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)

Transcript from https://podmenti.com/t/6a9b6be91b0ce461

You 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 output. The way that you scale to that is with agents. When all of that happens, the orchard
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. All of a sudden, these are these living organisms that just get better with the more interactions that happen. And 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 five's out We think about it as what season?
Than 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.
Osha'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 COO at Instacart.
And VPR product at Meta, where she ran messenger, Instagram direct, messenger kids, and remote presence.
She also sits on the boards of the Home Depot and Kupang.
And she's a second degree black belt in Taekwondo.
Osha has 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, Osha shares a bunch of trends and predictions that she's seeing 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.
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With that
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Asha, thank you so much for being here and welcome to the podcast.
Thanks for having me.
I wanna start with something that uh we were chatting about before this that I've never heard about as a concept that I think is uh
is gonna be really helpful for people to think about, 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 a it's been a pretty interesting shift, especially over the last
year or so because
When I got to Microsoft, um, it was kind of right after uh OpenAI and the large foundation models happened.
And then immediately after there was this explosion of models, uh proprietary open.
uh frontier models that were pushing the frontier curve and so they were
both uh more efficient and then we're starting to see domain level expertise in 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 action.
And I think that's just giving way to a new type of products that are uh starting to see uh some success. And so
All of a sudden products aren't just like these static artifacts that we start to ship. It'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 the whole KPI is what is the 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, whether it's price or performance, 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 to even think about
you know, products that think and live and learn, which is kind of exciting.
So when I hear this, what I'm thinking about is when I had uh Michael Terrell in the podcast, the cursor CO.
He talked a lot about how their big mode is the data that they capture from people using cursors 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.
It's just that the the models themselves like are so powerful. As of this year, Nat Lambert uh did this study that I thought was pretty interesting of all the top leader boards. And it showed that, you know, once a model uh hits thirty billion parameters.
the CapEx to actually train a model and put, you know, billions of tokens into a a kind of free run.
kinda 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, but
You can synthetically generate data. You have to come up with a rewards design. You have to actually roll it out. You have to A B test it rigorously. You have to find the job to be done or the use case that it makes the most sense for. And then yes, like that generates data that you can learn.
Uh, from I haven't ever seen it be one loop.
um for any any sort of product. I I think it's multiple tracks running in
in parallel that are kind of like assembly lines, if you will.
Um and kind of producing that.
And so is this kind of uh
thesis that we're moving towards product as organism is this.
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 um software as a primitive is changing and kind of the artifact inside of it uh is is
a model alongside the software components itself. And so in many ways I think that
you know, software products will all be model for products, if you will.
This reminds me why I just had uh Nick Turley on the podcast that we were talking about before we started recording head of chat GPT and
Uh, I was asking just like how much does chat GPT change with
GPT five coming out and he's just like it's the same thing.
They're the same product. It's just
Like Jeep the the model tell us what
to do in the product of Jet GPT.
And um and it makes me think about something else of just
Like you would think, why can't just GPT five 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 as 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. I one hundred percent believe that's where the world is going and that my experience feel different than yours. I mean that's kind of been the advent 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 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 let me kind of zoom out a little bit and ask you this question.
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 uh things that are applying to the people who are building the the AI uh products too.
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 we can raise the ceiling and
and kind of lower lower the floor for like all sorts of skills and and 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
uh something like customer support or taking fraud down from fifteen days to kind of cure to to ten days in like going through that entire loop of mapping out the process, applying AI to it, seeing some sort of impact, and then feeling the the P L or the kind of intrinsic benefits that that that looks like. The third thing then is like
Okay, great.
Now that you've seen impact, your 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 uh number of tasks.
I think that where companies fail is that they're doing AI for AI sake. 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 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 gonna do that end to end.
I think the tricky thing is for enterprises is the the technology is changing. There's something like seventy thousand enterprise tools like in the AI space launched 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 any one technology or any one tool, because the reality is the whole thing is going to change.
feel like you have to actually build for the slope instead of the snapshot of where you are.
So that's that's kinda what I see at the enterprise level. I think the the builders themselves are actually changing pretty fundamentally too.
Right, every single advent like change of technology has invented like a 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 you know, front end, backend and yada yada. And
Now I think we're seeing this advent of of the polymath and
Where um
Uh
I think that full stack builders are kind of having their renaissance where
If you take like an average organization, it takes probably 10 steps to launch a product. Um
It could be security review, it could be spec, it could be, you know, user research.
