Five of the most valuable software companies in the world — Microsoft, Google, Meta, Anthropic, and OpenAI, commonly grouped together as the Frontier Labs — have, within the past 18 months, independently re-oriented themselves around a common strategy that not a single one of them originated. It is a strategy that seeks to mimic the core organizational structure, technological primitives, and the philosophy of progress embodied by a single, much smaller software company.
It’s a company that does not share the Frontier Labs’ foundational focus on developing frontier AI models: systems that they, at least publicly, believe will soon be capable of automating all economically valuable work.
Its inner workings were, until very recently, more known and understood by executives at companies like General Mills and bureaucrats at three-letter agencies in Washington than by the average Silicon Valley AI researcher, engineer, or investor.
It is a company which professional investors on Wall Street still think bears a stronger resemblance to GameStop, AMC, and Nikola, than Google, SpaceX, or Amazon. Many engineers in Silicon Valley think it bears a stronger resemblance to Big Brother than to its antithesis.
That company is Palantir — founded 23 years ago and long dismissed as a company that provides data integration services, a category so unglamorous it’s functionally an insult.
But the defining technologies of the modern era are themselves clever flavors of data integration: the internet has integrated the world’s public knowledge, GitHub has integrated the world’s code, and the frontier models are, in a real sense, a compression of both. Each time a previously illegible corpus of knowledge and knowhow has become programmable: crawled, indexed, linkable, searchable, and versioned, discontinuities in progress seem to follow. Palantir’s founding insight, in some sense, was that the largest, most valuable corpus of all — the operational knowledge and knowhow of the institutions that run the world — remains API-incomplete: scattered across software systems that can’t talk to each other or, in many cases, in mediums that are undigitized altogether. It’s trapped in email inboxes, groupchats, ERPs, CRMs, Excel files, the airwaves of phone calls and also in the heads of (and in the conversations between) doctors, line engineers, and other front-line workers who embody the valuable knowhow they’ve earned through experience.
Crucially, Palantir recognized that this fragmented corpus would not reveal, version, and programmatically assemble itself. There would be no GitHub repo or version control system that encodes a real-time understanding of how SpaceX puts 100 metric tons into orbit at less than $200 per kg or how Airbus assembles four million discrete parts into an A380, unless someone tirelessly coaxed and captured it into existence. In the case of SpaceX, Elon had it handled, but Airbus and many others like them required a new kind of software foundry.
Palantir bet that the next generation-defining company would natively align itself with institutions to make their data legible, integrate it into computable ground truth, and in the process enables these institutions to solve problems and serve society more effectively. The dream was that they could finally connect the rapid progress the economy has been experiencing in the world of bits for the past 50 years to the relatively stagnant world of atoms, and, in this process of extending increasing-returns-to-scale characteristics to more of the economy, become fabulously rich themselves.
This 23 year bet has (rightfully) made Palantir the envy of techno-capitalism: since the November 2022 launch of ChatGPT, Palantir has seen its market capitalization rise more than 20 fold, outperforming every other publicly listed company on US major exchanges, including the most lauded beneficiaries of the AI buildout like Nvidia (~13x), Broadcom (~8x), and Micron (~17x). This stock-price performance is downstream of mindboggling business fundamentals — just look at the second derivative! Palantir’s revenue growth has accelerated for 12 quarters straight, from 13% year-over-year in Q2 2023 (the quarter GPT-4 was released) to 93% in Q2 2026, and profitability (operating margin) has expanded from 2% to 47%.
That sort of market vindication is hard to watch without jealousy, particularly if your business model has been tied up in a different bet on where the value accrues as AI progress accelerates.
The frontier labs’ theory has been that scale conquers all: acquire or create more publicly legible data, spend more compute training on it, and out will pop surprisingly powerful general-purpose models that first will be able to improve themselves, and eventually will be capable of automating economically valuable work writ large. Given the pace at which code is getting shipped, mathematical conjectures are falling, and frontier lab revenue is growing, that bet looks like it’s working.
But it’s not the only one you can make. You can also bet that as models become more generally capable, contextually-appropriate judgment, embodied privately within institutions, and the institutional right to exercise that judgement, is what remains scarce. In other words, general intelligence is much more useful in permissioned contexts, and cheap tokens make choosing the right prompt (and privately owning the upside of the output) more valuable.
