# Sierra's Clay Bavor on AI Strategy, Enterprise, and Frontier Models

**Podcast:** The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
**Published:** 2026-07-04

## Transcript

We have not yet appreciated the unbounded demand for, call it frontier levels of intelligence.
Part of the driver of the difference is probably the willingness of Chinese companies to do scale distillation of the frontier models.
If you can't build frontier models yourself, okay, maybe the next best approach is to distill them and offer them up.
Every one of our rounds, we actually guided to and took a lower price than we could have.
Some of our most effective employees at the entire company are 22 or 23 years old and have been completely AI pilled.
We completely changed our engineering interview process.
When Pat Grady at Sequoia and Neil Major at Green Oats tell you someone is special, well, it kind of means something.
Clay Bavor joining me in the hot seat, co-founder of Sierra, one of the fastest growing AI companies in the world.
Sierra has raised more than one and a half...
They work with some of the biggest companies in the world and they're valued at almost 16 billion dollars and they work with 40% of the Fortune 50.
But before Sierra, Clay spent an incredible 18 years at Google where he worked on some pretty cool projects.
Google Labs, naming one, Google Workspace, Gmail, Google Drive, Google Photos, Jesus, is there anything Clay didn't work on at Google?
On top of that, he's just an awesome dude.
Luckily, we had a chance to do it in person in London.
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I do this show three times a week.
That means three sets of research, three sets of prep, three sets of follow-up, and about 400 other things in between.
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Clay, I am so excited for this, dude.
I said to you downstairs, we do a lot of shows.
I often speak to people before a show.
When I speak to Neil Mater, Ravi Gupta, Sangin at GV, and I hear what I hear, I want to see there were some of the most astounding references I've had.
So thank you for joining me.
It's nice to hear.
No, pleasure to be here.
Thanks for having me.
No, I mean, listen, they paid a lot to be featured.
So, you know, you've got to drop the sponsor.
They are great.
So grateful to be working with each one of those guys.
It only cost 500 million bucks.
So I want to start with, I heard that Brett tried to hire you or start a company with you several times before.
Sierra?
Yeah.
Why third time lucky?
Why after 18 years?
Oh, third time's a charm?
Yeah.
Why after 18 years at Google were you like, ah, now?
Yeah.
So Brett and I met 20 years ago.
We both started our careers in the associate product management program at Google.
He was class one.
I was class three.
We met in the context of some kind of shared project that we were assigned to and kind of hit it off and ended up staying in touch socially through mostly a monthly poker group that in a good year might play two or three times, so not quite monthly, and had always wanted to work together and almost did a couple times.
I think when Brett left, I can't remember.
if it's for friend feed or equip tried to get me to join that the short answer is twofold one i just loved my time at google Culturally, it was me.
I learned more than I can ever imagine having learned in those years.
The people were so extraordinary to work with.
And I had a series of managers and leaders I got to work with who took bets on me, gave me, on paper at least, more responsibility than I deserved and got to work on just truly fascinating things.
And so I was just incredibly happy and engaged and growing as a person and professional.
And then in late 22, kind of the planets aligned.
in a way that I didn't think that they would probably align again.
I'd always wanted to start a company.
I started a very modest company when I was 13 years old and always thought I would start another.
And if you're going to start a company with someone, you want to make sure that they're excellent in competence and in character and then that the timing is right.
And we could see that language models were going to be a thing.
And if ever there's a time when the proverbial deck of cards are shuffled in the favor of smaller companies, it's when you're at the advent of the new technology.
So happy at Google, planets finally aligned and took the leap.
And we're, I don't know, three years and change in now.
18 years at Google is one hell of a stint.
Yeah.
Well, I started counting in colleges.
Gosh, I've been there one college, two colleges, three colleges, four colleges.
Yeah, it's a long run.
That's even more terrifying.
Yeah, it's a long run.
My question to you on the back of that is, and it's a terrible question.
You can chastise me for it.
What are your single biggest takeaways from that experience that you took with you to Sierra?
And what did you leave behind?
It's such an interesting question.
Of course, the scale of a two and then 10 and then 100 person enterprise software company is very different from, I think when I left Google, it was roughly 150,000 people.
Things that I've definitely brought with me.
Number one is...
a willingness to invest as far down the technology stack as you need in order to build the service and product that you want.
Google, I think from the early days, famously built its own, if not data centers, cluster architectures, and they were the first really to use commodity hardware.
That required building novel distributed systems for serving and data storage and so on.
And so we could see that language models.
as early as April of 23 when we started the company that agents were going to be a thing.
This was before all anyone wanted to talk about was agents.
And we realized, okay, this should be possible.
It's not yet possible, but we're going to have to invent frameworks for building these things.
Our own architecture is really from scratch.
So actually, our first founding head of research was the Princeton professor who literally wrote the paper on language model-based agents, the React paper.
And so we invented.
We invented and went further down the stack than I think some companies at that point would have been willing to.
And we're not doing our own pre-training.
We'll leave the capital expense there to the labs and the larger companies.
Before we move to two, can I ask, did you consider that?
Because I completely understand the desire to own as much as possible.
Yeah.
Did you consider training your own models?
And what was the thought process around not?
That's a great question.
We did briefly and discarded it.
If you recall at the time, so late 22, early 23, as a startup in AI, you were kind of nobody if you weren't doing your own pre-training and building your own foundation models.
Character, inflection, adapt.
great people at these companies, but the capital expense, the ongoing capital expense to create what is effectively a highly perishable bag of floating point numbers just doesn't work for any but a small number of companies.
And so our calculus was for areas that are deeply capital intensive.
How do we slipstream behind the investments that the labs, that the hyperscalers are making?
take as much as we can off the shelf while still being willing to engineer more deeply.
So today we have a set of our own proprietary fine-tuned models, but these are fine-tunes on top of open weights models.
So we're not going all the way down to the mega cluster training runs.
And I think it's important that you are in control of your own destiny enough and that you don't tell yourself a story that you need to go further than you actually need to do.
Is the future open models fine-tuned to specific company needs?
And if that is the future, with the realization that frontier models are too expensive, is that a bare case for frontier models?
I think it's a lot more complicated than that.
If you asked any software company, would you like to upgrade your staff-level software engineers to principal or distinguished-level software engineers, yes or no, a hundred out of a hundred would say, yeah, that sounds pretty great.
So I think...
