The other emerging fear among investors is that VantageScore 4.0 is disrupting FICO’s gold standard, which no financial data seems to support. When the market comes around to that realization, the upward correction in the stock quote may be violent, as implied in recent price volatility.

Company profile

August 25, 2026 Follow-up coverage
Direction: Buy
Previous fair intrinsic value: $2116, as of May 2, 2026

Symbol: FICO, Exchange: NYSE
Sector: Technology, Industry: Software - Application
Theme: High quality
Fair intrinsic value: $2269 (98%), as of August 25, 2026
Market capitalization: $26 055 million
Pricing data: P/S 10.9x, P/E 32x

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Data points to the market being wrong

Uncertainty tends to unequivocally have a downside impact on the stock quote of the targeted company, something evident in the sharp decline in FICO’s stock price. However, the market seems undecided because the company is delivering stellar results each passing quarter, suggesting no disruption is taking place. As a reminder, here is a policy timeline that has led us to today’s status, with bolded events causing direct crashes in FICO’s stock price.

In recent coverage, I have covered a response refuting each point raised by Josh Hawley, as well as discussed FICO’s status as the gold standard and why I believe it won’t be disrupted. Today I want to focus on the actual data from calendar quarter 2’s financial results for the publicly traded bureaus and FICO.

First things first, VantageScore has seen a genuine increase in usage. It appeared in fewer than 5% of TransUnion’s mortgage credit inquiries at the start of the year, to reaching almost 30% by Q2 across more than 900 lenders. Such rapid adoption at face value may seem like disruption of FICO’s gold standard being imminent, but what the headline does not cover is that VantageScore is being sent along for free with FICO score pulls. Meaning, it is pulled alongside FICO, not instead of FICO.

CEO Lansing also notes on the call that measuring market share is difficult, and a first instinct as an analyst is to note the spread between credit score-related revenue among FICO and the bureaus. However, Equifax and TransUnion resell FICO scores after buying them at FICO’s wholesale royalty. They mark it up and book the gross amount as their own revenue. When FICO increases its price, it inflates the growth contribution to the credit-file revenue at both bureaus. Both bureaus have begun disclosing commentary and numbers excluding FICO, and it shows that 50-75% of their mortgage revenue growth is FICO’s pricing power flowing through, at no profit for the bureaus.

Figure 1: Equifax USIS mortgage revenue growth mix (decomposition)

Note: CQ2 data assumes mid-point of 5% per Equifax Q2 earnings call: “USIS mortgage revenue was up 40% and up mid-single digits excluding FICO, with hard mortgage inquiries up only 1%.”

Equifax USIS Mortgage revenue and TransUnion’s mortgage revenue include the bureaus’ own products, which means that the ex-FICO revenue growth could come from sources other than VantageScore 4.0 adoption.

As a reference point, FICO does not disclose exact mortgage score volume growth, but noted in the Q3 earnings call that it grew “low single digit”. Against that backdrop, FICO still grew its mortgage originations revenue by 97% Y/Y, signalling that it still has immense pricing power. VantageScore has waited years for permission, but it turns out to be worth surprisingly little, and that is simply because of who pays for the score pull.

Figure 2: FICO Mortgage originations revenue growth

The marginal cost of pulling a score is passed to the borrower, meaning they don’t feel the impact of exercised pricing power. What they do feel, however, is the risk of a loan failing to qualify, which turns into scores becoming gamed. Lenders pull a free VantageScore next to a paid FICO because it is available, and pulling both means that the lender increases the chances of qualifying a borrower. The data shows that almost no one seems to be paying for VantageScore 4.0.

The CHLA puts the all-in credit report cost at an average of $540 in 2026, and they assume a $10 flat FICO take at a tri-merge rate of $30; it means that bureaus and resellers charge 17 times the FICO take, a 1600% markup. The $540 figure is up from ~$50 in 2022, branding FICO as the villain due to FICO’s price increases.

From another angle, FICO’s DLP performance model that charges a $33 funding fee for classic FICO, or $65 for FICO 10T, reflects a 50-90% reduction in average per-score fees compared to what resellers paid for FICO scores in 2025. This takes the markup out of the bureaus’ hands and lowers the all-in cost to lenders, and simultaneously defuses the political backlash FICO has been facing without cutting FICO’s own take.

The data suggests that FICO is capturing more mortgage dollars, not less, and it is accelerating rapidly. No data point that I can find supports the narrative that FICO is losing share to VantageScore.

Figure 3: Implied U.S. mortgage originations revenue share, TTM

It also seems that the fears of FICO being regulated further may be easing, as the FHFA Director Pulte signals that he is satisfied with recent developments in the credit scoring market.

FICO and Vantage Score are both doing very well. I appreciate the leadership of each company. Likely to have some new news on each soon. Exciting times ahead for the consumer.
- FHFA Director Bill Pulte on X, 21 August, 2026

Business developments

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Company profile

Theme: SaaS, Direction: Hold

Symbol: NOW, Exchange: NASDAQ
Sector: Technology, Industry: Software - Application
Fair intrinsic value: $112.6 (-10%), as of August 19, 2026
Market capitalization: $130 776 million
Pricing data: P/S 8.9x, P/E 78.3x

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The ever-present AI fears in the market

In recent times, the SaaS darling of the market has not been spared from the cannon of AI disruption fears. However, none of the reported financial metrics suggest that ServiceNow is being disrupted by AI.

Figure 1: Revenue and revenue growth, TTM

There are two angles to AI disruption fears: one to the business itself, and one to the business model. Anthropic sparked a majority of fears in the market when it started shipping products across a wide variety of categories, including creative and visual design, cybersecurity, and enterprise productivity and automation. The notion is that AI will be able to create software products within any given vertical and disrupt existing software companies. In regard to targeting the business model, the fear is that AI agents will take over seats in a seat-based SaaS, and with that, the revenue associated with the seat will be lost.

In the ServiceNow context, if ServiceNow only offers seat-based SaaS contracts, then agentic AI would be akin to an extinction event for the business, and that seems to be what the market has begun to fear for the company. Both volume and price would be under pressure, but such a scenario is not materializing. Instead, ServiceNow has embraced AI and integrated AI capabilities across its suite of products, which is resulting in increased volume and pricing for the offerings instead. The approach is a hybrid pricing structure of both seat-based pricing for predictability, as well as a consumption-based model for flexibility.

ServiceNow reports some of the most consistent financial figures that I have seen in a business, making it easy to spot any kind of disruption across the business. Renewal rates have stayed consistent, annual contract value (ACV) expansion across customers continues to accelerate through peak AI fear, and even the geographical mix is undisrupted.

Figure 2: Renewal rate

One chart that may seem alarming at first is the number of customers with ACV’s exceeding $1 million. The growth rate has steadily been dropping until it ultimately ceased to be reported on altogether, just as the growth rate was about to drop below 10% Y/Y

Figure 3: Customers with ACV exceeding $1 million

What replaced the metric is customers with an ACV exceeding $5 million, and that cohort of customers has been accelerating over the past year, through peak AI disruption fears.

Figure 4: Customers with ACV exceeding $5 million

Among the cohort of customers with an ACV exceeding $5 million, the average ACV per customer has steadily been increasing throughout the AI fear narrative.

Figure 5: Average ACV of customers with an ACV exceeding $5 million

To recap, AI disruption would show up in both volume and pricing, ultimately impacting growth. What we are seeing is consistent and healthy renewals, accelerating revenue growth, and expanding ACV both in terms of average ACV as well as the number of customers signing large contracts. ServiceNow’s own AI solutions are seeing rapid adoption, with a majority signing consumption-based pricing, and customers spending over $1 million of ACV on ServiceNow’s AI solution grew 130% Y/Y in Q1 2026, signaling expansion rather than contraction. If AI agents were displacing ServiceNow’s solutions, the outlook would look quite different.

ServiceNow’s software

ServiceNow and its “Now Platform” is originally an IT service management (ITSM) system covering the core incident, problem, and change service requests. Since then, it has expanded into covering wider parts of an enterprise’s operations, including:

• IT operations and asset management.

• HR and employee service delivery.

• Customer service and CRM workflows.

• Security, risk, and compliance.

• Low-code application development.

• Generative AI and agent platform.

These types of workflows and platforms are described in a myriad of acronyms: ITSM, ITOM, ITAM, CMDB, and so forth. However, it’s the last one, the configuration management database (CMDB), that matters competitively. A CMDB is a centralized repository that stores the enterprise’s IT assets and infrastructure components, as well as their relationships and dependencies. Configuration Items (CIs) that populate the CMDB include hardware, software, virtual resources, business services, and virtually any asset within the enterprise.

The purpose of a CMDB is to act as a single source of truth for the whole IT environment, which enables teams to understand the infrastructure of the business and its services. For example, the team can see that a specific application runs across these particular servers, in this data center, dependant of this database, which is maintained by that team, and during an outage, these applications and services will be impacted, which affects these teams, and so forth.

Understanding the importance of a CMDB is also a key to understanding ServiceNow’s competitiveness in the market, as well as its moat. Because of the CMDB serving as a single source of truth for the whole enterprise, the switching costs become massive as years of accumulated populating and mapping of a CMDB would be lost, quickly turning into an operational nightmare. It also helps ServiceNow to expand within existing accounts and sell more platform capabilities, as each module makes the entire platform deployment more valuable and more efficient. For example, if an enterprise’s security team purchases ServiceNow’s Security Operations module, they already have a CMDB dependency map to rely on to see which services, servers, and applications are vulnerable to threats.

The business

The revenue can be divided into two disaggregated revenue lines: subscription revenue, which encompasses the software itself, and a professional services segment, which is a small and limited part of the business. The professional services segment accounts for a mere ~3% of total revenue, which is on purpose as ServiceNow relies on a broad network of implementation partners. By outsourcing a majority of implementation, configuration, advisory, and training to partners such as Accenture, Deloitte, EY, KPMG, and others, ServiceNow can keep reporting high software-like margins and scalability. The professional services revenue is recognized as the work is performed, unlike the ratable recognition of subscription revenue.

Another reason to push professional services to partners is that a large consulting ecosystem gets built, and all of the world’s top consultancies have a reason to keep recommending and pushing the platform. In essence, it can be seen as a customer-acquisition infrastructure.

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The market can be quite generous at times to those who strive to understand businesses, and Robinhood at the time of writing is one such generous gift. What Robinhood has been busy doing has escaped surface-level investors, seeing the company as a mere brokerage that keeps adding products. The truth is a lot deeper, and a lot more exciting.

Company profile

August 10 2026 Follow-up coverage
Direction: Buy
Previous fair intrinsic value: $155.29, as of May 18, 2026

Symbol: HOOD, Exchange: NASDAQ
Sector: Financial Services, Industry: Capital Markets
Theme: Growth
Fair intrinsic value: $160.99 (73%), as of August 10, 2026
Market capitalization: $85 080 million
Pricing data: P/S 19x, P/E 45x

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The rotation in transaction revenue

As we are all aware, Robinhood is still a cyclical business and still primarily relies on customer activity in the markets to generate a majority of its revenue and profits. Q1 was a particularly weak quarter for Robinhood, and it traded down substantially after posting a mere 15% Y/Y revenue increase, after growing 100% just two quarters prior. However, Q2 sees a reacceleration to record revenue, and Robinhood grew its total revenue by 32% Y/Y, more than double the rate of Q1.

Figure 1: Total revenue

Over the past quarters, Robinhood has been on a systematic campaign to own the venue layer rather than just routing through third parties. MIAXdx was acquired in early 2026, which turned into Rothera, a CFTC-licensed exchange and clearinghouse through a joint venture with Susquehanna. Bitstamp became an owned cryptocurrency exchange, while Robinhood Chain became an owned settlement layer. Robinhood Banking is an owned deposit relationship in place of a sweep, and TradePMR and the related advisor network are an owned custody channel. Most recently, Robinhood Ventures became an owned issuance vehicle. We will cover all of these and their relevancy to the future of Robinhood in this report.

As most are aware, Robinhood discloses monthly results for two of three months in a quarter, which provides a lot of insight into what the earnings results might look like. I believe the primary cause for the stock quote’s recent collapse is the drop in cryptocurrency volumes. For a long time, crypto and options have represented the primary drivers of revenue and profits for the whole business, which is why a drop in activity can cause a lot of concern. However, crypto has been largely replaced by event contracts, an avenue with more impressive growth and way better take-rates.

