An Interview with Palantir CTO Shyam Sankar About the AI Religious War

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Listen to this post: Good morning, Today’s Stratechery Interview is with Palantir CTO Shyam Sankar. Sankar has worked at Palantir for 20 years, and has taken on an increasingly visible role as public intellectual, including authoring the book Mobilize, an exhortation “To Reboot the American Industrial Base and Stop World War III”. I previously interviewed Sankar in June 2023. That interview is a great place to start in terms of understanding Palantir, which we mostly discuss in this interview in the context of AI. To that end, in this interview we discuss why Sankar thinks the industry is insufficiently focused on AI diffusion, why companies focused on the top-line have more success adopting AI, and why he is frustrated at the labs refusal to take a systems engineering approach to AI safety. Then we discuss what Palantir has learned from AI, how companies can preserve alpha instead of the median, and whether or not we need more models. Sankar also explains exactly what a forward-deployed engineer is, why tech is in a religious war, and his retrospective on Anthropic’s battle with the U.S. government. This Interview is presented by Stripe, the economic infrastructure of AI, and for Stratechery itself. As a reminder, all Stratechery content, including interviews, is available as a podcast; click the link at the top of this email to add Stratechery to your podcast player. On to the Interview: An Interview with Palantir CTO Shyam Sankar About the AI Religious War This interview is lightly edited for clarity. Topics: Diffusing Intelligence | Top Line vs. Bottom Line | Safety and Systems Engineering | Palantir’s AI Lessons | Alpha vs. Beta | Model Questions | Forward-Deployed Engineering | Anthropic vs. the U.S. Government | AI Religion Background Shyam Sankar, welcome back to Stratechery. SS: Ben, thanks for having me back. Well, the last time we talked was in conjunction with Ted Mabrey, the head of Palantir Commercial. As is usually the case with dual interviews, we got right down to business — and I do have to say, I do a lot of interviews where I usually kind of know where I want to go, but that one was super helpful to me in understanding Palantir and thinking about AI generally. But before we get into that, we never got to do my traditional background questions — where you came from, how you got to where you are. So for you, do we start in Orlando, in Lagos, or in India? SS: At least Lagos. My father, in his early 20s, moved to Lagos and built one of the first pharmaceutical manufacturing facilities in all of Africa. He set up this whole life there for the better part of ten years. I was born in India, but really I grew up until I was three in Lagos. And then there was this horrible armed robbery that happened at the house — they decapitated the dog, pistol-whipped my father, and threatened my mother. So we left all of our earthly possessions behind and started over again in Orlando, which ended up being a truly phenomenal place to grow up. The profound optimism of Disney World, the shadow of the Space Coast, seeing shuttle launches in elementary school, running into your courtyard filled with this sense of optimism about how technology will inevitably lead to a better world — which, if you fast-forward twenty years from the ’80s, I think kids were fed a very different diet of Terminator and “nature always finds a way“, “How dare man try to do anything that creates progress?”, so that’s really where it started. Is that actually the ultimate example of man overcoming nature — not the space shuttle, but in fact Disney World? SS: (laughing) That’s true! The fact that millions of people go there every year, to the middle of the swamp — and it’s fantastic, a lot of fun. What does meritocracy mean to you? I was reading the recent profile of you in Colossus, and it opened with [Palantir CEO] Alex Karp’s statements in that regard, but it was put in the context of you growing up in Orlando, taking an entrance exam to go to Trinity High School, and how that launched you into what you became. Is that a sentiment from Alex, is it from you, or what’s your take? SS: It’s a sentiment for both of us, for different reasons. We’re both kind of acquired tastes, in many ways, and living in a world that judges you by the fruits you bear, rather than any dimension you have no control over, is obviously a better world to live in. I think it’s a precondition to the American Dream. I was going to ask you that. Because you have this background — born in India, originally living in Lagos, come to America. What does being an American mean to you? Is that the same question, or is there some tension between those two things? SS: They overlap profoundly. Being an American is this profound belief in your own human agency, and the community you’re in, and the ability to create a more perfect union — the recognition, the ability to hold