Palantir is the world's most successful forward-deployed engineering company; arXiv changes its rules on research papers; Getty loses lawsuit against Stability; meet OpenAI's "builder in chief"

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Nov 07, 2025

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Michael Burry is betting against two of the AI party’s hottest guests. In a regulatory filing made public on Monday, his hedge fund Scion Asset Management disclosed put options (trades that pay off if those stocks fall) on Nvidia and Palantir. The investor who was made famous by The Big Short wagering that a different kind of boom has gotten ahead of itself immediately sent the CNBCs of the world into overdrive, with Palantir’s CEO Alex Karp blasting the move in interviews as “market manipulation,” “batshit crazy,” and “egregious.”

But another, quieter shift in the AI industry helps explain why Palantir keeps punching above its weight, and why Burry’s bet may be more story than substance. The newest must-hire in the AI wars isn’t a research scientist, it’s a forward-deployed engineer. You basically take a software engineer, dress them in a suit from the same department store where all the McKinsey consultants shop at, and send them into the field to embed with customers and help them make AI actually work on real data, real processes, and real timelines. OpenAI, Anthropic, and Cohere have all accelerated recruiting for these roles in recent months because they’ve learned the obvious lesson of 2025: shipping a model is great for press headlines, but shipping outcomes is what drives the business.

If this model sounds familiar, that’s because Palantir popularized it years ago, and still practices it at industrial scale. The company built a whole operating system around data analytics: teams that sit with customers, map messy data into a usable ontology, and then assemble production software that the customer keeps using when the engineers go home. That hands-on approach is why Palantir keeps showing up in high-stakes environments, from defense work in the US to the NHS’s Federated Data Platform in England, where the data is gnarly, the stakes are high, and results can’t live in slideware.

This, combined with the AI vibes, is what has underwritten Palantir’s numbers for so many quarters. The company’s Q3 update showed sharp growth, led by its US commercial business and rapid adoption of its generative AI platform, AIP. Revenue, margins, and guidance all moved in the right direction, and management talked about beating expectations as customers scaled deployments from single pilots to enterprise-wide rollouts. That’s what execution looks like when you’re selling not just a product, but a workflow delivered by an embedded team that can wrangle a warehouse of CSVs into something decisions can sit on.

I was recently in New York for a dinner with a few people working for some of the large organizations that are Palantir customers, and the uncomfortable truth they shared is that the old world of data analytics and the new world of large language models has one common bottleneck: the real world. Corporate data is scattered, contradictory, insecure, and often trapped in systems that were never designed to talk to one another. AI models don’t magically resolve that. People do, specifically engineers. Palantir has spent a decade industrializing that human-in-the-loop integration playbook. That’s why the best-known and most imitated customer success program still belongs to Palantir, and why everyone else is now racing to hire their own version.

None of this means Palantir is immune to drawdowns. Valuations bake in a lot of future perfection. Burry could be expressing a short-term view on sentiment, hedging other exposure, or simply trying to time a comedown after a monster run. He’s done that before. But if your thesis is that software spending will shift from lab demos to durable line-of-business systems, then the companies that can turn AI potential into production workflows should be advantaged, not shorted. And right now, the top of that list includes Palantir, whose quarter and backlog say customers are buying not just the dream but the deployment.

Betting $1 billion against Palantir in 2025 because you think AI is hype is a big mistake. The next leg of the AI cycle won’t be decided in model leaderboards, it’ll be decided in the boiler rooms of the enterprise, where forward-deployed teams wire data to decisions. Palantir made that playbook mainstream.

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