Figure 2: Cryptocurrencies and event contracts yield

Robinhood entered prediction markets in Q4 of 2024 as a distributor by partnering up with ForecastEx, and then scaled the business from Q1 2025 with Kalshi. Robinhood provided the distribution channel, and Kalshi provided the platform; a partnership that quickly became a strategic vulnerability. By Q2 2025, more than half of all Kalshi volume was routed through Robinhood. As a response, in Q1 of 2026, Robinhood acquired MIAXdx, a CFTC-licensed designated contract market, derivatives clearing organisation, and a swap execution facility. Instead of going through the hoops of regulatory applications, Robinhood simply acquired it, which enabled the launch of Rothera in a short amount of time. Within a single month from its launch, Rothera became a top-three designated contract market.

Figure 3: Robinhood volume and share of Kalshi event contracts

Of a total of $156 million from event contracts, $139 million was still Kalshi-routed and $17 million went through Rothera. Rothera is not controlled by Robinhood, so the economics accrue partly outside of consolidated revenue since it goes through the joint venture and non-controlling interests. The yield fell 10 basis points to 1.1%, hinting at the unit economics not necessarily being more favorable through Rothera. However, only 15% of total event contracts volume went through Rothera, making it too early to draw conclusions. Robinhood’s estimated market share of the prediction market is ~12%, down from its peak in Q3 2025 of 26%.

However, Robinhood does not participate in the wider categories of predictions that more open platforms do, and as such, will keep losing share. The upside of Robinhood’s strategy of only offering more serious prediction markets is that they will have far fewer regulatory issues down the line. Prediction markets are quickly becoming more and more obscure, with contracts being offered for events that are subject to heavy manipulation, and some are downright luck-based, such as predicting the number of cars that will pass a crossing in an allotted timeframe.

Figure 4: Prediction markets market share

There are now ~2 million customers cumulatively that have used prediction markets on the platform, up from 1.5 million in Q1 2026. The increase is likely due to the FIFA World Cup taking place in Q2, and as an event-driven segment by construction, active events matter a lot for growth. In Q3, the football season will be big, and in Q4, the U.S. midterm elections will play a big role in growth.

The headline could be read as Robinhood swapping a 0.25% take-rate asset for a 1.1% take-rate asset without having to win a single new customer. However, it’s not like Robinhood dropped the ball on cryptocurrencies. Total cryptocurrency exchange volume has fallen off a cliff since Q4 2024, and declined 37% Y/Y in Q2 2026. On the contrary, Robinhood nearly doubled its market share of total cryptocurrency exchange volume Y/Y, from 0.8% to 1.5%. It is not Robinhood underperforming so much as the asset class having fallen out of favor, as is the reality of a cyclical asset.

Figure 5: Total cryptocurrency exchange volume

Speaking of market share, Robinhood is on the rise in the segment that arguably matters the most: options. Options is the segment that offers the most spread, and as such, is the most profitable. Robinhood now has an 8.6% market share of total U.S. options volume, an all-time high. In addition, Robinhood grew its equity market share of U.S. equities notional volume to 1.4%, another all-time high, eclipsing even the meme-mania bubble of 2021 of 1.3%.

Figure 6: Segmented market shares

Q1 2026 showed signs of genuine cyclical pain, only growing transaction-based revenue by 7% Y/Y. However, Q2 is a massive rebound, recording 44% Y/Y growth, primarily driven by event contracts, a segment where Robinhood is gearing up to be a major player for a long time to come.

Further diversification, the most important driver

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Palantir has always championed the concept of creating value through its software, and then charging its customers a portion of that value. In a sense, that concept is infinitely scalable: Create $100 million for a business, charge them $30 million for the software. Create $10 billion for a business, charge them $3 billion.

What has been transpiring outside of the Palantir context has been the opposite: charge customers for tokens, without caring about the value the token spend results in. The term associated with this is “tokenmaxxing”, which CEO Karp has mentioned in relation to how the AI labs conduct their business.

In addition, the results have once again surpassed analyst expectations, my own expectations, and Palantir’s own guidance, by quite a margin. The financial results are in a class of their own.

Company profile

August 4, 2026 Follow-up coverage
Direction: Buy
Previous fair intrinsic value: $268.5, as of May 9, 2026

Symbol: PLTR, Exchange: NASDAQ
Sector: Technology, Industry: Software - Infrastructure
Theme: AI Software
Fair intrinsic value: $356 (124%), as of August 4, 2026
Market capitalization: $409 039 million
Pricing data: P/S 66x, P/E 135x

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Stop tokenmaxxing

In a research note, I broke down a recent statement made by CEO Alex Karp in regard to the business being poised to hit $15-18 billion in FCF in two years. The implications from that statement would see Palantir grow its revenue by at least ~520% from its FY 2025 base by 2028, a massive undertaking which no one has even begun imagining (Wall Street consensus for 2028 is a mere $16 billion).

That statement implies an ~83% CAGR, and comes with massive implications for the intrinsic value of the business. Even more impressive, during a CNBC interview in relation to the Q2 earnings result, Karp stated that they are on a trajectory to grow at “this” pace over the next 18 months. During the Q2 earnings call, Karp clarified that the pace that he is referring to is the U.S. Commercial growth, which is 150% Y/Y.

Figure 1: Segmented revenue

The total revenue growth pace that Palantir showed in Q2 is what is required of the business to be able to live up to the statements made by the CEO in recent interviews. A trajectory of >150% growth across the business would far surpass any previous revenue assumptions derived from the FCF target of $15-18 billion in two years.

As investors and analysts, it is important to understand where that growth is coming from. For example, U.S. commercial revenue growth has accelerated for every passing quarter since Q1 of 2024, a feat which many thought would not be possible once that growth started exceeding 100% Y/Y. Now, Palantir has a full year of >100% Y/Y U.S. commercial revenue growth, and each quarter has managed to push the growth further and further.¹

Figure 2: U.S. Commercial revenue and revenue growth

¹ Q1 2026 saw a customer get reclassified from U.S. Commercial to U.S. Government. Without that impact, revenue growth would have been 143% for the segment during the period.

In order to achieve sustainable and robust growth for an extended period of time, there has to be scalability to the revenue. That ties directly to the notion of Palantir creating value for its customers, and as value is unlocked, so too should the contract value per customer increase. Net dollar retention has been consistently accelerating each passing quarter since the launch of AIP, which has usage-based pricing. If AIP is creating value, the use of the product should increase among existing customers (>12 months with Palantir), thus creating scale.

While the average customer is increasing their spend, as evident by NDR rising every quarter, so too is the cohort of Palantir’s largest customers. 2% (top 20) of customers account for 40% of total revenue, each providing >$120 million in annual revenue. That is over 3400%(!) more in annual revenue compared to the average customer outside of the top 20. For a year and a half, this massive cohort, which is already paying massive amounts to Palantir annually, is outgrowing the average customer base that has been with Palantir for over a year. That is strong evidence for the scalable nature of Palantir’s software.

Figure 3: Top-20 cohort and existing customer base average revenue growth (TTM)

Michael Burry has been one of the prominent voices criticizing Palantir for not being an AI company due to not having their own large language model (LLM), while CEO Karp made it clear from the very beginning that models are going to be commoditized.

We have been seeing evidence of the commoditization of LLMs at an alarming pace as of late, where open-source models distill frontier models (which also appear to be distillations to some degree) and replicate them at a fraction of the cost. In many cases, these distilled models, like Kimi K3, score better benchmark results than the frontier models.

At the same time, frontier AI Labs have been criticized for stealing IP with partners; a recent example includes Figma partnering with Anthropic, upon which Anthropic launched a competing product and Anthropic’s CPO, who was a Figma board member, resigned from the board.

The way it ties back to Karp’s criticism of tokenmaxxing is obvious in hindsight, but still caught investors and analysts off-guard. Palantir launched a partnership with Nvidia to deliver sovereign AI infrastructure, meaning that enterprises and governments can run models inside secure environments, with no risk of labs stealing IP. In addition, Palantir has started fine-tuning models for use in an enterprise context.

Palantir engineer Chad Wahlquist comments on the following observations from enterprise customers across the country:

• Customers are frustrated with having to assume risk for software products not working, while locking in the customers.

• Customers don’t want to subsidize AI labs that will ultimately launch a competing product.

• Customers are frustrated with having to exhaust tokens without receiving value in the output. For example, a PhD is overkill on a factory line where things are merely placed in a box. There is a need for task-specific models that are trained on the craft.

• Customers want to own the guardrails in their own environments; otherwise, the company risks not operating.

While models are being commoditized, and which frontier model reigns as king of the hill changes intermittently, Palantir has found a different approach to solve customer issues, provide value, and escape commoditization. They are fine-tuning models for enterprise-specific use cases, and their fine-tuned models outperform frontier lab equivalent models.

Palantir is uniquely positioned to fine-tune models because their software platform features Ontology, which is Palantir’s operational layer for the whole organization. Within the Ontology, the data of the organization is turned into objects, properties, links, and actions. The goal is to create a complete picture of the organization, and as such, provide an accurate context of the business to models.

Figure 4: Simplifed Ontology example

While the fine-tuning approach is rather new, through commentary, the demand for such solutions seems massive. Palantir’s software is model-agnostic, and it has already differentiated itself as a business that puts customer value-creation first, as evident by the sheer growth in existing customers. The fine-tuning approach is a smart way to build further on top of an already superior AI business.

Further sustainability of the massive growth

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Neoclouds’ soaring demand is the result of compute scarcity due to AI, but what the playing field looks like once first-term contracts are up could spell doom for the entire business model.

Company profile

Theme: Neocloud, Direction: Hold

Symbol: NBIS, Exchange: NASDAQ
Sector: Communication Services, Industry: Internet Content & Information
(legacy GICS classification)
Fair intrinsic value: $179.3 (-6%), as of August 02, 2026
Market capitalization: $58 831 million
Pricing data: P/S 67x, P/E 72x (inflated by revaluation gains)

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Business overview

Typically, my research reports contain a vast amount of financial data in order to understand core drivers of a business, its discipline, its strengths and weaknesses, its margin profiles, and the list goes on. However, with Nebius, there is not much to look at, at least not yet in terms of its current business.

Nebius has historically been an engineering organization that built Yandex, a search, cloud, and self-driving platform at technical depth few firms outside of the US have matched, but by using a fraction of the budget of a giant like Google. It was highly successful, and the growth in the business was impressive, reaching a peak of ~$10 billion (800 billion RUB) in revenue in 2023.

Figure 1: Legacy business revenue growth

In 2024, the company carved out the Yandex part of the business and emerged on the Nasdaq without a legacy business and a pile of cash. The company retained its Finnish data center and the cloud infrastructure teams, along with Avride, Toloka, and TripleTen. Nebius then built the most vertically integrated of the neoclouds by owning both land and power, self-designing its data centers and racks, and building a hyperscale cloud platform with a software layer above it. From a near-zero base, revenue growth has been in a vertical growth phase as it rides the secular tailwind that is AI.

Figure 2: Segmented revenue

Before we get into the intricacies of the neocloud business, it is worth highlighting all the business segments. In essence, Nebius Group consolidates one dominant business and two consolidated subsidiaries. The AI cloud business accounts for 98% of total revenue as of Q1 2026, making the other two immaterial both in terms of size and opportunity.

The Avride stake comprises an autonomous vehicle tech company, focused on developing and operating self-driving delivery robots and robotaxis. It was originally part of Yandex’s self-driving division, and now partners with platforms like Uber to provide driverless food deliveries and robotic rideshare services. It more than doubled its AV-capable fleet YTD, and launched robodeliveries in Philadelphia. It passed 500k cumulative robot deliveries in early April, and has received strategic investments from Uber.

TripleTen is an education tech platform that provides bootcamp program in technology fields such as software engineering, data science, and cybersecurity. The segment grew 10% Y/Y, with about 5000 new students in Q1.

The way to approach TripleTen and Avride in relation to Nebius Group is as free optionality. Simply, focus on the AI cloud business when looking at the business, and whatever the equity stakes produce, you get for free.

It is also worth noting that Nebius has two deconsolidated equity stakes in ClickHouse (real-time analytics database) and Toloka (AI data for frontier labs). The stakes matter both as assets and also serve as a GAAP reporting distortion. Nebius reported positive net income in FY2025, but it is the result of a revaluation artifact rather than due to the companies’ operating results.

The neocloud business

While revenue growth has been impressive across the neocloud space, the true economics and the attractiveness of the business are quite obfuscated. A neocloud converts MW into recurring revenue, and when looking at the unit economics, it seems as if neocloud contracts merely return the approximated capital costs of the assets, and nothing else.

Figure 3: AI Cloud segment growth

Looking at CoreWeave’s contracted power, the backlog of $99.4 bn revenue against its >3.5 GW of power yields about $28mm per contracted MW, which is the lower end of what an MW costs to build. The first contract is the financing instrument that pays for the whole asset, and not where the actual money is. For the economics of neoclouds to make sense, the second contract has to land. However, at years ~5-8, the hardware is already substantially depreciated, just for when the contract renewals are coming up.

