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 82 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 1 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: 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: No-code apps have a natural ceiling in the enterprise
“I feel like no code apps have kind of a natural ceiling”
DeepMind AI discovered unknown biological differences between male and female retinas
“DeepMind did a project with Moorfields, which is iHospital in the UK. And they were looking at retinas. And their system discovered a difference between male and female retinas, and apparently medical science didn't actually know there was a difference between…”
Evans: Meta Quest 3 lacks traction with roughly 10M active users
“Meta has the Quest III, which is a perfectly good, credible consumer device. It does not have traction. Does not have a, it's probably got, I don't know, maybe ten million active users. Huge abandonment rate in the past. It's not really good for anything other…”
Evans: Three to six organizations can create frontier AI models
“So that means that we've got, like, pick a number between three and six or maybe more organizations that can make a frontier model, and they keep leapfrogging each other every couple of weeks or every couple of months.”
Evans: 10% of population uses LLMs daily, 50% monthly
“If you look at the usage data, something like 10% of the population is using these things every day, but another 50% are using it every week or every month.”
Evans: Typical big US company uses 400 to 500 vertical SaaS apps
“And so this is why the typical big US company today has, depending on your numbers, like four to 500 vertical SaaS apps.”
Evans: Spreadsheets increased finance employment rather than causing job losses
“Spreadsheets did not result in a collapse in the number of people working in finance. They're quite the opposite. You have way more people in finance, because now it's possible to do all this more, all this new stuff that you couldn't have done before.”
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: Corporate audit costs stayed flat since early 2000s despite software progress
“Average audit data, all sorts of data for audit costs since the Barnes Oakley, which has basically been flat since about since the early 2000, despite everything that's happened in software”
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: Nvidia sells integrated computer systems, not just GPU chips
“People still think of NVIDIA as making GPUs in the sense that they make chips and sell chips. That's not what they do. They sell computers. They sell custom computers, kind of like Sun Microsystems did with a whole networking stack and a software stack on top …”
Evans: Big Tech data center spending will exceed $300B this year
“Google, Meta AWS, not Amazon overall, AWS only, and Microsoft spent about two hundred twenty billion dollars building data centers last year, and will spend about 300, maybe over 300 this year, depending on where their numbers come out.”
Evans: Apple has user interest graph data but refuses to use it
“Apple also in principle has an interest graph. It just refuses to use it.”
Evans: OpenAI lacks visibility into user search, purchases, and social media activity
“Back to open AI, they've got a partial view on you, but like, they don't know what you've bought. They don't know what you've searched for. They don't know where you go. They don't know what Instagram you look at and what TikTok you look at and what YouTube yo…”
Evans: Accenture recorded $1.4B in generative AI bookings in one quarter
“Accenture built 1.4 billion, booked 1.4 billion of new generative AI bookings last quarter.”
Evans: 20% to 30% of large companies have deployed AI projects
“Every big company is now, like, 20 to 30% of big companies have got stuff in deployment, but every big company's got pilots.”
Evans: Siri and Alexa were decision trees despite natural language capabilities
“There was like a trap with Siri and Alexa, which was that natural language processing worked, so you thought it was AI, and it wasn't. It was actually still just an IVR, it was still just a tree.”
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: 2023's immediate generative AI use cases were coding and brainstorming
“But we're still, and we had, I think in 20, 23, that wave of the initial, oh my God, you can use it for that right now things, which is basically coding and brainstorming.”
Evans: The typical enterprise today runs 400 to 500 SaaS apps
“Why is it that the typical enterprise today has four to 500 SaaS apps?”
Evans: Predictions that only tech giants had enough AI data were wrong
“And this is clearly what happened with the last wave of machine learning. There was a brief moment where people said it needs all this data. Only Google's got all the data. There's going to be, like, three people who've got enough data to do AI, and that turne…”
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.”
Benedict Evans: We cannot predict what happens when doubling LLM training data
“We can't do that with LLMs either. We don't know what will happen if you put double the data in or why, or we don't know why it works with this much data or not.”
Benedict Evans: Generative AI will inevitably experience a market bubble
“Clearly, if we're not in a bubble now, we're going to have a bubble. It's because that's just like the nature of the light, the cycle of life. There will be a bubble around each new technology.”
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 …”
Evans: Nvidia hit over $70B in trailing 12-month free cash flow
“I mean, I haven't looked at, I haven't updated my number here, but like, I think Q three last year, I think NVIDIA had something over seventy billion dollars of trading 12 months free cash flow.”
Evans: ChatGPT usage on Google Trends drops sharply in summer and Christmas
“I mean, it's funny if you look at Google Trends. There's a big sag in the summer and then a big sag in the Christmas week.”