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.

The Essence of Palantir

What’s the essence of an institution that gets more name recognition for the assassination of Osama Bin Laden than the Navy SEAL who shot him? That routinely convinces MIT Course 6 graduates to turn down offers from the best prop trading firms to spend their Friday nights optimizing processes at General Mills? That makes those same graduates feel like the highest-status people in their peer group, a peer group whose modal view also happens to be that Palantir is a company whose sole purpose is making ICE more effective at deporting parents in front of their children?

Palantir’s approach to building software is, in some sense, a bet on the power of the intellect; a bet that the organization with the clearest real-time view of their internal and external world will be best suited to make positive expected value decisions within it, whether that’s the CIA preventing a terrorist attack, Tampa General Hospital detecting sepsis in a patient early enough to cure it, or Airbus being alerted that it has a defective part before continuing an expensive assembly run. Palantir helps customers natively connect their operations to software to prevent very costly, often existential, errors of commission and omission. The institutional world models Palantir builds for customers (and that are ultimately owned by customers) help them model the real-time state of their institution, predict the impact of potential decisions, execute optimally based on that understanding, and learn from the outcomes to more accurately understand the current state and make better predictions in the future.

These world models are constructed by Palantir’s Forward Deployed Engineers, FDEs, who CTO Shyam Sankar has described as “pointillist painters” who “metabolize pain and excrete product,” turning an institution’s dark matter, its intangibles, into a computationally legible structure. Palantir calls this digital representation the Ontology. They go to work crawling and indexing a company’s assets, capabilities, knowledge and knowhow, and, importantly, the relationships between them to form this Ontology. When it comes to looking at a firm or institution as the unit of analysis, consilience is everything: understanding how all the disparate streams of information fit together to reveal some important truth about the nature of your business, market, or the world, is where all the alpha lies.

Google made a bet that by virtue of powerful computers existing and being linked together, the world’s important information and secrets would continuously reveal themselves for Google to organize and make useful. Traditional enterprise software companies like Microsoft made the bet that by giving everyone in an organization access to rows, columns, and formulas to manipulate them, that all of an enterprise’s information would automatically be represented in exactly the way that’s best suited to solving problems and avoiding catastrophes.

Palantir understood that consilience is rare and that the disparate streams of information that inform it don’t reveal themselves: FDEs must tirelessly kick down factory doors, scour hospital floors, and travel to clandestine military bases to uncover these secrets and piece them together into beautiful pointillist paintings. Importantly, they understand that these paintings make very little sense until they are near completion, and that while the map (Ontology) never fully matches the territory, the goal of the FDE is to ensure that it’s always getting less wrong.

“The enterprise software industry’s focus on custom software tools and applications is misplaced. Those approaches often only work briefly, if at all. The problems and needs of an organization often change before the software can even be deployed. Our partners require something more. They need generalizable platforms for modeling the world and making decisions. And that is what we have built.”

- Alex Karp, Palantir S-1, 2020

Ted Mabrey, Head of Palantir Commercial, responsible for their roughly $3B enterprise business, growing 157% year-over-year, tells Palantir FDEs to “act as if they are the CEO, but with zero authority.” We’d take it even further, and say that the best Palantir FDEs act as if they are Elon, who many would consider the best CEO in the world, but with zero authority. Like Elon, the prototypical engineer-CEO, the best FDEs are deeply curious and ruthlessly pragmatic. They seek to possess an almost superhuman understanding of the real-time state of capabilities and opportunities within their businesses, identify the key blockers (problems) to executing on those opportunities, and go to ground truth as a way to access and instrument the context required to devise systematic solutions.

In repeatedly doing this, they not only develop an exceptional big picture understanding of the businesses they work within, but also translate that understanding into software that embodies that representation with more and more accuracy over time, and allows everyone in an organization to use that better global understanding to better solve their local problems.

If you can master the meta-skill of figuring out what problems in arbitrary domains are computationally tractable, you have the opportunity to be a kind of “meta-genius.” You might not start out knowing the answer to anything about CPG supply chains or hospital triage, or even how to find it, but you have ever-better heuristics for the exact right questions to ask and the right temperament to make meaningful progress along the margin. This is the core process knowledge and IP Palantir has refined with their FDE model for the past two decades. And now, with their AI FDE product, they are turning that human FDE “meta-genius” into a computable system that inherently improves as frontier AI capabilities scale.