The renewal is uncertain

The question to be asking is: does the second contract exist, and at what price? The neocloud revenue largely exists on a few balance sheets, primarily concentrated at hyperscalers like Microsoft, Meta, and Google, as well as AI labs. However, while hyperscalers underwrite an overwhelming majority of neocloud demand right now, there’s a big question as to whether they will continue to do so since they are building out their own data center capacity.

┌────────────┬────────────────────────────────────────────────────┐
│ Buyer      │ Neocloud commitment                                │
├────────────┼────────────────────────────────────────────────────┤
│ Microsoft  │ ~$60B (CoreWeave, Nebius, Nscale)                  │
│ Meta       │ ~$62B ($21B CoreWeave, up to $27B Nebius)          │
│ Google     │ Lease guarantees (Cipher, TeraWulf)                │
│ OpenAI     │ Expanded by up to $6.5B in 2026 (CoreWeave)        │
│ Anthropic  │ Multi-year agreement (CoreWeave Q1 2026)           │
└────────────┴────────────────────────────────────────────────────┘

The four largest hyperscalers are guiding for roughly $700 billion in combined capital expenditures in 2026, all while simultaneously being the largest renters of third-party capacity. The reason for this is simply time, and not technology. It takes power and time to build out capacity. If a hyperscaler wants to build GW capacity in 2027, it must have secured its position to do so back in ~2023. For example, Microsoft has described in commentary that they can’t serve their full Azure backlog due to power constraints.

Renting out capacity at hyperscalers eases its construction pains, and as mentioned, they are throwing a lot of money at the problem. By the time the leases expire, will there be a second contract? The demand could be large enough to warrant another contract, but will the hardware still be up to par, and what price will it fetch? Neoclouds will need to continuously build out capacity, and at a projected lower $ per MW.

The reason is spelled inference, and is a double-edged sword. Inference is a reliable source of recurring workload and has a higher margin profile compared to training. Inference accounted for roughly a third of AI compute in 2023, which became about half by 2025, and now in 2026, it accounts for two-thirds (per Deloitte). However, cost per unit of inference is quickly collapsing.

Per the Stanford AI Index 2025, the cost of inferencing a GPT 3.5-equivalent model has fallen from ~$20 per million tokens in late 2022 to $0.07 in 2024, a 280-fold reduction. Inference is the reason the overall market keeps growing, but for neoclouds, it is also the reason why revenue per MW will keep falling.

Figure 4: GPT 3.5-equivalent inference cost

Recent developments see Meta building its own cloud business to sell compute, while simultaneously being the sector’s second-largest customer with roughly $62 billion in committed capital across CoreWeave and Nebius. That adds a lot of fuel to the fire that is renewal risk.

Nvidia has begun underwriting GPU residual values under a new six-year backstop. Nvidia will rent back unused GPUs at a pre-agreed price in exchange for a share of cloud revenue. It removes the capital constraint that has been rationing supply in the sector, making it good for volume, but bad for pricing and the revenue/MW which neoclouds depend on for their economics.

Nebius cloud

The Nebius stack is built in several layers, but before we dive deep into Nebius neocloud, it is important to distinguish between the different kinds of neoclouds that exist. Operators, which are the category that Nebius and CoreWeave fall into, sell compute and carry risks in terms of GPUs becoming obsolete, utilization, and price risk. Landlords, which are companies like Applied Digital, Cipher, TeraWulf, etc., lease powered shells for up to 25 years at a much lower rate. They lease them to investment-grade or investment-grade guaranteed tenants, and transfer the demand risk in exchange for a fraction of the revenue accrued.

Generally, there is roughly 4-6 times less revenue per MW that landlords collect compared to operators, but that is simply the price of risk transfer. Landlords contractually shed demand, utilization, and obsolescence risk. There is also a third category, the aggregators, which contract capacity from landlords and resell it, carrying even less risk.

Published estimates of the price per AI megawatt vary depending on what is included.

• Conventional data-center shell and core

  • $10-12 million per MW

• AI-optimized shell (liquid-cooled, high-density)

  • $15-20 million per MW (higher power density, liquid cooling loops, heavier electrical work)

• Tenant-adapted shell with GPUs and networking

  • ~$20-25 million per MW (GPUs alone exceed $20mm/MW)

• All-inclusive operational AI megawatt

  • ~$28-38 million per MW

Operator revenue run rates are roughly $8-12 million per MW at current pricing (CoreWeave $8.3bn annualized against >1 GW, Nebius $1.92bn ARR against ~200 MW active power), which is also expected to drop over time. With that in mind, gross revenue payback becomes >3 years, and that is before any dollars of operating cost. Once power, hosting, staff, SG&A get layered in, it’s more looking like ~5 years until payback. The weighted-average contract per CoreWeave’s disclosures is about 4 years, which means the contract approximately repays the asset, and that’s at current rates.

Nebius monetizes its physical assets in a variety of ways, each with a different margin profile.

• AI factories, including land, power, and DCs

  • Monetizes capacity. Is capital-intensive, but has a cost advantage layer since Nebius has in-house DC design.

• Reserved GPU capacity

  • Monetizes hyperscalers and AI labs through visibility and prepayments. Generally priced below spot, but finances the overall build.

• On-demand and short-term AI cloud (Aether)

  • Monetizes startups and enterprises across a variety of verticals. Has a higher rate per GPU-hour, but still retains utilization risk.

• Token Factory (managed inference)

  • Serving custom and open-source models on a per-token basis (Revolut, monday.com, etc). Has the same margin characteristics as a software business, and sits on top of the same GPUs. Becomes strategic for post-scarcity pricing power.

• Agentic and software layers

  • Monetizes search and inference optimization.

• Other stakes and businesses

  • Avride (AV/Robotics), TripleTen (edtech), ClickHouse, and Toloka stakes. Serve as optionality and are largely immaterial to revenue, but material to asset value.

Nebius has been aggressive in adding new locations and increasing the capacity potential globally. As of Q1 2026, there are 13 disclosed sites, with guidance of 16 total sites by the end of 2026. As of 2025, sites are operational in Finland, Kansas City, Vineland, Iceland, the UK, and Israel, with 2 under construction and the rest just being announced.

Figure 5: Site disclosures

A major expansion is needed in order to acquire credibility for future contracts. As of Q1 2026, contracted power is guided to be >4 GW by year-end. Of that, ~1 GW is expected to be connected power, a guide that has strangely stayed unchanged since 2025 while contracted power quadrupled.

Figure 6: Contracted power guidance, end-of-period 2026

While there are limited KPIs and financial data to observe in Nebius current state, there are a lot of metrics to track over time to ensure a proper monitoring framework for the sector.

• Contracted power, connected power, and active power conversion

  • Connected power hits guidance targets and active power tracks it within one or two quarters.

• Backlog conversion rate

  • These should be growing together to indicate a healthy reading.

• Revenue per active MW

  • Given the capital-intensive nature of the business, $mm/MW will be an early indicator of sustainability.

• Hyperscaler CapEx revisions

  • If capital expenditure continues to be revised up at hyperscalers, it signals an increase in demand. Once it starts getting cut, it indicates a sector-wide top signal.

Nebius ARR is defined as last-month AI-cloud revenue multiplied by 12, making it a run-rate snapshot and not contracted forward revenue. This is the prime metric for capturing the momentum of the business, but it won’t tell us about duration or churn, and will stay ahead of trailing revenue while the business ramps.

Figure 7: ARR

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Company profile

July 21, 2026 Initiated coverage
Direction: Hold
Previous fair intrinsic value: N/A

Symbol: MCO, Exchange: NYSE
Sector: Financials, Industry: Financial Data, Stock Exchanges
Theme: High quality
Fair intrinsic value: $413.13 (-18.5%), as of July 21, 2026
Market capitalization: $89 883 million
Pricing data: P/S 11.42x, P/E 35.98x

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AI as an accelerator or disruptor?

Moody’s operates two distinct revenue segments. Moody’s Investor Services (MIS) charges issuers for its credit ratings, similar to S&P Global’s ratings segment. They record a transaction fee whenever a bond, loan, or other financial instrument is rated, and then record additional revenue on the initial rating through fee schedules and monitoring fees for maintaining the rating. Moody’s Analytics (MA) sells workflow tools, risk software, and data feeds on a subscription basis, where close to all of the Analytics revenue is recurring.

Figure 1: Segmented revenue

Moody’s is flat year-to-date, but was down significantly earlier in the year on AI disruption fears. As mentioned, markets like compounders because they are a source of durability, and AI disruption fears left a question mark on that characteristic of Moody’s business. S&P Global made several statements in regard to how much of their data services are proprietary and unreplicable, and Moody’s has done the same throughout the year, though without S&P’s quantification.

There are two angles to the AI threat: one concerns which parts of Moody’s business can be replicated, and the other angle concerns whether it matters if they are.

For example, the credit rating value comes from the embeddedness of the standard; permissions and coordination relating to index eligibility, capital treatment, and acceptance by the bond market. In theory, an LLM could produce a credit opinion, and in some cases it could even be indistinguishable from a Moody’s rating, but that does not mean that it produces a rating that a bond mandate accepts. As a reference, FICO scores have had a competitor for two decades that hands out its credit scores for free, and the market still does not switch from FICO because FICO scores are simply too integrated into the financial system.

As for the more vulnerable part, the data in Orbis might be proprietary, but that does not mean that the raw facts behind the data are exclusive to Moody’s. Where the value lies is the curation layer, where the accumulated labor over decades has built an entity resolution with ownership graphs, identifiers, licensing agreements, and so forth. To use FICO as an example again, owning the raw data doesn’t get the standard, exemplified by FICO’s competitor VantageScore being owned by the big three credit bureaus, which actually own the consumer data that the FICO scores utilize. The very owners of the underlying data could not disrupt or displace the standard sitting on top of their data.

One key metric for evidence against AI disruption is remaining performance obligations (RPO). RPO is contracted rather than assumed, and as such, if AI were eroding demand for Moody’s, the backlog would take a hit before revenue, and it has not. The percentage of RPO that is recognized over the next 12 months is a tell for duration extension, and if it were to start increasing, it would mean that customers are less committed. A falling near-term share means more of the backlog is long-dated, meaning that customers are signing longer contracts.

Figure 2: Remaining performance obligations

Analytics

Perhaps the strongest counterargument is that we are about three years into the generative AI era, and Analytics organic revenue growth has not slowed. In addition, margins have been increasing, showing the increased attractiveness of the segment despite the market’s assumed AI pressures.

Figure 3: Segmented Analytics revenue

What Moody’s sees is that AI drives demand for its data, where customers adopting generative AI solutions from Moody’s grow at 2x the rate of their other customers.

However, the point at which the customers want to consume the data is shifting, and Moody’s has recognized it. Historically, customers would have to go to the data providers’ platforms to get the full breadth of the data, but that is quickly changing with AI implementations. Moody’s largest enterprise customers are building out their own AI infrastructure internally, and the data has to be able to live within those environments. The new consumption demand preference requires change, which is why Moody’s has built an MCP (model context protocol) integration to meet the demand before it becomes a threat.

Moody’s has partnered with both hyperscalers (Amazon, Microsoft) and frontier AI labs (Anthropic, OpenAI) to build a scalable agentic stack. For investors in Moody’s, this change further strengthens the predictable nature of the Analytics revenue as it further drives revenue towards a recurring mix. Moody’s is also deliberately partnering with several major AI platforms, positioning itself as an infrastructure layer, rather than betting on one winner in the AI frontier race. The infrastructure keeps monetization within licenses and stays neutral on which AI interface wins.

Analytics revenue is quickly approaching being rounded up to a full 100% recurring mix, and makes up ~45% of overall revenues.

Figure 4: Analytics recurring revenue mix

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AMD has become a second-source specialist, entering huge markets with a dominant vendor. The problem huge markets with a dominant monopolist face is that customers have a structural desire for alternatives, which moderates prices, supply, and pushes the pace of innovation of the whole industry.

Company profile

Theme: AI Accelerator, Direction: Buy

Symbol: AMD, Exchange: NASDAQ
Sector: Technology, Industry: Semiconductors
Fair intrinsic value: $503 (-8%), as of July 10, 2026
Market capitalization: $894 532 million
Pricing data: P/S 24x, P/E 179x

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Perpetual second

Even AMD’s original x86 business benefited from IBM’s refusal to let Intel be the sole supplier in 1982, thus forcing a second source to emerge. However, not everything was rosy for AMD; in fact, it was on the brink of bankruptcy before now CEO Lisa Su entered as CEO in 2014. Ruthless capital allocation decisions had to be made to ensure the survival of AMD, and since it lacked the R&D budget to compete against both Nvidia and Intel, Lisa Su hedged AMD’s survival on the CPU side and took the fight to Intel. The GPU division was starved of funding, while Nvidia poured billions into R&D, extending its lead in architecture and efficiency. Therefore, while AMD never seems to be the industry leader, the company has had to carve a path through other means.

