Lenny's Podcast: Product | Career | Growth - How 80,000 companies build with AI: products as organisms, the death of org charts, and why agents will outnumber employees by 2026 | Asha Sharma (CVP of AI Platform at Microsoft)

Starting point is 00:00:00 He said that we're just starting to scratch the surface of what an agentic society actually looks like. We're approaching this world in which the marginal cost of the good output is approaching zero. We're going to see exponential demand for productivity and outputs. The way that you scale to that is with agents. When all of that happens, the org chart starts to become the work chart. You just don't need as many layers. We were chatting about this concept you have that we're moving from product as artifact to product as organism. Because these models are so effective at this point, you want to start to tune them to certain types of outcomes.

Starting point is 00:00:35 All of a sudden, these are these living organisms that just get better with the more interactions that happen. I think this is the new IP of every single company, products that think and live and learn. Planning right now is just crazy. How does anyone plan a roadmap when there's just like, okay, GPT-5s out? We think about it as, what season are we in? Season one might have been prototyping of AI, and then it was all around models and reasoning models. and now it's the advent of agents. Today, my guest is Asha Sharma.

Starting point is 00:01:05 Asha's chief vice president of product for Microsoft's AI platform, where she oversees their AI infrastructure, foundation models, and agent tool chains, while also leading applied engineering, responsible AI, and growth for the core AI division. She was previously C-O-O at Instacart and VPR product at Meta, where she ran Messenger, Instagram Direct,

Starting point is 00:01:23 Messenger Kids, and Remote Presence. She also sits on the boards of The Home Depot and Ku-Pang, and she's a sales. second-degree black belt in Taekwondo. O'Sha is a really unique and rare role that allows her to see more than most anyone else in the world where things are heading with AI and what works and doesn't work for companies that are building large-scale AI products. In our conversation, Asha shares a bunch of trends and predictions that she's seeing

Starting point is 00:01:46 that I haven't heard anyone else talk about. Why we're moving from a product as artifact to product as organism world? Why GUIs are being replaced by code-native interfaces? Why post-training is the new pre-training, the coming agentic society. what it takes to be a successful builder today and going forward, and also her single biggest leadership lesson that she learned from Satya, who she works closely with. If you enjoy this podcast, don't forget to subscribe

Starting point is 00:02:10 and follow it in your favorite podcasting app or YouTube. Also, if you become an annual subscriber of my newsletter, you get a year free of 15 incredible products, including lovable, replet, bold, n8N, linear, superhuman, D, script, whisper flow, gamma, perplexity, warp, granola, magic patterns, Raycast, Chapier, D, and Mobbin. Check it out at Lenny's newsletter.com and click ProductPass. With that, I bring you Asha Sharma.

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Starting point is 00:04:06 and what impact AI is having on engineering productivity. To learn more, visit DX's website at getdX.com slash Lenny. That's getdX.com slash Lenny. Asha, thank you so much for being here. Welcome to the podcast. Thanks for having me. I want to start with something that we were chatting about before this, that I've never heard about as a concept that I think is going to be really helpful for people to think about,

Starting point is 00:04:34 which is this concept you have that we're moving from product as artifact to product as organism. Talk about what that means and what people need to understand here. It's been a pretty interesting shift, especially over the last year or so, because when I got to Microsoft, it was kind of right after opening, AI and the large foundation models happen. And then immediately after there was this explosion of models, proprietary open frontier models that were pushing the frontier curve. And so they were both more efficient. And then we started to see domain level expertise and a bunch of them. And then, you know, even more recently, models now can, you know, tool call and they can function call and they can take

Starting point is 00:05:18 action. And I think that's just giving way to a new type of products that are starting to see some success. And so all of a sudden, products aren't just like these static artifacts that we start to ship. That's not just like, hey, come up with an idea or an insight, go solve a problem, ship it into the world, maybe make it a little bit better, and then have a dashboard. All of a sudden, the whole KPI is what is the metabolism of a product team to be able to ingest data and then digest the rewards model and then create some sort of outcome? because these models are so effective at this point, you want to start to tune them to certain types of outcomes.

Starting point is 00:05:59 So there's price or performance or quality. And so it's pretty exciting because all of a sudden these are these living organisms that just get better with the more interactions that happen. And in many ways, I think this is the new IP of every single company. And it's a completely different way to build product and even think about products that think and live and learn, which is kind of exciting.

Starting point is 00:06:20 So when I hear this, what I'm thinking about is when I had Michael Terrell in the podcast, the cursor CEO, he talked a lot about how their big mode is the data that they capture from people using cursor, selecting, accepting certain suggestions, not accepting other suggestions. Is that what you're talking about here, just like the proprietary data that companies gather from people using their product or is there something beyond that even? I think why we're seeing, like, the rise of post-training happen is just that the models themselves, like, are so powerful. As of this year, Nathan Lambert did this study that I think, that was pretty interesting of all the top leaderboards. And it showed that, you know, once a model hits 30 billion parameters, the CAPEX to actually train a model and put, you know, billions of tokens into a kind of pre-run kind of doesn't economically make sense. And you can kind of start to optimize on the loop. And so, yeah, in many ways, I think you can, I think using your own data is the best way to do that,

Starting point is 00:07:14 but you can synthetically generate data. You have to come up with the rewards design. You have to actually roll it out. You have to A, B, test it rigor. You have to find the job to be done or the use case that it makes the most sense for. And then, yes, that generates data that you can learn from. I haven't ever seen it be one loop for any sort of product. I think it's multiple tracks running in parallel that are kind of like assembly lines,

