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Introduction

While the US trucking industry is likely considered outside the purview of investors seeking to invest in high-quality compounder businesses (it’s too cyclical and competitive), our experience is that a lot of opportunities (both long and short) tend to present when there are turning points in the US transportation market. After all, physical goods still need to move across the US economy, and shifts in transportation costs can have meaningful second-order effects on the margins of the companies that produce, distribute and sell them.

It is thus worth diving into some recent developments in the US transport and logistics industry that are causing trucking spot rates to skyrocket. Since late 2025, there has been a revenue per mile (RPM) rally, with ex-fuel van spot rates increasing by around 30% year-over-year (YoY) in May 2026, which followed increases of 11%, 23%, 20%, and 25% across January to April. This is the strongest stretch of performance since 2021.

Source: Bristlemoon Capital, DAT Solutions, Bloomberg

Notably these increases in trucking spot rates are excluding fuel surcharges (ex-fuel rates are referred to as “linehaul” rates). When we include the 60-70 cents of fuel surcharges that have been tacked onto trucking rates since March 2026, then all-in spot rates have been increasing at an almost 50% YoY clip in the recent weeks of June 2026.

For any company that needs to move goods, a 50% increase in the rate you’re paying to transport those items versus the year prior is clearly meaningful, so it’s worth us digging into what has caused this material spike in spot rates.

Some background on the trucking cycle

Before we look into what has catalyzed the current transportation market tightness, it is worth rewinding and looking at developments in the last few years that have affected the US trucking space, particularly as they relate to the level of trucking capacity.

During the pandemic years, there was a notable uptick in new active carriers in the US. There was a spike in demand for transporting goods (goods spending surged, inventories were rebuilt, and e-commerce volumes accelerated). This led to a sharp uptick in spot rates that created unusually attractive economics for owner-operators. This triggered a major wave of new carrier formation, with many new carriers popping up to take advantage of that heightened demand and elevated spot rates.

Source: Motor Carrier Management System (MCMS) Data Snapshot via FMCSA

However, it is the nature of the capacity added during that period which is noteworthy. If we look at the table below, we can observe that 65-72% of the truck carrier capacity being added in any given year during the pandemic period was from one-truck operators. And there was an 11.5 percentage point increase in the mix of new carriers added coming from one-truck operators in 2022 compared to 2019. It was a freight goldrush of sorts.

Source: Motor Carrier Management System (MCMS) Data Snapshot via FMCSA

But in 2022 the cycle started to turn. Goods demand normalized, retailers moved from restocking to destocking, and spot rates began falling. In fact, DAT dry van national average linehaul rates declined from above $2.60 per mile in January 2022 to below $1.60 in November 2022, a 40% decrease in less than a year. In other words, there was too much trucking capacity for the shrinking level of freight demand, and the spot rates swiftly deflated to reflect this.

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The downturn then became self-reinforcing. Spot rates fell below many small carriers’ economic cost of operating. And this was all while diesel, insurance, maintenance and financing costs remained elevated. Carriers that had bought equipment at peak prices during the pandemic frenzy were squeezed by falling revenue per mile and higher debt service. This led to trucking capacity exiting the market.

But it’s worth pointing out that while carrier capacity was quick to be added, this capacity was slow to exit, as many operators kept operating to generate cash, even at poor margins. Our belief is that this dynamic helped keep rates depressed for longer than in a normal cycle, and it was a grueling three year trucking bear market.

If we look at the Cass Freight Index that tracks the movement of goods by any mode of transportation, the index has suffered 40 consecutive months of YoY declines.

Source: Bristlemoon Capital; Cass Information Systems

This is notable, as we’ve been seeing enormous increases in trucking spot rates even with freight demand remaining weak. And this is due to some recent regulatory changes in the market that have led to further carrier exits.

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Table of Contents

1. Introduction to the business and recent financial performance

2. The power conversion challenge for AI data centers

   1. The legacy architecture and its limits

   2. Two converging architecture standards

   3. The 800V power delivery chain: from grid to transistor

3. The AI data center opportunity for Infineon

   1. Stage by stage

   2. Sizing the AI data center opportunity

   3. Competition and market share

4. Automotive: the bull case that never was

   1. The bull case that never was

   2. Should we be excited about an automotive recovery?

5. Green Industrial Power: the forgotten segment

   1. The solid state transformer opportunity

6. Is there an emerging MCU shortage?

Introduction

Infineon Technologies is not a stock that generates much excitement at dinner parties – nor at AI investor parties, it would seem – until recently. The Munich-headquartered diversified power semiconductor company lacks the headline profile of Nvidia, the Uber driver name recognition of Micron and SK Hynix, or the obscene torque of the optical bottleneck stocks. For most of the post-GFC cycle, Infineon occupied a corner of the market that can be loosely described as “European industrial cyclical with some EV exposure”, and the company’s stock price largely reflected this. Indeed, the last time we seriously looked at the company, it was coming off the back of the post-COVID automotive/industrial shortage and preparing for the global high voltage EV boom that never materialized. The stock spent most of the past four years treading water, before doubling in the space of ~8 weeks.

What has changed over the past twelve months is that the center of gravity for semiconductor demand fractured away from compute (GPUs and ASICs – too boring) and shifted across the supply chain into higher-beta areas such as memory, optical networking and packaging. The latest area to capture the attention (and imagination) of the market is power conversion and management within AI data centers. The AI DC buildout has surfaced a constraint that was obvious in retrospect: raw compute power is now scaling faster than the electrical infrastructure that delivers energy to it. Here, we refer specifically to the power infrastructure from the boundary of the DC facility to the point-of-load (POL) on the GPU itself.

Solving that constraint requires a complete architectural overhaul of data center power delivery, from the traditional 480VAC → 12VDC to 800V high voltage DC (HVDC) led by Nvidia’s 800VDC reference design and the +/- 400VDC Mt Diablo architecture developed by the hyperscalers. Regardless of which design becomes standard, the overhaul depends heavily on the wide-bandgap semiconductors, power MOSFETs and voltage regulator modules that form the core of Infineon’s product portfolio.

While the 800VDC architecture was unveiled last year, it was only in the last couple of months where it felt like everyone was talking about data center power and power semis as the next optical or memory play. The result: IFX and its power semis peers have rerated vertically since the end of March as this narrative gathered steam. Unlike some of the more speculative AI narrative pumps, we’d say this rerating is not without merit. Infineon reported Q2 FY2026 results that beat expectations, raised its full-year sales and margin guidance, confirmed its AI revenue targets for FY2026 and FY2027, and announced the early opening of its Dresden Smart Power Fab Module 4 to serve overwhelming AI power demand.

Furthermore, the 800VDC transition strikes us as a cycle that has legs. Unlike AI servers or memory where a double-digit sales growth trajectory next year could spell disaster for the relevant stocks, the 800VDC architecture is a quasi zero-to-one transition. That is to say, even if Nvidia’s Rubin and Rubin Ultra server generations sell like cold scones, the transition to 800VDC must happen as current data center power architecture simply cannot handle the power density of future AI racks.

In this piece we examine Infineon’s four main investment angles: overwhelmingly dominated by AI data centers today, but encompassing also a potential automotive recovery, industrial power and electrification, and an industry-leading microcontroller business. Ultimately, while we like Infineon as a business, the violence with which this sleepy power/analog stock has rerated to all-time highs (on both price and multiple) has likely pulled forward substantial future returns and leaves us lukewarm on IFX as an investment.

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Business overview

Infineon was spun out of Siemens in 1999 and has grown from a diversified industrial semiconductor company into a more focused power and embedded systems specialist through a series of transformative acquisitions: International Rectifier in 2015 ($3 billion), Cypress Semiconductor in 2020 ($9.4 billion), and most recently the Marvell automotive Ethernet business for $2.5 billion in FY2025.

The company’s competitive position rests on three pillars. First, it is the global leader in power semiconductors across silicon MOSFETs, IGBTs, and silicon carbide (SiC) MOSFETs – devices that control and convert electricity in everything from server power supplies to electric vehicle traction inverters. Second, it holds the #1 position in microcontrollers (MCUs) globally, with approximately 23.2% market share by revenue through its AURIX product family. Third, it occupies leading positions in several niche but strategically important markets including automotive radar sensors, smart card security controllers, and industrial motor drive power modules.

Source: Infineon

Through FQ2 FY2026 (March 2026 quarter), Infineon reported four segments:

• Automotive (ATV) at 50% of FY25 of revenue covers power semiconductors and MCUs for passenger and commercial vehicles;

• Power & Sensor Systems (PSS) at 29% spans power semiconductors for servers, consumer electronics, and mobile devices, plus automotive radar sensors;

• Green Industrial Power (GIP) at 11% covers power components for industrial motor drives, HVAC systems, solar inverters, and grid infrastructure; and

• Connected Secure Systems (CSS) at 10% covers smart cards, security controllers, and IoT connectivity.

Effective July 1, 2026, Infineon will reorganize into three divisions: Automotive (~50% of revenue), Power Systems (~30%), and Edge Systems (~20%). The restructuring consolidates AI data center and industrial power exposure into a single Power Systems reporting unit rather than split across two segments, making the AI opportunity more visible to investors.

Source: Infineon

Recent financial performance

Q2 FY2026 results came in ahead of expectations on the critical metrics, indicating the protracted cyclical trough is firmly in the rearview mirror. Indeed, management’s confident tone on the Q2 call marks a stark contrast to the cautious language that was still being used as recently as January 2026.

“A broader upswing across many end markets is clearly on the horizon. We are seeing rising demand in several key markets. While geopolitical conflicts continue to weigh on people and markets, our business indicators such as order intake, delivery times, cancellation rates, and inventory levels are showing a significantly improved picture. […] The market development in industrial applications is being supported by rising demand for energy infrastructure. In the automotive sector, order intake is rising as customers begin to replenish their low inventory levels. However, electromobility remains in difficult waters, while we are seeing a positive global trend in software-defined vehicles. Overall, demand in our end markets is improving significantly. We are preparing for a broad-based upswing.” – CEO, Q2 FY2026 analyst call

Compared to a quarter ago:

“Near-term indicators such as order intake or lead times are now starting to align in a positive way, and visibility is improving. That said, we feel our view of a gradual and uneven recovery remains appropriate for the time being. […] Automotive and industrial markets are beyond the cyclical trough, but demand has yet to really inflect. Consumer, compute ex-AI, and communication markets are in the early innings of the upcycle. AI remains the clear standout, continuing its extraordinary growth propelled by sustained massive investment in data centers and related infrastructure.” – CEO, Q1 FY2026

Revenue was €3.8 billion, up 4% QoQ and 6% YoY, with a segment result margin of 17.1%. The positive surprise was driven by stronger-than-expected AI power demand and a meaningful recovery in the GIP segment (+15% QoQ). Infineon raised its full-year FY2026 revenue guidance to above €16 billion with a segment result margin of approximately 20%, a step up from the high-teens margin guided earlier in the fiscal year and an upgrade to sellside consensus. The company simultaneously confirmed its dedicated AI power revenue targets of €1.5 billion in FY2026 and €2.5 billion in FY2027. The AI business is, per management’s own characterization, in allocation through FY2026 as demand exceeds available supply.

“Currently, our AI business is in allocation and we are converting capacities from other areas as well as ramping new ones as quickly as possible. Given the unabated momentum, we are reconfirming our guidance of dedicated AI power revenues of EUR1.5 billion for ‘26 as well as our indication of EUR2.5 billion for ‘27, irrespective of a weaker US dollar.” – CEO, Q2 FY2026 analyst call

“As we said carefully in our prepared script, the longer the allocation lasts on AI and adjacent markets, the more positive dynamic for pricing could be also visible still in the remaining quarters.” – CFO, Q2 FY2026 analyst call

The most significant near-term operational event is therefore the opening of Dresden Smart Power Fab Module 4 on July 2, 2026, earlier than the originally planned autumn timeline. Dresden Module 4 represents Infineon’s largest single investment at approximately €5 billion and is the primary production site for AI silicon MOSFETs. Its ramp through H2 FY2026 and into FY2027 is the central production lever underpinning management’s €2.5 billion FY2027 AI revenue target. The company is also converting underutilized silicon IGBT capacity (for EV and PV inverters) into AI silicon MOSFETs and further expanding power-related analog and sensor capacity by pulling forward an additional €500 million of capex.

“And for the time being, as I mentioned also in my summary, we are less hopeful for the XEV market. In fact, we believe that the development will be showing less growth than many market participants anticipate. And in the IGBT domain, for XEV as much as for certain applications in photovoltaics, we believe that it’s better to use this capacity, as I outlined in my intro, to convert it to leading-edge silicon MOSFETs and use it for the fast-growing, margin-accretive AI business.” – CEO, FY2025 analyst call

“These investments, including the conversion of existing IGBT capacities, are capital efficient and will underpin our market leadership. A large part of them will be used for a faster-than-originally-planned ramp-up of our new power and analog/mixed-signal fab module in Dresden. Fortunately, we are able to accelerate the opening of the facility to this summer with the additional capacity we project to achieve AI-related revenues of around EUR2.5 billion in our 2027 fiscal year.” – CEO, Q1 FY2026 analyst call

“The customers are very demanding. Technically, it’s very demanding. But we will be able also to supply in-house manufacturing Dresden Module 4, also what comes more and more that particularly our US customers ask for non-China, non-Taiwan supply chains which we can offer. So, plenty of aspects which speak in favor of Infineon.” – CEO, Q2 FY2026 analyst call

The power conversion challenge for AI data centers

Understanding Infineon’s AI opportunity requires some basic knowledge of the power delivery chain inside a modern data center – a topic that has received surprisingly little investor attention until recently compared to power generation and grid transmission. This is definitely rudimentary to anyone who has studied electrical engineering 101 (we didn’t) but we will keep it simple to not lose anyone. For more details on the power architecture of AI data centers, numerous excellent Substack articles have been published on the topic in the past two months, coinciding with (or perhaps triggering) the sharp rally in power semis-related stock prices. See here, here and here for example.

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The legacy architecture and its limits

Traditional data centers receive power as high-voltage alternating current (HVAC, typically 13.8kV at the campus boundary), step it down through successive transformers to 480VAC, protect it through uninterruptible power supply (UPS) systems, distribute it through power distribution units (PDUs), rectify it to 12V direct current through server power supply units (PSUs), and finally step it down again through on-board voltage regulator modules (VRMs) to the 0.7-1.2V required by the processor die. This multi-stage cascade was engineered when a fully-loaded server rack drew 5-10kW. It delivers around 70-85% of grid electricity to the compute die, with the balance dissipated as heat in power conversion components and current paths. For a 1GW AI data center, even 85% power conversion efficiency means 150MW of power dissipated as heat during voltage conversion.

Source: Nvidia technical blog

While burning off 100,000+ homes worth of electricity every hour as heat would be immensely regrettable, the more fundamental physical constraint forcing the transition to 800VDC is current. A fundamental identity of electricity is Power equals Voltage times Current (P = V * I). At a fixed voltage (e.g. 48V), higher power requires proportionally higher current. This wasn’t an issue when server racks would draw 6-12kW of power, requiring up to 250A of current which could be handled by a copper wire the width of your pinky. At 140kW per rack, the GB300 NVL72 starts to really test the limits of the 48V rack architecture. By the time we get to the Rubin Ultra generation, a rack is expected to draw 600kW+ of power, which exceeds 12,500A of current at 48V. Based on Ohm’s Law P_loss = I² * R, this 50x increase in current leads to a 2,500x increase in conduction loss assuming resistance R is constant. We would need a 2 meter long solid copper busbar with the cross-sectional area of an iPhone to deliver the required power to the GPUs.

Source: Vik’s Newsletter

This is obviously not physically or economically viable at scale, so what is the solution? For a given power envelope, power loss scales quadratically with current and linearly with resistance. Halving the current by doubling the voltage reduces conduction loss by a factor of 4, while halving the resistance by doubling the cross-sectional area of the copper busbar only reduces conduction loss by half. Hence the industry’s solution has been to shift to 800VDC rack power architecture: this reduces rack current by 16.7x compared to 48V, which reduces conduction loss by 278x assuming resistance remains constant. Of course, resistance will increase as the size of the busbar shrinks from the area of an iPhone, but the overall net reduction in power loss will still be many multiples of the prevailing 48V architecture.

NVIDIA’s GPU thermal design power (TDP) trajectory is familiar to most technology investors, but its implications for power delivery infrastructure are less widely appreciated. The A100 at 400W was manageable within the 480V → 48V → 12V architecture. The H100 at 700W was manageable with upgraded cabling. The Blackwell GB200 NVL72 rack draws approximately 120kW across 72 GPUs and 36 CPUs. The forthcoming Vera Rubin and Rubin Ultra rack designs will ramp power consumption from 200kW to 600kW, and by the time the Feynman generation rolls around, single racks will draw over 1MW of power.

Two converging architecture standards

The industry has coalesced around higher DC bus voltages as the solution to the current density problem. Two standards have emerged in parallel. Mt. Diablo, backed initially by Microsoft and Meta and subsequently joined by Google, proposes ±400VDC (effectively 800V differential) delivered from standalone power cabinets to IT racks, published through the Open Compute Project. Its commercial deployments are expected from 2026. The standard retains the existing 480VAC backbone at the facility level and inserts a DC conversion shelf at the rack boundary. This is a more conservative approach that lowers adoption friction for hyperscalers with large existing AC infrastructure estates (which can be retrofitted), and leverages the established EV battery pack supply chain that has validated ~400V components at volume for over a decade.

Nvidia’s 800V HVDC architecture, unveiled at GTC 2025, is more radical: it replaces the 480VAC backbone entirely, delivering 800V monopolar DC from the utility interface throughout the data center floor and directly into racks. The architecture explicitly targets 1MW+ rack power densities consistent with Rubin Ultra and beyond. It is the more aggressive standard as it requires native 800VDC data center designs (existing LVAC data centers can’t be retrofitted), but also the one best suited to efficient high density power delivery as the industry converges on megawatt-scale racks.

The practical differences between the two standards matter less for the semiconductor content opportunity than their common element: both converge on roughly 800V delivered to the rack, and both require wide-bandgap power semiconductors at every conversion stage. However, the Mt. Diablo standard preserves more of the 480VAC architecture and thus incumbent power supply vendors, while Nvidia’s 800V reference design is more favorable to high-voltage, wide bandgap power semis suppliers.

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The 800V power delivery chain: from grid to transistor

In the 800V HVDC architecture, power delivery from the utility interconnect to the compute die collapses from seven stages to three or four. A solid-state transformer (SST) converts 13.8kV AC directly to 800VDC at up to 99% efficiency, replacing the traditional multi-stage transformer-plus-UPS combination. An 800VDC bus runs throughout the data center floor: at 140kW per GB300 NVL72 rack, this requires only 175 amps which is manageable with a standard industrial bus bar and reportedly reduces copper requirements by approximately 45% versus an equivalent 54V architecture. A board-level HV DC-DC converter steps 800V to 12V (likely using SiC MOSFETs on the primary side and GaN on the secondary), achieving 97-98.5% efficiency at switching frequencies measured in hundreds of kilohertz. Finally, point-of-load VRMs adjacent to the GPU package step 6V or 12V down to the 0.7V core voltage required by the GPU.

Source: Nvidia technical blog

The cumulative efficiency of this architecture can exceed 90%, against 70-85% for the legacy AC chain. For a 1GW data center, the improvement can avoid multiple tens of MWs in conversion losses compared to best-in-class 480VAC hyperscale architectures, which can then be used to power more compute (and as we know, compute = $$$).

For semiconductor companies, the shift to 800VDC changes the technical requirements at every stage of the power chain. Low-voltage silicon MOSFETs in simple buck topologies are supplanted by wide-bandgap devices – SiC for high-voltage switching, GaN for intermediate bus and POL – alongside isolated gate drivers, reinforced isolation components, and power management controllers. The bill of materials is richer and more technically demanding at every node, which is why companies with broad power portfolios like Infineon stand to capture disproportionate content per dollar of AI infrastructure spend.

The AI data center opportunity for Infineon

Infineon participates at virtually every stage of the AI data center power chain, which in our opinion makes the opportunity more interesting, albeit at the cost of maximum torque to any particular power stage. The company is present in the LV AC-DC (480VAC → 54/48VDC) and intermediate bus conversion (54/48V → 12/6V) stages through silicon MOSFETs and GaN devices used in PSU reference designs, and more critically, in the second stage VRM market through its OptiMOS TDM quad-phase modules that enable vertical power delivery. Management has most explicitly tied the AI revenue trajectory to the VRM and GaN stages, which is where Infineon’s technical differentiation is most defensible. Additionally, given Infineon’s strong position in SiC established over the years in the EV industry, we believe the company is also well positioned for the Solid State Transformer opportunity as that market develops towards the end of the decade.

Source: Infineon

Stage by stage

Source: Infineon

At the PSU stage (rack-level AC-to-DC conversion), Infineon supplies CoolMOS silicon power MOSFETs and GaN devices to PSU manufacturers including Delta, Murata, Bel, and Acbel. This is an indirect market where Infineon is designed in by the PSU maker rather than by the accelerator platform owner, and the silicon content at this stage is subject to competitive substitution at design-in cycles of 12-18 months. Texas Instruments, STMicroelectronics, onsemi, and Navitas amongst others compete with Infineon for PSU content. Content per unit is meaningful but not highly differentiated, so the value tends to be lower than for stages closer to the GPU itself.

The intermediate bus conversion (IBC) stage is likely to see the most change during the transition to 800VDC architecture. The IBC stage itself basically exists as a compromise between distribution efficiency and POL feasibility as 54/48V is better for moving power across the server board (lower current), while 12/6V makes the final POL conversion down to ~0.7V easier. In the transitional 800V architecture, the 480VAC → 54/48VDC stage is replaced by an 800V → 54/48V DC-DC stage, with the LV IBC stage retained to preserve as much of the existing rack power infrastructure as possible to ease the transition. But the ultimate end state is to bring 800V straight into the server blade and perform a 67:1 conversion down to 12V near the GPU itself, thus minimizing the distance of the high-current low-voltage path. This would do away with the conventional LV IBC but require a much more demanding 800V → 12V high voltage isolated DC-DC converter.

We believe Infineon is well positioned for this shift because the HV DC-DC conversion stage moves the value pool towards the power semis components and module design where Infineon is strongest: high-voltage switching using a combination of Si, SiC and GaN, isolation, gate driving, sensing, protection, and system-level power control. The company forecasts the IBC SAM to grow at over 50% CAGR from 2025 to 2030, driven mainly by HV IBC, which supports our view that this stage will pull in a significant amount of high value power semis content from earlier stages.

In March 2026, the company introduced two HV IBC reference designs for 800V → 50V and 800V → 12V architectures using its CoolGaN switches. We understand that Infineon is currently the only company globally with scaled 300mm GaN wafer production versus the industry standard of 150mm or 200mm, which should enable materially lower die costs and tighter process control. This is not to say that Infineon will dominate this stage, as competitors like Texas Instruments and STMicro have both published their own 800V → 12V converter reference designs. But these kinds of major system-level transitions tend to reward vendors who can deliver a complete, integrated power semis portfolio.

Finally, the most strategically important stage is the second-stage voltage regulator module. These are the power modules that sit adjacent to or directly underneath the GPU package on the server board, converting the intermediate bus voltage to the GPU core voltage (typically 0.7-1V). This stage is technically demanding because it feeds voltage directly into the GPU power rails, must handle extremely high current (a 2,300W GPU package with 0.7V core voltage requires 3,300A of current), and is responsible for much of the power regulation required for the GPU’s very dynamic loads. Furthermore, as should be evident from the power delivery discussion above, this massive aggregate current at the chip level i) cannot be lowered unlike during earlier power stages, and ii) is where the largest efficiency losses occur. Therefore, VRM and power module vendors are increasingly turning to vertical power delivery (VPD) as a solution to the challenges presented by multi-kilowatt-rated AI processors.

Source: Infineon

Infineon’s OptiMOS TDM2454xx quad-phase modules, announced in March 2025, deliver 2A per mm² of current density and the company is working with Delta Electronics, the primary Nvidia power supply vendor, to enable VPD for AI processors. Management has committed to improving this to 3A/mm² by 2027, with substrate-integrated voltage regulation (SiVR) as the five-year horizon technology. By shortening the current path from centimeters as is the case with lateral power delivery where VRMs surround the GPU package, to millimeters through the compute board itself with VPD, conduction losses can be reduced by an order of magnitude (resistance scales linearly with the length of the conductor). Infineon also claims that its VPD modules enable higher power density, better thermals due to less power loss, and space savings on the compute board allowing for denser system designs.

Source: Infineon

The module business carries meaningfully higher gross margins than discrete MOSFETs because it integrates the gate driver, controller interface, and packaging in a form factor that reduces board space and design complexity for the customer, often supporting an ASP several times higher than the equivalent discrete component. Management has confirmed that AI power revenue is margin-accretive relative to the group’s high-teens segment result margin average. And on VPD specifically, management has also stated that these modules land at the high end of the power semis content range per installed kilowatt ($100-$250, more on this below).

“And for us, of course, the second stage part is giving us particular growth momentum. So we expect the share of the second stage in the overall numbers to increase over the quarters to come. As Andreas pointed out that vertical power deliveries, the modules have a much higher value. And again we are here teaming up with all GPU makers, or ASICS makers, whatever you want to call them (including in China).” – CEO, Q1 FY2026 analyst call

Sizing the AI data center opportunity

For paid subscribers, we discuss the size of Infineon’s AI power opportunity and the competitive landscape. Additionally, we dig into the positioning and outlook for the Automotive segment, the emerging AI-related opportunity within the Green Industrial Power segment, and consider whether an MCU shortage may emerge as the next opportunity for Infineon.

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In my recent travels to the U.S., I had a lot of conversations with other investors who are grappling how to adapt to this market environment. Market environment is a bit of a nebulous descriptor, so it’s worth elaborating.

With multi-strategy funds (a.k.a. multi-managers or pods) capturing a disproportionate share of hedge fund AUM growth over the past decade, they have become a more important part of how liquid securities trade. Given how much these pods lever up their equity capital and the high level of portfolio turnover, increasingly the pods are the marginal buyer (or seller) of liquid securities. Layer on top of that the rise of passive and retail participating more actively in pockets of the equity market, and it’s not hard to see how these developments have changed the manner in which stocks trade. This is the change in market environment I am referring to.

As we can see below, multi-managers account for around 10% of the total hedge fund industry AUM. Incredibly, the multi-strategy funds went from managing $91 billion of assets in 2010 to roughly $428 billion in 2025, a 4.7x increase in assets over the last 15 years.

However, data from the Goldman Sachs prime desk showed that multi-strategy firms accounted for around one-third of the gross market value managed by hedge funds in equities[1] (i.e., due to the leverage employed).

J.P. Morgan’s data for the month of November 2025 revealed that multi-strategy funds had leverage at 444.3%. In other words, for every $100 of capital from investors, the multi-strategy fund had almost $450 in long and short positions[2]. That’s a lot of leverage!

It’s thus no surprise that the pods operate within very tight risk parameters. They run market neutral (although some pods have flexibility to tilt their net exposure beyond strictly neutral), which basically means they need to net out their long exposure with an equivalent short exposure. With that much leverage and highly restrictive risk parameters, there is minimal scope for drawdowns.

There is no shortage of stories of pods being wound down if a manager blows through their risk limits. So, what this means is that the pods have minimal duration and have a strong tendency to be long the stocks that are “working” at any given time. The corollary of this is that the pods have an inherent bias towards being long momentum (bottlenecks, accelerating growth profiles, and the like).

If we think about the current market, the area of the tech sphere that is working (and in a gargantuan way) is semiconductors. The SOX (The Philadelphia Semiconductor Index) is up around 67% year-to-date, and some of the earnings beats during Q1 were simply eye-watering. If you’re a tech pod, you’re getting up and dancing while the music plays. In other words, you’re probably buying semis due to strongly positively inflecting growth profiles, good visibility into demand persisting (and there being little to derail near-term earnings beats), as well as just strong momentum in these stocks.