AMD, like Nvidia, is a semiconductor designer and outsources its manufacturing. Wafers are manufactured by TSMC , advanced packaging by OSAT and TSMC, and memory by SK hynix, Samsung, and Micron. Since AMD employs a fabless model, its capital intensity is structurally low. AMD is also able to scale free cash flow quickly with revenue growth, but the financial advantages also come with downsides. Supply has to be negotiated, thus becoming a forward-committed resource. Wafers, packaging slots, and high-bandwidth memory must be reserved quarters, or even years, ahead.

The revenue segments are divided by end market, ranging from gaming GPUs (Radeon), client encompassing desktop and notebook CPUs and APUs (Ryzen), to server CPUs (EPYC) and data center AI solutions (Instinct).

Figure 1: Total revenues

Data center will decide everything

The data center segment in particular is what makes or breaks the thesis for AMD, as everything hinges on whether AMD can stay a major player in the AI revolution. EPYC server CPUs and Instinct AI accelerators are sold to hyperscalers, cloud builders, enterprises, and AI labs. In a sense, the segment can be seen as two businesses at different stages of their lifecycles: a mature franchise in EPYC, and a hypergrowth business in Instinct. AMD reports them together, making it difficult to gauge how AI GPUs are performing against the backbone of server CPUs.

Looking at the market share at face value, it may seem like AMD has continuously lost its place in the market. However, that is due to Nvidia’s first-mover advantage in the AI space. Data centers have historically run on CPUs, solving sequential problems, one at a time. However, LLM training during this phase of the AI revolution requires a different kind of processing, namely solving many simpler tasks simultaneously. GPUs excel at parallel processing, and Nvidia was the first to capitalize on the opportunity, extending its dominance through its CUDA platform and NVLink technology. NVLink allows for multiple GPUs to process as a single, efficient cluster. Meaning, whenever a hyperscaler wanted to place a large order of GPUs, they couldn’t mix and match among vendors, allowing Nvidia to take most of the market. During the initial stages of the AI arms race, there was no time to wait for incumbents, and Nvidia’s inbound volumes exploded.

Figure 2: Data center revenues, segmented by vendor

In Q1 2022, AMD held an implied 8% data center market share. In Q1 2026, that market share has diminished to 6.7%. At a glance, it spells disaster for AMD; not only is AMD not gaining market share, but it is actively losing it over time. However, excluding Nvidia from the picture, the market share narrative is turned on its head. AMD has gone from 11% data center market share to overtaking Intel at 54% market share in Q1 2026.

Figure 3: Implied data center market share between Intel and AMD

The CPU business is a qualification business that spans several years. A hyperscaler qualifies a platform, meaning the platform (CPU, firmware, software stack, etc) over half a year to a year, and then it is deployed over several years. That dynamic has protected Intel’s server business for decades, but once share flips, it typically stays flipped for a long time. AMD’s server share, while veiled, has likely not retraced for close to a decade since every EPYC generation has launched on time, while Intel’s advanced nodes have been delayed, and transitions have slipped.

The AI business is starting to shift massively, just as Nvidia saturated most of the market. Nvidia’s had such an immense edge that a second supplier became desperately needed. Unlike CPUs, AI platforms are bought in large-scale commitments, typically sized in gigawatts of data center power rather than units. They are also negotiated at the CEO level, to then be deployed as complete racks, which involve the compute, interconnectivity, networking, and cooling, rather than as mere chips. Nvidia’s rack solutions created vendor lock-ins since there were no alternatives, but AMD is now becoming competitive.

Historically, AMD’s weakness was software, but ROCm, which is the equivalent of Nvidia’s CUDA, is now becoming mature. CUDA and ROCm are software stacks that enable the harnessing of the compute power of GPUs. CUDA, while proprietary, was cleverly made available for free to researchers and universities over a decade ago, resulting in a majority of AI libraries, math frameworks, and learning models being specifically written for CUDA. That in and of itself created a moat, since customers would be reluctant to change vendors, as it would mean having to rewrite code from scratch. ROCm 7 makes it close to seamless to migrate AI/ML frameworks like PyTorch from Nvidia to AMD, and as such, the underlying hardware language now matters less.

Another major hurdle for AMD that is about to be overcome is that NVLink, like CUDA, is Nvidia’s proprietary technology for cluster processing. NVLink became mandatory since there was no alternative for unifying racks, forcing vendor lock-in. However, a coalition including Microsoft, Google, Meta, Intel, AMD, and Broadcom has come together in order to form the Ultra Accelerator Link (UALink) consortium. UALink will enable interchangeability between various semiconductor vendors, tearing down the walled garden that NVLink historically has provided. To further strengthen its rack-scale systems capability, AMD acquired ZT Systems in March of 2025, but divested the manufacturing arm and only kept the rack design and enablement teams.

Like UALink and ROCm, other layers of AMD’s stack are also open standards. AMD uses the OCP (Open Compute Project) open-rack standard introduced by Meta for its data center hardware. Vendors who fear vendor lock-in have strategic reasons to fund the open stacks.

AMD’s key advantage is its price-per-performance, meaning tokens-per-dollar, especially in inference. That is also a key pivotal point in this stage of the AI buildout: large-language models are sophisticated enough to be widely deployed across enterprises, meaning that there is increasingly more demand for inference (using models) rather than just training. Customers, however, are unhappy with the costs of deploying AI, with many companies imposing per-employee token budgets.

Figure 4: LLM inference throughput, full system performance comparison against Nvidia

GPUs are versatile and can be applied to a wide variety of workloads, and while not the most efficient at every task, they are very flexible. As such, even though computational demand gradually shifts from training to inference, GPUs will still stay relevant as a jack-of-all-trades. However, the companies committing the most capital expenditures towards GPUs are the hyperscalers, most of which are now building out their own custom silicon (ASICs). That causes not only an introduction of new competition, but also a loss of revenue from the largest spenders.

The future of the AI data center market carries a lot of uncertainty, and forecasts by third-party experts are continuously revised. However, the consensus throughout the volatility in expectations all points towards a prosperous decade for AI accelerators. It is hard to predict how the near-term shift between GPUs and ASICs will play out, but it is looking like there is enough demand to stay distant from saturation for a long time to come. AMD has already de-risked the MI400 ramp by signing deals with both OpenAI and Meta for 6 gigawatts of AMD GPUs, which will take years to fulfill. AMD themselves target a >60% CAGR in the data center segment over the coming 3-5 years, citing a $1 trillion compute TAM by 2030 (>40% CAGR). That alone shows a lot of confidence in the experienced demand in the near-term.

AMD discloses very limited information about its AI business within the data center segment. Using those clues and vague disclosures, I have tried to put forward an estimated AI data center revenue line. The implied acceleration is astonishing, going from ~2% of data center revenues to >50% in 3 years.

Figure 5: Estimated Data Center AI revenue

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However, history has proven Karp’s ambitious claims to come true, even if they seemed extremely unlikely at the time. I believe his most recent outrageous claim will prove true, as well.

Company profile

Previous fair intrinsic value: $268.5, as of May 9, 2026

Symbol: PLTR, Exchange: NASDAQ
Sector: Technology, Industry: Software - Infrastructure
Theme: AI Software
Fair intrinsic value: $340.77 (163.55%), as of July 5, 2026
Market capitalization: $332 420 million
Pricing data: P/S 64x, P/E 145x

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The history of Alex Karp’s ambitious claims

I have made the latest Palantir coverage completely free to read by removing the paywall. I highly recommend reading it for a grasp on where Palantir is in its narrative.

It also serves as a free preview of the level of research I publish here on my Substack. You can find the research coverage below:

This article is not a full research piece.
It targets specifically the comments made by Alex Karp during his CNBC interview.

Alex Karp is not shy about sharing his thoughts, be it geopolitical views or forecasts of where he sees his own business over the coming years. When Palantir went public through a direct public offering (DPO) in September of 2020, one of the key financial targets was a compounded annual growth rate (CAGR) of 30% through 2025.

Looking at the financial performance surrounding the DPO, not only did 30% CAGR seem probable, but it even seemed understated. From Q1 of 2020 through Q2 of 2021, Y/Y revenue growth averaged 48%.

Figure 1: Total revenue and revenue growth, Q1-2020 through Q2-2021

However, what followed was a rapid deterioration of growth rates, with an absolute low of 12.7% Y/Y revenue growth rate in Q2 of 2023. During the decline, Palantir quietly stopped reiterating its 30% CAGR target, raising the eyebrows of investors.

Figure 2: Total revenue and revenue growth, Q3-2021 through Q2-2023

During those times, Palantir still had representation of various financial analysts, and Sanjit Singh of Morgan Stanley addressed the elephant in the room during the Q2 2022 earnings call. That was the first quarter that Palantir dropped below 30% Y/Y revenue growth since going public.

Alex Karp’s response was sharp, leaving no room for speculation:

$4.5 billion in 2025 would be a 33% CAGR from FY 2020, the year Palantir went public. Not only reiterating the 30% CAGR goal, but overshooting it. At the time, for each quarter that followed that quote over the next year, the target seemed more and more unlikely, and the stock quote reacted accordingly, hitting single digits and trading there for close to a year.

However, in Q2 of 2023, at the absolute rock bottom of Y/Y growth, Palantir launched AIP. Since then, for three years to date, every single quarter that followed the bottom of 12.7% in Q2 of 2023 has been higher than the previous quarter. A revenue acceleration out of this world, and one that most investors would find absolutely absurd as a claim if you told them that at the time of decelerating growth. Most did not believe Karp’s reiteration of the 30% CAGR, and the following quarters proved it to be more and more unlikely.

2025 came around, and sure enough, Palantir hit Alex Karp’s forecast of $4.5 billion in revenue, however unlikely it sounded at the time. In Q2 of 2022, when Alex Karp replied to the Morgan Stanley analyst, $4.5 billion would require revenues to 2.5x in 2.5 years, and it did.

Figure 3: Total revenue and revenue growth

The seemingly impossible lofty goals

That brings us to today and the new claims made by Alex Karp. History serves as the witness to the credibility of the ambitious CEO’s claims, and if nothing else, that they should not be disregarded easily.

Before we look at the most outrageous goals of them all, let’s rewind to a few months ago, when Alex Karp set a forecast for the company that changed intrinsic value massively.

After the amazing year of 2025, my fair intrinsic value for the business was $205. Not only was revenue growth accelerating for each passing quarter, hitting 70% Y/Y in Q4 of 2025, but margins were simultaneously exploding. Palantir has immense operating leverage as a capital-light business, unparalleled scalability, and sticky revenue. However, in a single quarter, that fair intrinsic value estimate changed to $270 in a single quarter due to a specific quote during the Q1 2026 earnings call:

U.S. revenue is ~80% of total revenue, and seeing two consecutive years of at least 100% growth has massive implications for the business. The fair value estimate of $270 may seem like a delusion at face value, but when one extrapolates the characteristics of how Palantir grows its revenues and combines it with the expanding margin profile that seemingly has no cap, that fair value estimate no longer seems as crazy.

Now, after Alex Karp’s CNBC interview a few days ago, my fair value estimate for Palantir is $341, an unfathomable number. However, the claim by Alex Karp is equally unfathomable, and once one understands what it implies, $341 no longer seems as a crazy fair intrinsic value estimate.

A bomb was dropped, and due to the nature of the intense ~18-minute interview, this quote seemingly passed by without much attention. However, $15 billion in free cash flow would mean a 5x in 2.75 years. A 2.5x in revenues in 2.5 years was impressive, but a 5x in free cash flow, when adjusted FCF margins are already at 57%, the implications are massive. If FCF margins were low, a 5x would be quite achievable, but as it stands, the implications of a 5x are titanic for the business, as it implies a massive increase in revenue. And that is just at the lower end of the target, $15 billion.

Unlevered free cash flow, commonly used in valuation models, which also accounts for reinvestment needs, is slightly lower than adjusted free cash flow. Karp’s target likely refers to adjusted free cash flow, which is adjusted for payroll taxes associated with stock-based compensation.

