Satya Nadella LIVE on TBPN | Alexander Embiricos, Kyle Daigle, Jay Parikh, Jared Palmer, Michael Grinich

You're watching TVPN. Today is Tuesday, October 28, 2025. We are live from GitHub Universe here in the Fort Mason in San Francisco and we have a ton of exciting interviews. Today we are interviewing the CEO of Microsoft, Satya Nadella. We're very excited. We've been on a Quest to interview BAG7 CEOs and we are very excited to sit down with him today. And it's a huge day because, because Microsoft just today announced that they have entered the next phase of the partnership between Microsoft and OpenAI. There of course are dueling blog posts one on OpenAI's website, one on Microsoft's website. We will go through some of the Microsoft update to give a little bit of background before we go into our interview with Satya Nadella. But first let me tell you about ramp.com, time is money save both if you easy use corporate cards, bill payments, accounting a whole lot more all in one place. Let's go. So this all started back in 2019. Microsoft and OpenAI, it says here has a shared vision to advance artificial intelligence responsibly and make its benefits broadly accessible. What began as an investment in a research organization has grown into one of the most successful partnerships in our industry. And I think that it might be one of the greatest deals of all time in business history. It is a remarkable, remarkable deal. I, I was, I was digging through some of the other deals that where big tech companies worked with each other or bought stakes in each other and there are some wild ones that people might not know about. I think it might be worthwhile to go through. Before we do, let me tell you about Restream 1 live stream, 30 plus destinations multi stream reach your audience. Yeah they are so in in back in what was it 1997 Microsoft bought $150 million of non voting Apple stock which settled some litigation committed to they were going to put Microsoft Office on the Mac for five years and it made Internet Explorer the default browser on the Mac.

And so before my time and I.

Was born by they did this deal but by, by, by 2001 Microsoft had converted all of the shares into common stock netting the company approximately 18 million shares of Apple. And then by 20 by 2003 they'd exited the position which I don't know if that's a good deal. They should, maybe they should have diamond hands it but it's still, it's still a wild, wild moment.

Yeah, I think you know something I'm excited to talk to Satya about is just like how Much. How much foresight he had.

Yeah.

Whether he knew whether, whether he was expecting a base hit or he really felt like it'd be a home run.

Right. Yeah. Yeah. It is a very fascinating thing. It's like you're doing a deal with this nonprofit. Sam Altman's obviously a big character in. Even in 2019, Sam Altman was an important figure in Tack of Force. But at the same time I was running the numbers and I was like, at least today Microsoft makes like a billion dollars in revenue every single day. And so if you think about it, I don't know if you actually think about it this way, but if you just think about it like it's your job as the CEO to steward capital and a billion dollars sounds like a lot, but you're making a billion dollars every business day. Like there's five business days a week, 52 weeks a year. You're basically like revenue for Microsoft right now is about a billion dollars a day. And so do you treat that deal like it's just another day at Microsoft or is it something that there's weeks of negotiation? Because you do have a sense that this is going to be one of the more important deals. And I always, I always look at.

When you look at the check size.

Yeah.

Relative to the capital available, it looks like a flyer if you were a VC fund.

Right.

And you know, I think it's fascinating because there's so many, you know, scaled platform VCs that have to just be faced. They have to look at this announcement to see that Microsoft owns 27% of potentially the most consequential company to come out of the 2010.

Yeah.

Right.

Yeah.

And they just have to look at that announcement.

And I haven't dug in, but I've seen there's a little bit of sour grapes from the venture capital community saying you didn't get enough of. We didn't get enough.

Yeah. If you look at the risk reward that, the amount of capital that the seed investors deployed into the company relative to their ownership today, it looks, you know, certainly they made a great return on paper, but did they actually make a great return relative to the risk of, you know, investing in a company that had went against every YC practice there is. Right. Like YC says, like, there's so many.

Videos of Ceremony saying, don't, don't reinvent the wheel.

Let me do, let me reinvent the wheel. And, and ultimately, I mean, this has led to like so much of the, you know, as, as ChatGPT has exploded, the chaos around the company has almost entirely centered around the corporate structure. So yeah, y. I wonder, you know this may be, you know, the final company for Sam. Right. In terms of. But I, but I wonder what he would do next time around.

I mean we have run the experiment because he has a bunch more companies and most of them I think are pretty clean C cores. But then again has a token and so like there are multiple things going on but I think probably if we dug into his BCI company we would see a cleaner C corp. Yeah.

And I'm excited in the fullness of time when we get the, when we get the books and the documentaries on both OpenAI and this investment. Yeah, I can't wait to see and try to understand where what OpenAI was really what they were facing at that moment when they did this series of deals with Microsoft.

Right.

Because the ultimate deal of you know, selling such a large amount of the company with a, with a rev share attached, which is the rev share wild Ask Ask, you know, a YC partner if a portfolio company, if one of the companies in their group came to them and said yeah, I have this investor, it's a large tech company, they want to invest like they want to buy like a lot of the company and they want a 20% rev share for like the next 10 years. They'd be like you need to walk away from that deal immediately. But. And Satya did it and here they are.

How about two on 20? Safe note.

Yeah, good old fashioned way.

Good old fashioned. Why are we reinventing the wheel? There are a couple other interesting cross industry deals. Jeff Bezos famously had a big stake of Google, wasn't an angel investor. There's also the time that Intel, TSC and Samsung came together to invest in asml which of course makes the lithography machine is kind of pulling forward kind of the initial like weird circular deal that people point to.

But it worked out.

But that one worked out for sure. It's interesting seeing the evolution of this deal in particular Some quick history. 07-22-2019. Microsoft invests $1 billion in OpenAI. Azure is named the exclusive cloud provider. Microsoft is named the preferred commercialization partner. In 2020, Microsoft receives an exclusive GPT3 license for its products and services.

And the, the foresight here is just from Satya is incredible. Like this is like at the, at the time, like it wasn't that. Yeah like only a couple years prior Elon was basically walking away because he said like there was no there. There were, I'm sure was material progress internally but to have that level of, of, of understanding and that love that much conviction to invest a billion dollars when you're years out.

It's more, it's more complicated than that, I think, because there are plenty of big tech CEOs who have taken $1 billion flyers on crazy ideas. We see that all the time with the, you know, oh, you want to build some new hardware thing or how much, how much did Apple spend on the car? Or they probably spent a billion dollars working on that car already. And like, you know, they kind of like the risk adjusted reward. The risk adjusted bet made sense, but then ultimately they pulled back from that. And that's happened probably all over the place. And Satya himself probably has other times when he's put down a big investment for something that was risky and it didn't pan out. What is interesting about the OpenAI deal is that I know investors personally who are looking at the deal before that and they couldn't get over the complicated structure and so they dipped out for that. And so it's not that it's like, oh, wow, we need to give a round of applause for someone who's helming a trillion dollar company to write a billion dollar check. Like, that's not that crazy. That happens all the time. What is crazy is to get over all the lawyers being like, you're doing what and how it's structured. What are you about, There's a nonprofit involved.

Why are we doing that? Back to me.

Exactly, exactly. And so, but knowing that, that we do live in a society where if things, if value is created, if a new platform emerges, everyone overcome it can.

Overcome any chaos, all the complexity.

Yeah. So, and so 2021, Microsoft followed on with more investment and then the OpenAI service went general availability on Azure in 2023. Before we move on, let me tell you about Privy Wallet infrastructure for every bank. Privy makes it easy to build on crypto rail super lightaber injections, integrate on chain and crypto all if you want a simple API.

And the reason that I say this is so notable and this conviction really matters is that think of when you look at other scale hyperscalers, how even years after the sort of ChatGPT moment. Grant, everything we've talked about so far predates ChatGPT.

Right.

And, and years after this ChatGPT moment, we still have people that are only now coming around to saying like, we're, we're ramping up, we're ramping up capex. Right. And so I just think that Satya was incredibly ahead of the curve. Here.

Yeah, it's, it's, yeah, it's easy to look at the deal and through the lens of, oh, well, Copilot was the glimpse of value creation out of what is effectively like a nonprofit academic lab. But it's important to remember Copilot happened three years after GitHub. Copilot launched the. Two or three years, 2022. Yeah. Two or three years fully after the, the, that initial $1 billion investment. So, yeah. Remarkable, remarkable progress. Let's go back to the Microsoft announcements today.

We can go through some of the key points, details on what's evolved. Do you want to read some? Yeah. So what is evolved? Once AGI is declared by OpenAI, that declaration will now be verified by an independent expert panel. So I'm assuming they're going to get Joe Rogan, Andrew Huberman, Lex Friedman. I would love and get it, you know, a panel of podcasters to decide now for this is a question that I want to dig in with Satya in just a few minutes. Trying to rate today like nobody, you know, some folks can agree on, on a definition of AGI, but it's, it's very much in flux. Right. Tyler Cowan was on our show a few months ago saying that he felt AGI had already been achieved, that we keep moving the goalposts.

Yes.

Others believe that we're in this era of spiky intelligence and we need sort of, you know, more broad intelligence before we can get to true general intelligence. But going forward, Microsoft's IP rights for both models and products are extended through 2032 and now includes models post AGI with appropriate safety guardrails. That feels significant. Microsoft's IP rights to research, defined as the confidential methods used in the development of models and systems, will remain until either the expert panel verifies AGI or through 2030, whichever is first. Research IP includes, for example, models intended for internal deployment or research only. Beyond that, research, IP does not include model architecture, model weights, inference code, fine tuning code, or any IP related to data center hardware and software. And Microsoft retains these non research rights. Microsoft IP rights now exclude OpenAI's consumer hardware. That's notable. They need to start figuring out carve outs. And OpenAI can now jointly develop some products with third parties. API products developed with third parties will be exclusive to Azure. Non API products may be served on any cloud provider. So again, Satya is forced to.

If you're just joining us, Satya Nadella will be joining us in five minutes to break all of this down live on TBPN for right now we are setting the table with some analysis and looking through the details of the story that emerged today.

Yep.

From so Microsoft can Now independently pursue AGI alone or in partnership with third parties. If Microsoft uses OpenAI's IP to develop AGI, prior to AGI being declared, the models will be subject to compute thresholds. Those thresholds are significantly larger than the size of systems used to train leading models today. The revenue share agreement remains until the expert panel verifies AGI, though payments will be made over a longer period of time. OpenAI has contracted to purchase an incremental $250 billion of Azure services and Microsoft will no longer have a right of first refusal to be OpenAI's compute partner. Again, like that 250 billion number is, you know, certainly not the biggest number we've heard, but. But it's quarter of a trillion is nothing to scoff at. Yep. And OpenAI can now provide API access to US government national security customers regardless of the cloud provider. And finally OpenAI is now able to release open weight models that meet requisite capability criteria. Yeah, so again, I feel like on a number of these points it feels like they are kicking the can down the road a little bit. Again, obviously this was important to complete the conversion from the LLC to the Public Benefit Corporation, which presumably can go public. But again, my question and my immediate thought is how many of these things are going to be critical to iron out before the ipo? Is there going to be enough. Yeah. Demand that it doesn't matter. Again, in the same way that certain investors, you know, you know, our friend Josh over at Thrive and others were, had incredible conviction to be, you know, deploying again and again and again into OpenAI's for profit subsidiary even when there was so much uncertainty around the structure. Right.

Yeah. Big open question in how Microsoft's internal AI research efforts evolve now that this is a little bit more concrete, will be very interesting to see.

Yeah.

If Microsoft uses OpenAI's IP to develop it prior to AGI being declared, who'd love that? OpenAI's dribbling, dribbling towards the basket. Satyr comes in with.

Who knows. Who knows. If you're just joining, we'll be live with Satya Nadella in 1 minute and 17 seconds.

There we go.

According to our timer. In the meantime, let me tell you about cognition. They're the makers of Devin, the AI software engineer. Crush your backlog with your personal AI engineering team.

So yeah, so yes, again there's so many of these points that leave kind of open questions. They'll need to be effectively renegotiated again and again down the road. But at least this provides pathway and it's no longer the elephant in the room.

Yeah. It does feel like the cap table is getting slightly cleaner. Yeah. And you're moving towards something where, I mean if you look at the, at the history of the Microsoft deal with Apple, they had a position, they eventually rotated out of that, sold out of that. Because there's, there's this question of, you know, if you're the CEO of Microsoft, you're Satya Nadella, should you be a venture capitalist as well? Oftentimes big tech companies do make investments. Minority investments.

Yeah.

Sometimes they make whole co acquisitions. But is that primary business?

Yeah, yeah. I mean it ultimately just comes down to feeling like potentially one of the greatest corporate venture investments of all time. And so I'm not coming up with any that are, that are better. It's pretty good or significant in terms of not just owning a massive piece of a generational company and a future, you know, potentially what looks like a future, you know, hyperscaler.

Yeah.

But also giving your business just this incredible strategic advantage in the race broadly.

So, yeah, to go any more impactful, you need to move over into the whole co acquisition world. You have to talk about Instagram. But even, even that is tough. But it's a very different, very different deal structure and something that is just down the fairway. Buy the entire company, buy the entire entire product as opposed to make this bizarro minority investment and then grow from there.

Yeah. The. It's worth noting too that OpenAI and Microsoft Office are on, you know, already on a collision course. Right. Like you can imagine that over time these products, you know, overlap. Today you can use Copilot for a lot of things that you can use ChatGPT for. That's only going to become. There's still going to be this like massive tension there.

Yep.

And we'll be covering it a lot.

Well, let me tell you about figma.com, think bigger, build faster. Figma helped design and development teams build great products together. And I believe we're ready for our first guest of the show, Satya Nadella, the CEO of Microsoft. Welcome to the show, Satya. Great to see you. How are you? And great seeing. Thank you so much for doing this. Please. There were a ton of bullet points and the announcement today. Can you just zoom out and explain it to me like I'm five. What actually, what, what, what actually changed because you've been in partnership with, with OpenAI for six years now. But this feels like an important moment. What happened?

