Sarah Guo - The Power of Conviction - [Invest Like the Best, EP.383]

Transcript from https://podmenti.com/t/836b004c420746af

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Hello and welcome everyone. I'm Patrick O'Shaughnessy and this is Invest Like the Best.
This show is an open ended exploration of markets, ideas, stories, and strategies that will help you better invest both your time and your money.
Invest Like the Best is part of the Colossus family of podcasts, and you can access all our podcasts, including edited transcripts, show notes, and other resources to keep learning at joincolossis.com.
Patrick O'Shaughnessy is the CEO of Positive Sun.
All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of Positive Some.
This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions.
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My guest today is Sarah Guo. Sarah is the founder and CEO of Conviction, an early stage venture capital firm built to serve AI companies.
She started conviction in two thousand twenty two after nine years at Greylock because she believes AI is the most important technological advancement of our lifetime.
In our conversation, Sarah discusses the challenges and rewards of leaving an established investing firm to start her own venture.
She shares her unique perspective on the AI landscape and reveals her predictions for what we should expect on the AI frontier.
Please enjoy this great conversation with a very impressive Sarah Gwell.

So Sarah, where to begin?
You're doing recruiting for your firm.
I like starting where people are focused with their attention. What
Would you teach us about
Awesome recruiting for an investing firm.
I think the hard thing in venture is the sample size is so small. And there is nothing on paper that is going to, at least in my experience, going to predict well if somebody's gonna be a great early stage venture capitalist.
If you're hiring somebody who is
fifteen years into an investing career and they have a track record. I think that is different, but
The alpha obviously is hiring people before they're obvious. And so
I think it's really tricky. We
Are recruiting for what we think of as some baseline understanding of the technology transition that's happening in AI.
And it's like really more just core technology understanding because AI is like a domain of this from both a
product and a technology perspective that you can learn, but it's some context around
Software and then core traits.
We care.
A lot about competitiveness.
Team orientation.
Judgment is the hardest thing.
T.
Look for.
in somebody who's really early in their career, but
You can use
Understanding of businesses
Interest in businesses
somebody's thinking about why a product works.
Or doesn't as a proxy.
And so
Kind of the core traits, but
It is hard.
When you think about early in your career before your judgment could be assessed through a track record.
What was the most
edifying investment that you made? Like what early investment did you make that taught you the most about this practice?
I will use two very different companies, and it's hard to say early in career because the learning cycle
on the company is just so long if it is successful.
And so the two I will mention would be
Me and my former partner, Ashim Chana, we incubated a company called Awake. It was a network analysis security company.
And this was the year I joined Graylock. We started with an entrepreneur named Michael Callahan, who had come from a math and storage data systems background. This company was eventually Arista. Great product, great team. This was educational because it's just a cult start. And I spent half my time working on building the company.
So recruiting co-founders and early employees, talking to design partners, drawing them architecture, working on the substance of building a company from scratch.
with the help of a venture firm and so
That's educational because you're doing the work.
It's such a privilege.
There are a lot of people
early on in venture careers that are in a hurry to level up.
And I understand where that comes from.
I'm an ambitious person. I want to have agency.
You wanna have the decision making capability, but it was actually this enormous gift that I wasn't
So sure I wanted to be an investor when I joined Graylock because I got to just work on the things that focus on the substance of the job and helping entrepreneurs. And your time just feels less precious than when you have the opportunity cost of a network of opportunities that you
are turning down meetings all the time and the ability to just learn and do company building.
with a little more patience was great. So
Huge gratitude to both.
Ashim and all the early customers and Michael on that journey. And the other one was just Figma.
We probably invest in this company year and a half or something into my tenure at
Grey Lock.

Dylan about when we met the other day and
This is ten years ago now.
And so Figma's obviously going to be a very important company.
Without the longitudinal exposure, it's really hard to have confidence that your
Judgment.
On
People and markets.
That are very early, but have potential.
It's hard to have the confidence that you're right.
the pattern match. This is one of the core challenges of early stage venture, especially with a company that
has a long build like fig night five years to a public release.
you don't get the feedback cycle for a while. And so I learned a lot about
Just
what an inexperienced but truly great entrepreneur looks like at the beginning and
that early process. It's also a different type of company than most. I think it's really
popular and even trite to say like oh
product led, user led growth now.
But it's a particular type of magic and knowing what it looks like when you do have user love.
adoption within the
leading influential group of a particular domain is.
Just useful for pattern matching.
Can you take me back to the moment that you decided to start conviction and tell the story around
That big decision.
I had the great luck.
Of being
At a venture firm, Greylock, with an extraordinary history.
Seventy five plus years old, been through multiple technology and internal generational transitions. One of the things that was really useful is just seeing
how unevenly the innovation happens in technology and how important recognizing that had been to the success of some of my legendary partners.
For me, I've been lucky enough to
just be intellectually interested.
in AI.
I had a friend who's an amazing academic entrepreneur himself, Andrew Ng, who
taught me about deep learning like he taught lots of other people about deep learning. On YouTube, not directly, but Yeah.
Yeah.
We had social friends in common, but one of the amazing things about Silicon Valley, and I know you believe this is just like a fundamental principle.
positive sum orientation is
It is not a meritocratic place, but is the closest I have found.
to that.
And an example, I'm not saying like I like deserve this, but the marketplace for ideas in Silicon Valley is pretty good.
So I I wrote some random blog posts as like an associate of Greylock a hundred years ago that was talking about like LSTMs or something.
And Andrew emails me.
cold and is like, hey, we should talk about this.
Oh my god.
Sure, Andrew.
And that's the beginning of a friendship and lots of other stories that you or I will have like that, but I was intellectually interested in this area.
I made a first investment that did not work at all in travel planning in like 2015 or something as a seed. Sold the company. And then
Going back to
You s results happening in academia, you s of
increasingly powerful models in the traditional machine learning science happening at all the large internet companies. And then you see transformers happen in twenty seventeen. And at some point
The curve just becomes clear.
If you think it is actually a huge change in
the landscape of software, not
An incremental thing.
Then the opportunity becomes
A little too big to ignore.
And so it took me a little while to arrive at that conclusion and also say
Uh, I like really want to do pure early stage versus a more multi stage firm.