And there's what?
five plus functions, uh, maybe s maybe six or seven. I'm being generous for for a normal organization. And then you have like six or seven layers.
So all of a sudden you have
five hundred different touch points that have to happen to get a product out.
And when there are five hundred models available a week, or five hundred new technologies, that is just insufficient. And so
I really believe in in the concept of the 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 then gives you the whole loop.
to start to actually uh metabolize and go through that much faster.
That's definitely a recurring theme on on 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 not the lane here. Um and so
I think that whatever uh
function you are, you have to be obsessed with trying to understand like
The efficiency
or the cost of of the product, the actual rewards or you like, you know, system design that you're going after, the actual UI U X, how that actually manifests for agents or people, you have to start to get really good at that really quickly.
I like this phrase you just use the uh the loop and not the lane. Can you s say more about that?
No, it's just going back to our our previous discussion on, you know, the signals loop and
uh products evolving and becoming these living organisms and not these artifacts and
If 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 um functions start to to blur.
Um in future workforces.
To make this even more real, is there an example of a product or company that is a really good example of this?
Of the doing this well.
Living this kind of loop life. I think most companies that we're seeing in the space from an AI perspective are doing this. I can tell you about a couple that we're working on. Obviously.
in in the coding space you mentioned cursor
GitHub has very similar features that we're using kind of a s an ensemble of models that have been
fine tune across, you know, uh thirty different countries, all of the languages to actually then go iterate in a loop for next set of suggestions or code completions and things like that.
We've got um an AI uh product uh called Dragon that's for physicians.
And uh we saw a massive difference from when we used you know synthetic fine tuning to
when we annotated six hundred thousand patient uh 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 thirty and sixty character acceptance rate, depending on the run, to something like eighty three percent. And so
that required a small group of individuals, not a large organization, that were 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
What I'm hearing here is that
If you can gather data on how things are going and then spend a lot of time creating high quality labeling
to feed back into it to fine tune it is
Basically the big advantage.
Is is how you win in them 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 kinda reference this from GUIs to code native interfaces. Yeah.
Talk about that.
means what that looks like and what this means for folks building product. I think it it kinda goes back to what does it mean to kinda be a product maker in the future. I think that everybody's instinct is like is a is a uh GUI, but if you kinda think back in history
Like databases kind of went from the desktop
kind of down into sequel.
You 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, it's like
Moore's lawn, it's getting faster. And so I think that's just accelerating. And if you think about like a stream of 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. 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 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 uh way of thinking, even though it's long been a trend, uh, that's happened in these changes.
So is the tr prediction here that it's uh
terminals like uh claud code sort of experiences.
Or is it that it's agents that are taking with
Or is it both? Is that kinda what you're suggesting? It's I yeah, it's I mean look, if I if I any of us knew, that would be amazing. Um
I just think that the reason why terminals and
Or uh
great and it feels really great when you code is because of the way you 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 will find
you know, new ways to actually do that, whether it's in the IDE, whether it's in
Get a compiler, whether it's in, you know, some new development environment. Um, 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 uh Brett Taylor in the podcast founder of Sierra and he had a similar
Prediction that all software companies are gonna become agent companies.
And it's essentially what you're saying here is that like your software will just be this thing that's running in the background and there's much less of a GUI.
Do you think it still becomes like this chat interface, the way we're kinda getting used to? Is that like the primary interface with agents or is anything some 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 people living in places of artifacts that I think can become
uh really important composable pieces of the picture.
And I think they should be. So
I I'm excited about that. I think that um chat chat will be important, but uh certainly not sufficient.
What's interesting is ChatGPT 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 uh he were in the MS Dos era.
Of uh of of Chat GPT and there's a w which is interesting. 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 gonna be like a Windows version where it's
Much easier to understand what the hell's going on.
Yeah, I mean look like I think that it's it's smart. You should
Uh, every company should be
bringing AI to where their users are and ChatGPT has
all of their users using chat and it's a phenomenal product. Um
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 I love. You said that we're just starting to scratch the surface of what an agentic society actually looks like.
Ah, I just love this idea of an agency society.
What is that what does that actually look like in the future?
