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 23 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 2 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
Krieger: DeepSeek release had almost no impact on Anthropic's go-to-market
“I got this question a bunch with deep seek when deep seek came out, like, all right, what does deep seek mean for you? And I think there's things that we learned from on the tech side, just looking at what they were doing, but from a go to market and place in …”
Krieger agrees with Alexandr Wang: Most future friends will be AI
“I've had so many conversations with Alex Wang about this because he has this whole thing about how in the future most friends will be AI friends. And you know, I don't think he's wrong.”
Krieger: AI foundation models will differentiate over time, not converge
“I think models over time get more different rather than more similar.”
Krieger: Top AI models will require both human and synthetic data
“So I think it absolutely has to be a mix. And I think the best models will come from that combination of great, like for code it's, you know, being, having good foundational understanding of code and good examples, but then also being able to explore a really …”
Krieger: Anthropic will build general-purpose software, not bespoke vertical AI
“We are going to be building things that are general purpose as a rule with maybe some specialization at the, like, user level, but not at the, I don't anticipate us building a lot of verticalized experiences that are, like, fairly bespoke to a given workflow o…”
Krieger: Anthropic is focusing on agentic tools, not building an IDE
“So when I think about the coding space and where we can play and add value, it really is on the agentic side. It's not on the ID side.”
Krieger: AI coding models cannot run autonomously for hours without humans
“Recognize that they're not yet at the place where for many use cases, you can let them kind of run free for hours. You need that more human in the loop piece.”
Krieger: Software engineers will become AI delegators within three years
“How do we evolve from being mostly code writers to mostly Delegators to the models and code reviewers. That's what I think the work looks like three years from now. It's coming up with the right ideas, doing the right user interaction design, figuring out a de…”
Krieger: AI models are three-plus years from solving product strategy
“That, that's still a very human problem that I think we're at least three years away from the models being, being solving at that level of abstraction.”
Krieger: AI labs will increasingly obscure model chain-of-thought outputs
“More labs either choose to not show or otherwise obscure the chain of thought down the line.”
Krieger: Anthropic trained Claude 3 with a much smaller team than competitors
“The Claude three, we were training a model at the frontier that was state of the art with a team that was much, much, much smaller. Than any other lab.”
Krieger: Claude and ChatGPT were initially built only as model showcases
“Claude AI and probably ChatGPT.com were, like, very much, like, initially just built to be sort of showcases of the models and not really built in a lot of ways to be the right, like, The sort of foundational for like a much more complex sort of multi product …”
Krieger: AI is not yet indispensable for most workers
“I still think we are in, like, day one around, is AI an indispensable part of most people's work? And I think the answer is no.”
Krieger: AI Labs Use Internal Distillation to Reduce Latency and Costs
“Even, like, let's take within the labs, like, I assume every single one of the labs is using, like, even within themselves, like, it is very valuable to be able to take, you know, the knowledge of your highest-end model and then be able to make it higher, you …”
Krieger: WeChat solved technical challenges on par with Facebook's scale
“People love talking about the, like the super app and we chat, and there was some technical challenges solved by those at scale that were of the same scale of challenges that Facebook was challenged was doing.”
Krieger: Internal dogfooding of Claude Code directly improved Claude 3.7 Sonnet
“As a really, you know, specific example with cloud code within, you know, a week of it being deployed internally, we had found a way in which one of the sort of tools that it has access to the model wasn't using as well as it could have, and that made its way …”
Novo Nordisk cut clinical trial reports from 15 weeks to 20 minutes
“On Novo Nordisk used to take, I think it was something like 15 weeks to do their clinical trial reports, and now they use cloud and get it done in 20 minutes, and like, that's a step change.”
Krieger: Day-one Instagram growth relied on filters, not network effects
“In day one of Instagram, you know, there wasn't really a network there. You were there because the filters made your, you know, photos from your iPhone three G. Or three GS look a little better or a lot better”
Krieger: Personalizing Instagram's Explore feed resulted in a 2x usage boost
“When we launched Explore and made it personalized rather than just like, most popular photos, like, that was a two X delta in usage.”
Anthropic locked Claude 3.7 Sonnet blog post hours before launch
“We were cloud three, seven sonnet. We launched on Monday and we locked the blog post for that. Sunday night at nine PM, which is not best practice from a marketing perspective. You know, we were briefing press that day on Sunday.”
Krieger: Instagram allocated roughly 95% to product and 5% to API
“On Instagram. It was easy. It was like, 95% product, five percent API, and it was, you know, that's all we really needed to do.”