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The open weights debate (3D Chess)
Kimi K3 turns open weights into a conflict between frontier-lab economics, national security, Chinese industrial strategy and broad access to intelligence.
Essays, investigations, and editions from The AGI Post.
Kimi K3 turns open weights into a conflict between frontier-lab economics, national security, Chinese industrial strategy and broad access to intelligence.
Continual learning may not be one problem with one solution. The challenge has changed from catastrophic forgetting in early neural networks to post-training, deployment learning, weight updates, and architectures built around experience.
This issue covers four linked feature recaps: Satya Nadella on enterprise AI harnesses, Tyler Cowen on initiative as the deployment bottleneck, Demis Hassabis on the AI decision window, and Alex Imas with Philip Trammell on who owns the upside if AI shifts returns toward scarce assets. It compresses about 3 hours and 13 minutes of primary source video into a 14 minute daily review.
Models are making it cheaper to generate more attempts: faster kernels, possible protein binders, new data-center demand, and software output from coding agents. The harder question is whether models can prove their own work, or where tests, labs, buyers, infrastructure, and human judgment still have to decide what is real.
AI is shifting scarcity from raw capability to the costs around it: inference budgets, chip data movement, reliable evaluation, durable memory, human supervision, capital substitution for labor, and weak-link institutions. This issue maps where leverage moves once models become useful enough that the hard question is no longer only what they can do, but who can allocate the remaining compute, trust, context, labor, power, and access.
AI is moving from model competition into an allocation stack: compute, wafers, power, deployment capacity, inference speed, trusted workflows, distribution surfaces, and capital markets.