the contradiction, that not everything is perfect today but it’s incumbent on us to make it better. That may sound a little Pollyannish and even corny, but that doesn’t exist anywhere else. Everywhere else, people are profoundly cynical — things are the way they are — and there’s a very thin line between cynicism and nihilism, and you kind of give up on everything. That’s the death spiral, that’s how you get the Dark Ages. After university and graduate school, you worked on Xoom, on remittances — and then you go to work for Palantir. SS: Xoom with an X. Xoom with an X, that’s right. Not the Zoom with a Z — which maybe I shouldn’t be talking to, since you’re now an executive at Palantir using Zoom with a Z, but that’s another discussion. You worked on Xoom and remittances, then Palantir, working on national security, arguing for building our homegrown industrial base. There’s an argument there that — to take a paradox that’s maybe unfair — how do you go from a career focused on sending money out of America to being an advocate for money here in America, rebuilding our capital base? Is there tension there, or am I creating paradoxes out of thin air to build a theme? SS: I think you’re creating a bit of a paradox out of thin air. First of all, there’s a policy question — if you have a working class trying to get money to support their family, how can they do that as efficiently as possible? The bigger issue is the profound lie of globalization. The calculus that globalization got wrong is this idea that we can do the innovation and “they” can do the production — “they” make the things, we do the innovation. The problem is that innovation is a consequence of productivity, if you stop making the thing, you cede the stimulus required to do the innovation around the thing. The example I always give young computer scientists is, what do you think motivated Google to do the research behind “Attention Is All You Need“? If you talk to that team, they wanted the 3% improvement in Google Translate. You cannot think of something more prosaic leading to something more profound. So we should be very careful where we give up — we buy a little too much into comparative advantage and give up the stimulus for production. Why do you think — what leads to that mistake of thinking that it’s okay to just own the inputs and not worry about the outputs? SS: Because at the margin, it’s true. At the margin, you can hire WuXi as a cheap set of hands at the lab bench to do a little bit of work. But at the limit, that’s a transfer of knowledge. At every increment it seems to make sense — you’re still doing the innovation — until one day you wake up and realize, “Oh, I don’t have it anymore”. Diffusing Intelligence You’re the only Stratechery interview that I’ve offered a job to. You sent over a list of questions you wanted me to talk about, and I have to say they’re really good — I did ask if you wanted to prepare all my interviews going forward, I don’t think I got a response, maybe the email got lost. SS: (laughing) I’m thinking about it. Okay, good. Well, let’s drill in — we’ll take your outline, why not, go through it and talk about it, and I think there are themes that might emerge. So number one, and this is from Shyam Sankar, does this count as a Shyam bomb, by the way, the long extended email detailing what we should talk about? SS: It’s not nearly vitriolic enough, so you’re spared. Got it, that makes sense. Number one, is the AI industry solving the wrong bottleneck? You argue we are over-investing intellectually in the supply of intelligence and under-investing in diffusion. Tell me what you mean. SS: If you thought about a crass metaphor of AI through electricity — there are people who generate the electrons, the power producers — for electricity to have any impact on our economy, there needed to be other people who built machine tools that run on those electrons. And that led to a profound revolution. Not only would you introduce one piece of machinery into your factory, you’d start redesigning your factories around the fact that we have electrons. Let’s call that diffusion, how does the miracle of electricity diffuse into the economy? There’s a similar question with AI. I think we are very focused on how to make better models, smarter models, more machine intelligence — and we should keep doing that — but we’re very under-indexed on how to accelerate the rate of diffusion. Where is the economic value being created? How do we turn these tokens into economic value more quickly with a great ratio around it? I’m obsessed with that, because I think there’s a license to operate in society where the benefits of this stuff diffusing really do matter and I think we’re getting the calculus around some of this pretty wrong. So who is “we”? Who are the ones insufficiently focused on diffusion? Who are you trying to convince here? SS: I think the labs are, but in general the entire market structure. People are being rewarded — and it’s very rational, if