For example, one reason Toyota’s cars have long been more reliable than Ford’s is the “andon cord.” At Toyota’s factories, anyone on the assembly line can pull a cord and stop the entire process if they see a defect, and the workers with the most local knowledge of the problem are expected and empowered to fix it on the spot. The economist Masahiko Aoki called companies like Toyota J-firms, where production is organized horizontally and where whoever has the most accurate local picture has the ultimate authority to act. This is in contrast to what Aoki calls the H-firm (H for hierarchy), where a worker who spots a defect must first report it to a line manager, who then escalates it up the chain until it reaches someone with the authority to act but without the local context or local actuation to act well or quickly enough for it to matter. The J-firm model only works because its workers carry a deep model of the whole plant and possess very strong judgment, earned through standard Japanese business practices like job rotation, broad training, and lifetime employment within the same firm. These practices are largely unheard of in the US, which explains why, when American car companies installed the andon cord in their factories, all they got was a much slower production process and almost no discernible increase in production quality to show for it. Palantir’s Ontology breaks this tradeoff by making the real-time institutional knowledge, knowhow, and judgment — previously only embodied in people who had spent thirty years absorbing it — accessible to every local decision maker at the exact moment of decision. Palantir’s software takes the andon cord, increases its upload and download bandwidth by a few orders of magnitude, and hands it to everyone. By giving you the ability to see reality clearly and act in accordance with that reality, Ontology allows you, like the mast did for Odysseus, to hear the siren song, experience the power of accelerating digital computation, without crashing into the cliffs.

A Philosophy Department with a P&L

But to really understand Palantir, you have to take a step back and realize that it is, first and foremost, a deeply intellectual institution. It’s basically the only available medium for the world’s most elaborate cross-disciplinary PhD thesis. (Granted, Alex Karp already has a PhD. But he’s achieved the impossible: being a genius dropout-coded person who can correctly be addressed as “Doctor.”) It’s an organization that’s architected for a never-ending quest to understand the nature and future of institutional fitness in a world dominated by ever more capable machines, and crucially, the logic that facilitates and even necessitates the connection between them.

Palantir understands that the progression and proliferation of machines, on their own, confer no inherent moral quality — technological progress does not automatically guarantee that humanity understands reality more clearly — or uses that understanding to increase human flourishing. It understands, too, that the coupling between machine progress and institutional fitness — each reinforcing each other — is not automatic, either. And that this symbiotic coupling is a fundamental requirement for technological progress to turn into civilizational progress, and what lets both continue.

Palantir’s implicit belief is that Silicon Valley has lost sight of this fundamental truth, believing instead either that technology will do so automatically (think the effective accelerationists), or that its own superior understanding of said technology anoints it to decide how institutions and society ought to be organized (think effective altruists). Palantir is a deeply paradoxical company in that what it shares with these movements is a grand vision, and what it adds is grounding this mission in deep respect for the parts of that status quo that work well. They take Chesterton’s fence, particularly in the context of institutions, seriously: you should never destroy or reinvent institutional norms without first deeply understanding why they exist.

Palantir is a “cult of skeptical people who care deeply and want to argue about where the world is going and how software fits into it.” – Nabeel Qureshi, Reflections on Palantir, 2024

Palantir is a cult that continuously asks: How can machines, particularly powerful and ubiquitous digital computers, continuously improve critical institutions’ ability to effectively serve the needs of their markets and constituencies, without trading off personal or institutional sovereignty and liberty? But, because they’re working with specific customers, that’s not a general question that leads to a ‘90s Wired cover story about how This Changes Everything, but a specific, concrete way to use particular tools to improve one thing at a time.

Palantir understands that high-g, generally intelligent agents capable of high-fidelity tool use have roamed the earth for hundreds of thousands of years, but it’s only been in the last few hundred that those agents have been somewhat optimally “deployed” to continuously improve our collective understanding of the world and translate that understanding into solutions that benefit humanity. Even in the last generation, talent-spotting has gotten far more rigorous across many domains — exceptionally tall and athletic kids will hear about the NBA earlier even if they don’t care about sports, kids with lightning-quick mental math skills recognize the initials “IMO,” and writers can find fame online even if the New York Times would have instantly discarded their résumé.