When I model my expectations for the business while keeping $15 billion in adjusted FCF in mind, the inputs seem fairytale-like. While I have allowed for reasonably probable margin expansion as well, the CAGR rates for the business segments eclipse my wildest imaginations for the company:

• U.S. Government CAGR: 80%
  Non-U.S. Government CAGR: 54%
  Total Government CAGR: 75%

• U.S. Commercial CAGR: 136%
  Non-U.S. Commercial CAGR: 12%
  Total Commercial CAGR: 112%

• Total Revenue CAGR: 93%

Figure 4: Historical segmented revenue and revenue forecast

When these figures are then extrapolated over 10 years, while keeping the sheer scalability and sustainable nature of the growth in mind, $341 fair intrinsic value does not seem that absurd anymore.

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It is doing so while deliberately investing through near-term margin to reap the rewards from structural levers in the future. A high-margin advertising segment, improved logistics economics, and a maturing credit portfolio are investments that will blossom from near-term hits to margins. The company is also increasingly spreading to more verticals, spearheaded by sharp management that has been executing and outcompeting the opposition in the region for decades.

Company profile

Theme: Commerce and fintech ecosystem, Direction: Buy

Symbol: MELI, Exchange: NASDAQ
Sector: Consumer Discretionary, Industry: Internet Retail
Fair intrinsic value: $3 394 (103%), as of June 28, 2026
Market capitalization: $84 923 million
Pricing data: P/S 2.67x, P/E 44x

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The monetization ladder

Looking at the revenue segments, it would be easy to assume that the business is split across two core segments: commerce and fintech. In absolute terms, there are indeed two core segments, but a better way to view MercadoLibre is through the lens of a monetization ladder applied to the users.

Figure 1: Segmented revenue

In a sense, MercadoLibre has built the rails and is now renting them back to an entire economy. Comparing MercadoLibre to Amazon is fair in the sense that both encompass entire ecosystems in the commerce space and are ambitious across layers. The offerings are comparable as well: both offer a marketplace, logistics services, payment solutions, subscriptions, and advertising. However, the paths diverge when it comes to core details. For one, the profit engines are vastly different. Amazon’s profits are carried by AWS, while MercadoLibre uses its consumer fintech as its second engine. MercadoLibre lacks the cloud, and Amazon has to lean on external banks and cards. Another large differentiation lies in how the businesses have grown. Amazon has been scaling on top of existing infrastructure, such as reliable carriers, credit bureaus, and ubiquitous credit cards. MercadoLibre operated in geographies where such things didn’t exist, and as such, it had to build payment ecosystems, credit, and logistics. MercadoLibre is structurally more vertically integrated out of necessity.

The result of MercadoLibre’s effort is a monetization ladder, where each step adds another stream of revenue from the same customer, increasingly at a high margin. Running steps at a loss is a good trade-off, as long as it feeds the higher margin ones. MercadoLibre doesn’t just aim to make money from a customer once; it aims to have the same customer spend money over and over, throughout various layers. Each step of the ladder is a way for MercadoLibre to make money from the same customer.

The first step of the ladder is the discovery and transaction step: the marketplace. The marketplace is the core of the cash source, where sellers pay MercadoLibre a commission on their sales. What follows is the shipping and fulfillment step. The logistics step is often subsidised to win volume, and can even be seen as a deliberate loss-maker, but the trade-off is well worth it. It buys frequency and trust, which enables steps three and four. Every time a customer pays with Mercado Pago (both on and off the marketplace platform), MercadoLibre keeps a yield on the transaction. When customers then park a balance in Mercado Pago, the company gains a cheap and sticky source of funding for the credit portfolio. When the customer uses the credit card or takes out a loan, the company earns interest at a high margin.

The fifth step involves monetizing customer attention at near 100% margins. Sellers pay MercadoLibre to promote products to the customers, and since those customers are already on the platform, almost all of that money is profit. The final step involves retaining the customer and turning them into subscribers. When customers subscribe to MELI+ for free shipping and streaming, or to earn loyalty points, it incentivizes them to keep coming back and keeps them sticky.

A customer may not be profitable on their first purchase, but the same customer, over time, as they start using the Mercado Pago card, hold a balance in Mercado Fondo, carry a loan, and click sponsored listings while staying a subscriber to Meli+, the customer becomes highly profitable. The monetization ladder is meant to monetize a customer’s lifetime, not a single transaction.

Operating segments

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However, despite it being unlikely, S&P Global is undervalued even if AI eats the whole market intelligence segment and reduces it to ashes. The rest of the business is strong enough to warrant an intrinsic value above where the stock is, and has been trading. That is the kind of optionality that I like to look for in businesses, especially high-quality ones like S&P Global.

Company profile

18 June, 2026 Follow-up coverage
Direction: Buy
Previous fair intrinsic value: $720.8, as of December 21, 2025

Symbol: SPGI, Exchange: NYSE
Sector: Financials, Industry: Financial Data, Stock Exchanges
Theme: High quality
Fair intrinsic value: $562.14 (37%), as of June 19, 2026
Market capitalization: $122 003 million
Pricing data: P/S 7.76x, P/E 25.54x

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AI is a tailwind, not a headwind

It has been a while since I revisited S&P Global for a report, simply because this type of high-quality compounder doesn’t require much maintenance. There generally are not many drastic risks or narratives that warrant watching over them like a hawk. They fit the bill of Buffett’s philosophy of buying stocks you would be happy owning even if the market shuts down for 10 years, as well as the farm test. Buffett likes to compare owning stocks to owning a farm, where the farmer buys land based on its ability to produce crops, not the daily fluctuations in real estate quotes of the farm.

S&P Global operates a high-quality business, but it is subject to fluctuations. The most lucrative and robust segment is its credit rating business, which is highly cyclical and subject to external factors. However, over time, the segment’s ability to generate cash is borderline unfair. Over the past 10 years, S&P Global’s revenue has increased by 178%, representing a moderately impressive ~11% CAGR. However, the cash the company generated over the same period increased by a staggering 364%. That is what investors should expect from this business in the future, and why compounders are attractive. Finding them out of favor is a lucrative proposition for investors; that’s where we are today with S&P Global.

Figure 1: Segmented revenue

However, any material threat still needs to be thoroughly investigated for its impact. Generative AI is indeed a threat to software businesses in general, and by extension, it is fair to assume that it can impact the market intelligence segment. Two-thirds of S&P Global’s revenue and three-quarters of its profits are tied to benchmarks, such as ratings, indices, and commodities. Those are very resilient businesses, and S&P Global is the sole source for that proprietary data. What investors are worried will be disrupted is primarily the Capital IQ platform, which is a competitor to the Bloomberg Terminal, Refinitiv, and other similar platforms such as FactSet. However, Capital IQ represents only 6% of total revenue, and even less of the overall profits.

One trend that can be observed with the AI revolution, and perhaps because of it, is vendor consolidations and the demand for value creation. Compared to competitors in the market intelligence space, S&P Global owns data across all parts of the market and, as such, has been well-positioned to increase its market share rather than see it diminish. Growing 6-8%, as management targets for the segment, is outpacing the general market segment, meaning that S&P Global is actually about to grow its market share in the face of AI, not have it diminish. That is likely due to S&P Global being at the forefront of implementing AI solutions and bundling data for their customers, allowing for vendor consolidation. For the most recent quarter, MCP connections grew 500% Q/Q and 100% M/M, showing that AI integrations are growing rapidly within S&P Global. MCP stands for Model Context Protocol and is an open standard that allows AI models to interact with data sources, applications, and tools.

The MCP growth shows inherent robustness to the data S&P Global has sole ownership of, since it is meant to be used with the client’s internal AI solution, even if they do not use Capital IQ. However, Capital IQ is seeing increased pricing power as well. Clients are cited to pay 35-45% more during renewals to gain access to AI-ready versions of S&P Global’s datasets, per CEO Martina Cheung during Bernstein’s 42nd annual strategic decisions conference (May 27, 2026).

Ratings outlook went from bright to dazzling

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Company profile

Theme: Shopping ecosystem, Direction: Buy

Symbol: SHOP, Exchange: NASDAQ
Sector: Technology, Industry: Software - Application
Fair intrinsic value: $128.63 (16%), as of June 9, 2026
Market capitalization: $144 385 million
Pricing data: P/S 11.7x, P/E 108.4x

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Capturing every vertical

Shopify has become the go-to e-commerce platform for SMBs, serves large brands, and is expanding across all verticals of payment. Most commonly associated with its e-commerce platform, Shopify now also offers offline point-of-sale (POS) terminals, a payments processing business, and a checkout solution. Shopify is rapidly expanding its market share in e-commerce, a sector slated for strong long-term growth.

Figure 1: Global e-commerce market and Shopify’s implied market share

The revenue is comprised of two business segments: subscription solutions and merchant solutions. Subscription solutions serve as a stable growth base and backbone, where merchants pay a monthly fee for utilizing the Shopify platform. Merchant solutions are the primary growth driver and include Shopify payments, transaction fees, shipping logistics, POS systems, and other merchant apps and services.

Over the past 10-year period, total revenue has grown at a 60% CAGR, and the company is still exhibiting a strong pace of growth, despite having grown massively in scale compared to 10 years ago. FY2025 saw 30% Y/Y growth, up from 26% in 2024. Q1 2026 grew 34% Y/Y, meaning that Shopify is still finding ways to accelerate its growth.

Figure 2: Segmented revenue

Shopify is consistently at the forefront of technology, which helps create a moat for the business. Switching costs are high once a merchant builds their business using the Shopify platform. A merchant sets up themes, apps, code, and designs for its storefront and has extended analytics on customer data and order details. The integrations are seamless for not only payments but also shipping, marketing, accounting, and inventory systems. For merchants using Shop Pay (Shopify’s own accelerated one-click checkout solution) and seeing the increased conversion rates, or for merchants who use Shopify Payments for fraud tools, payouts, reporting, and capital offerings, the friction is even greater. Knowing that the next technological innovation will inevitably find its way to Shopify and get integrated attractively is a comforting feeling for staying loyal to the platform.

One of the key pieces for strategically positioning Shopify at the helm of AI solutions such as agentic commerce is the vast data layer. By leveraging millions of merchants and billions of transactions, Shopify has a proprietary data layer that grows smarter with scale and, in turn, makes the solutions more attractive, creating a flywheel effect.

As more merchants join the ecosystem, more transactions and customer data occur, and the ecosystem grows more intelligent. That intelligence, along with the full transaction layer encompassing everything from the store-front to checkout, to taxes, serves as a powerful network effect layer for attracting more merchants and increasing GMV.

Figure 3: Gross merchandise volume

The network effects are the results of growing components within the ecosystem. There are thousands of third-party developers building on Shopify. More merchants mean a more attractive platform for developers, which means more applications, and in turn, more merchants. There is a large ecosystem of agencies, freelancers, and development experts who specialize in Shopify’s ecosystem.

There is also a buyer-side network effect through the Shop Pay buyer network, where shoppers save their payment information. Shop Pay adds convenience for shoppers and drives higher conversion rates, meaning it is more attractive for merchants to offer it. More merchants offering it means it is more convenient for shoppers, and the resulting increase in shoppers makes more merchants want to connect to Shop Pay.

In regard to Shop Pay, merchants report up to 30-50% conversion increases when Shop Pay is enabled. On mobile, improvements can be as great as 1.91x, which makes it a very attractive offering, and the growth reflects the prospects. It reduces cart abandonment since shoppers dislike having to type in addresses and card details, and the bigger the network, the more merchants want to offer it, and the better the offering becomes for shoppers since it becomes more widely available. A win-win situation. However, Shopify does not report Shop Pay KPIs in a consistent manner, forcing investors to stitch bits and pieces together from the available data. Since we get rough Y/Y growth rates and some GMV figures, we can backfill the GMV figures.

Figure 4: Shop Pay gross merchandise volume

Another important aspect of Shop Pay is that it drives Shopify Payments adoption. Shop Pay works best when merchants use Shopify Payments, which they are more likely to do when enabling Shop Pay. Higher adoption of Shopify Payments means that Shopify drives higher Gross Payments Volume (GPV), which results in significantly higher take rates. Shop Pay GMV as a percentage of total GMV has grown from 18% in Q1 2023 to 35% in Q1 2026, almost a double in three years.

Figure 5: Gross payments volume

It may be confusing to see GMV, GPV, Shop Pay, and Shopify Payments, and you might be wondering what the differentiation is. These are all distinct metrics and can be seen as follows:

• GMV, Gross Merchandise Volume: The total value of all orders processed across Shopify.

• GPV, Gross Payments Volume: A subset of GMV that is specifically processed through Shopify Payments

• Shop Pay: Shopify’s one-click checkout solution that stores returning customers’ shipping and payment details for an accelerated purchase process.

• Shopify Payments: Shopify’s built-in payment processor.

