
What happened
Amazon is halting active development of its flagship Nova Premier and Omni models, the Reel video model, and the Canvas image generator, placing them in "keep the lights on" mode for existing customers. The shift follows layoffs in the AGI division and closure of the AGI Lab (founded in 2024 after acquiring Adept). Resources are being redirected to a new Frontier Model Research group led by Pieter Abbeel, who joined through the acquisition of robotics startup Covariant.
Why it matters
Amazon is not exiting AI competition, but rather consolidating its in-house model efforts and refocusing on foundation model research. The company remains one of the largest backers of Anthropic and OpenAI, where investments could top $100 billion, suggesting its AI ambitions persist through both internal development and strategic partnerships.
What to watch
A new foundation model is due at the re:Invent conference this fall. Amazon has signaled that AI models remain one of its most important projects despite the Nova pullback.
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Amazon's pullback on Nova models marks a significant recalibration of its AI strategy, not an exit from the field. The company had unveiled Nova in December 2024 as a family of models spanning different sizes and capabilities, but internal changes have forced a pivot. The closure of the AGI Lab—which Amazon established in 2024 after acquiring Adept—suggests the company is consolidating duplicate efforts and layering responsibility more selectively.
The shift to the Frontier Model Research group under Abbeel, supported by his prior leadership at the robotics startup Covariant, indicates Amazon is betting on focused foundation model research over broad model families. This aligns with a broader industry pattern where companies shift resources toward core architectural breakthroughs rather than maintaining multiple variant product lines. Simultaneously, Amazon's reported investments in Anthropic and OpenAI (potentially exceeding $100 billion) reveal that the company is hedging its in-house AI bets with exposure to leading external teams, a position that reduces pressure to deliver immediately from internal labs.
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