Yeah, look, first of all, you know, it just feels. Yeah, you're. You said it. Right. It's a good. It's an important moment. And the story continues. But the story actually got started, even the OpenAI one. I've known Sam for a long time, since his first company pre YC days.

All the way back then. Wow, right?

That's right.

I was actually in the Double Polo day.

I remember him being at WWC DC presenting in the Double Polo. It's iconic. But I didn't realize that you were doing business with him back then.

And it started, I think in 2016. In fact, we were. Azure was the first cloud provider. That's right when OpenAI got started. In fact, I think Elon sent me the mail asking for Azure credits. So that's how it got started.

Hey, I have this nonprofit.

That's right. And they were obviously into reinforcement learning. They were doing Dota and all of that stuff. And then at some point it reached where I think they went off. I think they went to other clouds. And so I lost touch for a while. And then I think in 2019, Sam came and talked about sort of, hey, we're going to really. We think the scaling stuff works. I forget now. It's a little hazy when I read the paper. In fact, the paper was written by Dario Ilya and the scaling laws paper. And the thing that's, you know, Microsoft has been obsessed since Bill started at Microsoft Research in 95 is natural language. Yeah, it's just, you know, being the thing. We are an office company. We are a knowledge work company. And so we've always thought about text and AI as applied to text and natural language. So you could say it's the prepared mind. When sort of Sam said, hey, we're going to go take a run. You want to be on it. That's sort of what led to really coming together on this. It was a research lab, it is a nonprofit.

As opposed to. If they had stayed on the previous tech tree path of they were doing some humanoid robotics and were doing some video game stuff and Dota 2. That doesn't jump out to you as immediately relevant in GitHub.

It's interesting you bring that up because obviously RL has come back in a big way way in relation to sort of these large language models. But yeah, it's probably, you know, this is the funky path dependent way things happen.

Right.

Because I don't think I would have gone in full on to say, hey, let's go, you know, partner with these guys, build a computer that scales it. If it's not natural language, I'm glad we started there. And then our now is improving the quality of these models, that's for sure.

How big of a deal was writing a $1 billion check back then? I mean it's a big company, Microsoft, we think it makes revenues around $1 billion a business day. Was it one day of work for you or was it, you know, weeks of negotiation? Seriously, did you build memos? Like, were you building Excel sheets? Like, what were you thinking?

Even at Microsoft you kind of got to have to get a board approval just to throw a billion dollars out of there. But yeah, I must say it was not that hard to convince anyone that this is an important area and it's going to be risky. Like, I mean, in retrospect, I mean, who would have thought, hey, I didn't put in that, you know, billion dollars saying oh yeah, this is going to be whatever 100 bagger. That's not like what was going through our head. This is a partnership that I mean, by the way, remember this was a non profit.

Right.

And I think, you know, Bill even said, yeah, you're going to burn this billion dollars. Right. And yeah, we kind of had a little bit of high restaurant, don't worry. Yeah. And we said we want to go and give this a shot. And then of course we subsequently, you know, here we are at GitHub universe. In fact, this is probably the place where that billion to 10 billion happened. Because in 21 is when I first saw GitHub Copilot and I said, man, this is worse.

This is a year before the release.

No, actually, in fact I was fact checking my thing. I think GitHub Copilot launched in 21, ChatGPT in 22.

Sure.

And if I remember right, 23 is the blip, the November blip with OpenAI and then everything has been smooth since.

They have a blip.

It's a blip.

That's the bliss.

Nicest way you could put that. That's fantastic. Yeah. So that makes a ton of sense. Obviously it's been a wild ride up and down. You started with just natural language, let's predict the next word. Now let's rewrite the entire global economy. How do you think about the territory that you at Microsoft have, have kind of claimed? And what do you want to hold on to? What's, what's most important to map out where Microsoft, where the edges of your territory are and then where founders and other Business people can hold in partnership with you.

Yeah. So look, if you take sort of, I always say Microsoft's a, you know, a platform company and a partner company. And we define platforms as where the value capture of the platform is higher than by the platform. That's kind of who we are. And that's, you know, GitHub is a great place. So if you think about even at GitHub Universe today, it's interesting, right? As you said, we first started by saying, hey, code completions. Then we said, let's chat. Instead of getting distracted, stay in the flow of coding. You bring the information to the flow. And that's chat became the thing. Then we said agent mode. Then we said, hey, let's have autonomous agents. And then now we have multiple autonomous agents working across all these different branches, then bringing the PRs to me. And so this entire conference is about what we call agent HQ and mission control, where you have Codex, you have Claude, you have Grok, every model you want, each working across their own branches. Then you have the ide so you can bring up VS code where you can do the diff on each of the branches output, and then the story goes on. So therefore, to me, building a system that really brings the innovation across the ecosystem into some kind of an organizing layer is what platform companies do.

Well, have you seen anyone here that you think might be working on AGI? Do you have a personal definition for AGI?

Yeah, if you look at pretty much all the deal points, it, you know, it keeps coming back to this moment when AGI will be declared. Right. It's. There'll be a panel of experts. Maybe that panel is still being decided, but a lot of experts today have differing definitions. So I wanted to get a better sense of how you, how you imagine that kind of decision making process will go when the time comes.

And we'll put you guys on the panel.

We've been doing evaluations on AGI, specifically around comedy. Can it write fun comedy, Comedy event.

To me, I think, first of all, I think one of the reasons why, quite frankly, both Sam and I agree on this, which is it's become a bit of a nonsensical word. I mean, it's just changing and everybody defines it differently. And we now know what the issue is.

Right?

We know everybody describes even the intelligence we have, which has been exceptional as jagged.

Yep.

Yeah.

So, yeah. All right. And so if you sort of say, well, we have spiky intelligence, in fact, I think Andrej Karpathy's point in one of the podcasts recently, which is a good one. Which is even if you're having exception, you know, let's call it exponential growth in one of the spikes.

Sure.

It's not as if the Jags are getting worked out. That's the nines problem.

Yeah.

Right. That is each nine is maybe linear, even sublinear problem.

Yep.

Right.

In terms of rate of progress. So first step to me is even to get to broad intelligence, forget sort of general intelligence, we've got to get rid of these jagged problems. And that I think is the first place to do so. If you ask me, I think what may happen is we will achieve more robustness, let's call it that for different systems. Right. So coding is a good one.

Yeah.

I think the entire goal with GitHub and GitHub mission control and agent HQ is, is can I just like how I use compilers, can I use agents to generate better coding artifacts?

Right.

Today coding, I mean, so I sometimes think vibe coding is a sort of a slightly unfortunate term because it does lead to a lot of slop.

Right.

I mean, it's kind of like I'm sure you code away and then you lose control of the project and you got to put everything back into a markdown. And so.

Yeah. And even traditional knowledge work that's happening in the office suite, it's not like you want the biggest Excel model.

Right.

You want the one.

Yeah.

But even Excel is a classic one. In fact, one of the other things that's happened is even in when I see M3 Microsoft 365 copilot, man, the amount, just like right now, the number of repos on GitHub is exploding.

Yeah.

The other thing is everybody's generating PowerPoint and slide decks and sort of Excel models. The problem with Excel models is, you know, when intelligence is created, I mean, it is like a thing of beauty. Right. Assumptions are clear, the formulas are there.

Even the formatting and the support, all the stuff.

And you can iterate on it.

Yeah.

It tells a story.

It's not like a one shot and I want to change. I can't go back and say zero shot the entire thing. So in fact, the agent mode in Excel, which I like is it understands Office js. It puts the formulas, I can then iterate like I iterate on GitHub copilot. So those system. So if you asked me about, you know, first, how do you get rid of this jagged intelligence problem is you build a great knowledge work system that is multi agent, multi model, multi form factor. Get to a great benchmark in an eval where you can trust it at two nines, three nines, four nines. And until you achieve that, you're not going to be able to move and say, hey, we have anything called general intelligence.

Yeah. It feels like there was a lot of uncertainty in the tech community around oh, is super intelligence going to come out of the lab tomorrow and there's going to be this fast takeoff. Now it feels like there's more opportunity both for Microsoft to add those nines to products that you have and then also to entrepreneurs who are building products maybe on top of Microsoft. How are you thinking about the entrepreneurial opportunity in the age of a?

Very good point. Because at some level, if you sort of buy the argument I made that there's a lot more invention to happen.

Yeah.

By the way, the other thing that we should also talk about, I always say to this too. Right, Right. Today it's all the conventional wisdom is, oh, intelligence is just simple, straightforward log of compute. So throw more computer energy. Who the heck knows, man? One of the researchers comes from here, comes out of here and says, you know what, I got it.

Compute.

Any of you guys have thought about like, that's a game changer.

So.

Oh, by the way, we're all like excited about reinforcement learning. Guess what? Pre training is a more efficient form of training because you can advertise it. So there's a. I think pre training will have new breakthroughs, mid training will have new breakthroughs. RL will continue to improve. We will then have to add more innovation to it. And by the way, this is another part of this partnership which is, I'm glad, you know, OpenAI is continuing to do great work. Jakob, Mark, others are great and we'll partner with them and we'll continue to do so. And Mustafa has built a world class team. Right. You know, Karen, Amar, Lando, these are, I mean we have now three cool models, whether it's speech or image or text and we want to continue to have it. So we'll write our words as well.

Yeah. How are you thinking about the interplay between OpenAI what you do internally at Microsoft?

So our simple.

Are there certain things that you can take your foot off the gas because you're like, actually OpenAI's got that handled or do you want a duopoly? Like actually we're going to fight it out on everything.

I'm much more like, again, my mindset is all platform man. Like, hey, on Azure, do you run Windows? Yeah. Do you run Linux? Yeah. Do you run SQL Server? Yeah. Do you love Postgres, Apple, Net, Java. Hey, I'm happy with OpenAI. I would love to have anthropic Mai Turok anyone? If Google wants to put Gemini on Azure, please do so.

What is that like culturally? Like, what does it mean for the next such an Adela? Somebody who's working their way up in Microsoft, do they need to be okay, I'm building something internally, but my company isn't going to favor me. I need to fight it out with all my competitors across.

We all grew up in that culture where it doesn't mean. Because it's always we are going to bring our pieces together. Sure, we are going to innovate across these scenes, but as a platform company, you kind of want to support everything and most people don't. Office was born on the map before Windows was even.

Yeah, if you don't give people choice, developers here will churn.

Right.

They'll find other platforms.

Right.

The concepts Bill had when he started Microsoft was, hey, we're a software factory. We love all software categories and we're just gonna go create software. And so to me, we definitely want to sort of have that same attitude to innovation. We definitely need to stitch out stuff together so that they come together to solve bigger and greater problems, but doesn't mean we can't create opportunities. And in part the other thing that I grew up like for example, building SQL Server with SAP and so we've always partnered and our Intel, Microsoft.

Right.

I mean we owned up the PC industry, but you know, it is called the Grove greats model. That's a good model to create value.

Do you think that there's increasing returns? This is going to sound like a loaded question, but I promise you it's not. There's increasing returns right now to being a deals guy or innovating on the deal structuring side. And what I mean by that is there's, there's all these difficult problems to solve with energy and data centers and it feels like we, there's innovation in tech that we normally think of as like the code or the algorithm or the design of the system. But then there's also this difficulty sometimes to just marshal the resources. And is that, is that like a new phenomenon? Has that always been true? Is there, if somebody's pursuing a career in tech, is becoming a great deals guy or deal maker, like an important path now?

Yeah, I was just thinking about it, man, like, which is, yeah, you have this great investment and it has great return and no, carry all it, all the valuables to my shareholders. That's awesome. Maybe we should start a venture fir.

You might be well there.

I think the thing you're touching on is something that actually platform companies should think about which is what's the ecosystem up and upstream and downstream to your point right now we have to as an industry the reality is let's take power.

Right.

Which is if they sort of say intelligence is about tokens per dollar per watt, we've got to get efficient on all of it in order to get more efficient on it. You got to really think about even in our own industry the token factory itself really getting better order of magnitude. This is like again a renaissance time for systems architecture. And so we're, we see Nvidia is doing great work, AMD is doing stuff, Broadcom's doing stuff, all of us are doing great work. To just push that then the next barrier is going to be man, can we generate energy faster, can we build faster, can we build a cooling. I mean like, I mean I now know more about campus cooling systems than I ever thought I'll know.

Right.

I mean and these are all choke points.

How much do you want that to live within Microsoft versus you want to just be a buyer and the, the all the different power players are out there building nuclear, wind, solar and you're just dealing with it at a higher level of. Sure.

The vast majority of this infrastructure now now that you know, if you think about back at it. Right. Our data center builds mostly we built and we lease some because no one was in the business of building at the scale at which we were built. But now I think there's going to be opportunities for us to lease.

Yeah.

And there's going to be significant competition amongst builders. So therefore the lease prices also.

Yeah. Do you think you're more ROI focus than others that are throwing around big numbers?

I mean I'm, I'm always focused on long term return.

Well, well and, and we're at a time right now where there's people that have come out and effectively said I I don't actually care about roi, I just care about winning. Right. And it seems from your couple years.

Ago there was the mood of like if you this might be the last investment.

You always have someone else willing to give you the billion dollars when or the $10 billion. You can always be about I'm odd winning and the return. But at some point that party ends and everybody needs to sort of have a plan in that context in these platform shifts to be short term oriented doesn't help at all.

Right.

Because you kind of have you know, I always say long before it's conventional wisdom. I mean if you look back, you asked how we put the billion and the reality is we put the 10 billion.

That's right.

Or the 13 and a half was fully committed.

Yeah.

Before it became a thing.

Right.

And remember that was all done before Chad GPT became a thing.

