Latent Space / episode intelligence
Speakers in the public record
Claim mix
Evidence policy
Every row below preserves an exact excerpt. Identified speakers are linked; unresolved voices are labeled and excluded from people counts.
Claim ledger
20 published records
01 / evaluation
“Now what I think we underestimated perhaps is the real-world complexity of deploying these so that they actually deliver the value in the real world, right?”
02 / preference
“Like, in fact, like what I use to serve an inbox or a mailbox cannot be used to serve an agent.”
03 / evaluation
“But we’re not doing typing, we’re doing knowledge work. So that, to me, I think is it, right, which is whether it’s Microsoft or whether it’s any organization, is to give ourselves permission to do new types of metacognition, meta work, using these new tools to change the outputs that matter, uh, and then really make the impossible possible.”
04 / belief
“Um, yeah, just talk a bit more about what you’re seeing on the ground, like when you visit your- Yeah, I think there are two aspects of it.”
05 / belief
“I think the, um, the p- the most, um- Critical thing is the first question we even started with, which is we need to tell the story and make it real that everybody has a real shot to participate as a first-class participant in this new economy.”
06 / belief
“Whereas now I think you can with the agents that have learned through the h- through, through time, through all the traces.”
07 / belief
“See, the one thing, Eli, that I’ve now learned is I think the world is gonna be very skeptical of tech and tech companies that say, “Trust us, we’ve got it.”
08 / commitment
“Like, if we- if the broad economy is doing well and the communities are doing well, the dots get connected. It’s sort of the market forces are such that we will connect the dots.”
09 / belief
“Enterprise bundles Yeah. The way I- I think about this is always we’ve had... Like, let’s even take the per-user pricing.”
10 / belief
“I, I think, uh, I first of all have to congratulate you on basically building a frontier neo lab inside of Microsoft in two years.”
11 / evaluation
“Starting with pre-training, uh, with very good data quality, uh, doing all the ablations, making sure because in, in some sense it’s becoming even harder to build a clean lineage model just because there’s so much stuff out there, uh, that you truly need to ablate out to be able to have a fantastic [00:03:00] pre-trained model.”
12 / prediction
“Um, I’m sure you have heard much and participate in much of the debate about the end of software because all these workflows are, are cheap to generate now.”
13 / evaluation
“Because right now I think there’s, you know, a dozen different types of engineers that you can be, from QA, front end, et cetera.”
14 / evaluation
“In a fully agentic world. But that said, one of the things that we are starting to see, we started seeing with co-work, but even some of the work we, we showed with auto com- uh, um, autopilot Right on what you see with claws is a good one because if you sort of think about a lot of human capital is doing the glue work, right?”
15 / evaluation
“To me, that is so important because otherwise it, I, I don’t know how you achieve stable equilibrium, right?”
16 / evaluation
“Because we built a, a data model, right? We schematized some part of some business process.”
17 / evaluation
“Because right now I think when people say, “Wow, I don’t want a token max,” it’s an artifact of us not having thought ourselves as an industry that we are using tokens to create value every step of the way.”
18 / prediction
“If you don’t do that, it’s not been that great. And this time around, I’m a firm believer that ultimately if you do have a token economy that drives productivity, that drives economic growth, that drives broad spread, um, you know, participation, better health outcomes, um, then I think we’ll be in a great place.”
19 / prediction
“Uh, right? That idea that, “Oh, wow, my generalist skills have gotten higher leverage,” I think is what we’re gonna see across the board.”
20 / evaluation
“Uh, and then you’re feeding it with very rich context because that’s sort of the other hard lesson we have learned in the last two years is, oh my God, the amount of work you need to do to prep the context layer, uh, such that your plan can execute in the most efficient way is where the magic is.”