you’re a lab, you’ve been in your own reinforcement-learning loop, you do fundamental research, you make a more powerful model, and your valuation doubles or quadruples and every year it seems to keep working. Baked into that is the idea that eventually this stuff will diffuse, eventually there will be economic value. But one of the conceits I’m not sure people are getting right is, what is the atomic unit of the enterprise? Let’s leave consumer out of this. The presumption, as expressed through the product strategy you see from the labs, is that the atomic unit of the enterprise is the knowledge worker — if I can make every worker more productive, that’s the ROI, and that’s not actually true. The atomic unit of the enterprise is the workflow, you can make a single supply-chain analyst more productive and generate no incremental ROI, you have to make the entire supply-chain workflow measurably more productive to capture the value. There are some roles where the cardinality collapses — the knowledge worker is the workflow, like coding — so you have perfect product-market fit for those things, and you can chase unbounded productivity. But that’s a very small part of everything we do in the real economy. Again, I don’t understand the audience here. In a market, shouldn’t the model makers just do their job, which is making better models, and then there are lots of opportunities for companies to take those models and diffuse them? Isn’t that what Palantir is trying to do? Who is the target of this exhortation? SS: To those wondering, “Okay, we’re creating this, we’re spending trillions of dollars, where’s the return, how’s it working, why is the best example of excellence an AI assistant, which is nice and convenient but that’s not going to inflect our GDP?”. You think the big problems that I’m interested in solving — I think one of the fundamental issues, if you zoom out a lot beyond globalization. In 1971, the relationship between GDP growth and wage growth broke. Before then, every time the economy grew, wages grew. And now, adjusted for inflation, wages have been flat since. That’s a profound problem, a lot of our current challenges are downstream of that reality. There are many definitions of a thriving middle class, but the one that resonates with me is the most qualitative which is you believe your children’s future will be better than yours. If you go a couple generations without being able to cash that check, you get massive societal instability. Why does this matter to you? Why does it matter to my audience? Is this your mission here? Why do this interview? Is this concern about the overall public perception of the tech industry? Is AI going to lose public support? Is it about people opposing data centers? Where does the rubber meet the road, from theoretical discussion to actions you want to see happen in the real world? SS: Is AI just a dope technology, or is it something we can actually use to make our society better? I’m trying to solve for making the world better, our society better, through backpropagation. What would have to be true of AI for these things to be possible? As cool as AlphaFold is, there are zero drugs on the market because of AlphaFold. It’s dope — super dope — but nothing is better because of it, it would be a travesty to turn our back on AI in such a way that all it can be is something really cool. Top Line vs. Bottom Line Number two of the Shyam interview outline, why are ambitious customers winning? Why do some units and institutions put AI to consequential use while others struggle to move beyond experiments? How much of the difference is access to technology, and how much is leadership, urgency, authority, and an imperative to win? SS: This is one of the empirical things I’ve observed. It wasn’t obvious to me it would be the case, but if I segment the customers I’ve worked with — the ones who want to use AI to reduce headcount, to take costs out of the business — almost all of them have failed at even achieving those goals. If you compare it to the customers succeeding wildly, have real ambition, “I want to use AI to dominate my market”, “I want to use AI to put my competitors out of business”, “I want to use AI to do work that was always beyond the efficient frontier — things I always wanted to do but could never afford to do”. I think the conclusion I’ve come to is that there’s insufficient stimulus to motivate you to do the hard things when you have a myopic goal like getting rid of humans. There might be other factors too — human intelligence and machine intelligence aren’t exactly the same thing. How so? How are they different? SS: They don’t do exactly the same things in the context of the workflow. If you spend your time trying to delete the human, by definition the AI would have to be able to do everything the human does — even if it only does 98%, you’ll fail at the specific goal. Another way to reframe it, “I have this miracle intelligence now, how do I build the workflow around what it