Starting point is 00:07:39 if you will, and kind of producing that. And so is this kind of thesis that we're moving towards product as organism? Is this basically for model companies, or is this also, true for, I don't know, SaaS businesses and tools and user tools. Look, like, I think that software as a primitive is changing and kind of the artifact inside of it is a model alongside the software components itself. And so in many ways, I think that, you know, software products will all be model forward products, if you will. This reminds me why I just had Nick Turley on the podcast who we were talking about before we

Starting point is 00:08:16 started recording, head of chat chipt pt. And I was asking just like, how much does chat TPT change, with GBT5 coming out and he's just like, it's the same thing. They're the same product. It's just like the model tells us what to do in the product of JetGPT. And it makes me think about something else of just like you would think, why can't just GPT5 build its own user interface? Just like as you use it, it just evolved. It's sort of what it's doing with Canvas and all these things. But like that's like another way I think about when you talk about this idea of product, this organism is the product, the UX can shift based on how you're using it and evolve automatically without having product teams have to do anything.

Starting point is 00:08:52 I 100% believe that's where the world is going and that my experience should look and feel different than yours. I mean, that's kind of been the ad-ben and personalization, but now you can do it on the fly in the future. So I think that'll be a pretty fun world. I also think it will look different for agents and it will look different for kind of power users and new users and all of those things too. Let me kind of zoom out a little bit and ask you this question.

Starting point is 00:09:15 you work with a bunch of companies that are building AI products on your platform, other platforms. I imagine some just do an awesome job and are killing it. Some are struggling. What do you find are kind of common patterns across the companies that do really well and have a lot of success, building really successful AI products and ones that don't? Yeah. So I think there's things that are kind of more broadly applying to the organization themselves, and then there's things that are applying to the people who are building.

Starting point is 00:09:45 the AI products do. So more broadly, I think there's, there's a pattern that's starting to emerge for successful companies. Like one is they are embracing AI and everybody becomes AI fluent. So I think everybody's using some sort of code pilot or some sort of AI in their day-to-day workflows. Like job one, so everyone's not afraid of it, understands how it can raise the ceiling and kind of lower, lower the floor for like all sorts of skills and tasks. Number two, from there, they start to say, okay, how can I take a process that already exists and apply AI to making it better? That might be something like customer support or taking fraud down from 15 days to kind of cure to 10 days. And like going through that entire loop of mapping out the process, applying AI to it, seeing some sort of impact and then feeling the P&L or the kind of intrinsic benefits that that looks like.

Starting point is 00:10:38 The third thing then is like, okay, great, now that you've seen impact, everybody is using it, How do you actually use it to inflect growth? And that can be something like improving the customer experience. So your LTV or retention improves. It could be co-creating a new kind of set of concepts or categories. It could be, you know, going from agents that are embedded to agents that are embodied and then being able to take on, you know, exponential number of tasks. I think that where companies fail is that they're doing AI for AI sake.

Starting point is 00:11:09 They have a ton of projects that they're kicking off at the same time without a blueprint to understand how it actually worked from what their stack looks like. And they aren't treating it like a real investment. And so they don't have the measurement and the observability and the evals all kind of set up. It's going to do that end to end. I think the tricky thing is for enterprises is the technology is changing. There's something like 70,000 enterprise tools like in the AI space launch last year. It's really hard to know which one you should use for what outcome. And so you really need to bet on a platform or some sort of app server type layer that allows you to swap things in and out and not really be beholden to anything, any one technology or any one tool, because

Starting point is 00:11:51 the reality is the whole thing is going to change. I feel like you have to actually build for the slope instead of the snapshot of where you are. So that's kind of what I see at the enterprise level. I think the builders themselves are actually changing pretty fundamentally too, right? Every single advent like change of technology has invented like a changing set of roles um like mainframes to PCs like the whole garage engineers and then when we went from you know server to cloud and mobile there was like SEO specialists and CDNs and you know growth VMs and uxR and and and and you know front end back in and yada yada and now I think we're seeing this advent of the polymath and where um I think that full stack builders are kind of having their renaissance, where if you take like an

Starting point is 00:12:43 average organization, it takes probably 10 steps to launch a product. It could be security review. It could be spec. It could be, you know, user research. And there's what, five plus functions, maybe six or seven. I'm being generous for a normal organization. And then you have like six or seven layers. So all of a sudden you have 500 different touch points that have to happen to get a product out. And when there are 500 models available a week or 500 new technologies, that is just insufficient. And so I really believe in the concept of a full stack builder, you're seeing it with a bunch of the AI native companies that are coming up. I'm even seeing it in enterprises that have been around for 50 years starting to operate in that way. And I think that gives you velocity and throughput and

Starting point is 00:13:29 then gives you the whole loop to start to actually metabolize and go through that much faster. That's definitely a recurring theme in these conversations is just kind of the Venn diagrams of PM engineering design are starting to converge and more and more of other disciplines within your role. So PM needs to level up on design and or engineering. Yeah, I completely agree. I think it's all about the loop, not the lane here. And so I think that whatever function you are, you have to be obsessed with trying to understand like the efficiency or the cost of the product, the actual rewards or like, you know, system design that you're going after the actual UI, U.S, how that actually manifests for agents or people.