And the part of it is
Actually
Nothing to do with Greylock and the opportunity and more just
I still wanted to be an entrepreneur.
This is an important question because so many people listening, I'm sure, have thought at one point or another about starting their own investing firm. So you have this moment.
You see this wave.
It's worth going after. You want to be an entrepreneur.
What are the other steps?
Literally what did you do first? If you go back to that first week, like okay, I'm gonna do this thing.
What did you literally start doing first to get this thing off the ground?
What did I do?
I guess.
This is not perfect.
It is complicated to leave a venture firm.
that preoccupied me for a little bit.
Say a bit more about that, what makes it complicated.
You want to be responsible to the personal commitments you've made.
I carry some boards for graylock still, and I'm very invested in those founder successes. So I think you want to sort out that and just like leave things well.
I guess step one would just be I had planned to go take six months off.
And like hang out with my kids.
And
I don't know if I'm allowed to say like I'm glad I didn't. I don't think you're supposed to say that sort of thing about please. Well, it would have been a shame in terms of the opportunity, if that is your only lens.
It is my primary lens right now with lots of love for my family. We launched the month before Chat GPT.
Launched and then
There's some credit.
assigned in the market.
For
Taking a point of view that is actually a point of view versus like following.
And
I wish I'd done this even three to six months earlier.
So if anybody who is really convinced that they should do this, but they're like, Oh
It'll be better if I just optimize like another month or three months or like, no, don't do that. The market is moving, it's always gonna be hard.
And
The first thing was I was just like, Oh, I guess
There were LPs that I already knew who
wanted to talk when they heard I was thinking about doing this and I
talk to them out of love and respect in long term relationships and
then it just felt like I should go raise the fund. So I guess step one would be go
Talk to LPs.
In parallel, I was trying to figure out what the strategy was because I wasn't quite prepared for that yet, besides the fundamental orientation of
I'm not gonna do this unless we can be truly excellent.
try to be dominant in a some scope of asset class or domain or whatever it is.
I'm a partnership oriented person.
I need to go recruit.
And raise money, I guess. So those are the first few steps. But I I actually didn't think it's not very complicated and I wasn't terribly prepared for it.
What was your first investment?
My conviction.
Yeah, I guess the
First dated investment in
The fund would be a company in the legal application space called Harvey.
I invest as an angel.
right before the fund was fullosed and we kinda warehouse it.
Tell us about that company. That's a well known one.
Maybe it's actually a good excuse to talk about
what you look for since it's an application in an AI application company, a lot of the attention goes to
their core fundamental technology, which we'll talk about that too, but
What is your process for evaluating an application that's built on top of this?
set of models.
The core framework.
for evaluating software companies remains the same, which is all those things.
Distribution and quality of the people and size of opportunity matter. And so you make your judgment on that. If you ask me like what is different.
in the I application landscape, I would start with the premise of
Believing the narrative in the landscape is a very dangerous thing.
I'll explain what I mean for a second here. You, of course, remember maybe it's still going on. I think it's like being debunked a little bit, but.
When there's so much change and so much opportunity, it's very easy for investors to go ask themselves and each other and entrepreneurs, oh, where is the value gonna be? And some dominant narrative to emerge.
Like everything is GPT wrappers and there's no value in the application layer. So that was like
A very popular narrative for a period of time. I think there's still some of this.
But when I say it's
Dangerous to believe
in the popular narrative, first of all, that narrative serves somebody.
somebody's pushing that narrative. And second is just
It's the opposite of coming from first principles of saying
Okay we have this alien magical new set of capabilities.
Let's assume that set of capabilities is gonna progress.
In some way.
We need to make predictions.
About
How much that set of capabilities progresses.
How?
dangerous or positive it is for us to have this foundational technology coming from a single vendor, multiple vendors, and then what is the value of the
workflows and customer relationships
And specific data.
and change management and distribution that goes on top.
I think a lot of that was considered a little bit uh like it's last mile, everything's the foundation model and it's like who really cares because all the companies look the same.
because they're really thin. And I'm like, oh, that last mile, that looks like 99 of the hundred miles to me. It's very similar to a lot of the work that went into building a great company prior.
And I'd say what's different is you
don't want to build things that the foundation models are going to
replicate in terms of capability.
In the near term because it's wasted effort and then like commoditize. So this is what you do need to predict.
On the capability side.
But I think the ninety nine miles is a lot. And
In the case of Harvey, I do think
Another
perhaps distinctive characteristic of our portfolios.
At this point in time, we have a lot of teams.
with research backgrounds.
This is not all good, by the way. I'd say with total love for our research community and all of the people who are researchers in our portfolio.
I'm saying we have chosen to back a lot of people in research.
And it has some disadvantages because those people have often never met the market.
Academia or working at DeepMind or OpenAI is a very different environment than having to go talk to customers and build your own distribution and have more limited resources.
But if you think about this traditional diffusion of innovation curve, I think diffusion of innovation also happens
with talent.
If you have been working on
large model training or tinkering with these models for longer than other people, your depth of understanding of them is much better.
And so I still think we're like so, so early in the adoption and understanding cycle.
Of these models that
It is a significant advantage for people who understand.
How to build products around them.
where they should be advancing the state of the art.
and making good predictions at this foundational technology layer.
And so our teams often but not always have
more research DNA than traditional software companies.
If you ask me, is that gonna be true five years from now, I'd say, I really don't know. But I think at this point in time, there's overrepresentation that I is conscious.
So what has it been like to watch that team build a product from scratch using new technology? Like what would you say since you were in it early, like the discrete phases of that business so far?
And anything that's portable to others that want to build using this technology.
From having watched them.
The combination of DNA and technology fit for purpose is very obvious.
Where
Gabe is a researcher. Winston comes from the legal domain like White Shoe Law Firm. And then they have the core entrepreneurial traits.
of overall velocity, work ethic, and customer orientation. Not just customer orientation, but
ability to go out and engage customers and convince people to take risk with them. The phases for
most companies are like get a prototype out.
Go get first customers on board and show them value.
build the product suite and expand your lead and improve the organization and like scale.
We're early in that third phase now, but the company is in.
publicly reported tens of millions of runway with a bunch of
leading customers and think it's just credit to
Winston and Gabe and the team.
so far that
One of the hardest things is
Law is a more conservative
Industry.