can do, rather than shoehorning it into the error that always exists between the human and the AI?” — the AI maximalist would say, “I’m just going to keep training a better model”. But I’d say, what percentage of the problems we have today can we solve without going beyond the current frontier? I think we’ve left a lot of problems unsolved because we’re not thinking about diffusion enough, and wer’e not actually trying to maximize the AI for what it can do, we’re instead trying to judge AI by the standard of, “How do I eliminate humans?”, which may not even be that valuable, judging by the people who are succeeding with it. Does this lesson of the importance and power that comes from focusing on the top line versus the bottom line — was that a lesson you learned in the age of AI, or something you learned working with companies before current LLMs came along? SS: It’s very pronounced in the age of AI. Before then, you’d say it’s more exciting and you succeed more often going after the top line, but you’ll still succeed going after OpEx reduction. With AI, the outcomes are much more binary. It’s going to lead to a category of winners and a category of people who view this as something that’s not quite working. Safety and Systems Engineering Number three, who carries the burden of systems engineering? A capable model is not a working system — despite lab blindness, the idea that there’s anything other than the model. Who owns the integration of probabilistic models with deterministic software, human judgment, permissions, and real-world consequences? Explain that. SS: Just this weekend there was another article about how LLMs are able to communicate using timing differences in VMs for CPU cache. Again, it’s a complete absence of a systems-engineering approach to the problem. It’s saying, “Can we test and find things that can go wrong?”, rather than viewing it the other way around, “What would we have to build around this so we could actually use it to capture the economic value that exists?”. There’s a ceding of human agency and responsibility around solving these as systems-engineering problems. The tell is you look at these historical incidents — in a real company, you’d expect someone to publish a “Five Whys“, we’ve done a retrospective, I’m documenting not just what happened but everything I will do and build and fix and change so it can never happen again. Think about how [Hyman] Rickover built the nuclear Navy. These two positions are interesting to juxtapose, Admiral Rickover building the U.S. nuclear Navy, and the Soviet nuclear fleet. The Soviets had to rest their submariners every six months in Sochi so they could regenerate their white blood cells, because they treated safety from radiation as an unsolvable problem, “It is what it is”, and Rickover designed our submarines to a standard of safety a hundred times more than what scientists thought was even needed. Very clearly there you have a human who absorbed the burden of systems engineering, and a philosophy of continuous improvement. I think companies are going to have to absorb that, and the people who provide AI are going to have to absorb it too. All of these problems are solvable. We could wring our hands and talk about how dangerous this new thing is — we could have done that with the automobile. Four million Americans have died since 1899 with the invention of the automobile, but we just kept going, we have seatbelts, airbags, anti-lock brakes, there’s a systems engineering approach to reducing risk, dare I even talk about the airplane. You don’t see that coming out in the discourse — other than, say, Power Shelf from Nvidia, who has actually released toolchains meant to provide more safety? There should be a proliferation of that right now. Do you think that falls on the model makers to do that? Or does it fall on companies like Palantir or Microsoft? Or to what extent is it in the interest of Palantir and Microsoft to frame it that way because suddenly the value of what you’re wrapping around the models becomes much more apparent? SS: Well, certainly that’s the business we’ve ended up building, so you can say there’s some self-interest there. But I’m kind of gaslit by how little focus there is in the labs on this. It comes down to model blindness, there’s an aesthetic where if you need anything other than the model, that’s somehow a failure of the maximalist version of AI, and we should just be building more there. If you were more focused on the practical realization of benefits for your customers, you probably wouldn’t think about it that way. To what degree are the model companies really selling progress, not product? Isn’t that good for you? Palantir’s AI Lessons Well, here’s a question. You talk about the need to focus on the top line rather than the bottom line, and the need to build things around what these models are capable of as opposed to retrofit them into a human replacement paradigm. How does that apply to Palantir broadly? You built this