Starting point is 00:14:13 You have to start to get really good at that really quickly. I like this phrase you just use the loop and not the lane. Can you say more about that? Oh, it's just going back to our previous discussion on, you know, the signals loop and products evolving and becoming these living organisms. and not these artifacts. And if you think about getting really good at that loop, I think that is the product. That is the IP. That is the future of every organization. And I think feedback becomes continuous and observability becomes the culture. And I think that functions start to blur in future

Starting point is 00:14:47 workforces. To make this even more real, is there an example of a product or a company that is a really good example of doing this well, living this kind of loop life? I think most companies that we're seeing in the space from an AI perspective or doing this. I can tell you about a couple that we're working on. Obviously, in the coding space you mentioned, Cursor, GitHub has very similar features that we're using

Starting point is 00:15:11 kind of an ensemble of models that have been fine-tuned across 30 different countries, all of the languages to actually then go iterate in a loop for next edit suggestions or code completions and things like that. We've got an AI product

Starting point is 00:15:28 called Dragon Buds for Physicians, and we saw a massive difference from when we used, you know, synthetic fine-tuning to when we annotated 600,000 patient physician interactions by experts and actually fed that into the model and continuously optimized it to then produce, like, you know, I think we're sitting between 30 and 60 character acceptance rate depending on the run to something like 83%. And so that required a small group of individuals. individuals, not a large organization that we're able to actually iterate in this loop across functions and kind of all of those lines dissolving. That's super interesting. So kind of what I'm hearing here is if you can gather data on how things are going and then spend a lot of time

Starting point is 00:16:14 creating high quality labeling to feedback into it to fine tune it is basically the big advantage is how you win in a lot of this stuff. Okay. Along these lines, something else that you told me that you've been noticing that I want to hear more about is the shift from GUIs, and you kind of reference this, from GUIs to code native interfaces. Yeah. Talk about what that means, what that looks like and what this means for folks building products. I think it kind of goes back to what does it mean to kind of be a product maker in the future. I think that everybody's instinct is like is a, is a GUI.

Starting point is 00:16:47 But if you kind of think back in history, like databases kind of went from the desktop, kind of down into SQL. I think cloud was all about consoles and now it's about terraform. And so I think we're literally just seeing the same pattern that's played out in history start to play out in AI and like everything else in AI,

Starting point is 00:17:05 it's like Moore's lawn, it's getting faster. So I think that's just accelerating. And if you think about like a stream of text just connects better with LLMs. And so I think that there's a bunch of trends that are kind of working in the favor for like the future of products being about composability and not the canvas.

Starting point is 00:17:22 And I think that product makers really need to rewire their mindset around this because I think we spend an inordinate amount of time thinking about the UI of something rather than how something composes, how an agent's going to be able to read something, how do you actually get infinite scale, how does that collaboration start to work? And so I think it's just a new way of thinking, even though it's long been a trend that's happened in these changes. So is the prediction here that it's terminals, like Claude Code sort of experiences,

Starting point is 00:17:56 or is it that it's agents that are taking it? Or is it both? Is that kind of what you're just shared? Yeah, I do it. I mean, look, if any of us in you know, that would be amazing. I just think that the reason why terminals are great and it feels really great when you code

Starting point is 00:18:11 is because of the way it can interact with an LLM with the text stream. And I think that both can be true that humans will continue to commit code and we'll find, you know, new ways to actually do that, whether it's in the IDE, whether it's in get-up copilot, whether it's in some new development environment.

Starting point is 00:18:28 And I think that we'll do that with agents, and agents will do that with each other and we'll continue to kind of evolve from there. We had Brett Taylor and the podcast founder of Sierra and he had a similar prediction that all software companies are going to become agent companies. And it's essentially what you're saying here is that your software will just be this thing

Starting point is 00:18:47 that's running in the background and there's much less of a GUI. Do you think it's still become like this chat interface the way we're kind of getting used to. Is that like the primary interface with agents or is anything something else happening? Look like I think that conversation is a really powerful interface. I worked on messaging. I think it's um it's it's great for lots of forms of communication but it's not the only form of communication. I mean we use email today to to collaborate with each other. We use docs like everybody uses word and PowerPoint. Um, you know, there's a billion in people living in places of artifacts that I think can become a really important,

Starting point is 00:19:23 composable pieces of the picture, and I think they should be. So I'm excited about that. I think that chat will be important, but certainly not sufficient. What's interesting is chat GPT, the number one fastest growing product of all time, maybe the most important consequential product of all time, is chat. Yeah, it's great. It works. I think the question we have to ask ourselves is, will it only always be chat? Yeah. Yeah. The way Nick described it is we're in the MS DOS era of Chat Cheptie, which is interesting.

Starting point is 00:19:57 It's like the reverse of what you're saying. So it's like maybe if you start as that and then you have to move to GUI and then maybe it'll go back. But he said there's going to be like a Windows version where it's much easier to understand what else is going on. Yeah. I mean, look, like I think that it's smart. You should every company should be bringing AI to where their users are.

Starting point is 00:20:16 And ChatGPT has all. of their users using chat and it's a phenomenal product. And we've got lots of people around the world that do work in many different ways. And we should be thinking about how we use AI to enable that. So let's talk about agents. You spent a lot of time working with agents, building agents, helping companies build agents. You have this really great quote that I love. You said that we're just starting to scratch the surface of what an agentic society actually looks like. I just love this idea of an agentic society. What does that actually look like in the future? Oh, gosh. I mean, it's funny. You were telling me about your two-year-old, and I have my son, Rome just turned one, and I can't even imagine life that, too, because I'm just like, that is so far away, and what will have then developed. I look like, I think that in the future, work will look really different. I think that we're approaching this world in which the marginal cost of a good output is approaching zero. And I think when that happens, we're going to see exponential.

