--- schema: 1 type: video title: >- "Software engineering is not about writing code" — Benoit Schillings, Google DeepMind VP of Research description: >- A keynote exploring generative AI for code, deep-thinking algorithms, and the future of pre-training and transformer models for Gemini. Speaker: Benoit Schillings leads the Thinking, Reasoning, and Coding teams at Google DeepMind, directing foundational research toward AGI. His work focuses on advancing next-generation model reasoning and integrating software development best practices into AI code generation. Previously, as CTO at X, Benoit guided early-stage teams prototyping Alphabet's moonshot technologies across computing, biochemistry, and clean energy. LinkedIn: https://www.linkedin.com/in/benoit-schillings-2942a5 Timestamps: 0:00 Introduction and speaker background 2:35 The origin story of the Pitchfork project 4:43 Historical eras of software development 7:08 The current state of AI code generation 9:36 The role of self-play in training models 11:13 Changing economics of software engineering 12:41 Implementing guardrails and security 13:48 Inductive architecture and model planning 14:36 Evolution of evaluation benchmarks 15:45 Moving beyond simple chain-of-thought tokens 17:51 Future applications in chemistry and biology Key Takeaways from the talk: Benoit Schillings, VP of Technology at Google DeepMind, discusses the transformative impact of generative AI on software engineering and the future of model reasoning (0:49 - 2:35). The Era of Syntax Generation is Over: (4:43) Coding has shifted from a machine-constrained task to an AI frontier where syntax is effectively solved, moving the bottleneck to architecture and validation. The Power of Self-Play: (9:36) As human-generated training data reaches saturation, DeepMind is utilizing self-play, where models generate and verify their own challenges to reach superhuman performance. Shift in Engineering Economics: (11:13) With writing code becoming nearly free, the focus must transition to active guardrails, security, and managing the explosion of generated code. Inductive Architecture: (13:48) The next step for AI is moving beyond simple token prediction toward models that can plan, decompose complex problems, and transfer knowledge across domains. Scientific Breakthroughs: (17:51) AI's ability to experiment rapidly will transform fields like chemistry and biology, allowing models to uncover patterns and relationships that remain invisible to human perception. --- Watch along with the corrected transcript, summary, timestamps and resources on this talk's official AIE page: https://ai.engineer/talks/1P1hJ36rxM0-research-to-reality-with-google-deepmind Join our upcoming AI Engineer conferences (see https://ai.engineer landing page): - AIEi Shanghai, November 5–6 — the first for meeting top Chinese labs and engineers - AIE CODE in SF, November 10–12 — the top AI Coding conference returns - AIEi Sydney, December 7–8 — the first AIEi run alongside NeurIPS 2026 in Sydney --- canonical: 'https://liquidgold.cc/watch/1P1hJ36rxM0' markdown: 'https://liquidgold.cc/watch/1P1hJ36rxM0.md' llms: 'https://liquidgold.cc/watch/1P1hJ36rxM0/llms.txt' parent: 'https://liquidgold.cc/channel/aidotengineer.md' channel: aidotengineer updated: '2026-10-24T07:29:42.579Z' language: null children: [] videoId: 1P1hJ36rxM0 durationSeconds: 1226 publishedAt: '2026-07-17T15:24:44.000Z' searchChunks: - ordinal: 0 text: null startSeconds: 30 endSeconds: 163 watch: 'https://liquidgold.cc/watch/1P1hJ36rxM0?t=30' heldReason: rights - ordinal: 1 text: null startSeconds: 30 endSeconds: 163 watch: 'https://liquidgold.cc/watch/1P1hJ36rxM0?t=30' heldReason: rights - ordinal: 2 text: null startSeconds: 163 endSeconds: 175 watch: 'https://liquidgold.cc/watch/1P1hJ36rxM0?t=163' heldReason: rights - ordinal: 3 text: null startSeconds: 175 endSeconds: 285 watch: 'https://liquidgold.cc/watch/1P1hJ36rxM0?t=175' heldReason: rights - ordinal: 4 text: null startSeconds: 285 endSeconds: 295 watch: 'https://liquidgold.cc/watch/1P1hJ36rxM0?t=285' heldReason: rights - ordinal: 5 text: null startSeconds: 295 endSeconds: 428 watch: 'https://liquidgold.cc/watch/1P1hJ36rxM0?t=295' heldReason: rights - ordinal: 6 text: null startSeconds: 295 endSeconds: 428 watch: 'https://liquidgold.cc/watch/1P1hJ36rxM0?t=295' heldReason: rights - ordinal: 7 text: null startSeconds: 439 endSeconds: 584 watch: 'https://liquidgold.cc/watch/1P1hJ36rxM0?t=439' heldReason: rights - ordinal: 8 text: null startSeconds: 439 endSeconds: 584 watch: 'https://liquidgold.cc/watch/1P1hJ36rxM0?t=439' heldReason: rights - ordinal: 9 text: null startSeconds: 584 endSeconds: 