And so yeah, to me, how do you. I think there's a general consensus now that it's, it feels very possible to predict like a year out, two years out and then 10 years out is extremely fuzzy. What's your view on that? Given that you look like going back to the original OpenAI investment and the original partnership, it seems like you've had at least really good like six year, kind of like foresight abilities to sort of invest against like a six year time horizon. But how are you thinking about managing over, you know, the next decade?

Yeah, I mean I think, you know, to me, you know, one of the things about tech is as a percentage of GDP get right around 4 or 5%.

Yeah.

And if you ask me five years from now, 10 years from now, is that percentage going to be higher or lower? I think the answer is pretty straightforward. It's going to be higher. It's just a question, is it going to be 10 or 15? So why is that? Because the rest of the pie, the rest of the GDP would have grown faster. So that's why I always go back to. At the end of the day the only rate limiter here is the overall economic growth and the factors of sort of input to it. So tech as an input, I think AI and everything that it entails is going to be a core driver and some of it will come from just this intelligence and it's sort of continual march of capability. But it'll also come from, I'll call it great engineering and product making around it. Like when I look at GitHub copilot today with agent HQ and what have you, that's great because right now I'm in undated with multiple models and everything is slightly different except I have one repo and I want all of these agents to come work on all of my repo in different branches. So you need great product making to bring more coherence to the chaos. And that I think is going to be the big difference maker.

I was talking to Eric Lyman at Ramp who makes the show possible, of course. He had a question about how you. What, like what advice you would give to someone running a decacorn thousand plus employees in this age of spiky intelligence, where there is the possibility that tools are going to get better very rapidly and maybe you don't want to scale up too fast and then have to do layoffs or retraining like you run a huge organization. How do you think about managing human capital in what feels like an uncertain time? Does it feel more uncertain to you now than it did 10 years ago?

Great point.

I mean, Eric's a great founder. I know him well. And they're doing some unbelievable work. And so in fact, whenever I've talked to him, in fact, I learned from him even how he's rapidly changing the agents they have built. So to some degree, I think, as I said, Microsoft or that's ram, I think the key is learning the new production function. So when I look back at Microsoft, I feel like, hey, look, your platform ships. We've navigated. I joined Microsoft when our existential competitor was novella. Right. And so, you know, in 92 and here we are.

And so.

But the bottom line is we've over the years navigated many platform shares.

Yeah.

We've also navigated tough business model shifts.

Right.

When you suddenly have, you know, you have a 98, 99% gross margin server business and you move to the cloud and you don't even know, man, is there a margin here? And yet you have to make the shift and figure it out. This one is, interestingly enough, both the tech shift. Yeah, A business model shift. Because this is the first time you have marginal cost software. It's not like cogs of the SaaS world, but true marginal cost. And three, the way you produce your artifact, your software is changing. So the product development process is completely getting ripped and replaced. And that is a. Whether it's for ram.

Yeah, Even the competitive dynamic too, because you have people that can say, hey, we can build this product in two months previously would have taken us 12 months. Why don't we enter that category?

And it's kind of like rewiring yourself. Right. Unlearning is the hardest part. Learning is easy at times. If you have to unlearn and learn, it's much harder. And so to me, that I think is what all of us have to do. I mean, it's funny, I met a bunch of student developers right here. It is the first cohort of developers who grew up with GitHub Copilot a.

Standard issue when they create things completely different environment.

So there's a.

There was a word before git crazy.

Like I don't want to live in.

A completely different abstraction. On the topic of Changing business model, shifting your business model. It seems like the console wars are over. Take me through the journey.

You're a peacetime CEO now we're a peacetime CEO.

The war's over. But take me through the evolution of the business model. Shift on the gaming side of the business. It's one of the most interesting pieces of Microsoft.

I think you've got to remember, in fact, Flight Simulator, I think was the first product Microsoft built even before I think our dev tools were the first. Flight Simulator was second.

That says so much about the culture.

Yeah, it is.

As soon as you gave the developers the ability to write code, they were like, let's make a game. It's amazing.

And so to me, remember, the biggest gaming business is the Windows business. To us, gaming on Windows. And of course, Steam has built a massive marketplace on top of it and done a very successful job of it. So to us, the way we are thinking about gaming is let's first of all, now we're the largest publisher after the Activision. So therefore we want to be a fantastic publisher. Similar approach to what we did with Office. We are going to be everywhere in every platform. So we want to make sure whether it's consoles, whether it's the PC, whether it's mobile, whether it's cloud gaming or the tv. So we just want to make sure the games are being enjoyed by gamers everywhere. Second, we also want to do innovative work in the system side on the console and on the PC and bring. It's kind of funny that people think about the console PC as two different things. We built the console because we wanted to build a better PC which could then perform for gaming. And so I kind of want to revisit some of that conventional wisdom. But at the end of the day, console has an experience that is unparalleled. It delivers performance that's unparalleled, that pushes, I think, the system forward. So I'm really looking forward to the next console, the next PC gaming. But most importantly, the game business model has to be where we have to invent maybe some new interactive media as well. Because after all, the gaming's competition is not other gaming. Gaming's competition is short form video. Yeah. And so if we as an industry don't continue to innovate both how we produce, what we produce, how we think about distribution, the economic model.

Right.

Best way to innovate is to have good margins because that's the way you can fund.

So interesting saying gaming's competition is short form video. It feels like the entire world's competition is sure. Sharp Video.

Yeah, I mean we've heard this thing a while ago. It just comes up again and again with public SaaS, companies that are maybe a little bit more of a point solution and they have to go through a business model transition and that can be harder than a tech transition. And we hear about, oh well, if you want to change your business model and maybe you want to be private, but it feels like is there some sort of advantage of being a hyperscaler, a $4 trillion company that you can go and retool a piece of the business over here, change the business model and, and have almost the, the privilege of, you know, not having shareholders come to you and beat you down about a slight shift in a business model in a subdivision. That's that you don't get that.

You can't deny that, you know, diversity of business models, diversity of the portfolio that Microsoft has, has been helpful. I mean it's kind of. But that said, I don't think you can take that and say somehow you can make it. Yeah, if you don't reinvent yourself. See, I think what happens in tech unfortunately is that when these shifts happen, whether you like it or not, you have to first be relevant after having it doesn't matter what the business model is. The business model may be like hey I had whatever 90% margins, you are going to at best have 10% but you have to jump all in because even that 90 is going to zero. And so given the binary nature, you got to make it to the other side. But then the category economics matters because if you can't sustain long term innovation, if there is no category economics, I mean hyperscale is a great one. In fact the best day in hyperscale business was the day Amazon announced their operating margins.

Aws, ipr.

Yeah, because that's when everybody knew hyperscale business is an unbelievable business. It's a commodity, but at scale nothing is a commodity. And so to me that is kind of going to be the key here.

Even SaaS applications quickly unpack why that was so good for you. Again, just because the market recognized that you were in the same business. And it was fantastic.

That is one and more importantly, it was much more exciting. Expensive.

Right?

I mean think about our server business. It's super profitable except it was one tenth the size when I look at it compared to Azure. So we like, we sold a few servers but man we sell a lot of cloud VMs or containers. Who would have thought how expansive the cloud consumption model is going to be? In terms of people being able to sort of, it's kind of the gems paradox that sort of really played out in a massive way if you just make broadly. Yeah. So I think on the business.

Well, Jevons paradox post, by the way, back during the. The deep seek moment. Oh yeah, that was an important spot.

On with that analysis.

I wish we could keep going. I think we have to wrap up.

But we would love this gone and.

If it hit for 27%. Wow, that's a good, good hit.

As big as possible. Oh, there we go. That is a fantastic signature. Thank you so much. Thank you for coming on.

Thank you for having us.

It's a really great week. I've always, whenever these big news, these big tech news things happen, I always wish I could talk to the person who's making the news. And now I get to. And so what a wonderful conversation.

What a moment.

And what a CEO.

Yeah. What. Yeah. What a moment in the, in the tech world. Well, thank you. If you're new here and you tuned in just because of Satya Nadella, the CEO of Microsoft Live on tvpn, please follow us. Leave us a comment. Add us to your RSS feed. We have a 15 minute version of the show called Diet TBBN where you can hear both all the flavor. Only 15 calories. 15 calories. You'll also hear ads from our sponsors like Banta Automate Compliance Manage Risk Trust Continuously Managers Trust Earns replacable and takes the manual work out of your security and compliance process and replaces it with continuous automation. Whether you're pursuing your first framework or managing a complex program.

You could have kept going forever. We both could have. We should do a gigastream with Satya sometime. Just 12 hours straight.

It'd be super easy to get that on your calendar.

Sure. There's a 12 hour block for sure. Somewhere out in like the2030s that we could lock in.

But I mean there is really so much. I mean that's the problem with these, these conglomerate CEOs. They just got too many business lines. You know, you could do it. You could do a whole hour just on Xbox and Activision.

Yeah, they didn't even stand out. The thing that stands out to me is when you see some of these other hyperscalers or players.

Yeah.

Their reaction time just Satya makes them look incredibly slow.

Right.

He's been making these like sizable bets. Like he's been seeing the future and yet only this year you've had other players who. I won't, I won't directly name Deciding like, okay, I want to get in the game now. It's like, what were you doing when. When Satya was in the kitchen?

Cook.

It.

Was fun.

All right. What a. What a wild day.

All right, what else is on the timeline? What other news should we bring the folks while we are here? I'm seeing mostly people talking about 996 Porsches versus 996 ing working hard. What else is in the what else is driving the news cycle today? Of course OpenAI just did a live stream with Sam Altman and the head of research over there talking about their side of the deal. All parties kind of align to hey, we got a clean cap table. Let's move forward. We're excited.

Pbc.

Yeah. Which is what anthropic is as well, right? Yep. Microsoft now owns 27% or $135 billion stake in OpenAI. And OpenAI is contracted to buy 250 billion of Azure services. That's a lot of Azure. Should make it easy to underwrite future capex. On the Azure side, earnings is tomorrow. We'll be back in Los Angeles at our at the TBPN ultradome. And we probably won't be live when earnings drops post close but we will be bringing you the news on Thursday.

Of course, Meta also reporting earnings tomorrow which will be notable.

Yes, we also have Alex, the product lead on Codex, but we're we're wiring.

Him up so that we go here through a post from semianalysis. They have a green text here. They say be me Qualcomm Time to enter Nvidia AI chip market. Nvidia is making money. How hard can it be? Spend years developing AI200 chip. Finally ready for big announcement. Make fancy slide deck. Put 768 gigabytes of memory on there. Sounds big. 160 kilowatt power consumption. Sounds powerful. Add liquid. Cool. Sounds cool. O S H I T jpeg what about flops? Decided just to not mention it. Also don't mention price or how many chips per rack or actual benchmark numbers. Just vibes Launch presentation. Qualcomm AI200. It exists and uses electricity. Refuse to elaborate. Stock goes up 15%. That feeling when investors don't know what flops are either. My face went greater than 10x with no baseline ships in 2026. AI 250 ships in 2027. Still won't tell you the specs by then. Probably low. Tco. Trust me, bro. Confidential computing. The performance is confidential. Unreal.

I have more there. But let me first tell you about Julius what analysis do you want to run? Chat with your data and get expert level insights and seconds? No. You know what's funny is that in the 2019 blog post from OpenAI announcing the deal with Microsoft, they say Microsoft is investing 1 billion in OpenAI to help us support building AGI. But specifically we're partnering with, with, with Microsoft to develop a hardware and software platform within Microsoft Azure. And so it feels like, I imagine what they mean by hardware platform within Azure is just like a bunch of Nvidia GPUs at that moment in time. But it does lead to these, like the natural questions of like, how deep do you go in the stack? And if you're Sam and you're OpenAI and you're seeing that you're ultimately limited on cloud capacity and then chips and then at first dollars because there was plenty of Azure capacity, it's not like in 2019 they used all of it, but they needed the money, then they needed the data centers, then they needed the chips, and now they needed electricity. And it's just going deeper and deeper in the stock.

Yeah. The thing that's notable is just how married OpenAI and Microsoft are. When you look at OpenAI's relationships with Nvidia, AMD, Broadcom and these other players, everybody in the chip space is sort of like, you know, in these sort of like complex dynamics. Right. We got some backstory on the dynamic between just like the whole series of events between OpenAI and Nvidia and AMD and how that all came together. And it seems like everybody in the chip side is sort of, sort of, I wouldn't say desperately, but desperately sort of like competing for OpenAI's attention and resources. Meanwhile, Satya is able to just kind of sit back and ride this partnership out. So I wonder where Qualcomm's retraced, by the way. It's now only up 8 1/2% over the past five days, so dropped a little bit after.

Is that on public.com investing for those that take it seriously. Multi Asset investing in food Israel's. They're trusted by mo.

We got to throw in a post here from Spooks. Early, early friend of the show. He's quoting a post from Samuel Hammond who said melatonin in the US is sold in 5 milligram doses with the effective dosage range is.03 to 3 milligrams. Americans essentially overdose on melatonin by default for no good reason. Spook says, okay, well, if there's an easier way to soul speak with my ancestors, in the dream realm. Please let me know. I don't know if there's been a.

Read at all on this particular stream. We don't even have tweets.

Load Bangerpedia is now live.

Do you think. Do you think OpenAI will launch a Grokipedia competitor? Is. I clicked on. I clicked on a Grokipedia. Yeah. Yes. But I clicked on a graphopedia like entry and I was like, oh wow. It's like a pre baked deep research report on something that I already would have wanted to search for it actually effectively. Great product.

I mean research has. If Deep Research has disrupted in the same way that ChatGPT has disrupted search.

Yeah.

Deep Research has disrupted Wikipedia and there's so many, so many prompts that I run that I'm like, I shouldn't be like burning up the GPUs for this.

Yes.

This should be stored in a database somewhere. I should be able to access it.

This is the funniest thing is that like over time OpenAI becomes we have.

A changes from Codex.