And so
Your ability to take something that is
fundamentally probabilistic.
And sell it to a
relatively conservative
Or like risk aware non-technical audience.
Is challenging, and so they had to do a bunch of work to
Get people to understand how
huge the economic benefit would be.
And like go build that credibility.
And I think
If it be
Alan and Novy or PWC or some of their early partners, they're building that credibility.
in their lead.
What do you think the simplest way is to understand what that product does today for its customer?
You had to explain this someone that had never heard of it before. How would you describe the product today?
Yeah, I mean, assuming that they have heard of Copilot, I think the simplest thing would be to say,
This is co pilot for lawyers that does increasingly sophisticated tasks.
And the product makes the application of this co pilot increasingly automated.
And so more and more of the work that used to take a junior person in a legal firm hours and hours should be something you get an answer back.
automatically for.
And that could be search, it could be writing, it could be precedent, it could be hard grinded out.
legal tasks it can do in faster, more automated fashion.
Yes.
And so I think that's a significant piece of the product.
If we zoom out and look at this.
versus our general lens on what is valuable in AI applications. There's making people
Who
Might be billing.
two thousand dollars an hour more productive.
Hundreds to thousands of dollars an hour more productive.
And then there is do things that you can't do.
At scale today.
The perhaps simplest version of this is
I want to understand if a term exists in these twenty thousand contracts.
But it's not gonna be written exactly the same way.
And
If that costs you
X tens of thousands of legal hours in or out of house, you're not gonna do it.
If it costs you
A Harvey rod.
You might.
And so I think
These are the two of the forms in which the product is offering value today.
Can you say a little bit more about this term?
I've seen your writing, which is to avoid the path of incumbent strengths. And maybe Harvey's a good example of this where
Probably unlikely that
Anthropic or Open AI are going to build like a purely legal based model. So it's probably not part of their big roadmap and ambition. And so it's off that path of incumbent strength, but say a bit more about
that and how it relates to
Company.
Picking and investing.
I am
Very willing to bet.
that very large, very capable incumbents are not gonna go win every market.
And so part of the judo here is
being smart and really intellectually honest
About what incumbents
Let's consider some of the core foundation model companies incumbents now in terms of their resourcing and ambition.
They're not gonna be good at everything. The technology might be general, but if you think about the
layers on top and how that technology lands with an end user.
The distance is pretty far. So understanding where that distance is really far, and then what these companies most care about.
You need to make those predictions as an entrepreneur.
And so
I'm a big fan of Arvind and Perplexity, but if you look at the search GPT announcement that just came out.
I think it was pretty predictable that they were gonna do search.
At some point.
And that doesn't mean don't do it. It just means be aware and don't be surprised and have a strategy against it.
I still think entrepreneurs should go after these opportunities, but
The ability for an incumbent
To be truly great.
At solving a problem or a product that is very secondary.
in their business doesn't drive the enterprise value of the business.
Let's say for
Google, it might be anything out of ads and then cloud.
If you wanna go after ads, they're gonna attack you violently.
There's real reason for them too.
I'm gonna offend somebody out there with this. But if you're working on the like
Nineteenth most important product at Google.
Maybe that's okay.
Sometimes people look at incumbents and they say, like, oh, this thing is so scary and so powerful. And I'm like, Oh man, like that thing has a lot of political challenge.
You see projects end all the time based on how priorities shift.
And if you look at where this product
ID stack ranks.
in the economic impact to an incumbent and what the quality of people that are working on it in the organization are.
Maybe you feel a lot better.
Entrepreneurs should mostly focus.
on the problem they're solving for their customer.
But if they are gonna worry about incumbents
making richer predictions about what players in your ecosystem are gonna do and care about.
I would encourage.
Can you explain your notion of minimum viable quality?
There is still this theory of
In
B to B workflow software, you need to
do enough tasks and it might be like enough operations on a particular
Object type.
in order to fulfill core workflows where there's actually clean abstraction from the next task or piece of software, or you can just like replace an existing piece of software, a CRM or something.
If you're gonna do that in a particular segment, you might need to assign reps and quota and accounts.
have some cuing or allocation function.
And
Show BI against that.
There's a lot more in CRM, but let's keep the table of customers and assign reps to accounts and track it. And so you have this idea of minimum viable product scope.
What is a different concept that we use in
AI is mostly you're not deterministically
writing the source code for specific workflows, you're manipulating a model to produce an output.
that you can think of as some step.
in a workflow process that somebody had.
like an end user business customer had. And
It doesn't even have to be like a business customer. We can talk about Tesla and video output as two different examples.
But
You can use
Machine learning models to do
Many different things and the question is
Are they good enough to sell? I think really having a strong point of view.
as an entrepreneur about that.
And validating that with the customer.
is gonna be a huge part of finding product market fit for companies and being creative about how to
Improve the quality of the model, use user experience and a traditional product to wrap it such that the experience quality of the model is better.
dealing with failure cases. Well, there are many things you can creatively do.
as well as model improvement or model system improvement, but
If we're more concrete about two examples.
I'm on the board of this company called Hey Gen.
And they generate video with people in it today.
And people use it for all sorts of commercial use cases from like
A McDonald's commercial to a creator on TikTok.
making influencer
product videos or whatever.
But the North Star for the company in terms of how deep the video problem is.
Is well, is somebody gonna make
A thirty second
Super Bowl advertising spot with this.
Not yet.
That's pretty far. People want a lot of control. They want a lot of creative degrees of freedom.
Everything needs to be invested in. And
One of the ways this company Hey Gen thinks in working with their customers is
For some type of video.
Asking customers like are we good enough?
And one of the core breakthroughs for this company that's been growing really fast over the last year is
If you're
Patrick and you want a commercial
use video to promote
This podcast or positive some or one of your companies.
Is there generated video of you speaking by yourself in a room?
Good enough that you're comfortable using it.
And until this past year.
until Hen broke through that minimum viable quality barrier. It just wasn't.
For like any player.
Input ease matters. So for Hey Gen, it's like you give them two minutes of video. That's consumer quality. It could be you and me on a webcam.
not recording something perfectly and you'll still get good enough out.