595 watch: 'https://liquidgold.cc/watch/1P1hJ36rxM0?t=584' heldReason: rights - ordinal: 10 text: null startSeconds: 595 endSeconds: 673 watch: 'https://liquidgold.cc/watch/1P1hJ36rxM0?t=595' heldReason: rights - ordinal: 11 text: null startSeconds: 685 endSeconds: 764 watch: 'https://liquidgold.cc/watch/1P1hJ36rxM0?t=685' heldReason: rights - ordinal: 12 text: null startSeconds: 764 endSeconds: 777 watch: 'https://liquidgold.cc/watch/1P1hJ36rxM0?t=764' heldReason: rights - ordinal: 13 text: null startSeconds: 777 endSeconds: 833 watch: 'https://liquidgold.cc/watch/1P1hJ36rxM0?t=777' heldReason: rights - ordinal: 14 text: null startSeconds: 844 endSeconds: 882 watch: 'https://liquidgold.cc/watch/1P1hJ36rxM0?t=844' heldReason: rights - ordinal: 15 text: null startSeconds: 882 endSeconds: 895 watch: 'https://liquidgold.cc/watch/1P1hJ36rxM0?t=882' heldReason: rights - ordinal: 16 text: null startSeconds: 895 endSeconds: 945 watch: 'https://liquidgold.cc/watch/1P1hJ36rxM0?t=895' heldReason: rights - ordinal: 17 text: null startSeconds: 956 endSeconds: 1071 watch: 'https://liquidgold.cc/watch/1P1hJ36rxM0?t=956' heldReason: rights - ordinal: 18 text: null startSeconds: 956 endSeconds: 1071 watch: 'https://liquidgold.cc/watch/1P1hJ36rxM0?t=956' heldReason: rights - ordinal: 19 text: null startSeconds: 1082 endSeconds: 1226 watch: 'https://liquidgold.cc/watch/1P1hJ36rxM0?t=1082' heldReason: rights --- # "Software engineering is not about writing code" — Benoit Schillings, Google DeepMind VP of Research A keynote exploring generative AI for code, deep-thinking algorithms, and the future of pre-training and transformer models for Gemini. Speaker: Benoit Schillings leads the Thinking, Reasoning, and Coding teams at Google DeepMind, directing foundational research toward AGI. His work focuses on advancing next-generation model reasoning and integrating software development best practices into AI code generation. Previously, as CTO at X, Benoit guided early-stage teams prototyping Alphabet's moonshot technologies across computing, biochemistry, and clean energy. LinkedIn: https://www.linkedin.com/in/benoit-schillings-2942a5 Timestamps: 0:00 Introduction and speaker background 2:35 The origin story of the Pitchfork project 4:43 Historical eras of software development 7:08 The current state of AI code generation 9:36 The role of self-play in training models 11:13 Changing economics of software engineering 12:41 Implementing guardrails and security 13:48 Inductive architecture and model planning 14:36 Evolution of evaluation benchmarks 15:45 Moving beyond simple chain-of-thought tokens 17:51 Future applications in chemistry and biology Key Takeaways from the talk: Benoit Schillings, VP of Technology at Google DeepMind, discusses the transformative impact of generative AI on software engineering and the future of model reasoning (0:49 - 2:35). The Era of Syntax Generation is Over: (4:43) Coding has shifted from a machine-constrained task to an AI frontier where syntax is effectively solved, moving the bottleneck to architecture and validation. The Power of Self-Play: (9:36) As human-generated training data reaches saturation, DeepMind is utilizing self-play, where models generate and verify their own challenges to reach superhuman performance. Shift in Engineering Economics: (11:13) With writing code becoming nearly free, the focus must transition to active guardrails, security, and managing the explosion of generated code. Inductive Architecture: (13:48) The next step for AI is moving beyond simple token prediction toward models that can plan, decompose complex problems, and transfer knowledge across domains. Scientific Breakthroughs: (17:51) AI's ability to experiment rapidly will transform fields like chemistry and biology, allowing models to uncover patterns and relationships that remain invisible to human perception. --- Watch along with the corrected transcript, summary, timestamps and resources on this talk's official AIE page: https://ai.engineer/talks/1P1hJ36rxM0-research-to-reality-with-google-deepmind Join our upcoming AI Engineer conferences (see https://ai.engineer landing page): - AIEi Shanghai, November 5–6 — the first for meeting top Chinese labs and engineers - AIE CODE in SF, November 10–12 — the top AI Coding conference returns - AIEi Sydney, December 7–8 — the first AIEi run alongside NeurIPS 2026 in Sydney --- Canonical: https://liquidgold.cc/watch/1P1hJ36rxM0 · Channel: [AI Engineer](https://liquidgold.cc/channel/aidotengineer.md) > Search chunk text is held until public-source provenance is verified. ## Document chunks > Missing: no stored document chunks are available.