We have not yet appreciated the unbounded demand for, call it frontier levels of intelligence.
And now you don't need that in every domain.
For instance, in our own, we build AIs for companies to interact with their customers.
You don't need Mythos to return a pair of shoes.
You're good.
It's like, you want to do that well, but we've got some capability overhang, so to speak, for doing something like that.
But in a range of domains...
coding, certainly science, material science, legal, right, where the stakes are very high, there's a high degree of complexity, I think we're going to see effectively unbounded demand for greater levels of intelligence and therefore the frontier models.
That said, there will be an assembly line of cool.
GPT-4, which in March, April, May of 2023 was good enough to do some set of things is now 1,300 the cost for an intelligence equivalent token.
And so you'll have some assembly line of taking models that were once at the frontier to perform certain workloads and then build open weights, fine-tuned models for those.
And I think you'll end up with companies using both mixing and matching them depending on the task at hand.
As we see Open become more and more advanced, does that not mean the problem set for frontier models becomes more and more challenging?
As you said, we've seen the progression of Open so much that actually they can do the majority.
I get it for solving climate change, cancer treatments and materials, but actually for the majority, what percent of enterprise tasks can be done with Open today?
Well, I think if you look at what percent of enterprise tasks are completely automated today, it's a rounding error, right?
It's very low.
So is that a model gap?
Is that a diffusing the technology into the company?
Is that an application layer gap?
I think it's probably all of these.
You're obviously correct that as the open weights models become more capable.
The set of things they can do grows larger.
The set of things where all else being equal, if they're much less expensive, that you would want to point a frontier model becomes smaller.
But again, I think we're not imagining just how high the ceiling is in terms of demand for frontier intelligence.
Invention, discovery, building new products, building new services.
I think it's hard to get your mind around when you have intelligence that can work around the clock to invent, to build, to discover how you would use that and how much of it you could use.
Can you help me understand, when we look at token economics, we thought with chat tokens over time would go down in cost.
And with the movement from pure chat to chat and agents and agent economy based, we're seeing...
token costs increase, not decrease.
How do we see the evolution of token costs with the evolving formats, do you think?
Yeah.
You missed one thing in there, which is a large amount of token use is driven by reasoning models now, right?
Thinking out loud to themselves.
And I think actually one of the most underrated developments of the past few years was the O1 model from OpenAI in late 2024, where if you recall, there was a chart that showed okay, test time compute or amount of inference done, amount of thinking out loud and performance, and it just keeps going up into the right.
It's logarithmic, so it starts to level out.
But what it effectively demonstrated is if you have enough time and compute, the model will be that much smarter.
So as for what happens with token economics, I think there are many drivers underneath it.
One is you're going to end up with hardware that is able to produce more tokens at equivalent cost.
And so kind of the cost of the inputs, so to speak, will go down.
We talked about, I think you'll have this migration of certain workloads to...
OpenWeights models.
I think one of the drivers that's hard to predict how it will play out across both the OpenWeights models and the frontier models is just the availability of compute.
And it's classic economics.
It's microeconomics 101.
supply, demand, if you have unbounded demand for frontier level intelligence or GPUs to run open weights models and the rate limiter is the number of Blackwells and H100s you have, you end up with kind of a floor on the cost of tokens because you've got to pay for the energy, you've got to pay for the compute.
We had the founder of Nebius on the show the other day and he said that if they 10x supply, they could still sell out in a day.
I believe that.
I believe that.
And I think that makes the point, which is, okay, open weights models will be cheaper because you're kind of avoiding some of the margin stack in the hosted frontier models.
Okay, but what is the fundamental input?
It's GPU capacity, it's power.
That's still constrained.
One thing that could slightly alleviate that is actually running models locally.
People say that it could be the future.
On your cluster of Mac minis or whatever?
Yeah, or even on device on phones.
I don't quite understand that.
when we think about always on AI 24 hours a day, that's an awful lot to run locally.
Is it a pipe dream or do we think that's actually a reality that would alleviate the server side challenge?
Oh, it certainly wouldn't alleviate it.
I think it will make some consumer applications much better.
But I mean, the reality is you need, you know, petaflops, exaflops of compute, certainly for training.
And you want a whole bunch of compute quickly at inference time.
And you just run into thermal limits on your phone.
I do think it is shocking that we're all carrying around in our pockets, you know, hypercomputers these days.
And will they get better?
Yes.
Will you have language model optimized hardware rolling out in our phones, in our computers?
Yes.
I can see a...
sort of home appliance, which is, you know, you plug into the mains and you get, you know, on-demand access to a whole bunch of compute for things in your home.
And maybe that helps alleviate some of it.
Certainly for frontier workloads, though, it's like there's one place you can go to for that.
And it is a giant rack of TPUs or GPUs in a data center somewhere.
We spoke about kind of frontier versus open.
At Frontier, obviously, you have OpenAI and Anthropik in the US who are...
The dominant leaders everyone knows.
And my alma mater and Google.
And Google, of course.
We had Demis on the show.
Incredible, incredible.
I love Demis.
I love Demis too.
Also one of the most humble leaders I've ever met.
So I absolutely agree there.
Open in the U.S.
has lagged behind.
We see Chinese models being unbelievably advanced and impressive.
Do you agree that we have a challenging open ecosystem in the U.S.?
And does that worry you?
Part of the driver of the difference is probably the willingness of Chinese companies to do scale distillation of the frontier models from the labs.
My impression is many of the models, the open weights models coming from China, are derived from training runs done in the US.
I think if you have the US-based labs and hyperscalers developing the frontier models, there's an obvious, like, are they going to compete with themselves and drive price pressure on the frontier models?
by developing and releasing open weights models that are of similar capability.
If I was running that business, that's not something I would do.
So if you can't build frontier models yourself, okay, maybe the next best approach is to distill them and offer them up.
I think that's probably the main driver of the difference.
I have to ask, you mentioned earlier enterprise being a team sport.
I love that.
And you mentioned earlier about kind of who wouldn't want more advanced software engineers internally.
You had a Lovable announced yesterday hitting, I think, 500 million ARR with 149 people.
And in a show that comes out tomorrow, Rory, who's one of my co-hosts on this kind of weekly show that we do, says, well, if you're Sierra, you can't do that.
And we actually mentioned Sierra.
It's a enterprise.
business and you have to have a different structure of the team.