If you had
thirty cameras in a studio in front of a green screen that are all super high def professional cameras, this problem's a lot easier.
It is a little bit multi-dimensional, but trying to define within a company what's good enough quality as the customer experiences it is like this really important new thing.
as a moving target as you expand scope of use cases.
If you go look at self driving, like
The
scoping of minimum viable quality for a particular use case also seems really important.
I don't know if they're allowed to call it full self driving or not at this point, but
Stay in my lane.
They reached a long time ago. Now you have minimum viable quality as provable to safety regulators in the city of San Francisco for a taxi with no driver. We just got to this year.
You can use this concept across a lot of different machine learning products, and then it feels like quite different than scope for.
Other types of companies.
It brings to mind that classic idea of the uncanny valley in a lot of this stuff.
Talk through where the valleys are still the most uncanny or the hardest to cross.
Is
crazy how
so quickly some things just seem to have crossed it and just seem to work fine.
We were on a road trip recently and I had one of these
audio models like narrate our journey for my kids, like teach us about what we're driving through as we're driving through it.
And I had this thought of like holy crap, like it's good enough.
This is fine. This is better than having a human guide in the car.
'Cause it's more knowledgeable and we can tune it how we want and have it explain things a certain length and
The valley is cross, so to speak.
So I'm curious where you see the most interesting valleys that are left. Some of them have been crossed, some of them haven't.
What hasn't yet been crossed do you think?
Oh, I think the world is full of valleys.
It's all valleys from here. Like we've crossed three of them.
In little ways.
Even in what should have been
What is considered to be like one of the very first domains, like writing.
You're someone who cares a lot about
quality of content.
One way to think of the uncanny valleys and how to cross them faster.
what the foundational capabilities offer you.
And increasingly the labs are all working on a reasoning that should continue to improve.
writing output generation. It's a super multidimensional problem. And for example, if you look at a writing product,
Whether or not I use something.
for writing is gonna depend on
Oh well.
Man, I'm so OCD about what I will actually publish.
If you generate something for me and it's gonna take me just as much time to edit it into what I want.
Then
I'm not gonna do this again.
Versus part of the new product thinking for these AI companies is going to be how can I make the cost of
Managing errors.
Or
going from eighty percent quality to acceptable quality.
Really, really low cost.
for the end user.
Another way to think of this would just be
There's increasing interest in
Just how to do things that are really cheap for
models to create a better end user experience. And so more and more model systems, they generate a bunch of candidates.
And then they have some sort of verification or ranking against those candidates to get to an experienced outcome for the end user that's better.
And like cross the valley that way.
A really important valley, just to make sure I answer your question directly, is
Well, how much code can you generate from a natural language specification? Not much today.
There are a bunch of different benchmarks out there, but like if you look at sweet bench or something, it's like, oh, well, all of a sudden we got to thirteen, fifteen, even twenty percent.
That's not good enough.
We have some software engineering interns right now.
If they generated stuff that was good enough twenty percent of the time, they're fired.
Go fix that.
The question is
Well, also classical problem in computer science and math, but like it's something more easily, more cheaply verified.
Than solved. I think that's going to be true for lots of classes of model problems where people are designing verification.
And designing ranking.
because this is I think one of the most recent progressions in code generation, but if I can
like the Google team did, generate a million candidates and then come back with a reasonable view of the top three, the end user experience is much better.
If I have seven interns.
Little twenty percent right, but then I know which of the best two intern
Answers I should look at.
That's better.
And so
We're gonna have foundational
improvements. We're gonna have company specific improvements, but they're gonna be
a combination of different systems approaches and research approaches to this. So I'm very
Optimistic that
lots of valleys, lots of people pushing for valleys to be crossed really fast, as experienced by
Us or other end users.
What have been the biggest mistakes that you've seen
application companies make.
That we haven't talked about so far.
in this fast changing landscape.
I know I just slammed this idea of the GPT rapper narrative at the beginning, but there is a seed of truth in it.
Maybe I'll backtrack a little bit.
State
Truism here, which is like, well
If something is really easy for you to build.
Unless your distribution is totally unique and defensible.
you're probably not gonna be able to capture rent or economic value on that for a long time.
And so we still see
Plenty of entrepreneurs.
Get into initial traction with something that amounts to
Weeks to months of work in a handful of
Um templates essentially. But
The idea that
Could that be?
An entry wedge to get customers to engage with you so you can serve them more deeply? Totally.
We're not expecting that companies
Building
billions of dollars of enterprise value with a software project that is three months old.
But if they think that is the long term answer.
We disagree.
Because if they
Have done it in that period of time, and it is that simple, competition is coming. There's a lot of people who recognize the opportunity. And so I'm somewhat surprised at how
Short term.
people are in terms of approaching the strategy piece of this.
What problems do you hope the next generation of frontier models solves?
What do you most hope for from
as leaps forward in the next generation.
This is more
Optimism
grounded in a lot of really smart people at
the labs and large players having confidence this is true, but
If you could have models that were
better calibrated on whether or not they are correct. They can tell when they have a good answer or not.
They become
Much, much more useful.
Hallucination management is the
blocker for many, many use cases in the enterprise.
And there's a lot of confidence.
that the next generation of models improves against this.
Well the most obvious answer is just gonna be multi step reasoning.
It is a core mission of what the labs are working on in terms of solving that in
Um or
General.
Why?
So the things that I'm most hopeful for, multi-step reasoning and self improvement, the things I am confident on that have commercial value.
Hallucination management.
Let's say you get everything, like we get the best possible version of Oh my goodness. GPT five.
Where are you like going to run towards in terms of seeking
new company opportunities, like what spaces will get unlocked or use cases will get unlocked in your mind.
by like the perfect version of GPT five that would get you excited to invest behind.
I would say
I mean the perfect version of GPD five's a lot. So you know rooting for you, Sam. Bring it bring it out. But well, if all of those things are true.
I think we're in a really different zone.
If we just narrowed the scope of it to
Models that were more verifiable, more calibrated, better hallucination management.
These are things that block the enterprise from adoption. There is, I actually think people are asking this question for good reason, but there's so much enthusiasm and so much capex spend.
Against
AI model development right now.
The idea that we're gonna hit an air pocket because
adoption of these tools in the enterprise where there's real economic value is lagging, I think that
Is real.
