Dwarkesh Podcast / 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
25 published records
01 / prediction
“I am thrilled that I'm going to be leasing a lot of capacity in '27, '28 because I look at the builds, and I'm saying, "This is fantastic.”
02 / evaluation
“That's one of the reasons why even the disclosure on the inference revenue... It's interesting that not many people are talking about their real revenue, but to me, that is important as a governor for how you think about it.”
03 / evaluation
“Except, of course, we missed what turned out to be the biggest business model on the web, because we all assumed the web is all about being distributed, who would have thought that search would be the biggest winner in organizing the web?”
04 / belief
“I know David Autor and others have talked a lot about this which is, 60% of labor- I think the other question that needs to happen, let’s at least talk about our democratic societies.”
05 / belief
“Is intelligence that bottlenecked on prices going down? Because when I think about, at least my use cases as a consumer, intelligence is already so cheap.”
06 / belief
“Now, when I think about what's going to be possible in the next 250 years, I'm thinking like space travel, and space elevators, and immortality, and curing all diseases.”
07 / commitment
“The cool thing is what I'm excited about is bringing--we're going to have a catalog of games soon that we will start using these models, or we're going to train these models to generate, and then start playing them.”
08 / belief
“I need a smarter thing than a chat interface to manage all the agents and their dialogue. That's why I think of this Copilot, as the UI for AI, is a big, big deal.”
09 / belief
“You can say, oh, that's all alignment and this, that, and the other. That's why I think you have to really get these alignments to work and be verifiable in some way, but I just don't think that you can deploy intelligences that are out of control.”
10 / belief
“I think in the long history of these curiosity-driven research organizations, to just do a research org that is about fundamental research and MSR, over the years, has built up that institutional strength so when I think about capital allocation or budgets, we first put the chips in and say, "Here is MSR's budget.”
11 / belief
“" You will want to be able to take your SaaS application and make it a fantastic agent that participates in a multi-agent world. As long as you can do that, then I think you can even increase the value.”
12 / belief
“We think that no society cares about it, right? There can be rogue actors, I'm not saying there won't be rogue actors; there are cyber criminals and rogue states; they're going to be there.”
13 / belief
“The way I think about it is hey, distributed computing will remain distributed, so go build out your fleet such that it's ready for large training jobs, it's ready for test-time compute, it’s ready- in fact, if this RL thing that might happens, you build one large model, and then after that, there’s tons of RL going on.”
14 / belief
“In fact, we have what, I think that the last thing that we announced was 24 logical qubits. So we have also got some fantastic breakthroughs on error correction and that's what is allowing us, even on neutral atom and ion trap quantum computers, to build these 20 plus, and I think that'll keep going even throughout the year; you'll see us improve that yardstick.”
15 / belief
“In consumer, that could happen. In the enterprise again, I think there will be, by category, different winners.”
16 / prediction
“I think in models there is one dimension of, maybe there will be a few closed source, but there will definitely be an open source alternative, and the open-source alternative will actually make sure that the closed-source, winner-take-all is mitigated.”
17 / commitment
“Therefore, I think we will have to build enough software engineering around the deployment side of these, and then inside the model itself, what's the alignment?”
18 / prediction
“Ultimately, not as tech for tech's sake, but solving some of the fundamental things that we, as humans, want in our life, and more, we want them in our economy, driving our productivity. And so if we can somehow get that right, then I think we will have really made progress.”
19 / evaluation
“If you take the R&D budget that we will spend this year, it’s all speculation on what's going to happen five years from now.”
20 / evaluation
“Is there a business even in hyperscale?” And it turns out there is a real business, just because of the know-how of running, in the case of Azure, the world's computing of 60-plus regions with all the compute.”
21 / commitment
“We invested, quite frankly, because- here's an interesting thing about our history: We built our first game before we built Windows.”
22 / recommendation
“She's going into a tumor board meeting, and the first thing she uses Copilot for is to create an agenda for the meeting because the LLM helps reason about all the cases, which are in some SharePoint site.”
23 / prediction
“One is the hyperscalers that do well, because the fundamental thing is if you sort of go back to even how Sam and others describe it, if intelligence is log of compute, whoever can do lots of compute is a big winner.”
24 / evaluation
“Except once we started putting servers in the cloud, suddenly people started consuming more because they could buy it cheaper, and it was elastic, and they could buy it as a meter versus a license, and it completely expanded.”
25 / recommendation
“I think when I use something like Deep Research, even, the minimum assurance I think we want is before we especially have physical embodiment of anything, that I think is kind of one of those thresholds, when you cross.”