business, this concept of the ontology, all these disparate data sources, and you bring them in — and when we talked in 2023, it was a very compelling story about how this makes sense in the context of AI, with AI almost being a user interface across this data. At what point are you approaching AI and it seems awfully convenient that, “We’ve had it right all along and it fits right in” — especially in conjunction with a message that “You need to rethink what you do around AI”. To what extent has Palantir rethought what you do for customers because of AI? SS: Enormously. On the big macro things, we were right. The enduring value of ontology — I was just with another big customer who was reasserting how ontology is what prevents the kind of AI slop from accumulating in their enterprise, and provides a layer they can build their supply chain on. But it would be crazy not to think AI has gotten us to rethink lots of small things, including how the work gets done. The majority of work in the platform now happens through what we call AI FDE — using the models themselves to accelerate the programming of what’s happening in the platform. Also thinking about it as part of a toolchain. If you zoom out, we view the enterprise as a state machine. Before AI, every one of those state transitions, some human was doing — the state machine is very complicated, and you’re interacting with lots of legacy systems. So the point of the ontology was never just a semantic representation of your data in your enterprise, it was also to model the verbs, the actions you could take. It made the enterprise programmable — a harness for humans to the enterprise. The opportunity, and what we had to invest in, was turning that harness into something that models can use. And thinking about it idempotently, enterprises aren’t just going to rip out the humans and suddenly have AIs running the entirety of it. Some workflows will start with humans, move to AIs, come back and forth. So you need a state machine that’s invariant to how did this work start — mammal or AI — and how is this work going to end, and how I govern the transitions through that state machine accordingly and the actions that can be taken? How would you have changed your approach to the ontology and what you’ve built had you known back in 2006 that LLMs would be the ultimate interface to what you’re doing? SS: Since the core ontology is kind of enduring, I’m not sure we would have changed that. It has changed how you get value out of the ontology — the applications you build on it, how they relate to each other, and it’s changed how you get data and model systems into the ontology. The counterintuitive thing, maybe a-year-and-a-half ago, I mandated that every new employee learn to use the platform through AI FDE in their first week, “This is the future, let’s go maximalist on it”, and it actually had a profound retarding effect over time, which was that there were lots of young employees who only knew the platform through this abstraction layer and, as a consequence, didn’t understand the underlying building blocks that the platform used and was composed of. Perhaps the most important question, even as someone who is an AI maximalist, is knowing when not to use AI. It’s been very interesting, when you ask the AI to design ontologies for enterprises, Going back to the alpha-beta frame — when you ask the AI to design ontologies for an enterprise, it does a mid job. Going back to the alpha/beta frame, it’s all beta, it models you like it would model anyone else, smoothing over everything that makes you special or different. A reversion to the mean. Alpha vs. Beta Number five — we’ll come back to four — has enterprise software made companies more alike, and will AI make that worse? This is an argument you’ve put forward on multiple occasions, the idea of conventional enterprise software as beta, not alpha — software required you to sell to as many folks as possible to get the return on your investment, and if you’re selling to as many folks as possible, it’s defining workflows, so everyone ends up with the same workflow. Isn’t that fine? Aren’t most workflows non-essential to the top line — software is inherently a bottom-line endeavor, and in that case it’s fine? SS: In the old world, that’s the only thing you could use software for. Go back to the earnings calls during COVID — what were CEOs talking about as the technology successes? Zoom, Teams. Meanwhile, many of these companies who I’ve known had just spent five billion dollars on an ERP implementation that fell over like a paper tiger two weeks into COVID. So the question is, what parts of the software-industrial complex are actually working? I don’t besmirch them — essentially they’re solving for, “Can I sell you the software?”, which is not the same thing as, “Did the software work?”, “Does the software actually matter?”