But if you talk to companies like, oh, there's the things that are just the enterprise overall, like change management. But when you ask them like what is a problem from a risk perspective, the adoption of
AI in large foundation model based applications.
internally or externally built in large financials, traditionally a huge spender in technology.
is marginal. It is extremely low. And it's because from a
risk compliance reliability perspective, the bar is really high. We haven't hit minimum viable quality.
And so part of that is all the clever things we're talking about in terms of entrepreneurs.
Designing systems and product scope such that the experienced
quality by the user is good enough.
But part of it is just we need a little bit more from the models, but it's coming.
We need lower hallucination from models.
Maybe say anything more you've learned about
Who
is overrepresented and underrepresented in the buyers of AI products. Like that financials point is really interesting.
Who are the biggest buyers of this stuff already? And is there any other group like financials that you see as big laggards?
That will become addressable when the quality gets higher.
This AI thing is making me personally rethink a number of my assumptions that were long held as an investor, but.
We may be surprised.
I think we're gonna get some leapfrog effect as we get over this minimum viable quality bar in different domains because the areas.
Where we're just talking about healthcare operations.
I have not been super enthusiastic about selling health care IT.
Over the last decade.
Looked at it off and on.
Healthcare is a quarter of the American economy. I mean globally.
It's
very important to every single human being.
It is not particularly efficient. It is especially inefficient in the United States. It is hard not to want to work on.
Except then you look a little closer at the companies that actually work in this space and you're like, ah.
The incentives are a mess. It's super slow.
the ecosystem is extremely complicated. There's regulatory capture in every zone.
But there's so much to unlock there.
Because the number of people employed and the number of hours wasted.
that in the administration of health care in the United States that causes
A good deal of why our healthcare is so expensive is really high. If you ask me about another area that I wasn't
excited about in terms of investing in, but generally.
Please still call me, especially now.
But might be as the models get better is government services.
These are all huge parts of the economy that are incredibly inefficient, where people are
doing analysis and moving data around
The workflows you can picture in a way that make a ton of sense.
To apply these model capabilities against
Creatively.
I guess maybe my fundamental optimism comes from this place of well
If we make it a hundred times cheaper, no matter how complicated that ecosystem is.
I think we can sell it.
That's one thing that we've begun to see. And the leapfrog effect is
The types of businesses and the types of functions that had the most inefficiency.
can be most ripe for selling new solutions.
What's your best guess as to what the market structure will look like for the foundation
model providers in like three to five years.
What do you think the healthiest version of
Maybe don't predict it. What do you hope it looks like?
We work closely with
All the large providers.
And that means take our portfolio over to see the foundation model companies, make sure they have research connectivity, co invest with them.
And we are also first
round investors and miss straw.
I'm very loath to make
predictions here because
AI is just really hard. Anybody who like really says
that they know what is coming more than six months from now, that doesn't need a large lab is likely. And even so is
I think the large labs were quite surprised.
Bye.
success of open source over the last year.
And so when you say like what do I hope for? I want a million flowers to bloom because the last mile to
humans with more joy and play and productivity in every corner of the economy and every part of the globe.
That last mile is really long, and there's a lot of them. And so it's very hard for me to imagine.
A single company or two or three
walking that mile, essentially.
And so
What I would like to see is you have these amazing assets, you have multiple options, you have open source.
And there is enough
choice and competition and robustness at this layer that you can have companies built with true economic value on top and in partnership with them.
I think that will happen.
The alternative point of view is that the
Race for development gets increasingly expensive.
I mean, that's gonna happen anyway, but like that it becomes harder and harder for people to keep up. But
An interesting dynamic here is the number of people who know how to train large models is increasing.
And
The second comer.
Discount?
Is very high.
As that expertise is known.
as you get to use modern hardware.
as you get to not train all of the intermediate models and just
try to be at the state of the art with the state of the art techniques.
It is a competitive market.

commitment of Zuck to that market makes it also much more interesting. I still think that enterprises have
Some challenge believing that Facebook is gonna be a great long term partner.
To them.
But the more they work with the ecosystem of deployment and inference and consulting partners, the more credible it is.
And
A rich and competitive ecosystem of models.
Means that there's
you know, not total rent collection at that layer and lots of opportunity for many companies to walk that last mile.
What's your model of thinking about Mistral versus
Entropic and up in AI.
How should we think about the difference between them?
I know one's open source, but like maybe say a bit more about
Yeah, how you think about it, maybe even while you invest it early on.
Going back to the description of the ecosystem that I think is great for innovation and great for the end user.
It is one of
Faster, more democratized progress.
And m economic value at multiple layers in the stock.
And I think that is Miss Strahl's view of the world. Arthur and Gam and Timothy are amazing researchers with a bent toward both state of the art performance, but also a view on efficiency.
And
Again, going back to
Blindly believing some dominant narrative in
The AI landscape is very dangerous.
There was a narrative.
Maybe it still exists, I don't know. That efficiency doesn't matter. Model efficiency doesn't matter. It doesn't matter how expensive it is to run.
Or how big it is, you just want the best performance.
And nowhere in the history of computing
has efficiency not mattered.
Because somebody is paying for it in the end.
And so maybe the model company can absorb it for a very long time if they've got like a great other business model, like ChatGPT.
Facebook, Google.
But
In a vacuum.
more efficiency is better for latency and cost and
Scalability reasons.
And so one
Big
potential change in the ecosystem is
whether or not people are going to start using a
mix of distilled models, models of different scales, putting them in compound AI systems and getting to performance and efficiency. And so
Eficiency was not a huge consideration.
amongst research labs and application companies until relatively recently.
But I'm sure you've heard this term.
Towards intelligence too cheap to meter.
And I will make you a claim that there's no such thing as cheap enough.
Because we've been working a compute for a long time as an industry, and it is not
So cheap that nobody cares.
Because we just keep doing more with it.
There's no company that isn't like, Oh, if compute resources were free, similarly we'll look at this and be like, if intelligence resources were
free we wouldn't do more, but they're not free.
I do think efficiency, especially if we start getting to
the next generation scale of models, even limits to data center size and power.
Thinking about using the resources that we have.
well both in training and in inference.
for every particular application problem is just gonna become a much bigger
consideration. You see it now.
