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Part 2 follows the choices behind useful model improvements: architecture, evaluation, training budgets, context, serving costs—and the research habits that sustain them.
Goal-reaching agents benefit from depth when the learning objective, architecture, and supply of experience scale together.
Shrinking model advantages put more weight on product design, integration and execution. The Unsupervised Learning × Latent Space crossover explores where that work creates value—and where technical limits still matter.
Human task duration makes AI capability easier to interpret. Productivity, autonomous research and catastrophic risk depend on more than the work a benchmark captures.
Part 1 follows five research bets—from longer inference to online models—then examines Sora's physical consistency, Groq's utilization economics, and Gemini's million-token context.
Omar Qazi’s public commentary spans local language-model inference and Tesla’s Full Self-Driving system.
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A conversation with Greg Brockman about OpenAI’s path to AGI, alongside our analysis of GPT-5, Codex, and the shift to agentic coding.
Thariq Shihipar on Claude Code’s next era: prompts, harnesses, plugins, and agents—and what it takes to keep pace with the frontier.
Marc Andreessen on the future of the browser, Pi and OpenClaw, and why this wave of AI feels different.
Priscilla Chan and Mark Zuckerberg on Biohub’s plans for frontier AI and virtual biology, and the ambition to cure or prevent disease.
Anima Anandkumar on modeling the physical world, from weather to fusion reactors, and why physics needs more than language models.
John Platt on automating science, tackling climate change, and how the next generation can contribute in the age of AI.
Anthropic brings interactive apps into Claude with an open MCP standard for richer AI interfaces.
What Sam Altman’s town hall reveals about building startups in the age of AI.
Moonshot’s multimodal model brings competitive coding, native image and video understanding, and parallel agents.
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