Anthropic’s September 2026 threat report suggests the near-term risk is not full autonomy but cheaper, more scalable malicious workflows. The evidence is useful—and limited to what Anthropic can observe.
NVIDIA and CoreWeave are positioning Vera Rubin as part of a full agentic production loop—not just a faster accelerator. The key question is whether that translates into durable unit economics.
Google’s Gemini 4 Argon pairs a much larger output budget with strong enterprise and cyber benchmarks—but its staged release and mixed results matter as much as the headline scores.
OpenAI’s DevDay 2026 points to a larger shift: from prompt-response chatbots to persistent agents with durable sessions, cloud execution, software access and lower-cost models.
Stanford CS329A Part 9 examines future research in self-improving AI: reasoning diversity, verification, self-generated curricula, continual learning, and intelligence per watt.
Stanford CS329A Part 8 examines long-horizon agent evaluation through METR, GDPval, and DeepScholar-Bench, arguing that longer task horizons are not the same as reliable, delegable AI.
Stanford CS329A Part 7 connects AlphaCode, AlphaCode 2, Search-o1, and Search-R1 to show why search only helps when generation, selection, and context refinement improve together.
Stanford CS329A Part 6 connects STaR, DeepSeekMath/GRPO, and DAPO to show how verified reasoning can be turned into persistent train-time improvement.
Stanford CS329A Part 5 compares METR, GDPval, and DeepScholarBench to show why agent capability, reliability, context, and real-world deliverable quality must be evaluated separately.
Stanford CS329A Part 4 connects ReAct, execution feedback, and Constitutional AI to one question: where should a self-improving agent get corrective signals it can actually trust?