When you look at the future of teams, are we seeing a world of dramatically leaner, fewer people in teams?
Or actually, is it still very much dependent on customers and we will still have very large teams for companies like Sierra with Enterprise?
I think the general direction of travel clearly is towards smaller, higher leverage teams.
We have software engineers who are completely AI-pilled and using cloud code.
Kodak's, our own internal agent we call Pinecone that we used to run much of the company on, they estimate they are between three and 20 times more productive in terms of features shipped.
The productivity gains certainly in software engineering and data science, data analysis, and other area we're seeing it in spades.
But I think in time it will touch all parts of really every company.
So that's the general trend.
I think within a company like Sierra where We serve, in particular, the large enterprise.
We work with 40% of the Fortune 50.
We have 50% of our customers doing over a billion in revenue.
We have 30% doing over 10 billion in revenue.
These are some of the most complex and, in cases, regulated organizations in the world.
And to be able to sell and implement our product and solution successfully for organizations which are snowflakes.
The process of selling and more importantly, successfully implementing and deploying a solution like ours into the large enterprise is still a lot about deeply understanding our customers' business outcomes and objectives, about understanding their technology stack, integrating with it successfully, building relationships, earning trust to show up, not just as a vendor that...
throws some software over the wall, but is a true partner in diffusing this technology into, in our case, all of the front office, sales, support, marketing, and so on.
I mean, there's so many things for me to unpack there.
I was scribbling furiously.
I have to ask, you mentioned the internal agent, Pinecone.
Can you talk to me about what that is, how it was built, what it does?
I'm just intrigued to see how companies change and how they operate.
Yeah.
It's one of the more significant developments in how we run the company of the last six or nine months.
And we began by building what we call our MCP gateway.
This is a single MCP server that aggregates all of the main systems and services that we use to run the company.
And so you can add this single...
gateway to your Claude instance, to your Codex instance, and indeed to Pinecone.
And basically via any one of those agents have full access with the permissions, of course, that you as an individual at the company, you can't read someone else's documents, but you can read your own.
You can read your own Slack messages.
And it's kind of like having superpowers, right?
You can interrogate.
in essence, the entirety of the company, all information that is published, whether it's Slack messages or presentations or operating reviews and so on, and use access to all of that information to better reason, make decisions, get things done.
Pinecone, of course, incorporates that MCP gateway, but then is a purpose-built harness for all of Sierra.
Pinecone knows how to...
build Pinecone.
There's a whole harness around the engineering of Pinecone and our engineers there are phenomenally productive.
We have a whole harness around the core of our platform, our agent architecture, agent studio, where you build and deploy agents, speeding up software development there.
And then we have a shared library of skills that anyone at the company can build.
You can build one that's private to you.
I have a whole bunch of skills, including one that is basically the clay scanner of interview packets.
So I, to date, review and approve every single hire we make.
And I get some help from Pinecone.
And I've basically taught it what are the things that I look for?
What do I scan for?
flag these if there are any instances of them.
And it's kind of a shortcut to a faster, deeper read of every packet.
So Pinecone has just become this approaching indispensable.
I think we're not quite there yet, but approaching indispensable tool for running the company.
I could go on on some of the other interesting things we built.
I've been working on what I call Sierra Brain and some other things in the same.
What's Sierra Brain?
Sierra Brain is, it starts with a 20 or 30 page document that grounds any agent in what we are as a company.
What we do, how we're organized, our team structure, the competitive landscape, our strengths and weaknesses, all of these things.
On top of that...
I've given it access to every one of our recent board letters, every one of our recent operating reviews, other insights and observations we have about what we believe to be true about the world.
I can then use it to reason about what we should be doing as a company.
And so it's a bit like a strategy thought partner, if you will, that knows the company, if not inside and out, very deeply.
We're going to get to your board letters because I heard about these and how you have boards every six weeks, not every quarter.
Yeah.
Because the world moves too fast, apparently.
Oh, trust me, I stalked the shit out of you.
But I just want to kind of stay on my internal builds.
You mentioned that the internal agent that you have, we're having a lot of CEOs who I speak to.
I've got no idea.
Do I just let my devs teams run wild on token spend?
Do I give them some form of budget?
Token maxing.
Yeah, what's your personal take?
And what do you sit around the fire and say, should we put a cap on this?
Do we just encourage them to go wild?
How do you approach it?
Yeah.
I think over the past six months, using a bunch of tokens was a proxy for you're using AI.
You're leaning into it.
You're trying to be more productive with it.
So I think it's generally been a positive signal.
top engineers who are really leaning in to cloud code, codex, and so on, are spending more than $100,000 on a run rate basis on tokens per year.
That's a meaningful fraction of an engineering salary.
So I think the direction that we're headed is some amount of token budgeting on a per-employee basis.
I think for CFOs in the future, like capital allocation will look more like, how do we allocate OpEx?
And then headcount.
And headcount will be both headcount for salaries and SBC.
and also tokens associated with headcount.
And so here's your salary, here's your token budget, have at it.
We are not yet at that point.
Our usage compared to some of those larger numbers is modest.
And I think the benefit of learning at the fastest rate possible outweighs the capital discipline at this point.
We prefer to learn quickly, see what works.
It'll be interesting to see how the...
rate limiter in software development moves around, how the Andy Grove breakfast factory, what is the constraining factor?
It used to be writing code.
Now it's probably reviewing code.
Pretty soon it will be deciding what is worth building and editing what could exist to what should exist.
The dynamics there will be interesting.
I think the cool question for us to understand if...
Everything is slightly overhyped is what percent of developer salary will be spent on tokens in the future.
Mark Benioff, you said larger companies, said that he spends 300 million a year on Anthropic for his dev teams.
That works out about 3.8% of developer salaries.
Not actually as much as the headline 300 million makes you feel.
If it stays at 3.8%, a lot of the companies that we're investing in and see around us are actually grossly overvalued.
If it goes to 20%, they're undervalued.
And I had Brandon at McCaw on the show who says he spends more on tokens than he does headcount.
Yeah.
I think 3.8% is wildly off from where the steady state will converge.
Where do you think it, I'm not going to hold you to it in five years' time, but do you see it being a 20%?
Oh, I do.
I do.
So the $100,000 a year actually will be normalized because if you think about a great dev in the Valley, I presume $500,000 is kind of where they're at.
Sure, that would be the upper end.
So it feels kind of normal.
I would not bet on 3.8%.
