A two-dimensional framework for evaluating AI applications maps technical performance (does it work as claimed, is it overhyped, or is it snake oil) against ethical implications (harmful because it doesn't work vs. harmful because it works well); harmful applications include video interviews for hiring, criminal risk prediction, AI cheating detection, and mass surveillance with facial recognition, while benign applications that work well like autocomplete fade into the background.

Self-driving cars face solvable engineering problems; dramatic progress has already been made; they will eventually be widely used, become part of physical infrastructure, and the word 'car' will come to mean self-driving car, requiring a new name for current manual cars.

The approach of attempting to build general-purpose AI without domain-specific knowledge is failing; companies tried this with simple wrappers around large language models for a year or two and the approach 'miserably failed' because these products could not achieve the 99.99% reliability required for real-world use, whereas they currently only reach ~80% reliability.

The path forward for AI involves slow, sector-by-sector deployment with iterative feedback loops from real users and domain experts; this is modeled on self-driving cars, which required two decades of slow scaling (driving 1,000 miles → improving → 10,000 miles → 100,000 miles) rather than sudden general capability jumps.

AI progress has been consistently over-predicted for 70+ years; when universal computers were invented, developers thought building hardware was the hard part and that software emulation of human intelligence would follow within years; the 1956 Dartmouth Conference proposed a two-month, ten-man effort to make substantial progress toward AGI, which was miscalibrated.

Human intelligence is not primarily a consequence of our biology but of our technology—centuries of external tools for learning about the world; prized knowledge comes from large-scale experiments on people, and this will hold AGI back because we won't allow AI systems to conduct unrestricted experiments on humans, creating bottlenecks that apply to both human and artificial learning.

The solution to hallucinations in generative AI is not to improve the technology but to identify specific areas of workflow where it is easier to verify the AI's output than to do the work yourself; if this answer to 'why is verification easier' cannot be found, AI should not be used.

Content moderation using AI is overhyped in its capabilities despite being discussed as an important application.

Public opinion surveys show that more people are worried about AI than excited about it, and this variation across countries depends on the types of worker protections people have come to expect.

The new paper 'AI as Normal Technology' proposes a fourth narrative about AI as an alternative to three dominant framings: (1) AI as super intelligence leading to utopia, (2) AI as super intelligence leading to doom, and (3) AI as overhyped fad that will soon pass.

Sasha Kapoor joined Narayan as a graduate student to conduct ~5 years of research that became the basis for 'AI Snake Oil', including publications in papers leading up to the book.

Generative AI is fun to use and Narayan uses it in personal life for activities like creating educational tools for his young children, a capability that enriches relationships with family.

Narayan would prefer hard-coded criminal risk algorithms where the logic is apparent to all (including defendants) over opaque neural networks, even if both achieve identical accuracy, because transparency enables accountability and understanding of how decisions are made.

It is hard to predict the future—not because of limitations in current technology but because we fundamentally lack knowledge of who will commit crimes—and therefore we should not easily accept determining someone's fate based on predictions of future crime rather than determinations of guilt.

Facial recognition technology has improved dramatically and now works very well, which is precisely part of the reason it is harmful if used without appropriate guardrails and civil liberties protections, especially for mass surveillance.

Scaling up foundation models on larger chunks of the internet and expecting emergent capabilities to emerge without domain-specific engineering has run out as a productive research direction because the new capabilities to learn are tacit knowledge, which requires active deployment and feedback loops, not passive training on internet text.

AI developers would benefit greatly from understanding domain experts' superior knowledge about what AI can and cannot do in their field (law, medicine, etc.) compared to AI developers' own understanding, and from not making overhyped claims; at the same time, the public deserves clear communication from companies about why they are confident enough to make trillion-dollar bets and what emerging capabilities justify those bets.

Hiring automation software that analyzes 30-second video recordings to predict personality and job suitability through body language and speech patterns analysis cannot work, and after six years no company has released evidence that these techniques can predict job performance.

AI is not a single technology but an umbrella term for loosely related technologies; ChatGPT and credit-risk classification algorithms are both called AI because they both learn from data, but they differ fundamentally in how they work, what problems they solve, how they can fail, and what the consequences of failure are.

The origin of the book 'AI Snake Oil' came from a talk Narayan gave at MIT in 2019 after observing hiring automation software; he called the talk 'How to Recognize AI Snake Oil' and the slides went viral because people suspected many AI claims were false but didn't have confidence to call them out, and they felt reassured when a computer science professor studying AI confirmed skepticism.