Starting point is 00:21:16 demand for productivity and output. And I think that the way that you scale to that is with agents. And it's agents that are embedded and their tools and their pieces of software. And I think there's going to be a ton of those far more than the software that we use today. And then I think there could be a set of embodied agents that are developed. And we start to see that now, right? You can assign a pull request, a co-pilot. You can create a software development rep that's,

Starting point is 00:21:46 agentic that can kind of do some of the lead generation and mining for you. And so I think that when all of that happens, the work chart, the work chart starts to become the work chart. I think that tasks and throughput become more important than they have been before. I also think that you just don't need as many layers. Like I think the whole kind of organizational construct might start to look different in a few years. And so I'm pretty excited about it. I think I think meetings will still be meetings and they'll be weird, but I think that will be a bit better. And I think there'll be lots of changes. I think that for the average employee, my hope and kind of my optimistic view is that they will be

Starting point is 00:22:32 able to expand their skill set because now they have their own agents stack that they can bring with them to work just like you can kind of bring your own device. And you can start to have access to a set of skills that you never have. before. And so if you think about, you know, the 20 million people that maybe sit in that that space across America and they get 20% more skilled, but it's like pretty exponential for GDP. And so it's pretty fun. This comment you made about the work chart, the work chart becomes the org chart. It's such a profound concept because I don't know if this is what you meant, but what imagining is you build these teams and here's your mission and goal and KPIs. And it's humans.

Starting point is 00:23:12 and like, oh, cool, go do this for us. And what I'm recognizing as you're talking is like, okay, but if you have agents doing that, that is their prompt, go drive conversion. And then you have all these agents, and that's the org. This is the conversion onboarding team. And that's like a bunch of agents off doing their work. Is that what you mean? Yeah, I mean, yeah, I think like today we think in terms of, hey, who reports to who in the

Starting point is 00:23:37 org chart and who's responsible for these areas. And I think at the end of the day, when you have a set of capable, agents and people are capable of more things, you're not going to start to think in hierarchy and communicating upward or going to start to figure out like kind of outward task-based type of opportunities. I think that humans will always decide in organizations how AI is used and what we want to apply it to. But yeah, it's kind of exciting when a new issue comes up or a new task comes up. How do you actually automatically decide where to route it? Who's working on that task? How do you actually go work on it? How do you observe if age it's doing the right thing?

Starting point is 00:24:12 how do you fine tune it if they're not, like all of those things. So I think that, I'm just speculating, right? That there's a world in which that could be pretty exciting. And I think that's great because we can just accomplish more. You touch on this point that reviewing the work is going to be increasingly important. If you have like a thousand agents off doing work, it's just like, holy moly, that's a lot to look at, make sure they're doing the right thing. How do you think that evolves, just like being able to scale your ability to review the work that's being done?

Starting point is 00:24:40 Yeah, I think that, um, The same kind of loop that we talked about becomes increasingly important, like fine-tuning and self-healing, observability, really good e-vows, all of that. I mean, the good news is that there are systems that manage this for billions of people today that already exist. And so I think that, you know, we don't have to reinvent the wheel. There's certainly going to be a bunch of new things to learn if that world ever plays out. But I think, you know, managing devices and policies and group access, all those things are solved problems, which is good. This episode is brought to you by Finn, the number one AI agent for customer service. If your customer support tickets are piling up, then you need Finn.

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Starting point is 00:26:29 People are using agents in all these different ways. Is there any way you, your team, have found a value in working? with agents of some kind other than coding, I imagine, is a big part of it, but just anything there that's like, wow, that's a big deal. At this point, we have AI and agents and many of our workflows. Like, one of my favorite ones. So right now are my engineering partners out. So I jump on the live site bridges when something goes down.

Starting point is 00:26:54 And, you know, as something as simple as, like, you can automatically get a summary of everything that just happened because usually there's 15 people talking. You don't actually know where the incident started, where it's going to end and everything. And then all of a sudden I have that and I can kind of figure out and ask questions and get updates. Like, awesome. Like, I think that kind of the entire kind of DevOps areas is changing. We use it to, we use Spark to create prototypes. So everybody on the team is expected to code.

Starting point is 00:27:24 But, like, you know, sometimes just chatting in and, like, talking in real words actually gets you to a prototype that's more interesting and, like, more expressive and reflective of your creativity. So we use that. I mean, I think everybody's using AI to write. Everybody's using AI to kind of find ways to have efficiencies and like coming up with documentation and things like that. And so I think it's everywhere, which is cool. I think that we're just scratching the surface, though, for kind of like what's possible in terms of working with agents.

Starting point is 00:27:58 That's how I always feel when people ask me how I use AI. It's just like everywhere. It's just like in every little sprinkled in everything. thing I do now. I don't even know how to describe it. Yeah, it's hard to remember a world where it didn't really exist. Yeah, there's a product manager that I collab with, Peter Yang, who talks about how he just doesn't, I don't even know how to do a strategy doc anymore without AI. How did people do this? Without having someone, you think there will be strategy docs in the future? That's going to be interesting. I have this, like, I wrote this post once of like which skills of a PM job will be

Starting point is 00:28:29 most replaced by AI. And strategy is the one that people are the most, have the biggest debate on. Like, you could argue, I don't know, let's get into it briefly. You would think if some AI had all of the information you had about where the market's going, your metrics, your product today, it would be so good at developing a strategy for you. Many people think that's the one thing AI will be really not good at for a long time

Starting point is 00:28:55 because that's where we need all this human judgment stuff. I don't know. Do you have any thoughts? I think that some of the most consequential products in the world required a bunch of kind of deterministic, like, logical sets of inputs and like sparks of creativity and imagination and judgment and vision that could not be achieved without humans. Like Microsoft is like the vision of a software factory

Starting point is 00:29:26 in creating what Microsoft did. wasn't inevitable. Instacart, you know, there was web vans and webbands didn't work, but Instacart did work because of a different way of thinking about it. That came through judgment and iteration and a bunch of things that you couldn't have learned unless you actually went through the process, you know, the iPod. Like, you go forward. So I think it's there.

