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 18 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
Garman calls Anthropic Claude models the best performing in the world
“I think people love using Anthropix Claude models. Those are fantastic, and right now, those are the best performing models in the world which is fantastic.”
Nvidia runs its own AI training clusters on AWS infrastructure
“NVIDIA actually runs their AI training clusters in AWS, because we actually have the most stable infrastructure of anyone else, and so they actually get the best performance from us”
Garman says Google and Microsoft forced new app paradigms in early cloud
“I think if you look at some of those others like Google and later Microsoft they kind of went at this space for first, we were the first ones out there that had anything like this, but even soon after that, I think they went after the space, like that was goin…”
Garman estimates 85% of enterprise workloads still run on-premise
“Now that we're at a hundred billion run rate you look at, you still go out there, and I think, 85% of workloads are still running on-prem today, by most estimation, somewhere in that range, you know, pick your number, whether it's 80 to 90, whatever it is, lik…”
Secret intelligence contract win against IBM marked AWS inflection point
“One of the big Inflection points we saw is we went after the intelligence agencies for the U S government, and we won that contract and it was secret. And it, you know, we pushed really hard to go in that. It was against all the incumbents, HPs and IBMs and Or…”
Garman predicts foundation models will command less attention over time
“Today the models is the front and center thing that everybody pays attention to, but I think increasingly it'll become a smaller percentage of the thing that people pay attention to”
Multi-year fab lead times will keep AI hardware supply chains constrained
“Look, I think we're probably going to be in a constrained world for the next little bit of time. Just, you know, that some of these things are, they take time. Like, look how long it takes to build a semiconductor fab. Like, it is, it's not a short lead time, …”
Inference workloads must dominate training for AI capital investments to pay off
“Inference is, is one of those workloads that today it's, you know, fifty-fifty maybe of training in Inference, but in order for the math to work out, Inference workloads have to dominate, otherwise all this investment in, in these big models isn't really gonna…”
Garman predicts liquid cooling will make on-prem AI clusters too difficult
“Increasingly, I think that's going to get harder and harder as you move to liquid cooling and larger clusters”
Amazon Titan is by far the most popular embeddings model on Bedrock
“In fact, the Titan embeddings model, Is by far the most popular embeddings model that we have inside of Bedrock today for people that are building search indices and thinking about things like that.”
Next generative AI leap requires integrating action-taking agentic workflows
“The next generation of and the next step forward and what we can get out of AI systems is going to depend a lot on, How well we can integrate agentic workflows and actually get these AI systems to do things, not just kind of summarize and tell us information.”
Most enterprises will eventually stop building their own AI models
“Most companies are not going to build their own models over time. They might tweak them a little bit, but I think a lot of companies are going to build, are going to use the applications that use the software and the models underneath.”
AI inference will become a core cloud computing primitive like storage
“As you move forward, generative AI honestly becomes one of the compute building blocks that you think about. You're going to need storage, you need compute, you need databases, you need inference, if you will, for your application, largely. And I think that's …”
Jeff Bezos mandated internal move to modular services in 2003
“If you scroll back all the way to 2003 or so, Jeff Bezos basically mandated across the company that in order to move from a big monolithic stack that wasn't going to scale anymore for Amazon, we had to move everything to services.”
AWS is building an easy button to migrate legacy mainframes
“If I had an easy button and by the way, we're trying to build an easy button, but if I had an easy button that would just migrate mainframes to a modern cloud architecture today, almost everyone will push that button, but it doesn't quite exist today.”
AWS began developing proprietary AI processors five to six years ago
“We've been investing in AI broadly for the last 10 years, and that's why we started five or six years ago investing at the infrastructure layer and building our own processors”
Adobe built its Firefly AI models entirely on top of AWS
“Where we see folks like Adobe building Firefly is a all built on top of AWS purpose built for the own thing, their own thing that they're building.”
Garman's first startup burned $27 million in 18 months before folding
“My very first startup. We raised, I think at the time was a lot of money. It was twenty seven million dollars. We ran out of money in like 18 months, and then, you know, the 2000 came around, and there wasn't any more funding, and we went out of business.”