But, if you’re running market neutral like the pods do, you can’t simply buy semiconductor stocks and call it a day. You have to fund that exposure, and have an offsetting short book. Software and consumer internet names have been prime funding source candidates. We’ve previously noted that there’s been a lack of narrative nuance, with the tech landscape bifurcating into perceived AI winners and perceived AI losers. The way in which the multi-managers execute their strategies has likely exacerbated this dynamic.

Just take a look at the spread between the IGV and SOX. While the SOX is up c.67% year-to-date, the IGV (a software sector ETF) is down around 12%. That’s a 79 percentage point outperformance of semiconductors versus this software basket in less than six months.

This has split the fortunes of fund managers. If you’d owned a lot (or even just a little) of semi and AI-beneficiary companies, your Fund is probably shooting the lights out. If you had been caught on the wrong side of this trade, it’s likely a completely different story, with a different set of conversations with clients. I have never witnessed a period in markets where capital has rotated from one sector into another at such a scale and in such a short period of time. It feels truly unprecedented.

We admittedly have had too much of what was in the IGV, and too little of what was in the SOX. And that’s hurt our year-to-date performance. I’m not writing this to lament this soft run of performance, and I’m also not blaming the market (as they say, “no crying in the casino”). Rather, I thought this was a good opportunity to relay the thoughts and discussions that flowed from conversations I had with investment managers who I met during my travels to the U.S., many of which were grappling with similar challenges in navigating this market.

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How are managers adapting?

One of the biggest challenges is that in order to keep up in this hot market, it has required most fundamental managers to pivot dramatically from their process which has worked historically (e.g., deep fundamental work, valuation sensitivity, buying and holding quality businesses that compound over the long-term, etc). The current market is faster moving, more narrative driven, and if you’re constrained by fundamentals, you probably won’t get there on many of the higher torque AI-related names that are further out on the risk curve.

This is certainly not to say that all AI-related stocks are expensive. But many of the stocks that are going vertical require a continuation of heady demand years into the future, such that these stocks can grow into their lofty valuations. Investors dabbling in the more speculative part of the semis complex are increasingly underwriting demand that extends into 2030 and beyond. That demand might transpire, but these stocks become increasingly risky to the extent that investors are treating that growth as a sure thing.

There was broad acknowledgement from managers I spoke to that these stocks are expensive. But in terms of how those stocks are being traded and the market structure changes highlighted above, the absolute valuations of these stocks have been relegated to a secondary concern. Rather, the focus is squarely on whether these companies are accelerating their topline and producing earnings beats. So long as that is happening, seemingly expensive stocks can march higher. It’s the rate of change of the fundamentals in the short term that has mattered most for these stocks, and more and more non-pod investors are recognizing this and changing the manner in which they invest.

This approach of playing near-term accelerations/decelerations is anathema to fundamental investors who typically underwrite businesses based on the earnings and cash flows they will produce over long stretches of time. This approach acts as an anchor that makes it hard for fundamental managers to simply pivot into the stocks that are working.

That discipline helps avoid overpaying for speculative stocks, but it also makes it difficult to perform well in markets where investors are willing to pay up for outer year cash flows that rely on a chain of assumptions around ever increasing demand, restrained capacity additions, tech roadmaps that do not upend favorable supply/demand dynamics, and accommodating capital markets. In some of these AI stocks, the path to getting paid appears exceptionally narrow and deceptively multiplicative.

It does feel like there’s a huge spread in the willingness of the market to give credit to outer year cash flows for AI winners versus AI losers, which speaks to the prevailing confidence in the AI trade persisting.

The difficulty in adapting to this market is exacerbated by many managers feeling the need to adhere to what they’ve marketed to their LPs (i.e., sticking to what’s on the tin of the fund strategy). This dynamic is more acute for managers with more institutional mandates that will give far greater scrutiny to any process changes.

I heard numerous stories of fund managers who can invest on their personal account chalking up massive year-to-date returns, but being down in their fund. This likely reflects the tendency of a manager’s approach to investing to become sclerotic if their LPs are likely to penalize them for process changes (even if those process changes are necessary and a sensible course of action). I think the impetus to change is always greatest during periods of drawdown/underperformance but it was a topic in virtually all the conversations I was having.

Semis is the FOMO trade

Semis is now firmly the FOMO trade. Regardless of fundamentals, every manager is feeling the pull of the seemingly infinite money glitch of owning semis. Lots of generalists I’ve spoken to during my trip have sharpened their pencils on the space, with some shifting meaningful portions of their book into select hardware names.

So, while no doubt pods are in semis and using internet/software as funding sources, the rotations of capital out of software and into semis are to an extent also being directed by long-only funds that are obviously very conscious of lagging their benchmarks.

Interestingly, the generalists venturing into semis are unconstrained by the knowledge/experience of how low quality some of these names were considered previously, or how cyclical the fundamentals of these stocks have been historically. Rather, it’s easier to come to the space with a blank slate and, for example, form a view around the sustainability of the supply/demand imbalance in memory persisting.

We have not done a great job at playing the AI boom. We own NVIDIA and ASML as they are quality businesses and we can get comfortable with the valuations of these stocks. But they have much lower torque to the AI trade compared to say memory prices going parabolic, and they won’t have the same insane earnings beats. We have been waiting for a pullback which very much has not occurred in some of the semi stocks we’d be comfortable owning.

Semis tourists make the trade more fragile

The problem with so many generalists chasing semis is that it makes the AI trade more fragile. Having spoken to a number of investors in recent weeks who have recently initiated positions in semis, they have openly admitted that they’re not across the tech details and acknowledged that they could be blindsided by tech changes that are negative to fundamentals.

This is perfectly fine when there’s upside momentum in the stocks. But the risk is that an increasing portion of the shareholder bases of these hot semis stocks lack true conviction in the fundamentals, which increases the risk of an unwind to the extent that there are any unforeseen developments that cast doubt on the fundamentals (or perhaps even just if these stocks stop working, and momentum reverses).

Lots of managers are entering these stocks simply because they’re the stocks that are working and the fear of underperforming/career risk is forcing them to dance while the music plays. But because of this, they’re also much more likely to pull the plug once these stocks stop working. So while the fundamentals for many semi stocks are currently amazing (monstrous earnings beats and accelerating growth profiles), some of the valuations require conviction around very strong fundamentals extending into 2030+. We have been looking at names in the space and have struggled to build that conviction.

We manage money on behalf of high net worth individuals, family offices, a charity, and we have our own money, and our families’ money in the Fund. For this reason we do not feel compelled to chase the popular stocks in a hot market, and unnecessarily risking the capital we’ve been entrusted with managing. If we do not assess the risk/reward to be attractive, and we lack the conviction in that stock, we will pass, even if others are making a lot of money. These are just not risks we think are sensible to take.

It will be interesting to see how it all plays out, and as always, we welcome constructive pushback on any of the above!

[1] https://www.reuters.com/business/finance/hedge-funds-double-down-using-near-record-leverage-quest-boost-returns-2025-12-03/

[2] https://www.reuters.com/business/finance/hedge-funds-double-down-using-near-record-leverage-quest-boost-returns-2025-12-03/

Disclaimer / Disclosures

The information contained in this article is not investment advice and is intended only for wholesale investors. All posts by Bristlemoon Capital are for informational purposes only. This article has been prepared without taking into account your particular circumstances, nor your investment objectives and needs. This article does not constitute personal investment advice and you should not rely on it as such. This document does not contain all of the information that may be required to evaluate an investment in any of the securities featured in the document. We recommend that you obtain independent financial advice before you make investment decisions.

Forward-looking statements are based on current information available to the author, expectations, estimates, projections and assumptions as to future matters. Forward-looking statements are subject to risks, uncertainties and other known and unknown factors and variables, which may affect the accuracy of any forward-looking statement. No guarantee is made in relation to future performance, results or other events.

We make no representation and give no warranties regarding the accuracy, reliability, completeness or suitability of the information contained in this document. To the maximum extent permitted by law, we do not have any liability for any loss or damage suffered or incurred by any person in connection with this document.

Bristlemoon Capital Pty Ltd (ABN: 22 668 652 926) is an Australian Financial Services Licensee (AFSL Number: 552045).

George Hadjia and Daniel Wu are associated with Bristlemoon Capital Pty Ltd. Bristlemoon Capital may invest in securities featured in this newsletter from time to time.

If you haven’t subscribed, you can join 5,954 others who enjoy our deep dives and investment insights here:

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Australian wholesale investors looking to invest in the Bristlemoon Global Fund can do so via this link.

Business Overview

Today we’re taking a quick look at Montana Aerospace (AERO SW), the Swiss aerospace company. AERO is one of the fastest growing aerospace suppliers, having grown its Aerostructures revenue at an 11.5% annual rate between FY22 and FY25. This has primarily been driven by market share gains and ramping OEM build rates. The aim of this writeup is to jot down our thoughts on the business and whether it’s interesting enough to be considered for the Bristlemoon portfolio.

AERO is a supplier of complex, mission-critical components for aviation and space applications. The company’s product portfolio includes structural components for fuselage (think of this as the structural long beams that comprise the skeleton of an aircraft’s fuselage), wings, and landing gear, critical engine parts exposed to extreme thermal and mechanical stresses, as well as large-format extrusion products.

Source: Company filings

The company’s aerospace portfolio is split as follows:

1. Universal Alloy Corporation (UAC) – manufacturer of aerospace structural components and assemblies.

2. ASCO Industries – designer and manufacturer of high-lift structures, complex mechanical assemblies, and critical functional components for the aerospace industry.

Montana’s products are on all major commercial aircraft platforms (historically the company has had a roughly 50:50 split between Boeing and Airbus). Back in 2023 the company had around €350k of average shipset content across its platforms, with this expected to ramp up to €450-500k in the years following, driven by new content share wins and further OEM outsourcing[1].

The way AERO works as a business is that they win content on various aircraft platforms. Think of this as Boeing contracting with Montana as a supplier of say, a landing gear product. In reality, there are many different parts that Montana will supply into an aircraft platform (note that AERO offers over 100k SKUs in aerostructures).

This all ties together into a contracted shipset value – that is, the total dollar value of all the components that Montana is contracted to provide for one single aircraft of a specific model. And recall that AERO has significant content on all Boeing and Airbus aircraft types.

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So, in this sense you can think of AERO’s sales as being driven by the shipset value multiplied by the number of aircraft that Boeing and Airbus will produce in the future. And how much certainty we can get over Montana’s future sales depends on how long Montana remains as the supplier to that aircraft platform, and how the economics of supplying those components change over time.

There’s reason to believe that AERO will be able to keep selling its products into these aircraft platforms for many years because most of the company’s contracts are “single source” – that is, AERO is the exclusive supplier of that product, creating strong visibility over future revenues and providing barriers to competition (given the time and cost of qualifying alternate suppliers). However, we found it more difficult to get confidence around the pricing power and economics of AERO supplying into those platforms (as we discuss further below).

Vertical integration

AERO is vertically integrated – it covers the aerospace value chain from processing raw materials to the assembly of entire components. This removes some of the issues that result from multiple third-party suppliers, whereby there might be delays that are capable of disrupting the entire production process. In contrast, a non-integrated value chain means that the final outcome of the component for the OEM is multiplicative – that is, it requires execution from multiple suppliers and if there’s a hiccup with one then it stalls the entire process (basically one of the suppliers being a “zero” in the multiplicative string of events). AERO can mitigate these risks by having greater control over the value chain.

Source: Company filings

Read more

This was the most severe drawdown the Fund has experienced since inception. Importantly, it was not driven by a deterioration of the underlying earnings power of the businesses owned by the Fund, but rather by a sharp and rapid repricing of what investors were willing to pay for those earnings. In this letter we will explain what contributed to the weak performance and how we plan to address this with our compounding/conviction position sizing framework, discuss how we have adjusted the Fund’s positioning in light of developments from the Iran War, and provide an update on the Fund’s AppLovin, FICO, Hemnet, and PAR positions.

We believe the current opportunity set is the most compelling we have seen since the Fund was launched. For those contemplating an investment in the Fund, we are seeking to partner with long-term, aligned investors, and we would welcome the opportunity to discuss our investing approach in more detail. For new investors looking to enter the Fund, more information can be found here or by emailing info@bristlemoon.com.

By accessing the link below, you acknowledge and agree that you are an Australian wholesale client as defined by Section 761G of the Corporations Act 2001.

Bristlemoon Global Fund March 2026 Quarterly Report Vf

792KB ∙ PDF file

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U.S. Trip

Our Chief Investment Officer, George Hadjia, will be in Los Angeles on 28-29th April, in Omaha for the Berkshire Hathaway weekend, and in New York on 4-12th May. For investors and allocators located in these cities, please reach out at info@bristlemoon.com if you would like to catch up. We are always looking to connect with likeminded investors to exchange stock ideas, as well as allocators who align with our investing approach.

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Disclaimer / Disclosures

The information contained in this article is not investment advice and is intended only for wholesale investors. All posts by Bristlemoon Capital are for informational purposes only. This article has been prepared without taking into account your particular circumstances, nor your investment objectives and needs. This article does not constitute personal investment advice and you should not rely on it as such. This document does not contain all of the information that may be required to evaluate an investment in any of the securities featured in the document. We recommend that you obtain independent financial advice before you make investment decisions.

Forward-looking statements are based on current information available to the author, expectations, estimates, projections and assumptions as to future matters. Forward-looking statements are subject to risks, uncertainties and other known and unknown factors and variables, which may affect the accuracy of any forward-looking statement. No guarantee is made in relation to future performance, results or other events.

We make no representation and give no warranties regarding the accuracy, reliability, completeness or suitability of the information contained in this document. To the maximum extent permitted by law, we do not have any liability for any loss or damage suffered or incurred by any person in connection with this document.

Bristlemoon Capital Pty Ltd (ABN: 22 668 652 926) is an Australian Financial Services Licensee (AFSL Number: 552045).

George Hadjia and Daniel Wu are associated with Bristlemoon Capital Pty Ltd. Bristlemoon Capital may invest in securities featured in this newsletter from time to time.

If you haven’t subscribed, you can join 5,698 others who enjoy our deep dives and investment insights here:

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Australian wholesale investors looking to invest in the Bristlemoon Global Fund can do so via this link.

Introduction

“The airline industry’s demand for capital ever since that first flight has been insatiable. Investors have poured money into a bottomless pit.” – Warren Buffett, 2008 Annual Letter to Berkshire Hathaway Shareholders

Yes, we are indeed starting this missive with a Buffett quote. But while Buffett in his usual folksy manner has consistently deplored the subpar economics of the airline industry, we would posit that the quote above (and the many other Buffett airline quotes) are reductive and fail to account for the distribution of economic fortunes of the various airline industry participants.

Let’s dig into this idea. As we can see in the chart below, there is a distribution of value creation amongst various industries. But in the smaller sub-charts, we can also observe that within any given industry there is a distribution of value creation. In other words, the average level of value creation (or destruction) for an industry masks the fact that beneath the surface there will be some companies creating value, and others destroying value.

Source: Credit Suisse

(N.B. the blue bars measure the spread between the cash flow return on investment (CFROI) and the cost of capital across 68 global industries, classified under MSCI’s GICS framework, using 5,500 publicly listed companies)

Admittedly, when we first started looking at AerCap (AER) in mid-2024, our thinking was prejudiced by the bad reputation of the airline industry as a cash incinerator. You see, AER is the world’s largest aircraft lessor, and we initially struggled to fathom how this would be a business worth investing in for Bristlemoon. As we did more work on AER, this uneducated view very much changed.

As we can see from the industry map below, the airline industry is obviously a lot broader than just airlines.

Source: Credit Suisse

So how does an aircraft lessor stack up? Based on Michael Mauboussin’s profit pool chart below, we can observe that lessors have generated a healthy amount of economic value (measured as a function of the economic profitability of the industry – that is, the CFROI less the discount rate – and the aggregate invested capital).

Source: International Air Transport Association, “Vision 2050”, February 12, 2011; via Credit Suisse.

(N.B. Returns data is from 2002-2009, and investment data is as of 2009. CRS = computer reservations systems; ANSP = air navigation service provider)

A recap on AerCap

While this is a high level, rather academic approach to industry analysis, it’s worth reiterating what attracted us to invest in AerCap back in 2024. We would point readers to our original write up on AER back in July 2024 which can be found here. We have removed the paywall to that article so all readers can view the writeup in full (notably the majority of points in the article continue to provide tailwinds for AER today).

The stock is up more than 60% since July 2024, having compounded at 33% per annum. However, despite this stock price increase, there are still reasons why we continue to own the stock in the Bristlemoon Global Fund.

Source: Bristlemoon Capital; Bloomberg

As a recap, AER is a multiple arbitrage story. The company is selling planes from their fleet at 2x book value, and buying back stock at around 1x book. This is, of course, financial engineering. But it is also wonderful value generation mechanism given the very high degree of confidence we were able to build around the aircraft undersupply situation persisting.

Source: Bristlemoon Capital; Company filings

AER CEO Aengus Kelly has done a spectacular job of creating value for AER shareholders (it’s no wonder – his interests are quite aligned given that he owns more than $770 million of AER stock). For example, AER repurchased 22.1 million shares in 2025, shrinking its diluted shares outstanding by around 11% year-over-year. In fact, the number of shares outstanding for AER is now 31% lower than it was just three years ago.

In this sense, owners of AER over that period now have a 45% greater claim on the business due to these buybacks. This is remarkable, and there are very few examples in public markets where companies retire almost one-third of their share count in such a condensed period.

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These buybacks have led to the company’s book value per share (BVPS) compounding at a healthy clip (and note that this BVPS is understated for a variety of reasons we state in our original writeup).

Source: Bristlemoon Capital; Company filings

Sandbagged Guidance

Earlier this month the company reported its Q4 2025 earnings result and gave guidance for FY26 EPS of $12 to $13. With the stock trading around the $150 mark, that guidance sounds wholly underwhelming when we consider that AER has historically traded at an 8x P/E (implying a share price range of $144 to $152 per share). This misses a few things, and we will mount a case why we believe that the guidance given is highly conservative.

Firstly, we would note that the $12 to $13 EPS range does not include any further gains on sale from selling aircraft. Thought of another way, the guidance assumes that AER simply stops selling planes for high prices and realizing material gains on sale that benefit its EPS. We would round the probability of this occurring down to zero, particularly given the company is already assuming $2.0 to $3.0 billion in aircraft sales in FY26 (the company disclosed this when it disaggregated the various P&L projections that support the company’s FY26 EPS guidance).

It is worth noting that in FY25 AerCap sold $3.9 billion worth of aircraft and generated $3.95 per share from gains on sale of these owned aircraft (i.e., almost 26% of FY25 adjusted EPS of $15.37 came from gains on sale). While these gains from asset sales are difficult to underwrite and will be lumpy by quarter, perhaps we can bake in an additional $3-4 of EPS from gains on sale in FY26. This bumps up the FY26 EPS range to $15-$17.

It is also worth noting that even outside of any gains on sale, AER has a tendency to be conservative when guiding the base business EPS. For example, in Q4 2024, the company guided FY25 EPS to a range of $8.50 to $9.50. As we progressed through 2025, the company continued to upgrade this EPS guidance. Excluding gains on sale, AER achieved $11.42 of base business EPS in FY25, a whole 20% to 34% higher than the initial FY25 guidance range given in Q4 2024.

Our expectation is for this FY26 EPS guidance to be upgraded as we move through the year, and this is based not so much on precedent but on some of the conservative assumptions underpinning the guide relative to what we think the future holds for AER.

Below we can see the various assumptions the company is using to arrive at its $12 to $13 of FY26 EPS.

Source: Company presentation

What immediately jumped out at us was the projection for $6.7 billion of basic lease rents in FY26. This is mostly because AER generated $6.7 billion of basic lease rents in FY25. So, in other words, in one of the tightest aircraft markets we’ve seen in decades, the company is assuming that basic lease rents will be flat in FY26.

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For example, AER’s owned aircraft average utilization rate has been 99% for the last two consecutive years. In FY25, AER extended 87% of leased aircraft, up from 79% in FY24, highlighting very strong customer demand for leased assets. This is all putting upward pressure on lease rates which should be supportive of basic lease rent growth.

However, it should also be noted that the company is selling planes from its fleet, which naturally has a deleterious effect on lease revenues. Nevertheless, basic lease rents increased by 5% and 2% year-over-year in FY25 and FY24, respectively. These were years when there were a heavy number of asset sales. We would also point out that AER continues to replenish its fleet via aircraft purchases, which helps offset the impact on basic lease rents from aircraft sales.

As a final point, 12% of AER’s current fleet is comprised of COVID-era leases, which will roll off linearly out to 2031-2032 (i.e., around 2% of AER’s fleet will reprice each year out to the early 2030s). This is an important point due to the magnitude of repricing. During AER’s May 2024 Investor Day, management disclosed that AerCap extended an aircraft for an existing operator in 2020. It was a three year extension struck at a sub-$120,000 rate. The aircraft came up for re-lease and was extended again at a lease rate above $165,000. In other words, AER was able to achieve a 40% increase in lease rates even though the aircraft was three years older.

We suspect that given lease rates have continued to increase since then that AER could achieve a very healthy pricing uplift on the 12% of its lease book that will roll off in the coming years. For example, if 2% of the lease book were to reprice with a 50% lease rate uplift, then this should boost basic lease rents growth by 1 percentage point.

It’s possible that another $2 of EPS could emanate from management upgrading its base business guidance. Tying all of this together, we believe a more reasonable EPS range for FY26 is $18-19 per share. The next part in framing the return shareholders are playing for is to determine what multiple AER might trade at in the future.

What multiple is fair?

Historically, AER has roughly traded at between 0.6x to 1.2x of its book value.

Source: Bloomberg

The stock has also traded at a c.7.5x P/E on average over the last decade, struggling to sustain above 10x.

Source: Bloomberg

The fact that AER’s multiple is at the top of its historical trading band, sitting at around 9.5x is not a great starting point. And there’s clearly downside to the extent to which the multiple de-rates back to its historical levels (this is a key reason why the Fund’s position in AER is sized much smaller today).

It is, however, also worth turning our minds to future opportunities that could cause the multiple to rerate. AER management was asked the following question on the Q4 2025 earnings call:

“In the industry, another owner of engine assets announced they’d be looking to convert engines to power turbines to service data centers. Is that something you guys are exploring?”

CEO Aengus Kelly’s answer highlighted AER’s willingness to participate in converting aeroderivative opportunity – that is, converting aerospace engines such as the CFM56 to turbines that power data centers:

“And of course, we’re looking at this. And if it turns out that the demand for what are currently commercial aerospace engines, if that demand to convert them into ground-based power generation for data centers is very durable and is long lived, then of course, we will participate in that, either directly or indirectly, directly by converting the engine into ground-based power generation or indirectly by taking advantage of this surge in demand. But at the moment, we want to make sure that this demand is a durable demand and it’s not fleeting, and that’s our focus at the moment is sizing the market. And from there, then we will participate, as I said, one way or the other.

In terms of the quantum of engines that we have ourselves, if you look at what’s installed on our aircraft, take the CFM56 model, we are the largest owner of CFM56s in the world. And I would remind you that all of our engines are serviceable and have to be returned for the most part in full life conditions, which is very different to a portfolio of engines that is half life or run out engines, and you’re swapping modules to make a serviceable engine. So, when we look at the engine portfolio that we have…on any of those metrics, we would have more engines than anyone in the world.”

The recent pop in the share price of FTAI Aviation (+55% YTD) highlights the immense uplift in value the market is currently willing to ascribe to companies hitching their cart to the AI data center buildout. FTAI recently announced FTAI Power, an initiative to convert CFM56 engines into 25-megawatt turbine units that can generate electricity to power data centers. The stock went vertical.

Source: Bristlemoon Capital; Bloomberg

Our belief is that energy is one of the key bottlenecks in the AI infrastructure buildout. Existing grid capacity is insufficient to power these energy-hungry data centers, which is why we’re already seeing an increasing prevalence of behind-the-meter (BTM) power solutions (i.e., energy systems installed on the customer’s side of the electricity meter rather than on the utility/grid side). The demand for energy is likely to be so great that these BTM energy systems such as aeroderivative turbines could become permanent fixtures to providing power, as opposed to a stop-gap solution that will temporarily bridge these energy needs while the grid plays catch up.

Given the multi-year tailwinds around data center energy demand that we think there is decent visibility over, combined with Aengus Kelly’s openness to participating in the aeroderivative opportunity, there is a scenario where AER significantly re-rates above its past historical multiple range if the company did start selling CFM56 engines to FTAI or if it embarked on converting the engines itself. We believe the former is much more likely, given that FTAI Power required a year of investment to operationalize and it was built on FTAI’s Maintenance, Repair & Exchange (MRE) platform (which is not something AER has).

If the market were to catch wind that AER is now selling engines to FTAI which are then being converted for data center power generation use cases, then this could lead to a re-rating. However, this is a rather speculative prospect to be banking on for further multiple expansion (again, which is why the Fund’s AER position is currently small) and we suspect that Aengus Kelly would prefer that there was not a significant re-rating in AER’s multiple. Rather, we’re almost certain he’d prefer to surreptitiously profit from the aeroderivative opportunity and then use those profits to repurchase large amounts of AER stock at a depressed multiple – the same playbook he’s been using for years.

This isn’t a terrible setup. Management has indicated that the aircraft shortage should persist into the 2030s, meaning that they should be able to continue selling aircraft assets at a healthy premium to book and buying back stock at a lower multiple. But if investors were to suddenly become giddy about AER being an indirect beneficiary of the data center buildout and subsequently bid up the stock, then AER stockholders obviously do well in that scenario too.

AER is one of those rare opportunities where you have very good visibility over the value drivers, but with the stock having re-rated somewhat and many other stocks in this market having significantly de-rated, we are currently holding AER as a smaller sized position in the Bristlemoon Global Fund.

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Disclaimer / Disclosures

The information contained in this article is not investment advice and is intended only for wholesale investors. All posts by Bristlemoon Capital are for informational purposes only. This article has been prepared without taking into account your particular circumstances, nor your investment objectives and needs. This article does not constitute personal investment advice and you should not rely on it as such. This document does not contain all of the information that may be required to evaluate an investment in any of the securities featured in the document. We recommend that you obtain independent financial advice before you make investment decisions.

Forward-looking statements are based on current information available to the author, expectations, estimates, projections and assumptions as to future matters. Forward-looking statements are subject to risks, uncertainties and other known and unknown factors and variables, which may affect the accuracy of any forward-looking statement. No guarantee is made in relation to future performance, results or other events.

We make no representation and give no warranties regarding the accuracy, reliability, completeness or suitability of the information contained in this document. To the maximum extent permitted by law, we do not have any liability for any loss or damage suffered or incurred by any person in connection with this document.

Bristlemoon Capital Pty Ltd (ABN: 22 668 652 926) is an Australian Financial Services Licensee (AFSL Number: 552045).

George Hadjia and Daniel Wu are associated with Bristlemoon Capital Pty Ltd. Bristlemoon Capital may invest in securities featured in this newsletter from time to time.

If you haven’t subscribed, you can join 5,600 others who enjoy our deep dives and investment insights here:

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A reminder that you can register for our upcoming fireside chat with Timee CEO Ryo Ogawa via the following link.

Australian wholesale investors looking to invest in the Bristlemoon Global Fund can do so via this link.

Introduction

Towards the end of last year, we started sifting through what we thought was the rubble of the software sector looking for attractive opportunities. Software sector multiples as measured by the IGV had fallen to the lows of the 2022 bear market on fears that AI would “kill SaaS” for a multitude of reasons: some sensationalist (vibe coding replacements for systems of record) and some real (seat loss, margin compression and/or more competition). Evidently, we were still too early as the rubble has subsequently been nuked into glass.

Source: Bloomberg. Note non-GAAP EPS

One stock that looked derisked enough for us to initiate a starter position is Adobe Inc (ADBE), which had derated to sub-20x forward GAAP P/E compared to a post-SaaS transition history spent almost entirely north of 40x. We laid out some initial high-level thoughts in our December quarterly letter as to why the AI disruption narrative for Adobe might be overstated. However, as we spent the past month or so drafting this article (while watching the market speedrun the diSaaSter —> catSaaStrophe —> SaaSmageddon trade), we’ve refined our framework for software stocks we want to own and formed a much more nuanced view on Adobe, which could reasonably be summarized by Superman:

This is not an article where we pontificate our views on why the SaaSpocalypse narrative is overblown, at least for some SaaS companies. There is already so much public discourse arguing both sides that we don’t have much incremental to add; but more importantly, the market doesn’t give a damn about what you or I think and anyone who resolutely believes they know what the world will look like in two years (let alone the 20+ years of software terminal value) is the real irrational actor.