Starting point is 00:29:49 I think docs themselves, like, for every idea, for every, you know, need, we'll just start to kind of fade into, you know, applications. and different artifacts in the productivity suite, which, you know, is just a different way of working. Yeah. Like your original question, which I didn't quite answer, but I think is important, you're asking, like, do we even need strategy docs?

Starting point is 00:30:13 And I guess it's just like somehow everyone needs to be aligned on the strategy. Maybe it's not a doc. Yeah, it could be some other. I mean, if you architect an organization the right way to keep up with AI, you know, you need a different alignment mechanism. then traditional ways of actually work. So let me ask you actually about that. So planning right now is just crazy.

Starting point is 00:30:35 How do you like, how does anyone plan a roadmap when there's just like, okay, GPT-5s out? Okay, great. What works for you for setting an actual roadmap and a strategy for your team? Like how far out do you plan? How often do you have to rethink everything? I mean, I'll caveat this by saying like everyone's just figuring it out and it's a lot harder to figure it out when you're a larger organization than when you're, you know,

Starting point is 00:30:56 much smaller and you get to kind of, you know, run something yourself and there's pros and cons to both. So here's what we do. We, the company historically, at least in our product teams, had kind of semesters that they planned again. So I think that as every six months, there's kind of a strategy with back look forward, all of those things. I think that's very valuable. I think, like, the idea of six months, though, and really understanding what's changing out in front is truly challenging to kind of have a overbake situation. And so we kind of think about it as, you know, what season are we in? And so a season, which is very uncomfortable, can be denoted by a set of secular changes that are happening in the industry or that are happening from customers. And so,

Starting point is 00:31:40 you know, you can think about season one might have been like, you know, the prototyping of AI and kind of the early GPT work. And then it was all around models and reasoning models. And now it's the advent of agents. And so that can last a year. That can last six months. That can last three months. But like grounding everybody on the ethos of what are the secular changes? What are the customer problems we need to solve? What does winning look like? So everybody has that shared sense. What is the North Star metric is something that we do? The second thing that we do is that we have kind of loose quarterly OKR. So like, okay, if we believe that, what do we need to do next quarter to actually put ourselves on a path to that. And then from there, you know, teams are operating in squads and

Starting point is 00:32:26 they're kind of setting out, you know, four to six week goals that they're trying to go after for problem areas to go ladder up to that, you know, and especially as the platform for the company and the platform for our Azure customers with AI, I will say we go through lots of changes to that all the time. And I think we have to just have an openness that that is the business that we're in. I think the other thing is just like, we, try to leave slack in the system, not just for the unplanned, but for the slope. I think that we have to continuously be thinking about how we're going to disrupt the platform in our thinking and what we need to be investing in to make that possible. And so we try to do a little bit of both.

Starting point is 00:33:06 This is awesome. So what I'm hearing here is there's this concept of seasons and everyone's a line. Okay, this is time for agents. This is what's happening right now. We're going to center around or strategy around agents. And then there's these loose quarterly OKRS you plan for three months roughly and then you leave some slack in the system for things to change. Yes.

Starting point is 00:33:24 Is the current season agents? How would you describe what season right now? Yeah. Okay. It's agents. Okay. It's the rise of agents.

Starting point is 00:33:31 The rise of agents. It sounds like a Terminator movie. Do you have a sense of what the next season might be? Is there any like, oh, this might be coming next? Gosh. I don't. But I think that, look like we have you know more than 15,000 agents that are deployed on our service today

Starting point is 00:33:52 at least at the Azure service there's a bunch of other platforms in the company and I would just say that I think that we should really focus on making sure that we have all of the alignment accountability observability evals to making those agents like great I think that Manus's breakthrough in the space was that they could do these tool-calling loops and have agents kind of do longer-running tasks that really no other platform was able to do. I think stuff like that is critical. Memory is critical. Like, there's still a bunch of building blocks that I think are leaving agents incomplete in the wild that I think we have to really sweat the details on before we move on. So it's just like agents until the end of time until superintelligence and

Starting point is 00:34:41 then we're just on beaches, chill. Yes, agents until dank memes. Look, like, yeah, I think the cool thing is, is like, something new could come in three months, something new could come in 13 months. I think, like, we kind of have this conviction on a set of building blocks that we want to provide to enable these agents to endure and have high endurance. And so that's what we're folks. When you said there's 15,000 agents, what does that mean?

Starting point is 00:35:09 Is that 15,000 types of agents you can? can use or is it like that's how many processes are. That's, you know, customers, 15,000, I think I should re-reference the numbers. 15,000 customers who have produced agents. I think the number of agents is actually like millions. 15,000 customers that are building a specific kind of agent on your platform. And they're running and the number of agents is in the millions just running. Yes, in the cloud.

