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 10 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
Sreenivas: Deploying custom open-source models eliminates major AI cost pressures
“If we ran our entire business exclusively on frontier models, I would care a lot about costs, and I would think a lot about that, but once you're already, once you've already made the jump to saying, okay, now, now we know how to think about open source models…”
Sreenivas: Early-stage AI startups need forward-deployed engineers for unproven workflows
“Forward deployed engineers are necessary or newly necessary for early stage AI companies because the workflows are new. Right? If you're building a SaaS company five years ago, most SaaS products are pretty well explored, right? Like you roughly know what the …”
Sreenivas: AI productivity gains will drive companies to build more, not fire
“Everybody has access to these tools, and so if our competitors are going to use them and build more things, we need to build more things, right? If somebody else said, oh, here's our roadmap, and now we can get through it in, you know, a third of the time, and…”
Sreenivas: Optimizing AI agents breaks the cost, latency, and performance tradeoff
“Performance latency and accuracy is definitely the driving factor for most of this, right? Cost is a nice benefit in that, you know surprisingly, this is one of the few like tasks where you kind of get all the things for free, right? Like we don't actually hav…”
Sreenivas: Automating customer support workflows will not trigger mass layoffs
“Automating things doesn't necessarily result in just kind of people laying off their entire teams.”
Sreenivas: Frontier Models Are Best Suited for Open-Ended Auxiliary Tasks
“So we think for jobs like that, Frontier models that are very smart, that can try out a lot of things, make a lot of sense.”
Sreenivas: Enterprise AI requires continuous model retraining rather than one-time projects
“We don't just build our set of open source models and then, you know, it's done, we can move on to our next thing, and maybe we'll revisit this in two years. You often need to train new models all the time because As the frontier changes, as the capability of …”
Sreenivas: Tailoring evaluations to customer outcomes beats tracking loss curves
“When we have open source open source models that we want to fine tune, we find that if we can clearly tailor our evals to customer outcomes, it's way better than just looking at, like, loss curves over time, right?”
Sreenivas: AI agents across support and sales fundamentally execute business processes
“The thing that we built and we kind of built this intentionally from the start was not an agent that does customer support well, but rather an agent that follows business process well. And executing on operational workflows, doing sales lead qualifications, an…”
Sreenivas: Improved model instruction-following unlocks open-ended workflows like sales discovery
“When you had models, you know, let's say a few years ago, you'd have to give it very, very tight guidance, very specific instructions that you didn't want it to deviate from, and as the models got smarter, you could kind of give it broader and broader guidance…”