Why? Primarily because of institutions. Institutions are generators of economic and civilizational reward functions. The reward functions that drive progress are the ones that reinforce actions that improve our collective understanding of the universe — giving us dopamine from productive curiosity, for improving our ideas — and that translate that understanding into useful solutions to human problems. Reward functions that reinforce essentially anything else (particularly sensory novelty at the expense of epistemic novelty, or certainly anything that creates fear), will probably lead to stagnation. And stagnation almost always reinforces zero-sum behaviors that tend toward civilizational collapse.

Joel Mokyr, Philippe Aghion, and Peter Howitt won the Nobel Prize last year for formalizing this insight: progress accelerates during periods of time when the feedback loop between improving our collective understanding of the universe (science) and “making things happen” with that understanding (technology) are tight. Institutions that tighten the feedback loop — that reward epistemic novelty, and the application of epistemic progress to solving real problems — facilitate progress. Institutions that do this with the highest fidelity and speed create upward discontinuities in progress. Those that do almost anything else create downward ones.

Capitalism, free markets, and Western institutions created the preconditions that allow us to continuously generate reward functions aligning our actions to the objective improvement of our collective understanding of nature and ourselves. Technology is a physical instantiation of that understanding, and when it’s applied against these reward functions, it helps us solve important problems. And the ultimate goal of technology is to ensure that our institutional reward functions increase our collective consciousness.

Free-market capitalists would say that this reward function is as close as we have ever gotten (and are likely to get) to one that leads to never-ending progress and human flourishing: People who invent world-changing technology get heavily compensated for it. They might be panglossian about this from time to time — sports gambling may be a triumph of capitalism for particular capitalists, but it isn’t Western civilization’s greatest achievement. But overall, Silicon Valley would probably say that its reward function, its understanding of capitalism, truly reinforces this ideal today. They’d say that it’s a place where social validation and sensory novelty happen to correlate with actual progress. Bad environments give you dopamine for signaling; good ones give you dopamine for building, and Silicon Valley, they’d argue, is a good one. It’s good because the people who built it and live in it have high IQ scores so their approval tends to track genuine insight rather than performativity.

One of Palantir’s core insights (mostly that of Palantir cofounder and chairman Peter Thiel) is that the Church of Silicon Valley has been largely usurped by the Pharisees (the signallers). That the reward function of capitalism, Silicon Valley’s or not, has become increasingly decoupled from progress (we wanted flying cars, but we got 140 characters); but also that this can be corrected. Palantir’s counter-position is that institutions are the ultimate meta-technology, and that natively connecting them to material technology is fundamental to re-accelerating progress. In other words, we must harness material technology to improve our institutions’ ability to generate the right reward functions. Material technology lets us mutate the world in countless ways, but it is our institutions that select the useful among these mutations. Human brains actually weren’t all that useful before institutions connected them to these reward functions and coordinated them at scale toward productive ends!

Palantir doesn’t believe that superior understanding of computers confers some supernatural ability to create more effective institutions, to dictate how society ought to be organized, or to posit the optimal set of moral and philosophical norms that should govern it. Being a billionaire, or an AI researcher, or an enterprise software founder is orthogonal to your ability to litigate the big questions facing humanity. Instead, Palantir’s answer, in the context of software, is to go to ground truth. That means embedding would-be Silicon Valley engineers into institutions that have trusted access to rich, local realities. These are environments where domain experts already understand their problems on a fundamental level but would benefit enormously from connecting that localized knowledge to the accelerating power of digital computation and making it programmable.

To scale the fitness of pro-human institutions with the increasing power of digital computation, Palantir had to fundamentally be in the business of breaking tradeoffs. From the beginning, particularly in the post-9/11 era, it rejected the pervasive notion that leveraging computation to improve state security capabilities inherently required sacrificing privacy and sovereignty, a false dichotomy back in vogue as AI capabilities accelerate. The core innovation of Palantir’s original Gotham platform was an architecture that gave intelligence analysts the exact information necessary to achieve their mission at the right time, but enforced strict, purpose-based access and full accountability. It didn’t allow for a sweeping ability to find out anything about anyone.