I would bet on much closer to 20%.
In software engineering, the gains to me seem unequivocally there.
You can debate, is it 2x, 10x, 20x?
Even if it's 2x, okay, you've just effectively doubled the size of your engineering team.
That's remarkable.
You mentioned the 40 of the Fortune 50 being customers.
You use that quite a lot in a lot of your marketing materials.
And it strikes me as like a very enterprise company.
Is it difficult or how do you retain a real product focus, a real closeness to customers when you're so enterprise?
Is that difficult?
I think it's a little bit of a false choice you're implying there.
I think being large enterprise doesn't necessarily mean you need to be distant from your customers.
In our case, our customers' customers.
So Brett and I are constantly building agents ourselves.
One of the more interesting things of the last six months, we released Ghostwriter.
This is an agent for building agents.
It's kind of agents all the way down.
It's pretty cool.
But we are constantly in the products ourselves.
And Brett is actually still an extraordinarily capable software engineer.
It's remarkable.
So some of the code that is in production, he has written.
I've probably got a couple lines here or there, but it pales in comparison.
And so, of course, we can't on our own simulate the complex multi-system environments that characterize many of our largest enterprise customers.
So that, you know, we have to kind of simulate in our heads.
But we're in the product.
And then one of the things I think a lot about is we will, in short order, be in a way one of the larger B2C companies.
We're doing that via our customers.
We're serving hundreds of millions of interactions, soon billions of interactions.
So staying close to the end experience there as well, voice fluency, latency, the quality of the experience, all of that stuff is very energizing and things that we're close to.
So I don't feel distant from the product, either from our customers' perspective or from their customers' perspective.
I always looked at the space itself.
And I was like, amazing space.
What a huge town.
What a problem.
And AI perfectly suited for it.
And then I kind of peek under the covers.
And I'm like, oh, my God, like 15 companies funded with 100 million bucks.
Salesforce, Lassie and Zendesk, all the other incumbents.
Oh, my God.
What is the market maturation of this space?
Help me understand how this evolves in like a five to 10 year period.
I think, first of all, to state the obvious, the great thing about being in a giant market is it's a giant market.
The challenging thing about being in a giant market is it's a giant market, and other folks know it too.
And it's startups, it's longstanding companies, it's the incumbents.
And so your point on it being competitive is certainly right.
Five or 10 years, especially in the age that we're in, is a long time.
I think what I would point to is amongst the startups, customers are voting with their feet.
So we are at multiple larger size than one of our next nearest similar vintage startup competitors growing faster, as I said, are working with many of the great companies in the world.
Do you think it's like an Uber lift market or do you think it's an AWS, Google Cloud, Azure market?
It's hard to know.
I think...
Because the economies of scale in terms of depth and breadth of platform, experience in specific industry verticals and so on really compounds.
My hunch is it will be more like an Uber-Lyft market.
And we obviously think we're in the pole position to be the bigger of those two.
Again, we sell to some of the biggest enterprises in the world.
I had a guest on the show the other day say you can't sell to enterprise without an FDE motion.
Would you agree with that, knowing all that you know now selling to 40 of the 50?
I would like to think at least in the AI space, I would say rediscovered and borrowed this model from Palantir.
And we came to it almost accidentally.
So we started the company.
And the first thing we did was reach out to people we trusted to understand what are the biggest unsolved problems that you were looking at?
And so, oh, interesting service and support as a...
a foothold into something much broader, helping support customers across the entire lifecycle.
We then enlisted half a dozen design partners.
that we built the first version of our product and platform with and for.
And these are, in the history of the company, legendary, legendary companies.
Olokai, SiriusXM, Sonos, Weight Watchers.
We built the first version of our platform with our engineers deeply embedded inside those companies.
So much so that our founding engineer, Mihaly, was actually an employee of Weight Watchers, including getting like its performance review time emails and so on.
What we realized in that was no one has ever deployed an AI agent.
No one has ever put AI in this way in front of their customers.
And in order for us to build the best thing as quickly as we and our customers would like, Being so close to the business, the mechanics of it, the people, their business model, that we understand it, I won't say as well as our customers, but approaching that, we saw so much power in that.
So starting in early 2024, we really started building out this forward deployed team.
Customers use it in widely ranging ways.
Our platform is highly extensible and very transparent.
You can see exactly how an agent is built.
You can export agent definitions and completely build your own.
So no need for Ford deployed if you don't want it.
What we generally find, though, is in getting started, having Sierra and help from our teams kind of drive while our customer is in the passenger seat, but navigating for the first version.
It's what has enabled us to take companies like Next live in six weeks.
from kickoff to live behind their phone number and chat in six weeks, or Cigna, one of the largest healthcare companies in the world, live in I think it was 58 days.
And so time to market, time to impact, time to value, and then the quality of the result, we think it makes a big difference.
I wouldn't say it's binary, though, as you framed it.
I do think you can sell without a forward deployed team.
But I think for getting to the impact of this technology as quickly as possible and at the magnitude that we know is possible, boy, is it an important catalyst.
Are we at a unique time in history where for this specific moment in time, every buyer is in the market for the product?
Normally, not everyone is in the market for a product at the same time.
Every CEO is being told by their board, how are we using AI?
Is it a unique time because there is buyer pull like never before for this specific moment?
There is effectively unbounded demand, I think, in two areas.
One, we've talked about coding agents.
The other is a space where we're the category leader.
One of the reasons we've grown as quickly as we have is to meet that moment and meet that demand.
We're now 100 people here in Europe.
We recently acquired a company in Japan, Opera Technologies.
You and I were talking about this.
To hit the ground running there and to have a team that can be attuned to the cultural nuances of Japan and the concept of omotenashi, which is extreme hospitality.
That is what is expected in Japanese service, and that's what we intend to build there.
Starting with the kind of beachhead in customer support and customer service, to scale into the company you want to be, you have to move out of customer support into complete lifecycle management, I guess.
Is Sierra...
a sales platform in the future?
Is it a conversion platform?
Is it a marketing platform?
What is it?
I think Rocket is actually a pretty good indicator of the direction that we're headed.
You think about the life of a Rocket customer, it begins with search and discovery of a home they might want to buy.
We worked with Redfin to rethink their search experience.
We help Rocket reach out to folks who've expressed interest in a refinance, make contact that way.
We worked with them to build Rocket Assist to help bring people in and help them shape and size their loan, gather all the information needed, and so on.