Alex, welcome to the stream. Let me tell you about fall while he hops on general the fire phone for developers. Nice to meet him. Nice to meet you.

Welcome back to the show. Thank you.

Congratulations. Incredible event. How many have you been to give us? Give me, give me a little read on the ground of like what's the. The scale? Is this the biggest ever? Tell me a little bit about what's going on today.

So we just had OpenAI dev day two weeks ago. That was awesome. It was massive actually. I got Covid like the day before so I was not there. So yeah, this is actually the biggest event like this I've been to this year.

Yeah, it's with the COVID now. You don't get the same level of. You don't get the same level of like oh, I'm sick. It's like, okay, so you're safe.

I didn't think it was a thing anymore.

But anyways, here today we announced a couple things. We announced that Codex is coming natively to GitHub.

Okay, nice.

And then we announced that today actually we're bringing Codex to Copilot VS code. Sorry, I'm going to mumble this. Try Copilot Pro plus subscribers and VS.

Code can use throwing Matt part of this amazing.

Walk me through the different flows and the different actual user journeys because there's something very interesting about GitHub has the ability to even host pages.

Yeah.

And then Codex allows me to from my phone potentially like write a web app. That then can be deployed. Is that helpful to actually, like, instantiate a web app on the fly that actually lives on the Internet that I can send to a friend? Is this, like, is there, is there the beginnings? Are you starting to see, like, what the next era of vibe coding might look like?

So totally. I mean, so mostly codecs is used by professional software engineers, although we have a good amount of people who don't aren't as familiar with coding using this. But, like, I think the best analogy is to think of Codex kind of like a human teammate. Right? Sure. If we're working together, I could talk to you in Slack, I could talk to you in GitHub, I could text you, I could come by your desk and we could like, jam on something on your computer. And so it's kind of the same you. But you're present in all those tools. So that's what we're trying to build codecs into. It's just like an AI software engineering team that works with you wherever you like building.

Sure, sure, sure.

So, yeah, use it from your phone and, like, make some updates there. Maybe that creates a PR you push into GitHub. You know, maybe you. You use Codex to review your PR on GitHub and then you land the PR, like all those things. No matter where it is, it's just the same Codex agent. Yeah, yeah.

What is the. Is there some sort of, like, business model flow through then, like, you have to be subscribed to OpenAI, but then you also subscribe over to GitHub, and there's kind of like. That's just like the default stack for a lot of people.

So this is like actually a. Kind of an interesting part of the deal. So to use Codex today, the main way that most of our users use us is to have a ChatGPT account. Of course, you know, they're on Pro Plus Enterprise, and then they can use Codex. Now as part of this deal, what we figured out with GitHub is how to partner so that if you have a Copilot Pro plus account and you don't need to have a ChatGPT account, you get the full power of Codex anyways. And when I say the full power, I mean you get to use our model and you get to use our model harness, which is kind of like the code that provides the prompt tools to run loop. Yeah, and so, you know, our goal in this is just to, like, make codecs as ubiquitously available as possible. And so, yeah, you don't need. There's no, like, flow through there. It's just like, you know, you just need your copilot account.

There's somewhere out there there's somebody that just started CS in college and they're only going to live a life that like just running codex in GitHub just naturally.

Yeah, well, I mean, look, so actually it's interesting, right? Like if you think of GitHub. GitHub does a lot of things. Right. But at least like where I personally spend the most time in GitHub is like actually collaborating with the other people contributing to the code base, right?

Yeah.

And so like I actually think it's quite unlikely that you would only spend your time doing that type of activity. You're also going to spend a ton of time in tools like VS Code or like the Codec CLI or ID Extension because that's where you're doing your work yourself.

Right?

Like again, like, I think the human teammate analogy kind of works pretty well here. It's like Most of us, 90% of the work we're doing is kind of at our desk. We're not like having a, hopefully not having meetings like 90% of the time as a software engineer, right. So probably that, you know, that person who's going to become an engineer but is currently in college, they'll spend a lot of their time with superpowers, but working at their computer, doing stuff individually like commanding fleets of agents. Right. And then they'll spend some of their time collaborating with their team. Like obviously quite a lot of their time, but not all of it. I don't think the sort of individual productivity is going away.

Yeah, Tiles.

How much? One kind of follow up question, like how much, how important is the metric of like how long Codex is spending working? Is that something that you guys are like explicitly like tracking and trying to scale? Because it just means that goes from.

Like 10 more value to 2 hours. We're tracking like the meter thing, but it was like 200 years. I click it and I come back. My ancestors come and they watch.

What's it into stuff.

Give me the 200 year AGI.

So like this, it's interesting. I actually shared at the keynote today that we lost Leak and then here on the Codex team ran codex for over 60 hours on a single incredibly hard task. And that's crazy. And we're like, we're excited about the capability of that. From the perspective of it means the model is actually able to do like very productive work for a long time. And it means the model and the harness are working together to manage the context window because More than one length. However, it's not like we have an eval that's like, how long did the model work? And let's maximize that. That's much more sort of a lagging indicator of, like, the intelligence capability of the model. Like, what we're really trying to drive is, like, how smart is the model?

Yeah. Right.

And how. How easy is it to work with? Yeah. And then it just turns out that as you make the model smarter and smarter, it can work. It can take on, like, longer and longer tasks.

Oh, how, How. What. What do you think about the user experience? If you're setting Codex off to go work for 60 hours, there's some risk that, you know, it's doing things maybe incorrectly. And you come back after 60 hours and you're like, I just. I kind of just blew 60 hours that I could have been doing this myself.

I mean, in that 60 hours, all.

Of Game of Thrones, that's possible. Or you're watching Subway Surfers here. We gotta go. No, but my question is, like, the workflow, that. That feels like the most natural. Give it. Using the teammate analogy is you just get a ping and it's like, hey, can you double check this before I continue? Is that. Is that. Is that a workflow that you're thinking about? Like, you just. You're a developer, you get a push notification, you're at the gym, and you're like, yeah, it looks good.

Yeah. I mean, so I think, like, there's two things you said that make a lot of sense to me. Like, one is just, like, steerability. That's something we're working on. We want us to be able to steer the model, like, short task, long tasks, whatever. Right?

Yeah.

The other thing is kind of like proactivity. Right. Like, again, when. When you hire someone onto your team, maybe at the beginning, you're hanging out, you're collaborating directly, then you start delegating small tasks, and at some point, you know, you will give them, like, a 60 hour task without, like, specifically prompting every single detail, right?

Yeah.

And what you expect of, like, a good employee is that they know when to ask you questions, right?

Yeah.

And so it flips from, like, the bottleneck of my productivity is how frequently I'm able to prompt an LLM to the bottleneck of my. Of my productivity is actually kind of like how I can structure the work so that, like, independent agents or humans or whatever can, like, go do the work and then ask me questions when they ask.

Because in. We're so used to now, like, trusting a Crud app, right? Like you can trust that you can put data in and you're going to come back and it's going to be there.

Right.

That faith. And it feels like with agents as a product category, broadly the agent, if you're building agents, you need to be focusing on like, how do I develop trust with the user? And it's come, you know, again, maybe it's like focusing on short term tasks initially and having that serability.

So this. So if you think about it like right now, the place where most people are using coding agents is to write code, right? Like CodeGen. And again, I keep coming back to the human analogy, but like imagine you had a human teammate and the only thing they can do is write code. They can't read user feedback. They're not in Slack. If there's an outage, they're not going to see it. Right? So like Zarag.

You mean a teammate?

Even if this is the smartest human teammate in the world? Like no. Right. So what we need to do to build this trust is we need to extend, extend what agents can like look at across the software development life cycle, right? So they're like present in more of the team conversations and ideation and prioritization and planning. They're also able like more and more capable at the code review stage. And actually that's one of the recent product releases we ship is like Codex code review and people loving that. But more present at code review, more present at like the deployment stage and like code maintenance stage. Aware of like what's going on in like your telemetry tools. And like I think that's actually how you get to the trust. So some of this is like increasing model capability, but a lot of this is actually changing the form factor of like how these models are harnessed so that they can like interact with more of what you need.

So it feels like a couple years ago we were just trying to predict the next token and it was like, oh wow, I can do poetry. Oh wow, I can write code. Like this is amazing and very like undirected fundamental research. Now we're in the age of spiky intelligence. Is there a feedback loop where you're actually trying to take feedback from. Okay, Carpathy says that the agents can't write, you know, nano GPT, so let's go work on that. Or oh, we've seen in the data that it's easy to write a, you know, website in Django and Python, but if you're trying to use FORTRAN to refactor some obscure High frequency trading system or something. Like, we're falling down on that. Let's actually put a team on this. Like, how does actually, how does feedback work now? And like, what are the humans on the team doing? Or is it all just zoom out and hope that the emergent properties solve? Like, is it deus ex machina?

This is OpenAI. So the way that we build is constantly evolving. Like the Codex team was just like five engineers a few months ago and now we're like, I actually don't know, but I think we're like 25.

Okay.

So it is constantly evolving. But what I can say is that our team is like possibly slightly unhealthily on social media, just like reading all the feedback. So we love it when people see send FEC to us. We're also starting to try to get a better understanding of, okay, how do different model snapshots and stuff compare. And so, yeah, we're starting to build up a more and more systematic way of doing it. But I still think it's quite early days and there's still a lot of taste involved.

Yeah, it does feel like we're entering the era where if you see in the data that maybe there's developers here that are like, I want to use Codex for this specific thing. You're falling down on this. You're great at everything else, but you're not with this. There is the world where you can actually go in and, and run the action. How do you.

I want to make sure you don't get the wrong signal from social media because, like, there's a certain type of person who posts about, post product feedback publicly and then there's. For every one person that does that, there, there could be, you know, a number of people that just turn and never say anything. There could be a number of people that are just like super hungry power users. And yet these people think like getting a push notification and somebody is saying like, you know, they're DMing you something that somebody said about Codex. It's like you need to make sure that it doesn't, you know, consume like 100% of your like worldview on how the product is actually resonating with users.

Totally.

So, yeah, let me add to that and I actually want to go quickly back to the for topic as well. But on that note, I think like, yeah, a lot of the feedback you're going to get on social media is like your power users. Yep. Right. And so power users, I think are really good. Like, I kind of put that in the, like, how do we advance the capabilities. And like we are trying to advance capabilities. So the feedback, like, what are they doing? Like, what should the product do? Make easier for them.

Yeah.

And then at the same time I think I like to balance that kind of with like, just like, what is the first mile of the product? Like literally like the first 20 keystrokes, like what are those? Right. And I think for me, I just like kind of focus on mostly these two extremes. So we're constantly looking like, okay, what is the new experience to get to the product? And frankly I think there's a ways to go. It's still a very power user reproduct and yeah, lots improve there on the sort of like the FORTRAN question. Like one of the interesting things about building codecs in open source, which we're doing, is that we're seeing like larger enterprises with very bespoke needs who are excited about the capability. You know, maybe an engineer was using codecs on the side, wants to bring it to work, notices like, oh, in this code base, it's not doing as well as it's doing in like the code bases that OpenAI has more, you know, see more. So like what we're actually seeing is like certain customers are starting to like fork the CLI or work with us to deploy it in a very specific way where you can inject like, you know, more instructions for the like company specific language, like into the context.

So if I'm a business, if I'm a big enterprise and I have a million lines of Fortran for whatever reason.

Yeah.

And I, you know, authenticate with Codex. You're not training on my, on my code, which is good for privacy but maybe bad for performance. So what you're saying is that there's a world where we could work together to figure out how to actually fine tune the model or train the model or work together to, to have the actual product work better on my code base.

Yeah. And I think like fine tuning and training are like, definitely levers that exist. But I think even before that there's like a ton of work you can do. Yeah. In the harness. Even in like, in terms of like, you know, agents, md and just like how you tell the model what it needs to know.

If it doesn't, every layer of abstraction we can do, there's, there's opportunity to squeeze out extra performance before we go back to like, hey, let's pre train on your data. Exactly.

There's like important. Yeah. I think there's a giant capability overhang for models today. And so, yeah, it's exciting. We're seeing like a lot of pull from enterprise now. And it's exciting because we get to kind of like, go deep. Right. And invest a lot of time on our side to figure out how to make it work, even with, like the current model on the front.

The pull from enterprise. Do enterprises care about benchmarks or do they feel like they've been hacked? Going back to the social media thing, I think people were really into benchmarks and then pretty quickly everyone kind of assumed that they were saturated, they were gamble. But what are you hearing on the enterprise side?

I think, yeah, I don't hear a ton about benchmarks, to be completely honest. I mean, maybe folks read it, but I think it very quickly comes down.

Think of the Sasser. It's like our CRM is a split second, actually fast. The competitor, like, you should use us instead, actually.

Yeah.

So mostly I think what it comes down to, at least on a lot of things we're seeing. Well, it's actually this kind of two motions.

Sure.

One is like, it's just like they give the tooling to developers and it's like, do you like it?

Yeah.

Right. And it's just like, what do developers like more? Luckily, developers love Codex, so that's great. The other side is actually, it's like, hey, like, we have this like, really big project that we want to do. It's like a re. Platforming, like a migrate migration from one cloud provider to another.

Yep.

Or something like that. And that's where we're actually like working more closely with enterprises to figure out like, okay, let's. Let's actually set up like a meta harness almost for like codecs to do this work. This started with like, some customers. Like Instacart runs Codex, like in one of these. I don't know if they don't. Probably don't call it a meta harness.

Sure.

But like in basically a system that runs codecs automatically to do stuff, you know, that they want to do for code maintenance. And then now we've been like, okay, this is actually a pretty good idea. Like, we can go help customers who have these, like, larger things that they want to do to set up this kind of like workflow automation. So there's kind of the two sides.

Last question. Are you feeling GPU rich or GPU poor right now?

Any. Any requests for Sam and Sarah?

We.

We.

So the Codex team is getting a ton of support. So you're feeling GPU rich?

