Ai

Turing's 1950 paper posed the question "Can machines think?" and then methodically dismantled every common objection, from theological arguments to Lady Lovelace's claim that machines can only do what they are told. The…

Licklider's argument is not that computers will replace human thinking but that the interesting future is in the partnership — humans setting goals, computers handling the mechanical. He funded ARPANET to make this visio…

The earliest and most philosophically rigorous critique of symbolic AI — written when the AI community was making promises remarkably similar to today's. Dreyfus draws on phenomenology (Heidegger, Merleau-Ponty) to argue…

The first narrative history of artificial intelligence, written by someone who personally knew the founders — McCarthy, Minsky, Newell, Simon, Samuel. McCorduck traces the dream of intelligent machines from antiquity thr…

The Chinese Room paper — ten pages that generated four decades of debate about whether machines can think. Searle's thought experiment argues that syntax is not sufficient for semantics: a system can manipulate symbols a…

Philosophically the most serious book of the symbolic AI era. Haugeland coined the term "GOFAI" — Good Old-Fashioned Artificial Intelligence — and gave the clearest account of what the symbolic programme actually claimed…

The mind as a society of simple agents — none of them intelligent on their own, but collectively producing what we call thought. Minsky's book is hard to classify: part science, part philosophy, part manifesto, structure…

Winograd built SHRDLU, one of the most celebrated early natural-language AI systems, and then wrote this book to explain why the entire approach was wrong. Drawing on Heidegger's phenomenology, Maturana's biology of cogn…

The 1992 update to Dreyfus's 1972 original, written twenty years later with the critique deepened rather than softened. Dreyfus adds new introductions addressing connectionism, neural networks, and the failures of expert…

The foundational text of embodied and extended cognition. Clark argues that the mind does not stop at the skull — it extends into the body, the tools, the environment. This reframes what it means to design a product: you…

Dyson traces the idea that machines might evolve intelligence from its seventeenth-century origins — Hobbes's Leviathan as artificial organism, Leibniz's calculus of reason — through Samuel Butler's 1863 essay that gave…

Sixteen hundred pages covering the entire history of cognitive science from its cybernetic origins through connectionism, evolutionary psychology, situated robotics, and dynamical systems theory. Boden — herself a partic…

A comprehensive technical history of artificial intelligence written from the inside by a Stanford pioneer who was there from the 1960s onward. Nilsson covers the full arc — from early cybernetics and logic through searc…

The first machine age augmented physical force; the second augments cognitive capacity. Brynjolfsson and McAfee argue that we are at an inflection point where digital technologies begin doing for mental work what the ste…

Markoff, who covered technology for the New York Times for three decades, structures the history of computing around a fundamental tension: artificial intelligence (replacing human capabilities) versus intelligence augme…

Three simultaneous rebalancings: from the human mind to the machine, from the product to the platform, and from the core (the organisation) to the crowd (external networks). Brynjolfsson and McAfee argue that firms have…

A rigorous theoretical framework on the competition between automation (which displaces labour) and the creation of new tasks (which generates employment). Acemoglu and Restrepo offer the analytical counterweight to Bryn…

A philosopher-computer scientist with fifty years in the field argues that the difference between calculation and judgment is categorical, not a matter of degree. Smith distinguishes between reckoning — the formal manipu…

Brynjolfsson draws a clean distinction between AI that substitutes (automation) and AI that augments (augmentation). When AI imitates the human and replaces them, workers lose bargaining power and value concentrates. Whe…

The accessible version of Surfing Uncertainty, written for a general audience without sacrificing intellectual depth. Clark shows how prediction shapes everything from basic perception to emotion, pain, culture, and the…

Empirical study with more than 5,000 customer-support agents. AI increases productivity 15% on average, but the effect is uneven: less experienced workers improve 30% in speed and also in quality, while the most experien…

Huyen writes the book that treats machine learning as an engineering discipline rather than a research project or business buzzword. The focus is on building reliable ML systems in production — data pipelines, model depl…

Acemoglu and Restrepo solve a puzzle that haunts every conversation about AI and work: if automation increases productivity, why don't wages rise accordingly? Their answer is rent dissipation — automation systematically…

The task-based framework provides product directors with a rigorous way to think about what automation actually does: it doesn't just replace workers, it reallocates tasks between humans and machines. The key insight is…