Oh gosh. I mean it's it's funny, y you were telling me about your two year old and I have my sunroom just turn one and I can't even imagine life at two because I'm just like that is so far away and well

Look, like I think that in the future uh
work will look really different.
I think that we're approaching this world in which the marginal cost of um a good output is approaching zero.
And I think when that happens we're going to see X
exponential 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 they've their tools and their pieces of software. And I think there's going to be
uh a ton of those far more than s the software that we use today. And then I think they're
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
uh create uh software development rep that's a genic 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
uh the orch chart starts to become the work chart. I think that tasks and throughput become more important than 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
Uh
I I'm pretty excited about it. I think
I think meetings will still be meetings and and they'll be weird, uh, but I think they will be a bit better. Um
And I think there'll be lots of changes. I think that
For the average employee, my hope and and kind of my optimistic view is that
they will be able to expand their skill set because now they have their own agent stack that they can bring with them to work just like you can kind of bring your own device and
you can um start to to have access to a set of skills that you never had before. And so if you think about, you know, the twenty million people that maybe sit in that that
space across America and they get twenty percent more skilled. But it's like
pretty exponential for GDP and so
It's it's pretty fun.
This comment you made about the orc ch the work chart becomes the orc chart is such a profound

I don't know if this is what you meant, but what I'm imagining is
You build these teams and here's your mission and goal and KPIs.
And it's humans and like oh.
Cool, go do this first.
And what I'm
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 part.
Your job. This is the walk the conversion onboarding team and that's like a bunch of agents just off doing their work.
Yeah, I mean
Yeah, I think like today we think in terms of hey, who reports to who in the 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, you're going to start to figure out like kind of outward task base.
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 it's kind of exciting when a a new issue comes up or new tasks 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 the age it's doing the right thing? How do you fine tune it if they're not? Like all of those things. So
I think that um
I'm just speculating, right. Yeah. Uh
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 gonna 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? Yeah, I think that
Um, this same kind of loop that we talked about becomes increasingly important. Like
fine tuning and self healing, observability, really good evals.
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. Um
But I think, you know, uh managing devices and policies and group access, all those things are solved problems. Uh, which is good.
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So a lot of this it feels like it's in the future. I know a lot of this already happening. 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 in many of our workflows, like
one of my favorite ones. So right now are uh my engineering partners out. So I jump on the the live site bridges when something goes down and
you know, as something as simple as like you can automatically get a summary of everything that just happened because usually there's fifteen 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 um
kind of the the entire kind of DevOps areas is is changing.
We use it to uh we use Spark to create prototypes. So everybody on the team is
expected to code, 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 um
Everybody's using uh AI to kind of
uh find ways to have efficiencies and like coming up with
uh documentation and things like that. And so
I think it's everywhere, which is cool.
Um, I think that we're just scratching the surface though for kind of like what's possible in terms of working with agents.
That's how I always feel when people ask me how I use AI. I'm just like it's just like everywhere. It's just like in every little sprinkled in everything 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 a product manager that I collab with, uh, Peter Yang, who talks about how he just doesn't I don't even know how to do a strategy doc anymore without AI. Like how did people do this without having someone's. Do you think there will be strategy docs in the future? That's gonna be interesting. I I have this like
I this wrote this post once of like which skills of a PM
job will be most replaced by AI.
And strategy is the one that people are the most
di have the biggest debate on. Like you could argue. I don't know if like let's get into it briefly. Uh
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 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. Um
Like
uh Microsoft is like the vision of a software factory and creating what Microsoft did uh wasn't inevitable. Um
Інстакарт
you know, there was web bands and web bands didn't work, but Instacart did work because of a different way of thinking about it.
uh that came through judgment and iteration and a bunch of things that you couldn't have learned unless you actually went through the process. Uh
you know, the iPod. Like you you go for it. So I think it's there. I think docs themselves like for every idea, for every, you know, need.
will just start to kind of fade into
you know, applications and and different artifacts in the productivity suite, which
Um
You know, is just a different way of working.
Yeah. Like your your original question, which I didn't quite answer, but I think is important. You're asking, like, do we even need strategy docs?
And I guess it's just like somehow everyone needs to be aligned on the strategy. Maybe it's not a
Yeah, it could be some other thing. I mean if if you if you architect an organization the right way to keep up with AI
You know
You you need a learn you need a different alignment mechanisms than traditional ways of actually work.