None of that is service and support.
We do do that, right?
Loan servicing and so on.
So I think that's a good example of where things are headed.
That's an inbound sales machine.
Inbound and outbound.
You're right.
And it's not just Rocket alone.
Next, we work with them on personalized product recommendations.
How do you help someone build an outfit, a bigger basket of things they will love?
Again, that's much more sales than support.
This sounds and feels more like kind of a Fortune 50, Fortune 500.
Palantir, but more consumerized, where you're building these kind of amazing solutions for these products to fit their needs.
Is that unfair of me?
First of all, Palantir is an amazing company.
We have taken a lot of inspiration slash, you know, copied from them elements of our...
forward deployed approach.
My understanding is Palantir has kind of low hundreds of customers.
We are and intend to be at a lot larger scale than that.
And I think where that will come from in particular is building real domain expertise in specific industry verticals.
Of course, our first, second, and third customer deployments were by definition unique, one of a kind.
I think we've learned some things about how to help build a basket in the retail setting.
and in some of these other industries, how best to handle a question about the status of a healthcare claim, a healthcare insurance claim, or questions about a fee around a checking account.
And so I think we're going to have these deeper and deeper lessons in specific industries and be able to apply those in a much more scaled way.
Will you build products that aren't uniformly applicable across customer bases?
So if one customer needs specific cart abandonment product features, is that something we build or is it, no, that's not applicable to the platform?
One of our approaches in building the company, and this kind of goes back to where we started, you can build a platform and hope that people come, right?
The applications get developed on it, or you can build applications to inform a platform that makes building the third, fourth, and fifth that much easier.
And so wherever we can, we're scanning for opportunities to...
strengthen our platform.
So commonality is much better than something that is truly a one-off.
That said, if we're working with a Fortune 50 or Fortune 20 or Fortune 10 or Fortune 5 company, and there is some element of that company that is literally unique.
Of course, we'll build that.
And one of the neat things is it's actually become feasible to build that because of coding agents, because of the pace at which you can move.
There's a real unlock there in being able to build a solution on an already deep platform, but extend it in ways that may apply to a single customer.
My hunch, though, is that if you build it for one, someone else is going to have that same problem, right?
And so it's less common than you would think.
True one of ones.
I do want to go to the way that you run the company.
It was so important in so many of my conversations before this.
If we start with the board meetings, I suppose, as I said, many of the investors, every six weeks, not every quarter.
Can you talk to me about your biggest lessons on how to really get the most out of your board and run the best board meetings?
We do a couple of things.
You mentioned the...
six-week cadence.
We have kind of a tick-tock, a three-hour meeting and a one-and-a-half-hour meeting.
We've done this since the beginning of the company because we could just see if you're on the AI time clock, it moves a lot faster.
Things are changing.
Most recently, we came back from winter break, and suddenly coding agents were amazing.
You had Claude 4.5, Codex 5.2.
There is a fundamental step change in the capabilities of these models.
It changed our approach to software development.
It changed our approach to the core product.
And so having a cadence where you can take in information, even from the last six weeks, update your priors, and then change course, I think is quite important.
As for running the board meetings themselves, we don't have board decks, we have board memos.
So Brett and I write a usually six to 10 page memo.
There's a saying, writing is just thinking on paper.
And I think it's very hard to hide from writing.
And so getting our thoughts clearly out onto paper, sending that in advance, giving each of our board members some kind of soak time.
to think through the issues and come prepared rather than be like presented to and managed, I think is a big part of it.
And then the contents of the board letters themselves, I think is notable.
We've done quite well in our first eight quarters in market.
And generally the format of a board letter is like, we exceeded forecast by a wide margin yet again, things are going well.
We landed these customers.
And here are the seven things we think we could be doing better.
Where we're unhappy, we could be going faster here, need to hire in this area.
And so on.
And then the board meetings kind of take form on their own based on that.
You get the scaffolding right.
You get the people right.
You get kind of setting the table of the big questions we're asking.
And then genuinely, genuinely inviting our board members in to challenge us and improve and sharpen our thinking.
Those are some of the ingredients.
I heard that you write also about everything that you suck at.
Yeah.
What's one of the most memorable writings on what you suck at?
One early on was...
We had such good indicators of the demand we were going to see.
And we just didn't hire fast enough to meet that demand.
It was like, oh, like we could have taken on this additional set of customers.
We had the data in front of us, like we could see it.
And we didn't act decisively enough to build out a recruiting team, right?
Like scale faster.
This was early 2024.
So early days in the company, you know, we've since corrected.
But that was one that stands out.
We are going to go to hiring.
You mentioned some of the people around the table.
You know, often people say this, but you really can't.
You and Brett, I mean, it's the dream team.
The best of the best operators.
You can choose any investors at almost any price, which is kind of hard.
How do you and Brett sit down and discuss price on a new round?
Because investors will pay anything to get in.
You want it to be high, obviously, but also not too high.
How do you actually think about that?
Is it like, okay, three years on next year's target?
What does it look like?
It's generally been inbound, is the answer.
We think about it, honestly, not in terms of valuation.
We think, what is the amount of capital that we need to raise to get to the next unequivocally higher watermark in terms of revenue, company scale, and so on?
So we think of it as like milestone to milestone funding.
And then we're sensitive, but not maximally so to dilution.
And so how do you balance those things?
And every one of our rounds, we actually guided to and took a lower price than we could have.
I, again, spoke to them.
They said about the values within the company.
Craftsmanship, intensity, and family.
Craftsmanship, intensity, and family are three that I wouldn't normally see.
Can you talk to me a little bit about why those are so important?
I'll start with craftsmanship.
Both Brett and I...
Just because of the way we are, we care about doing things well.
If you're going to do something, do it with excellence.
Two ways in which doing things with excellence mean much more than just sweating the details.
One is what is a great company?
A great company is an aggregation of thousands and thousands of things that are themselves great.
It's great people.
It's processes that are well designed.
It's a great product.
It's a great culture.
And so how do you build an excellent company?
Well, you build everything with excellence.
And so I think holding ourselves to the standard, like if it is worth doing, it is worth doing well is one part of that because that adds up to a great company.
How else do you get there?
The other is you think about what our customers are trusting us with.
Back to the trust value, but I'll make the connection with craftsmanship.
It is with their most precious.
asset.
It is their customers.