Starting point is 00:35:33 Okay. How's, it's wild. Some crazy numbers here. Okay. So let me just kind of go in a slightly different direction. you're kind of in the center of the storm of a lot of AI, just like seeing everything that's going on. Is there something you wish you'd known before stepping into this role that you're just like,

Starting point is 00:35:50 okay, I see. I didn't expect this. When I first took the role, it was kind of described as like the belly of the beast. And I had spent most of my career building products at the center of machine learning and applications or businesses. And I think that to my surprise, a lot of the learnings have translated in terms of what makes a great platform is what makes a great product.

Starting point is 00:36:17 And like the thing for me is like it's often in the invisible work or the like not the pixels that actually drives that. So like for example, one of the first companies that I worked out was a company called Porch Group. I was employee seven and we knew we wanted to hope people take care of their home. And I think we invented so many features like the home report or like a wait for a manager home or like house style inspiration where you could like see all of the houses and it's map every room and the single most important thing that we could have done and did during my time there was create a matching platform that matched the six million professionals with the 300 service types of the 37,000 zip codes and all of the homeowners in North America to actually take care of their their home

Starting point is 00:37:05 and that was just the game of inches and kind of optimizing that engine in order to create higher quality leads, essentially. That's what got us to the first $500 million valuation. That's eventually what we built on to actually have other vertical services and software platforms that IP of the company. Same with messaging. The number one learning that I had was, look like WhatsApp didn't win because it had stickers or stories or dark mode.

Starting point is 00:37:38 In fact, I don't even think it had all of those things when it won. It won on a few premises because one was the phone book. You knew that when you use WhatsApp, you could reach every single person because you had their phone number. And those are the people that you care about when you're using messaging. It was the reliability and how fast it was. I could text my grandmother in India and know that she would get my text message all the time. And then it was the privacy. Like when you are sending 200 messages a day to the four people,

Starting point is 00:38:08 you care about most, you want to make sure no one else can read the messages. And so the end-to-end encryption really mattered. And so it wasn't the hundreds of features. It was all in kind of the infrastructure and the platform. Same with Instacart, right? Like there are so many loved features of Instacart, but at the end of the day, it's a billion items that updates 3,000 times every single minute to get homeowners, their groceries from the store that they love. And so I think I, I wish I had known that because I think it would have curtailed my learning curve to say that it's not all the features for the platform that matters. It's the data residency. So the hospital in Germany that's fine-tuning a model can do so in confidence and the data isn't going to leave the region. It's the

Starting point is 00:38:52 availability. It's the reliability. It's, you know, making sure you have the right selection of the tools that enterprises need and the right way to retrieve the knowledge. And that's kind of the platform that we've built. But just didn't fully have that picture that those learnings would translate. That's really interesting. So what I'm hearing is people kind of undervalue who just have the simple bottom of the Maslow hierarchy of things you get of things that help you win in platforms, especially in messaging platforms, including. So it's like reliability, privacy, I don't know, availability. Yeah. Performance, reliability, privacy, safety, all of those things. Let me ask you kind of a totally different question when we were going to record this previously.

Starting point is 00:39:37 and you're like, oh, I have a big meeting with Satya, I got to do instead. And so we moved to a different time. Very few people get to work with Satya. He's quite a successful leader. What's something you've learned from him about, I don't know, leadership or product building? I've learned that optimism is a renewable resource. Like, this company for 50 years has had, you know, every reason not to succeed in it has. And even as it's had early success in the AI era and challenges and other successes,

Starting point is 00:40:12 like, and the space is developing so quickly, I think that his ability to generate energy and to use his optimism to kind of renew everybody's dedication to the mission is unbelievable. And I think it's such an important part of the culture. everybody talks about the growth mindset. That's real, huge part of the culture. But I think the ability to generate energy and clarity on what we need to go do and use optimism to renew the commitment every single day for every single person in an entirely competitive talent space is like, is pretty amazing. Is that something you think that was just innate to him or it's something that he's worked on to just generate this optimism on behalf of everyone? I have no idea. We should ask him,

Starting point is 00:41:02 but I am like deeply impressed by it. It's interesting that a lot of this comes down to just vibes. It's just like this vibe of, you know, like imagine it's not him just the words he uses. It's just like this energy that exudes optimism and energy. I mean, thinking about it, we all choose to, you know, someone he just said this to me. And I thought it was great. We all choose to close the door on our kids every single day to go work on something. And so you have to work on something that is like deeply moving to you and is like, you know,

Starting point is 00:41:31 you have a deep belief that is going to make the world a better place. And I think that's why it's vibes. I think you have to follow and have a sense of duty towards a mission that is bigger than yourself. Makes me think of a line that I've referenced a couple times on this podcast that's really hits people really hard. That the only people that will remember you working late are your kids. Okay, I don't know where we're going with that, but that was like, you know, now you're like. It's too much. We've gone too far. Oh, man. Okay, well, let me ask you this. What's driving you? You could have said our customers. We could have gone a different route on that one. This is the real stuff. What's driving you? What's driving you? What's keeping you excited about the work that you're doing?

Starting point is 00:42:17 What AI will help us do from a workforce perspective, what it will help us do from a health care perspective. Like, you know, my mom has cancer. And I think a lot about how, wow, we might find a way to solve the form of cancer she has in my lifetime. And I never. thought that was possible three years ago. All of that's deeply profound. And the thing that, like, I personally think a lot about now that we know that we're living in this time working with such powerful technology is the effects of it and how I can, you know, best build a platform where people can make use of it. So, like, the reason why I work at Microsoft is because, like, the whole ethos of the company is, like, how do I help people and businesses achieve more?