It’s fruitful to contrast this symbiotic model to the big consumer tech companies built on ads like Google and Meta. These companies are also trying to understand the world, and their profits measure how well they’re doing it. But ad companies have a double incentive to hoard their knowledge. First, their users and their regulators care about privacy: Google might target an ad for a product that helps with an embarrassing medical condition, or a substance abuse problem, but they don’t want to be in the business of selling advertisers a bundle of personal information about everyone whom they’ve inferred has these problems. Second, an ad system that’s an inscrutable black box to advertisers, where they set a budget and a goal and have no idea how this is accomplished, is great for pricing power. Advertisers know if they’re getting an acceptable return on their investment, but they don’t know how much of the return the platform itself is capturing. Over time, that commercial imperative is to cede more of the customer relationship, business knowledge, and their own agency to a few big platforms.

Palantir funds this quest, and commercializes its understanding, by building software that, by design, helps improve the fitness of institutions that empower humanity, and hurts those that don’t. Crucially, beyond a clear commitment to the US’ elected government (Karp has said that their goal is to power the West to its obvious innate superiority), it doesn’t claim final authority over which institutions are moral and which are not. Palantir instead delegates the question of “how society should be organized or what justice requires” to democratic institutions and the free market. Palantir’s job is to help them execute that vision better. Their technology, like the Company’s namesake (the palantiri, seeing-stones in The Lord of the Rings), strives to help pro-human institutions more effectively discover the Thielian secrets that make them better at serving their markets and the public, and more antifragile against adversaries.

“Effective Software” and

Economic Computing

Palantir calls this technology “effective software,” and is wholly oriented toward its successful creation. To Palantir, effective software is that which enables critical institutions to turn information about the world into the decisions — useful states — that benefit their constituents in the good times, and ensure survival in the bad. It lets pro-human institutions scale with the increasing power of digital computation, rather than be left behind, obviated, or destroyed by it.

“Our culture and means of organizing ourselves are preconditions for the creation of effective software.”

- Alex Karp, Palantir S-1, 2019

All this might sound like it has very little to do with software. To many, it probably bears a closer resemblance to how we traditionally think about the ideal form of Firms, Economies, or Governments themselves: systems that, when functioning properly, continuously improve their ability to turn information into economically or societally valuable solutions.

One way to conceptualize Palantir is that it is not a software company at all, but a meta-institution architected to turn institutional fitness into an engineering and information-management problem. A system that continuously searches over complex institutional environments with a reward function tied exclusively to improvements in institutional fitness, and with Ontology-armed FDEs as the actuators that close the learning loop.

Palantir is spiritually and organizationally closer to TSMC than it is to OpenAI or Anthropic, in the sense that they embody extremely complex process knowledge, born from an organizational structure and execution philosophy that has, from day one, prioritized trusted access to scarce institutional context — and over and over again, honored that trust by increasing their customers’ ability to wield computation in an increasingly sovereign way. TSMC may help you manufacture your chips, but they will never, ever compete with your design, or use your IP to help others compete with you. It’s organized in every way to maximize your odds of success. Similarly, Palantir will help you build the computational substrate that makes programmable your process of turning local information about human needs into economically and societally valuable solutions, but without ever using that information to compete with you and in a way that you can own.

Progress in this economic computing revolution that Palantir is powering is not measured by Moore’s Law, FLOPs per watt, “intelligence per watt,” tokens per dollar, or the like but by something much more ambitious: the density of programmable economic complexity (economically valuable knowledge and knowhow) accessible by and sovereign to pro-human people and institutions, per unit energy.

For example, Sepsis Hub, built on Palantir’s Foundry and owned by Tampa General Hospital, is a system that encodes clinicians’ tacit knowledge of what early sepsis looks like, monitors around 1,000 inpatients at a time against that embodied knowledge, and integrates vitals, labs, and other indicators to accelerate antibiotic treatment. Since launch it’s credited with halving overall sepsis mortality, a 68% reduction in 48-hour mortality, and saving roughly 900 lives as of June 2026. It’s also led to a 30% reduction in sepsis length of stay. Other systems that Palantir helped build for Tampa General have led to hospital wide efficiencies, including an 83% reduction in patient placement time.