How will a company know, how will a set of people who are considering working with us know how we will show up with their customers?
A lot of it is how we show up with them.
Sweating the details in how we show up and interact with our customers, the level of professionalism, care, dropping everything when something matters.
I'll give you an example there.
In our first Black Friday, Cyber Monday, with a set of retailers, One of our lead engineers, our head of operations, me or Brett, was in real time personally reading every single conversation that our agents were having.
We wanted to make sure we were doing right by our customers.
So that's craftsmanship.
And again, adds up to a great company.
And it's, I think, very meaningful in helping our customers understand the care we will have for their customers.
Intensity and family.
Yeah.
Can you expand on those again, two that I don't often get?
So I think it's back to this great thing about giant market, giant market, hard thing about giant market, giant market, and others are in it too.
I think there is an inevitability to companies interacting with their customers via really sophisticated agents that...
capture all that they know and all they can do on behalf of their customers and get the job done on their behalf that handle the complexity as opposed to pointing you to websites and so on where the conversation is the interface, right?
I think there's an inevitability to that.
And therefore...
In order to win, in order to build the best company in this space, it is about pace.
It is about winning.
It is about building the best product.
It is about being competitive and being intense about it.
Knowing that we don't have the luxury of patience, there's nothing written in the wind, right, that any particular company will be the company.
Showing up in our fifth engagement.
and 500th engagement as intensely as we did our first, like you have to do that.
And so I think there's also, I talk about the Venn diagram of who we hire for, smart, nice, intense.
And it's hard actually to get all of those three in a single person.
When you do, it's fantastic.
You can feel it in the office.
And another way of translating intensity is doing things with excellence, doing things with pace, relates to craftsmanship as well.
Is there anything that you can do or add to an organization to increase or to maintain intensity, be it timelines, be it rewards, incentives?
How do you keep intensity with scale?
I think it starts with the founders.
Like Brett and I are quite intense.
We have to be the pace setters.
We have to be the examples of intensity.
It shows up in how we manage the company.
He and I are deep in details and are constantly, is this good enough?
How could this be better?
How could we go faster on this?
Why can't it happen tomorrow instead of next week?
So I think it has to start with the founders as one.
How do you determine what you should be in versus what you shouldn't?
We've seen the resurgence of founder mode, of founders being in the weeds.
It's also not possible in everything, and it's not right in everything.
How do you determine that?
You have to edit it.
You have to have judgment for it.
And I think you have to look at, what is the thing that is not going to happen or won't happen as quickly without direct applied force from one of us or both of us?
And so, you know, it's pointless to be in quote founder mode, you know, 17 layers in the details and something that doesn't matter.
It matters a lot if it's...
our next generation agent architecture and there's something that we can add.
We try to be selective about where we engage at that level, but it's anything but kind of hands-off management.
So I think it starts with the founders.
Ambitious goals have a way of becoming self-fulfilling.
You set out a goal, whether it's the quality of a product or a revenue number, it's like, well, what would have to be true in order to get there?
Like, let's suspend disbelief and just imagine like what would have to be true to cover this much ground this quickly?
Why can't we do that?
Okay, why shouldn't we?
Japan is an interesting example of that.
Why can't we have a giant business in Japan this year and not next year?
What would have to be true?
Oh, we would have to have like 10 people on the ground.
I was like, why don't we buy a company there?
You see how this stuff hangs together.
Ambitious goals can take the form of a date.
Sure, you know, date-driven development can sometimes work.
I think also...
work is like a gas and tends to expand to fill all available space that you give it.
And so there's a danger in setting dates as well, where it's like, well, we've got this long.
It may not need to take that long.
It ties to the third one, which is like work expands to the room that you give it.
Yeah, I give everything to my work, and I love that.
Third family as a value?
I'm just interested by that one.
Each of our values comes directly from Brett and me.
And actually, one of the best decisions we made was, I think it was the time we were five or six employees.
We spent half a day.
Brett and I have a technique we call think apart, think together, where we'll initialize on a prompt.
And the idea is not to group think one another.
So we want to get kind of the best of our independent thinking.
And so we did a think apart, think together on values.
Went off and spent an hour kind of writing up what our view was, came back and compared notes.
And there was, first of all, a shocking amount of overlap, which I guess shouldn't have, in retrospect, been surprising.
It was like we had wanted to work together.
We'd been friends.
I think deeply similar in many of our values, really all of our core values, I would say.
Family comes from, I've got four young kids, Brett's got three kids, and I married my high school sweetheart.
And I think for both of us, the only thing that's more important than Sierra is our families.
Our belief is that you can be part of something that is growing fast.
You can be intense about your work.
You can turn on the afterburners when you need.
And it doesn't just mean kids.
It's picking up your parents at the airport when they get in from out of town.
It's going to the friends, you know, extended birthday weekend.
It's being, yes, at the parent teacher conference or whatever it is.
And I think there is too often an image of kind of a sometimes performative grind in certainly Silicon Valley startups.
It's not that we don't believe in hard work, like, boy, do we?
Like, again, intensity.
But it's in working smart and finding some balance that gives you space for.
Again, translate family to things that matter to you in the sense of your whole being beyond just work.
Are you literally able to work as hard, though, when you have a family and you have four, I mean, Clay, four kids?
That's a lot of kids.
I find I work a lot.
Of course, if you just magically handed me, you know, 15 hours in a week that I, you know, wasn't with kids, I'd probably do something with those.
I find...
I am intensely focused and efficient.
And so like, boy, do I get a lot out of every hour I have.
One of the things I've done, I spend a lot of time on 101, going to and from the office.
We are all about in-person.
And coming out of the pandemic, it was something of, you know, a novelty being opinionated about being in-person.
So I spend like an hour and a half, sometimes two, on the road every day.
I now have a very complex networking setup that combines two sessions.
networks and a Starlink Mini so that I have uninterrupted beautiful connectivity to and from the city every day.
You're efficient.
You get everything that you can out of every hour.
Why are you so opinionated about in-person?
In particular for a young company, I think it is, I won't say impossible, but very challenging to build a culture, a set of shared norms, camaraderie.
I think so many of the things we talked about, enterprise software as a team sport, it feels great to be part.
of an amazing team.
And it's different when your connection to that amazing team is via a Brady bunch of Zoom squares.
We have rituals that we've developed that we only would have developed if we were all working in person.
I think for younger employees, apprenticeship and mentorship that happens.