I think.

I think the Codex team feels supported, But I think OpenAI, like, we could definitely, like, things are growing and more GPUs would be more good.

So, yeah, definitely fire.

Okay, so, so internally GPU rich.

Yeah, every time.

I wouldn't say that. That's probably overdue.

You know, every, every time I'm in chat GPT now and I, I prompt it on something that it should really think, you know, you. And it's like, you know, I had a request yesterday for like, give me a list of like 50 companies that meet these criteria. And it was like, I can't do that. And I was like. And I was like, yes, you can. But then in that time it was like, yeah, the, the GPUs were like, this is you guys, I'm sure. And somewhere out there there's millions of Codex agents, you know, running.

And yeah, this is the, this is the, the, the, the, the endless product feedback. But thank you so much for coming.

It's great doing all your curses. Thanks for coming by, guys. How's it going?

We have a few more people joining this show. If you're tuning in for the first time, please subscribe, follow us on YouTube or add us to your Spotify. And before our next guest, don't forget about LinkedIn. Yeah, honestly, honestly, I know a lot of you who are listening on another platform. Do not follow us on LinkedIn. Head over there. We are actively hiring someone. We just hired someone. We're, we're working on ramping up our LinkedIn presence. Very excited for that. We're also excited to tell you about turbopuffer search. Every byte, serverless vector and full text search built from first principles and object storage. Fast 10x cheaper. Thank you. Extra scaled in.

Thank you.

Next up we have Kyle, COO of GitHub. Very excited, very excited.

We are wiring him up.

We'll be coming on in a second. Next we'll have Jay, EVP of Core AI.

I sold to J a couple months ago. At this point, I was in the back of a car. It was, it was kind of hard to get to get to bring like the level of enthusiasm that I had. But I'm very excited to talk to him because a lot of the questions that I got when I asked people, hey, we're going to Microsoft, we're talking about their relationship with OpenAI was we want to hear what Microsoft's doing inside. What is the core AI team? Where's that driving the business? So we're excited for that.

And then next we'll have Jared Palmer, VP of Product, Core AI and the SVP of GitHub. And then we'll finish it off with Michael, founder and CEO of work.

That's very exciting.

Excited about as well. Ken heads back to the timeline. Tomorrow for the show, we'll have to dive more into Grokpedia. We gotta start using it, checking it out while we have time. There's an app launched by a product designer at Meta. Did you see this? It's an AI app that if you can't afford a vacation and the Verge is saying an AI app will sell you pictures of one. So you upload images of yourself.

I saw.

And then it put. It creates a bunch of vacation photos for you. So I guess short the tourism industry.

This was announced by a founder who just said like, I wanted to feel the feeling of like the warm and fuzzy vacation photos. And so, yeah, I used AI to generate those. It was. It was pretty well received originally, but I think that they're spinning it and so the narrative might be getting away for them. But we have Kyle from GitHub coming into the studio or the studio. Hey guys. Hey.

Great day here.

Thank you so much. Stop by, Michelle.

Yeah.

Mask day. Beautiful event. Good weather too.

I know it's a little here when the weather's not great, but when it is, it's been.

So give me a little background on you. How'd you wind up here? How'd you wind up at Microsoft?

Yeah, So I joined GitHub 12 years ago before we had managers. We call it open allocation now, but it was anarchy. Freeman Nat joined in 2018 as part of the acquisition.

There you go.

So, yeah, we were 140 employees when I joined back then.

Wow.

And do you even think about employee count in GitHub now because it's so merged into Microsoft, but it's still its own brand?

Yeah. I mean, we have over 3,000 employees that have worked full time on GitHub. Then obviously we partner with Microsoft Teams, do a lot of, you know, the AI model hosting, training and so on and so forth on.

On sort of a meta question. I mean, AI and you know, AI can do so much. How are you thinking about scaling that team over the next 10 years? Is it harder to forecast like human capital allocation in the age of AI?

Yeah, I mean, part of the problem is that there's places where AI is like, incredibly helpful, like in software. I think for sure there's a ton of places that AI hasn't hit. Yeah, you know, I mean, there's that we talk about, like it so many of the sort of business operations side that AI hasn't proven to be as Valuable to us yet, but I think over time it'll get there. Events like this take people.

Yeah.

You know, full in, full out. And so there's just an imbalance, a little bit of where software has been so great and then the rest of what makes GitHub. GitHub, yes. These people still.

Yeah.

Earlier with Satya, before we jumped on, we were catching up with him and I said my words, GitHub co pilot is criminally under hyped. And I think the reason for that is like, you guys don't need to go out and raise a venture round every couple months and you know, you're obviously well, well capitalized. But can you give us a sense of the scale and kind of the growth of Copilot over the last couple of years?

Yeah, I mean, you know, GitHub is used by like 80% of everyone that joins GitHub right now. It's 36 million, I believe, joined in the last year in the first week, like one of the first things they do when they join. So we're definitely still hitting the world's developers with Copilot. Yeah, all the time. Like every day now, these days, just like, I don't know, 10 years ago, devs are using whatever tool they want. Things are changing so quick. You got to keep up, you got to try all these tools. But we keep seeing folks using, you know, Copilot over here and then trying out a new tool or using Copilot over here and finding this new flow. That's a big part of this, like reopening up like GitHub has done over and over.

Yeah.

Let's bring us all together so you can collaborate in that single place while you're going to pick whatever tool you're going to use. And that's cool. I can't tell you that part of.

Being, that's part of being a platform. I was saying when he was on the air, it's like you can't, if you want to be a platform, is the more closed off you get, the more you're encouraging other people to go elsewhere because the tools are changing so quickly. You just, you need to be able to give people that flexibility. Right. And we had Alex on from Codex and that's a good example of it.

Yeah, I mean, this AI moment, it feels a little bit like before every app had an API back in the day because that like, that wasn't the norm. And now we're in a kind of quasi walled garden moment where everyone's making their thing really, really great, you know, Their model, their app, their service, whatever. But in order for all those tools and agents to actually be valuable, we have to interconnect them. So my hope is that just in general, not just for software devs, we can go back to that platform first approach, just like as an industry, because then each of our products will be more valuable for our customers because we're not going to have to deal with, well, how do I actually place the grocery order? Yeah, because there's not an API for that.

You know what, what were the kind of key moments for you and understanding that AI would completely change software engineering? Because when you, you know, we were just going back through like the history even of Microsoft's investments in OpenAI, obviously because of the announcement today. And it just feels like Satya had this incredible foresight, you know, in, in 2019, in the early 2000 and 20s that only now a lot of other CEOs are kind of reacting to. But I want to know for you, you've been here for 12 years, like, what were kind of the key moments that were eye opening to you where you thought, I've seen the future. We need to just invest heavily, heavily in this.

Yeah, I mean, the first moment was kind of a pure open source moment. Right. Like when it first started happening and we were talking about Transformers and everything. Like you see the groundswell on GitHub from the open source side. So we started talking about that. And then when we got access to the first, you know, GPT3, I think, you know, model to ultimately build Copilot, the thing that was so interesting was we were building it to write docs. Like that was what Copilot was. Copilot was taking. Oh yeah, Copilot was taking that model. We were going, oh yeah, what we're.

Going to do, we're going to like writing code.

They hate documentation.

So we're going to generate the docs.

And then what hackathon was. Code is exactly the same characters. Absolutely.

Exactly. So then we flipped it and then we got to this from. Exactly. And then I think, you know, while it seems very simple now, like the idea of ghost text, like more than just like an auto complete or whatever, the first time we used it, that was truly the moment where we've said, oh crap. Because no one had to do something differently. And I feel like that's the big problem with some of the AI tools. You got to go interact with them in a way that's not normal. You got to go, okay, I want to go write an email. Write an email for me that that's not how our brains work. We just start typing. And when we were able to do that with the ide, that very, very quickly kind of shook all of us because it meant, oh, it won't be the same anymore. Now with agents and whatnot, like because we can verify the code, we have an advantage in software versus some other agents where it's harder to verify. But that all started that first time. You like wrote something and then it just appeared and I didn't have to learn anything, it just happened. And now we just, you know, we all take that for granted because it's de facto. Yeah.

Do you have a philosophy of, of how where inference happens will change over the next few years? Like in a lot of worlds there's, it used to be there's a decision between like fire off an agent, wait 20 minutes, wait an hour or something, or do it quicker, you know, a couple of seconds. But then there is the world where a lot of the work that's going on in software development is so high value. Like why not just do all three inference it locally immediately and then also in the fast model and then also kick off an agent for every single task. Is that where we go or is there sort of like some sort of shift in where inference happens over time?

Yeah, I think, you know, we clearly are going to have more and more inference that's has that, that should happen locally. Yeah, that seems pretty obvious at this point, I think. And then I think when we're talking about, you know, how much we're going to kick off into what cloud A and models, et cetera, I think the thing that's really interesting is that we are pretty close to having that now. Like we're, you know, we talked about it a lot today. Other folks have that the real problem is, is like the age old like garbage in, garbage out problem.

Yeah.

Which is like we talk about abundance and you just just fish off five tasks and we pick the winner. Well, if your input was crappy, then probably all five of those are also kind of crappy. They just variants of crap, I guess, you know, and so I really think it's about when you're having a discussion with a colleague or you're on a zoom call, or you're in an issue or in linear using a ticket. That is the moment when we can actually get as much context as possible and ask questions. Then like, why isn't copilot in that moment going, I think I know what you're trying to build. But like, are you sure about that.

Yep.

Why do I have to carry that even via a click and then go, let's plan to build this again? Humans don't do that. Yeah, you just read it and we get started. So I think it's. It is a bit about where inference happens, but I think it's how early. And I have a problem that needs a solution. I should be doing inference in the background immediately before I ever invoke something, you know, to go say, now it's time to work. It started while we were in the shower thinking about the idea. That's, I think, what we need to get the AI to do more of.

Yeah. I'm thinking about, like, AI at GitHub is the funniest thing because it's like seven different initiatives. Walk me through your thinking on. With a lot of companies, I see this idea of AI above the fold.

Or below the fold.

So above the fold is kind of like I put a search box and I let you interact with my app, my SaaS, my CRUD app, via natural language. But then behind the fold or below the fold is like in the. In the. Behind the scenes, I'm running inference over the data to improve the user experience, but I'm instantiating it with HTML basically, at the end. But with GitHub, you also have inference that you're selling directly. You have a whole bunch. Are you seeing any exciting developments on, like, that behind the scenes? Like using AI to improve GitHub as a product that isn't actually bubbling up to the user experience directly in the form of just like, you know, a text box? Have you. Have you seen developments there?

Yeah. So, I mean, a big part of what we've been figuring out is, like, we have so much information about your interactions.

Yeah.

You know, pull requests, the ones you got closed. Like, I told a story. The first pull request I ever shared didn't make it. And, like. But now you know that about me. And so I think the thing that we're figuring out is, like, we have, like, new models that allow us to really, deeply understand your code more.

Sure.

Beyond just like the tabbing and asking a question, because then if we have that, then we have to understand what does Kyle. How does Kyle work in a pull request? Yeah. What mistakes does he make every single time?

I'm super fascinated by this. That's the thing we were just talking about with, like, Grokopedia today, where basically it seems like the XAI team went and ran a bunch of deep research reports for all the topics that you'd want to know and Then you just have that pre cached output. And I'm so fascinated by this idea of you have a ton of user data, you have a ton of inference. It's going to be really inference expensive. But what, what is some sort of, you know, cron job that you can run over your entire user base all the data and then just have surfaced results or surfaced action items. That seems like an interesting, like underexplored territory.

Yeah. If you think about today, we're saying we're going to bring all these coding agents. Sure, sure. Why, why does each coding agent have its own memory of how I've interacted with it? Yeah, I've been a developer for 20 something years. We could just go, here you go, take this with you. Yeah, you know, you can understand how I work. So I'm going to get a result that matches what I'm looking for.

Okay, walk me through the game theory around enterprise pre training for coding agents. So if I'm Coke and he's Pepsi and we both have written a bunch of corporate code and have a massive GitHub installation and if we both say yeah, we're going to train on us, maybe we both get better models, but at the same time we don't want to leak information. So what's the current thinking among big enterprise customers around? Will they jump over and say yeah, you know what, it's worth it. Or from a, from an actual, like when an AI scientist just be like yeah, I don't need that code anyway. What, what's the current thesis?

So we spent a long time trying this and the problem is that everyone goes, hey, our code's very different. It's super unique. You work a certain way, you don't like. Well so many companies don't. Now there are, there are examples of where that's not true. Particularly really companies with a really long legacy of like COBOL mainframe code, etc. We've been kind of discussing with them like what would it take to get another hundred million lines of COBOL code? Because then that does matter.

That actually moves the needle under quality.

Of the coding 100%. Because the problem is, is that the practices and principles don't change that much. And then most of these companies are also trying to modernize. So they don't want the code to look like their old code. They want to use their unique IP and look like the thing they want it to look like in the future.

But If I have 100,000 line Django project and he has a hundred thousand line Django project, like you're not like, oh, if only we had that.

No, no, because we know. Because we've, like, done it in, like, there's, you know, margin of error improvements, but nothing major.

Yeah.

When we look at the, like, looking at the chain of commits, then you can get to some interesting information.

Okay, how was the enterprise built?

Exactly.

Exactly.

And why was that choice made? There's a little.

You can obviously still instantiate that on the fly with. With an enterprise partnership, there's just always the question about, like, is there something beneficial to all the companies working together? But that's very helpful. Thank you so much for hopping on this show. This is a lot of fun.

You have incredible voice for podcasting.

Come back on anytime if you ever wrap up. We have Jay Parikh next. Oh, no. We are moving on. Okay. We are going to take you back to the news. Thanks for tuning in. We also have to do an ad read for Google AI Studio. We are behind enemy lines here at a Microsoft event, but we are presented by Google, Google AI Studio. It's the fastest way from product to production with Gemini, chat with models, vibe code monitor usage. We are obviously very happy to be supported by all of our sponsors who make crazy events like this possible. We are obviously able to come up here on short notice due to our sponsors and what else?