A year and a half ago, I'd say
Focus on
data quality.
wasn't considered the highest status part of research.
People care a lot about it now because we run up against some limit, which is, well, we took the internet data and improved quality is going to lead to improved model quality. And so
I just think the dimensions on which people
optimize change over time. I'm very loath to make any prediction, but I think it's going to be a rich ecosystem, and I'm hopeful of that.
We've been told that software is like the ultimate business model for lots of reasons. And if you look at some of the growth for these firms like OpenAI's
revenue numbers are incredible for how young the company is. It's like this huge revenue business.
Maybe the only thing more staggering is like their OpEx, like an incredible amount.
of cost in these businesses.
so far business models that are don't look like the traditional
Zero marginal cost software business model.
Say a bit about
margins and the basic stuff.
And how you think that will evolve.
Given that so far we've seen these things are freaking expensive to run.
I mean, we just had a conversation about how efficiency
Matter.
Will matter more and more.
I am
Not particularly concerned about open AI's OpEx.
The winds behind them, not that they like shouldn't care, but I am not concerned that this is like an existential thing for them, where like chat GPT can never be a good business.
It depends on whether or not chat GPT is like a differentiated product in market, but the wins behind them look like
The rest of computing.
Investing in
efficiency against these workloads. And so we're super early.
Right now we're in this
Crazy
Immature.
part of the infrastructure cycle. So let me like say a little bit more about that.
Look at the history of
Where'd you get a server for, let's say, a
client server application web application, you had it on prem.
And then you co-located it. Somebody gave you like power and real estate and cooling and you put your server there.
And then you had hosted
renting server and the data center from somebody and maybe they're even running an application for you.
And then you have virtualization.
I'm renting an instance.
And
The kiddies I graduate from computer science programs now, if they're not
Thinking about AI, serverless. I don't want to know that there's hardware behind that. Click button, Amazon, Google.
Infinite capacity.
In AI, we're like step
One or two.
You are
Building and deploying your own clusters in data centers, you're in three year reservation mode, you're managing your own depreciation cycle, and you're like, Oh no, our B one hundreds from NVIDIA gonna totally destroy the amortization schedule on this. I don't know.
That's a really hard environment to innovate in. And so
Power to the labs in every company.
inside outside our portfolio that thinks about it because they're training on large resources in this super immature ecosystem.
Think about the amount of compute.
That we have accessible
In our phones, when like a huge ecosystem is focused on the efficiency of getting more and more power into our hands here in any other machine.
Could we use?
As an ecosystem, more competition to NVIDIA.
Yes, this stuff will get cheaper if there are multiple players.
I mean NVIDIA is innovating pretty fast, but
Pushing NVIDIA and then eventually challenging a 90% margin product. But overall,
I expect
The industry to get much, much better.
Uh
hardware utilization.
and at efficiency in a bunch of different ways at every layer of the stack.
And so
When I think about the opex of OpenAI as an example, but any other application company, the optimizations that a company itself can do, then in infrastructure management.
I'm on the board of this company called Base 10.
You want to do inference on GPUs of different types that is serverless and not write all of your own scheduling and GPU failure manage and whatever else.
You can do that.
And then
There are
People working on everything from
memory bandwidth to chips to systems.
The entire ecosystem has innovation that we expect to
as an industry reap the benefits of over the next five or ten years.
No reason to me that won't happen. I can't predict how quickly the cost curve comes down for the same level of compute.
And I think it will still like feel like you always need more.
Think about progression of PCs.
You always want a latest processor. But
I am not worried that the
And margin structure.
Of any application company.
Is
bad or any worse in this era. It's not clear to me that that's a structural problem versus the software era.
That's probably the best answer I've gotten to that question and I've asked a lot of people, which makes me wonder like
What else do you want to tell us about infrastructure that you've learned?
I'll claim one thing that's maybe interesting to a broad investing audience like you have.
There are a lot of people thinking about
Why is there not a legitimate challenger to NVIDIA?
And what are
all of the structural advantages that NVIDIA has.
And
Broadly basic analysis of the landscape would land you at
Okay.
AMD is working on this and their software stack has not been competitive to date, but maybe they're getting closer.
Software and hardware, but weakness in the software stack.
Then you have
A series
of proprietary chip efforts.
Sometimes from acquired companies.
Uh
Microsoft, Amazon, Google
Rumored now opening eye.
And then you have upstarts who are trying to do either systems
With a particular
DGX boxes like more like a mini data center than a chip.
But either like a full system or a chip. And so you have rock and cerebrus and etched and matics and whatever else.
I would love to see most of the industry that isn't a giant holder of NVIDIA, but even some of those.
Would love to see more innovation at this layer. People understand the power of CUDA and the optimization and effort that's been
going into that for
More than a decade and a half.
One of the core challenges that people don't recognize
Is
We had a portfolio company.
Who used
A
Different chip.
Not NVIDIA GPUs.
to try and train a large scale model. These are X large lab researchers who know what they are doing.
And
No one is saying that the like engineering and infrastructure management work that they did was perfect, but
It exposed me to an understanding of what is making this problem so hard for people to attack.
If you don't have
People
Running.
large scale training workloads at multi thousand node cluster size.
on your chips, you do not know if they are going to work.
And so
This isn't something that you can do.
As easily
Okay, we're gonna do the test and simulation, test an FPGA, and then like
We have as much confidence as we can that Chip will tape out, Chip fits the spec.
It is useful.
The types of failures
That can happen in
Large scale training, which is a huge driver of consumption that a lot of people are going after.
It seems to, at least with some of the new contenders, only come out at scale.
And so that's a really difficult risk to mitigate where I'm like, I don't know, can you do that in SIM?
Because it's a new problem.
I think that's kind of interesting because
Then if you look at, for example
Google
And their chip efforts.
Those work.
Actually, because Google has the workloads where they're training on their own ships at scale.
And
I would ask you.
Do we know that the other chip efforts are working?
You either need the patient zero or that patient zero to be yourself, because it is very hard thing to answer without that workload.
Doing it physically is really expensive because then you need to go deploy 10,000.
