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
13 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 / 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.”
04 / 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?”
05 / evaluation
“To me, that is so important because otherwise it, I, I don’t know how you achieve stable equilibrium, right?”
06 / evaluation
“Because we built a, a data model, right? We schematized some part of some business process.”
07 / 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.”
08 / 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.”
09 / 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.”
10 / 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?”
11 / 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.”
12 / 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.”
13 / 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.”