Shop Pay is more than just a checkout solution. As I mentioned, it drives Shopify Payments adoption, but it also plays a big role in the future of Shopify and its competitive advantage. Adoption helps prepare Shopify for agentic commerce, where AI agents shop on behalf of users.

AI is an accelerator

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The story somewhat echoes that of the Japanese equity market, which has seen two decades of stagnant returns, only to rise up and accelerate massively. The Nikkei 225-index is up 77% over the past twelve months, and international eyes are turning to the Tokyo Stock Exchange once again. Sanrio is one of the more interesting names on that list, and one that is severely undervalued.

Price chart 1: Nikkei 225 index, 1-year quote history

Company profile

Theme: Compounding, Direction: Buy

Symbol: 8136, Exchange: TYO
Sector: Consumer Discretionary, Industry: Leisure, Apparel & Entertainment Retail
Fair intrinsic value: ¥1 696 (89%), as of June 2, 2026
Market capitalization: ¥1 109 696 million
Pricing data: P/S 6.05x, P/E 14.95x

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The explosive turnaround

Seldom does a chart paint a visual so vividly as the revenue history of Sanrio. A beloved brand with storied characters that frustratingly stood stagnant as a merchandise company until management recently engineered a revival. By bringing in new leadership and by centralizing brand management from a historically fragmented approach, Sanrio was able to have its second founding in 2020.

Figure 1: Historical revenues

Sanrio, famous for characters such as Hello Kitty, can be described as a character IP platform. Sanrio designs, licenses, and commercializes character-based IP across merchandise, retail, theme park experiences, and other emerging entertainment formats such as VR.

There are three main revenue segments, licensing being the most lucrative line of business. Royalties through licensing feature much more attractive margin profiles compared to traditional retail, are easily scalable, and do not carry inventory risk. Sanrio grants third parties the rights to feature Sanrio characters on merchandise such as toys, plushies, clothing, beauty products, stationery, and even food. It also grants the right to feature the characters in collaborations and various media content. Licensing and royalties currently account for about 42% of overall segment revenues.

Figure 2: Royalty and licensing revenue

Accounting for about half of overall revenues is product sales, which comprise direct sales of its merchandise through Sanrio retail stores, e-commerce, and outlets. A simple, easy-to-understand line of business that has found rapid growth, even though a mix shift to licensing is preferred from a margin and risk perspective.

Figure 2: Product revenue

The third main segment is the theme parks Harmonyland and Puroland, as well as other types of experiences. The theme parks feature attractions, parades, character interactions, and in-park merchandise. The revenue the theme parks represent as a percentage of total sales is less than 10%, but their existence is important in order to drive overall enthusiasm for the brand. The average number of visitors to the theme parks keeps increasing, and so does the spending per person who visits the theme parks. The theme parks, despite limited capacity, drive an increasing amount of traffic through seasonal events and limited-edition merchandise.

Figure 3: Theme park visitors and spending per visitor, TTM

The reason the business was able to turn around and accelerate has several key reasons. The company had grown too reliant on Hello Kitty, and there was no clear path forward to grow interest in the character roster that now spans over 450 different characters. The first step was to start monetizing its global IP strategy, which included aggressive pushes into markets outside of Japan and diversification in its licensing. Hello Kitty was joined by other characters such as Kuromi, My Melody, Cinnamoroll, and others, many of whom are now more beloved than Hello Kitty in key markets.

The push for a more diversified portfolio of characters was bolstered by digital marketing in order to appeal to younger fans and new demographics. That included leaning into YouTube, social media channels, games, and other channels frequented by younger demographics in order to gather feedback and reduce reliance on physical retail stores. In the most recent character ranking globally, Hello Kitty ranks fifth, with Pompompurin, Cinnamoroll, Pochacco, and Kuromi ahead, in order from first. In some key markets like the U.S., Hello Kitty is not even in the top 10 at the time of writing. Another key reason for the turnaround was an improved licensing structure, where Sanrio focused on acquiring higher-quality partners and pushed more character mix and category selection. Going for quality over quantity allowed for improved brand control, as well as monetization per license.

Today, Sanrio looks less like a toy and gift seller and resembles a global, multi-channel character operator. The value now stems from a growing, globally monetized IP flywheel with better reach, better licensing, broader character use, and tighter brand control. The flywheel is simple:

Character creation and brand building → Spread awareness through merchandise, stores, digital channels, collaborations, and theme parks → Scale the characters into many product categories and geographies through licensing → Higher visibility feeds fan engagement and character demand → Reinvest in digital content and further expansion, IP protection, and further character creation → Repeat the cycle.

Understanding the geographies and segments

While the revenue chart tells of an impressive turnaround narrative on its own, it is necessary to look deeper in order to grasp the dynamic fueling the growth. Most important of all is understanding why a mix shift towards licensing is substantially more lucrative for Sanrio. I already mentioned that licensing carries less risk and that it has a more attractive margin profile. However, much like the turnaround story, a chart vividly paints the picture of how much more attractive the licensing margins are.

Figure 4: Segmented domestic operating margins

Segmented operating margins in the 80-90% range are on par with the most oppressive, high-moat, and unfair businesses in the world.

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If investors are purchasing the SpaceX IPO, it is for the narrative, not for the numbers that the business has put up historically, or even reasonably will put up over the next 10 years. SpaceX may have unparalleled potential among any publicly traded company, but it also comes with severe risks, most of which are tied to the valuation.

Company profile

Theme: Frontier, Direction: Sell

Symbol: SPCX, Exchange: NASDAQ
Fair intrinsic value: $458.14 billion, as of May 29, 2026
Targeted IPO valuation: $1,750- $2,000 billion
Major underwriters: Goldman Sachs, J.P. Morgan, BofA Securities, Morgan Stanley, Citigroup
Implied pricing data at $2 trillion: P/S 107x, P/E N/A

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All about the numbers

SpaceX operates three main business segments: space, connectivity, and AI, all of which are synergistic. Adopting X and its AI business at SpaceX enabled a powerful self-reinforcing loop, and it is all driven by the overarching goal of making human life multi-planetary.

Space and connectivity:

• Reusable rockets drastically reduce launch costs, enabling more frequent deployment of low-Earth-orbit (LEO) satellites at a fraction of competitors’ costs.

• Starlink (LEO) generates recurring, high-margin revenue that feeds directly back into capital expenditures for next-gen rockets and scale manufacturing.

AI:

• Space’s payload capacity and orbital refueling capacity will enable orbital data centers in the future, where solar power and natural cooling provide AI with a structural cost and energy advantage.

• AI is already present in rocket design optimization, autonomous landing and flight control, constellation management, and collision avoidance. Furthering SpaceX’s AI capabilities and Colossus compute accelerates SpaceX’s development cycles.

• Starlink’s network traffic routing, predictive maintenance, and spectrum allocation can be optimized using AI. X integrations can drive new consumer and enterprise use cases on the network.

In short, connectivity’s cash flow subsidizes both Space and AI. Space’s cost advantages make connectivity difficult to compete with. AI’s compute scale and data from X and Starlink create a new high-margin revenue stream that further funds the other segments. The more satellites that are launched, the more cash SpaceX generates, which means more AI build-out, which turns into smarter rockets, which makes satellites cheaper, and the cycle repeats.

Figure 1: Segmented revenue

The business flywheel is solid, and it is hard to root against a company with such an inspiring long-term goal, but is it worth paying for? Total revenue has grown 33-35% over the past two years, and at a targeted $2 trillion valuation, the implied price-to-sales ratio is 107 times, and that’s without a price-to-earnings ratio since the business is not profitable.

As most readers know, I don’t put a lot of weight on pricing metrics as I prefer to focus on intrinsic value, especially in companies that are still relatively early-stage in their growth story. However, the satellite launch vehicle market (SLV) is expected to compound at 13% over the next 10 years (Global Market Insights), and the LEO market is expected to compound at 17% over the same period (Goldman Sachs). That’s not a market that would justify excitement to pay ~100 times revenue for. AI, on the other hand, is hard to gauge, but third-party estimates place expectations at 44% CAGR over the next 10 years (Precedence Research), something to definitely be excited about. But it is also the segment with the most competition, and Grok is not in a pole position to capture a majority of it.

Space

None of the markets is clearly defined, and the total addressable market size can change rapidly per segment. What we can do as potential investors, however, is to gauge how the businesses are developing and how SpaceX is setting up to capture potential TAM.

Space revenues have only grown in the single digits over the past two years, but the business development has been significant. Two core operational metrics that SpaceX provides in its S-1 filing are the number of launches and the mass to orbit. Higher mass and more frequent launches mean more billable payload, as government contracts and commercial customers pay based on capability and reliability.

SpaceX had 170 launches in 2025, which accounts for about half of all global orbital launches. Number two is China as a whole, with about 90 launches, most of which were expendable rockets, and with lower mass per launch. Third place is Rocket Lab with 21 launches, with much smaller payloads. The launch cadence alone speaks of SpaceX’s clear market dominance, but it is also important to note that almost all competitors operate on expendable vehicles and have higher costs per delivery. The resuability advantage is massive, and Starship will further widen the gap.

Figure 2: Mass per launch and revenue per ton to orbit

Looking at the operational metrics, it is clear that SpaceX is becoming more efficient with its payloads, as they are able to bring more tons into orbit per launch. However, the revenue per ton to orbit is rapidly declining, which may seem alarming. Revenue per ton to orbit translates to pricing power per unit of capacity delivered, where a falling metric means that scale is beating price, which is actually the bull case. Capacity is scaling far faster than the revenue dollars, which is expected when launch costs are collapsing, and Starlink is the dominant payload, as they fly their own cargo for free. SpaceX is already dominant, and making use of the capabilities to accelerate Starlink’s growth will feed right back into the company’s multiplanetary ambitions as Starlink generates funds to advance space capabilities even faster. When the payload mix shifts more heavily towards contracted payload, the unit economics of the business will change rapidly.

Connectivity

Global connectivity has an increasingly broad application and market, one that is just now starting to take shape. SpaceX themselves estimate the total market opportunity to be $1.6 trillion across consumer use cases, and that is without accounting for connectivity evolving into a critical infrastructure layer for the global economy. Historically, Starlink has primarily been used to serve the 40% of the global population that lives in rural areas, where terrestrial broadband infrastructure is not developed. And while rural and underserved geographies have been the primary target for Starlink historically, that is now quickly changing as Starlink begins to compete materially in suburban and urban environments.

Consumer broadband opportunities are just one side of the coin, though, as enterprise solutions are quickly gaining traction as well. SpaceX’s connectivity offerings are used across a wide variety of industries, including construction, agriculture, retail, telecom and hospitality. For example, we have seen an increasing number of airlines adopt Starlink for their on-board WiFi, most recently American Airlines.

Applications across government use cases are also rapidly increasing, as demand for resilient, low-latency, and secure communications in contested and remote environments is increasing.

In a mere two years, Starlink subscribers have increased by 448%, and Q1 2026 connectivity revenue alone just about equals the revenue for the full 2023 fiscal year. Not only is the adoption and revenue ramping up quickly, but so too is the margin expansion in the segment. As mentioned earlier, Starlink is the main cash generator of the business, which is necessary to allow the advancement of the space and AI segments.

Figure 3: Starlink subscribers

Similar to revenue per ton to orbit, Starlink's average revenue per user (ARPU) is dropping rapidly. However, much like with revenue per ton to orbit, it is not alarming but by design. The metric has to be looked at from a broader perspective. While ARPU is dropping, revenue for the segment has tripled over the same period, and the subscriber base has exploded. Simultaneously, operating margins have more than tripled.

Figure 4: Starlink average revenue per user

It is a case of a classical market penetration strategy, as is the case with many now successful platforms like Netflix, Amazon, and even Tesla with its Roadster. Early adopters are premium customers who pay high rates, and as the service expands globally and across more diverse use cases, ARPU drops. In order to reach the full breadth of the market, Starlink has to move down-market, which means pricing the service competitively. A dropping ARPU would be alarming, but only if it happened without a corresponding subscriber growth. SpaceX is prioritizing market share, global coverage, and long-term network effects over shorter-term per-user metrics.

AI

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I will do something different in this coverage and attack Robinhood from a bear’s perspective, and still show why Robinhood is severely undervalued as a business.