This is also not a deep dive into the history and nitty-gritty details of Adobe’s business segments and products, because that’s not where we think the alpha lies in the present environment. As such, for those who are unfamiliar with the company, we’ve enlisted ChatGPT to provide an overview of Adobe’s product suite and use cases.

Source: Bristlemoon Capital, ChatGPT, Adobe

With this preamble out of the way, we can move onto the real question.

Is Adobe an AI loser?

To us, this question is ultimately about whether the value proposition of Adobe’s creative applications lies in “creation” or “orchestration/workflow”. In a generative AI world, do high-powered professional grade content creation/manipulation tools need to exist? No serious customer is going to vibe code an Adobe product replacement regardless of how frustrated they are with Adobe’s pricing. But might existing and prospective customers decide they need less Adobe in their lives?

To determine whether Adobe stock is a generational buying opportunity or a melting ice cube (or value trap at best), we believe it is critical to answer the following questions:

1. What does being an AI loser look like for Adobe?

2. Are Adobe’s products susceptible to being disrupted by AI?

3. Is Adobe’s business model or economics susceptible to being disrupted by AI?

4. Can Adobe self-disrupt using AI before an AI competitor or startup disrupts it?

5. If AI disruption does occur, where will it materialize?

In seeking to answer these questions, we focus mainly on Adobe’s Digital Media segment, which consists of Adobe’s Creative Cloud suite of applications plus Acrobat and is central to the stock’s terminal value. We believe that any decision to own Adobe stock must be underpinned by the view that the Digital Media business is insulated from AI disruption, as the Digital Experience (DX) segment is very unlikely to offset a deterioration of the core creative business.

We flag upfront that the remainder of this article expends no words contemplating the positioning of the DX business. At a high level, we think the DX products are more insulated from AI disruption because of consumption pricing and potentially higher usage if AI agents proliferate the internet. It is, in a sense, a narrow System of Record for the marketing organization of an enterprise. However, this segment is only 25% of total revenue (unchanged since FY19, so it hasn’t increased in importance), and likely a lower share of earnings given its low-70% gross margin compared to 95% for Digital Media. So, if Digital Media is disrupted by AI, DX is not going to save the day.

What does Adobe being an AI loser look like?

As one of the pioneers of the perpetual license to SaaS transition, Adobe’s core business model is underpinned by monthly Price * Quantity. If AI was disrupting Adobe’s digital media business, we would expect to see any of the following:

• Seat count reduction

• Pricing power erosion

• Margin compression

It has been more than three years since the ChatGPT moment, and Adobe’s financial performance is not representative of a company that is an AI loser.

Source: Bristlemoon Capital, company filings

Adobe does not report seats, churn or net retention, but we can infer from the ARR growth above and price increases in recent years that seat count has not declined post-ChatGPT, nor has pricing power been eroded. The net increase in Digital Media ARR looks healthy, and margins have remained stable at a high level. By all indications, this does not look like the financial profile of a business that is being disrupted by AI, and the first-order conclusion is that the current narrative-driven derating has completely dislocated from fundamentals. But, seeing as the market price is the consensus NPV of all future expectations (rational or otherwise), we need to turn our minds from past performance to the future and think about how things are evolving.

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Are Adobe’s products susceptible to being disrupted by AI?

Before answering this question, let’s take a step back and briefly look at the broad categories of software that exist and what characterizes them as more or less susceptible to AI disruption. There is plenty of existing material on the Internet written about this topic and we are not trying to reinvent the wheel here, so we enlist ChatGPT again to provide a high-level overview.

Source: Bristlemoon Capital, ChatGPT

As we can see, Adobe’s core creative software falls within the most vulnerable category of software – productivity-based application software. This explains why Adobe stock has experienced one of the more extreme de-ratings within the software sector and currently trades below 15x forward GAAP P/E and ~1x PEG ratio. It is this objectively, rather than subjectively, cheap multiple that attracted our attention initially.

To answer whether Adobe’s products are susceptible to AI disruption, it is helpful to separate Adobe’s “job to be done” into creation and workflow. Within the creative fields, some software tools are used mainly for the creation of digital assets (e.g. Autodesk Maya, Blender, Adobe Photoshop in some cases) and other tools are used mainly for workflow orchestration or getting a project from ideation through to finished product.

If workflow is the most important job to be done by Adobe software, the company should ultimately prove to be a beneficiary of generative content creation. Generative image and video models that enable much faster and cheaper content creation should drive an explosion of content, some of which will require software like Adobe Photoshop or Premiere Pro to polish into a professional finished product (i.e. TAM expansion).

If creating content is the most important job, the value of Adobe’s software is at risk of erosion because an alternative tool (an image or video model) can do it more efficiently and at lower cost. A high-powered, hard-to-master application like Photoshop gets relegated to the utility workflow layer where minor edits are made to already-generated content. Even if the Adobe workflow remains essential, pricing power may be impaired if users and/or expense approvers perceive greater value in the creation stage.

In simple terms:

• Creation is highly susceptible to AI disruption and is already happening

• Workflow orchestration is less susceptible to AI disruption and is not happening (yet)

Much of the bear case for Adobe’s creative software rests on people (whether amateur or professional) being able to create images, illustrations, videos etc. using just natural language prompts instead of spending years mastering Adobe’s feature-rich but complex user interface (UI). Thus, even as demand for content creation explodes, Adobe’s seat-based business model may shrink.

We think this perspective largely misunderstands Adobe’s role in the content production process. Even before the proliferation of generative AI, “creation” – as in something from nothing – rarely started within Adobe’s product suite. For example, take Photoshop and image editing. The image most likely originated as pixels on a digital image sensor, which was then uploaded into Photoshop for the editing process. If the intended output is a composite image, Photoshop’s role as the orchestration layer is even more important, allowing the user to combine and manipulate disparate visual elements within one image.

The same can be said about software like Premiere Pro (and competitors such as Avid Media Composer or DaVinci Resolve) for video post-production, i.e. they are used for the editing stage once the raw footage has already been produced. Sure, Nano Banana Pro or Kling 3 might be able to one-shot a simple creative prompt, but natural language edits using these models can be clunky and imprecise (essentially re-prompting the model to generate the output slightly differently). In these cases, having precise human-controlled editing tools may even be more time and cost efficient.

Of course, this will depend on the creative field as well, e.g. illustrators or digital artists who start with a blank canvas and a flexible creative brief will be most disrupted by “good enough” AI image generators. So, we don’t dispute the assertion that some Adobe products will face some disruption, but AI’s raw generation capability is likely overstated as a risk.

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Having said all this, the one reason we find it so hard to disprove a steelman AI disruption thesis is that the AI we have today is the worst it is ever going to be. Therefore, dismissing the disruption risk because AI image gen can’t one-shot a complicated scene with perfect fidelity or AI video still occupies the uncanny valley is likely to be shortsighted. Likewise, a strongly held belief that Adobe will retain its central role as workflow orchestrator is a bet that AI development won’t advance to a stage where precise compositions and edits are possible via text prompt or that Adobe tools won’t be relegated to the backend of an agentic natural language UI.

Source: X

Ultimately, all tools are a means to an end. For the commercial creative field, the end is a piece of content that adheres to some specification. Adobe is one of multiple tools used to achieve that end, and AI that can directly (now, or in future) create the output poses a threat to the incumbent tools and workflows used to create that same output. For this reason alone, it would be difficult to size up ADBE as a high conviction investment.

The remainder of this article is reserved for paid subscribers, where we discuss whether Adobe’s business model and economics are susceptible to AI-disruption, what Adobe can do to mitigate any pressure on seats and pricing, and turn our minds to how AI disruption might materialize in Adobe’s business in the future. A big thank you to our paid subscribers!

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We’re pleased to share that Bristlemoon Capital will be hosting a live webcast discussion with Ryo Ogawa, Founder & CEO of Timee.

Timee is Japan’s leading marketplace for spot-work labour, enabling real-time matching between flexible workers and employers to address the acute labour shortages that exist in Japan. Ryo-san, who launched Timee in 2018 when he was just 21, has scaled the platform to 12.7 million registered workers, reshaping how short-term labour is matched within Japan.

The conversation will be led by George Hadjia, Chief Investment Officer of Bristlemoon Capital, and will explore Timee’s business model, long-term strategy, and the structural forces shaping the flexible labour market in Japan.

For background reading, you may wish to review our most recent December 2025 quarterly letter, which includes our high level investment thesis on Timee and reasons for holding it in the Bristlemoon Global Fund.

Zoom registration link: https://us06web.zoom.us/webinar/register/WN_8GFWs9ImTUKWz8spxMtAgQ

Webcast details

• Date: Thursday February 12th

• Time:

  • 8am JST

  • 10am AEST

  • 6pm ET (Wednesday February 11th)

  • 3pm PT (Wednesday February 11th)

• Format: Zoom webcast

• Duration: ~45–60 minutes discussion, followed by audience Q&A

• Language: Japanese with English interpretation

Discussion topics will include

• Timee’s origin story and founding philosophy

• Product-market fit and user engagement dynamics

• Unit economics, monetisation, and scalability

• Competitive landscape and barriers to entry

• Management’s long-term vision and capital allocation priorities

George will be asking Ryo-san a prepared set of questions, followed by time for audience Q&A.

A replay will be made available to registered attendees following the event.

We hope you can join us for what should be a thoughtful and wide-ranging discussion.

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Disclaimer / Disclosures

The information contained in this article is not investment advice and is intended only for wholesale investors. All posts by Bristlemoon Capital are for informational purposes only. This article has been prepared without taking into account your particular circumstances, nor your investment objectives and needs. This article does not constitute personal investment advice and you should not rely on it as such. This document does not contain all of the information that may be required to evaluate an investment in any of the securities featured in the document. We recommend that you obtain independent financial advice before you make investment decisions.

Forward-looking statements are based on current information available to the author, expectations, estimates, projections and assumptions as to future matters. Forward-looking statements are subject to risks, uncertainties and other known and unknown factors and variables, which may affect the accuracy of any forward-looking statement. No guarantee is made in relation to future performance, results or other events.

We make no representation and give no warranties regarding the accuracy, reliability, completeness or suitability of the information contained in this document. To the maximum extent permitted by law, we do not have any liability for any loss or damage suffered or incurred by any person in connection with this document.

Bristlemoon Capital Pty Ltd (ABN: 22 668 652 926) is an Australian Financial Services Licensee (AFSL Number: 552045).

George Hadjia and Daniel Wu are associated with Bristlemoon Capital Pty Ltd. Bristlemoon Capital may invest in securities featured in this newsletter from time to time.

Key performance contributors in the month of December included AppLovin, Synopsys, and PAR Technology Corporation. Notable performance detractors over the same period included Timee, Fair Isaac Corporation, and Uber Technologies. Notable positive contributions from the Fund’s short book in December included National Vision Holdings, Nike, and Starbucks.

In the letter (accessible via the link below) we discuss some of the new positions in the Fund – Adobe, Baltic Classifieds Group, and Timee – as well as give a more detailed investment thesis around our recently initiated position in Floor & Decor Holdings.

By accessing the link below, you acknowledge and agree that you are an Australian wholesale client as defined by Section 761G of the Corporations Act 2001.

Bristlemoon Global Fund December 2025 Quarterly Report Vf

1.29MB ∙ PDF file

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Investing in the Bristlemoon Global Fund

For new investors looking to enter the Fund, more information can be found here.

We appreciate your interest in Bristlemoon Capital and hope this letter provides a clear view of our investment philosophy, current portfolio positioning, and ongoing efforts to compound capital prudently over time. If our investing approach resonates with you, we encourage you to connect with us to explore an investment in the Fund.

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Disclaimer / Disclosures

The information contained in this article is not investment advice and is intended only for wholesale investors. All posts by Bristlemoon Capital are for informational purposes only. This article has been prepared without taking into account your particular circumstances, nor your investment objectives and needs. This article does not constitute personal investment advice and you should not rely on it as such. This document does not contain all of the information that may be required to evaluate an investment in any of the securities featured in the document. We recommend that you obtain independent financial advice before you make investment decisions.

Forward-looking statements are based on current information available to the author, expectations, estimates, projections and assumptions as to future matters. Forward-looking statements are subject to risks, uncertainties and other known and unknown factors and variables, which may affect the accuracy of any forward-looking statement. No guarantee is made in relation to future performance, results or other events.

We make no representation and give no warranties regarding the accuracy, reliability, completeness or suitability of the information contained in this document. To the maximum extent permitted by law, we do not have any liability for any loss or damage suffered or incurred by any person in connection with this document.

Bristlemoon Capital Pty Ltd (ABN: 22 668 652 926) is an Australian Financial Services Licensee (AFSL Number: 552045).

George Hadjia and Daniel Wu are associated with Bristlemoon Capital Pty Ltd. Bristlemoon Capital may invest in securities featured in this newsletter from time to time.

If you haven’t subscribed, you can join 5,449 others who enjoy our deep dives and investment insights here:

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Australian wholesale investors looking to invest in the Bristlemoon Global Fund can do so via this link.

Introduction

Private equity in public markets – it’s a phrase often used by fund managers, and one that borders on being trite. But it’s no wonder that fund managers tend to overuse the expression – private equity as an asset class has performed decently well. The Cambridge Associates US Private Equity Index achieved a pooled annual net return of 12.09% over the last 25 years, compared to the annualized returns of 8.46% and 9.38% for the Russell 2000 and the S&P 500 indices, respectively[1].

There is, however, a publicly listed company that has implemented the “private equity in public markets” concept better than anyone else. That company is TransDigm (TDG), and its financial and stock price performance trounces that of even the most respectable private equity outfits.

TransDigm was formed in 1993 with initial equity of $25 million, after which not a dollar of additional equity was contributed to the company. Today, TDG’s equity value is $72 billion. In other words, TransDigm has returned 2,880x its initial invested equity.

Since its 2006 IPO, the stock has increased from $21 per share to $1,270 per share, a 23.1% annualized return for almost 20 years! This has been supported by revenues and earnings growing at 17% and 23%, respectively, over the last 20 years. TransDigm, with these numbers, is one of the all-time great stock market compounding stories.

We will look at what makes TransDigm a phenomenal business, some of the risks inherent in the company’s approach to achieving such stellar results, and what the future could hold for TDG.

Business Overview

TransDigm is a designer, producer, and supplier of highly engineered aircraft components. In fact, the company sells its components into nearly every commercial and military aircraft worldwide. If you’ve ever boarded a plane, it’s a virtual certainty that you’ve interacted with one of their many products.

TDG’s products are numerous and varied, with the company supplying components such as aircraft passenger seatbelts, cockpit security systems, actuators, and the list goes on. Below is an overview of TDG’s products.

Source: 2024 TransDigm Analyst Day

TDG’s business is organized into three segments:

1. Power & Control – includes operations that make components and systems to provide power to or control the power of the aircraft using electronic, fluid, power and mechanical motion control technologies. This includes products such as mechanical actuators, ignition systems, specialized pumps and valves, batteries, etc.

2. Airframe – includes operations that make products used in airframe applications. This could be latching and locking devices, connectors, cockpit displays, lavatory components, seat belts, thermal protection and insulation. In addition to systems and components, TDG also has some services based businesses such as wind tunnel and jet engine testing services, and equipment testing solutions.

3. Non-aviation - includes seat belts for ground transportation, actuators and controls for space applications, parts for gas turbines, and refueling systems for heavy equipment used in the mining and construction industries.

TDG’s operating units sell components into the original equipment and aftermarket channels. Their customers include OEMs such as Boeing and Airbus, suppliers that sell to the OEMs, airlines, third party maintenance suppliers, and military buying agencies.

Below is a snapshot of TDG’s revenue mix by end market for FY24.

Source: Bristlemoon Capital; company filings

It is worth noting that TransDigm is a holding company that owns a collection of mostly aerospace component suppliers. Each business operates separately under its own brand, as opposed to going to market under the TransDigm name (we touch on this highly decentralized approach below). Importantly, all of these businesses have been acquired.

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Private equity approach

TransDigm is essentially a private equity firm that specializes in the aerospace industry. Since its founding in 1993, TransDigm has acquired more than 90 businesses. Today, these acquired businesses are split into 51 independently run operating units that collectively produce parts for nearly every commercial and military aircraft platform.

Below is a history of the numerous acquisitions TDG has made both as a private company as well as after it went public.

Source: 2024 TransDigm Analyst Day

The company employs significant leverage to enact its acquisitive growth strategy, running net debt / EBITDA at almost 6x on average for the last 10 years. Usually this significant level of leverage would concern us and rule out the company as an investment prospect.

We’d point to two things that give us comfort: 1) TDG derives a significant portion of its earnings from the aftermarket (c.3/4 of adjusted EBITDA comes from the aftermarket), which is a much more stable earnings stream; and 2) the business navigated and survived two of the most severe exogenous shocks (the GFC and COVID), testing their highly levered strategy and showing that their model can withstand material demand shocks and credit crunches. Aside from a -6% EBITDA decline in FY20 and -4% in FY21, TDG has never experienced a full-year EBITDA decline, hinting at a well-managed, resilient business that is capable of supporting elevated amounts of leverage.

OEM and aftermarket

TDG sells its products into original equipment manufacturer (OEM) markets as well as the aftermarket. TDG supplies components to OEMs which include the major global aircraft manufacturers (e.g., commercial aircraft manufacturers such as Boeing and Airbus, as well as private aircraft manufacturers like Gulfstream, Bombardier and Cessna) and defense contractors (e.g., Lockheed Martin and Northrop Grumman). Often, however, TDG is selling products to other suppliers, who then sell to companies such as Boeing and Airbus.

The OEM market is defined as the sale of components to the aircraft manufacturer (or related suppliers that sell to that aircraft manufacturer) for the production and initial delivery of new aircraft.

The aftermarket, on the other hand, refers to the supply of replacement parts for aircraft that are already in operation. It is a much more stable stream of revenues that is correlated to the distance flown by aircraft; revenue passenger kilometers (RPKs) have grown at 5-6% for decades, providing a nice demand tailwind. In other words, more flight hours lead to more wear on an aircraft’s components, necessitating periodic replacement of those parts.

It is worth noting that the aerospace industry follows a two tier pricing model. The OEMs (e.g., Airbus and Boeing, or a supplier selling into these firms) pay TDG one price for the original part and additional warranty parts. The OEMs exert their duopolistic leverage over the aerospace supply chain to push down the prices they pay for these parts so that they can improve the margin they make on selling planes.

These planes are then sold to airlines, who are then responsible for purchasing aftermarket replacement parts at a higher price than what the OEMs initially paid. Sometimes there are long-term agreements (LTAs) that specify the aftermarket pricing that TDG can charge, but typically there’s flexibility for TDG to exercise its pricing power in the aftermarket.

The graphic below gives a snapshot of the aerospace value chain and highlights where TDG derives the bulk of its earnings from.

Source: Compounding Labs

In essence, the OEM market is where TDG gets its proprietary parts designed into a platform, securing an annuity-like revenue stream for the decades-long aftermarket phase (which is much higher margin than selling to the OEM market). In this way, TransDigm is implementing a razor razor-blade model: sell the parts cheaply upfront and make the margin on the long tail of replacement demand for that part (note that TDG’s philosophy is that you don’t have to lose money on the upfront sale to the OEMs, even if many other suppliers do).

In the original 2003 investment memo from Berkshire Partners when they were looking to acquire the then-private TransDigm, it was estimated that the company will usually achieve $100 of aftermarket sales over the lifespan of an aircraft from each $1 of OEM sales[2]. This long-tail of high margin replacement demand is one of the most attractive aspects of TransDigm’s business.

Why TransDigm is a Great Business

There are reasons why TransDigm’s aftermarket-focused strategy has worked spectacularly well. Essentially, the businesses TDG has acquired have unique characteristics that have enabled the company to raise the prices of its products over long stretches of time without customers switching to other suppliers. We will dig into each of these characteristics in turn.

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Introduction

As a recap, Hemnet is the monopolist for online real estate listings in Sweden with a c.90% share of listings. Think of it as the Swedish-equivalent of Australia’s realestate.com.au. The growth in the business had been spectacular: Hemnet’s property seller revenue increased by 6x between FY18 and FY24 (a 34.8% annual growth rate), and consolidated operating profit increased by 8.6x over the same period (a 43.3% annual growth rate). We wrote about the stock in our March 2025 quarterly letter, with a bullish thesis that mapped out the potential for a continuation of this strong performance. We also discussed Hemnet’s pricing actions here in a subsequent article.

What has happened in the interim has been a deterioration in the growth rate of Hemnet and a steep decline in the share price. For context, Hemnet’s share price fell from SEK 420 in February 2025 to around SEK 160 in recent weeks. We wanted to explain in greater detail what has happened to cause such a precipitous share price decline, whether the bear case has merit, and what the future could look like for Hemnet from here.

Is Hemnet’s moat broken and its future growth prospects permanently impaired, or is this a monopoly asset whose price has been buffeted by erroneous narratives, with the company’s shares now trading at an abnormally low 22x forward EPS?

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Table of contents

• Introduction

• Are we in an AI bubble?

  • Demand

  • Willingness to pay

  • Unit economics

  • Supply

• TSMC N2 capex

• ASML EUV demand

• Nvidia revenue outlook

Introduction

Over the past few weeks, we decided to take stock of the ongoing AI investment cycle in an attempt to gauge where we are in the cycle and how much further there is for fundamentals to run. Essentially, we wanted to understand the magnitude of AI capex baked into current share prices and whether there could be substantially more upside or downside to estimates and thus prices.

Unfortunately, Jensen Huang stole some of this post’s thunder with his revelation at GTC last week of $500 billion of booked Blackwell and Rubin orders to ship in CY 2025 and 2026. We arrive at similar magnitudes of revenue and explore what we think this means for a subset of the AI supply chain. We have limited exposure in the Fund to AI capex beneficiaries and instead have preferred to own early and obvious AI application (verb) winners. As such, this discussion is limited to the supply chain stocks that we are interested in (ASML, NVDA and TSMC), but the implications might be applicable to the AI cycle participants more broadly. As always, readers should do their own due diligence and draw their own conclusions.

The chart that pushed Nvidia to $5 trillion market cap

This exercise is also motivated by a quote from Eowyn, Shieldmaiden of Rohan: “The women of this land learned long ago that those without swords can still die upon them.”

In other words: if the AI capex beneficiaries leading the markets higher were to falter, we believe they could bring the entire market down with them; being diversified or underweight AI exposure is no consolation on the way up or down. It should not be lost on anyone that AI related stocks have accounted for 75% of S&P 500 returns, 80% of earnings growth and 90% of capex growth since ChatGPT launched in November 2022[1]. We also can’t ignore the development that in 1H 2025, AI capex contributed more to US GDP growth than the US consumer[2].

Are we in an AI bubble?

We don’t think hyperbole around the “AI bubble” (on both sides) is helpful or instructive for rational decision making, so we endeavored to approach this analysis from both a top down and bottoms up perspective while checking any preconceptions at the door.

Starting with the top down view, we compare the available data points on demand and supply for AI.

Demand

Consumer demand for AI remains robust. OpenAI reported 800 million weekly users in September, up from 500 million in March. Google just disclosed 650 million Gemini app MAU on its Q3 2025 call, up from 400 million at Google I/O in May. Over 2 billion monthly users benefit from AI Overviews, while the initial rollout of AI Mode reached 100 million users in July. Meta likewise has disclosed some big user numbers – over 1 billion Meta AI MAUs on its Q3 call.

Source: NBER “How people use ChatGPT”

We know that WAUs and MAUs are metrics that don’t necessarily align with actual usage. For that, token demand is the better measure. Google has sporadically disclosed monthly tokens processed, and that number has grown exponentially. The company also disclosed that the Gemini API processes 7 billion tokens per minute, while OpenAI’s API processes 6 billion per minute – good for 300 trillion and 260 trillion tokens per month, respectively. Yes, tokens processed have been inflated by reasoning models, but we don’t have a breakdown of reasoning vs query growth. In any case, we believe higher quality output from small reasoning models vs non-reasoning models should drive greater AI usage.

Source: Bristlemoon Capital, Google

While OpenAI hasn’t consistently disclosed tokens processed, the “How people use ChatGPT” research paper by NBER does include data showing daily messages per WAU is increasing for all cohorts.

Source: NBER “How people use ChatGPT”

Earlier this month, there was some data from Apptopia (a third-party app intelligence firm) showing OpenAI app DAUs have plateaued since September, while average sessions and time spent have also declined. This period coincided with both the botched rollout of GPT-5 and Google’s launch of Nano Banana which pushed Gemini to the top of the app download charts, so we would be hesitant to exclaim “Aha! OpenAI has peaked!” on this data alone. Based on the most recent OpenAI and hyperscaler commentary, it appears that demand is still well in excess of supply and expected to remain so into 2026, despite the rapid AI server capacity additions.

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Enterprise AI demand has, unsurprisingly, lagged consumer adoption. One report recently making waves is a July study by MIT NANDA canvassing 1H 2025 that found 95% of enterprises were getting zero return on $30-40 billion of AI investment. To borrow the 20 second executive summary:

“Tools like ChatGPT and Copilot are widely adopted. Over 80 percent of organizations have explored or piloted them, and nearly 40 percent report deployment. But these tools primarily enhance individual productivity, not P&L performance. Meanwhile, enterprise grade systems, custom or vendor-sold, are being quietly rejected. Sixty percent of organizations evaluated such tools, but only 20 percent reached pilot stage and just 5 percent reached production. Most fail due to brittle workflows, lack of contextual learning, and misalignment with day-to-day operations.”[3]

Source: MIT NANDA

Our interpretation of the findings is that 1) enterprises are generally not ready to successfully implement AI, whether that be lack of sponsorship, data cleanliness, or resistance to change; and 2) the models – particularly narrow domain agentic models – are just not good enough yet. We don’t have a strong opinion on what enterprises can do to overcome the former limitations as each organization is different, but on the latter, we would just remind readers that the AI in deployment today is the worst that AI will ever be.

Yet even so, one very compelling enterprising use case for AI has already emerged – the AI coding agent. Many tech companies have revealed a high and growing percentage of their code base being initially written by AI coding agents (whether true or not, we can’t verify). Anthropic is the leader in this area and has just recently projected $20-26 billion in annualized revenue in 2026, up from $9 billion projected exiting 2025, and $1 billion in January 2025[4]. We believe a substantial majority of that revenue comes from enterprise, and specifically Claude Code. So, it seems that if an AI use case achieves strong product market fit, enterprises are willing to pay up for measurable improvements in productivity.

As an aside – AI bubble doomers often cite de minimis AI revenue as the key reason why the bubble must pop. Yet this MIT report analyzing 300 public GenAI implementations captured $30-40 billion of GenAI investments. Sure, a majority might be spent on headcount and consultants, but some AI service provider must be receiving some of this as revenue…

Willingness to pay

Having insatiable user or token demand is great, but means nothing and is unsustainable if given away for free. When we consider AI demand from a willingness to pay angle, the picture is perhaps somewhat less rosy. We think there is a strong case to be made that enterprises will pay if they see value in a product or service (basically the entire premise of the SaaS market). However, consumers evidently haven’t shown a strong willingness to pay for access to more capable models and higher rate limits.

It’s been reported[5] that of ChatGPT’s 800 million WAUs, only 5%, or ~40 million, are paying users. Subscriptions account for 70% of OpenAI’s $13 billion ARR, with API access contributing the remainder. This basically says to us that the 800 million users, taken as a whole, find ChatGPT no more valuable than a freemium mobile game, where a single digit percentage of “whales” account for substantially all a game’s revenue.