Jay looks like he's getting miked up here.

You would run Jay Parikh in me?

I brought this up yesterday, John. Remember? I brought up. I did not know that Interstellar. That with Interstellar, Christopher Nolan spent $100,000 to plant 500 acres of real corn in Alberta.

Yes.

Then sold the corn for a profit after filming.

Yes.

And it remains the most profitable commodities trade in Hollywood history.

Yes.

You acted like this was.

This is old news. I knew about this years ago.

This is what I heard in the AI area of, like, well, I could just generate the scene with AI, but.

I want to miss out on the commodities trade for sure. No, I think Christopher Nolan just got lucky here. Honestly, it is pretty hilarious. I also wonder how. How apocryphal is this story? Because it doesn't account for everything else that went. Like, if. If the corn was planted by production assistance. Right. Like, you have to burden that cost into the actual roi.

Yeah.

Is to plant corn.

Who planted the corn? I'm just saying, like, who planted the corn? Is this gross? Is this gross profit or net profit? That's what I want to know. Christopher Nolan. Everyone's talking a big, big game about the interstellar trade, and it might not have been as good as you think. Also, who owns the, who owns the rights? Does the value of the, of the corn accrue to everyone who has points on the back end? Like, does Matthew McConaughey make a couple dollars off of that corn trade? I don't know. We'll have to get to the bottom.

Of it or just get to the budget.

We have our next guest. Welcome to the Stream day. How are you, Derek? Thank you so much.

Welcome to Sweetie2XP.

Introduce yourself for anyone who's been living under the data set and explain a little bit about what you're working on today.

I'm Jay Pari and I am the EVP of Core AI here at Microsoft.

Okay.

Important job.

Do you have anything to share that updates your job today based on the news with OpenAI, or is it just exactly the same?

It's exactly.

It's exactly the same, yeah. Really? Okay. So are you marching towards AGI? Is it a waste on developers? Is it a race? If Microsoft becomes a platform for AGI and OpenAI can compete there and Microsoft's AI, internal AI team can compete, is there a world where you're racing to AGI against them?

I think we, we have a process for figuring out what AGI is. Yeah. And that is something that both companies will continue to collaborate, work on research. And in the meantime, we have this mission, which is to focus on developers and how we unlock way more creativity and to build a ton more things. So I have this idea which is, or this concept where you think about all of the potential. You think about the Hoover Dam, for example, right? You guys are familiar with the Hoover Dam. There's 9.3 trillion gallons of water behind that.

It's about 1 gigawatt, right.

And it's like massive. Right. In terms of the amount of energy that it can generate. So think about all of these large language models, whether they be small ones, big ones, closed open ones, multimodal video, audio, text, et cetera. And you think about how we're going to unlock that intelligence. And in order to unlock that intelligence, we have to write a lot of software, Right. And so if you think about the history of Microsoft, Satya commented on this earlier. You know, it's like Microsoft has been around 50 years, right? And you think about all the software that's been written by Microsoft and everybody in the last 50 years. And I would posit that only 1% or less than 1% of the software that has been written in history, and that what we're going to see in the next 10 years is just this like prolific expansion of the reservoir that's.

Going to create you might be the enderpone, right?

Yeah, it's going to be crazy, right? So that is why we're all here today, right? Which is like, how do we really drive and use agents, use this technology with the right guardrails, with the observability, being able to customize this, personalize it, be able to tap in and bring in open source, being able to bring in your enterprise specific knowledge and controls and all of that and to really just change that trajectory of creation of imagination in a building. Right. And I think actually the notion of even what we think of as a software developer is going to change right now to make this way more approachable by anybody who has an idea. Being able to translate that into showing something, building an app, getting out there, getting feedback, iterating on it way faster than we've historically been able to in, in codegen.

Like I want to get your read on how you're thinking about like today developers are, you know, maybe they have some favorite tools, but they're willing to constantly be experimenting, trying new things. You guys are in a great position to be able to support that through partnerships and sit, you know, at a foundational layer with GitHub. But how are you thinking about what's your view on switching costs today and how that might evolve as, you know, in five years from now, do you believe developers will continue to just, you know, want to always be trying the latest thing or do you think they'll like, eventually switching costs will get to the point where it doesn't make sense to just constantly be looking over in other places. It really makes, makes more sense to just focus on what you have.

Yeah, I think there's like an element to, you know, developers, builders around craft and I think you're always going to want to find like the best tool or the tools that suit your sense of craft. Right. Whether you're a wood, like a wood maker, you're a painter, you know, and there's like a big element of craft. So I think that there's going to be use cases where, hey, this is the way to do it. These are the best tools to say modernize or upgrade some version of like old Java code that you may have and there may be just like this is the one or two just true tried ways of doing it and proper tools that you use. Then there's going to be new use cases that we haven't even discovered seen yet today you think about some of these rapid prototyping apps And I think, you know, it's great for the ecosystem that we're seeing different startups. We have different, you know, we have GitHub, Spark, we have different ideas that are all kind of competing and trying different versions of this. Those things do mature and there may be a smaller, narrower field. But I think right now with this inflection that we're seeing in terms of building and velocity of change, that there will always be lots and lots of things to go try out. And I think that's good for developers. Right. And I think our platform is such that we care a lot about that ecosystem of startups. Other companies that can bring that choice, bring those tools into it, but then we can help like hook those things together from an observability controls, like just a sensibility perspective. So if you want to scale this adoption inside of your enterprise, you need those Rails, so to speak, right?

Yeah. There's so much like practical on the ground. Just make the piece of software 5% better. With AI today there's so much low hanging fruit. It's a very exciting time. At the same time we're in this like I feel like we're taking a breather from all the AI. Fast takeoff. And it's exciting because they can go build so much enterprise software, so much value, so many new companies, so many things built on top of Azure and Microsoft. But at the same time it feels like there is a new need for going back to the roots of academia or these like academic labs or these scientific labs. Do you have a pitch for if there's someone out there who thinks that they're going to be the. They're going to write the next. Attention is all you need. They're going to write the next transformer paper and you know what, in the short term they're not actually going to help optimize knowledge retrieval or Codegen for this next couple of years, but they believe they want to do that. Do you have a pitch to them where they can come and work at Microsoft and do that level of research?

Yeah, absolutely. So I think there's lots of different adventures you can pick inside of Microsoft EAP4 and focused on builders developers. Because one of the other fascinating and fun things about the core AI team is we have this super tight collaboration with Microsoft Research. Yes, right. So Microsoft Research has all of 30 plus years of history in science research and programming language research, compilers, security and you name it. Right. So we actually have a lot of collaboration and joint problem solving right. Where they can focus more on that open ended Research, whether it be, hey, here's how I'm going to go optimize this model. Here's how I'm going to do formal verification of the code that comes out. Here's what I'm going to do in terms of how to secure this code better. And so those things are out there, they're like big unsolved problems. There are longer time horizons. Then as those innovations, those inventions happen in research, we can do the tech transfer, we can do the combined product making together and then that accrues into GitHub or in VS code or into Foundry, whatever is the right avenue to bring that stuff to our customers, to developers.

Yeah.

Where do you stand on the should you learn to code Debate?

Oh, that's a good one.

I think. Yes. I think you should learn everything you can learn about these systems because the fundamentals, you know, ultimately if you can understand like how this stuff shows up and it's instructing a computer, a gpu, a mobile phone, then I think that. And it's less about maybe even knowing kind of the, the code, but it's that systems thinking mindset. Right. It's the cultural aspect of it. It's like, hey, I'm creating, I'm prompting and guiding this thing, but here's how the code is going to generate. I understand what these models can and can't do, how to guide them more with a higher efficacy. Right. So absolutely. But I think of it more as like, less of a narrow question of like, hey, should I learn to code or not? Yeah. It's like, how do I understand the system, the new system for how we're going to build software? Build innovation. There's understanding the hardware, understanding the software, understanding, for example, evals.

Yeah.

Super, super like important concept. Totally underreported. Like. Right. In terms of what's going to happen. You have these offline evals. We have these. What we're saying is Ter said in the media. Yeah. In terms of like how important that is to get higher, like quality outputs of these things. Because there's the offline evals that we can sit there and we can score and say we got these evals. Then there's the online or the lived experience. Right. Where you put this AI into this product. You're like, wait, that doesn't quite work the way it is. Eval said it was going to work. Right. And if Marco is selling it's weird ways. Right. In terms of.

Sorry, I have one more on that. We got your answer on should you learn to code? I want to Know, should you learn to deal? Should you learn to do deals? Is deal making underrated in 2025? In the age of AI, being a deals guy, understanding incentives bring people together, around a table, iron out a deal. This is something that's. It feels like it's growing. We saw it with the Microsoft OpenAI deal. That was a very unique deal. That was something that a lot of people, if they were just saying, oh.

Well, much more than a, than a traditional.

There were a ton of reasons not to do, and it got done. And it's probably one of the greatest deals in tech history. So is there value in learning how to do deals and becoming a deals guy?

I don't know that that's a 2025 question. I think that is a life skill to know how to collaborate and how to negotiate and how to compromise and how to see, you know, and sometimes, like, there isn't a deal to be made, and other times there's a greater output or there's sort of a greater, like a global maxima that you can attain. Right. And that's where, even if you look at the news today, with our announcements of partnering with OpenAI and with Anthropic, bringing that all into this platform together, I think is the. What we can go build and what we're going to discover and how we're going to accelerate our joint learning, I think is important. Right? And that can turn into a deal. But I think that comes up with this, like, hey, there's a greater good, there's a greater opportunity, there's sort of a greater market, there's a greater problem, a bigger problem to go solve, then, yes, Figuring out how it's going to work. Nuts and bolts.

I like it.

How do you think about Jevons Paradox in the context of code? During the deep seq moment, Satya quickly came out and I think he posted the Wikipedia link to Jevons Paradox and it sort of like, steadied the market broadly. There was people that just weren't familiar, but I think it was well timed from his side. But when it comes to, you know, on our side, you know, we're a media company and we have a developer on our team, and I think that, like, five years ago we wouldn't have had a developer, and as it's become basically cheaper and faster to create software, we now want to make software and we're a company that historically just wouldn't have. So I'm curious how you think of that in the context, you know, going back to your earlier point of like, we might have a hundred thousand, a hundred thousand times more code. So what's your view there?

Yeah, I think that's what we want to see, the acceleration. Right. I think we talked about today, there's 180 million developers in GitHub today. Right. And new developers joining GitHub.

Every sector.

It'S a second. And I was like, that sounds like miles per hour. But this is just such an abstract concept.

That's how countries talk. They're like every second.

Yeah, there's a baby born every second. Yeah.

But to think about, you know, it's not, you think about the, the types of personalities and backgrounds, right? You can be a product person, you can be a designer, you can be a, you can be a marketer, you can be a deal maker, like all of this stuff. You can join GitHub, you can start building, you can start checking in code, you could start mashing up different things. So I actually think it's a super exciting time to see, see what the industry is doing. Right. And I think it's hard to predict the future, but I do actually really, really fundamentally believe, like from a mission perspective in core AI, our job really is to unlock that creativity both in the AI powered tools that you heard about today, plus the platform, making these things secure. And really like anybody who's got an idea, wherever you are, in whatever department you are in an organization or an individual, you should be able to actualize that. Like, you know, we have this saying in our team which is like, you know, more demos, less memos, Right. It's like all about building and showing and iterating. Lots of stuff gets like, we don't like it, you know, but the fact that I can in 15 minutes go through 15 iterations versus in the past, I might get a quarter of an iteration done that I think is going.

To, no matter how good a memo is like seeing, seeing the product tells you you're 10 times more, it sort.

Of gets more creativity from the team, a small group of people. Now we have to make sure we also spend time dealing with the fact that there are, there are gaps in these, in the technologies. Right. They don't like work perfectly. Right. So we've got to keep building those guardrails, we got to keep building that training. The models will get better, the tools have got to get better as well. And that's where I think the GitHub community, working together with these different partners that we have the platform, we just have to keep learning faster and faster and faster. That's what we're focused on.

So more demos, less memos. Let's role play for a few more deals. More deals, potentially. That's role play. We're trying to do a deal. If I'm a Fortune 500 CEO and I'm coming to you and I'm saying I want to transform my business with AI, I don't want to make mistakes. What pattern should I avoid? What mistakes have you seen? Broadly trends that I want to stay away from so that I can move forward with something that actually drives shareholder value and isn't just rah rah. I'm doing AI now.

Yeah. So the first thing I would say is like really understand what the top 1, 2, 3 outcomes are. More like specifically like, hey, I want to transform my business. Okay, well, what does that mean? Yeah, okay. Do you know what that means? Are you saying, hey, I need to. I'm in an understand phase where I even just need to even create some bright lines around what is the ideal or kind of my dreams around the outcomes of what transformation means. So get into the specifics of the what that actually means. Is it some revenue thing? Is it some product thing, Is it some.

Even if it is the difference between are you trying to cut costs or are you trying to grow top line?

Right.

That's number one is just understanding like one, how are you in the customer? I keep asking why. Yep. And to try to get more grounded in what those specific things are. Number two is one of the things that I will always encourage them or talk to them about is to then don't just talk about these things. Right. It's like, what are you doing to start learning? Because if you're early in that journey of understanding AI, there is only so much that you can sort of like read and talk about and conduct meetings. You do need to have like this internal adoption. Right. Where people are and you're encouraging, you're incentivizing, you're really driving that experimentation, that curiosity of your organization. Right. So how do you understand what your base level of curiosity and risk taking is? If you are a more risk averse company in a slower moving company, then how do you change that culture? Right. So cultural transformation is. It comes up in 90% of my customer conversations. We'll talk some tech stuff. And then they're like, okay, Jay, how do we do this people wise? Right. And then the third thing, headcount.

