Arvind Narayanan
Professor of Computer Science at Princeton University and director of the Center for Information Technology Policy; co-writes AI as Normal Technology with Sayash Kapoor.
Arvind Narayanan did not write this page.
We collected these quotes from things they published elsewhere, and every quote links to where it was said. They have no account here and have not endorsed this site. Quotes are word for word; the short line under each one is our own restatement, not their wording. Their own site. Is this you? Claim it or ask us to remove it. Or tell us what is wrong here.
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Their wordsFor the time being, you can have only two out of these three properties in agents: general-purpose (a language model based agent that can be instructed to do different tasks rather than purpose-built for a task like traditional software); deployed in high-stakes scenarios, and automated.
↗What will be left for us to work on?normaltech.ai 1st of 3 in this piece
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Their wordsI think it is already the case in software engineering, and will increasingly be the case in many professions, that we can think of knowledge workers as similar to a crane operator or a forklift operator.
↗What will be left for us to work on?normaltech.ai 2nd of 3 in this piece
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Their wordsAI is still in a state where it is much better at verifiable tasks than non-verifiable tasks.
↗What will be left for us to work on?normaltech.ai 3rd of 3 in this piece
- 4 days earlier
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Their wordsFirst, infrastructure providers rarely capture the value they create.
↗Up the Stack: How AI’s Escape From the Commodity Trap Risks Enterprise Lock-innormaltech.ai 1st of 3 in this piece
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Their wordsWe believe that AI, at least in its current hyperscaler form, risks falling into the same commodity trap that has bedeviled so many previous infrastructure builders.
↗Up the Stack: How AI’s Escape From the Commodity Trap Risks Enterprise Lock-innormaltech.ai 2nd of 3 in this piece
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Their wordsOver the long run, we expect that AI will be an essential part of most knowledge work, and that the technology will be as transformative as the industrial revolution.
↗Up the Stack: How AI’s Escape From the Commodity Trap Risks Enterprise Lock-innormaltech.ai 3rd of 3 in this piece
- 4 weeks earlier
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Their wordsAI has compressed the middle of the sandwich, but has left the two ends largely unchanged.
↗Why AI hasn’t replaced software engineers, and won’tnormaltech.ai 1st of 3 in this piece
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Our reading
Production software cannot be built by unskilled vibe coders replacing trained software engineers.
Their wordscompanies can't ship production software by hiring unqualified vibe coders instead of software engineers.
↗Why AI hasn’t replaced software engineers, and won’tnormaltech.ai 2nd of 3 in this piece
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Their wordsThe barrier isn't actually learning the syntax. It's having enough skilled judgment to make good decisions while maintaining accountability.
↗Why AI hasn’t replaced software engineers, and won’tnormaltech.ai 3rd of 3 in this piece
- 4 months earlier
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Their wordsIt is really a statement about what the AI community finds it worthwhile to work on. It doesn't have any predictive power about which problems are going to be easy or hard for AI.
↗Fact checking Moravec's paradoxnormaltech.ai 1st of 3 in this piece
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Their wordsSince diffusion of new capabilities takes a long time, that gives us plenty of time to react - time that we often squander, and then panic!
↗Fact checking Moravec's paradoxnormaltech.ai 2nd of 3 in this piece
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Our reading
Their wordsMuch of my writing is based on the idea that superhuman AI reasoning is a myth.
↗Fact checking Moravec's paradoxnormaltech.ai 3rd of 3 in this piece
- 5 months earlier
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Their wordsThere is a long causal chain between AI capability increases and societal impact. Benefits and risks are realized when AI is deployed, not when it is developed.
↗A guide to understanding AI as normal technologynormaltech.ai 1st of 3 in this piece
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AI alignmentdesignrecursive self-improvement
Their wordswe don't think RSI will lead to superintelligence because the external bottlenecks to building and deploying powerful AI systems cannot be overcome by improving their technical design.
↗A guide to understanding AI as normal technologynormaltech.ai 2nd of 3 in this piece
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Their wordsWe predict that AI will not be able to meaningfully outperform trained humans (particularly teams of humans and especially if augmented with simple automated tools) at forecasting geopolitical events (say elections).
↗A guide to understanding AI as normal technologynormaltech.ai 3rd of 3 in this piece
- 5 months earlier
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Their wordsWe do not think that viewing AI as a humanlike intelligence is currently accurate or useful for understanding its societal impacts, nor is it likely to be in our vision of the future.
↗AI as Normal Technologynormaltech.ai 1st of 3 in this piece
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Their wordsThus, the speed of diffusion is inherently limited by the speed at which not only individuals, but also organizations and institutions, can adapt to technology.
↗AI as Normal Technologynormaltech.ai 2nd of 3 in this piece
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Their wordsIn the context of AI-based product development, the bitter lesson has never been even close to true.
↗AI as Normal Technologynormaltech.ai 3rd of 3 in this piece
- 4 months earlier
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Their wordsRegardless of whether model scaling will continue, industry leaders' flip flopping on this issue shows the folly of trusting their forecasts. They are not significantly better informed than the rest of us, and their narratives are heavily influenced by their vested interests.
↗Is AI progress slowing down?normaltech.ai 1st of 3 in this piece
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Their wordsThe connection between capability improvements and AI's social or economic impacts is extremely weak. The bottlenecks for impact are the pace of product development and the rate of adoption, not AI capabilities.
↗Is AI progress slowing down?normaltech.ai 2nd of 3 in this piece
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Their wordsIt is impossible to predict whether progress in AI capabilities will slow down. In fact, forget prediction - reasonable people can have very different opinions on whether AI progress has already slowed down, because they can interpret the evidence very differently.
↗Is AI progress slowing down?normaltech.ai 3rd of 3 in this piece
- 5 weeks earlier
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Their wordsIn reality, the target variable - the output - is often more important for fairness than the features.
↗Does the UK’s liver transplant matching algorithm systematically exclude younger patients?normaltech.ai 1st of 2 in this piece
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Their wordsFor this reason, utilitarianism has been ascendant in many, many domains over the last few decades, including medical ethics and public health.
↗Does the UK’s liver transplant matching algorithm systematically exclude younger patients?normaltech.ai 2nd of 2 in this piece