We would argue that ChatGPT provides more value than Candy Crush, but the difficulty is clearly in getting consumers to pay for the premium experience when a small, non-reasoning model is probably enough to satisfy the average user’s requirements. To that end, it is clear that OpenAI is preparing a shift towards an advertising model for ChatGPT, having hired Fidji Simo and several other Meta product alumni to build out the “applications” (i.e., non-research/AI) organization.

It remains to be seen whether OpenAI can build an advertising business rivaling the scale of Meta or Google, but the precedent of supporting $100+ billion annual capex buildouts using ad dollars is a strong one. Obviously, ad dollars don’t just materialize out of thin air, so unless AI adoption is driving incremental GDP growth, OpenAI will need to take share of ad budgets from somewhere.

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Unit economics

Taking a step back, a lack of consumer willingness to pay should not be conflated with weak unit economics. The “AI is a bubble” justification we take the most issue with is this misconception (often magnified by the media) that serving AI models to users is a loss-making endeavor. This is most often misrepresented as spending $2 or $3 dollars to generate $1 of revenue, with the implication being that these AI labs will never be profitable.

A rudimentary understanding of accounting is all that is needed to disabuse oneself of this misrepresentation. The Information reported that OpenAI generated an operating loss of $7.8 billion in H1 2025 on $4.3 billion in revenue, good for $3 in costs to generate $1 of revenue[6]. However, buried at the end of the report is a line stating that cost of revenue was only $2.5 billion, meaning gross profit was $1.8 billion for a 42% gross margin. This indicates that serving current models to users carries positive unit economics, with substantially all the operating loss attributable to training the next generation of models. This means that in time and at scale, frontier model providers will be capable of generating positive net profit.

To understand just how profitable model inference can be, we ran our own back-of-the-envelope tokenomics analysis. Note there are a lot of assumptions that go into this – we are aiming for ballpark estimates here. In summary, depending on model size and API pricing or paying user mix, inference gross margins might range from ~50% to as high as ~80%.

Source: Bristlemoon Capital

For the purposes of the analysis, we used DeepSeek V3 to approximate a medium-sized Mixture of Experts model that might be used in production. We also assume DeepSeek V3 API pricing, which is extremely low compared to the closed source frontier model pricing (e.g. GPT-5 is $1.25/$10.00 input/output and GPT-5 mini $0.25/$2.00). We haven’t found any reliable information on the sizes of the various GPT-5 models or which models are used for ChatGPT, so we’ve gone for the most conservative assumptions that generate the most conservative revenues.

As we can see, even with some of the cheapest API pricing in the market, served on the most expensive (on a GPU/hour basis) AI servers currently available, inference via API still commands a respectable gross margin for the model provider. This of course assumes the provider also owns the servers (e.g. Google or Microsoft) – if they rent the servers from a CSP, the gross margin will be lower.

We can also see that for a subscription model where 5% of users are paying $20 a month, the service provider can still generate attractive gross margins depending on the level of utilization and other optimizations they can achieve. Apptopia data indicates US ChatGPT users spend 20-25 minutes a day in the app, so one could even argue that 10 minutes of GPU active time is excessive, especially for non-thinking chats (i.e., the majority of free ChatGPT usage). Of course, these are averages, and the many free users who might use ChatGPT once a week may be more than offset by the power users on the $200 pro tier.

One final thing to note for OpenAI specifically is that it has a revenue share with Microsoft of just under 20%, in addition to renting server capacity from Azure. Yet even with these incremental contra revenues and COGS, the company is still achieving a 42% gross margin. As that revenue share declines through 2030 and OpenAI starts owning more of its servers (the 16GW it has signed directly with Nvidia and AMD), we expect gross margin to expand, even before considering additional monetization avenues such as advertising.

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Supply

Finally, we get to token supply, or more accurately, the demand for AI infrastructure. We see two key debates here: 1) how much (more) will the hyperscalers spend in capex, and 2) what kind of ROI are they earning on this capex?

The debate over how much AI capex can or will be spent is, in our opinion, a straightforward one to settle. The hyperscalers – mostly comprised of the world’s biggest cash flow printing presses – see the pursuit of AGI as existential. Some are more fanatical (Meta, Google, Oracle) than others (Microsoft, Amazon) but all have said they would rather have too much compute than too little. The ones pursuing AGI in particular are caught in a prisoners’ dilemma where the Nash equilibrium is for all involved to invest as aggressively as possible in that pursuit.

These hyperscalers have significantly more capacity to spend. If we compare current Bloomberg sellside consensus operating cash flows to capex for the five hyperscalers, we see that capex is “just” ~2/3^rd of operating cash flow for the group. Only Oracle is currently, and is projected to continue, spending more than its cash flow on capex. If the group were to spend up to 100% of their operating cash flows on capex (i.e. free cash flow = zero), current sellside capex estimates would need to increase by as much as 50%. If these companies were to collectively lever up to 1x EBITDA to invest in AI, their capex spend would be 3x higher than current estimates!

Note this doesn’t include any OpenAI investment in owned infrastructure (to the extent they can afford to pay for it…) nor any neocloud capex.

Source: Bristlemoon Capital, Bloomberg

How much the hyperscalers decide to invest in AI capex will ultimately come down to what return on investment they expect to generate. The ROI side of the debate is undoubtedly more subjective/biased because no one can predict with any degree of confidence what revenues might (or might not) be unlocked in future years as a result of ongoing AI investment. AI bulls will point to the acceleration of revenues at the hyperscalers (short term), plus historic precedent of the value unlocked by massive capex cycles (railroads, fiber etc; long term), as evidence of at least an acceptable level of ROI. Bears will point to the massive and growing operating losses reported by the frontier labs and say there is no ROI on AI investment.

Our opinion is that the returns will (for now) fall somewhere in the middle. We’ve established above that unit economics of serving inference are quite attractive, with the hurdle being a relatively low willingness to pay for today’s models. Outside of the standalone frontier labs (e.g. OpenAI, Anthropic), it is difficult to precisely ringfence how much revenue the hyperscalers are generating specifically from their AI investments. It is clear that Meta, Google, Azure and AWS are reporting accelerating revenue growth; what’s not clear is the extent to which AI is driving this acceleration.

We think it would be unfair to ascribe zero benefit to these companies’ AI investments, particularly for the likes of Meta and Google where AI-based recommender and ad models are clearly driving results. Likewise, it would be intellectually dishonest to claim that none of the AI capex is wasted and that these companies couldn’t have achieved similar results with some fraction of their current spend. For example, we know that Meta allocates some portion of its total AI server capacity to “core AI” (the ads business) and some portion to “gen AI” (the frontier superintelligence stuff), and that mix changes over time depending on the opportunity set.

The simplest way we can think about ROI is to again look at sellside estimates. We know that the sellside capex forecasts broadly reflect the continuation of the current AI investment cycle (even though they tend to underestimate the capex). We strongly believe that, collectively, sellside are not explicitly modeling specific AI revenue streams for these hyperscalers. Rather, they are typically modelling company guidance and extrapolating growth rates into the future. As such, any AI monetization breakthroughs are not captured in sellside earnings estimates, while the capex burden is. Even so, we can see that the incremental ROI on the hyperscalers’ total capex is still expected to be decent, and certainly above their cost of capital (while returns for individual hyperscalers may vary).

Source: Bristlemoon Capital, Bloomberg

We think the market has gotten accustomed to these companies generating outlandishly strong ROIC year-in year-out with their increasing returns to scale and dominance of their massive end markets. We note the above ROI is incremental, meaning their strong historical ROIC is simply being diluted during this capex investment cycle, rather than being imminently reset downward to a mid-teens level. In any case, a mid-teens post-tax ROIC with nearly 100% reinvestment rate at the scale these companies are operating at would be a textbook example of an attractive “compounder”.

Should the sellside be underestimating the monetization potential of all this AI investment, the realized incremental returns will be even more attractive. But should this mostly cashflow-funded investment really be a waste of capital, these companies can reduce their capex by 20%, 30% or even 50%, without necessarily hurting the performance nor the fundamental attractiveness of their core businesses.

Now that we have established our position on AI demand and supply, we discuss in the paid section what we think this means for TSMC’s capex, the demand for ASML EUV tools, and implications for Nvidia’s revenue. Consider becoming a paid subscriber for full access to our analysis.

TSMC’s N2 capex

When TSMC reported its Q3 2025 earnings last month, the company narrowed its 2025 capex guide to $40-42 billion from $38-42 billion but did not comment on the 2026 capex outlook. Sellside consensus for 2026 has been raised by ~5% to $46 billion, but we think this may still be too low.

The first thing to note is that TSMC typically spends ~70% of its capex on advanced nodes (N7 and below), 10-20% on specialty mature nodes, and 10-20% on advanced packaging and other backend processes. Within its budget for advanced nodes, TSMC is typically investing across multiple nodes in any given year. The majority is allocated to the current leading edge node being ramped (N2), with the remainder spent on both further expanding capacity in the sub-leading node (N3) and laying the physical groundwork (land, fab shell construction) for the next node (A14).

TSMC management has consistently said that they expect N2 to be a bigger node than both N3 and N5, based on the number of N2 tape outs so far. Sellside estimates of N3 and N5 capacity are all over the place, but we have seen estimated peak capacity of 150-170kwpm for each node. If N2 is to be TSMC’s biggest node, it would suggest peak capacity of at least 170kwpm, possibly even 180kwpm. In addition to the volume of N2 tape outs to date, HPC and smartphone volumes are also expected to converge at N2.

Historically, smartphone customers (namely Apple) would lead the ramp of each new node, with HPC – both CPU and GPU/ASIC – lagging by one or two generations. This meant that as Apple prepared to move to the next node, HPC customers would step in and fill the capacity that Apple vacated in the prior node. With N2, however, AMD is expected to lead the high-volume ramp alongside Apple and MediaTek. Nvidia will be using N3 for its Rubin generation of GPUs after using N4 for both Hopper and Blackwell, and is expected to jump to N2 or even straight to A16 with its Feynman generation in 2028.

Noting the above, we believe TSMC may need to spend as much as $85 billion in capex to build out 180kwpm capacity for N2, spread over ~3 years starting in 2025. This translates to ~$4.7 billion capex per 10kwpm, which is materially higher than some of the early sellside N2 capex estimates we heard in the ~$3 billion per 10kwpm range.

We believe the reason for this is twofold: 1) sellside likely significantly overestimates ASML’s EUV tool productivity – which as we will show below may be only a third of nameplate throughput in practice; and 2) for N3, TSMC had the benefit of situating those fabs alongside its existing N5 fabs at its Tainan Gigafab, allowing for tool reuse and conversion of spare N5 capacity into N3. TSMC’s N2 fabs are in Hsinchu, located on the opposite end of Taiwan island, thus limiting capacity conversion of N3 into N2. The Arizona Gigafab situates N5, N3 and N2 fabs together, but given the strategic importance of TSMC’s leading edge capacity and lack of alternatives in the US, we do not believe TSMC will be doing node conversions in the US any time soon.

We arrive at our $85 billion estimate using a bottom-up approach starting with ASML EUV tools given we have the specs for these machines and a rough idea of EUV layer count. We then apply some rules of thumb to scale up our estimated EUV capex into lithography capex, total WFE capex and finally total fab capex. For context, this is likely to be more than the capex for N3 even though EUV layer count remains flat (the most expensive process step), for the reasons we described above.

Source: Bristlemoon Capital, ASML filings

ASML EUV demand

It may be apparent now that although we are discussing TSMC capex, we are actually using this to frame demand for ASML’s EUV tools. Our estimates suggest as many as 80 EUV tools are required for 180kwpm of N2 capacity assuming no tool reuse, again likely spread over a 3-year ramp. This means even if TSMC purchases 20 EUV tools this year, it will still need to materially step up its purchases over the next two years compared to its previous peak of 2021-22 when it was ramping up the N3 node.

We also make an attempt to estimate HBM and DRAM demand for EUV, considering the memory vendors collectively are now the second largest source of EUV demand after TSMC. We would note that this is even more of an approximation than our estimate of TSMC’s EUV demand, considering 1) estimates of HBM demand over the next few years could be wildly off target, and 2) memory vendors engage in even more tool reuse than TSMC as the former typically do not maintain production capacity for more than two trailing nodes.

Source: Bristlemoon Capital, broker estimates

The memory vendors are shaping up for two big years of ASML equipment purchases in 2024 and 2025, so we believe 2026 may be a slower year considering the lack of cleanroom space and a general reluctance by the big 3 to invest in greenfield capacity expansion when they are currently enjoying unprecedented pricing power. However, by 2027 we expect EUV demand to pick up again as the industry will be forced to expand capacity by HBM customers (unless the AI bubble has burst by then…), and lest they start losing share in commodity DRAM to CXMT.

Overall, we see more upside to ASML’s EUV shipments in 2026 and 2027 than the 51 and 61 units being modelled by the sellside. We think TSMC alone could account for more than half of these units, along with a strong rebound in memory in 2027. TSMC’s N2 price – rumored to be $30,000 per wafer – is now said to be a 10-20% increase from N3 compared to a previously rumored 50% jump, but only because N3 prices have now risen from ~$20,000 to above $25,000 each. On Monday, TSMC unveiled an unprecedented four-year plan to raise advanced node pricing (N5, N4, N3 and N2) by as much as 10% a year, citing cost pressures and tight capacity. It is evident that the industry needs an alternate source of leading edge foundry capacity, for both geopolitical and competitive reasons. A resurgent Intel (or even Samsung Foundry) could drive further upside to ASML’s numbers, especially as they are far less efficient with their tools than TSMC.

Nvidia revenue outlook

The last piece of the puzzle for us is reconciling TSMC’s capacity estimates with Nvidia and the broader AI accelerator industry’s wafer demand. Our objective is twofold: 1) to gauge if TSMC needs to further expand its N3 capacity (and the implications for ASML), and 2) to get a sense of Nvidia revenues based on TSMC capacity.

On 1), we have recently seen sellside analysts revise up their TSMC CoWoS capacity estimates to the ~100kwpm range exiting 2026, with ~50% allocated to Nvidia. As CoWoS has historically been the bottleneck for AI accelerator output at TSMC, this is a good gauge of what the upper limit to Nvidia’s revenue could be. 50kwpm CoWoS capacity translates to approximately 12 million reticle-limit GPUs.

On the N3 capacity front, 12 million reticle-sized GPUs at mature yields would require in the vicinity of 430k wafers to produce, or nearly 36kwpm. We estimate that the CPU and networking-related silicon take up half the GPU die area or another 18kwpm. We further assume that Nvidia is 65% of total AI accelerator wafer demand, based on its 50% share of CoWoS and reticle-sized dies compared to smaller custom ASIC dies or AMD’s chiplet architecture. This gets us to over 80kwpm of AI accelerator wafer demand for N3, which would be more than half of N3 capacity assuming 160kwpm peak.

Source: Bristlemoon Capital

This doesn’t quantitatively tell us whether TSMC needs to build or convert additional N3 capacity. However, looking at past nodes, when flagship smartphone customers stepped up to the next gen node, HPC (mainly CPU) and non-flagship smartphone customers would backfill the vacant capacity and then some. Those nodes certainly did not have to contend with tens of thousands of incremental wafer demand from AI customers, which is largely a new dynamic for N4 and especially N3, since both Rubin and TPU v7 are moving to N3P. So, at least qualitatively, this says to us that TSMC will be more constrained on N3 than it has been in sub-leading nodes past. Whether this means entirely new EUV tools are required or just upgrade kits to older converted tools, ASML should stand to benefit next year.

As it relates to revenue for Nvidia, 12 million Rubin GPUs in the NVL144 configuration would be something like $440 billion in revenue, assuming unchanged $/W pricing vs Blackwell. Once we layer networking revenues on top, we get potential VR200 generation data center revenue of as much as $500 billion, likely spread across 5-6 quarters. Note that this is not like-for-like with what Jensen presented at GTC, which was $500+ billion in 2025 and 2026, most of which will be GB200 and GB300, plus the initial ramp of VR200 in late 2026. Our estimates for Rubin are for FY28/CY27, with some overflow into late 2026 and early 2028. We have also not seen established pricing for the VR200 NVL144 yet, so our estimate of $5.3 million per rack based on the GB200’s $/W could be too high. Having said that, we’d be surprised if Rubin GPUs were not priced at some material premium to Blackwell, considering Rubin is a full node jump and also packs more HBM than GB200 (288GB HBM4 vs 192GB HBM3E).

Source: Bristlemoon Capital

We are not suggesting that there will be 30% upside to Nvidia’s consensus revenue estimates in FY27 based on our napkin math. Rather, we simply sought to identify the potential magnitude of delta between consensus and what could be achieved based on Nvidia’s capacity allocation at TSMC. Also, assuming Nvidia’s $500+ billion in AI server revenue across 2025-26 implies over $300 billion in 2026 (FY27), our Rubin estimates suggest 30%+ server revenue growth in FY28 is possible compared to consensus Data Center revenue growth of 20% off a smaller base. Tying this back to our earlier discussion of supply and demand, the hyperscalers alone have sufficient capex capacity between remaining FCF and modest leverage to close the gap between consensus and our estimates, assuming they see a path to an acceptable ROI.

Ultimately, our (perhaps unsatisfying) conclusion is that whether we are in an AI bubble or not is largely irrelevant to making sound investment decisions. What we care about is whether there is a rational basis for further upside to the forecasts of those companies involved in various aspects of the AI infrastructure buildout. Based on our work, we believe there is scope for the buildout to continue and for estimates to move higher through 2026 at the very least, backed by the hyperscalers’ capacity to pay, before we even get into enterprise and sovereign demand.

One final point, which we have not discussed here but did allude to briefly in this note, is that there are bottlenecks and constraints across the AI supply chain, from generation to transmission to chips, which militate against fervent overbuilding and stockpiling – behavior that typically triggers a cyclical downturn or in extreme cases, the popping of a bubble.

Disclaimer / Disclosures

The information contained in this article is not investment advice and is intended only for wholesale investors. All posts by Bristlemoon Capital are for informational purposes only. This article has been prepared without taking into account your particular circumstances, nor your investment objectives and needs. This article does not constitute personal investment advice and you should not rely on it as such. This document does not contain all of the information that may be required to evaluate an investment in any of the securities featured in the document. We recommend that you obtain independent financial advice before you make investment decisions.

Forward-looking statements are based on current information available to the author, expectations, estimates, projections and assumptions as to future matters. Forward-looking statements are subject to risks, uncertainties and other known and unknown factors and variables, which may affect the accuracy of any forward-looking statement. No guarantee is made in relation to future performance, results or other events.

We make no representation and give no warranties regarding the accuracy, reliability, completeness or suitability of the information contained in this document. To the maximum extent permitted by law, we do not have any liability for any loss or damage suffered or incurred by any person in connection with this document.

Bristlemoon Capital Pty Ltd (ABN: 22 668 652 926) is an Australian Financial Services Licensee (AFSL Number: 552045).

George Hadjia and Daniel Wu are associated with Bristlemoon Capital Pty Ltd. Bristlemoon Capital may invest in securities featured in this newsletter from time to time.

[1] https://assets.jpmprivatebank.com/content/dam/jpm-pb-aem/global/en/documents/eotm/the-blob.pdf

[2] https://am.jpmorgan.com/us/en/asset-management/adv/insights/market-insights/market-updates/on-the-minds-of-investors/is-ai-already-driving-us-growth/

[3] https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf

[4] https://www.reuters.com/business/retail-consumer/anthropic-aims-nearly-triple-annualized-revenue-2026-sources-say-2025-10-15/

[5] https://www.ft.com/content/a169703c-c4df-46d6-a2d3-4184c74bbaf7

[6] https://www.theinformation.com/articles/openais-first-half-results-4-3-billion-sales-2-5-billion-cash-burn?rc=oc3j7k

Key performance contributors in the month of September included AppLovin, ASML, and Alphabet. Notable performance detractors over the same period included PAR Technology Corporation, Salesforce, and Hemnet.

In the letter (accessible via the link below) we talk about PAR Technology Corporation, a performance detractor during the quarter, and explain why we still like the stock. We also outline our investment theses for several investments that performed well in the quarter, including ASML, Alphabet and Synopsys.

By accessing the link below, you acknowledge and agree that you are an Australian wholesale client as defined by Section 761G of the Corporations Act 2001.

Bristlemoon Global Fund September 2025 Quarterly Report Vf

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For new investors looking to enter the Fund, more information can be found here.

We appreciate your interest in Bristlemoon Capital and hope this letter provides a clear view of our investment philosophy, current portfolio positioning, and ongoing efforts to compound capital prudently over time. If our investing approach resonates with you, we encourage you to connect with us to explore an investment in the Fund.

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Disclaimer / Disclosures

The information contained in this article is not investment advice and is intended only for wholesale investors. All posts by Bristlemoon Capital are for informational purposes only. This article has been prepared without taking into account your particular circumstances, nor your investment objectives and needs. This article does not constitute personal investment advice and you should not rely on it as such. This document does not contain all of the information that may be required to evaluate an investment in any of the securities featured in the document. We recommend that you obtain independent financial advice before you make investment decisions.

Forward-looking statements are based on current information available to the author, expectations, estimates, projections and assumptions as to future matters. Forward-looking statements are subject to risks, uncertainties and other known and unknown factors and variables, which may affect the accuracy of any forward-looking statement. No guarantee is made in relation to future performance, results or other events.

We make no representation and give no warranties regarding the accuracy, reliability, completeness or suitability of the information contained in this document. To the maximum extent permitted by law, we do not have any liability for any loss or damage suffered or incurred by any person in connection with this document.

Bristlemoon Capital Pty Ltd (ABN: 22 668 652 926) is an Australian Financial Services Licensee (AFSL Number: 552045).

George Hadjia and Daniel Wu are associated with Bristlemoon Capital Pty Ltd. Bristlemoon Capital may invest in securities featured in this newsletter from time to time.

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Free subscribers will only receive a partial preview of our reports. The remainder of our reports, which contain the deeper analysis, are reserved for paid subscribers. Consider becoming a paid subscriber for full access to our reports.

Bristlemoon readers can also enjoy a free trial of the Tegus expert call library via this link.

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Lessons from launching a hedge fund

Last year, George Hadjia discussed the journey of launching Bristlemoon Capital, following an X (Twitter) post that garnered interest from the FinTwit community. One year following the launch of the Fund, George details further lessons from building Bristlemoon.

It is worth opening this post with some of the more memorable comments I received from people before launching Bristlemoon Capital.

• "How are you going to raise any money?"

• "If you don't partner with someone [a capital partner] I can tell you right now it's going to be a miserable failure".

• "You should go to another fund first and get a track record".

• "You're not smart enough to work at a hedge fund".

Starting a fund requires grit. Most people will be encouraging and supportive when you divulge your fund launch plans. Others won’t be. Having full conviction in what you’re trying to build from the outset, and the tenacity to see it through, is critical. Without a clear vision, any negative comments along the way might dissuade you from taking the fund launch plunge.

At the risk of stating the obvious, the odds of successfully scaling up a fund are low. An investment fund as a business requires a lot of AUM to become self-funding. With this in mind, it maybe sounds foolhardy, even crazy, to have launched Bristlemoon Capital last year with no major investor commitments, instead bootstrapping the business from my own savings.

Launching Bristlemoon has been the most challenging, but also the most fulfilling thing I’ve ever done. In this post I will detail some lessons I’ve picked up in the first year since launch that may be relevant for those contemplating starting their own fund, or starting a business in general. Launching any sort of business can be a difficult process – it helps to have a good reason for starting a fund, and to know what you’re signing up for.

Why start a fund?

Many investment analysts, whether secretly or overtly, harbor ambitions to one day start their own fund. I was no exception. During an interview for a hedge fund role in 2015, I was asked “where do you see yourself in 10 years?” My response, somewhat naively and perhaps far too candidly, was “I want to be running my own fund”.

To best frame the rationale for starting your own fund, it’s instructive to think through when it makes sense to not start a fund.

There are broadly two reasons for staying at an existing fund as an analyst: 1) you are learning a lot from those around you; and/or 2) you are being paid well for your contributions.

If you are achieving both, and are given research autonomy by your portfolio manager, then this can make for a satisfying job. If you are achieving neither, then the impetus to leave and start your own fund will be higher.

While it makes sense to hone your craft at an established fund early in your career, the problem is that your learning curve as an investment analyst will inevitably begin to flatten. In other words, the rate at which you are accumulating new investing knowledge from those around you will naturally slow down over time. This dynamic is especially noticeable as you get more senior in your career, given you have already built up a base of expertise.

As the competence gap between yourself and your portfolio manager narrows, the potential for pushback on your portfolio manager’s thoughts and ideas increases. This pushback often serves as the fuel for wanting to start your own fund. It can become more difficult to maintain job satisfaction if your stock and business suggestions are being overruled by a portfolio manager you increasingly view as an equal.

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Another factor that feeds into the fund launch calculus is the way that a fund’s economics are split, with your superiors typically taking a larger slice of the pie.

Starting a fund may seem like a neat way to kill two birds with one stone, enabling you to: 1) capture full decision-making autonomy; and 2) benefit from a greater share of the fund’s economics.

Sure, these are possible benefits from starting your own fund. But you are giving something up for them; don’t forget that.

When you are working at a fund as an employee, you will only capture a mere sliver of the value you are generating. But in return, you enjoy a level of stability and safety. You will receive a salary, knowing precisely how much will be deposited in your bank account each month. Many of the strategic and operational decisions will be borne by somebody else, enabling you to spend the majority of your time researching stocks. You will likely receive mentorship and there will often be a clear, pre-defined path for career progression.

Launching a fund means abandoning these benefits that stem from being an employee, and not an owner. You will be out on the open seas and it’s incumbent upon you to navigate the route forward when managing your own fund.

You have to acknowledge and be prepared for everything that comes with the fund launch route. It is far more than just picking stocks and reaping the associated rewards. You will be running a business – the operational, compliance, regulatory, tax, and marketing burdens will need to be considered. You will be running a team as the fund scales, and you must be equipped to deal with a multitude of people issues. You are responsible for building your fund’s brand, letting the world know that you’re open for business and are worth investing in.

Much of your mental bandwidth will be consumed by the venture, so you must accept that simply shutting off is not really an option. There is no convenient “off switch” when you have been entrusted with managing the capital of others.

If all of this sounds like an acceptable trade-off, then it’s worth thinking through the various ways in which you can approach a fund launch.

The different ways to start a fund

The number of fund launch options available to you will heavily depend on your personal circumstances. The key considerations are as follows:

• Your experience and whether you have a marketable track record;

• The amount of personal capital you have saved;

• Your relationships with individuals and organizations that would be comfortable backing you; and

• Your risk appetite.

We can plot the different fund launch archetypes along a spectrum. Notably there’s often an inverse relationship between the level of risk taken in the fund launch approach and the level of fund economics you will capture (but not always).

Launching an internal fund within an established fund manager

This route is the least risky from a financial sense. You will likely be underwritten with a salary, and the fund manager bears the expenses of the fund. The trade off is that you will keep a much smaller share of the fees – often just some portion of the performance fee for the capital you have discretion over. While these arrangements vary, at best you might be able to capture half of the performance fee.

This is potentially still very lucrative, particularly if the fund has a good reputation and established distribution capabilities to scale up your internal fund.

Partnering with a capital partner

In this arrangement, you will ostensibly be running your own fund and will be able to build a brand. You will split the economics with the capital provider on the basis that they will do the heavy lifting on raising money for the fund. This can work very well. It can also be problematic to the extent that often these capital partners are representing multiple investment managers, with potential conflicts of interest to the extent there are other managers in the same asset class with a similar strategy that are also being marketed to allocators.

Furthermore, you won’t have control over the distribution personnel, which are interfacing with end clients and representing your brand. This is fine if the capital partner has professional and competent personnel. It can be an enormous distraction to the extent that they don’t, and any decision to separate from a capital partner could be a messy undertaking given the sharing of economics.

An alternative is to launch the fund independently, build up a track record, and then retain the option to partner with a capital partner if the fund’s AUM does not reach sufficient scale.

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Launching a fund independently

Within this option there are a variety of flavors. Launching out of a high-pedigree, name-brand fund with your own track record will translate into a very different AUM scaling path compared to launching from a no-name fund with no track record. While success in the latter scenario is not impossible, and there are cases of under-the-radar managers launching with $1-2 million of AUM with no pedigree and no track record, who then scale to hundreds of millions of dollars of AUM in a matter of years, it is a much harder path.

