What the book covers
- Origins and scaling: training pipelines, data, and the norms that shaped frontier labs.
- Inside views: first-hand perspectives from founders and researchers on breakthroughs and setbacks.
- Behavior of LLMs: alignment attempts, emergent capabilities, and safety concerns.
- Future trajectory: possibilities from explosive growth to AI governance and risk scenarios.
- Reference material: 170+ definitions, visuals, classic essays, and unpublished interviews.
Notable voices
Anthropic (Dario Amodei), DeepMind (Demis Hassabis), OpenAI (Ilya Sutskever), MIRI (Eliezer Yudkowsky), Meta (Mark Zuckerberg), and others share how they approached scaling, safety, and productization.
Themes & takeaways
- Scaling laws in practice: Bigger models, more data, and emergent behaviors.
- Safety vs. speed: Tension between rapid capability gains and alignment/governance.
- Tooling & infra: Training stacks, evals, and ops lessons from frontier orgs.
- Economic impact: Paths to explosive productivity and the need for guardrails.
- Plurality of views: Optimists, cautious builders, and alignment-first critics in one volume.
Why it matters
It’s a contemporary snapshot of how AI was actually built between 2019–2025, capturing the mindset of the people who pushed scaling forward—and the debates about what comes next.
Quick prompts
- How did scaling norms emerge across labs, and what would you change?
- Which safety practices should be standard before the next model generation?
- What governance or market structures best handle billions of capable AIs?
- How can product teams translate frontier capabilities into reliable systems?