So let me ask you actually about that.
So planning right now is just crazy. How do you like
How does anyone plan a roadmap when there's just like okay, GPT five's out. Okay, you're right. Uh
What what what works for you for setting an actual a 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, much smaller and you get to kind of uh you know, run something yourself. And there's pros and cons to both. So here's what we do. We
Uh, the company, um historically, at least in our product teams, had kind of semesters that they planned again. So think of that as every six months. There's kind of a strategy, but back forward, all of those things.
Think that's very valuable.
I think like the idea of six months though and really understanding what's changing out in front is is truly challenging to kinda 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, 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 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
Uh
you know, teams are operating in squads and they're kinda setting out, you know, four to six week um 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.
uh with AI, I will say we 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 the slope. I think that
We have to continuously be thinking about how we're going to disrupt the platform.
um 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.
This is awesome. So
What I'm hearing here is there's this concept of seasons and everyone's aligned, Okay, this is time for agents. This is what's happening right now.
We're gonna center around our strategy around agents.
And then there's these loose
quarterly okay R C plan for three months.
Roughly, and then you leave some slack in the system for things to change.
Yes.
Is the current season agents? How would you describe what season wrote right now? Yeah. Okay. It's agents. Okay. The rise of agents. That sounds like a terminator movie. Do you uh is do you have a sense of what the might next season might be? Is there any like, oh, this might be coming next? Gosh. Um
I don't, um, but I think that
Look, like we have, you know
More than fifteen thousand agents.
that are uh deployed on our service today.
Um, at least at the 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 um making sure that we have all of the alignment, accountability, observability evals to making those agents like
Great.
I think that uh Manus' breakthrough in the space was that they could like 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
uh a bunch of building blocks that I think like are m 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 uh agents
Till the end of time until super intelligence and then we're just on beaches.
Chilling. Yes, agents until dank memes. Um
Look, like I I I think the cool thing is is like
Something new could come in three months, something new could come in.
uh thirteen months. I I think like we
kind of have this conviction on a set of building blocks that we wanna provide to enable these agents to
uh endure and and have high endurance. And so that's that's what our books.
When you said there's fifteen thousand agents, what does that mean? Is that fifteen thousand types of agents you can
Use or is it like that's how many processes are uh that's you know, customers, fifteen thousand I 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.
And they're running and the number of agents is in the millions just running there in the cloud.
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 this
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, Okay, I see. I didn't expect this.
When I first
took the role it was kinda described as like the belly of the beast. And I
Um, had spent most of my career
Building products at the center of machine learning.
And applications or businesses.
Um
And I think that
To my surprise
a lot of the learnings have translated in terms of what makes a great platform, um is what makes a great
product.
And 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 at was a company called Porch Group. I was employee seven.
And we knew we wanted to help people take care of their home and I think we invented so many features like the home report or
Like a way to manage your home or
Like
house style inspiration where you should like see all of the the houses and it's map map every single room and
The single most important thing that we could could have done and did during my time there was
create a matching platform that match the six million professionals with the thirteen hundred service types with the uh thirty seven thousand zip codes and all of the homeowners in North America to actually take care of their their home.
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 the first five hundred billion uh five hundred million dollar valuation. That's eventually what
we built on to actually have other vertical services uh and software platforms that that IPO the company.
Same with messaging. Um
The number one learning that I had was
Look like WhatsApp didn't win because it had stickers or stories or dark mode. In fact I
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. Like 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. Like 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 two hundred messages a day to the four people 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 hundred of hundreds of features. It was all in the 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 three thousand 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'cause I think it would have
curtailed my learning curve to say that
It's not all the features for the 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 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 the platform that we've built. But
Just didn't fully have that picture that those learnings would translate.
Mm, that's really interesting. So what I'm hearing is
People kind of undervalue just how the sim
The simple
Bottom of the Maslow hierarchy.
Of of things you get of of things that help you win and in platforms especially and
messaging platforms including. So it's like reliability.
Uh
Privacy.
I don't know, availability.
Yeah. Performance, reliability.
Privacy, safety, um, all of those things. Mm-hmm.
Let me ask you kind of a col totally different question.
when uh we were gonna record this previously in
You're like, Oh, I have a big meeting with Satya, I gotta do instead.
Uh and so we moved to a different time.
