Governments started the space age. Now, billionaires and private firms are reinventing it. Guest host Steve Levitt explores what the past can teach us about a high-stakes future. (Part two of a two-part series.)
SOURCES:
Blaise Agüera y Arcas, vice president and fellow at Google, C.T.O. of technology and society.
Rosanna Hoffman, head of space law, policy, and sustainability at the United Nations Office for Outer Space Affairs.
Alex Macdonald, former first chief economist at NASA, senior associate at the Center for Strategic and International Studies.
Will Marshall, co-founder and C.E.O. of Planet Labs.
RESOURCES:
"The mission to create a searchable database of Earth's surface," by Will Marshall (TED, 2018).
The Long Space Age, by Alex Macdonald (2017).
Planet Labs A.I.
EXTRAS:
"Should A.I. Move to Space?," series by Freakonomics Radio (2026).
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At first it sounds ridiculous. But it might be inevitable. Guest host Steve Levitt talks to a team that hopes to push A.I. infrastructure off the planet. Part one of a two-part series.)
SOURCES:
Blaise Agüera y Arcas, vice president and fellow at Google, C.T.O. of technology and society.
Travis Beals, senior director of product management at Google, Project Suncatcher lead.
Will Marshall, co-founder and C.E.O. of Planet Labs.
RESOURCES:
What Is Intelligence?, by Blaise Agüera y Arcas (2025).
"Towards a future space-based, highly scalable AI infrastructure system design," by Blaise Agüera y Arcas, Travis Beals, Maria Biggs, Jessica V. Bloom, Thomas Fischbacher, Konstantin Gromov, Urs Köster, Rishiraj Pravahan, and James Manyika (Google, 2025).
Reason, by Isaac Asimov (1941).
EXTRAS:
"Are Our Tools Becoming Part of Us?," by People I (Mostly) Admire (2024).
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What if life itself is just a really sophisticated computer program that wrote itself into existence?
Blaise Agüera y Arcas presenting at ALife 2025 — the most technically detailed public walkthrough of the ideas in his *What is Life?* and *What is Intelligence?* books that we've come across.He covers the BFF experiments (self-replicating programs emerging spontaneously from random noise), the mathematical framework connecting Lotka-Volterra population dynamics with Smoluchowski coagulation, eigenvalue analysis of cooperation matrices, and his central claim that symbiogenesis — not mutation — is the primary engine of evolutionary novelty.The experimental results are genuinely striking: complex self-replicating code arising from random byte strings with zero mutation, a sharp phase transition that looks like gelation, and a proof that blocking deep symbiogenetic ancestry trees prevents the transition entirely.A few things worth flagging for critical viewers:— The substrate is more carefully engineered than the framing sometimes suggests. The choice of language, tape length, interaction protocol, and step limits all shape what emerges. Their own SUBLEQ counterexample (where self-replicators *don't* arise despite being theoretically possible) highlights that these design choices matter substantially — and a general theory of which substrates support this transition is still missing.— The leap from "self-replicating programs on fixed-length tapes" to "life was computational and intelligent from the start" involves significant philosophical extrapolation beyond what the experiments directly demonstrate.— The Bedau et al. (2000) open problems paper he references at the start actually sets a higher bar for Challenge 3.2 than BFF currently meets: it asks that "the internal organization of these 'organisms' and the boundaries separating them from their environment arise and be sustained through the activities of lower-level primitives" — whereas BFF's tape boundaries are fixed by design, not emergent.
---
TIMESTAMPS:
00:00:00 Introduction: From Noise to Programs & ALife History
00:03:15 Defining Life: Function as the "Spirit"
00:05:45 Von Neumann's Insight: Life is Embodied Computation
00:09:15 Physics of Computation: Irreversibility & Fallacies
00:15:00 The BFF Experiment: Spontaneous Generation of Code
00:23:45 The Mystery: Complexity Growth Without Mutation
00:27:00 Symbiogenesis: The Engine of Novelty
00:33:15 Mathematical Proof: Blocking Symbiosis Stops Life
00:40:15 Evolutionary Implications: It's Symbiogenesis All The Way Down
00:44:30 Intelligence as Modeling Others
00:46:49 Q&A: Levels of Abstraction & Definitions
---
REFERENCES:
Paper:
[00:01:16] Open Problems in Artificial Life
https://direct.mit.edu/artl/article/6/4/363/2354/Open-Problems-in-Artificial-Life
[00:09:30] When does a physical system compute?
