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Amazon's AI Self-Development Hits a Snag: It Scales Back the Nova Models and Reorganizes the Team
According to a Bloomberg report, after its self-developed large language model failed to achieve the expected results, Amazon reduced investment in most Nova models and placed its text, video, and image generation models in a state where only minimal resources are maintained. The company shifted computing power and personnel to a single frontier model project led by Pieter Abbeel; a week earlier, it had also closed an 80-person AGI research office in San Francisco.
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The Shape of AI Capabilities: Jagged Frontiers, Bottlenecks, and Reverse Protrusions
In an article, Ethan Mollick points out that AI capabilities remain distributed in a jagged pattern, that weak spots such as memory have long shown no marked improvement, and that the system as a whole is constrained by its worst link. Taking Google's Nano Banana Pro as an example, he explains that image generation quality was once a bottleneck, and that after it was broken through, NotebookLM could directly generate full-page slides from source materials; he argues that judging where AI is heading should focus on bottlenecks rather than benchmark scores.
In an article, Ethan Mollick points out that AI capabilities remain distributed in a jagged pattern, that weak spots such as memory have long shown no marked improvement, and that the system as a whole is constrained by its worst link. Taking Google's Nano Banana Pro as an example, he explains that image generation quality was once a bottleneck, and that after it was broken through, NotebookLM could directly generate full-page slides from source materials; he argues that judging where AI is head
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