The Great AI Pivot

This presentation argues that the era of simply scaling models is ending and that AI is entering a new Age of Research. From 2020 to 2025, progress was driven by neural scaling laws and the ability to buy more compute, scrape more data and train larger Transformers. That playbook is now hitting hard limits. High quality human data is finite, synthetic data risks model collapse and returns from bigger pre training runs are shrinking. Models excel on benchmarks yet still show a jagged frontier. They can outperform experts on exams or coding contests and still fail on simple abstraction tests or long horizon tasks.

The industry response is a pivot to reasoning, inference time compute and new architectures. Models like o1 and o3 spend many more tokens thinking before answering and use verifiable rewards to improve math and code performance. At the same time, research is shifting toward value learning, hierarchical reinforcement learning, intrinsic motivation and neurosymbolic hybrids that combine neural intuition with symbolic verification. A second safety track focuses on unlearning, so hazardous capabilities can be removed without destroying general reasoning.

Institutionally, Ilya Sutskever’s Safe Superintelligence Inc. embodies this new phase. SSI rejects incremental products and pursues a straight shot to safe superintelligence. This insulates safety work from short term revenue pressure, but also creates a shadow development zone that current regulation barely touches, especially where research exemptions apply. The danger point moves from public deployment to powerful internal systems that are hard to observe and govern.

The article closes with a roadmap for labs, enterprises and policymakers. Labs should rebalance their portfolios toward value learning, reliable inference and safety engineering. Enterprises should adopt model agnostic routing, invest in evaluation infrastructure and stop waiting for the next GPT to fix structural reliability issues. Policymakers need capability based rules for internal use and inference compute, plus mandatory safety cases for high risk research. In the Age of Research, winners will be those who can engineer robust values and reasoning, not just larger clusters.