The trick is properly appreciating how the AUM ramp up might look for the type of fund launch you choose and then appropriately budgeting for that. A common reason emerging managers fail is that they run short of personal capital before they can get enough traction. In order for your fund to be able to scale, you need the financial runway to stay in the game long enough to prove yourself to investors.

It’s critical to be realistic about how long it takes to properly scale a fund. It is an almost certainty that it will take longer than what you think.

The advice I was given was to haircut your AUM launch expectations by 90%. This was precisely our experience – to the dollar, in fact. Virtually every fund manager we have spoken to launched with significantly less capital than what they were expecting. That’s just how it goes. But it does make the journey harder, and it reinforces the need to be very sure that you do indeed want to start a fund.

The personal side to starting a fund

Much of the fund launch decision will hinge on your personal circumstances. Here are some questions you should consider:

• Do you have the personal capital to give yourself sufficient runway to build a track record and raise capital? At this point I have gone more than two years without a salary. It is an uncomfortable adjustment, but a necessary part of the journey of building something great.

• Are you willing to drastically lower your personal cash burn in order to extend this runway? This means significantly curtailing discretionary expenditures. For some, the associated hit to one’s lifestyle might be wholly unpalatable, particularly if a spouse is not okay with making the same sacrifice.

• Do you have a spouse, and are they onboard with you taking on this monumental challenge? Absent this support, the demands of running the fund, in terms of time, energy, and money, could put strain on your relationship.

• Do you have relationships with wealthy individuals who will give you money to manage? The reality is that no matter how good of an investor you are, if you don’t have a network of people that trust you enough to invest with you, then you will struggle to convert your investing acumen into a viable business. There is a sales component to building a funds management business that can’t be ignored. Be honest with yourself around your ability to network and build relationships with wealthy individuals. Any shortcomings here will make the scaling process much more difficult.

Launching a fund too early can be detrimental. If you are too young, allocators (most of which are likely to be older than you) will not take you seriously.

However, launching a fund too late can also be problematic. One’s personal cash burn tends to increase over time due to mortgages, additional dependents such as a spouse and children, schooling fees, etc.

There is likely a sweet spot for launching a fund where you have sufficient experience, but do not have a personal life that has become too financially encumbered. You will need to factor all of this into the level of personal capital you have when starting the fund.

Launching a fund is launching a business

When you launch a fund, it can be difficult to retain your investing purism. For example, if you were managing only your personal capital, you might invest with a certain desired approach. However, when running a fund your capital is co-mingled with the capital of external investors. Their expectations and goals might not perfectly align with yours. In fact, it is almost certain that their expectations and goals won’t perfectly align with yours. However, it is the fees from these clients that will underpin your business, so their expectations and goals must be duly reflected in the fund’s strategy.

At the end of the day, a funds management business isn't so much managing a portfolio as it is creating an investment product that is saleable and well-received by the target market. This requires product market fit. So, the investment strategy must be carefully designed so as to be attractive to potential investors. It is sometimes a delicate balance in reconciling how you might want to manage the portfolio, and how capital allocators want the portfolio managed.

If you are too much of a purist, you will trade AUM scalability for job satisfaction. If you are too commercial and design the product so that it has maximum appeal to the target market, you risk running a strategy that is poorly-aligned with how you want to manage capital, and in the worst case, is impaired in its ability to generate good returns.

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Some of the fund parameters that are important to consider are when structuring an investment strategy are as follows:

• Gross and net exposure ranges and limits for a long/short fund

• Maximum cash weighting for a long only fund

• Maximum position size

• Sector concentration limits

• Sector or geographic specialization

• Quality bias (or not)

• Portfolio turnover

• Level of volatility

• Fee structure

Be very careful with how you craft your fund’s strategy. Often there is a push and pull between retaining enough flexibility in your investment mandate to generate good investment returns and having a narrow enough focus to carve out a niche that makes you stand out to allocators.

Many allocators will already have investments in a variety of funds – if yours doesn’t stand out, it might be hard to get a look in. This is where a fund with a more narrowly defined mandate can help open doors with allocators. Specialization in a niche with a compelling argument for why that niche is ripe with investment opportunities will resonate more with some capital providers than simply saying you’re a good stock picker and will find great opportunities wherever they present.

For example, a fund that only invests in SMID cap technology stocks in Southeast Asia will probably generate more initial interest from allocators than a fund that has an open investment mandate. The lack of constraints in the latter fund makes it harder for allocators to mentally pigeonhole that strategy. This simply makes it more difficult for an allocator to determine how that fund will blend with the remainder of their manager investments.

The other consideration with a niche strategy is that there will be periods where the market will be favorable for that strategy. Performance will be good, making it easier to get traction with capital allocators. However, there will also be periods when the market environment for that targeted niche becomes unattractive, perhaps even hostile. It then becomes increasingly hard both to make money and attract investor interest.

By committing to a more narrowly focused strategy, particularly if investors are allocating to that strategy to get a certain exposure, you are then locked into that approach. Launching a fund with a narrow mandate means you are basically rolling the dice, hoping for a market environment that is kind to the investing approach you’ve adopted. This is clearly problematic to the extent that you launch into an environment that is punishing stocks in your targeted domain. This can create genuine business risk.

The timing of when you start a fund matters, an incredible amount actually. You could be a savvy tech investor, but if you had launched a U.S. tech fund at the beginning of 2022, there were few places to hide in that sector from the carnage that ensued over the course of that year.

The narrower the approach, the more you will need luck on your side in the early years of the fund to help you build a solid track record. Absent this, and if bad luck strikes early in the fund’s life, then it is likely to elongate the AUM scaling process as it will take time to get the fund’s performance back on track.

Launching solo or with a business partner/team

I launched Bristlemoon as a solo operator. My thinking was to get the business up-and-running and build a track record with my own capital in a structure that could later onboard external capital. It was a leap of faith, and frankly I didn’t know for sure whether other investors would enter the fund at such a nascent stage.

Thankfully I was able to secure capital from some early backers, and our AUM has grown healthily since then, allowing me to bring on my close friend and former colleague, Daniel Wu, as a business partner.

Launching a fund as a solo operator has its benefits. You have total autonomy. You also get to intimately understand the various aspects of your own business (versus outsourcing the various functions to a team). The problem, however, is it is more difficult to scale as a solo operator, and unfettered autonomy doesn’t always translate into the best decisions.

Numerous investors we’ve spoken to also expressed more comfort in having more than one person running the fund.

Bringing Daniel into Bristlemoon has been a gamechanger for the business. We are able to share the workload and he’s served as a great sounding board to pressure test ideas. All of this translates into greater rigor in the decision making process.

Managing a portfolio with someone else requires a huge amount of trust and knowing how to calibrate the other person’s judgment. For example, Daniel saying “I think this stock is good” is probably the equivalent of another investor banging the table and saying “this is a generational buying opportunity where we need to back up the truck”.

People express their views and opinions differently; having familiarity with how that person communicates their stock calls is an important part of a solid investment partnership. Often there’s no substitute to achieve this level of understanding outside of having countless detailed investing discussions and knowing that person for a long period of time to understand the quirks of their personality.

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Before starting Bristlemoon I spoke to many who'd started their own investment firms, but had later decided to wind down their funds. I found the stories of why some of those firms had subsequently shuttered to be illuminating. Many cited an irreconcilable dispute with their co-founder that led to them parting ways. An investment partnership is akin to a marriage; choose your partner wisely.

Prior to Bristlemoon, I had known Daniel for 14 years, and we had been talking about launching a fund together for a decade. We were housemates in Sydney and New York, and worked together for years. Becoming business partners was a pretty straightforward decision for both of us.

But even with a shared history, it is still vital to figure out roles and responsibilities, as well as economics before you set out. Figure out exactly who will do what, and formalize a shareholders agreement before you set out on the journey. The absolute last thing you want to be doing is figuring out how to share the pie mid-journey. You particularly don’t want to be negotiating the economic split on an ongoing basis. That’s a recipe for contention and disagreement. Just agree on how the economics will be divided between yourselves and then stick to it.

Markers of legitimacy / building credibility

Step into the shoes of a prospective client who’s contemplating investing money in your fund. Even if they’ve met you multiple times, there’s not a lot from the outside they have to go off to get comfort – after all, most funds are a bit of a black box. Maybe they can see your website and research you’ve written. Regardless, there’s often a trust and credibility gap that typically needs to be bridged.

For this reason, many prospective investors rely on heuristics, typically seeking out markers of legitimacy. This manifests in prospective investors asking us similar questions:

• How big is the team?

• How much AUM do you have? (Rarely do investors want to be the first to invest in a new fund. AUM has a high signaling function and more of it gives investors confidence that others have confidence in you)

• Where’s your office? (Working from home, while perfectly fine, is far less comforting to some investors than having a city address)

• Where do you live? (I get this question all the time, believe it or not. Prospective investors, rightly or wrongly, draw comfort if you live in a good suburb rather than a rough part of town. It shouldn’t matter one iota to your ability to generate investment outperformance, but it hints at the mental shortcuts prospective investors take to try to assess whether you’re credible.).

To the extent that you can tick these boxes, you give a prospective investor fewer reasons to say no to investing in your fund. In fact, every strategic business decision you make should be in service of reducing the number of reasons a prospective investor might be inclined to say no. Many of these frictions reduce over time. Others will depend on structuring decisions that are made at the outset of the fund launch journey.

No one cares about your fund more than you

The funds management space is crowded and competitive, with limited vectors across which you can truly differentiate yourself. There is no shortage of fund managers saying the same things: “We take a long-term view, buying quality stocks that are mispriced”. This certainly makes it more difficult to stand out, and without something to make you stand out, you will likely be reduced to a commodity product in the eyes of an allocator.

The truth is no one will care about your fund more than you do. The wealthy individuals you will be pitching to have their own priorities, and investing in your fund is probably not super high on their list. They are probably not thinking about how critical it is to promptly invest in your fund when they’re out vacationing in Saint-Tropez. Now, this doesn’t mean that they’re not interested in your fund whatsoever, but it does mean that there’s typically a healthy dose of persistence needed to chase up prospective investors to build those relationships. It usually requires a balance between being politely persistent, but not so persistent that you become a pest.

I hope you enjoyed these thoughts and reflections. It’s an honor to get to share these learnings and I hope they’re of use for anyone who has contemplated starting their own fund. If these thoughts resonated with you, or if you have been on a similar journey, I’d love to hear from you and can be reached at info@bristlemoon.com.

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Disclaimer / Disclosures

The information contained in this article is not investment advice and is intended only for wholesale investors. All posts by Bristlemoon Capital are for informational purposes only. This article has been prepared without taking into account your particular circumstances, nor your investment objectives and needs. This article does not constitute personal investment advice and you should not rely on it as such. This document does not contain all of the information that may be required to evaluate an investment in any of the securities featured in the document. We recommend that you obtain independent financial advice before you make investment decisions.

Forward-looking statements are based on current information available to the author, expectations, estimates, projections and assumptions as to future matters. Forward-looking statements are subject to risks, uncertainties and other known and unknown factors and variables, which may affect the accuracy of any forward-looking statement. No guarantee is made in relation to future performance, results or other events.

We make no representation and give no warranties regarding the accuracy, reliability, completeness or suitability of the information contained in this document. To the maximum extent permitted by law, we do not have any liability for any loss or damage suffered or incurred by any person in connection with this document.

Bristlemoon Capital Pty Ltd (ABN: 22 668 652 926) is an Australian Financial Services Licensee (AFSL Number: 552045).

George Hadjia and Daniel Wu are associated with Bristlemoon Capital Pty Ltd. Bristlemoon Capital may invest in securities featured in this newsletter from time to time.

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Bristlemoon readers can also enjoy a free trial of the Tegus expert call library via this link.

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Introduction

Investors love businesses with pricing power. The ability to raise prices without losing customers is one of the clearest indications of a business moat. Fair Isaac Corporation (Nasdaq: FICO) is one of the more notable examples of a public company exerting its pricing power in recent times. The company dramatically raised the prices of its eponymous credit score in a very short span of time.

For some context, the company didn’t raise its credit score royalty rates in real terms for nearly 30 years. But following an intense process of contract renegotiation that permitted FICO to begin raising prices, which we’ll explore, the price of a FICO Score went from around 50-60 cents in 2022 to $4.95 per score in 2025. In other words, FICO raised its prices by 800% in just three years! Clearly, FICO has been making up for lost time on the pricing front.

The company argues that there remains a demonstrable price-to-value gap with its FICO Score that it is seeking to narrow (i.e., via further price increases). Regulators, however, have taken notice, and have sought to break FICO’s monopoly by making changes that attempt to foster competition in the scores market. The key question is the extent to which these changes will erode FICO’s monopoly as well as the company’s ability to continue its heady price increases.

This has caused investors to fret over whether FICO still has pricing power. This tends to be problematic when a company’s stock trades at over 60x earnings, as FICO did earlier this year, and where the future growth is almost wholly reliant on price increases. Consequently, the regulatory rumblings have brought about an enormous destruction of value in FICO’s equity, with the share price declining by 45% from the all-time high of c.$2,380 in November 2024, to c.$1,300 in August 2025.

Source: Bloomberg; Bristlemoon Capital

This report is intended to serve as a primer so that you can understand how FICO’s business works, from the nuances of its Scores business, its complicated relationship with the credit bureaus, as well as some financial analysis to better understand the economics of FICO’s growth story. We also unpack the key debates surrounding the stock, and analyze the potential ways in which FICO’s business might develop in the future.

Key Takeaways

• FICO is not only a monopolist in the credit score market, but its credit score is used as the industry standard for consumer credit risk in the U.S. The company has aggressively increased prices, raising them by 800% over the last three years. This growth has led to 95% incremental operating margins for the Scores business, and operating margins that are near 90%.

• The FICO Score is incredibly entrenched in the US mortgage ecosystem, and it is infused into multiple systems, workflows and regulatory requirements across the point of loan origination, as well as the secondary market securitization value chain. The FICO Score very much is not just a number referenced by lenders, but is integral to the plumbing of the U.S. mortgage system in a way that makes the score very sticky.

• FICO is used at different stages of a mortgage application, and we estimate that there are roughly 10-12 credit pulls in a typical mortgage application, resulting in $49.50 to $59.40 of royalties for FICO. FICO’s royalties comprise a small part of total mortgage closing costs – in the vicinity of 90 basis points for a typical mortgage loan. FICO, however, has attracted regulatory scrutiny from the FHFA, despite the fact that the bulk of incremental credit report inflation can be attributed to the credit bureaus and other third parties layering on fees and marking up the cost of the FICO Score when a tri-merge credit report is generated.

• FICO has a somewhat complicated relationship with the credit bureaus – one that we would categorize as “frenemies”. While the three major credit bureaus compete with FICO via their VantageScore JV, the bureaus also make more revenue from marking up the FICO Score than what FICO makes from its scores. Both FICO and the credit bureaus are well-incentivized for FICO to continue its aggressive pricing campaign, and we believe there is an exceedingly low chance that any traction from VantageScore would result in a race to the bottom on prices.

• The securitization market, in particular the standardization of the to-be-announced market where more than 90% of agency MBS are traded, acts as a gating factor on VantageScore getting traction. Heterogenous loan pools – that is, those that use both FICO and VantageScore – are harder to price and we believe that MBS investors will demand a discount for loan pools scored with VantageScore. There is also the risk of loan putbacks that create a disincentive for lenders to deliver loans to the GSEs with an alternate score such as VantageScore, in the case that those loans default and the GSEs force the lender to buyback the loan at par.

• We estimate that for every basis point of VantageScore risk premium over FICO, a borrower will end up paying over $900 more over the life of a 30 year, $400,000 mortgage underwritten using VantageScore. We estimate that the value of the savings over the term of a mortgage are potentially 100x greater than the $50 in royalties FICO might receive in a dual borrower mortgage origination.

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Table of Contents

• Business Overview

  • Software – a brief mention

  • Scores – the key value driver for FICO

• The FICO Score is the Industry Standard

  • Creating an industry standard

  • Moving away from an industry standard

  • Are there examples of an industry standard being displaced?

• Business History

• FICO’s Special Price Increases

• The Real Cost of Price Increases

  • The CRAs have contributed more to credit report cost inflation than FICO

• Regulatory Woes

• Increased Competition in the Credit Score Market?

  • FICO 10 T vs VantageScore 4.0

  • VantageScore is more inclusive?

• FICO’s Relationship with the Credit Bureaus

  • The quirks of the VantageScore JV

  • Do the credit bureaus make more from a FICO Score than from VantageScore?

• The GSEs and the Securitization Market

  • FICO Score the industry standard for mortgage securitization

  • The To-Be-Announced (TBA) Market

  • Risk of loan putbacks

• Why are lenders complaining about the FICO Score if the cost is passed through to consumers?

• What is the Value of the FICO Score?

• Concluding Comments and Valuation

Business Overview

FICO’s business is comprised of two segments:

1. Scores (FY24: 54% of revenues and 75% of operating income)

2. Software (FY24: 46% of revenues and 25% of operating income)

The Scores segment is the key value driver for FICO as a business. Not only does it comprise three-quarters of the company’s profits (it is incredibly high margin, with 88% segment operating margins in FY24), but it also contributes the bulk of incremental profit dollars, given that the Scores segment is growing relatively faster and at much higher incremental margins than the Software business (Scores revenues have come in at 95% incremental margins on average over the last six years).

Software – a brief mention

FICO’s Software business covers six categories: fraud detection, customer management, originations, financial crimes compliance, customer engagement and marketing. The company typically sells its software as multi-year subscriptions, with payments based on usage metrics such as the number of accounts, transactions, or decisioning use cases.

FICO’s Falcon Fraud Manager dominates the fraud detection market. It can determine whether a transaction is fraudulent or not in 15 milliseconds, and it protects two-thirds of credit card transactions worldwide. FICO TRIAD Customer Manager, which is a decision management software suite used by lenders to optimize and automate credit decisions, is similarly dominant in its market segment. We could spend pages talking about FICO’s Software business but it’s the Scores segment that is going to drive the value of the stock, so we will spend the remainder of the report on Scores. We would just lastly note that FICO management have expressed openness to one day selling the Software business, and the Software and Scores segments operate independently.

Scores – the key value driver for FICO

FICO is an applied analytics business, with the company best known for its flagship FICO Score product. The FICO Score – a three-digit number ranging between 300 and 850 – is the standard measure of consumer credit risk in the U.S. This metric is used by over 90% of the top U.S. lenders when making consumer lending decisions across mortgages, auto loans, credit cards, and other areas[1]. Typically, the higher your FICO Score, the lower your credit risk, and thus the greater the likelihood that lenders will write you a loan.

The FICO Score is used to rank-order individuals against a certain variable, such as the likelihood of making a credit payment on time. It utilizes five major factors in determining the score, weighting each of those factors based on predictive importance:

1. Payment history (35%)

2. Amounts owed (30%)

3. Length of credit history (15%)

4. New credit (10%)

5. Credit mix (10%)

FICO uses predictive modelling to uncover patterns in historical data, which enables classifications about future credit outcomes to be represented by a single FICO Score number (although notably there are over 50 variations of FICO Score for various markets, including FICO periodically releasing new generations of FICO Scores which refine how the algorithm interprets credit bureau data, or incorporate the ability to process additional datasets).

FICO is really a licensing business. The company has a set of intellectual property that is used in the underwriting process to assess credit risk. FICO licenses out this intellectual property to the three U.S. consumer reporting agencies (CRAs) – Experian, TransUnion, and Equifax – and in return receives royalties from the CRAs. It is worth noting that the three major credit bureaus mentioned above accounted for 45% of FICO’s revenues in FY24, and 84% of the company’s Scores revenue.

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Below is a visual depiction of the process to generate a credit score:

Source: Jefferies

FICO is not actually the entity that produces the FICO Score. It is the CRAs that apply FICO’s proprietary analytic algorithms to their respective credit data in order to generate a FICO Score. The CRAs then sell the resulting FICO Score to lenders and other end-users (typically via resellers). In this sense, the credit bureaus act as distributors, and are an intermediary between FICO and the ultimate user of the scores product. Importantly, FICO doesn’t house or handle any of this sensitive consumer credit data.

The end users of FICO’s products are mainly financial institutions, with the company’s products being utilized by three-quarters of the 100 largest financial institutions in the U.S. It is then perhaps unsurprising that FICO’s largest market segment is financial services, comprising 92% of FY24 revenue.

The Scores business is further broken down into business-to-business (B2B) Scores (77% of total Scores revenue) and business-to-consumer Scores (23% of Scores revenue). Lastly, FICO is primarily concentrated in the Americas region, with this geography representing 84% of FY24 revenue.

The FICO Score is the Industry Standard

It is worth spending a moment to discuss what industry standards are, why it’s valuable to be an industry standard, and how FICO’s deep integration into the U.S. lending markets has made it the industry standard for assessing and monitoring credit risk.

An industry standard refers to a technical specification, practice, or product that is widely used across an industry as a baseline for compatibility, quality, and interoperability. Essentially, an industry standard is what joins different parties together in a way that allows them to unlock value.

In the context of FICO, credit data on a single borrower is not particularly useful. Data in isolation is simply far less valuable than datasets that have been joined together. If the credit data of millions of borrowers can be joined via a singular representation of credit risk, then that data becomes exponentially more valuable. This is what the FICO Score represents: a “join key” that links these millions of credit datasets in a way that unlocks value in the form of not only more predictive loan underwriting, but a common language for participants in the lending industry to communicate credit risk.

In a great article on Data-As-A-Service, Auren Hoffman made the following insightful observations[2]:

“One of the big ways that data becomes useful is when it is tied to other data. The more data can be joined, the more useful it is. The reason for this is simple: data is only as useful as the questions it can help answer. Joining, linking, and graphing datasets together allows one to ask more and different kinds of questions…the more join keys (and joined data sets) you can find, the more valuable those data become…As you keep joining data, the number of questions you can ask grows exponentially.”

There are two things to bear in mind for industry standards: 1) it is incredibly hard to make something an industry standard, and it often requires a unique set of goldilocks circumstances; and 2) once a market builds around an industry standard, it is enormously challenging to move off that industry standard.

Creating an industry standard

An industry standard can arise from voluntary consensus (e.g., agreement on a standard by an industry consortium), widespread market adoption (i.e., one company gets so much market share that it becomes the de facto standard), or it can be mandated by regulators.

In the case of FICO, it became the de facto standard due to widespread lender adoption. U.S. lending institutions adopted the FICO Score for two key reasons: 1) it allowed them to write better loans, given the FICO Score did a better job of predicting default risk than those banks’ internal methods at the time; and 2) a number of regulatory changes, such as the Equal Credit Opportunity Act in 1974, supercharged the adoption of the FICO Score, given that algorithmic credit scoring was an ideal way to assuage regulators that a given lending decision was not discriminatory.

The adoption of the FICO Score as the industry standard came from a set of irreplicable circumstances – namely being the first mover in creating an algorithmic scoring system at a period in time where regulatory changes made it highly convenient for lenders to adopt said scoring system.

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Moving away from an industry standard

Shifting to a new industry standard is very difficult. It is a bit of a chicken-and-egg problem. Industry participants might be interested in utilizing a new standard but will be reluctant to adopt that new standard until others are already using it. So, this means that either a very large industry participant, or a group of participants need to switch to the new standard en masse to drag along the other players. In an industry as fragmented as that of lending in the U.S., we believe that there is an exceedingly low likelihood of the industry shifting away from the FICO Score. Industry standards, due to the immense switching costs when moving to a different system, often have significant longevity.

A good example of an industry standard is the QWERTY keyboard. It is a subpar arrangement of keys in terms of typing speed and ergonomics, yet it’s hard to imagine switching from this standard, given it’s the keyboard arrangement virtually everybody has become accustomed to, as well as how integrated it is into computer hardware. This is the power of industry standards: once they’re entrenched, they are really difficult to move away from. Regardless of any incremental ergonomic benefits from a redesigned keyboard, it would be a tall order to convince consumers to switch to a different keyboard design (e.g., Dvorak or Colemak) when virtually no one else is using those alternate designs. And because of these frictions consumers face when switching, keyboard manufacturers have very little incentive to push for change (particularly given they do not bear the cost of a slightly less ergonomic keyboard).

It's possible to map the value of an industry standard using Metcalfe’s Law. Metcalfe’s law states that the value of a network is proportional to the square of the number of connected users. In other words, the more industry participants that use the FICO Score as the industry standard, the more useful that standard becomes. This is particularly so when you have multiple types of industry participants all rely on the industry standard – in the case of the FICO Score, it is relied upon by borrowers, lenders, investors, and regulators.

The FICO Score is infused into multiple systems, workflows, and regulatory requirements across the point of loan origination, as well as the secondary market securitization value chain. The FICO Score very much is not just a number referenced by lenders, but is integral to the plumbing of the U.S. mortgage system in a way that makes the score very sticky. Below are a few ways in which FICO is integrated into the mortgage ecosystem:

• Loan Origination Systems (LOS) – LOSs such as ICE’s Encompass and Black Knight’s Empower integrate with resellers to pull a tri-merge credit report (which includes a separate FICO Score from each of the three credit bureaus). The LOS then stores the middle FICO Score as the representative score for the borrower, and uses this score in loan eligibility calculations.

• Automated Underwriting Systems (AUS) – Fannie Mae’s Desktop Underwriter (DU) and Freddie Mac’s Loan Product Advisor (LPA) require a FICO Score as an input. Without a FICO Score it is currently impossible to get GSE conforming loan approval.

• Loan pricing engines – FICO is a key input into Loan Level Pricing Adjustments (LLPAs), with secondary desks explicitly bucketing pricing by FICO bands (e.g., 720-739, or 700-719). Fannie Mae and Freddie Mac publish LLPA grids (these are essentially rate sheets that determine basis points price hits (or credits) to the loan amount based on risk factors such as the FICO score, loan-to-value ratio, occupancy type, etc.) but it is the secondary desks within lenders that operationalize these LLPAs (we will dig into this and the role of GSEs later in the report). In this sense, the LLPA grids, which are based on the FICO Score, allow lenders during the origination phase of a mortgage to efficiently establish and adjust loan pricing based on the individual risk profile of the borrower.

• Servicing and loan performance models – Mortgage servicers monitor borrower credit health post-origination by using FICO Scores. Both GSEs and mortgage servicers will rely on periodic FICO credit pulls to make updated assessments of delinquency and prepayment risk (which feeds into mortgage servicing rights (MSR) valuations and loss mitigation waterfalls).

• Secondary market and MBS structuring – when GSEs package loans into mortgage backed securities (MBS), pool-level disclosures include the weighted average credit score. For decades that score has always been the FICO Score, and this continues to presently be the case. Credit risk transfer securities issued by Fannie and Freddie (i.e., CAS and STACR, respectively) explicitly reference FICO as the risk metric.

• Regulatory capital and supervisory models – FHFA stress testing and GSE capital frameworks (e.g., ERCF) use loan-level FICO distributions as key drivers of expected default and loss models. Furthermore, banks holding MBS also bucket their capital requirements based on FICO-driven credit quality segments.

• Private Mortgage Insurance (PMI) – the FICO Score is an integral part of PMI pricing models. It is what underpins insurance eligibility decisions as well as setting insurance premiums.

• Other systems – there are a range of additional mortgage industry players that are reliant on FICO. For example, the Mortgage Industry Standards Maintenance Organization (MISMO), which is responsible for developing standards for exchanging information in the U.S. mortgage industry, has a FICO-centric data schema. Quality control and loan audit systems such as QC Ally use FICO thresholds for defect categorization. Servicing transfer data tapes (i.e., the loan-level dataset that the current servicer of a mortgage portfolio delivers to the new servicer when MSRs are sold or transferred) carry FICO fields.

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If you can appreciate the challenge of moving away from the QWERTY keyboard in a scenario that involves only consumers and keyboard manufacturers, imagine trying to find ways for all of these disparate parties in the U.S. mortgage industry to make the changes necessary to support a second scoring method such as VantageScore. The problem becomes even more difficult when you consider that many of these industry participants have little incentive to make the investments necessary to support a second credit score beyond FICO.

