AI Is in its 1997 era: Presume Radical Uncertaintiy

@benedictevans on @lennysan's Podcast, May 31, 2026

Benedict Evans, former a16z analyst-in-residence and now an independent tech researcher with a six-year track record of widely-read annual presentations, sat down with Lenny Rachitsky to argue that AI is exactly as big a deal as the internet or mobile - no more, no less - and that this comparison is the most useful frame for thinking about jobs, value capture, and what to do next. His core posture is "presume radical uncertainty," and his bottom line for everyone worried about being replaced is to stop hiding from the technology and start using it.

1. AI is as big as the internet or mobile, and only as big as the internet or mobile. Evans calls this his most controversial opinion. People in tech who think it's bigger - the industrial revolution, the singularity - are doing themselves no favors. People outside tech who think it's smaller are doing the same. Both are wrong in opposite directions, and arguing over whether it's 20% bigger or 100% bigger than the internet is a waste of breath.

2. We are in 1997, not 2007. Most things don't work yet. Most things people will eventually build haven't been built. Anyone telling you they know how this plays out is selling you their cluster of Mac Minis. The honest version of an 80-slide deck on AI is 80 slides saying "we don't know."

3. The job apocalypse is mostly fanfic. Every technology shift in 200 years has automated jobs and unlocked new ones we couldn't name in advance. The new job sounds dumb in retrospect: railway engineer in 1820, web designer in 1985. Even the AI labs themselves - the companies most positioned to fire everyone tomorrow - are adding headcount, not cutting it. Evans calls the people predicting blanket layoffs "morons."

4. The McKinsey test exposes the flaw in "X% of jobs will be automated." If Claude can produce a 75-slide deck, does that replace the consultant? No - because you weren't paying for the deck. You were paying for someone to walk through your company, talk to your customers, and figure out what the politics actually are. Same with the lawyer, the accountant, the engineer. The visible task is rarely the actual job.

5. Foundation model companies probably don't have lasting pricing power. There are no network effects between models. There's no radical differentiation users can feel. There are at least three serious competitors. That math has one answer: commodity. Evans expects models to end up looking more like AWS than like Windows - infrastructure you don't pick, layered under apps you do.

6. The value moves up the stack, again. Telecom revenue is a trillion dollars a year and the stocks have gone nowhere in 25 years, while the things built on top of mobile networks made trillion-dollar companies. The same pattern is likely for foundation models: huge revenue, thin margins, all the interesting wealth created by the people building on top.

7. When the product becomes commodity, distribution becomes the moat. Google and Meta are already spraying AI across every surface they own. OpenAI's "shipmas" sprint was an attempt to build a flywheel before that happened. Apple - whose 2024 vision of personal on-device AI was the most compelling demo of the year - is the last shoe to drop.

8. The anti-AI backlash is a fuzzy mess of real and unreal grievances. Some are true (electricity bills going up in specific places). Some are nonsense (data centers use 0.017% of US water). Some are an artist class watching the floor drop out on illustration commissions. Most discourse conflates them, which means none of them get addressed properly.

9. You can't predict which jobs are exposed. In 1997, the obvious safe job was taxi driver - what does the internet have to do with hailing a cab? Uber answered that. Today the supposedly safe jobs include personal trainer. Prop your phone on a rack, point the camera at yourself, ask AI to coach your form. Maybe that doesn't work. The point is you couldn't have predicted it.

10. Software engineers thought their job was the hardest to automate. It turned out to be the most transformed. Evans's read: engineers didn't realize that most of what they did was boring manual labor that could be automated. They thought it was creative work. The lesson generalizes - your job is probably not the thing you think it is.

11. The only useful response is to dive in. Going on Bluesky to shout about how evil AI is gives you a great feeling of moral superiority and accomplishes nothing. Walking into a law firm interview and saying "I think AI is bullshit and I'll never use it" is not the move. Submerge yourself in it. Come out the other side knowing what it can and cannot do. That's the only career insurance available.

12. AI corner - what Evans actually uses it for. Proofreading. Generating images for apartment redecorating ("here's a picture of this room, repaint it, add this rug, change the rug color"). Voice dictation that auto-transcribes to text. The general pattern: AI is good at the stuff computers used to be bad at, and bad at the stuff computers were good at. The boring precise retrieval tasks he most wants automated are still the things it does worst.