"Part of what is driving this is that the scaling benefits of pre-training - that initial process of teaching AI models using massive datasets, which is the sole domain of foundation models - has slowed down. That doesn't mean AI has stopped making progress, but the early benefits of hyperscaled foundational models have hit diminishing returns, and attention has turned to post-training and reinforcement learning as sources of future progress."
"In short, the competitive landscape of AI is changing in ways that undermine the advantages of the biggest AI labs. Instead of a race for an all-powerful AGI that could match or exceed human abilities across all cognitive tasks, the immediate future looks like a flurry of discrete businesses: software development, enterprise data management, image generation and so on. Aside from a first-mover advantage, it's not clear that building a foundation model gives you any advantage in those businesses."
The scaling benefits of pre-training foundation models have slowed, producing diminishing returns from ever-larger models. Attention is shifting toward post-training techniques, reinforcement learning, fine-tuning, and interface design to create superior task-specific AI products. Startups often treat foundation models as swappable commodities and focus on custom tuning and user experience instead of massive additional pre-training spend. Success cases from foundation-model companies show skill in these downstream areas, but that advantage is less durable. The near-term AI landscape looks fragmented into discrete businesses like enterprise software, data management, and image generation where owning a foundation model may not confer clear advantages.
Read at TechCrunch
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