. You can juxtapose that to our entire approach to software has been solving for, “What software must exist to solve the problems I care about?”, it’s building software through backpropagation. That both lets you continuously up-level the ambition of the problem you’re trying to solve, and allows you to think through the abstractions you want to codify in software versus the parts you leave as what is going to be unique to the customer. That’s allowed us to pierce the trade-off between alpha and beta, “How do I build software that makes each customer more different, not more similar?”, and therefore is a strategic weapon they can use in the market. So should customers be using Palantir for driving top-line revenue and still hiring a bunch of SaaS companies for everything else? SS: Empirically, that’s the case. No customer I work with is a greenfield where we’re the only thing that exists. If you’re happy with the beta you’re getting but you’re beating your head against the wall trying to figure out why the beta doesn’t let you capture the opportunities in front of you — are you fighting your software stack? Are you fighting decisions made in the past, borne out of an aesthetic desire for something easily governable and maintainable by your IT organization? IT will always yoke toward beta — decisions that are mimetic, closer to best practice, what everyone else uses. The people most interested in alpha are in the line of business — the person who feels frustrated that their strategy is hard to implement, versus “I have a precise steering wheel to do this”. So do models deliver beta or alpha? SS: Depends how you use them. If you just think about it at the limit, we’re just going to keep betting that these models will get better and I’m going to rent my intelligence from a lab — how are they getting better? They’re somehow compounding all the world’s knowledge into their system, which you’re maybe tacitly helping them do that, maybe you’re not, but that’s the trajectory you’re on. There should be an existential question about, “What is the theory of my firm?”, “What do I do if all the intelligence is compounding into weights I don’t control?”. A big part of what we’ve been pushing is this idea of Sovereign AI, that our customers have really taken to. It’s not a binary — you don’t need to leave the lab ecosystem. But for what workflows is actually the most valuable asset your post-training pipeline — it’s not about a specific open-weight model, it’s not about a specific post trained open-weight model, it’s actually that you have sovereignty over a post-training pipeline filled with the operational exhaust of your enterprise. The operational decisions that you’re making in the context of the operating system that is our software is generating data that allows you, every month, to train a new checkpoint and evaluate it against everything that came before, in a way where you know that model is yours that you’re going to be relying on. The stakes of doing that in something like government were obvious, your competitive advantage is everything, you wouldn’t rent the brain from someone that your competitors could rent the brain from. But it’s equally true in commercial world where frontier capabilities seem to be a function of unique data that pushes you past the frontier. If you talk to the Mercor’s or the data companies of the world, they’ll tell you the way they sell to a frontier lab is they generate a dataset that they post-train an open-weight model on to be provably past the existing frontier of the model the lab is selling. So if that’s how this game is happening, every company has this exhaust as a unique asset, why should they sit around and wait? And then as a consequence have a bigger question of, “If everyone has access to this, what is my alpha as a firm? Is everything reduced to beta?”. That makes sense — you give everything to an Anthropic or to an OpenAI, that learning is going to benefit everyone, which is going to transfer your differentiated knowledge into beta instead of being alpha. But why is the correct stopping point having a partner like Palantir? Why shouldn’t they just do all of this themselves? Why is it that you are a trusted partner in a way the models are not? SS: You can think of it as a model factory. What systems are you using today to make operational decisions? What system is generating the exhaust that has your decision-making processes and all the constraints that is effectively its own RL environment that you can go off post-train these models in? And as you post-train those models, how do you evaluate those models against historical decisions you’ve made, how are you going to manage deploying them to production? Having the entire vertically integrated chain where it just works and you’re able to leverage — a lot of what we’re investing in now is auto-research, how do I, without the customer investing in a big post-training team, use the decisions they’re making in supply chain or in production planning, use that exhaust to automatically start training models behind the scenes and surface to them, “Here’s a model that’s better