Maybe that's a just an interesting anecdote where we've been thinking a lot about
Is there opportunity here and what are the barriers and
Love and respect for Jensen and Nvidia, spending time with him as well. I think the barriers are pretty high.
back to like the thousand flowers blooming. It's cool to imagine all
Of the smart people.
That are
economically and just
Curiosity motivated to
Tackle all of this all at once.
It makes you, of course, wonder about like past technology bubbles where
Lots of money has been spent trying a million things and then we find our way through and money's lost and money's made.
Wear with your like bubble hat on.
Do you have a bubble hat? Like what does your bubble hat tell you?
Talk to us through that specific lens.
Oh my goodness, of course I have a bubble hat.
We have skin in the game. I'm a large owner in my own fund and
This is the main thing.
If the promise I made to LPs was multiple
We gotta go make good on that.
I think most people should not be training
general foundation models.
Maybe that's obvious.
That's a way to lose a lot of money.
I would also say as somebody who wants
I do think that there will be multiple players.
A tea
Chi and Systems layer.
And chip systems networking the new data center layer.
The opportunity is too large. It capitalism works. It will happen. It's just
Barriers are high. But
I don't think that the market is not infinitely deep for another
L M player that isn't particularly differentiated, but is just a
handful of
great researchers working on something
That feels very general or feels directly in the path of the existing large players. And so I would say.
Companies keep getting funded.
That are of this shape.
biggest people look at and are
envious the
value creation at OpenAI and Enthropic and others. It's the great game. It's really interesting, of course. Like you and I are interested in that too.
But when the entry price is now
more than half a billion dollars to
do pre-training of something interesting or at least get into the game of a general language model.
That's a really easy way to lose money.
Can I ask you the question I asked Martin Cassado, which I quite liked his answer to, which is
What do you think the twenty thirties look like?
in the world as a result of
all of this, intentionally creating like a little buffer between now and then. And we're always so focused on like the next three years, but
Your kids are young. My kids are young.
What's our kids like teenage years gonna be like?
I think they're gonna be a lot more intelligent and creative.
So
Twenty thirty is really hard because
I can assign a probability to it, but there is a piece of me that says
Real probability of abundance.
I don't know if we're all
Sitting on a beach partying in Europe.
Or something. But
I say that like a little bit glibly because the
Ability of society to ingest that much change in productivity is not great.
There will be winners and losers and the
Job displacement.
dynamic I think is real.
I also think it's probably not quite as fast as people are worried about.
So
I'm splitting my mind between
Okay.
model progression happens.
very, very fast and we solve a bunch of really important problems.
And
We have abundance. If I take a step
Back from that.
In terms of just optimism of timeline.
I'd say
The things that I can picture
very clearly.
Is
The ability to
learn and
Express a capability that would have been a very skilled thing before.
Is gonna just be completely changed.
your expectations around creativity of entertainment. Everybody has desktop Pixar.
Everybody has the world's knowledge in the form that is most useful to them immediately.
Andre's working on this educational tutor.
thing, somebody can become an expert in something at a like an incredibly accelerated rate.
because of that. There are a bunch of areas that should
market structures be willing buying processes be willing magnitude of improvement
being enough.
become a lot more efficient. So I don't think I have a clear version of the future. I can picture a lot of
really amazing individual experiences.
Is there any?
commercial or product thing.
That you're surprised we haven't seen yet.
That you're like got your eyes peeled for and it hasn't happened and you're surprised it hasn't happened.
I'll describe two opportunities that I'm excited about and
One of them I know why it doesn't exist yet, even though it's so big.
The vast majority of the time.
Incubating companies is a terrible
Idea.
Because
My former partner Jerry used to say there's no such thing as inception. People like have to live the problem and the ideas and go after and make it their own. The entrepreneurs that we want to back, they have the agency to go do it. But
One of the places where I think this is not true.
Is where
Essentially the context and capability.
to build a particular type of company rarely come together.
This is one of the things that
Is so powerful about Harvey.
How many times do you get somebody who is going to be
entrepreneur on forward thinking about workflow, that they are a
lawyer working ninety hours a week at a good firm and are still like
I make ChatGBT do my job and then his
roommate is a great researcher.
It's uncommon because y you ask your average
researcher at a lab, what does the legal workflow look like?
That's not part of their life experience.
And so one of the really interesting opportunities that we think about and actually do support incubation around is
Places where
The context
of like what people are doing that could be
automated or improved is so far away from the engineering and research community that understands the capability at the state of the art today.
So an example would be
There are many billions of dollars spent a year.
On
configuration, monitoring, and maintenance of enterprise software systems.
E R P
HR, CRM workflow systems like ServiceNow.
That gets done.
Bye.
large consulting shops.
In the US.
In India.
And
I think if you asked
If we look at the capabilities that are coming in software generation.
And code generation. I think some of these tasks are pretty tenable.
Or you could at least create a services org that has a
very different margin structure than today's orgs.
Better margin structure.
Бетер СЛА Бер энд узперієн.
maybe change the industry structure of software because this configuration and maintenance and depth of embedding in a particular enterprise system is the thing that makes it so sticky.
the fact that you configured SAP 20 years ago. It's just all your business process. You couldn't possibly get out of it.
You can replicate in a different system.
That's a very high mountain to climb, but pieces of that
That could change the industry of software of enterprise software. And that that's really interesting.
But if you think about
All of that work.
That type of software engineering work.
That's considered very low status work.
in Silicon Valley engineering circles.
What's happening over there?
Who's writing?
configurations of this platform. It's so far away. But if you think about the economic value and what
Technology is actually super important to the largest companies in the world that they feel like they can do nothing about or they can't improve.
That's kind of it.
That's to me
The type of under exploited opportunity where the domain is so far away from the technologists.
Even that's still software. Never mind.
healthcare operations or government administration or call center operations or something like that.
And so I think there in Laura's a little bit of the opportunity, another one that we're just really excited about in terms of potential.
value for society and clear the data set is not
generated and owned at the large
Labs today.
is material science as a domain for foundation models.
But if you ask me like why doesn't that exist, yep, well, I think you need to
have the right talent, you have the right thesis on data, hopefully some cleverness around efficiency of collection of that data.
Would you get excited by some sort of pitch around
We're gonna create the next Accenture, but built on top of
Basically the question is like
What you get excited about a services?