Company profile

May 18, 2026 Follow-up coverage
Direction: Buy
Previous fair intrinsic value: $158.88, as of February 27, 2026

Symbol: HOOD, Exchange: NASDAQ
Sector: Financial Services, Industry: Capital Markets
Theme: Growth
Fair intrinsic value: $155.29 (101%), as of May 18, 2026
Market capitalization: $70 586 million
Pricing data: P/S 16x, P/E 38x

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Take-rates explain everything

Most investors forget that cyclical businesses have volatile results until they are reminded of it. And when they are reminded, the stock chart does not look pretty. My forecasting for Robinhood accounts for the business’s cyclical nature, which is why the periods are normalized. I was criticized for expecting 27% Y/Y growth in 2026 when 2025 just posted 52%, almost cutting the subsequent year’s growth in half. That is the result of normalization, and I have done it for all the forecasted fiscal years. Robinhood is the type of business that will post negative growth in transaction-based revenues in one year (2023), and then grow 110% the subsequent year. That is the nature of the business, and as long as investors account for it, seeing near-misses won’t sway emotions.

Instead of starting with the top-line results like usual, let’s begin by looking at the building blocks for getting that top-line. Here is a summary of earnings expectations from Wall Street, and what Robinhood reported:

• Total revenue estimates of $1.14 billion, reported $1.07 (-6%).

• Net interest revenue estimates of $383 million, reported $359 (-6%).

• Transaction-based revenue estimates of $658 million, reported $623 (-5%).

Drilling into transaction-based specifically:

• Equities revenue estimates of $83, reported $82 (-1%).

• Options revenue estimates of $280 million, reported $260 (-7%).

• Cryptocurrencies’ revenue estimates of $153 million, reported $134 (-12%).

• Event contracts revenue & other revenue estimates of $122 million, reported $147 (+20%).

How are such large misses possible when Robinhood reports monthly results? The reason is that Robinhood only reports volumes every month; therefore, take-rates can’t be derived.

Equities were basically in line with estimates, which is because equity markets are mature and yields from the volume are pretty stable. There was a relative yield drop of 3% Q/Q, which resulted in a 1% revenue miss. However, in order to capture that 3%, we would have to break out four decimals; that’s how mature equity markets are.

Figure 1: Equities yield

The options yield dropped by 7%, which is also what the miss in options revenue amounted to. On a more drastic note, cryptocurrencies’ yield dropped a substantial amount, by 24% Q/Q. Analysts seem to have accounted for a drop in yield, since crypto revenues missed by 12%, but not enough of a drop. That is because ever since Robinhood introduced Bitstamp, yields have more than halved from the peaks of 0.55% in Q1 2025 and continue to dwindle. The bottom is unpredictable and difficult to forecast, which is likely why the discrepancy from analyst expectations was so big in Q1 2026.

If we apply Q4 2025 yields to Q1 2026 volumes, we would have options revenue in line, equity revenue in line, and a big 15% beat in cryptocurrencies. More importantly, transaction-based revenues would beat analyst expectations.

Figure 2: Cryptocurrency yield

The take rates (yields) were directly addressed by management during the Q1 2026 earnings conference call.

The CFO’s comments make it appear that gaining market share is a good trade-off for the drops in take-rate. In addition, it is implied that there’s more active trading occurring on the platform. However, Robinhood lost market share in three out of four trading segments, only gaining market share in cryptocurrencies, while losing in equities, options, and event contracts.

Figure 3: Trading market share summary

The CFO’s comments also imply more active trading, but the quarter sees fewer cryptocurrency daily active revenue trades (DARTs) Q/Q, fewer equity DARTs, and static options DARTs. When looking at the average trade sizes for cryptocurrencies and equities, it is roughly static compared to Q4. This means that the comments about winning market share and having more active trading on the platform are not supported by the publicly filed data.

Figure 4: Segmented average trade sizes

In general, I would agree that take-rates (yields) are a good trade-off in return for sharp gains in market share, but that did not happen this quarter to explain the drops across the board.

Turning to net interest revenue, a similar dynamic can be observed. Monthly data disclosures don’t include broken-out assets so that investors can measure how much of AUC is interest- and fee-bearing, but it does include total AUC. Interest-earning assets as a percentage of total AUC have been roughly the same over the trailing twelve months, both in Q4 2025 and Q1 2026, so analysts would have been more accurate assuming the yield was roughly in line. Both Q3 2025 and Q4 2025 had ~3% in annualized yield on interest-earning assets, a figure that dropped to 2.3% in Q1 2026.

Figure 5: Segmented interest yield

Assuming the same yield on interest-earning assets as in Q3 and Q4 of 2025, Q1 2026 net interest revenues would have come in at $457 million, beating expectations by 20%. Summing up transaction-based revenues and net interest revenues based on Q4 yields would have left Robinhood beating earnings expectations by 5% on total revenue.

Business is thriving

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The intrinsic value growth that this business has been exhibiting over the past twelve months is nothing short of extraordinary. Not only because Palantir keeps accelerating its pace of growth and expanding its margins, but because of how durable the growth is. This is not short-term; this business will accelerate for a long time into the future.

For the observant investors, the interest in Palantir is skyrocketing, and it’s not only sourced from Palantir’s own commentary. CEO Alex Karp and other executives have been saying that the volume of inbound they are receiving is more than they can keep up with for several quarters now, and that was reiterated once again.

To double-check the interest, I ran a screen on the word “Palantir” across all publicly traded companies in the U.S., and the results surprised me. There are 647 separate instances of Palantir being mentioned across different companies’ investor relations. The customer testimonials are exactly what Palantir has been saying; they are changing the unit economics of businesses at scale, and as Palantir’s numbers also suggest, the customers can’t get enough of Palantir.

The reason I highlight this particular customer is that the CEO was an AI skeptic. He does not follow hype on social media or delve into AI thoroughly. He thought that AI was a mere chatbot, with no tangible benefits to his business.

I realize that what I read and what we see talked about in the AI world and what Palantir’s operating system actually is, I can barely recognize the reality of what they’re doing on the ground with the talk that goes on around AI.

[…]you’re talking to a guy who’s not on social media, doesn’t know anything about it, doesn’t care to know anything about it. I was full grown when that came out. I skipped all of that. I thought AI and the chatbot and having conversations with an AI system was of the same ilk.

What I see, though, is the reality that Palantir creates a walled garden, if you will, where every piece of data in our network across all the systems and all of the handoffs and all of the spreadsheets and all of the memos and the hundreds of hours a week that we spend as individuals trying to explain and connect information from one system to another to another to then even be able to guess what our profitability is, let alone audit it.

Palantir’s Foundry system contains all of that information and drains the need for all of those systems and all of that activity. We’re talking tens of millions of dollars of benefit over the next three to five years annually in a business our size at only a $1.3 billion run rate.

Company profile

9 May, 2026 Follow-up coverage
Direction: Buy
Previous fair intrinsic value: $204.45, as of February 5, 2026

Symbol: PLTR, Exchange: NASDAQ
Sector: Technology, Industry: Software - Infrastructure
Theme: AI Software
Fair intrinsic value: $268.5 (94.85%), as of May 9, 2026
Market capitalization: $354 273 million
Pricing data: P/S 80x, P/E 220x

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Growth is exceeding everyone’s expectations

Palantir has once again exceeded Wall Street’s expectations, my own expectations, and their own guidance in terms of topline growth. Q1 2026 saw 85% Y/Y revenue growth, but as always with Palantir, the mere number is not the impressive part; it’s the composition of the number that tells the whole narrative.

Figure 1: Segmented revenue

When a company is exhibiting accelerating growth to an extent that it becomes exponential in nature, the natural question is to ask “how long can this be sustained?” That is why I find it important to understand the pillars supporting the growth that the company is experiencing, which also allows investors to understand the scalability of the business. Looking at where the growth came from on a cohort basis, 50% of the overall growth came from existing customers, and 35% came from new customers. That means, even if Palantir saw no new customers in the past year, they still would have grown 50% in Q1 2026 purely through their existing customer base from a year ago.

Figure 2: Existing customer and new customer cohort growth

The accelerating trend in how much existing customers are spending on Palantir software is truly a marvel to behold, but it does not end there. Palnow now has over 1000 customers, and 2% of those (top 20) account for 41% of overall revenues. Over the trailing twelve months, the top 20 customers average $108 million in revenue per customer, while the average customer outside of the top 20 averages $3.1 million. The top 20 customers’ average spend is 3500% higher than the average of customers outside the top 20.

What’s astonishing is that these massive companies that are already spending huge amounts on Palantir software are actually growing their spending faster than the overall average of existing customers. The companies that already utilize Palantir on a large scale are still extracting so much value from Palantir that they find it necessary to increase their spending at an increasingly accelerating pace.

Figure 3: Top 20 customer cohort revenue growth (TTM), and percent of total revenue (TTM)

Those 3 charts are really all you need to tell the narrative of how Palantir is the only company in the enterprise software world that is actually delivering real, tangible value from its AI platform to its customers. The customers, who are already spending millions of dollars annually on Palantir software, are rapidly accelerating their pace of spending because they see how much value they are extracting from utilizing Palantir. In addition, the ones who are spending the most are the ones who want to also increase their spending the most.

While contract configuration seems to vary, we know from historical research that AIP has usage-based pricing. Meaning, implementing AIP to solve only a single use case may not yield a large increase in contract value, but those expanding AIP to power most of their business could yield a contract value increase of 2-3x, without a real ceiling. The more you employ Palantir, the more value you extract from it, and the more you want to keep implementing it. The reason Palantir can charge over $100 million annually for its software is that it generates more than $100 for the customers who are using it; meaning, if Palantir can save your business a hypothetical $100 billion in annual spending, you wouldn’t mind paying $50 billion per year for that software.

The scalable nature of the software is a core part of the sustainability of Palantir’s growth, but the more you dig, the more convincing it becomes. Existing customer cohorts and their accelerated growth are impressive, but there are many more financial results that point toward the sustainability and even acceleration of the growth that Palantir is seeing, for a long time into the future.

One of the more shocking comments from Palantir’s Q1 earnings call was Alex Karp mentioning that he will drive the company to double U.S. revenue in 2026 and also extend that to 2027. Two years of consecutive 100% growth in U.S. revenue, which is ~80% of total revenue, raises the intrinsic value of the business massively.

The comment can be analyzed from multiple angles. First, Palantir has continuously implied that they do not have the capacity to handle all the inbound demand.

So the question then becomes: why isn’t Palantir increasing its sales force to meet customer demand? The answer to why Palantir is intentionally not meeting demand, that I can think of, is quite attractive for Palantir.

There are two angles that I see this from. First, more demand than what can be met implies increased pricing power. Second, we know that AI FDEs (forward-deployed engineers) are being scaled out. AI FDEs solve several of the most expensive components of integrating Palantir across a business. AI FDEs can quickly and efficiently build out ontologies (the context of a business so that it can be effectively used by AI), which would otherwise require human resources and time, a pain point since Palantir has a limited number of FDEs.

As for improved pricing power, it is starting to show up in average revenue per customer. As mentioned, the top 20 customers’ growth is outpacing that of the overall existing customers on average. The revenue per customer metric is really difficult to maintain, let alone increase, as a business starts to scale with an efficient go-to-market strategy (Palantir’s bootcamps). The metric was steadily declining until Palantir AIP launched, upon which it stabilized. Now that customer acquisition is slowing down due to Palantir’s inability to capture all the demand, the pricing power is being shown through the average revenue per customer (TTM), reaching 2021 levels in Q1 2026. I expect this metric to continue to accelerate over the coming quarters.

Note: the metric excludes the top 20 customers. Since 2% of customers account for 41% of all revenue, it would inflate the metric.

Figure 4: Average revenue per customer, TTM

As an investor, it is difficult to complain about any perceived lack of growth due to not meeting demand when the business is accelerating the way it is. The stock sold off on a perceived U.S. commercial revenue estimate miss of $605 million versus a reported $595 million. The miss also resulted in a deceleration in Y/Y revenue growth for the segment since Q1 2024 (which is when AIP got momentum). However, that is simply due to Palantir reclassifying a major U.S. commercial customer as a U.S. government customer, and without the reclassifiation, Palantir would’ve grown U.S. commercial revenues to 143% Y/Y, to $620 million, handily beating estimates.

Figure 5: U.S. commercial revenue and revenue growth

The U.S. government side of the business would have kept accelerating even without the reclassification during the quarter. With the addition, USG saw a large jump in growth from 66% in Q4 2025 to 84% in Q1 2026. USG is often undermined because the U.S. commercial segment is taking all the spotlight, but the USG business is also expanding rapidly. Much of the growth can be seen as a result of recent geopolitical events, and while that is true, Palantir is also widely expanding across multiple agencies simultaneously. Palantir software, much like on the commercial side, is delivering immense value to the agencies that adopt it, and Palantir is continuously receiving new contracts and expanding existing ones.