So much of what I learned, and I think the initial conditions of my career, were from experienced people.
taking me under their wing or letting me, in cases, literally look over their shoulder at how they were doing something.
I think there's an element of paying it forward that's important and in-person has a role to play in that as well.
There's a talk that I love by the renowned computer scientist Richard Hamming, you and your research.
For any new graduate, probably the single best thing on a per-word basis I think you can read.
One of the central theses is...
Find great people, work with them, and learn from them.
It sounds obvious, but there's something deeply correct.
Like, how do we learn as human beings?
We observe someone doing something, and we effectively copy it.
So find great people and copy them.
That's how you accumulate skills and capabilities.
And one of the other points Hamming makes in this talk is knowledge and hard work are like compound interest.
And we all know the earlier you start saving because of the miracle of compounding interest, change the trajectory of your life.
And so any young person should be intensely focused on learning as much as they can as early as they can, locking in those kind of lessons and capabilities, if you will, because it is literally trajectory changing.
Do you want to hear two funny things?
One, we used to do five shows a week.
I just worked harder than anyone else when I was starting out.
That's a lot of shows, Harry.
Yeah, yeah.
It was 11 years ago, but it was five shows a week.
And then two, I didn't have a thousand listeners per show for three years.
And I never made a dollar on the show for three years.
It was never about money or recognition.
I only cared actually about using this as a method to learn from you.
Probably, especially when I was 18, it was harder to meet amazing people.
And so I completely agree with those two.
There are a lot of young people today.
You have kids, I can picture them leaving university, who are uncertain about where the world is, what to do.
What would you advise them, knowing all that you know?
The obvious kind of tsunami coming is AI.
Okay, what are the implications on jobs for that?
I think there's been a lot of concern.
understandably about, okay, what happens to entry-level jobs?
How do you apprentice and so on?
I think the unfair advantage that young people have coming out of university is you've just had four years to spend effectively unlimited time.
You got to go to class and pass some exams and stuff.
You have huge control over your time and disposable hours.
Coming out of university as a master of these AI tools, Boy, let me point you to a thousand companies that would love to have you infuse what you know into how they're doing things.
I can't remember a time when a young person with no work experience, but with the right mindset and experience using some of these tools has ever been so valued.
Some of our most effective employees at the entire company are 22 or 23 years old and have been completely AI pilled and have a comfort and facility with these tools that many of our more experienced folks don't.
Has the way that you hire changed for the profile that wins in this AI-pilled world?
Yes.
We completely changed our engineering interview process.
So it now looks much more like, here's a kind of prompt.
Think through an application you would like to build.
Cool.
Okay, here's $150 to spend on choose your coding agent.
You can use whatever setup you want.
Use whatever tools you want.
Bring your own laptop, bring your own tools.
We're going to pay for your tokens.
And then build it.
Tell us how you went through building it and so on.
So at least in engineering, it is an AI native interview.
And of course, we test for architecture, systems design, product thinking, culture, smart, nice, intense, the extent to which we think people manifest our values.
But that's changed very significantly.
I will be disappointed if in the next.
No more than two months.
Not every one of our interviews has some strong AI native component to it.
You mentioned architecture there.
Do you think we are entering a golden age for cyber and for cybersecurity, given the proliferation of code generated by AI that may not be as secure as it needs to be?
In terms of importance, it is obvious to me that it has never been more important, given that the kind of offensive capabilities...
just ratcheted up five notches.
I think cybersecurity seems like a pretty good bet to me.
The question is whether the offensive tools turn to be defensive tools.
If actually Mythos and Codex 5.5 Cyber, if they themselves are the solution, not a more narrowly focused cybersecurity product.
What was the most recent disagreement you and Brett had?
A couple of weeks ago.
We were trying to figure out how to get something to move much faster in one space.
It's interesting.
We basically always converge.
It's like we're highly truth-seeking.
It's like, what is, we have a funny expression like, this is correct.
Okay, what does that mean?
From some objective truth-seeking perspective, like this is the right way to do it.
So we try to get to, okay, what is the correct solution?
I was on one side.
It was like, I think we need better kind of process and structure around this thing.
Brett was on the side of people.
Maybe we need different leaders or a different leader in this space.
The answer, as with most things, like turned out to be some of both, right?
Turned out to be some of both.
We started from, no, it can't just be solved with problems.
It's like, no, it's not just people.
And we pull on those threads.
And this wasn't think apart, think together so much as just kind of interrogating each other, again, with the goal of just getting to the right and best approach to something.
What when Brett says something, are you like, yep, I'm sure he's a G at that?
And what when you say something, is Brett like, yep, play as the expert?
We think about rather than dividing up the company, we think about...
majors and minors for every part of the company.
So Brett's majors are definitely sales and then engineering.
He is really good at selling software.
He is really good as a software engineer still.
I major in what I've got like the running of the company.
So operations, finance, legal, and so on.
I do a lot of first calls.
Like he understands our most important contracts, you know, for things that are highly consequential on how we run the company.
You know, we have kind of two nuclear keys that we turn on those.
Brett.
having spent time at Salesforce, really learned from the best.
Like Mark is extraordinary.
And so he's the best seller I've ever met.
Unbelievable.
The goat, the goat.
Unbelievable.
How's the weather, Mark?
Have I told you about agent force?
Just but but honest, honest respect when it comes to instincts on how to sell.
It was like, yep, OK, makes sense.
Brett's instinct on system design and architecture are second to none.
And so I trust his judgment more than I trust my own on people stuff, on the building and running of the company.
It was like whatever.
says.
I would go with that.
So I think that's probably the rough yin-yang major minor split.
Dude, I would love to do a quick fly around if it's okay.
Yeah, let's do it.
What was your biggest lesson from working with Sundar?
It's such a gift.
Sundar has a remarkable ability to look at a problem from wildly different zoom levels.
His dynamic range in thinking is second to none, zoomed all the way out, highest level strategy.
How is this going to unfold over the next five years?
all the way into the details, the pixels, the drop shadows, the sound, the texture of something.
And I have tried to emulate that.
Talk about surrounding yourself by great people or having the privilege of working for someone.
I observed a leader who is extraordinarily focused on the product, the work, building something great, also is just a wonderful human being and deeply focused on the humanity and folks around him.
What does no one know about Google that you think everyone should know?
