app / uses
Deep Research
“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.”
Public evidence record
CEO · Microsoft
Books, apps, and tools
app / uses
“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.”
Claim ledger
47 transcript-backed 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 / 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.”
03 / 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.”
04 / 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.”
05 / 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.”
06 / 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?”
07 / evaluation
“To me, that is so important because otherwise it, I, I don’t know how you achieve stable equilibrium, right?”
08 / evaluation
“Because we built a, a data model, right? We schematized some part of some business process.”
09 / 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.”
10 / 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.”
11 / 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.”
12 / belief
“I think that there are going to be some network effects of continual learning—I call it data liquidity—that any one model has.”
13 / belief
“Therefore, whether you’re ramped to some revenue number or you’re ramped to some audience number or what have you, that I think is what’s going to happen.”
14 / belief
“In fact, that’s one of the other reasons why we want to think about what an Azure region looks like and what is the networking between Azure regions. This is where I think as the model capabilities evolve and the usage of these tokens evolves, whether it’s synchronously or asynchronously, you don’t want to be out of position.”
15 / commitment
“In fact we will take leases, we will take build-to-suit, we’ll even take GPUs-as-a-service where we don’t have capacity but we need capacity and someone else has that.”
16 / commitment
“Because from a hyperscale perspective, we want to build the most scaled infrastructure fleet that’s capable of supporting all the models the world needs, whether it’s from open source or obviously from OpenAI and others.”
17 / commitment
“The way we are going to do it is to have a close loop between our own MAI models and our silicon, because I feel like that’s what gives you the birthright to do your own silicon, where you literally have designed the microarchitecture with what you’re doing, and then you keep pace with your own models.”
18 / prediction
“I think that’s going to be one of the biggest places of innovation, because right now I want to be able to use multiple agents.”
19 / prediction
“I think that the key, key priority for the US tech sector and the US government is to ensure that we not only do leading innovative work, but that we also collectively build trust around the world on our tech stack.”
20 / prediction
“If I think about all of the Office artifacts being structured better, the ability to do the joins between structured and unstructured better because of the agentic world, that will grow the underlying infrastructure business.”
21 / prediction
“In this case, because we have all the IP from the GPT family, we are taking that and putting it into the core middle tier of the Office system to teach it what it means to natively understand Excel, everything in it.”
22 / evaluation
“During the transition from server to cloud, one of the questions we used to ask ourselves is, “Oh my God, if all we did was just basically move the same users who were using our Office licenses and our Office servers at the time to the cloud, and we had COGS, this is going to not only shrink our margins but we’ll be fundamentally a less profitable company.”
23 / prediction
“That fully autonomous agent will have essentially an embodied set of those same tools that are available to it. So this AI tool that comes in also has not just a raw computer, because it’s going to be more token-efficient to use tools to get stuff done.”
24 / prediction
“I want to be able to take the flops that we use to generate a GPT family and maximize its value, while my MAI flops are being used for… Let’s take the image model that we launched, which I think is at number nine in the image arena.”
25 / 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.”
26 / 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.”
27 / 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?”
28 / 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.”
29 / 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.”
30 / 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.”
31 / 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.”
32 / 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.”
33 / 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.”
34 / 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.”
35 / 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.”
36 / 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.”
37 / belief
“In consumer, that could happen. In the enterprise again, I think there will be, by category, different winners.”
38 / 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.”
39 / 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?”
40 / 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.”
41 / 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.”
42 / 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.”
43 / commitment
“We invested, quite frankly, because- here's an interesting thing about our history: We built our first game before we built Windows.”
44 / 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.”
45 / 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.”
46 / 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.”
47 / 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.”