Figure 6: U.S. government revenue and revenue growth

As time passes, more and more international business is thriving as well. Until now, the exponential growth has only been observed within the U.S., but other regions of the Western world are starting to adopt Palantir at a wider scale as well. With global geopolitical tensions, NATO allies and other Western allies are likely to adopt Palantir solutions such as Maven Smart System NATO. There are several contracted awards regarding intelligence, data analytics, AI-enabled warfighting, and operational decision-making being awarded to Palantir across various nations. There are seldom any specific details as to what exact products are being adopted, but nations that are on the international government list include the UK’s MoD, Australia’s defense department, Canada’s DoND, France’s DGSI, Germany’s state-level police, Europol, and others, such as Ukraine and Israel.

Besides the warfighting context, there are examples of large and expanding international government contract awards, such as the NHS federated data platform (FDP). The NHS in the UK has seen a lot of media backlash, on the pretenses of Palantir mining UK citizen data, but the results are objectively massively beneficial to the UK taxpayers. Here are a couple of statistics:

• Over 100,000 additional patients have undergone procedures.

• 800,000 patients safely removed from waiting lists through validation tools.

• 300,000 patients safely discharged (15.3% reduction in average discharge delays).

• 7% improvement in patients with suspected cancer referrals receiving a diagnosis.

• 28% reduction in waiting list size.

• 36% fewer patients stay over 21 days, improving patient flow.

The list is long, and there are many trust-specific improvements widely reported and tracked by NHS England.

Figure 7: International government revenue and revenue growth

International commercial revenue has been more or less static since Palantir went public, with frequent commentary during earnings calls about Europe's unwillingness and inability to adopt AI solutions. However, other geographies have been more willing to use Palantir, primarily seeing large customers stemming from Japan and South Korea. The growth, despite Europe staying stagnant, is finally starting to accelerate, seeing 26% Y/Y growth, which is the fastest growth recorded for the segment in trackable history.

Figure 8: International commercial revenue and revenue growth

Efficiency and profitability have no ceiling

Palantir’s employee force is often touted as maybe the most brilliant group of engineers in the world. A simple approach to measuring employee efficiency across an industry is to look at revenue per employee. If Palantir’s engineers are world-class, they should generate more revenue per employee compared to peers, and Palantir does that handily. In fact, Palantir’s revenue per employee is more than double compared to some SaaS peers, including Palo Alto Networks, Salesforce, Autodesk, ServiceNow, Datadog, Fortinet, CrowdStrike, and Oracle.

Figure 9: Employee efficiency

Efficiency extends across the whole business; no matter which stone is turned, there is a metric that is improving massively. Since the launch of AIP, Palantir went from having an implied customer acquisition cost payback time of 65 months down to 3 months in the past few quarters. This implies that any acquired customer has paid for themselves within one period, which is a very impressive dynamic.

Figure 10: Customer acquisition cost and CAC payback time

When you have efficient operations, it is only natural that margins expand as well. Palantir has immense operating leverage, managing to accelerate growth from 13% to 85%, all while also expanding margins. As Karp mentioned in an earlier quote, Palantir does not have a big sales force. In addition, while the engineers are world-class, R&D expenses are not really going up. Palantir has been shipping major platforms, services, and launching new products without meaningfully increasing R&D spending since the start of 2019. G&A and S&M are also incrementally inching upwards at a very slow pace, despite the business growing massively in scale in a relatively short period of time.

Figure 11: Operating leverage

Palantir is unique in the whole stock market in being able to grow revenue and profitability simultaneously to an unprecedented extent. Some companies can have brief periods of matching Palantir in terms of measuring the quality of growth through the rule of 40, but no one is consistently improving the metric to levels seen by Palantir. For example, Palantir recorded a rule of 40 score (Y/Y revenue growth, adjusted operating margins) of 145 for the quarter, but is outpaced by Micron and SK Hynix (265 and 270, respectively), which are seeing explosive cyclical demand. Those companies don’t have the inherent robustness and consistency of Palantir, since they are cyclical in nature, while Palantir is operating towards a linear market with an essentially unlimited addressable market.

Figure 12: The rule of 40

Looking at the resulting margin expansion, it has far outpaced any historical expectations from Wall Street and me. I don’t know where the ceiling for Palantir operating margins may lie, and my valuation model caps operating margins at 50%. I see Palantir achieving 50% operating margins by 2029, but they could report 50% as soon as the next quarter. That is the magic of Palantir; they keep surpassing everyone’s expectations each passing quarter.

Figure 13: Operating margins and net income margins

A majority of Palantir’s revenue is already being converted into cash, as the business is very capital-light. On a TTM basis, Palantir has already had a full year of over 50% FCF margins, and in terms of FCFF (which accounts for reinvestment needs), the figure is 48% in Q1 2026.

Alex Karp had an interesting quote during the earnings call, which I had not considered prior to him mentioning it. It really goes to show how quickly this business is moving.

Figure 14: Free cash flow - firm margins, TTM

We are all still early to the Palantir story

One of the most common comments I see whenever I discuss Palantir is that investors recognize Palantir as a great company now, but that they are too late to the story. The question being asked is “What’s the next Palantir?” instead of looking at Palantir itself and realizing that Palantir is the next Palantir. We are still very early to the narrative, and despite a sharp increase in the stock quote from the single-digit days a few years back, there’s still a lot of upside in Palantir. Palantir passed 1000 customers this past quarter, which is still marginal. As a reference, Salesforce is approaching 200,000 customers, while fast-growing peers CrowdStrike have more than 20 times the customer count of Palantir.

As we have seen, growth is not tied to growing the customer base quickly. The majority of Palantir’s growth is coming from scaling existing customers, and if the top 20 customers are any indicator, each commercial client has the potential to grow massively for Palantir. If Palantir stops growing customers and only focuses on scaling its current customer base to the same level as the top 20 customers, Palantir would have annualized revenue of ~$110 billion. But Palantir is growing its customer base, and there are a lot of customers, both on the commercial side and government side, for Palantir to keep growing for a long time to come.

Figure 15: Total customers

U.S. commercial saw an increase of 44 customers to 615, government saw 1 to 175, and international commercial increased by 8 to 217, after losing 3 customers in Q4 2025. The customer count in no way indicates anything near maturity. The customer growth can be argued to be relatively slow given how much demand there is, but that should change once AI FDEs roll out on a larger scale. That also gives support for the narrative that there is a long runway of hypergrowth still ahead for the company.

Another good indicator for future growth is billings, since GAAP rules prevent recognizing upfront payments for future services as revenue. Billings grew 93.3% Y/Y, and is accelerating for each passing quarter over the last twelve months. If billings are above revenues, it indicates that further revenue acceleration can be expected.

Figure 16: Billings

Q1 is seasonally the slowest quarter in terms of new contract value, and we see a sharp drop-off compared to Q4 2025. TCV is still up 61% Y/Y, and should accelerate into Q2 and the rest of the fiscal year. Not observing the seasonality may have left some investors worried about the metric.

Figure 17: Total contract value

TCV is made up of the deals signed during the quarter, and looking at all deals signed of at least $1 million, Palantir reached a new all-time high in Q1 2026 despite TCV looking weak. The composition is strong on deals closed between $1-5 million, but weaker on larger deals. A similar dynamic can be observed in Q1 of 2025, and is not alarming, knowing the seasonal nature of contracts.

Figure 18: Deals closed of at least $1 million

Remaining deal value reached $11.8 billion, which means that there is a lot of revenue left to recognize across both commercial and government. While up 98%, the increase looks small relative to the Q3-Q4 2025 jump. Even if Palantir were to cease acquiring new customers and expanding existing customers, it would still recognize ~$12 billion in revenue from existing contracts and options. To note regarding RDV is that not all the revenue is guaranteed, as it also includes contractual options that may not be recognized.

U.S. commercial RDV is 42% of total RDV at $4.92 billion, and grew 111% Y/Y.

Figure 19: U.S. commercial remaining deal value

Looking at the remaining performance obligations, which, unlike RDV, are guaranteed revenue, it grew 134% Y/Y to $4.45 billion. RPO is primarily composed of the commercial segment, since government contracts tend to be uncertain in nature, relating to timing and recognition. Of the RPO, 39% (~$1.75 billion) will be recognized over the next twelve months, which is higher than the total revenue recorded in Q1 2026. Keep in mind that a major customer was reclassified from commercial to government during the quarter, which may have impacted total RPO.

Figure 20: Segmented remaining performance obligations

Contract balances and the aggressively scaling existing customers all point towards a continued acceleration of growth across the business. The trends also point towards an extensive period of hypergrowth, meaning it is not about to end anytime soon. This further strengthens the argument that Palantir is still early. Palantir has relatively few customers, is seeing more demand than it can handle, and contract balances show that there is a lot of growth still to recognize, even among the existing contracts.

Another forgotten dynamic is that Palantir has no debt. They are not expending excessive amounts of cash to achieve the current hypergrowth cycle, nor do they need to put capital to work in order to develop new products. The business is highly efficient across each vertical, and the cash keeps accumulating. The question is, what happens when that cash gets put to work, what may growth look like then? I personally don’t see a need for Palantir to burn cash, as they have already proven that they can be at the forefront of the most exciting revolution in the history of the financial world. Instead, I would like to see them invest across high-quality, aligned businesses. Former employees of Palantir have left to found their own businesses, some of which are doing extremely well. Also, there are obvious synergies across other companies with aligned values, such as Anduril, which Palantir could acquire a stake in. For now, treasury yields are still attractive, and I don’t mind parking cash inside of treasuries until yields drop. However, that won’t last forever, and it will be interesting to see how Palantir utilizes the cash pile in the future.

Figure 21: Net cash

Diminishing returns risks

Business risk has been absent from the company for quite a while; all investors had to worry about was not overpaying for Palantir stock. That sentiment has now turned into a risk of not owning enough Palantir before it is too late, as they keep increasing intrinsic value tremendously for each passing quarter. The risk is related to not having enough foresight into the aggressive expansion of revenue and margins that we are seeing.

Similar to past quarters, political risk remains material, and Palantir is seeing a lot of pushback driven by a false media narrative against the company. The outcry against Palantir is politically driven and does not seem to be impacting the business on a corporate level at all, as evidenced by the demand for the product.

The bear arguments are dwindling for each passing quarter as well. Most of the historical bear arguments have already been dismantled, such as the one relating to Palantir purchasing revenue (SPACs), Palantir only being profitable due to interest from treasuries, and now also the argument that Palantir diluted shareholders into oblivion. Historically, an argument could be made that Palantir was paying excessive amounts of stock-based compensation and, in turn, diluting shareholders. However, SBC as a percentage of revenue is about half of what the SaaS average is, and continues to drop. Furthermore, the current dilution of existing shareholders was only 0.7% Y/Y (1.8% in Q4 2025), which is considerably low when looking at the stage of maturity Palantir is in.

Figure 22: Weighted-average diluted shares outstanding and share dilution

Intrinsic valuation and fair value

The market is currently experiencing a sector-wide sell-off due to fears of AI labs disrupting software companies, and Palantir is among those punished. Despite the jaw-dropping financial results, the stock traded down post-earnings. We saw P/E ratios in the 400-500 range, and P/S ratios over 100. However, I am not so sure that most people in the markets could call Palantir expensive now. Price-to-sales is at 68 times, which in isolation seems expensive, but then you look at the growth that Palantir is exhibiting. Is 68 P/S expensive in the context of Palantir growing 85% Y/Y and ~100% for the year, and another 100% next year? I would even argue that the P/S ratio is looking cheap for this kind of business, especially considering the sustainable nature of the growth we are seeing.

It does not matter to me personally, as I do not put weight into short-term pricing when evaluating businesses; I strictly focus on intrinsic value in my research and investing. From an intrinsic value perspective, it is almost scary how undervalued Palantir became over a single quarter. Then again, I have been saying that for each passing quarter. The intrinsic value growth, as we learn more and more about the business for each passing quarter, has been remarkable.

The model sees a revision down on dilution per period, as Palantir is now below 1% Y/Y dilution. That change has boosted the intrinsic value to increase substantially. In addition, I have revised U.S. revenues to align with the expected 100% into 100% growth as per Alex Karp’s commentary. I am also relatively conservative with operating margins, still expecting 45% for FY 2026, and not reaching 50% until 2029. With these changes, Palantir has ~95% upside, with a fair value per share of $269. It looks crazy at first glance, given where Palantir traded a mere year or two ago, but the narrative is clear and easy to defend.

Table 1: Intrinsic valuation model

A capital-light business that is growing massively while simultaneously expanding margins aggressively is the recipe for perhaps the most dominant business in software.

Type-F Capital, Palantir Technologies: Absolute Dominance, February 5, 2026

The coverage update retains the previous buy rating.