“We are paid, and have always aspired to be paid, as a derivative of value creation. Our results represent a subset of the economic value that our software has created for our customers.” – Alex Karp, Shareholder Letter, Q2 2026

Palantir makes institutional memory, an institution’s search and learning process, computable. But these computers (which Palantir helps design and manufacture) are wholly owned by the institutions they power. Palantir understands that you cannot separate this economic computer from the institution it powers: doing so is like removing memory from a GPU, stripping a business of its core means of production. There may be different bundles for different use cases — a personal computer is useless without a monitor, but giving a data-center computer a monitor is useless when its behavior is being orchestrated at scale. You also cannot aggregate the memory of many different economic computers to create one that performs equally well for all institutions: the state of the computer, and its ability to create new useful states, is entirely a function of the singular context from which it’s born. In other words, “geniuses in a data center” is an incoherent concept. Aggregating genius misunderstands what genius is: singular intellectual synesthesia born of selective adaptation to a specific pocket of reality.

“We have always declined, and will continue to decline, entering into a parasitic relationship with our partners. It is true that our approach has diverged sharply from the market in this regard. And yet we are one of the few companies in the technology industry that is fully aligned with its customers.”

- Alex Karp, Q2 2026 Shareholder Letter

Palantir vertically integrated to understand the logic of economic computing, to tease out its value networks, and to build a map for optimal institutional design in a world of accelerating computation. Most fundamentally, they believe that durably selected knowledge, embodied in Western institutions and free markets, should be made more effective, and preserved, by technological progress. It is not something that a few engineers, by virtue of understanding computers, have the mandate of heaven to rewrite in their own conception.

So, in a way the labs are right to try copying Palantir, but they don’t realize they are actually copying the wrong thing, and missing why Palantir’s customers actually trust them. The labs’ implicit goal with the vertical integration of deployment seems to be increasing their models’ ability to do everything in the economy, by seeing everything in the economy. But being aligned with your customers, partners, and the broader ecosystem of economic actors is logically incoherent with that goal. So, the labs end up competing directly with the enterprises building on top of them — from encroaching on Cursor’s territory with native coding agents (think Claude Code and OpenAI Codex) to threatening Harvey with specialized offerings in the legal domain.

And lately, this has also meant competing with the small businesses that form the character of our economy. For example, Thrive Holdings (a private equity firm partially owned by, and deeply intertwined with, OpenAI) has acquired 70 account and IT services firms, and essentially turned them into reinforcement learning environments for OpenAI’s frontier models to hill climb. It’s easy enough to understand why any private equity firm would be interested in such businesses and making them run better with technology; but here, the interest is tied to the business practices, clickstreams, and localized context that OpenAI’s researchers and engineers need to build models capable of automating work in those domains. In this paradigm, vertical integration is purportedly about empowering the customer, but in the fullness of time is about capturing their means of production.

“The businesses we acquire represent the right reward systems for this evolution, bringing together industry expertise and real-world data that can help improve models on specific tasks and capabilities.” – Joshua Kushner, OpenAI Partnership Announcement

“By training the most advanced models for specific tasks within our businesses, guided by both company-specific data and expert feedback, we believe we can continuously improve model capabilities…” – Thrive Holdings, OpenAI Partnership Announcement

“There are Marxist overtones and undertones to our business. Others, including many of those building large language models, intend, knowingly or otherwise, to capture the means of production of their purported partners.”

– Alex Karp, Q2 2026 Shareholder Letter

Palantir, on the other hand, vertically integrated deployment to build the capabilities and process knowledge (Forward Deployed Engineering, Ontology, AIP) required to make building institution-specific economic computers as easy to build as possible. If Palantir builds a sovereign economic computer for the CIA, the UK’s NHS becomes more amenable to the idea, and so do market leaders like Airbus and Kirkland & Ellis. Soon, the most critical institutions in society are the ones most invested in pushing forward the economic frontier: they reap some of the rewards, while gaining more control over their own destinies. But if FDE and vertical integration of AI deployment simply mean using trusted access to institutional context as a way to improve your ability to rent-seek, or worse, to build your customers’ or partners’ replacement (in the case of Thrive Holdings’ partnership with OpenAI), you jeopardize the whole promise of economic computing: making progress programmable in a way everyone can contribute to and own.

The lesson from Palantir’s 23 years is to vertically integrate for innovation. Do it if it’s the only way to get permissioned access to the ground truth knowledge needed to help your customers and society uncover new, valuable secrets. But don’t vertically integrate for rent or to concentrate power. Not only does rent-seeking actually reduce your own institutional fitness, but it also creates a broader negative selection pressure in society — one born from the false idea that there are no secrets left to discover.