Starting point is 00:43:01 And like more for me and the thing like I think about at night outside of, you know, GPUs is, you know, I think about like, will my son have classmates in the future? And that's not because agents are going to replace them. It's because the fertility rates are declining, right? Like the average birth rate in the 90s when we were growing up was like three. And now it's 2.3. And in 2050, it's estimated to be, you know, below replacement. And I think that AI can have such a big effect on it and already is. Like, who's just reading about a hospital in London that's, you know, able to improve pregnancy rates by using AI to match, you know, eggs and spurs. And they're cutting costs at the same time. You saw with the ChatGPT 5 launch yesterday, such an amazing story about how ChatGPT is helping in healthcare. You know, Stanford's one of our big customers with the platform that I build and they're working on using AI for tumor reviews. And it's just like that is like it is these sets of things that will like move humanity forward and expand our lifetime and give us the like privilege to solve 100 year problems. And so that's that's why I'm excited.

Starting point is 00:44:21 And that's why I do what I do. Yeah. Especially in your role where you're building the platform that enables all of this. I could see how impactful. that could be. Asha, is there anything else that you wanted to touch on or share or double down on anything we've talked about before we get to our very exciting lightning round? We touched on it a little bit, but I think that with the advent of agents and products of think and can act and reason, there's going to be this kind of new wave around RL. And I have a

Starting point is 00:44:53 deep belief that that will become one of the most important product techniques kind of of the next season or at least the next few seasons. And RL is reinforcement learning. Yes, yes, exactly. Like, I believe we will see, you know, just as much money spent on post-training as we will on pre-training and in the future more on post-training. We talked a little bit about Nathan Lambert's study where his review was that, you know, when a model hits 30 billion parameters, it makes more sense to kind of fine-tune and optimize

Starting point is 00:45:20 that. You know, 50% of developers, according to surveys, are now fine-tuning. And we know fine-tuning is good, but, like, if, you know, you know, If you actually go through the full loop, you can get better results. So I think there's a bunch there. And I think there's a whole new set of infrastructure and platforms and companies that will be created that are all around this part of the stack. And so I think it's an exciting time to be in the platform space, but it's also an exciting time to be starting companies and be thinking about those problems. I want to make sure people truly understand what you're saying here because not everyone truly understands post-training, pre-training.

Starting point is 00:45:56 What's the simplest way to understand the difference there? and just why it's such a big deal that investment is moving to post-training? The way that I think about it is, you know, to create a foundation model, it requires a tremendous amount of compute, a tremendous amount of science expertise, as we're seeing, which the cost for scientists or the average value is raising dramatically. And I think, you know, an expertise that we've seen isn't everywhere in the world. world right now. And so it's just a big KAPX investment to do that. And with this explosion of models that we talked about in the beginning, there's a lot of good models to choose from for different

Starting point is 00:46:39 domains. And so I think that you just get more leverage economically. You get more leverage from a taste perspective of how you actually want to steer a model if you're actually doing reinforcement learning or some sort of fine-tuning to actually start to optimize what's off the shelf for some outcome like price, performance, quality. If you think about that, that's not crazy, right? Like, you know, ranking is an age-old optimization problem where you don't want to just take what's off the shelf because there's like amazing frameworks and UI and kind of components that, you know, the world, this React components that are out there. You still want to tailor the experience to a set of use cases or set of people. I think it's just the same kind of industrial logic.

Starting point is 00:47:22 So in practice, what this means is there's like, like a GPT-5 model, you're saying there's a lot of opportunity and a much more efficient way to spend money, which is take something like that and then train that on additional custom data that you have, whether it's data or just reinforcement learning, maybe even with humans to align it with what you wanted to achieve. Yep, and it could be your own data. It could be data that you buy. It could be synthetic data. It could be, you know, something else. But I think that we're kind of going to start to see, you know, more and more companies and organizations kind of start to think about how do I adapt a model rather than how do I take something off the shelf as is or invest a bunch of money and building my

Starting point is 00:48:05 own models. Yeah, I forget. I know Cursor, when he was on the podcast, he shared that they have a bunch of models that support your experience with Cursor. And over time, they're just going to have their own thing. I forget who has WinSurf or one of those guys just uses their own model now. They don't just plug into Cod. I'm much more in the model system. camp. Like I believe in model diversity. I think that in experience like Claude like Sonnet 4 is awesome for a set of use cases versus GPT5 is different for different use cases. I think that there's some tasks where you care about the latency of the model. You want you're like cool with the thinking time or you kind of want a quick retrieval and things like that. Like I think the beauty is there's a lot of

Starting point is 00:48:50 models that can kind of help you achieve that. And so I'm much more in the, like model system rather than one model to rule them all. Is that the right term? I've also heard ensemble model, ensemble of models. I think about an ensemble of models as a set of multiple models that then you can, you know, fine tune and deploy independently. But, you know, at this point, we're all making of different terminology to define things that we like have deep beliefs on that have like, you know, limited sets of data points because everything is moving so fast. Yeah. With that, we've reached our very exciting lightning round.

Starting point is 00:49:22 I'm very excited for our lightning around and I'm like turning down the lights. And then they'll come back on, I imagine, and one second. Okay, first question. What are two or three books you find yourself recommending most to other people? At work, it's probably thinking machine. So it's all about treating the cause, not the symptoms. The like, you know, prototypical example is like, you know, if you want to solve traffic, you don't actually put up, you know, speed bumps or speed limits.

Starting point is 00:49:50 you actually have to like solve walkability and mobility and kind of like why people actually use cars. Outside of that, I'm kind of personally, the CMO of Instacart recommended to me tomorrow and tomorrow and tomorrow. And I read it like last month and last year and the year before because I love it so much. It's like this like beautiful story over 10 years.