The following quote from the Founding President and former CEO of VantageScore (via Tegus) is revealing:

“My first call on Fannie was in October of 2006. They had already tested us and said, "Will you outperform FICO on predictiveness?" I thought, "Well, this is going to be a slam dunk." Here we are 19 years later. I just think there's so much inertia.”

In other words, when VantageScore was introduced in 2006, it was believed that it would be able to encroach on FICO’s stronghold in the mortgage market. Almost two decades later and VantageScore’s impact has been negligible.

The truth is the mortgage industry is just very slow to change. The following quote from a former VP at Guaranteed Rate highlights this dynamic:

“We've been waiting for 15 years for them to allow electronic signatures on mortgage closing documents. That technology has been around forever and nobody allows it in. If they're not going to allow something as simple and verifiable as an electronic signature, what makes you think they're going to allow something as potentially life-changing or industry-changing as a VantageScore to have any role in the risk management of these assets?”

The majority of financial institutions that utilize the FICO score are relatively sophisticated and use the FICO Score in conjunction with their own risk models. Introducing an alternative credit scoring model, such as VantageScore, or modifying the current FICO scoring model would require lenders to undertake a comprehensive recalibration of their internal risk models and protracted trial period. FICO management have noted that it is close to impossible to accurately and reliably map a Classic FICO Score to a VantageScore 4.0, given that the relationship is nonlinear and changes based on the economic environment. The fact that VantageScore has not been stress-tested in a real-life economic downturn is a fairly steep impediment to lender adoption.

Financial institutions are typically reluctant to change scoring methods, and we can best see this reflected in the slow uptake of the new generations of FICO Score.

Some lenders upgrade quickly to new versions of the FICO Score, while others take much longer; observe below how FICO Scores 2, 4, and 5 are still being used for the mortgage lending market, despite FICO Score 10 and FICO Score 10 T having been released. If it was easy to switch scores, then the mortgage industry would have likely shifted to using newer generations of the FICO Score that incorporate trended data. The truth is it’s a heavy lift to change scores.

Source: MyFICO

It takes years to drive adoption of these updated and more predictive generations of FICO Score, hinting at an industry that is reluctant to change.

“So going from FICO 8 to FICO 9, it took four years before 50% of the market was on FICO 9. And I think you'll see the same kind of thing. It'll be four years again before FICO 10 is half the market. So it's a very slow adoption rate.” – Will Lansing (CEO), MS TMT Conference 2025

Back in October 2022, the Federal Housing Finance Agency (FHFA) announced that a dual-score mandate would be implemented, whereby the GSEs would be required to use both a FICO Score and VantageScore on conforming mortgages. The FHFA then backed away from this proposed move. There is signal in the fact that in July 2025, after years of consultation and trying to get the industry ready for this shift, the FHFA announced that it was moving to lender’s choice, whereby lenders can choose to use either FICO or VantageScore (i.e., a somewhat watered down policy compared to the original dual-score mandate that was proposed).

A dual-score approach would simply double credit scoring costs and introduce immense complexity across the origination and securitization value chains. A Goldman Sachs research note highlighted that the FHFA had previously estimated that it would cost the mortgage industry $600 million or more to move to a dual-score mandate. These heavy implementation costs, and the fact that the absolute cost of a FICO Score is so low relative to mortgage closing costs, and that this cost is passed on to consumers, leads us to believe that there’s a low incentive for lenders to make the switch to VantageScore.

Inertia is a fairly safe bet when it comes to making changes that risk disrupting a high-value ecosystem that depends on an industry standard product like the FICO Score. There is $13 trillion worth of outstanding mortgages in the U.S.[3] – there is an extremely high bar for implementing changes that risk dysfunction in such a financially, socially, and politically important market. For this reason, we are of the view that any changes to the status quo of FICO as the credit score used in the U.S. mortgage market will be slow and marginal.

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Are there examples of an industry standard being displaced?

If industry standards are so defensible, there’s merit in looking at historical examples of when those standards get displaced. We view there as being two primary ways in which industry participants will pivot away from a standard: 1) technological change leads to a new industry standard that is significantly more performant; and 2) a new standard is mandated by a regulatory body.

The inexorable changes stemming from technological innovation have led to numerous industry standards being unseated. Email usurped fax. The consumption of media content moved from VHS to DVD to Blu-ray, before changing form factor entirely with the advent of streaming services like Netflix. In the case of FICO, we are not at all worried about technological change displacing the FICO Score as the industry standard.

If we look at newer generations of FICO Score, there is typically only a minor incremental performance uplift compared to the prior generation of FICO Score. In other words, the predictiveness of scores has already begun to asymptote, and we don’t foresee a new credit scoring method materializing that can predict loan defaults at a significantly higher rate of accuracy than the FICO Score. Any improvements are likely to be incremental and minor. This militates against a step change improvement that is capable of catalyzing a broader industry shift towards a new standard of assessing creditworthiness.

We would also note that standards are most easily adopted when they are free. The unique history of FICO, whereby it kept its Scores prices both fixed and at a very low level for almost three decades, created the perfect conditions for adoption of the FICO Score as the industry standard. We deem it highly unlikely that a competitor score such as VantageScore will give away its score for free for years in order to drive adoption, particularly when we consider the unique financial incentives of the VantageScore JV (which we dissect later in the report).

Finally, as it relates to the potential for regulatory change to cause a displacement of the FICO Score as the industry standard, we believe that the disruption to the all-important mortgage industry would be far too great to make these changes.

Business History

The history of FICO is fascinating, and one that is entwined with the history of lending in the U.S. It is certainly worth exploring the company’s past in some detail to understand the significance of FICO’s contribution to improving underwriting standards and expanding access to credit in the U.S. Only then can we truly appreciate the impact that the FICO Score has had on the U.S. lending landscape and the value the FICO Score provides.

The company was founded in 1956 by engineer Bill Fair and mathematician Earl Isaac. The two had met at the Stanford Research Institute while they were working as operations researchers for the U.S. military. Fair and Isaac saw an opportunity to apply their operations research methods to the private sector in a for profit enterprise. They both put in $400 as an initial investment and set out to start a consulting business that aimed to use data to improve business decisions. The duo eventually settled on consumer lending as the domain in which they would apply their analytical efforts.

Fair and Isaac sent letters out to the 50 major U.S. lenders, requesting a meeting to explain their approach of employing statistical techniques to measure credit risk. They received one reply. A lender called American Investment Company (AIC), the nation’s fourth largest consumer finance company at the time, agreed to let Fair and Isaac analyze the company’s credit files. The resultant credit scoring system, after analyzing 13,000 “good” accounts (those devoid of significant delinquencies) and 1,000 “bad” borrower profiles, purportedly enabled AIC to reduce its bad debt charge-offs by 25% with only a 3% reduction in total loan volumes[4]. The data used was rather rudimentary: how long borrowers had lived at their current residence, whether they had a telephone, and other questions such as age and marital status. Regardless, AIC served as a proof point of the systematization of credit risk analysis which led to other lenders taking interest.

During this initial phase of the Fair Isaac Corporation, the company was a consultancy that worked on bespoke credit analysis projects for lenders, a far cry from the intellectual property licensing business FICO is today. What is so remarkable about the FICO story is that the company managed to bring statistical rigor to consumer credit analysis before the digitization of credit data.

A dissertation on the history of the Fair Isaac scorecard by Martha Ann Poon made the following comments regarding the laborious process by which the company originally generated its credit scorecards[5]:

“In the late 1950s the ability to calculate involved executing a series of physically demanding activities. Credit scoring required a steady traffic of materials back and forth, between Fair Isaac, a second party firm with access to computers and engineering skill, and storage rooms of credit operations spread out across the country. To change credit screening practices on the ground, the company had to command an intricate process of moving people, paper and things around.”

In the late 1950s and 1960s, Fair Isaac Corporation pioneered a new industry by doing the incredibly labor-intensive, manual process to generate credit scorecards. FICO was essentially turning stacks of paper (i.e., consumer credit data), using physical labor, into an easy-to-use cardboard printed table that mapped out the odds of defaulting (although the credit scoring process became increasingly automated in the ensuing decades due to improvements in computing technology). Each borrower attribute (e.g., age, time on job, delinquencies, etc.) was assigned a point value which was then tallied. This numeric score correlated with the odds of repayment, and each bank or finance company could raise or lower its own cutoff score to reflect their risk appetite and willingness to grant credit. The beauty of this new system, devised by Fair Isaac Corporation, was that it provided a standardized approach to making credit decisions that was consistent and scalable, much unlike the subjective judgment of loan officers that often fell victim to biases (cough… prejudices).

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If we trace the history of lending in the U.S., much of it was done based on a character assessment and personal relationships. Obviously, it’s difficult to scale this approach, and the proliferation of consumer credit with the advent of the credit card demanded a more rigorous, consistent, and fair approach. FICO’s credit scorecards came at the right time and place, particularly when we consider some of the later regulatory changes to fair lending practices.

The 1970s were a period of immense social change in the U.S., and this was reflected in a number of hearings conducted by Congress during that era to create equal credit opportunity as a civil right[6]. Legislation was subsequently introduced that banned creditors from basing lending decisions on factors such as race, religion, sex, marital status, or age, promoting the idea that credit should be granted based on merit rather than an individual belonging to some group which is beyond their control. While the intent of this regulation was noble, it posed difficulties for lenders. The Equal Credit Opportunity Act (ECOA) which was passed in 1974 required lenders to provide reasons for why a borrower was turned down for a loan. Over time, the objectivity of a credit scoring method such as that offered by FICO provided the necessary cover for lenders to point to their compliance with the definition of fairness as espoused by the ECOA. As is often the case with regulation spurring change, these new fair lending rules accelerated lender adoption of the credit scoring systems that FICO offered.

Back in the 1800s, the bulk of credit activity in the U.S. was between lenders and commercial enterprises (early forms of credit scoring can be traced back to the 1830s when local credit bureaus helped firms decide whether to extend trade credit[7]). The corollary of this is that most Americans paid cash for goods; borrowing was deemed risky and irresponsible. It wasn’t until the 1900s that the advent of the motor vehicle, which was a higher sticker price item relative to average wages, necessitated some inventive forms of financing to drive sales. General Motors created the General Motors Acceptance Corporation (GMAC), making car ownership possible by popularizing the concept of the installment plan (as a fun fact, GMAC was the precursor to Ally Financial).

Retailers similarly adopted these installment plans. However, in the early 1960s, retailers began transitioning from installment plans to revolving credit plans, where customers paid a percentage of the balance and interest accrued on the rest (and notably there was no risk of repossession upon default, as there was in an installment plan arrangement). This shift was what paved the way for the modern credit card.

It wasn’t until the late 1960s that we saw unsecured consumer credit from financial institutions become a more meaningful part of the lending landscape. Up until that point, most of the credit granted to consumers was extended by retailers. This rapid expansion in consumer credit prompted a desperate need for the systematization of credit evaluation at scale. While FICO was making inroads on this front, the data used in its scorecards was siloed within the banks and retailers that it was commissioned by to provide bespoke scoring systems. What was needed was a scaled, centralized repository of consumer credit data.

Over the course of the twentieth century, there was significant consolidation of the U.S. credit bureaus – the companies that controlled the consumer credit data. This industry consolidation accelerated following the introduction of the Fair Credit Reporting Act (FCRA) in 1970, which introduced a regulatory framework that favored the larger credit bureaus that could afford to make the necessary investments in infrastructure to be compliant (many of the smaller bureaus lacked the capital and technical resources to make these upgrades).

This culminated in the emergence of the big three credit bureaus: Equifax, TransUnion, and Experian. These three credit bureaus provided exactly what FICO needed in terms of centralized credit data at scale – a development that was made possible by the digitization of data that stemmed from computing advancements. It was thus in 1984 that FICO released what was called PreScore. FICO’s PreScore used credit bureau files to screen consumers for pre-approved credit card offers (rather than blanketing an entire region with solicitations for credit, only to reject a large portion of those unscreened applicants[8]).

While the PreScore was a standardized score used for marketing credit products, it wasn’t until 1989 that FICO introduced the FICO Score, which was used as a general-purpose credit score to standardize and automate the assessment of credit risk. Rather than the hitherto manual review of consumer credit applications – which were time-intensive, costly, subject to the biases of the loan officer, and often saw inconsistent outcomes – the FICO Score underpinned an automated credit underwriting process that was fairer, more consistent, and expanded access to credit. The FICO Score also emerged at an opportune time – the recession in the late 1980s led to an increasing number of credit card delinquencies. Of the more than 200 million Visa, MasterCard and Discover card products at the time, 14% were subject to credit scoring in 1989, which jumped to more than 50% in the early years of the 1990s[9].

The pivotal moment for the FICO Score came in 1995 when the GSEs Fannie Mae and Freddie Mac directed lenders to use FICO scores for all new residential mortgage applications. Notably, this was when Fannie Mae and Freddie Mac were for-profit enterprises owned by private shareholders; these corporations voluntarily adopted the FICO Score, given that lenders were already using it to evaluate credit risk in mortgage and non-mortgage markets (i.e., FICO’s monopoly in U.S. mortgages was not granted by government decree but rather guided by market forces). This cemented the FICO Score as the industry standard for U.S. residential mortgages, but lender adoption more broadly also saw the FICO Score become dominant in other verticals such as auto loans and credit cards.

The erroneous notion that FICO is only dominant because of its government mandated use in the conforming mortgage loan market is best rebutted when we see that the majority of FICO Scores are used outside of mortgage originations. In a blog post by the President of FICO’s Scores business, Jim Wehmann revealed that 99% of FICO Scores are used outside of mortgage originations.

“In fact, the vast majority, approximately 99%, of FICO Scores used for decisioning across the consumer credit industry are used outside mortgage originations. Total mortgage originations, including GSE-purchased mortgages, constitutes only 1% of all FICO Scores used for decisioning across the consumer credit industry.”

This recount of FICO’s history helps us better understand the importance of FICO in the U.S. consumer credit ecosystem. But to properly appreciate FICO as an investment, and to assess the company’s future growth prospects, we must understand the company’s special price increases.

The remainder of this 16,000+ word deep dive is reserved for paid subscribers. We cover FICO’s special price increases, the royalties FICO receives in a typical mortgage loan origination, the economics and incentives of the CRAs (including why the CRAs are incentivized for FICO to continue raising prices), as well as views on where FICO’s scores pricing might go in future years. Consider becoming a paid subscriber to receive full access to the remainder of this FICO report.

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For a more detailed writeup of MELI, please see our deep dive on MercadoLibre (MELI) – Amazon in the Amazon which we published earlier this year.

We hope you enjoy the Business Breakdowns episode on MELI and as always, we welcome any well-reasoned thoughts and pushback.

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Disclaimer / Disclosures

The information contained in this article is not investment advice and is intended only for wholesale investors. All posts by Bristlemoon Capital are for informational purposes only. This article has been prepared without taking into account your particular circumstances, nor your investment objectives and needs. This article does not constitute personal investment advice and you should not rely on it as such. This document does not contain all of the information that may be required to evaluate an investment in any of the securities featured in the document. We recommend that you obtain independent financial advice before you make investment decisions.

Forward-looking statements are based on current information available to the author, expectations, estimates, projections and assumptions as to future matters. Forward-looking statements are subject to risks, uncertainties and other known and unknown factors and variables, which may affect the accuracy of any forward-looking statement. No guarantee is made in relation to future performance, results or other events.

We make no representation and give no warranties regarding the accuracy, reliability, completeness or suitability of the information contained in this document. To the maximum extent permitted by law, we do not have any liability for any loss or damage suffered or incurred by any person in connection with this document.

Bristlemoon Capital Pty Ltd (ABN: 22 668 652 926) is an Australian Financial Services Licensee (AFSL Number: 552045).

George Hadjia and Daniel Wu are associated with Bristlemoon Capital Pty Ltd. Bristlemoon Capital may invest in securities featured in this newsletter from time to time.

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A blowout quarter for Meta

Meta delivered incredibly strong Q2 results and Q3 guidance that handily beat expectations:

• Revenue growth of 22% YoY beating consensus by 6%, and a stable 2-year stack of ~44% growth for the past four quarters.

• Operating income growth of 38% YoY and 500 bps of operating margin expansion on the back of ongoing cost controls and operating leverage.

• EPS growth of 38% YoY to $7.14, smashing consensus by 21% and similar to last quarter’s beat.

• Q3 revenue guidance of $47.5 to $50.5 billion or +17% to +24% YoY, beating consensus of $46.2 billion.

While P&L performance remains very strong, we see ongoing deterioration of cash results as capex continues to step up. Operating cash flow of $25.6 billion was largely absorbed by $17 billion of capex, resulting in just $8.5 billion of free cash flow. FCF margin is down to 18% and could potentially fall to 10% in 2026 given the capex step-up compared to 40%+ operating margin.

Source: Bristlemoon Capital, company filings

The 2025 capex guide was tightened slightly to a range of $66 to $72 billion representing an over $30 billion YoY increase at the midpoint. But the real kicker is the initial outlook for 2026, which is expected to step up to ~$100 billion! Yet even with this nearly 3x capex increase over two years, the company is still compute constrained per the CFO:

“But even with the capacity that we’re bringing online, we are still not quite perfectly meeting the demand that teams have for compute resources across the company.”

The biggest driver of the 2026 capex increase will be Gen AI instead of Core AI investments. When we spoke to the company last year, Core AI was still the majority of capex (2024 capex: $37 billion), and we suspect that this year the balance may flip. By next year, we expect the majority of capex to be for Gen AI initiatives, which don’t yet have a monetization timeline. While management still maintains that the capex is fungible between Gen AI and Core AI, our back of the envelope math suggests that at a quantum of $100+ billion capex every year (and growing), it may prove difficult to earn an adequate return on investment from Core AI advancements alone.

But before we consider the AI capex vs ROIC debate, it is worth highlighting how Core AI investments have been driving improvements across the business.

Core AI investments powering growth

For those who don’t follow Meta, Core AI is essentially the investments into the company’s recommendation and advertising systems that drive engagement and revenue across the core platforms. The improvements that stem from these investments are bucketed into two categories: engagement initiatives and monetization efficiency.

On engagement initiatives, the clearest indicator of success is the reacceleration of DAU growth over the past three quarters. Despite nearly 3.5 billion people already using Meta’s apps daily, DAU is somehow still growing at 6% YoY. This suggests to us that AI – whether it be through better recommendations or Meta AI itself – is driving higher daily engagement.

Source: Bristlemoon Capital, company filings

Other engagement improvements that were highlighted in Q2 include over 20% YoY growth in video time spent on both Facebook and Instagram, driven by enhancements to the recommendation systems. This comes on top of a 7% increase in time spent on Facebook and 6% increase on Instagram reported in the six months through Q1, which came on top of a 8%/6% increase in time spent on Facebook/Instagram in the nine months through Q3 2024. So there do appear to be strong incremental gains in time spent across the apps from better AI recommendations. The company is also making good progress on longer-term content ranking innovations such as cross-surface foundation models and extending LLM architectures into existing recommendation systems.

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We generally subscribe to Ben Thompson’s (of Stratechery) view that ad networks are at their healthiest when revenue growth is being driven by ad impressions rather than price per ad. Strong growth of ad impressions supports robust ad revenue growth in future quarters, while an over-reliance on eCPM growth tends to degrade advertiser returns over time. For the past few quarters, ad impression growth had slowed materially while price per ad grew double digits, but in Q2 there was a meaningful inflection in ad impression growth driven by rising engagement on the core platforms and some further ad load optimization on Facebook. Meta has also started inserting ads into Threads and the WhatsApp Updates page, though these won’t be material contributors to ad impression growth this year (or next, for WhatsApp).

Source: Bristlemoon Capital, company filings

On monetization efficiency, CFO Susan Li noted that the 9% growth in price per ad benefited from improved ad performance, suggesting that underlying ROAS continues to increase. Susan spoke extensively about improvements to ad retrieval and ranking systems, each of which drove 3% to 5% increases in conversion on Facebook and/or Instagram. During the quarter, Advantage+ was also rolled out as the default option for sales and app campaigns, and adoption has been higher than expected.

"The Andromeda model architecture we began introducing in the second half of 2024, powers the ads retrieval stage of our ad system, where we select the few thousand most relevant ads from tens of millions of potential candidates. In Q2, we made enhancements to Andromeda that enabled it to select more relevant and more personalized ads candidates, while also expanding coverage to Facebook Reels. These improvements have driven nearly 4% higher conversions on Facebook Mobile Feed and Reels."

"In Q2, we improved the performance of GEM by further scaling our training capacity and adding organic and ads engagement data on Instagram. We also incorporated new advanced sequence modeling techniques that helped us double the length of event sequences we use, enabling our systems to consider a longer history of the content or ads that a person has engaged with in order to provide better ad selections. The combination of these improvements increased ad conversions by approximately 5% on Instagram and 3% on Facebook Feed and Reels in Q2."

We think it is easy for investors to dismiss or underappreciate small improvements to engagement and ad efficiency, especially when viewed against massive capex investments. But an 8% increase in time spent could, ceteris paribus, translate into a $15 billion increase in revenue, and a stack of 3% improvements to ad efficiency could support a 10% increase in eCPM with no degradation to ROAS.

The example below shows how the cumulative benefits from Core AI investments can generate an attractive incremental pre-tax return on capital at current capex levels.

Source: Bristlemoon Capital

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Finding religion in superintelligence

Moving on from Core AI, it is clear that Mark Zuckerberg has found his new messianic calling: to usher in the Metaverse superintelligence. From our perspective, it is interesting watching his descent into an AI maximalist. Throughout 2023, Zuckerberg appeared content to do his own thing with AI (open source, product-focused, school project-ish etc.) and let the frontier labs compete for the state-of-the-art models. In early 2024, Zuckerberg changed his position and believed Meta needed to pursue AGI. Fast forward to 2025, he has been on a multi-billion-dollar binge to acqui-hire the best AI talent in a bid to build superintelligence. This seems to be spurred by his observation of the exponential rate of AI progress (so far).

"I think it's basically just what we're continually observing how this works, what the trajectory or the pace of AI progress has been. I think it continues to be on the faster end. And that I think informs a lot of the decisions, from everything from the importance and value of having the absolute best and most elite talent-dense team at the company, to making sure that we have a leading compute fleet so that the people here can do, so the researchers here have more compute per person to be able to lead their research and then roll it out to billions of people across our products, making sure that we build and drive these products through all of the different things that we do, which I think is one of the things that our company is the best in the world at, is basically when we take a technology, we're good at driving that through all of our apps and our ad systems and all that stuff.

So, yeah, I mean, we're just going to push very aggressively on all of that, but, at some level, yeah, this is—there's sort of a bet in the trajectory that we're seeing, and those are the signals that we're seeing, but we're just trying to read it."

As Meta shareholders, it is not clear to us why Meta needs to achieve “superintelligence” (whatever that means) at all costs. We understand that Meta has enormous consumer distribution and cash flow, but we don’t understand i) why a personal AI assistant for the median human needs to be a “superintelligence”; ii) how a superintelligent personal assistant can realize its full potential without access to email, calendar, encrypted messages etc; and iii) how a highly engaging personal assistant does not cannibalize time spent on Meta’s monetized surfaces.

Even as we give Zuck the benefit of the doubt when it comes to (eventual) monetization, we have to wonder what it might look like. With OpenAI hiring Fidji Simo to build out an advertising business, it is evident that subscriptions alone are insufficient to cover the cost of a freemium service where the substantial majority of users are non-paying. Yet Meta’s engagement-driven ad model is not well suited to conversational use cases – WhatsApp ads sit in the separate Updates tab, and Susan revealed on the follow-up call that the company abandoned plans to introduce Messenger inbox ads after initial testing.

We believe Meta will ultimately figure out a sustainable monetization strategy for “superintelligence”, but the $100 billion a year and growing capex burden adds a certain level of urgency. Investors are currently cheering Meta’s strong P&L performance and letting the enormous capex outlay slide. But should revenue growth disappoint, we imagine the market reaction could be reminiscent of 2022, especially if Zuck is unrelenting in his pursuit of superintelligence. As we mentioned earlier, even if the Gen AI investments are fungible with Core AI, we struggle to see how forcing $100 billion a year of capex into the core platform can deliver adequate returns.

Source: Bristlemoon Capital, Bloomberg consensus

Two observations here: 1) a low-teens post-tax ROIIC is not yet value destructive in the sense that it is likely still above Meta’s cost of capital, but presumably shareholders would prefer buybacks; and 2) obviously if the superintelligence investment was not meeting internal expectations, Meta should cut capex. Our biggest concern is that instead of rationally reducing capex, an irrational decision is made to throw even more money at the problem (in line with current LLM scaling laws).

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Disclaimer / Disclosures

The information contained in this article is not investment advice and is intended only for wholesale investors. All posts by Bristlemoon Capital are for informational purposes only. This article has been prepared without taking into account your particular circumstances, nor your investment objectives and needs. This article does not constitute personal investment advice and you should not rely on it as such. This document does not contain all of the information that may be required to evaluate an investment in any of the securities featured in the document. We recommend that you obtain independent financial advice before you make investment decisions.

Forward-looking statements are based on current information available to the author, expectations, estimates, projections and assumptions as to future matters. Forward-looking statements are subject to risks, uncertainties and other known and unknown factors and variables, which may affect the accuracy of any forward-looking statement. No guarantee is made in relation to future performance, results or other events.

We make no representation and give no warranties regarding the accuracy, reliability, completeness or suitability of the information contained in this document. To the maximum extent permitted by law, we do not have any liability for any loss or damage suffered or incurred by any person in connection with this document.

Bristlemoon Capital Pty Ltd (ABN: 22 668 652 926) is an Australian Financial Services Licensee (AFSL Number: 552045).

George Hadjia and Daniel Wu are associated with Bristlemoon Capital Pty Ltd. Bristlemoon Capital may invest in securities featured in this newsletter from time to time.

Key contributors to the Fund’s performance in the June 2025 quarter included Uber Technologies, PAR Technology and APi Group. Notable detractors were AppLovin and Hemnet.

To be upfront, we are not satisfied with the Fund’s performance this quarter. The lacklustre performance was driven by two factors: 1) being too bearish regarding the impacts of tariffs; and 2) the volatility inherent in a concentrated strategy, with some of our larger stock positions declining during the quarter.

We want to spend this quarterly letter walking through both of these factors. At the same time, it is also worth noting that we are the most excited about the Fund’s portfolio of stocks that we have ever been. Later in the letter we detail three new stock positions that were initiated during the quarter: APi Group, Salesforce, and UnitedHealth Group.

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By accessing the link below, you acknowledge and agree that you are an Australian wholesale client as defined by Section 761G of the Corporations Act 2001.

Bristlemoon Global Fund June 2025 Quarterly Report Vf

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We are incredibly thankful for the support of our investors. It is difficult to verbalise how much it means to us that you have entrusted us to manage your capital. We hope to be good stewards of your capital for many years to come. For new investors looking to enter the Fund, more information can be found here.

As a business update, Bristlemoon Capital now has an office. In June 2025 the Bristlemoon team moved into Level 10, Hudson House, 131 Macquarie Street, Sydney. Having a physical space has made collaboration easier and we are thrilled at how the new arrangement has been working thus far.

Disclaimer / Disclosures

The information contained in this article is not investment advice and is intended only for wholesale investors. All posts by Bristlemoon Capital are for informational purposes only. This article has been prepared without taking into account your particular circumstances, nor your investment objectives and needs. This article does not constitute personal investment advice and you should not rely on it as such. This document does not contain all of the information that may be required to evaluate an investment in any of the securities featured in the document. We recommend that you obtain independent financial advice before you make investment decisions.

Forward-looking statements are based on current information available to the author, expectations, estimates, projections and assumptions as to future matters. Forward-looking statements are subject to risks, uncertainties and other known and unknown factors and variables, which may affect the accuracy of any forward-looking statement. No guarantee is made in relation to future performance, results or other events.

We make no representation and give no warranties regarding the accuracy, reliability, completeness or suitability of the information contained in this document. To the maximum extent permitted by law, we do not have any liability for any loss or damage suffered or incurred by any person in connection with this document.

