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 17 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: OpenAI has 900M weekly active users, but only 5% pay
“You've got nine hundred million weekly active users, but most of them are not using it every day and can't think of anything to do with it. And only five percent of them are paying for it.”
Evans: OpenAI's Deep Research inverted numbers in its own marketing example
“Then the interns typed the number in wrong. Like, it was literally the wrong percentage. It was like, 65, 35, instead of 35, 65.”
Evans: Submitting 1,000 prompts placed ChatGPT users in top 20%
“Turns out that if you did a thousand posts, if you did a thousand prompts last year, you're in the top 20%.”
Evans: Google and Meta delayed LLMs in 2022 due to high error rates
“This is why Google and Meta didn't launch their own LLMs in twenty-twenty-two when they had them as well, because they looked at them and said, well, they're wrong too much.”
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: 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: 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: 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: 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: 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…”