Jeff Bezos is quietly building one of the most ambitious AI labs on the planet, and his latest hire shows just how serious he is. Project Prometheus, his industrial AI start-up, has brought in Kyle Kosic — a co-founder of Elon Musk’s xAI and a former OpenAI infrastructure lead — to help power its next phase.

Kosic is not a flashy frontman, but in AI circles, he is exactly the kind of engineer everyone is fighting over. He helped design and run the infrastructure behind xAI’s Colossus supercomputer, the massive computing backbone that trained Musk’s models, and before that, he spent years deep inside OpenAI’s stack. After a stint back at OpenAI in 2024, he is now decamping to San Francisco-based Project Prometheus, where he will once again focus on large-scale AI infrastructure.

The move lands at an interesting moment in the AI talent wars. All eleven of xAI’s original co-founders have now left the company, several in the last few months, with some complaining privately about Elon Musk’s management style. That revolving door has turned into a recruiting pipeline for rivals, as deep-pocketed labs dangle huge compensation packages and a chance to work on more focused, less chaotic projects. Kosic leaving, after leading infrastructure at xAI and then returning to OpenAI, underlines just how fluid the top of the AI job market has become.

Prometheus is not trying to build yet another chatbot to answer emails or write code snippets. The company’s pitch is physical AI: systems that actually understand and operate in the real world, not just the internet. Think AI that can reason about jet engines, factory lines, chip fabrication equipment, or the airflow around a new aircraft wing — and then help design, simulate and optimise those systems long before anything is built. That means training models not just on text and code, but on vast, highly specialised engineering datasets and physics-heavy simulations.

To do this, Prometheus is hiring at a scale that looks less like a start-up and more like a new research division of a Big Tech company. It has already recruited hundreds of engineers and researchers across San Francisco, London and Zurich, with a particular focus on people who have experience “building out massive infrastructure projects” — exactly the kind of background Kosic brings. Many of those employees have been poached from Meta, OpenAI, and Google DeepMind, the very organisations that defined the first wave of generative AI.

Bezos and co-chief Vikram Bajaj, a former Google executive, are also assembling an extraordinary amount of money behind this effort. Project Prometheus reportedly launched with roughly $6.2 billion in initial funding, part of it from Bezos himself, putting it among the most well-financed early-stage AI companies ever. That is just the starting pot. The pair are now trying to raise “tens of billions” more for what insiders describe as a permanent capital vehicle, structured a bit like a Berkshire Hathaway-style holding company for industrial AI bets.

The model is simple to describe and hard to execute: buy stakes in established engineering-heavy businesses — in sectors like aviation, architecture, design, chipmaking, and advanced manufacturing — and use the data and real-world experience from those companies to train Prometheus’s AI. In return, Prometheus embeds its own people inside those firms as “forward-deployed engineers,” who work on the ground to improve margins, smooth operations and push through AI-driven redesigns of processes and products. For the companies that promise better performance and higher profits; for Prometheus, it creates a flywheel of proprietary industrial data that competitors cannot easily copy.

What makes this especially interesting is that it attacks one of the core weaknesses of today’s AI: a shallow grasp of the physical world. Most state-of-the-art models are trained overwhelmingly on text, images and code, which helps them mimic language and learn patterns but does not give them deep understanding of mechanics, materials or complex physical interactions. Rival labs are experimenting with training on video, robotics logs and simulations, but high-quality, domain-specific engineering data is rare and usually locked away inside industrial companies. Prometheus is trying to buy its way into that data, at scale.

Kosic’s arrival fits neatly into this strategy. Building “world models” that can reason about physical systems is incredibly compute-intensive: it demands gigantic clusters of GPUs or custom accelerators, ultra-fast networking, and a carefully tuned software stack that can feed data to models without wasting cycles. At xAI, Kosic helped architect exactly that kind of infrastructure, and he has already navigated the realities of scaling training workloads while hardware supply is tight and demand is exploding. For Prometheus, which wants to stand up massive simulations of everything from engine components to production lines, that experience is almost the perfect résumé.

Behind the scenes, the financing push hints at how far Bezos wants to take this. Reports suggest he has been exploring raising up to $100 billion over time to acquire and modernise a portfolio of industrial companies with AI, effectively building a new kind of tech-enabled conglomerate wrapped around Prometheus’s models. Sovereign wealth funds from Singapore and the Gulf are said to be among the investors courted, attracted by the idea that AI could unlock productivity gains in the “physical economy” that dwarf anything seen in consumer apps or advertising. If that money comes together, Prometheus starts to look less like a start-up and more like a long-term infrastructure play on the future of manufacturing, aerospace and energy.

For workers inside those industries, the implications are more nuanced than the usual “AI will take your job” headline. If Prometheus and its rivals succeed, AI should reduce the time and cost of testing physical ideas, allowing engineers to run thousands of virtual experiments before committing to a design. That could lead to new products, faster iteration and, arguably, more demand for skilled people who can work with these tools rather than being replaced by them. As one FT commenter put it, AI in this context is less about eliminating roles and more about expanding what becomes possible, which tends to create new work rather than wipe it out.

There is also a competitive subtext. Bezos is re-entering the front line of tech leadership just as his old company, Amazon, is racing to catch up in AI infrastructure and as Musk, Altman and other founders become the public faces of different AI ideologies. Where OpenAI and Anthropic are still primarily associated with chatbots, and xAI leans into a contrarian, less safety-heavy approach, Prometheus is staking its reputation on being the lab that actually moves atoms and not just words.

Kosic’s hire will not make or break that vision on its own, but it sends a clear signal to the rest of the industry: Prometheus is willing to pay for top-tier infrastructure talent, even if that means pulling co-founders away from rival labs. In a field where access to compute and the ability to scale training runs are becoming as important as research breakthroughs, that kind of bench strength could be decisive. For now, Prometheus remains mostly in the shadows — no consumer products, no splashy demos, very few public details — but the people and capital flowing into it suggest that when it does step into the light, it is aiming to reshape how physical industries work, not just how people chat with machines.