Bristlemoon Capital Pty Ltd (ABN: 22 668 652 926) is an Australian Financial Services Licensee (AFSL Number: 552045).

George Hadjia and Daniel Wu are associated with Bristlemoon Capital Pty Ltd. Bristlemoon Capital may invest in securities featured in this newsletter from time to time.

Source: Novi

While a somewhat trite and erroneous analogy, it vividly captures the concept of creeping normalcy: the phenomenon where significant changes are accepted as normal when they occur gradually, through a series of small, often unnoticeable, increments of change. This idea around the importance of the rate of change in shaping the end outcome is one that investors should deeply reflect on. It is an idea that is pertinent to the pricing decisions of companies, particularly those with genuine pricing power.

Pricing power is great. If you’re an investor, you want the businesses you’re invested in to have pricing power. However, the speed at which a company seeks to extract value from its customer base via price increases matters – a lot. This is because the speed at which a company raises prices can directly affect the ultimate price level that can be achieved.

Essentially the human mind needs time to habituate to change. So, if a company raises prices too quickly, it interferes with our ability to incorporate these higher prices into a new normal.

Jared Diamond, who coined the term creeping normalcy in his book Collapse: How Societies Choose to Fail or Succeed, also introduced a related concept called “landscape amnesia”: the idea that people forget what the surrounding landscape looked like 50 years ago because the year-to-year change has been so gradual.

This concept of landscape amnesia is why there are examples of companies increasing prices by many multiples over time. People continue purchasing those companies’ products or services, with minimal fuss, because the gradual nature of those price increases afforded them time to mentally adjust, relegating the formerly lower prices to a distant, forgotten memory.

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Think about the health insurance premiums charged by UnitedHealthcare (the average health insurance premium has outpaced inflation for almost all of the past 25 years, but seldom by more than a double-digit percentage point spread). Or the price per pound for a box of See’s Candies (which has increased by around 5% per annum for more than 50 years). These price increases have been relatively gradual, occurring over the span of decades.

Thought of another way, a necessary precondition for a high level of pricing-led value extraction is for those price increases to be gradual. Pull the pricing lever too aggressively and you risk upsetting your customers, and occasionally the general public and regulators.

Risks from pulling the pricing lever too hard

A rather extreme and morally reprehensible example of rapid price increases involved Daraprim, a 62-year old drug which is used to treat life-threatening parasitic infections. Turing Pharmaceuticals, run by a controversial fellow named Martin Shkreli, acquired the Daraprim drug and overnight raised the price from $13.50 a tablet to $750 (a 5,456% increase for those counting).

While Shkreli was sentenced to prison for an unrelated securities fraud matter, a U.S. federal judge later ruled that Shkreli violated antitrust laws via actions that sought to monopolize the market for Daraprim (and thus enable Turing to continue charging exorbitant prices for the drug).

Martin Shkreli was banned for life from the pharmaceutical industry, an ignominious end to one of the most egregious spectacles of price gouging in recent history. It turns out that raising prices by 5,000%+ in one fell swoop is not a sustainable strategy for value generation!

There are other examples of companies dramatically raising prices in a short span of time (although not in a nefarious way like the Daraprim example). The consequence of these speedy price hikes is similar: they can jeopardize the company’s ability to raise prices in the future.

FICO is an interesting, and topical, example of this dynamic, with the company having put through gargantuan price increases over the last seven years. Importantly, these price increases were from a miniscule base, and after decades of precisely zero price increases.

Incredibly, the royalty rates that FICO received from the consumer reporting agencies (CRAs) remained virtually unchanged for nearly 30 years after the FICO score was first introduced in 1989. After a multi-year renegotiation of its license agreements with each CRA that began in 2012, FICO put through its first increase in the royalty rate in 2018. At that point it was charging around 50-60 cents per score for mortgage lenders.

Investors salivated over the latent pricing power. And FICO certainly did its best to extract it. More recently, FICO put through its fourth major price increase: in October of 2024, FICO notified its credit bureau customers that in 2025 the company’s wholesale royalty will increase to $4.95 per score for mortgage originations (a 41% increase over the 2024 wholesale price for mortgage lenders of approximately $3.50 per FICO Score).

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Objectively, $4.95 does not appear to be an extortionate amount. In fact, it comprises an infinitesimal cost within the mortgage value chain. Consider that the $4.95 per score royalty comprises on average 15% of the $80 to $100 tri-merge bundle cost – that is, a single report which combines credit information from the three national credit bureaus. Furthermore, FICO comprises just two-tenths of one percent of the total average mortgage closing costs of $6,000. It would be a long bow to draw to argue that a 0.2% cost amounts to an impediment to home ownership in the U.S.

However, regardless of the rationale or fairness of these price increases, FICO has still raised prices for its FICO Score by almost 900% since 2018. This has caused regulatory consternation.

The large and rapid price increases enacted by FICO are likely a major reason behind the recent rumblings of Bill Pulte, Director of the Federal Housing Finance Agency (FHFA), who has threatened imminent regulation to reign in FICO’s pricing power.

If FICO had instead raised prices gradually over multiple decades, no one would have kicked up a fuss about its current pricing. We would bet that no one would have protested if FICO’s prices were demonstrably higher than they are today, so long as FICO’s price taking had been gradual. The lesson from this is it’s perhaps best not to play catchup and make up for lost time when it comes to price increases!

The ultimate prices a business is capable of charging are path dependent: raise prices too quickly and you risk people taking issue with that value extraction. This is especially so if you are a monopolist and customers have no viable alternative.

Thoughts on Hemnet

With this background around pricing increases and rates of change, it is worth now turning our attention to Hemnet Group AB, an investment held in the Bristlemoon Global Fund and one that we wrote about in our March 2025 quarterly letter.

Let’s start with some context. Hemnet has raised prices aggressively in recent years. The average revenue per listing (ARPL) has increased by 4.5x in the last five years, going from SEK 1,414 in FY19 to SEK 6,383 in FY24. Over the last nine years, the ARPL has increased by a whopping 13.1x. While some of that growth reflects product mix – property sellers opting into higher-priced listing packages – the company has still put through material like-for-like price increases.

Investors with a penchant for quality businesses get starry-eyed at this sort of pricing power – successfully raising your prices on customers is one of the clearest indications of a business moat.

However, there’s a dichotomy that results from pulling the pricing lever too hard. On the one hand, investors in the stock benefit immensely from this sort of growth and the associated high incremental margins; often this causes these types of businesses to trade at rich earnings multiples. On the other hand, customers on the receiving end of these price increases can grow increasingly frustrated and resentful at the increasing value extraction, particularly when the pace of value extraction is rapid.

Unfettered pricing power doesn’t exist: every pricing-led growth strategy has a breaking point where those prices will be extreme enough to prompt user churn, or egregious enough to draw the ire of regulators who can issue fines, or worse still, regulate future price increases. The trick is making an accurate assessment of where that breaking point is. We do not think that Hemnet has reached this breaking point, but we will discuss some of the issues that have resulted from the historically speedy ARPL growth.

There have been some recent developments in Sweden that have muddied the narrative for Hemnet and its pricing-led growth story, namely around: 1) regulatory risk; and 2) competition.

Regulatory scrutiny?

In late May 2025, an article published in Affärsvärlden flagged concerns about Hemnet’s agent compensation structure as well as the fairness of its listing fees. There were comments in the article from the Swedish Estate Agents Inspectorate (FMI) around agents being incentivized, via higher remuneration from Hemnet, to recommend more expensive advertising options on Hemnet to property sellers, and that they would assess if this was a violation of the Estate Agents Act.

We are not concerned about this. The FMI has no jurisdiction over Hemnet, only on the actions of real estate agents in Sweden. Any changes over what Hemnet can pay real estate agents in Sweden would likely also be a boon for Hemnet’s margins – in this scenario they would not need to pay away as much of their revenue to real estate agents.

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We did have some concern, however, over comments in the Affärsvärlden article from Martin Mandorff, the Head of Market Abuse Unit at the Swedish Competition Authority (SCA). The reporter asked Mandorff a number of hypothetical questions, so we would note that his comments, without the relevant context of that hypothetical question, do appear much more alarming than otherwise. Mandorff made the follow comments to Affärsvärlden for the article (translated from Swedish):

• “During the spring, we have noticed that the issue has grown with the new price models. We are constantly considering whether there is anything that we should investigate with our tools.”

• “It is clear that this market is not opening up and that pricing has skyrocketed. This makes us more concerned now.”

• “Based on the Competition Act and the abuse rule, you may find that a company has contract terms or pricing that excludes other players. Prices can also be too high, although this is more unusual. In that case, we can order the company to stop doing so. There is also the possibility of a competition fine if you have harmed competition…However, overpricing can constitute an abuse.”

• “Then it becomes more a question of whether we see indications that there is pricing that seems so high, that given the cost structure of the industry, it is simply no longer reasonable. I don't want to speculate on when that will happen.”

Let’s be clear – an official at the regulatory body going on record making these sorts of comments is in no way a positive for Hemnet. Fears around regulatory risk were no doubt exacerbated given this article was hot off the heels of the Australian Competition and Consumer Commission (ACCC) announcing that it was investigating the pricing practices of REA Group, owner of the Australian property portal realestate.com.au.

We suspect Mandorff’s comments are simply a shot across the bow from the Swedish regulator, and do not signal imminent regulatory intervention, but we also cannot brush these risks aside completely.

The problem with regulatory risk is that 1) it’s difficult to handicap the likelihood that it transpires; and 2) it can lead to outsized negative outcomes. The presence of so-called stroke-of-a-pen risk for an investment simply makes it harder to underwrite a set of future value outcomes with confidence. It’s just hard knowing what the business might look like in the future if an external party can step in and begin interfering with how the company conducts its affairs.

It is difficult to know whether Mandorff’s public comments indicate an actual investigation from the SCA is forthcoming. Perhaps the more subtle risk is that these comments may have a diminutive impact on future price increases, surreptitiously curtailing the level of pricing power the company is capable of exerting over property sellers due to fear of a regulatory crackdown. We don’t know but will get answers in future quarters when we see the degree to which Hemnet adjusts the prices of its listing packages.

We would make a few points that support Hemnet’s listing fees:

• A property is in all likelihood the seller’s largest asset. These assets are transacted infrequently, and chances are the seller doesn’t even recall how much they paid Hemnet to advertise any past properties they’ve sold. We believe that the cost for advertising on Hemnet to show your property to the largest audience in Sweden is miniscule. For example, an SEK 7 million (c.$730,000) property in Stockholm would cost SEK 7,950 (c.$830) to advertise on the Basic starter package. Expressed another way, it costs just 11 basis points of the home’s value to list your property on Hemnet, on the least expensive tier.

• Property sellers opt in to higher priced packages. Their choice is voluntary. Typically, they will upgrade because of priority placement and more views per listing. In other words, they’re paying more for the premium listing packages on Hemnet because they’re getting more value. For example, the company recently disclosed that Hemnet’s Max listing tier receives 88% more views per listing than the average Basic ad[2].

• Using the Stockholm property example above, it costs SEK 26,150 for a property seller to purchase the Hemnet Max package (the most expensive option), which equates to roughly $2,700. This is around 37 basis points of the value of the property. Is this worth it? All it would require is for the extra exposure you get from the Max listing resulting in an additional buyer who is willing to pay $2,700 more for the property. This, to us, does not seem inconceivable. If the Max listing leads to an additional interested party who offers just 1% above the SEK 7 million asking price (i.e., an extra SEK 70,000), then this results in a 3.8x return on investment on upgrading to Max (i.e., the additional SEK 70,000 of property value realized compared to the incremental SEK 18,200 it cost to upgrade from the Basic package to Max for that Stockholm property).

The worsening of the narrative around Hemnet has also been driven by fears of increasing competition, a somewhat surprising fear given history has shown it to be incredibly difficult to assail the leading property portal in a market.

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Competition heating up?

More recently, the narrative has gained traction that Hemnet’s aggressive pricing has inadvertently opened the door for competition, namely Booli. Booli, which has been around since 2007, is a property portal owned by SBAB, a Swedish bank. It’s free to list on Booli. The way it works is that agents will list a seller’s property on their own real estate website. Booli then “scrapes” this listing data from the real estate agent’s website. This results in Booli having a holistic view of the listings landscape, and there are listings that appear on Booli which are not on Hemnet.

Booli gained a foothold in Sweden via its free property valuation tool (reminiscent of Zillow’s “Zestimate” feature, which served as the initial wedge for the company to get early traction in the U.S.). People are curious to know what their property is worth, and Booli has racked up 800,000 registered homeowners that it sends property valuation updates to on a weekly or monthly basis.

Traditionally people have gone to Booli to get updates on the value of their property and how neighboring valuations are trending. To some extent more recently, people are going to Booli to look for a property to purchase.

The primary reason why we have been unconcerned with Booli is because listing dollars follow eyeballs, and most of the eyeballs are on Hemnet. For example, Hemnet receives 16x the number of clicks on listings compared to Booli. However, there is data to suggest that Booli has been gaining traffic share, narrowing the gap between itself and Hemnet.

Let’s take a look at app downloads for the two property portals (app users for portals tend to be stickier, higher quality users). If we were to look at Sensor Tower data for app downloads, there appears to be a wide divergence between Booli’s cumulative app downloads and MAUs. The MAU line is so small you can barely register it off of the x axis of the graph. Note that for both the Booli and Hemnet charts below, the cumulative downloads are only starting in January 2017 because that is where our Sensor Tower dataset begins, not because this is when the Booli and Hemnet apps were first introduced.

Source: Bristlemoon Capital; Sensor Tower

This enormous delta between cumulative downloads and MAUs potentially signals that many of the Booli app downloads fail to convert to regular users of the app. This could be due to a poor user experience, or some other reason. The data does look strange to us. Hemnet’s graph, on the other hand, looks much more like what you’d expect from a healthy app, with a more normal ratio between MAUs and cumulative downloads.

Source: Bristlemoon Capital; Sensor Tower

We would further note that Hemnet is currently maintaining a 69x MAU lead over Booli, and despite Booli gaining traffic share and having solid download activity, Hemnet has still grown its ARPL by around 6.8x since 2017 (a 31.4% CAGR over this seven year period).

So why has Booli been gaining traffic share in recent years?

Pre-market listings

It is worth now turning our attention to what is known as the pre-market for listings in Sweden. These are properties that are being prepared to be sold, but are not officially for sale. Think of it as a “Coming Soon” section for properties that are yet to officially list for sale.

Basically, the property gets listed on a real estate agent’s website, and then Booli scrapes that data. For this reason, Booli has more listings than Hemnet – Booli includes all the pre-market listings that appear on the agents’ websites. The trade off of Booli scraping listings, as opposed to agents directly uploading those listings like what happens on Hemnet, is that a portion of the listings on Booli are stale (i.e., incorrect, out-of-date information about the listing, or even where the property is no longer for sale). The lower information quality for listings on Booli feeds into a lower quality user experience (which could explain the abovementioned poor MAU retention data).

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The pre-market in Sweden gained prominence during the very hot property market during the pandemic. A listing could be shown in the pre-market with minimal preparation, and buyers would find that listing and make offers for that property. Around the pandemic, the listing days on Hemnet were around two weeks, meaning that a property would transact just two weeks after listing on the site. As of Q1 of 2025, listing days on Hemnet have blown out to 47 days. In other words, it’s a much slower market where it’s harder to find buyers.

Logically, one would think that in a slower market there’s an even greater impetus for property sellers to advertise on Hemnet. This would result in more eyeballs, and thus a higher chance of that property transacting. However, there are nuances in the Swedish market that appear to have derailed this dynamic.

For example, the slower market has made it more competitive amongst real estate agents in Sweden. Some are running on fumes, struggling to eke out commissions given the much smaller volume of properties that are transacting. We have heard instances of agents lowering their commission rates to entice the property seller to choose them as their representative agent.

One lead generation tactic used is for agents to tell a property seller that they can potentially save them money by first listing the property on that agent’s website as a pre-market listing. Booli will scrape this listing and there’s a chance that somebody sees the property and lobs in an offer. If the property doesn’t transact on Booli, then the agent will recommend listing on Hemnet.

This tactic lowers the friction and risk for property sellers – it’s free, after all. It also creates optionality for agents in the off chance that somebody does make an offer for the property. The key question is what portion of these Booli listings ultimately end up on Hemnet due to an inability of Booli to surface a buyer. Our view is that these are lower quality listings, some portion of which are uncommitted sellers who are hoping for a good buy offer that meets the price they’re hoping for.

We believe that the sensible move for property sellers is to always list on Hemnet in order to maximize the number of views per listing. However, while Hemnet pays agents a commission (which in theory should incentivize them to recommend sellers to list on Hemnet), the competitive nature of the Swedish property market for agents creates a binary proposition. Agents either contract with the seller so that they’re the representative agent on the listing and receive a commission for selling the property (even if it’s a smaller amount due to a lower sale price), or they don’t secure the listing and thus get nothing.

Notably, agents in Sweden typically do not get a salary but rather their pay is entirely commission based, exacerbating this dynamic. The Hemnet commission, while nice, pales in comparison to the commission that an agent gets from the property seller when the property sells. This is especially so, given that in many cases the commission paid by Hemnet is retained by the real estate brokerage, not the individual agent, and is used to fund things like Christmas parties and company vacations.

It is also worth mentioning that in a slower real estate market, sellers are reluctant for their property to remain unsold on Hemnet for too long. If this happens, potential buyers might interpret a long listing duration as there being something wrong with the property which could turn them off making an offer.

We think that a risk for Hemnet is if the market remains weak, then the pre-market might continue to gain traction which helps Booli increase its share of traffic. To us, this pre-market dynamic is a symptom of a paucity of seller leads for agents. We believe that Hemnet should be able to address this if it were to introduce a seller lead generation tool for agents. Responding to Booli directly, in our view, would be strategically challenging for Hemnet.

The fact that listings can appear on Booli for free makes it difficult for Hemnet, which subsists off of a paid listings model, to mount an effective response. Booli does not charge for listings. Rather, the site serves as a mortgage lead generation funnel for its bank parent company, SBAB. There’s an element of counter-positioning here. Hemnet will no doubt be reluctant to mimic Booli’s free pre-market listing stance due to the damage it would cause to its paid listings. We have seen Hemnet respond to Booli by introducing a Coming Soon tab, but sellers must pay to list their properties here (unlike Booli). Instead, we believe that Hemnet needs to better demonstrate the value that its platform offers to sellers, which might require an increase in marketing spend, as well as introducing better lead generation functionality for agents (as mentioned above).

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Much of the above might also be resolved if the Swedish property market were to tighten, which would likely result from the Riksbank (Sweden’s central bank) cutting rates. However, there’s maybe another wrinkle in this scenario.

The slower Swedish property market has almost certainly been a factor in driving a high upgrade rate to the Premium listing tier. The Premium listing tier had historically been the exclusive tier with the unlimited renewal feature (the recently introduced Max tier now also includes unlimited renewals). What this means is that the property listing can be renewed every 30 days at no additional cost (versus the Base and Plus packages where you need to pay an additional cost to maintain your listing after 30 days).

Given the slower market, with listing days extending to 47 days, unlimited renewals has become the killer feature of Premium and Max. The problem is that if the market were to tighten and listings transact in a sub-30 day time frame, then the benefit of having a perpetual listing for a one-off cost enormously diminishes. We suspect this would be a negative for upgrade activity to Premium and Max, but note that Hemnet can always rejig the pricing of its other packages to steer users towards these more premium listing tiers.

Conclusion

Let’s be clear: our Hemnet thesis has not played out as expected. The company was once the Fund’s largest position, and we were too bullish on the ability of Hemnet to extract value via price increases, as well as the speed at which sellers would upgrade to the new Max tier.

We suspect that the introduction of Max (which was rolled out earlier than initially scheduled) was a consequence of a faster-than-expected upgrade rate to Plus and Premium. Essentially, the change to the agent commission model on 1 July 2024 drove very strong upgrade rates to Plus and Premium, which turbocharged the company’s growth but also forced the need for a new listings tier (so that sellers could better differentiate themselves via the more expensive Max tier).

It's possible that the strong upgrade rate following the agent commission changes caught Hemnet’s management team off guard, creating fears around the ability to sustain this heady growth, as well as the difficulty in managing investor expectations. The problem with such a robust upgrade rate, and the resulting strong ARPL growth, is that you are pulling forward growth from the future, potentially creating a growth air pocket. Growth air pockets usually aren’t good for stock prices. It’s likely that Hemnet is now trying to manage the company to a lower, but more consistent growth path. This has been problematic for the stock to the extent that most investors, including us, were expecting very strong ARPL growth in FY25, which might now be less likely.

Our enthusiasm towards pricing-led growth strategies has cooled, particularly if the pace of price increases has been rapid, and the company is well into their journey of hiking prices. This is because pricing-led growth is inherently lazy if there is no accompanying uplift in value to the end user. Contrast this with a company like NVIDIA which is rapidly innovating, such that new GPUs are released at far higher prices than previous generations, but there is an enormous performance benefit that justifies that price increase. Customers are far less likely to balk at price increases if there is a commensurate uplift in the value that the company’s product or service.

Investing teaches many lessons – but one stands out: the speed at which a company raises its prices will ultimately determine the sustainability of that company’s pricing power.

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Disclaimer / Disclosures

The information contained in this article is not investment advice and is intended only for wholesale investors. All posts by Bristlemoon Capital are for informational purposes only. This article has been prepared without taking into account your particular circumstances, nor your investment objectives and needs. This article does not constitute personal investment advice and you should not rely on it as such. This document does not contain all of the information that may be required to evaluate an investment in any of the securities featured in the document. We recommend that you obtain independent financial advice before you make investment decisions.

Forward-looking statements are based on current information available to the author, expectations, estimates, projections and assumptions as to future matters. Forward-looking statements are subject to risks, uncertainties and other known and unknown factors and variables, which may affect the accuracy of any forward-looking statement. No guarantee is made in relation to future performance, results or other events.

We make no representation and give no warranties regarding the accuracy, reliability, completeness or suitability of the information contained in this document. To the maximum extent permitted by law, we do not have any liability for any loss or damage suffered or incurred by any person in connection with this document.

Bristlemoon Capital Pty Ltd (ABN: 22 668 652 926) is an Australian Financial Services Licensee (AFSL Number: 552045).

George Hadjia and Daniel Wu are associated with Bristlemoon Capital Pty Ltd. Bristlemoon Capital may invest in securities featured in this newsletter from time to time.

[1] https://www.abc.net.au/science/articles/2010/12/07/3085614.htm

[2] https://efn.se/hemnets-svar-efter-kritiken-ska-absolut-tas-pa-allvar

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Table of Contents

• Introduction to US healthcare system

  • US healthcare expenditure

  • Participants in the US healthcare system

• Health insurance in the United States

  • The rise of managed care

  • Medicare

  • Medicare Advantage

  • Medicaid and other safety net programs

  • Employer-sponsored health insurance

  • Economics of managed care

• UnitedHealth Group and its place in the US healthcare system

  • UnitedHealthcare

  • OptumHealth and value-based care

  • OptumRx and pharmacy benefit managers

  • OptumInsight

• What went wrong in 2025 (so far)?

• Regulatory risks

• What does the path to recovery look like?

Introduction to the US healthcare system

The US healthcare system is a complex, multi-payer system where healthcare is provided by a mix of for-profit and nonprofit providers, and paid for by a combination of government programs, private insurance, and out-of-pocket payments. Unlike many other developed nations, the US does not have universal coverage, which means its citizens do not have guaranteed access to healthcare. Despite the lack of universal coverage, the US spends more on healthcare on an absolute and per-capita basis than any other country. This has enabled a (formerly) $1+ trillion market cap industry to coalesce around the provision of healthcare services in the US, and directly supports the multi-trillion market cap pharmaceutical, biotech and medical devices industries.

We acknowledge at the outset that the state of the US healthcare system is a politically and often emotionally charged topic. It is not our intention in this deep dive to make normative statements about the perceived or actual political, economic and social shortcomings of the US healthcare system. We simply explore the system as it is and aim to provide factual analysis – this “primer”, if we may, is not so much for the benefit of the reader but rather for ourselves.

We have historically dismissed the US healthcare system as a bit of a black box and largely relegated it to the “too hard” basket. However, with the unprecedented implosion of UnitedHealth Group (UNH), the largest private player in the industry, we decided it was finally worthwhile investing the time (and collateral brain damage) necessary to better understand the US healthcare services space. If, ultimately, this space remains in our too hard basket, it will not be for lack of trying.

In attempting to understand the healthcare services industry, we center our analysis on UNH, in part because there may be opportunity in the brutal selloff, and partly because UNH’s operations are so expansive as to cover health insurance (aka managed care), healthcare provider, pharmacy benefit manager (PBM), and healthcare IT services. It seems logical to us to cover as much ground as possible with one stock.

US healthcare expenditure

According to the Centers for Medicare and Medicaid Services (CMS), US national health expenditure was $4.9 trillion in 2023 or 18% of US GDP. Healthcare expenditure has grown substantially faster than CPI, driven by medical cost inflation and an ageing population.

Source: Bristlemoon Capital, CMS data

Per capita healthcare spend of $146 in 1960 has expanded 100x to $14,570 per person in 2023 and is approximately double the OECD average on a PPP-adjusted basis.

Source: KFF

In terms of where the expenditure goes, inpatient hospital care is the largest category at nearly one third of total healthcare spend. Outpatient physician services is the second largest category at 20% and retail prescription drugs is the third largest category at 9%. There is a perception that US healthcare spending is driven by out-of-control drug costs, which is not immediately evident in the data. However, we have heard that total expenditure can alternatively be decomposed into roughly one-third each of inpatient (hospital settings), outpatient (clinic/other settings) and drug spend, as the CMS data below allocates drug spend by setting e.g. hospital drug infusions are included in hospital care.

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As for expenditure by payer, government programs are the largest source of funding at nearly half of total healthcare spending. These include Medicare, Medicaid and other safety net programs (federal and state), military-related programs and public health initiatives. Private insurance is predominantly employer-sponsored health insurance, with a small portion of individually purchased insurance mainly on the health exchanges established by the Affordable Care Act (ACA). Finally, patients spend over $500 billion on out-of-pocket expenses, though we believe this mainly reflects deductibles, copays and coinsurance rather than the healthcare spend of the uninsured.

Source: Bristlemoon Capital, CMS data

Participants in the US healthcare system

We summarize below the key participants in the US healthcare system:

• Consumers – the 340 million US population are the primary consumers of healthcare. Approximately half of the population is covered by employer sponsored health insurance, either as direct beneficiaries or dependents. 8% of the population is uninsured, down from the mid-teens post-GFC following the passage of the ACA, which expanded the Medicaid program and established the ACA exchanges for individual health insurance. The remainder of the population is covered by some form of government sponsored program, whether for the elderly (Medicare) or the various safety net programs of which Medicaid is by far the largest.

• Payers – the payers in the system fall under three broad categories: the government, employers, and individuals. Note that the US does not have a single payer healthcare system, unlike Canada, the UK, and Australia among other countries. The government only acts as single payer for the elderly and through safety net programs.

• Managed care organizations (MCO) – MCOs are the health insurers and as their name suggests, their role is to manage the cost and utilization of healthcare, ostensibly to maximize health outcomes and minimize total healthcare expenditures. MCOs design health plans (health insurance policies), collect premiums from the payers and establish networks of providers where they can offer volume in exchange for lower cost of care. There are a handful of large national MCOs of which UnitedHealthcare is the largest, regional and state MCOs including the nonprofit Blue Cross Blue Shield companies, and integrated health systems such as Kaiser Permanente that control health plans, hospitals and primary care groups.

• Healthcare providers – providers are the organizations that provide healthcare services to patients. These include hospitals, primary care clinics and physician offices, ambulatory surgical centers, behavioral health centers, nursing facilities and home care among many other care settings. Typically, “inpatient” refers to hospital care, while “outpatient” refers to all non-hospital care settings. This distinction is important when it comes to insurance coverage and fee schedules for medical procedures (inpatient typically being more expensive than outpatient).