Starting point is 00:50:13 What are some favorite recent movie or TV shows? You really enjoyed. Formula One saw twice. For All Mankind. For all mankind, I like season four. I don't know. I like kind of playing out alternative theories to kind of how the space race might have looked. Do you have a favorite product? Do you recently discover they really love? Could be tech, could be gadgets, could be clothing. So I just joined the board of the Home Depot and we're doing a little renovation project. And so there's this new kind of new to me, DeWalt kind of power pack and they use pouch cells.

Starting point is 00:50:49 And so it's like 50% like lighter, but with all the power. And it's like awesome for drills and like things that, you know, I need to lift up with one hand that feel heavy. So I love that. We also are testing out this new brilliant smart home kind of system. So it's like kind of four inches of like high res middleware that allows you to kind to kind of connect to everything. And I've like reached peak kind of this out with like the explosion of all the technology

Starting point is 00:51:16 required to actually use your home. So it just might be the middleware that like sticks, but we'll see. You see dissat. Is that short for dissatisfaction? Yes. Sorry. I'm speaking in acronyms. Whoa.

Starting point is 00:51:29 I've never heard that. Dis sat. It's like, I love that. By the way, I love that you're on the board of the Home Depot. What a different part of the spectrum of work. Yeah. It's been awesome. The very first board meeting that had of philanthropy,

Starting point is 00:51:45 has been at the company for decades. said, welcome to the greatest company on the planet. It's pretty special. You're like, no, Microsoft. Is there something you've learned from working with that, with them that you've brought to Microsoft? Like, it's, it's new. It's this year. But I've long worked on products that kind of had that impact. So like when I was at porch, it was pros. At Instacart, we had 600,000 shoppers. And obviously, the Home Depot has associates. One of my favorite things, things about the company culturally is they have this inverted pyramid where instead of having

Starting point is 00:52:23 like executives at the top, the associates are at the top. And the stores themselves are headquarters. And then the kind of traditional HQ is kind of support. And so it's just like it's so customer centric. And when I think about amazing execution and creating these durable long term institutions and kind of how culture and ideology and kind of leadership is formed. Like I think about that. And I think about at the end of the day, you know, AI is going to have an impact on every single person and every single job. And it's like amazing to kind of just spend time with people outside of our bubble and kind of really try and learn what their real pain and problems and how they think about AI and how they think about technology and kind of what we need to do. Okay. Two more questions.

Starting point is 00:53:12 have a favorite life motto that you find yourself coming back to sharing with friends or family? I used to use the kind of minimize regret framework. And it's great. And I've used that for a long time. I think that probably once I got into my adult years and started to kind of have a family and things like that, my kind of just worldview changed a little bit. And it was all about maximizing kind of option value. And it just gave the things that I naturally cared about, like family and health and trust and relationships. Like it was just kind of like a new level of like value associated with those because all of a sudden learning rest on the weekend can like compound in the future or, you know, having good health can compound in the future. You don't

Starting point is 00:54:04 like have to trade that off of working extra hours or, you know, the importance of family. and all of those things. And so I think that, like, my worldview is, like, when I'm 70, it's not about what do I look back on in my life and count the number of regrets. It's really about, like, looking forward in the number of adventures I will still have because I have, like, accumulated this wealth of skills and trust and, you know, people and family and impact and things like that. Speaking of skills, the internet tells me that you have your second-degree black belt in tech-old. Why, oh gosh, is this true?

Starting point is 00:54:41 And then I have a question about it. This is true. Okay. That's incredible. What's some, why is this embarrassing? That's an incredible thing. I'm generally embarrassed anytime anything is discussed about it. Okay, great.

Starting point is 00:54:56 No problem. What's something that you learned from Taekwondo that has helped you with life or work? Taekwondo is more mental than it is physical. And so I think that's the same with kind of like all of our jobs and making product. Like I think it's like mental clarity. It's it's courage. It's it's kind of the ambition to kind of see things through and be unwavering. And so I think that's literally, you know, what it taught me outside of meditating,

Starting point is 00:55:29 which probably took me the entire time to like actually learn to meditate and clear my head. But yeah, I think it's awesome. Like, I think everybody imagines like, you know, flying psychics or running up a wall and like you can do those things too. But the real value is like the mental pursuit of it all, you know? And you can do those things too. Wow. Okay. I'm good.

Starting point is 00:55:50 I got to get into this. Asha, this was awesome. Is there, oh, actually two final questions. Where can folks find you online if they want to maybe follow up on anything if you want people to reach up? And how can listeners be useful to you? You can hit me up on LinkedIn. or email or text. I think all of those are traceable.

Starting point is 00:56:11 Look, like, how can you be helpful to me? I think, like, we're all early in this journey and great platforms that are built on great use cases and built on great customers. And so, like, if you have feedback, you have ideas, you have, like, things you want AI to be able to do to help you achieve more. I'd love to hear it.

Starting point is 00:56:28 I think the thing about all of these changes is that all of these new products and use cases will be developed everywhere. And so I'm always just thinking about how can we be the platform to support that. Amazing. Asha, thank you so much for being here. Thanks for having me. Bye, everyone.

Starting point is 00:56:46 Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenny. podcast.com. See you in the next episode.

Lenny's Podcast: Product | Career | Growth - How 80,000 companies build with AI: products as organisms, the death of org charts, and why agents will outnumber employees by 2026 | Asha Sharma (CVP of AI Platform at Microsoft)

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