The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 28 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 0 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable
Evans: AI scientists possess no special expertise on global geopolitical threats
“One of the sort of fallacies here is a sort of an appeal to authority, which is, you know, well, that person is an AI scientist, so they must know if this is a threat to world peace. No, they don't. They're an AI scientist. They don't know anything more about,…”
Evans: Meta and Google do not need standalone LLM monetization
“Because if you are Meta or Google, You've got this whole other highly profitable business, which now needs to have LLMs inside it, powering all sorts of capabilities and features, and you probably want them to be your LLMs rather than somebody else's. But you …”
Evans: Incremental AI accuracy improvements don't reduce necessary human review
“But if you've got a bunch of use cases where you need the right answer, as opposed to sort of the right answer, then saying that the model is better doesn't mean anything. I mean, literally, it is literally meaningless. What you're telling me is, I asked the m…”
Evans: Differentiating a chatbot is like differentiating a web browser
“The chatbot itself is kind of like trying to differentiate a web browser in that you've got an input box and an output box, and how can you make them different if the whole point is that you can type in anything and get anything out.”
Evans: AI product teams are strategy takers, not strategy setters
“You start from the technology. You don't control the product strategy, which is of course how science works, but you don't know what's going to happen. You don't know what's going to get built. You know, obviously you've got like Sam and Dario and so on are li…”
Evans: Most SaaS companies are fundamentally just database wrappers
“Most SaaS companies are database wrappers, where somebody realized that here is this problem, and here is the people who have it, and here is a way of turning it 90 degrees, and this is your insertion point, this is how you build it and take it to market.”
Evans: Consumer chat interfaces are thin wrappers, not vertical SaaS
“The only thin, thin GPT wrappers are what you get when you go to chatgpt.com and claude.com and grok and all these others. That's a thin wrapper on a model. Whereas you know, name your vertical enterprise SaaS company. That's not a thin wrapper.”
Evans: GPTs could lower software creation costs by an order of magnitude
“The analogy that's been floating around, I think, is, is to compare this with AWS, in the sense that AWS was a sort of an order of magnitude change in how easy you could get a startup out of the door. You didn't need to write all this stuff yourself and buy in…”
Evans: Meta and AWS both benefit from AI becoming cheap commodity infrastructure
“In a sense, like, AWS and Meta are on the same page, and then Meta wants this to be cheap, generic commodity infrastructure that's sold at marginal cost, and they will differentiate on cool Facebooky stuff on top. Amazon want this to be cheap, generic commodit…”
Evans: Consumer AI apps currently lack viral loops and network effects
“There's no viral loop. There's no network effect. There's no reason why you should use the one your friends use. There's no reason this one gets better because everyone else uses it, at least not yet.”
Evans: Every enterprise software company is unbundling Oracle, Gmail, or Excel
“Basically every enterprise software company for the sake of argument is unbundling Oracle, Gmail, or Excel.”
Evans: Major software failures stem from institutional rot, not missing regulation
“You don't look at this and say, well obviously the solution is that we needed to have a database regulator that makes sure that databases don't have bugs. Rather you look at it and say, well, this is an institutional failure. A in Fujitsu in the post office an…”
Evans: AI app design is currently at the 'PDF catalog' stage
“And we're still at the stage of, you know, taking a PDF of your catalog and putting it in your company website as we try and work out what we should do with AI.”
Evans: AI is a continuation of 50 years of software abstraction
“Tell that next time you hear a software developer saying, like, AI is a completely different thing, and nobody has ever abstracted software like this before, like, yeah, we've been doing this for 30 years, 50 years.”
Evans: Almost everyone in a tech bubble acts rationally
“Well, you know, everyone in, in, generally in a bubble, everybody's a rational actor. Almost everyone's a rational actor given their situation.”
Evans: The hardest part of building software isn't writing the code
“The hard part of writing software is not writing code. It's all the other stuff around, like, what should the code be doing? And how would we tell people that they should be using it? And what should we charge? And how do we go to market? And which bit of the …”
Evans: You cannot trust AI research reports on unfamiliar topics
“But if you go to it and say, give me a 40 page report on something I don't know much about, you can't trust any line of that report, because most of it will be right, probably, or it will be roughly right, but if there's anything, but you won't be able to depe…”
Evans: Memory features in AI models create user stickiness, not network effects
“I think that stickiness, I don't think it's a network effect.”
Evans: Humanoid robots represent mechanical mobility, not AGI
“And there's a trap with these humanoid robots, which is some people look at them and think it's AGI, and it's not, it's just a robot that's got legs instead of wheels, but it's still a robot.”
Evans: Silicon Valley fails to respect the difficulty of non-tech industries
“Silicon Valley really has this problem in not understanding that other industries are hard. Like, the airline business is hard. They're not just idiots. It's difficult.”
Evans: AI differs from past tech shifts because its physical limits are unknown
“The one way this is unquestionably different is that with all the other platform shifts, we knew what the physical limits of the science were.”
Evans: Building AI models is getting costlier while usage costs fall
“Building a model gets more expensive, but the cost of using the model gets cheaper.”
Evans: Lower AI error rates cannot replace required absolute accuracy
“And if you cannot depend on this to be right all the time, as opposed to slightly more of the time, then you either can't use it, or you have to use it in very different ways to the ways you could use it if it was always right.”
Evans: Users are forcing non-deterministic AI into deterministic roles
“We're still at that beginning of, like, forcing it to do deterministic, forcing it to be a deterministic system, which of course it isn't.”
Evans: Legal AI adoption is slowed by plausibility versus accuracy
“There's a huge difference between a legal brief that looks right and a legal brief that is right.”
Evans: Framing machine learning as pattern recognition unlocked enterprise adoption
“And it took a while to work out that the right level of abstraction was to think that this is pattern recognition.”
Evans: Incumbents Always Try to Turn Platform Shifts Into Features
“The classic pattern of the platform shift is the incumbents always try and make it a feature.”
Evans: Machine learning functions as an infinitely fast intern
“One of the ways I used to talk about machine learning is that it gives you infinite interns. Like you would like someone to listen to every call coming into the call center and tell me if the customer is angry. Like you've got a million calls a day. You don't …”