Jeff Bezos and Vik Bajaj's startup Prometheus raised $12 billion in a Series B funding round, valuing the company at $41 billion, according to TechCrunch. The capital will fund an artificial intelligence system designed to accelerate the design, modeling and prototyping of physical objects, potentially compressing decade-long engineering cycles into months.
Funding and investor backing
This round follows a $6.2 billion raise at the end of 2025. New investors include JPMorgan, Goldman Sachs, BlackRock, DST Global and Arch Venture Partners, with Bezos also participating. Prometheus plans to allocate a significant portion of these funds toward computational capacity, building an internal cluster and purchasing resources from external providers.
The universal AI engineer
The founders describe the product as a universal AI engineer. It targets the early stages of production rather than factory automation or robotics. The system will focus on design, modeling and process optimization.
Bezos said altering a jet engine design to increase thrust by 10 percent currently takes years due to manufacturing constraints. Prometheus intends to reduce such cycles by a factor of ten or more.
"If a task currently requiring 100 engineers and 10 years could be accomplished by 10 engineers in one year, we will simply build a lot more things," Bezos said in an interview with Axios. Bajaj added that simplifying the realization of ideas will lead to more inventions and greater involvement in the process.
Data constraints and training
Prometheus relies on data from physics laws, internal tests and corporate collaborations to train its models. The company acknowledged the absence of an "internet of manufacturing data" to feed the system.
Details regarding the product's public launch timeline and specific training methodologies remain confidential. The startup operates without disclosing how it trains its models, relying instead on proprietary physics data and corporate partnerships.
Why this matters for IT and Development professionals
For software engineers and technical teams, this funding indicates a shift from purely digital automation to physical engineering workflows. As AI models improve at generalizing skills, developers will increasingly need to integrate these systems into existing CAD and prototyping pipelines. Professionals working in AI for Product Development will face new challenges in bridging simulated physics data with real-world manufacturing constraints. Building expertise in these new workflows will require a strong foundation in AI for IT & Development practices.
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