Headcount planning. They're like, what's your plan? Maybe I'll adopt.

Right. And then the third thing that I always will encourage folks to do and we'll have a conversation is like, raise your level of ambition, like wherever you think you are in terms of ambition and that outcome, I promise you it's not enough because the technology, the models are growing way faster. They're getting way smarter than we humanly understand. So whatever ambition you have for this fiscal year or this half or this quarter, take it up a notch or two and then strive and push and lead to that point. Yeah.

Do you have a right line internally with. I feel like there's some organizations where core AI means not generative AI, but I don't think you use that exact dividing line. But should there be a dividing line between like machine learning recommendation systems, how Netflix recommends me, the next thing to watch, for example, like that is an AI system. What pops up on my newsfeed is AI, but it's not generative AI. It's not what we think of when we think of generative image models. Is it worthwhile in 2025 to have a bright line between those teams or those skill sets or is everything bleeding together?

I think things are definitely blurring together and there's stuff that's informing from one set of techniques to the other and vice versa. I do think that those systems are very, very sophisticated. They're very, I would say powerful in terms of like user experience today. There are definitely places where people are using gen AI when they shouldn't be and they should be using machine learning techniques that just really work and are faster, better, cheaper.

Cheaper. Yeah, you can imagine a bunch of things.

Those are the things that we have to watch for in organizations where gen AI is the hammer and everything looks like a nail when we actually have these mature, optimized and really exceptionally bright people technology to use those and not forget about those. But I do think that in at scale, the stuff that we've learned in these, more maybe, you know, more mature, more scale out machine learning systems will feed back into how we make products using gen AI.

Well, thank you so much for coming on the show.

This is always take care.

We have Jared Palmer, the vice president, a product porter I and SVP of GitHub, the SVP, the V. We're. You're both a vice president and a senior vice president.

Yes.

Is this like a two faced situation?

I might just back up so to regional branch manager.

Yes.

Right.

Yes.

Yeah, yeah.

It's like Assistant to the CEO. Assistant CEO.

Technically it is VP product core AI and SVP of GitHub.

Okay. Does this incredible.

Welcome.

Welcome to the gig. Thanks. Monday 3013 oh 13 yeah.

Okay.

What were we doing before A month.

I was VP at AI of Vercel.

Great. We had Guillermo on the show yesterday.

Rad. A little thing called V V0 V0 and resolution.

Yes.

So in been in a game mod for, I don't know, a little bit doing that stuff. So. Yeah, it's been fun.

Vercel has been great.

Yeah. So I mean have you had time to actually develop like a vision for what you're building here? Is it too early to ask or. Or are you still just in kind of like. Let me assess the tools in the tool chest over here.

Yeah, it's day 13, so definitely. But I've been a long time GitHub user for. For very, very long time. Like I don't think over 10 years.

Yeah.

I made my account. So.

And I imagine you've been thinking about like the broader developer experience and what this means in the age of AI all through the last. I mean the last five years have been like a deafening ring of like AGI and takeoff and timelines and stuff. You must have engaged with that.

Of course.

Yes, yes. And we obviously have Vercel. We thought deeply about totally developer experience. I think that's really the vision is how do we bring apart with core AI and the formation of it. We just had J on.

Yeah.

I think by combining Microsoft's assets across the stack. Right. We've got the S code, Visual Studio, GitHub and putting these actually all in one. Org make for the ultimate developer experience. And that's what our goal has to be. And focusing just on that is I think my first and foremost.

Yeah. Do you think that developer label just melts away eventually? You think there will be a dividing line in five years, 10 years?

I don't know, five or 10.

I mean it just feels like there's a world. I don't care about that but. But it's just feeling great, you know, like there was a time when. When to take a photo. You needed to be a professional photographer because you need to. Needed to understand how to change film in a dark room. And now everyone has the. A smartphone camera and everyone's a photographer. That feels like it's coming. I don't know, I just see like I can open up an app on my phone, type a prompt, get code.

Sure.

It's like kind of hard for me to. I need to link my GitHub account instead of pages to like actually deploy it. But like we're only a couple months away from that, I feel like. And then eventually it becomes like More prompt driven. But then there's still value. I don't know. How does all this play out?

I think there's always gonna be a market for people who get stuff done.

Yeah, right.

Yeah. Just high agency people, lighter people.

Clarify.

So builder who and whether it shifts into more product focus.

Yeah.

Knowing how to build like systems that are big and large.

Yeah, yeah.

That may be outside the training set. I think it's always going to be important. I also think that some of the, the way I think about it at least is some of the, the pipes, the tooling probably aren't changing as fast as the AI is.

Yeah.

What I mean by that is like the way that packages and code is distributed, tested, spilled. I don't think that's going to change as fast as maybe the models will. That makes sense. So with that infrastructure in place, I think you're still going to have human involvement for quite some time. I think the things that people will build may be more ambitious. I think that's really exciting and our job is to facilitate that and empower developers and think about what they need. But in five years or so, I still think people are going to be building stuff with. You're still going to be coding. In some respects it just may look very different.

Where do you want to see model progress? People talk about the models are going to get better, like they're just going to get better.

Right.

And plan around that. But like when you're, when you're talking to labs, like when you, when you're at Vercel or when you're now, now at Microsoft, like where specifically are you even thinking and kind of pushing them to say like, hey, like it needs to be better here.

Yeah, I mean that's a great question. At Vercel we worked deeply with the model labs. We obviously were very focused with a product like B0 on a specific subset of what models can do. Even in the coding realm, Vercel was always focused on front end. Right. And specifically Next js. So not just one language but one specific tech stack. And so we're always, you know, engaged with how can we make it better for next JS. Switching gears for a second to GitHub. Obviously we're now multi languages. We care about everything, but we do care about coding. That's the primary, the primary focus point. But coding involves so much more than just generating like more than auto source code. Right. It's more than autocomplete. We need models to be great at research, to be great at reasoning and I think. And then Also delivering mergeable code. Right. That's I think slightly different than just complete my comment. So we've been focusing a lot there and focusing on quality and something that we look to continue to hill climb on as time goes on.

How much have you studied the, the open source company like scalable business model? Like what, like what Vercel did with Next js. Like are you, are you familiar? Can you give me like, like the, the crash course? If I'm like, I'm a developer, I want to build a business. I'm. I'm going to open source a, you know, a package that does something and then I want to build a business about it. Like what are the pitfalls that I need to avoid? How do I actually balance, like what are the trade offs that I'm making to actually build a great like open source for profit company? Because there does seem to be some tension there. But it's held, that model's held for going back to Red Hat Linux all the way to Vercel today.

Sure.

I think I'll. I have a controversial take.

Please.

There aren't as many pure open source companies where their core product itself is open source.

Sure.

I think the more successful strategy is actually if you really dig into Vercell is Vercel is not open source, but Next JS is open source and Next JS is a complementary satellite product that drives attention that is used by Vercel to make a better product. They got this amazing feedback loop of internal dogfooding. But there is a community around the project which then I think Vercel has a material amount of Next JS overall builds and developers use Vercel. But it's not like Vercel is this open source business. Right. It just has Next JS as one of its largest pieces of the open source portfolio. But it also has now aisdk. And with Vercel the idea was to do something what we used to call framework defined infrastructure. So framework defined infrastructure. And the idea was you can build this framework and with no configuration you can deploy it and you don't have to think about scaling it. And so the, the analogy I would make is like imagine you were asked to, I don't know, cook food for everybody here at Universe.

Yeah.

With Vercel the idea was like well what if we gave you the pots and pans and all you had to focus on was like cooking for your family of four and then Vercel would worry about like scaling it to everybody here. And so I think to your point about like Open source. My suggestions for the crash course is a common pitfall that you should not run into is just assuming that your free open source users are going to directly translate into paying customers. Interesting. I think that's actually really hard because you've set up expectations that you're giving away a free service with this free. This code. Right?

Yeah.

And then all of a sudden they're going to convert and pay you X dollars a month and you're going to have an enterprise business which you haven't been really honing in on and grinding on. And that's just going to happen overnight. I think that's, I think that's. And yes, walls. You need to start from the beginning with both and also set expectations with your, with your user base that this is paid, this is open source. So if you can find a beautiful symbiosis between those two, that's where I see like it really being successful.

Is there some sort of like barbell strategy where you should like actually go really broad with your, with your open source package. Anyone's using it, but probably like, you know, small developers, startups, solo indie devs are using it. And then if you jump all the way to like, oh, you notice some big corporations are using it. So you go with an enterprise plan on day one, it's like they're not going to be. They have no ground to stand out. If they complain, they're like, so. So it's a lot easier than being like, okay, actually I'm nerfing the open source thing and now all the indie devs need to pay me 25 bucks a month. You know, that's way different than going like hey look, Fortune 100 company was using this. Now we got a million dollar contract with them. Is that best practice?

It's hard sometimes those big contracts early on can really be devastating.

Oh sure.

Because they can remove your focus on grow inertia that and so you have to be careful. Obviously they're great. But focusing on your core value proposition, your core customers and it's really great to get feedback by those enterprises early on. And many, many projects I've been involved with, whether it was TurboRepo, whether it was next JS, even V0 like we didn't launch enterprise for almost a year or so and we even I even. It was a big debate between me and Guillermo and I think we were actually early. We should actually delayed it even further really getting that groundswell is so important and you can always do enterprise.

Okay.

You'd be careful. I say always your enterprise you'd be careful. Yes. Somebody could come in. But just driving up, even like ChatGPT, by the way, didn't have enterprise for like a lot people were egging for it.

Yeah, yeah.

And then when, hey, a lot of companies report like, yeah, we don't pay for ChatGPT, but our employees all use it.

And then you go to the csun, you're like, hey, by the way, we have a lot of your data.

I know.

Exactly. A wild choice.

How do you think, you know, a lot of. There's so much excitement around the potential of AI and science and law, these other categories, and obviously adoption is happening. But how do you think adoption will. Will kind of. How would you imagine adoption will look in those categories? Because I think AI adoption in software engineering is very natural because the people that are building and, and doing the research or adopting the product and like it's a super tight feedback loop and you're not really going to see in the same way in some of these other categories. So.

Yes.

And I don't know, I, Before I got into software development, I, I was actually a banker and so let's go. Yeah. Goldman Sachs, fake. Thank you very much.

Let's go.

So I did, I did my sanker.

Yes, I remember.

Banker.

Right.

So I, I think, I gotta tell you, I'll be honest, like, I think, you know, anthropic. I was talking to Mikey. They announced Claude for Excel. I think that's going to do wonders. I think if you talk to any Goldman Sachs analysts, they'll be very excited to, to have that deeply integrated. And if you're building film autos all.

Day, there's whole businesses that are built just on like templates.

Totally.

So and it's like an.

Obviously what's interesting though is if you look at Claude for Excel, I think their core foundation is still the coding agent and that there's something about the coding, the coding runtime that can be then augmented to other verticals. I think that's what you're going to see in next year or so is these model labs build out these harnesses and go vertical by vertical, whether it's banking, healthcare or consulting.

Right.

They're going to go through that, through knowledge work and they're going to iterate on that. Just like in the hill climb.

Yeah, yeah. What do you think on how do you, how do you think about switching costs now and over time? If you're a products company and you're leveraging intelligence from a lab, like, do you think the labs will over time make it Harder and harder to kind of like rip out one model provider and use another. Because right now it feels like there's this land grab happening in enterprise and this race between Anthropic and Gemini and OpenAI. But like how do you, how do you think that evolves?

I think most of the product builders that I've talked to like the, from the companies you that are on here all the time, most of their teams are working with multiple models and they have and they're constantly evaluating whatever sort of product analytics or test harnesses they're looking for any edge they can.

They're.

They're so competitive.

Yeah.

At least in like the startup space that like and, but switching models is not easy. That takes time and especially when there's big re architectures like when reasoning came out for example, that may require a rewrite of all the prompts and all the edge cases that you've been massaging. And these models have different characteristics. But I think most of the high performance teams are dialing in harnesses for each and every lab and they're just.

So hungry that so switching, switching costs are high. So the answer is like use. You get all of them in the beginning.

Correct.

And there may even be certain subsystems or certain tool calls where you're going to switch models and mix them together. And that just is, you know, part of the, part of this. If you look at like what Windsurf did with a released sweegrep that, that specialized model for research, you know, they're combining, they're mixing and matching. I think that's the, the next, you know, we'll see that throughout the next year. I don't think it's like, oh, we're just going to use anthropic models or we're just going to use OpenAI models or we're just going to is, you know, whatever you're not allowed to. I think you'll see a lot of combination.

Thank you so much for coming on the show.

Thanks guys.

Big fan.

Congrats on have a great come back Rest anytime.

Before we bring in Michael Greenwich from work os, we got some breaking news. Did you see the blimp? Did you see the blimp? Do you know whose blimp that is? It's Sergey Brinz blimp. Let's go.

He.

I mean this is bag seven on bag seven.

I'm actually going to take him going.

To take a little bit of courage it for that.

I told the Gemini team great to finally meet you friends David, mutual friend David.

Love David, I got you. Brought you guys both.

Oh please.

One of our highly coveted super rare enterprise enterprise ready.

Okay. Beside, how are you enterprise?

All right.

Square ready. What is does it mean? Well, pretty much every software company eventually when they get product market fit and go up market, there's a ton of stuff they have to add to their app to go sell the enterprise.

Yes.

So the guys at Microsoft and GitHub, they did this years ago. But if you're a new company, you have to add all this stuff to your product.

Sure.

And it's things like single sign on user provisioning logs, security workos just does all that for you as a developer.

Got it.

Yeah.

Okay. So you can just focus on the core products. The actual.

Yeah.

In the same way you use Stripe for payments or Twilio for messaging. Work OS is really that or this.