Uh very few people get to work with Satya. He's
quite a
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.
Mm.
Like this company for fifty years has had
you know, every reason not to succeed and it has and
Uh, even as it's had early success in the AI era.
and challenges and other successes
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.
Um 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 to generate energy and clarity on what we need to go do and uh use optimism to renew the commitment every single day for every single person in a
entirely competitive talent space.
He's like
It's 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. Um we should ask him, 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 he exudes optimism.
And energy.
I mean think about it, we all choose to to you know someone you 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, you have a deep belief that is going to make the world a better place and like
I think that's why it's vibes. Like I I think you you have to follow
Um
and have a sense of duty towards a mission that is bigger than yourself.
It makes me think of a line that I've referenced a couple of times on this podcast that's really hits people really hard.
that the only people that'll remember you working late
Or your kids?
Okay, I don't know where we're going with that, but that was like you know, now we're like, too much, we've gone too far.
Oh man. Okay. Well let me ask you this. What's what's driving you? We could have said our customers. We could have we could have gone a different route on that one.

This is the real the real stuff.
Um
What's driving you? What's driving you, what's keeping you excited about
Uh the work that you're doing.
what AI will help us do from a workforce perspective, what it will help us do from a healthcare 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.
Like 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 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?
And like more for me in the thing like I think about at night.
Um, outside of um
You know, GPUs is
Um, you know, I 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 the fertility rates are declining.
Right? Like the a the average birth rate
In the nineties when we were growing up was like three and now it's two point three. And in twenty fifty 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 was 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 sperms and their cutting costs at the same time.
Uh you saw with the Chat GPT five launch yesterday such an amazing story about how Chat GPT is helping in healthcare.
Yeah, Stanford's one of our big customers with the with the platform that I uh build and they're working on using AI for tumor reviews and
Um
It 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 hundred year
problems. And so that's that's why I
I'm excited 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
Uh
impactful that could be.
Usha is there anything else that you wanted to touch on or share?
or double down on uh 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 um
With the advent of agents and products that think and can act and reason, there's going to be
this kind of new wave around RL. And it I have a deep belief that 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 our all is reinforcement learning. Yes. Yes, exactly. Like I believe we will see, you know, just as much money spent on post training, uh as we will on pre training and in the future more on post training.
Um, we talked a little bit about Nathan Lambert's study where his review was that, you know, when a model hits thirty billion parameters, it makes more sense to kind of
Fine tune and optimize that.
you know, fifty percent of developers, uh, according to surveys are now fine tuning. Uh, and we know fine tuning is good, but like if you actually go through the full loop, you can get better results. So I think there's
There's a bunch there. And I think there's a whole new set of infrastructure and platforms and
uh companies that will be created that are all around this part of the stack. And so
Um, 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 wanna make sure people truly understand what you're saying here,'cause
Not everyone's
Truly understands.
Post training, pre training.
What's the simplest way to understand the difference there and just why it's such a big deal that that investment is moving to post training? The way that I think about it is.
You know, to create a foundation model, um, creates a trem uh it requires a tremendous amount of compute, a tremendous amount of science.
uh expertise as we're seeing who's uh which
The cost for scientists or the average value is rising dramatically.
And I think you know
W
uh an expertise that uh we've seen it like isn't everywhere in the world right now.
And so
It's just a big capex 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 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.
uh 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 that's not crazy, right? Like
You know
ranking is an age old uh 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 is 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.
So in practice what you're what this means is there's like a GPT five 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
With humans to line 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 um
I 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
uh off the shelf.
as is or invest a bunch of money in building my own models.
Yeah, I forget. I know Cursor when he was on the podcast he shared that
They have a bunch of models that
that support your experience with cursor.
And over time they're g just gonna have their own thing. I I forget who it was, Winsurf or one of those guys.
Just uses their own model now. They don't
Just plug into Cloud.
I'm much more in the model system camp. Like I believe in uh model diversity. I think that
inexperience like
Clone.
uh like Sonnet for is awesome for a set of use cases versus GPT five 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 uh quick retrieval and things like that. Like
I I I think the the beauty is there's a lot of models that can kind of help you achieve that. And so I'm much more in the like
model system, uh, 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 up different terminology to define things that we like.
have deep beliefs on that have like, you know, uh limited sets of data points because everything is moving so fast. Yeah.