• Pharmacy benefit managers (PBM) – PBMs are the middlemen in the prescription drug supply chain, connecting drug manufacturers with retail and specialty pharmacies – not physically, but financially. PBMs play an important role in drug price setting, create the formularies that define which drugs are available at which pharmacy networks under which health plans, reimburse pharmacies for prescriptions filled, and generally exert influence over prescription drug consumption in the US. The PBM model is opaque even by healthcare standards, and there is long-running controversy surrounding their pricing practices. President Trump has singled out PBM reform as a means to lower prescription drug prices for Americans. We will discuss PBMs in much more detail later as it is an important component of UNH’s model.

• Suppliers – suppliers into the healthcare system include drug manufacturers, drug distributors, medical device makers, medical consumables, and healthcare IT service providers. These participants are outside the scope of this deep dive, though we may revisit some of these industries in the future.

Health insurance in the United States

The current design of the US health insurance industry can trace its origins back to the Great Depression and WWII era. It was during this time that the original nonprofit Blue Cross (hospital cover) and Blue Shield (physician cover) health plans were created to help pay for rising healthcare costs. During WWII, wage controls were legislated to stabilize prices and contain inflation, but employers were allowed to offer up to 5% of employee wages as fringe benefits. The IRS ruled that employee health benefits were a tax-deductible expense, and this gave rise to employer-sponsored health insurance. By 1960, over half of the US population was covered by employer-sponsored insurance, a share that has remained relatively stable to this day.[1]

In 1965, Medicare and Medicaid were created under President Lyndon B Johnson’s Great Society program. With employed Americans and their dependents largely covered by employer-sponsored insurance, there was a pressing need to address healthcare gaps for the elderly and the poor/unemployed. Medicare was created as a federal government single payer program for people aged 65 and over, while Medicaid would be a state and federal-funded safety net program for those living at or below the federal poverty line.

These early programs were essentially traditional forms of indemnity insurance - i.e. the insured would incur a cost and the insurer would cover or indemnify that cost. While these programs socialized the burden of healthcare costs, they did little to contain healthcare cost inflation.

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The rise of managed care

The highly inflationary decades from 1965 through the mid-1980s saw a blowout of healthcare costs. According to CMS data, national health expenditure (NHE) grew at a CAGR of 11%, 13% and 11% in the 1960s, 70s and 80s respectively, such that NHE per capita grew twenty-fold from 1960 to 1990. For comparison, NHE per capita has increased five-fold in the 33 years since 1990.

Several legislative developments through these decades were aimed at bringing healthcare costs under control. The Health Maintenance Organization Act of 1973 was passed to facilitate the establishment of Health Maintenance Organizations (HMO), a type of health plan that combined prepaid monthly premiums with care delivery through a narrow network of healthcare providers. The motivation behind the HMO model was to contain medical costs through fixed payments (known as capitation), funnel volume through a narrow network of providers at lower cost, and engage in utilization management strategies such as prior authorizations and tight network coverage. HMOs disrupted conventional indemnity-style health insurance and spawned further permutations of the model, such as Preferred Provider Organizations (PPO) which are more flexible with out-of-network providers and may not require referrals for specialists, but come with higher premiums, copays and out of pocket limits.

On the government program side, the Tax Equity and Fiscal Responsibility Act of 1982 authorized Medicare to enter into risk-based contracts with HMOs and other private health plans. The shift towards risk-based contracts, where health plans receive a capitated payment per member per month (PMPM) and are at risk for cost overruns, was aimed at reducing Medicare’s ballooning costs. Subsequently, the Balanced Budget Act of 1997 introduced Medicare+Choice (later renamed to Medicare Part C), which sought to offer Medicare beneficiaries access to more private health plans with more comprehensive benefits, thereby incenting them to move off traditional fee-for-service Medicare. The Medicare Modernization Act of 2003 renamed Part C to Medicare Advantage (MA), further expanded plan options for beneficiaries, and established the Medicare Part D prescription drug benefit for both traditional Medicare and MA.

During the 1990s, industry consolidation was also taking place due to the disruption wrought by HMOs, PPOs, and the necessity of scale to effectively manage healthcare costs, especially under risk-based models. Cost containment by payers directly translates to lower revenues for providers, so the healthcare provider market also saw a wave of horizontal and vertical consolidation, ultimately resulting in the large health systems that exist today. Provider consolidation begot further insurer consolidation, as relative bargaining power determined which party got to keep more of the pool of healthcare expenditure.

In the late 1990s, UnitedHealthcare, then a health plan rollup, began acquiring healthcare technology services, and by the early 2000s also began to acquire specialty services, physicians’ practices, and a PBM to boot. The other large, listed insurers eventually began to replicate UNH’s vertical integration strategy by acquiring care providers of their own, giving rise to today’s integrated managed care organization model.

But before we can unpack the economics of managed care, we need to understand the three main funding programs for the healthcare system: Medicare, Medicaid and Commercial (aka employer-sponsored).

Medicare

Medicare is the federal government-funded health insurance program for US residents aged 65 or older or are disabled. Medicare consists of four parts: A, B, C and D.

• Part A provides hospital insurance (typically for free)

• Part B provides medical insurance for outpatient settings e.g. primary care physician visits (typically for a monthly premium, $185 in 2025)

• Part C is Medicare Advantage as discussed previously

• Part D is the prescription drug benefit established by the MMA (typically for a ~$45 monthly premium)

Original/Traditional Medicare consists of Parts A and B and is a fee-for-service (FFS) model administered by the CMS whereby the government will pay healthcare providers for medical services rendered, without a cap. Medicare beneficiaries must meet an annual deductible before insurance kicks in and are then subject to 20% coinsurance on all medical costs (uncapped). Beneficiaries can take out separate MediGap insurance to cap their annual out-of-pocket (OOP) expenses. Part D had an OOP maximum of $8,000 per year that was reduced to $2,000 by the Inflation Reduction Act, starting in 2025.

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Medicare Advantage

In the context of MCOs, our interest lies in Medicare Advantage, as that is the managed care version of Medicare. MA plans must cover everything that is covered under Original Medicare, but can also provide supplementary benefits such as dental, vision, fitness etc., and increasingly, quality of life benefits such as transportation and grocery cards. MA members must still pay the Part B and Part D premiums, though some plans apply their benefits to help cover part or all of these premiums. MA plans typically do not require the 20% coinsurance under Original Medicare, have lower copays for cost sharing, and have capped OOP expenses ($9,350 maximum for 2025). The main drawback of MA is a limited provider network and tighter utilization management per the earlier HMO/PPO discussion, compared to Original Medicare where beneficiaries can visit any provider for any service that accepts Medicare.

Evidently, the attractiveness of supplementary benefits and capped OOP costs far outweigh the drawbacks, as MA has seen significant enrollment growth over the past two decades. MA members account for substantially all new enrollment into the broader Medicare program essentially since MA’s inception, and has now exceeded 50% of the eligible Medicare population. The MA program has been a crucial growth driver for many MCOs, particularly UNH and Humana (HUM) who together have enrolled over 45% of total MA members and historically traded at premium multiples to other listed MCOs. Given the last of the baby boomers will turn 65 by 2030, there are a few more years of favorable demographic tailwinds for the MA insurers. However, recent changes to how CMS calculates MA rates, and the risk of future tightening have made the MA program less attractive than in the past.

Source: Bristlemoon Capital, CMS data

Medicare Advantage rate setting process

The CMS sets the PMPM benchmark payment for MA plans on an annual cadence based on the growth in per-capita FFS Medicare costs, further adjusted by several factors to better align the MA population with the FFS population. The CMS releases its MA rate-setting Advance Notice in early January for the following year (i.e. January 2025 for 2026 rates), and publishes the final Rate Announcement by early April. MA plans then have until early June to submit their bids for the following year.

We present below the year-over-year change in MA benchmark rates for the past several years.

First, we provide a brief explanation of each material rate component:

• Effective growth rate – this is the estimated growth rate of per-capita FFS Medicare spend based on observed trends through rate Year minus 2 (i.e. the rate announcement for 2026 is published in April 2025 using actual 2024 data) and actuarial projections. This means MA payment rates always lag actual medical cost trend. In an environment where medical costs and utilization are rising, MA plans will face challenges containing their medical costs within the confines of their capitated payments from CMS. We can see for payment year 2024, the effective growth rate was only 2% due to favorable FFS cost trends in 2021 and 2022. We, and the entire MA industry, are not clear why the 2025 effective growth rate was equally low, considering the rising utilization that MA plans began to experience starting in 2023. This resulted in MA-reliant MCO stocks, particularly Humana, suffering steep drawdowns. CMS seems to have now realized its underestimation of utilization, resulting in a sharp increase in the 2026 rate announcement.

• Change in Star Ratings – MA plans are assigned star ratings under the Quality Bonus Program which we discuss below. The brief summary is that plans rated 4.5 or 5 stars receive a 5% increase to their benchmark rate. The change in star ratings reflects the impact on benchmark payments from the average increase or decrease in star ratings.

• Risk model revision and FFS normalization – the MA benchmark PMPM payment is scaled by a risk adjustment factor for each member. We discuss the process of coding and risk adjustment in detail below, but this factor essentially reflects CMS’s attempt to align the risk scores of the MA population with that of the FFS population. CMS is well aware that MA plans are incentivized to code their members more intensively to receive higher risk-adjusted PMPM payments. CMS risk adjustment models have a base year e.g. 2015 or 2019 where the average risk score is 1.0 which then inflates over time; the FFS normalization factor is intended to reset each payment year to an average score of 1.0. For years 2018 to 2023, the negative adjustment mainly reflected the FFS normalization with marginally positive risk model revisions. For payment year 2024, CMS introduced a new risk adjustment model V28 intended to further curtail MA coding intensity. V28 will be phased in over three years and is expected to reduce risk scores (and thus benchmark payments) by up to 8%.[2]

• Change in benchmark rate – this is the net impact of the above adjustments and reflects the “like for like” change in benchmark payments to MA plans, i.e. assuming no change to average risk scores. CMS then estimates an “MA coding trend adjustment” which recognizes that risk scores do in fact inflate over time as MA plans are incentivized to code their members with as many diagnoses as they can to receive higher risk-adjusted payments.

• Expected MA payment trend – this is the growth in total MA PMPM expenditure that CMS expects to fund. MA plans of course claim that the MA coding trend adjustment is merely remuneration for sicker members and not a true increase in MA program funding. When MCOs and industry lobbies complain about funding shortfalls, they point to the proposed change in benchmark rate; when CMS pushes back, it points to the expected MA payment trend.

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Benchmarks, bidding and rebates

As outlined above, CMS sets the benchmark PMPM rates for the MA program. While the rate announcement is a single set of numbers reflecting the national program average, CMS actually sets individual county-level benchmark rates. These represent the maximum rates that CMS will pay in each county for a plan member of average health (risk score 1.0). Every year in June, plans submit their bids for MA contracts. Bids below the benchmark rate will receive a rebate equal to a percentage of the difference between the bid and the benchmark. Bids above the benchmark will receive the maximum benchmark rate and must charge plan members an additional premium for the difference.

These benchmark rates can be increased by a number of adjustment factors, the main two being a county quartile adjustment and the star quality bonus. Because benchmarks are set based on per-capita FFS spending, counties in the lowest quartile of FFS spending receive a benchmark set at 115% of FFS, while counties in the highest quartile of spending have a reduced benchmark at 95% of FFS.

The star quality bonus falls under the MA Quality Bonus Program (QBP) established by the ACA. MA plans are assessed across 40+ quality rating measures and assigned a star rating from 1 to 5. The intention of the QBP is to incentivize MA plans to improve quality and member experience by awarding plans rated 4 stars or higher a bonus 5% increase to their benchmarks. Rebate levels are also determined by star rating, with 4.5-5 star plans receiving a 70% rebate, 3.5-4 star plans receiving 65% rebate and 3 star or lower plans receiving 50% rebate.

MA plan profit margins tend to bounce around 3%, so it is quite evident how important star ratings are to the financial performance of a plan. A 4.5 star plan can bid against a 5% higher benchmark, which translates to a 3.5% rebate – essentially the entire profit of the plan. Of course, MA plans are required by law to apply all rebates to the benefit of its members, whether through supplemental benefits, premium buydowns or other forms of reduced cost sharing. Nonetheless, a 3.5% bonus rebate means a plan can offer 3.5% additional benefits without eroding its profit margin, making the plan more attractive to existing members and prospective enrollees. Losing the bonus can mean redesigning the plan to offer fewer benefits, a higher cost share burden for members, or exiting some markets entirely.

For the 2025 star ratings, which were released in October 2024 based on assessments conducted through 2023, CMS appears to have raised the star cutoff points by more than expected, resulting in multiple plans receiving material rating reductions, including the two largest plans from UnitedHealthcare and Humana. Humana in particular saw its average star rating fall from 4.37 in 2024 to 3.63 in 2025, essentially wiping out up to $3 billion in quality bonuses (Humana’s operating income was only $2.6 billion in 2024). Both UnitedHealthcare and Humana filed lawsuits against the HHS; the former won its claim to force CMS to recalculate its stars rating, while the latter litigation is still ongoing.

UNH is actually our second pick for a healthcare system deep dive. We were initially intrigued by HUM given the stock had sold off by over 50% from late 2023 to late 2024, with two distinct drawdowns – one in January 2024 following the CMS 2025 Advance Notice, and another in October 2024 following the 2025 star ratings. However, upon learning that bonus rebates were paid the year following the star rating, we were less enthused about the time it might take for Humana to return to its previous profitability levels. Considering Humana didn’t know about its stars implosion until October, it only had two months to implement widespread improvements for the 2024 assessment period that would determine the 2026 stars (to be paid in 2027). If that isn’t successful, it would have to try again in 2025 for the 2027 star ratings, and would not receive meaningful bonus payments until 2028!

Risk adjustment

Finally, we want to touch on the topic of risk adjustment. The CMS benchmark rate reflects the average health of the entire FFS Medicare population, and this imaginary beneficiary of average health is assigned a risk score of 1.0. Of course, some individuals will be sicker than others, and CMS will pay MA plans more for members with higher acuity, and less for healthy members with lower acuity. The process of scaling the PMPM payment to reflect the health of the individual member is known as risk adjustment.

When new members enroll in an MA plan, the plan doesn’t know anything about the member’s health status. The default PMPM payment the plan receives for these members is based solely on their demographics-only score, which reflects age, sex, disability status etc. It is then the plan’s responsibility to get these new members to see their primary care doctors for their annual checkups, so the plan can obtain clinical diagnoses for any conditions the members may have. CMS maintains an exhaustive database of health conditions known as the Hierarchical Condition Categories (HCC). Each health condition has a code, and each category of conditions is assigned a risk adjustment factor (RAF) score. These new members thus get “coded”, and the plan submits these diagnostic codes to the CMS to update the members’ risk scores. The default PMPM payment is then multiplied by the total RAF to calculate the risk-adjusted payment.

Risk adjustment is a practice that is highly susceptible to abuse due to capitation. CMS and its advisory bodies (e.g. MedPAC) have found that the risk scores of MA populations tend to be higher than FFS Medicare, thereby costing taxpayers more. In theory, seeing as MA and Original Medicare represent ~50/50 of the eligible Medicare population, both population pools should be statistically significant samples of the overall Medicare population, and one should not be persistently sicker than the other. As expected, competing partisan interests have different explanations for this risk score discrepancy.

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MA advocates argue that higher average risk scores are due to higher coding intensity – i.e. MA plans are more proactive in identifying and thus managing medical conditions, which ultimately reduces total medical costs in the long run. Detractors argue that plans are incentivized to “upcode” – the practice of diagnosing conditions that might not be supported by clinical evidence – because higher risk scores lead to higher capitated payments from CMS and more profit for the MA plans.

It is worth highlighting that V28, CMS’s latest risk adjustment model that is being phased in through 2024-2026, has made significant changes to the HCC, eliminating or consolidating a number of conditions and remapping RAF scores. As we mentioned in passing earlier, this is expected to have up to an 8% impact on risk scores upon full phase-in. This is one of the Biden administration’s more prominent attempts to crack down on risk adjustment profiteering in the MA program. CMS maintains that the current model will reflect more recent utilization trends, costs and diagnostic patterns, leading to less overpayments. The model is also expected to more closely align the coding intensity of MA plans with that of FFS Medicare.

The table below provides a stylized example of the impact of V28 vs the previous V24 risk adjustment model. This is likely on the more extreme end with the person’s total risk score declining by -33%. Note that the default demographic factor for this person is 0.737.

Source: https://www.agshealth.com/blog/understanding-the-changes-in-the-cms-hcc-model-v28/

Putting all of the above together, the chart below summarizes how MA payments are calculated.

Source: MedPAC

Medicaid and other safety net programs

We now turn briefly to Medicaid and other safety net programs such as the Children’s Health Insurance Program (CHIP), which at the cost of over $900 billion, represents almost as much healthcare expenditure as Medicare. Medicaid was originally established in 1965 alongside Medicare to provide insurance for people living below the federal poverty line (FPL). The program has seen multiple expansions, including CHIP during the Clinton era and by the ACA under Obama, which raised the eligibility threshold to 138% of the FPL. The ACA also established public health insurance exchanges (aka Marketplace) to provide subsidized insurance for individuals who did not qualify for any other programs.

Source: Bristlemoon Capital, CMS data

The Medicaid program is administered by the states but jointly funded by state and federal governments. Federal funding ranges from 50-80% of total funding and varies by state, with poorer states receiving more federal funding. The funding is actually on a matched basis – for every $1 the state spends, the federal government matches by some multiple. Similar to Medicare Advantage, some states have elected to outsource their Medicaid programs to private MCOs, on a similar PMPM capitation basis. However, unlike MA which typically lets plans keep the savings they generate and reinvest back into plan benefits (and plan profits), states typically adjust capitation payments annually to recapture some or all of those cost savings. This is because as a safety net program, there is already de minimis cost sharing with Medicaid beneficiaries, and no incentive to provide supplemental benefits beyond basic medical care.

Source: KFF

Consequently, in our opinion, managed Medicaid is a less attractive business for MCOs than MA. Medicaid growth over the decades has largely come from program expansion rather than favorable demographic tailwinds as is the case for MA. MA has higher premium, higher acuity (more opportunities for cost containment), more flexible plan design (more opportunities for cost containment), and historically higher plan margins.

Source: Bristlemoon Capital, Bernstein

In recent years, Medicaid plans have enjoyed abnormally high profit margins due to the COVID public health emergency (PHE), which mandated continuous enrollment for Medicaid beneficiaries in addition to a temporary 6% increase in Federal funding. What usually happens is that each year, Medicaid beneficiaries are assessed for eligibility and disenrolled if they are no longer eligible. Disenrollment was paused during the PHE, which resulted in Medicaid enrollees jumping from ~66 million in the five years through 2019 to 94 million at the peak in March 2023. Following the expiration of the PHE, Medicaid redeterminations resumed in April 2023 and has resulted in ~15 million people being disenrolled through the end of 2024. So Medicaid MCOs, especially Molina and Centene, enjoyed additional payments for otherwise ineligible members, and lower utilization during the 2021-2023 period.

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Conversely, as we explained in the MA rate setting section, MA plans faced lower like-for-like PMPM payments for 2024 and 2025 while utilization rates surged, resulting in severe margin contraction. The MA-heavy MCOs are arguably still recovering from this today, and the higher rates announced for 2026 should help. We’d also note that the MA plan margin compression in 2013-2015 was largely due to the ACA cutting MA benchmark rates to 1) fund the ACA Medicaid Expansion and 2) to bring MA payments closer to FFS Medicare costs. MA payments were already seen as excessive back then, so the current media attention on MA risk coding is nothing new.

This brings us to the final reason why we think the Medicaid business is less attractive than MA – regulatory risk. Both Medicaid and MA face regulatory scrutiny, but Medicaid has historically been subject to far more sustained federal scrutiny and threats of funding cuts.

This is because:

1. The federal government has limited control over funding due to the state matching requirement (which states can abuse);

2. The over-65s are the most consequential voter demographic so cutting their entitlements is a political non-starter; and

3. Republicans tend to see the Medicaid program as a form of open-ended government welfare.

The 3^rd point is especially relevant today given full GOP control of Congress. The House reconciliation bill currently working its way through Congress threatens to cut federal Medicaid funding by up to $880 billion over ten years, impose 80-hour per month work requirements as part of eligibility criteria (currently zero), and require semi-annual eligibility verification, amongst other proposed changes. In the lead up to the House bill, there was also discussion about potentially converting federal Medicaid funding to block grants – essentially fixed lump-sum or PMPM payments to the states – from the open-ended matching model that exists today.

Employer-sponsored health insurance

The last of the three main funding programs is employer-sponsored insurance (ESI), also known as commercial insurance. Here, employers contract with MCOs/health plans to provide health insurance to their employees. This can either be done on a “risk” basis aka Commercial Risk whereby the employer pays a fixed premium and the insurer takes on the risk of funding medical benefits, or on a self-insured basis aka Commercial ASO where the employer bears the funding risk and pays the insurer a fee for administrative services only e.g. care coordination, claims processing and access to the insurer’s provider network. Typically, large employers with diversified risk pools will choose to self-insure while smaller employers will opt for commercial risk insurance.

We don’t have much to say about employer-sponsored insurance as this is the most straightforward and least controversial “program”. Premiums are negotiated and set between private parties, contracts can be repriced to reflect actual realized utilization and medical cost trends, and funding is not a political debate. ESI is the most mature health insurance market and enrollment has remained stable in the 160-180 million range for decades. Competition between plans largely falls along the vectors of lower per-unit medical costs or better cost management. The Blue Cross Blue Shield plans tend to have the highest enrollment in any given state and can leverage their scale to negotiate the highest discounts from healthcare providers. Smaller for-profit MCOs might not be able to secure the best discounts (even UnitedHealthcare is #2 in most states) but can engage in superior utilization management to achieve comparable total medical costs.

Source: Bristlemoon Capital, CMS data

From the perspective of the healthcare provider, there is a big difference between government and employer-sponsored insurance. For FFS Medicare, CMS sets the physician fee schedules and hospital reimbursement rates – there is no negotiating with the government. Medicaid reimbursement rates are typically lower than FFS Medicare due to it being a safety net program for the poor. MA plans, while serving the Medicare population, do negotiate rates with providers, and these rates tend to be even lower than FFS Medicare due to network concentration and risk-sharing arrangements. On the other hand, reimbursement rates for commercial/ESI tend to be much higher, sometimes multiples higher, than government rates for the same procedures.

Economics of managed care

A concise textbook definition of “insurance” would be a policy that is intended to protect against unexpected loss. Health insurance, like other forms of insurance, is a means of socializing the burden of catastrophic loss. However, one unique feature of health insurance is that unlike almost all other forms of insurance, a health insurance policy is designed to be used frequently. We don’t buy car insurance with the intention of crashing our car every year, but we buy health insurance with the intention of availing ourselves of doctor visits, dentist checkups and prescription drugs. In a sense, a health insurance policy can be thought of as a discount card to access healthcare services. Readers in the US are probably familiar with the cost delta between an in-network doctor’s visit and an out-of-network visit.

This unique feature of health insurance has implications for insurer behavior. If you’ve ever wondered why companies such as Travelers or Progressive are known as P&C insurers but the Elevances and Humanas of the world are referred to as managed care organizations, then you are a) a nerd, but b) an astute one. Underwriters of “traditional” forms of insurance typically price their policies ex-ante based on actuarial factors and once issued, have little ability to “steer” the behavior of the insured. Sure, the policy might contain exclusions for intentional damage and unlawful behavior (e.g. driving under the influence), but the primary avenue of loss mitigation is claims adjudication after a loss event has occurred, and secondly by increasing future premiums to try and recover excessive prior year losses.

Health insurers, on the other hand, receive capitated payments and are on the hook for potentially unlimited medical cost. It is much easier for a health plan member to unintentionally and without malice overconsume healthcare services than it is for a P&C policyholder to have their house burn down multiple times a year. As such, the primary role of MCOs is in the name – to manage the (medical) care of its plan members by balancing the outcomes/quality of care with the cost of care.

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We can decompose the total cost of medical care into a classic P x Q equation: the cost per unit of care, and the number of units consumed. The cost per unit of care is typically set within the contracts between the MCO and its network of healthcare providers, or in the case of Medicare and Medicaid, set by the government. The cost per unit is easier for the MCO to predict because it is contractually negotiated and can typically be priced into the plan premium ahead of time. The number of units consumed is also known as utilization and is influenced by factors such as members’ underlying health status (vs what’s been priced, especially for MA), more physician visits, higher incidence of seasonal illnesses, higher-acuity hospital stays, increased prescription drug use etc. Utilization is much harder to predict and can improve/deteriorate throughout the year, so MCOs spend a lot of time trying to manage utilization. Overall medical cost inflation or medical cost trend is the combination of unit cost inflation and changes in utilization.

The best MCOs from a shareholder perspective tend to be the ones that can consistently predict (and price for) their medical costs and effectively manage plan utilization in line with those predicted costs. Below, we highlight some of the main strategies that MCOs use to manage their medical cost trends.

1. Network design – starting at the top, plans need to decide if they want a wide or narrow network of providers. If plans can direct higher volumes of patients to a narrower network of physicians, hospitals, pharmacies etc, they can negotiate steeper per-unit discounts with these providers. The tradeoff is a more restrictive network for plan members and higher OOP costs for out-of-network services.

2. Benefit design – MCOs can also design plan benefits and costs to incentivize certain behavior e.g. higher copays or coinsurance to steer members away from certain services or higher cost care settings, or inclusion of supplemental benefits like certain testing, screening or even acupuncture if there is evidence such benefits reduce overall cost of care.

3. Utilization management – in addition to using OOP costs to disincentivize unnecessary care consumption, MCOs try to steer patients to lower-cost care settings e.g. outpatient surgical center for hip replacement instead of an inpatient hospital stay. MCOs also use prior authorizations to limit medically unnecessary specialist visits and medical procedures, case management programs for members with comorbidities, step therapy protocols especially for prescription drugs, and claim denials at the extreme. Some of these utilization management techniques are seen as controversial (essentially denial of care) and are a source of the recent public backlash against MCOs.

4. Risk sharing and value-based care – just as MA is a risk-based arrangement between CMS and private MCOs, MCOs can also enter risk-sharing or value-based arrangements with care providers. We will explore value-based care in the next section.

In terms of the economics of an MCO, the two most important metrics are premiums and the Medical Loss Ratio (MLR). Premiums are the revenues that MCOs collect from payers – for government programs it will be based on their bids relative to benchmarks and the change in those benchmarks as determined by CMS, and for commercial programs it is directly negotiated with the payers (predominantly employers). The MLR is the totality of medical costs incurred by the MCO’s members as a percentage of the premiums collected. A listed MCO’s primary objective is to contain its medical costs within the bounds of its premium revenue to generate a profit for shareholders. A lower MLR indicates the MCO is effectively managing its medical costs, while a higher MLR, especially over 90% (let alone 100%) suggests the MCO is struggling to contain its medical cost trends.

But an MLR that is too low is also not ideal as it suggests the MCO is either overearning on premiums or not providing sufficient benefits to members/excessively denying care, neither of which are sustainable. The ACA established a minimum MLR floor of 80% for individual and small group markets and 85% for large group and MA. Hence, we typically see employer focused MCOs (e.g. Cigna) running at low-80s MLRs and MA/Medicaid focused MCOs (e.g. Humana, Molina) running in the high-80s range, with more diversified players somewhere in the middle.

Source: Tegus. MRQ = most recent quarter. Note CVS (Aetna) is missing due to its large retail pharmacy operation.

The listed MCOs run at operating cost ratios in the high-single digit to mid-teens range as a percentage of revenue, although direct comparability is difficult as these companies tend to also have material non-insurance operations. Putting the MLR and operating cost ratios together, the big listed MCOs achieve operating margins in the low- to mid-single digit range for their insurance businesses. Given these thin margins, we can understand why their earnings are so sensitive to changes in capitation rates and utilization.

Source: Tegus

This concludes the payer and MCO sections of this primer. For paid subscribers, we will cover the two remaining major system participants – the providers and PBMs – more specifically in the context of UNH below. This means we will omit hospital providers because they are not relevant to UNH’s business, but may revisit them in future considering hospital care accounts for the largest percentage of US healthcare expenditures.

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