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Jeff Bezos stands beside two former Waymo and Verily executives on a stage with a Project Prometheus banner behind them.

Jeff Bezos launches USD 6.2 B Project Prometheus, adds ex‑Waymo and Verily exec

2 min read

Jeff Bezos is putting $6.2 billion behind Project Prometheus, an AI-driven push to speed up engineering cycles. The plan, unveiled this week, aims for quicker design iterations and tighter feedback loops between hardware and software crews. Money aside, the move also brings in a senior engineer who once helped build the self-driving prototype that later turned into Waymo, co-founded the life-sciences lab Verily, and then led Foresite Labs.

It’s not clear yet how well that background will mesh with Bezos’s goal of reshaping how engineers work. I’m curious whether the same playbook that got a prototype on the road can be applied to a broader, AI-heavy workflow. The report says Project Prometheus…

Bajaj contributed to projects like the self-driving car that later became Waymo. He also co-founded the life sciences lab Verily and most recently led Foresite Labs before joining Project Prometheus. How Bezos's new venture aims to reshape engineering work According to the report, Project Prometheus is developing AI tools for engineering and manufacturing in fields like computing, automotive, and aerospace.

The Times says the company has not yet decided where it will be based. Until now, the startup has operated mostly out of public view, so it is unclear when it was founded. The team already includes about 100 specialists, including researchers from OpenAI, Deepmind, and Meta.

The startup is targeting a rapidly growing and increasingly competitive segment: AI systems designed to speed up physical processes. That includes tools for robotics, scientific research, and materials development. Other companies moving into this space include Periodic Labs, which is building a lab where robots can run scientific experiments at scale.

Related Topics: #Jeff Bezos #Project Prometheus #AI-driven #Waymo #Verily #Foresite Labs #OpenAI #Deepmind #robotics

Project Prometheus looks like it could speed up engineering, but it’s still early days. With a $6.2 billion war chest the startup sits among the most heavily funded early-stage ventures you’ll see. Jeff Bezos is back in an operational role for the first time since 2021, sharing the helm with Vik Bajaj - a physicist-chemist who spent time at Google X (the early self-driving car effort that became Waymo) and co-founded Verily.

Their résumés point to an AI-centric take on product development, yet the playbook is still hazy. The press release talks about reshaping engineering work, but it doesn’t spell out how the tech will actually be used. Funding is plentiful, and that certainly signals confidence, but money alone doesn’t guarantee delivery.

It’s unclear whether this duo can turn their high-profile experience into real-world speed gains. As the company moves past the hype, we’ll have to watch whether it lives up to the lofty expectations set by its backers.

Common Questions Answered

How much funding did Jeff Bezos commit to Project Prometheus?

Jeff Bezos pledged a $6.2 billion war chest to launch Project Prometheus. This massive investment places the venture among the most heavily financed early‑stage startups in the AI‑driven engineering space.

Which former Waymo and Verily executive did Bezos bring onto Project Prometheus?

Bezos hired Vik Bajaj, a physicist‑chemist who helped build the self‑driving prototype that later became Waymo and co‑founded the life‑sciences lab Verily. Bajaj most recently led Foresite Labs before joining the new venture.

What sectors are targeted by Project Prometheus’s AI tools for engineering and manufacturing?

Project Prometheus is developing AI‑driven solutions for computing, automotive, and aerospace industries. The tools aim to accelerate design cycles and improve feedback loops across both hardware and software teams in these fields.

How does Project Prometheus plan to accelerate engineering cycles?

The venture promises faster design iterations and tighter feedback loops by integrating AI across hardware and software development. This approach is intended to shorten engineering timelines and boost productivity across multiple technology domains.