https://arxiv.org/abs/1309.7979
[00:15:00] Computational Life
https://arxiv.org/abs/2406.19108
[00:27:30] On the Origin of Mitosing Cells
https://pubmed.ncbi.nlm.nih.gov/11541392/
[00:42:00] The Major Evolutionary Transitions
https://www.nature.com/articles/374227a0
[00:44:00] The ARC gene
https://www.nih.gov/news-events/news-releases/memory-gene-goes-viral
Person:
[00:05:45] Alan Turing
https://plato.stanford.edu/entries/turing/
[00:07:30] John von Neumann
https://en.wikipedia.org/wiki/John_von_Neumann
[00:11:15] Hector Zenil
https://hectorzenil.net/
[00:12:00] Robert Sapolsky
https://profiles.stanford.edu/robert-sapolsky
---
LINKS:
RESCRIPT: https://app.rescript.info/public/share/ff7gb6HpezOR3DF-gr9-rCoMFzzEgUjLQK6voV5XVWY
Blaise Agüera y Arcas explores some mind-bending ideas about what intelligence and life really are—and why they might be more similar than we think (filmed at ALIFE conference, 2025 - https://2025.alife.org/).
Life and intelligence are both fundamentally computational (he says). From the very beginning, living things have been running programs. Your DNA? It's literally a computer program, and the ribosomes in your cells are tiny universal computers building you according to those instructions.
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Blaise argues that there is more to evolution than random mutations (like most people think). The secret to increasing complexity is *merging* i.e. when different organisms or systems come together and combine their histories and capabilities.
Blaise describes his "BFF" experiment where random computer code spontaneously evolved into self-replicating programs, showing how purpose and complexity can emerge from pure randomness through computational processes.
https://en.wikipedia.org/wiki/Blaise_Ag%C3%BCera_y_Arcas
https://x.com/blaiseaguera?lang=en
TRANSCRIPT:
https://app.rescript.info/public/share/VX7Gktfr3_wIn4Bj7cl9StPBO1MN4R5lcJ11NE99hLg
TOC:
00:00:00 Introduction - New book "What is Intelligence?"
00:01:45 Life as computation - Von Neumann's insights
00:12:00 BFF experiment - How purpose emerges
00:26:00 Symbiogenesis and evolutionary complexity
00:40:00 Functionalism and consciousness
00:49:45 AI as part of collective human intelligence
00:57:00 Comparing AI and human cognition
REFS:
What is intelligence [Blaise Agüera y Arcas]
https://whatisintelligence.antikythera.org/ [Read free online, interactive rich media]
https://mitpress.mit.edu/9780262049955/what-is-intelligence/ [MIT Press]
Large Language Models and Emergence: A Complex Systems Perspective
https://arxiv.org/abs/2506.11135
Our first Noam Chomsky MLST interview
https://www.youtube.com/watch?v=axuGfh4UR9Q
Chance and Necessity [Jacques Monod]
https://monoskop.org/images/9/99/Monod_Jacques_Chance_and_Necessity.pdf
Wonderful Life: The Burgess Shale and the History of Nature [Stephen Jay Gould]
https://www.amazon.co.uk/Wonderful-Life-Burgess-Nature-History/dp/0099273454
The major evolutionary transitions [E Szathmáry, J M Smith]
https://wiki.santafe.edu/images/0/0e/Szathmary.MaynardSmith_1995_Nature.pdf
Don't Sleep, There Are Snakes: Life and Language in the Amazonian Jungle [Dan Everett]
https://www.amazon.com/Dont-Sleep-There-Are-Snakes/dp/0307386120
The Nature of Technology: What It Is and How It Evolves [W. Brian Arthur]
https://www.amazon.com/Nature-Technology-What-How-Evolves-ebook/dp/B002RI9W16/
The MANIAC [Benjamin Labatut]
https://www.amazon.com/MANIAC-Benjam%C3%ADn-Labatut/dp/1782279814
When We Cease to Understand the World [Benjamin Labatut]
https://www.amazon.com/When-We-Cease-Understand-World/dp/1681375664/
The Boys in the Boat [Dan Brown]
https://www.amazon.com/Boys-Boat-Americans-Berlin-Olympics/dp/0143125478
[Petter Johansson] (Split brain)
https://www.lucs.lu.se/fileadmin/user_upload/lucs/2011/01/Johansson-et-al.-2006-How-Something-Can-Be-Said-About-Telling-More-Than-We-Can-Know.pdf
If Anyone Builds It, Everyone Dies [Eliezer Yudkowsky, Nate Soares]
https://www.amazon.com/Anyone-Builds-Everyone-Dies-Superhuman/dp/0316595640
The science of cycology
https://link.springer.com/content/pdf/10.3758/bf03195929.pdf
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Today we’re joined by Blaise Aguera y Arcas, a distinguished scientist at Google. We had the pleasure of catching up with Blaise at NeurIPS last month, where he was invited to speak on “Social Intelligence.” In our conversation, we discuss his role at Google, and his team’s approach to machine learning, and of course his presentation, in which he touches discussing today’s ML landscape, the gap between AI and ML/DS, the difference between intelligent systems and true intelligence, and much more.
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