Acemoglu and Restrepo's task-based framework offers the most rigorous economic lens for understanding how AI reshapes work — not just which jobs disappear, but how comparative advantage shifts between humans and machines…

Based on Patel's long-form interviews with leading AI researchers — Ilya Sutskever, Dario Amodei, John Carmack, and others — this book documents the period when AI capabilities began scaling predictably with compute, dat…

AI not as a tool but as a cognitive companion that transforms how you work, decide and organise. Mollick, from Wharton, argues that AI changes the nature of work not because it performs tasks for you, but because it alte…

Anthropic's framing of context engineering as the discipline of designing, preparing and maintaining the information a model sees, beyond the narrow craft of prompt writing. The piece reframes the relationship between or…

The title is a direct nod to Hayek (1945). Brynjolfsson and Hitzig argue that AI can shift the optimal locus of control in organisations through two channels: by codifying local knowledge that used to be tacit, and by ex…

Firm-level empirical evidence for the productivity J-curve associated with AI adoption. Companies that adopt AI initially see no productivity gains — they can even get worse — because they need complementary investments…

This Nature paper reveals a fundamental problem with AI-generated training data: models can transmit behavioural biases through pathways that appear semantically unrelated, creating a form of technological inheritance th…

Erlei and colleagues apply Akerlof's classic 'market for lemons' framework to AI system adoption, addressing a critical gap in understanding why organizations struggle to evaluate AI capabilities. The information asymmet…

Watermarking is positioned as neutral infrastructure for AI content authentication, but Nemecek et al. reveal how its effectiveness varies systematically across cultural and demographic lines — what they term the 'plural…

Peterson frames a problem that most AI governance literature ignores: compliance layers built to make algorithmic decisions reviewable can also be gamed by successive administrations who learn to satisfy the form of over…

Falk and Tsoukalas construct a task-based competitive model to show that the real problem with AI-driven displacement is not ignorance but a classic collective action failure: each firm rationally automates while the agg…

Walker responds directly to Brynjolfsson and Hitzig: AI does not automatically codify knowledge — someone has to prepare, structure and maintain the context that makes knowledge usable by the model. What is missing from…

The paper tackles a foundational problem for product directors working with AI: how do you know when users are trusting the system too much? Traditional metrics miss the behavioral patterns that emerge during interaction…

Ustynov takes a flat-footed but important observation and works it through with unusual patience: for sixty years, the conventions of software engineering — naming, design patterns, project layout, SOLID, logging formats…

Barkett applies Jasanoff's framework of sociotechnical imaginaries to decode how OpenAI and Anthropic construct authority over technological futures through shared rhetorical strategies that transcend their apparent diff…

De Neve's argument for augmentation over automation revisits Coase's firm theory through an AI lens: the choice between replacing human judgment versus amplifying it becomes a new frontier in organizational design. HBR s…

Vertesi, boyd, Taylor, and Shestakofsky argue that AI accountability debates are not failing by accident — they are being shaped by the same networks of power they nominally critique. The concept of 'decoys' is analytica…

The paper's central finding is unsettling in a precise way: giving users the ability to edit AI reasoning increases their sense of control but also increases over-reliance when the AI is wrong — an illusion of control th…

This paper resolves a puzzle that sits at the heart of the automation debate: if machines are replacing workers, why do most firms show rising labor shares? The answer is heterogeneity — large firms automate and drive do…

Most AI critique leans on Dreyfus's embodied cognition argument — the claim that intelligence requires a body situated in the world. Chirimuuta's contribution is to trace the problem further back, to the structuralist mo…

Aral (MIT, one of the most cited scholars in digital economics) and Caosun construct a formal dynamic model that makes precise what practitioners sense but cannot yet argue: AI adoption can be individually rational at ev…

Mironov, a product management consultant, likely addresses the operational economics of AI deployment — the gap between prototype costs and production-scale costs that many organisations discover too late. The economic c…

This paper addresses a fundamental challenge for product directors building AI-powered products: how do users develop trust and delegation strategies when the same AI system performs differently across different tasks? T…

Palminteri and Wyart tackle the fundamental question of how technology changes who makes decisions and how. Their framework distinguishing behavioural analysis, nudging, and boosting offers product leaders a vocabulary f…