Is an interesting business because it feels like it's not something that you could just like go through YC and like sell to another startup. So like who was the first client? How did you get into this? What were you doing before?

I worked on. Started working this a long time ago, almost seven years ago. So workaway success. It's kind of. Yeah, it's like we're also like a pre AI company. Is that what Paul does the other day? Which also kind of hurts a little bit. I know. Shook my ears dinosaur.

I started as AI native before AI existed.

Yeah.

For real.

I saw this problem with another company at Start.

Okay.

We had built an email product, got a bunch of usage, got a bunch of adoption.

Enterprise tried to sell it to these.

Guys and they said, no way we can let this country though is the CTO, CISO, 50 engineering leaders, co founders, VP of Eng, whoever is kind of responsible for the technology.

But is it because they want those features or they need need them for legal reasons?

They gotta have them. They usually have deals that are blocked because they don't have these features.

Okay, got it.

So you'll start growing up market and there'll be some customer that says, hey, we'd love to use your product, we'd love to roll it out, you know, at Coinbase or Microsoft or something. But we can't do it unless we have these features.

Has demand just been insane because people are building products so quickly and then they start, you know, employees, employees of companies start adopting them kind of personally and then they realize compounding. Yeah, sure.

So we had a lot of growth, you know, years ago through the kind of the early cloud era. SaaS like Vercel is one of our customers. Carta plaid folks like that. In the last year and a half, two years. In the last year and a half or two years, what we found is it's actually perfect for all these AI companies. So today we're powering Enterprise Auth for OpenAI anthropic perplexity, cursor Sierra, all these guys that are growing faster.

So spend a lot of time for that fun. Yeah. Okay, walk me through this thesis. In the YC era, it became like the YC trade was basically you could be a kid in college, you know, graduate and move to Mountain View or Silicon valley. And for $100,000 and some cloud credits from Azure or whoever, you could set up a website and go kind of build the first era of consumer. And we got our Airbnbs from there. We got a variety of consumer companies. But in the AI era, it's becoming easier to go enterprise on day one. Is that real? Is that, is that, is that a reasonable thesis? Do you see any data to that effect? Absolutely.

So I think that previous era, the privilege that those companies had is they could take a while to get to enterprise. So if you look at Dropbox, Figma.

Yeah, yeah, yeah.

It was years, it was like three, four, five, six, seven years before they actually went after enterprise. What we're seeing today is AI businesses get pulled up market way faster.

Sure.

And it's way more competitive. So companies like Cursor or Plexity pretty much in year one, year one or two, yeah. They get pulled at market to the enterprise. And that's why they need.

Yes, because of that competitive dynamic, but also the tools that they're building, like the enterprise is just so ready for them.

There's another piece of it as well. It's not just that they grow faster at market, but you think about these AI products. They are touching sensitive data. You have one of these things, it's only valuable if you get access to all of your stuff. You give it access to do things on your behalf. So suddenly it becomes this huge security concern. Maybe an old product like figma, you could say to the design team, just don't put any sensitive data in it. But you get one of these agents or something connected, you need it to access everything. And so they're scrutinized at a higher amount. Plus they grow faster. Plus in their life cycle, it's a perfect storm where we come in and help them.

Talk to me about domestic versus international. I imagine a lot of your clients are already international. So does that mean you're international or are you focused on Making American companies enterprise ready immediately and then maybe you'll go after the European market later. How do you think about.

So many of our customers are actually right here.

Yeah.

Like probably at Universe. Literally right here. We were joking. We could cut up our sales territories by north and south of Market street in SF because we have so many businesses that are here that are growing quickly. What we find is their customers are international, of course.

Right.

So they're going and selling to larger organizations elsewhere in the world.

Okay.

So our products that are kind of our customers, customer, those types of things, we, you know, we localize. We just did a big project to Translate everything using AI launched with 100 languages to do that kind of stuff. But we find that the best product, the best companies that are using work OS are these high growth AI businesses that are taking off. And of course they're mostly here. You know, they're. They're mostly here.

Yeah.

What's your philosophy around operating the business? I, I don't remember. I don't necessarily recall the last time you guys raised money. Like I'm sure people are throwing money at you all the time when they see the logos was.

Yeah, we raised our, our Series B almost exactly four years ago actually. Which is.

That was the last financing.

Yeah, that was the last financing. Which is like an eon in the SAS era. Yeah.

Since then we've just got necks to their hands.

You've just been building it slowly since then, you know, bit by bit by bit, brick by brick, you know, slowly. Actually, I do have something to announce. It's pretty exciting. You know, you had Asati here previous talking about how they do a billion in revenue every day. We're very proud to announce that we just crossed 30 million in annualized revenue.

So that's our congratulations.

That's our big number overnight success.

We're sitting today. That's great.

We're a bit smaller than Microsoft, but we're coming for you. We've been compounding since then. The AI stuff has really been this huge tailwind for us. And it's so fun to build infrastructure where we get to see into all these things. Companies like my customers are the fastest growing, most exciting AI businesses out there.

Invest. Do you angel invest?

I do some, yeah.

As you're seeing these companies at this like crazy point, I've had VC start.

Asking me for the data they want to invest just to get the growth data out of it.

It's a little.

Yeah, it's a little difficult. Yeah, we don't, we don't share that kind of stuff, but. But just through, you know, building for developers and running events. And I mean, I love GitHub universe. This is like Coachella for, like, you know, developer stuff. You meet founders and meet other people building stuff.

And so who are some of the entrepreneurs that you look up to or where the. What's the story from a founder or business person that you keep coming back to is like, oh, that one.

I'll go first. David Senra.

Just his story. Just how he built us.

No, I, I do. I think, I think I, I, like, he is like, I, I feel like I have the blessing of like, Like, I. By being friends with David, I'm friends with history's greatest entrepreneurs. It's the most efficient role model. Right. He has a specific type of business, but whatever business you're building, you can learn from.

Yeah.

I mean, yeah, there's a lot of other things you could have said, but that one's pretty good.

I was trying to think for mine. David's great.

I'm just laughing because like, like, like the stakes are like, you know, Henry Ford inventing the, you know, the, the. What's it called? The actual assembly line.

Yeah, yeah.

The manufacture. The automated assembly line or like, you know.

The most impactful versus, like, the most valuable for you.

Yeah, yeah, I guess. Who, who do you come back to? I think I always come back to the market. We're very much in a marketing business, and so I always come back to a quote that I attribute to David Senra, but is actually from David Ogilvy. You are not advertising to a standing army. You are advertising to a moving parade. And so the question is, like, why.

Are you heard that for the first.

No, I read movie on advertising. I'm familiar with the book. I read it before David read it, but he did stick it in my brain and he advertised it to my moving parade. And I've always liked that idea of even if you've shown someone an advertisement once or you've sent them a message or you've given them a pitch once, like, you there. There is a moving army. There is so many distractions. There's attention all over the place that you need to be hitting again and again. That's why they don't just do one GitHub universe and say, yeah, we did it. We're good. They do it every single year.

And the message is different every year, for the two are evolving. Yeah, man. There's so many. There's so many to choose from. I feel like a little bit of an old soul in that When I, when I heard Satya talking about, like the early days of Microsoft and Bill, I love building platforms.

Yeah.

Like, I built all this other stuff earlier in my career and as soon as I started building stuff for developers, other people making stuff, sure. I was like, ah, that's really sick. Like, you see other people make stuff with the thing you made and then build their own businesses on top of that. And to me, Microsoft is like the first big software platform company. You know, Windows enabled so many developers to build and ship these experiences to change the world. And it's, it's, you know, previous cycles, I think a lot of people forget it, but it was this huge enabler, this huge, like, democratization of access to technology.

Is there a, a specific sales funnel that flows through GitHub with your product?

We do just a ton of stuff with developers, I think. You know, I mean, we do everything from, you know, sponsoring podcasts and newsletters, meetups and doing developer events. We did our own conference last week. We do a lot of open source stuff. We run a really popular open source design system project called Radix.

Oh, that's.

Yeah. So that's on GitHub. But I think, I think my GitHub account, it's probably one of the earliest, like personal identities online. I have an account, you know, I've had it since before I was in college, so I'm thrilled to be here.

Yeah. Yeah. That's very cool.

Are you speaking at all today?

I am. I'm giving a talk tomorrow, all about AI and identity for agents.

Okay.

So this is a new thing. WorkOS is kind of like an identity security company. You help people with sign in and off. And there's this big question right now of how we're going to secure agents.

Yeah.

You know, if we have 7 billion people on the planet, we'll probably have trillions of agents running around and doing stuff for us. Connecting to different systems. And security is even more important. You can think of an agent, kind of like a crazy hyperactive intern. You're going to have access to all of your systems. And so there's this question of how do you authenticate them, how you build security around it? Permissions, approval prompt injected.

Right, Right.

Yeah. There's this old quote, you know, to err is human, but to screw up 10,000 times per second, you need a computer to do that. Agents are kind of like that, Right. They make it really easy to do stuff really quickly, but also make mistakes. So my talk is all about that and some ideas that we have around security.

For.

Yeah. Do you think agent security bifurcates along the consumer and business to business access? Do you think there's a discrete enterprise versus B2B layer? It feels like we talked to the CEO of 1Password, for example. And it does feel like the. Wait, my, the password to my Yelp account. I might not be using like work OS for that in the future.

It's definitely going to be some blurring, you know, and, you know, talking about.

API communities, the way I'm thinking about is like a small business might have 200 agents that are out in the world. Maybe some are selling, maybe some are doing customer support. And it's like, right, when should a CX agent be able to share account data?

Right.

When, when can a sales agent like provide pricing? I mean, like, there's so many different things. And you, you know, CEOs, when they're working with teams, right? They have like processes in place for individual people. And so I think, I think this like really important problem area, it's, it's.

Completely changing the way people think about security. I think if you go to any of these like security focused conferences, it's the topic on everyone's mind. Yeah, exactly. Within. Exactly. Yeah. Because within companies previously you've had these kind of silos of information or control. You have permissioning systems that are pretty static with agents, you know, you might have 200 today and 0 tomorrow. You might spin them up and down, depending on a task, depending on a project. And so that permissioning model is like completely changing and it's really exciting. You know, we're right in the middle of it. Like us working with all these different AI businesses, they themselves are building their own agentic workflows. Whether it's, you know, stuff like Codex or, or Claude code or what Cursor's building with their.

Is there about an AI horror story and security yet?

Oh yeah, people can talk about it.

I mean, there's ones like.

Yeah.

I think it feels notable if there hasn't been like a specific day on the Internet that everyone was like.

Oh, we went down because of AI. Yeah, that hasn't happened yet.

Not, not yet. I feel even probably just latest AWS outage.

Like, I don't think no one pinned that on AI. No one pinned that on generative AI or stochastic systems.

There was one a few months ago where Jason Lemkin from Saster, he was vibe coding an app on Replit. Oh, I saw, Remember that? And he was like, he was pure prompting, right? He's not writing any code he's just talking to the thing and he asked the agent to do something and it deleted the full production database. And then he was like, what the hell? And then the agent lied about it. It was like, no, I didn't do that.

Think it's.

Agent was like, yeah. He's like, yeah, covering.

You know, it's like.

You're like, oh, yeah. At one point, I think agent did just.

Yeah, you got.

My bad. Yeah, my bad.

But.

But I do think they were able to roll it back. So I read your getting in the thread and kind of just to close it out and not leave the.

Yeah. And they've built a lot of stuff since then as guardrails, but that just shows you, like, early on, people pushing these systems to their limit and they can have catastrophic effects if you don't put up these guardrails.

Yeah.

So that's what the talk's about. We're doing a lot of innovation and research here, but it's. It's going to take a while to get right.

Yeah. Yeah.

Well, awesome. Amazing to finally have you on the show. Long fan. Have you come back on again.

I will take care see it.

We'll talk to you soon.

There are a lot of posts here in the timeline that I want to share that we can't.

Timelines.

We can't. I mean, we're not paying them off, being held back by. I want to just come back to them tomorrow.

Okay.

Held back tomorrow.

You have our word. We're doing lots of timeline. If you're new here, leave us a subscription. Follow us on AX. Follow us on LinkedIn.

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Yeah.

Well, it has been a fantastic day here in San Francisco. Thank you to everyone.

Stunning out. It is. I'm gonna go try.

We gotta go hunt that. We gotta find out. It's not the Gemini blimp. I'm telling you. It's Sergey's blimp. His personal blimp. It's not a Gemini project. It's not a single project.

I thought you said it was branded. No, it was branded.

No.

Sergey individually funding a blimp company and they're testing it.

So funny, because I sat down with Logan and the Gemini team. We were just talking about marketing ideas and I was like, the obvious thing that you should do is get a blimp, wrap it with Gemini branding, and just fly it around. San Francisco's on top. And we were talking. I was like, okay, finding a blimp I was doing some research, there's like six active blimps. I was like man, it's going to be hard to find a blimp can get to SF that can be wrapped. And of course Google, incredible foresight from Sergey to create a beautiful billboard in the sky that's just waiting for branding. But waiting for branding. Well, super fun day. A surreal moment, a lot of fun talking to a great time. One of the greatest living CEOs.

And thank you to everyone on the Microsoft Team. Thank you to everyone on the GitHub team who helped organize this. Thank you to our sponsors. Profound linear numeralhq.com sales tax on autopilot here. Got a nice BIM AI the number one AI agent for customer service Addio of course customer relationship magic 8 sleep didn't sleep on my 8 sleep last night. Can't wait to get back to it tonight. Of course we mentioned public.com also adquick.com getbezel.com your bezel concierge was able now Jared Palmer had a nice yacht master that was good.

That was looking good.

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Thank you folks. We will be back in Hollywood tomorrow.

The Podcasting will continue 11am Sharp Pacific. Cheers. See you then. Goodbye.

Bye.