What people underestimate about Google is when you have the alignment of an ambitious, enduring mission, incredibly smart people, and a culture that values truth and building and service of that mission, that company can kind of solve anything.
People sometimes criticize Google for a thousand flowers bloom.
If you have smart, well-meaning people caring for every one of those flower beds and they're directed in the right way, it is quite a force for invention and discovery and building new things.
I got asked to ask you about your book list.
I hear you read a lot.
That's a shit question.
Forgive me for it.
What's the must read for me leaving this conversation?
Oh.
Give me one.
David McCullough, The Wright Brothers.
It's so good.
It's so good.
It's a tight.
history of obviously the invention of the first heavier than air aircraft and to me it is as accurate a portrait of entrepreneurship and invention as has been ridden anywhere.
The aircraft could not have existed without this kind of network of pre-existing inventions, most importantly, a lightweight internal combustion engine.
And then it was try, it didn't work.
Try, it didn't work.
There are scenes of them stuck out in North Carolina, being eaten alive by mosquitoes.
It's the hardship and then the triumph of having built something that flies.
I've never said this before in a show, but have you seen a wonderful film called Those Magnificent Men and Their Flying Machines?
No.
I'm going to send this to you.
Okay, sounds good.
It is about the kind of pursuit to fly from mankind.
Fantastic.
It's amazing.
Well, thanks for the tip.
It's like the 1940s, 50s.
That's great.
I'm looking forward to that one.
Parenting, four kids, and an unbelievable operator and founder.
What's your biggest advice?
First of all, Having kids is the greatest gift.
It is such a privilege.
And a few things I would say.
First of all, you will be a changed and different person, you know, on the other side of holding your son or daughter.
It's just it is the, in my opinion, single fastest rate of change, single biggest change that anyone experiences in their life after they themselves are being born.
Right.
Welcoming your first child.
I try to carve out time.
family dinner.
We have many mornings on Sundays, maker mornings with two of my sons, where we block out an hour or two and we build something at home.
And so rituals and discipline around making time and space, anything important in life, my view is a product of clear goals and good habits.
And so I think if you have a clear goal around how you want to be as a parent and then habits that help you build towards that, I think that's a very important ingredient.
And then the other is making kids' interests your own.
And so I'm terrible at basketball.
My eldest son is an incredible basketball player.
I am so proud of him.
I go and watch him play and say, he does things.
I could never do that.
Not only can't I do that, I could never do that.
And so I follow the playoffs.
I've learned about the sport.
I've learned about the best players.
I've learned about coaching so that I can try to enjoy and support him in this interest more fully than I otherwise would be.
And so I think for each of our four, it's being aware of what gets the synapses going for them, what they light up about, and then making that interest my own.
Would you say that's the same for your partner?
Do you need to have aligned interests in a partnership, in a marriage, or is it good to have different ones?
I mentioned I married to my high school sweetheart.
We will have been together for almost 30 years.
And I'm not that old.
I think a great marriage is a partnership.
And a partnership means you are working in pursuit of and service of some shared set of goals.
And so you asked about interests.
I think having shared interests in what you are pursuing.
as a partnership is deeply important.
Happy kids who grow into adults who can enjoy their lives and contribute meaningfully to those around them, building a set of values in one's family that are aligned with your own.
And then I think ensuring as part of that partnership that the other member in it, themself, thrives and fully realizes themself.
And there are those interests.
may be different, but there can be a shared interest in enabling each other to become the best that you're able to become.
Final one for you, but I do like it.
What's the kindest thing that anyone's ever done for you?
I feel such gratitude to my parents.
I'm sorry if it's a straight down the fairway answer.
My father was a career cardiologist.
My mom is a very talented quilt maker.
Neither of them were in engineering or technology.
And they saw that when I got a hold of my first computer, I just lit up.
Not really understanding what computers were about.
My mom was good with them in the 80s, but it was not at all clear where they would go.
But they could see that I was obsessed with them.
They supported that interest to the hilt.
I remember going with my father and my mom and dad to buy an early Power Mac.
And my dad was...
pushing like, would you be able to do more if we had more memory in it?
I think I would be.
And it's like, well, we should get more.
I was like, is this real life?
My mom would take me out of school one day a year and we would go to Ken's house of pancakes, get breakfast.
She would make up a doctor's appointment or something for me.
And then we'd go to Macworld and I get to spend the day at Macworld, which for me was like nirvana.
And so I feel such gratitude to them and seeing.
in me that interest and how I lit up about this thing that was unfamiliar to them, but that they then pushed and enabled.
And of course, there's a direct line from that to wonderful 18 years at Google starting Sierra and today.
They must be very proud of you.
I think they are.
I know that they are.
The thing that strikes me from this show is, I don't mean to sycophantically, it's just like what a good person you are.
Do you know what I know?
I interview a lot of people and they're brilliant and they're intellectually brilliant.
You obviously are that.
It looks like what a genuinely good person you are, which is really, it's very tangible.
So I really can't thank you enough for doing this and you've been an incredible guest.
Thank you so much, Harry.
I really appreciate it.
But before we leave you today, a quick shout out to a company I've been genuinely blown away by and have been tracking closely, ROX.
I've been watching this team closely and the speed they're operating at and the level of applied AI talent they've assembled, it's honestly remarkable.
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This isn't another productivity app.
Christ, we've all had enough of those.
Rocks gives reps a single interface on top of their GTM stack, powered by a knowledge graph across your internal and external data.
So if you want to boost AE productivity, increase revenue per rep, and consolidate your stack, try Rocks at rocks.com slash sign up.
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While Framer helps your website convert, Superhuman Go helps your inbox move faster.
I do this show three times a week.
That means three sets of research, three sets of prep, three sets of follow-up, and about 400 other things in between.
My inbox doesn't sleep, and neither do I really, which shows in my wrinkled face.
But I was juggling a dozen tabs just to keep up.
Another window, copy this, paste that, lose your place entirely.
That is why I started using Superhuman Go.
It's an AI chat that's always there when you need it.
Already up to speed on what I am doing.
Last Tuesday, two minutes before...
I went live I had a 40 message thread I hadn't read I asked Go to summarize it right there in my inbox no new tab no switching apps from the makers of Grammarly Superhuman Go works inside the tools and sites you already use your browser your inbox your docs it handles the repetitive stuff so you can focus on the work only you can do AI that works with you not on top of you Superhuman Go keeps up so you can move forward find out more at superhuman.com