Palantir popularized a very specific role around this idea, the Forward-Deployed Engineer: someone who can understand a customer’s business problem, propose a technical solution, and, yes, write mountains of code that connect database X and third-party product Y to internal dashboard Z. This requires a mix of technical skills, business sense, and the discipline to give realistic answers instead of overselling. One of the core benefits of this structure is that it solves a classic incentive-alignment problem in sales: the person making specific promises is also the one who has to make those promises come true. It’s a clever way to organize information and skills within a company.
The labs are increasingly realizing this. Within the last four months, OpenAI, Anthropic, Google, Meta and Microsoft have deployed $30B of capital, acquired or funded four ex-Palantir founded or staffed companies (Tomoro, Fractional, Northslope, and Thrive Holdings), launched partnerships with the largest PE firms (e.g, Blackstone, TPG, Bain Capital, Thoma Bravo), and have set out on a hiring tear for 9,000+ FDEs of their own — equivalent headcount to two Palantirs — to gain native access to and deploy AI within institutions. Microsoft in particular has taken this mimicry to almost comedic lengths: naming its enterprise AI data-integration and deployment platform Foundry, roughly a decade after Palantir launched its flagship enterprise offering of the same name. All of this serves the same basic goal: get trusted access to, and make programmable, the scarce institutional context that Palantir has long known is necessary to turn machine tokens into economic value.
So, we must ask: why are the labs trying to copy Palantir? Are they copying the right thing? Can you even copy it — or is a company’s model of delivering value more like a constitution: something that evolves out of a specific context, and that can’t be copy-pasted into some new context and expected to deliver the same results? It might turn out that it’s relatively easy to distill frontier models, but that it’s almost impossible to distill institutions. And, in the big picture, why does this matter beyond the narrow world of big enterprise software contracts?
To motivate things, we’ll answer the last question first. If valuations (which are well into the trillions) are any signal, the frontier labs represent the capital markets’ best judgment on what kind of architectural knowledge — organizational, technological, and philosophical DNA — is necessary to enable and benefit from the cognified economy: a future that promises to scale economic growth and human prosperity with abundant machine intelligence.
But the fact that all of these firms have converged, recently and independently, on a new common strategy that they didn’t originate — focused on FDEs, deployment, and institutional knowledge — illuminates a potential shift in their inside view. A shift in what our best-capitalized technological institutions actually believe is required to achieve their shared goal of beneficial artificial general intelligence, and their intuition about what they are missing. The labs independently converging on Palantir’s strategy and founding insights, and aggressively deploying capital and labor to mimic it, tells us they believe that Palantir is what’s missing. We’re going to argue they’re right: the development of beneficial AGI is in fact contingent on deeply understanding and internalizing Palantir’s lessons, but that it’s far from clear whether the labs actually have.
The labs seem to have a general sense that Palantir has solved AI deployment. But we think Palantir solved a deeper problem, one that the labs don’t seem to grok: the problem of connecting powerful computation to local institutional knowledge without destroying an institution’s sovereignty or its incentive to reveal that knowledge.
Envy is usually a sign that you strongly desire someone’s capabilities and traits, or at least the rewards that come with possessing them. The unhealthy response to envy is to signal or superficially copy those capabilities, which makes you averse to actually understanding their nature, why they matter to your goals, and how to truly develop them yourself. (Whenever someone plaintively complains that they did everything right, and didn’t get a fair outcome, pay attention to how much of what they did right involves credentials rather than skills, or is plainly unfalsifiable.) The healthy response to envy is to acknowledge the feeling without judgment, then use it as a map to learn why the capabilities the other person has are important to achieving your goals, and use that as motivation to truly understand those capabilities and develop them. Done well, envy becomes inspiration.
The labs are currently responding to envy in an unhealthy way, leading them to create Palantir Cargo Cults that achieve the opposite outcome the labs’ desire, the outcome that makes Palantir enviable in the first place. Palantir’s chairman, Peter Thiel, likes to say that competition is for losers. Or as Rene Girard, one of Thiel’s strongest intellectual influences, made a related point: imitation starts with external forms but ultimately reflects borrowed desire.
The charitable framing is that the labs are Cargo Culting Palantir because they don’t actually understand it. The uncharitable framing is that the labs are Cargo Culting Palantir because Palantir threatens their credibility and long-standing strategy, and the best plan they have for addressing that is to have a Palantir of their own.