With that we've reached our very exciting lightning round. I'm very excited for our lightning round and I'm like turning down the lights. And then they'll come back on, I imagine, in 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. Uh so it's all about treating the the cause, not the symptoms. The like, you know, prototypical example is like
You know, the
If if you want to solve traffic, you don't actually put up, you know, speed bumps or
Or speed limits, you actually have to like solve walkability and mobility and kind of like why people actually
Use cars.
Um, outside of that
Uh kind of personally, um the CMO Vince Descartes uh recommended to me tomorrow and tomorrow and tomorrow, and I read it.
Like.
last month and last year and the year before,'cause I love it so much. It's like this like beautiful story over ten years.
Yeah.
What are some favorite recent movie or TV shows you really enjoyed?
Formula one, saw twice.
Uh, for all mankind.
For all mankind, I like season four.
Uh, I don't know. I li I like kinda playing out alternative theories to kinda
How a space race might have looked.
Do you have a favorite product you recently discovered that you really love could be uh
Tech could be gadgets, could be clothing.
So I just joined the the board of of the home depot and we're doing a little renovation project and so
Uh, there's this new kind of new to me, DeWalt.
kind of power pack and they use pouch cells. And so it's like fifty percent like
lighter but with all the 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.
Uh, so it's like
kind of four inches of like high res middleware that allows you to kind of connect to everything. And I've like
reach peak kind of this sat with like the explosion of all the technology required to actually use your home.
So
It just might be the middleware that like
Mistakes, but we'll see.
Did you say diss sat? Is that short for dissatisfaction? Yes. Sorry. I'm speaking in acronyms. Whoa. I've never heard that. Dissat. It's like
Yeah.
I love that. By the way, I love that you're on the board of the home depot. What a
What a different part of the spectrum of of w of work.
Yeah. I it's been it's been awesome. The f very first board meeting, the head of philanthropy
been at the gum uh has been at the company for decades and she said, Welcome to the greatest company on the planet.
Mm.
Very special.
You're like, No, Microsoft.
Uh, is there something you've learned from working with that?
with them that you've brought to Microsoft.
Like it's it's it's new, it's this year, but I've long worked at
on products that kind of had that impact. So like when I was at Porch, it was pros at Instacart, we had six hundred thousand shoppers.
Um, and obviously the Home Depot depot has associates. One of my favorite things about the company culturally is
They have this inverted pyramid where
Instead of having like executives at the top, the associates are at the top. And the stores themselves are uh headquarters uh and and then
the the
kind of traditional HQ is kind of support. Um
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 h how culture and ideology and and kind of leadership is formed, like I think about that. And I think about at the end of the day,
You know
A I is going to have an impact on every single person and every single job. And it's like amazing to kinda just spend time with
People outside of our bubble.
And 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.
Do you have a
Favorite life motto that you find yourself coming back to sharing with friends or family?
I I I used to use the kind of minimize
Regret.
framework. Um and I it's
It's great and I've 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 uh kind of just world view changed a little bit and I was 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 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 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 the my worldview is like when I'm seventy, 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
a accumulated this wealth of of skills and trust and
you know, people and and family and impact and things like that.
Speaking of skills, uh, the internet tells me that you have your second degree
Black belt in Techland.
Uh
Why, oh gosh, is this true? And then have a question about it.
Uh, this is true. Okay. That's incredible. What's some why are you why is this embarrassing? That's an incredible thing.
Uh
I'm I'm generally embarrassed any time anything is discussable. Okay, I think Okay, great, no problem. Uh what's something that you learned from Taekwondo that uh that has helped you with w life?
Or or work.
Taekwondo is more mental than it is physical.
Um
And so
I think that's
the same with kind of like all of our jobs and make a product like
I think it's like mental clarity. It's it's courage. It's it's kind of the
Um
ambition to kinda see things through and be unwavering. And so
I I think that's literally, you know, what it taught me outside of meditating, which probably took me the entire time to like actually learn to meditate and clear my head. Um
But uh yeah, I 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. I gotta get into this.
Usha, this was uh awesome. Is there uh 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. Um
Or email or text. Um
I think all of those are traceable.
Look, like how can you be helpful to me is I I think like we're all early in in this journey and great platforms 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, um, to help you achieve more. I'd I'd love to hear it.
I think the
The thing about all of these changes is that all 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.
Everyone.
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