The Crooked Timber of AI

Alex Imas on Twitter / X

New essay for the @GoogleDeepMind Institute with James Manyika on AI x Science, "Bending the Curve of Discovery".While accelerating existing scientific practices is certainly useful, the real promise of AI is its potential to act as an invention of a method of invention (IMI) a… pic.twitter.com/XMw80ToQcf— Alex Imas (@alexolegimas) October 8, 2026

The Age of Wonders and Terrors

Twenty years ago, when the idea of AI taking over the world in our lifetimes still struck most of us as the unconstrained fantasy of those who knew too much science fiction and too little science, …

Terence Tao – Kepler, Newton, and the true nature of mathematical discovery

“And what those stories teach us about how AI will revolutionize math”

AI FOR EPISTEMICS & COORDINATION

Civilization and technology have radically improved the human condition. Nonetheless, the world sometimes goes in directions which essentially nobody would prefer — e.g., nuclear arms races, unexpected financial crashes, predatory marketing, or ubiquitous political misinformation.

The fall of the theorem economy

How AI could destroy mathematics and barely touch it

The fall of the theorem economy

How AI could destroy mathematics and barely touch it

Artificial intelligence and illusions of understanding in scientific research

Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists’ visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community’s ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.

AI is not superhuman

What metaphor should drive the field of AI research?

What we can’t measure about AI – yet | Aeon Essays

The costs of transformative innovations are immediately clear: it’s the longterm gains that are hardest to understand

AI, Ethics, and Society — Home

The Real Existential Risk Is AI CEOs Avoiding Oversight: “Alternative Nobel” Winner Timnit Gebru

We speak to the prominent AI researcher and leading critic of the AI industry, Timnit Gebru, who was recently awarded a 2026 Right Livelihood Award for her work investigating the harms and hype behind the global buildup of artificial intelligence. “Instead of discussing faulty, unreliable systems,” Gebru says tech leaders like SpaceXAI’s Elon Musk, OpenAI’s Sam Altman and Anthropic’s Dario Amodei are “marketing their systems as all-knowing, superpowerful, superintelligent,” and lobbying for “self-policing and self-regulation” to avoid being held “liable through existing laws.” Gebru expresses skepticism over increasingly widespread claims that AI algorithms pose an existential risk to humanity, warning that such claims are overblown and that “what is actually happening is regulatory capture.”

The Scaling Era: An Oral History of AI, 2019–2025

An inside view of the AI revolution, from the people an…

Field Theory: AI as Social Science Question, Object & Tool

Uses of advanced artificial intelligence are changing how societies organize labor, govern, produce knowledge, and make meaning. In light of these developments, this essay argues that AI models, tools, and systems pose three interrelated imperatives for social science: they demand renewed attention to social theories of how technology, human experience, and social order are entangled; they require study as objects of inquiry in their own right; and they offer capabilities that may transform—or upend—the practice of social investigation itself. From Weber’s analysis of rationalization to Du Bois’s study of technology and inequality to contemporary scholarship on algorithmic governance, the essay examines what social science distinctively offers: the capacity to historicize the apparently unprecedented, to trace connections across scales, and to center those most affected by technological change. It identifies how algorithmic systems are remaking the distribution of opportunity and risk as a central task of social inquiry and asks what futures social science might help bring into being.

Separating AI’s Technological Problems From its Capitalism Problems

Nathan E. Sanders and Bruce Schneier say integrating a technology as disruptive as AI responsibly requires deep structural reforms.

No. 008: AI co-mathematician, compiler-verified protocols, open infrastructure bets - The Hallway Track

The Hallway Track No. 008 - How AI is affecting the practice of science