Business.
that just was built around using and deploying
All this technology to make.
the offering better and faster.
And higher fidelity.
First service business is just too lame of a business model for you.
The answer is yes.
But
What would get me jumping out of my seat excited would just be somebody with the
relentless ambition to figure out if we can make the margins as good as a software business.
And let's think less about Marge is more about scalability.
Because you could go like okay, fine, why are services businesses lame? Well, because
You're fighting on human capital quality and volume. You can only scale so fast. It only gets valued X.
multiple in the public markets eventually. It is only so profitable.
And hey, like to some degree, because you can't really tell.
It's a brand game in the end, go with a big consulting firm. Those are some of the reasons people don't like services businesses.
And so
For
A story I could believe around
scalability and ambition of scalability and all the things that come out of that. Like how much of it is a technology business.
Yes. If the answer's high enough, yes.
That's true.
When you look at some of the most interesting application layer companies today.
They may not frame themselves this way or even be consumed this way. And that might be the difference.
the what we're talking about is consumed as a
Services experience.
But the replacing
Things that were services before.
Is there anything that scares you a lot?
The things that are
Interesting to be afraid of.
Bio risk.
runaway model that is optimizing paper clip production.
China weapons system or something.
I think we should go figure out.
If those
Risks are real.
But
I actually think they're like a huge distraction from
the near term abuses.
And so
For
You and I investors in technology.
Technologists themselves.
The
Ability to adapt.
Two
New tech.
Is a built skill.
But it will not be true across the entire world that people adapt very quickly.
And so there are some
Incredibly
Basic abuses.
Where I would be excited about solutions.
And it's very easy to say
misinformation and
Fraud?
But I think those are quite large.
I'm more oriented toward wow, we have like a lot of abuse that's happening already.
With
older technologies that just gets
so much more amplified, gets much cheaper.
With leverage.
Of
this generation of models and improving capabilities.
We should address that.
pretty immediately and in a sophisticated way.
Is there any debate?
in this field that you find really interesting?
Personally.
This is a little bit in the weeds, but I think the debate about how you improve
multi step reasoning in a general way.
Is open question.
I'm also labs. And this is like a very fundamental question of where does the next step function of intelligence come from beyond just scaling?
Because on both a compute and a data side, that is more difficult than it was in the last generation. People have
varied points of view on this in the different labs.
What are the sides of it? Like how would you describe the sides of that?
Debate or the different opinions.
Labs and upstarts.
I will describe
one directional
Effort.
That
people are investing in which is
I just had Oriel Vineyals from Deep Mind on
our podcast to talk about this.
But
Дозить мек сенс.
to invest in
На
And computer science.
As a domain.
Where it feels closer to pure logic, you are doing multi-step reasoning and in terms of
improving model capability specifically against this domain.
and using generated data in this domain as new training data for the model.
Lots of people are doing this.
There's a contingent of folks that say that is not a general approach.
human reasoning is much richer than that, and so
Looking at code or looking at math.
translating problems into lean and verifying them.
That's
To some degree.
A dead end because we cannot
do this with the rest of think about the qualitative reasoning that you do on
An investment. There's no proof.
That's one open question of are there more general answers to improving multi-step reasoning that are not in a particular domain?
Because
Some people will look at code and math as an extension of
R L and games.
And
Is that the right path forward? I think lots of people are interested in it. It's m like much
Less clear consensus in the research efforts than
More scale, more data, human expert data.
And better quality of data.
improvement of the quality in the pre training.
as well.
Do you think there are any important I'll use the term crossroads?
That we're at.
as an industry in AI today.
Or we have to go one direction versus the other.
I think a
Point of change.
Now that's kind of interesting is
A tea
Scale of
cluster required for the next generation of training.
These are
For training essentially like
Single use
gigantic constructions of cat pecs.
And they're not
Easily extensible to the next generation like obviously you can use
those for training smaller models, experimentation, inference, whatever, but
The idea that we're
Just going to build
larger and larger data centers consuming more and more power in order to
Improve.
model performance.
feels like it has some limits.
And it probably goes back to
the discussion we were having before of
Efficiency has always mattered.
And maybe there is increasing investment in
Some sort of
modularity or that efficiency of training matters in a way it hasn't in the past.
Because that is when you start talking about the
multi billion dollar training run that gets you to state of the art for
Some period of time.
It's a hard capex investment.
If you could commission a research paper, like a crazy well done, in depth research paper.
On any topic.
An AI, what would you pick?
three domains that I think are interesting. I should can't choose one, but one is
The problem is nobody's gonna write this research paper because it's all like proprietary knowledge. That's okay. You have a special magic wand.
The comparison of
generalization of multi step reasoning.
I think is an interesting area. A open question is
We have all of these benchmarks.
exams that humans in different domains take.
M M M L U or a legal exam, a medical exam, et cetera.
We are surpassing the benchmarks in a bunch of different ways where
It begins to open the question of
How do we do evaluation of these models as they are?
Super intelligent.
Not in any super philosophical sense of the word, but only in the sense that they are
better than our human experts.
at a particular task.
And so human eval becomes hard.
How do we understand progress as we go from there?
Something like Ark?
Would that satisfy what you mean?
That's one version of it. I think another example of an idea is have you ever heard of the term.
Centaur play.
If you play chess. Oh yeah. A lot of people in AI don't believe in this idea anyway.
One theory of it would be
Patrick.
with a model can come up with better answers.
than Patrick or the model.
that will be the bar for human eval for
the next set of models, but that's a version of it. But it's an interesting question.
How do we continue to
Evaluate models as models progress.
understanding the different theories on
reasoning traces leading to
model self improvement.
What is it in your personal experience
That has led
To you caring so much.
And willing to work so hard.
In this field.
This is the most important change to happen in technology in our lifetimes.
And so
If you believe the impact is very large, not in like a religious AGI way.
But if you just think the opportunity for productivity and abundance is very large, and you can create
New economic platform players.
And the landscape is so open because your opinion
Actually really matters now versus if you were investing in SaaS.
Like you and I were in like twenty fiftin.
There were
Incremental discoveries and understandings about companies and technology that did matter.
I'm not trivializing that, but it was at a very different scale.
If software one dot oh was about human engineers writing explicit instructions and source code.
And that was decades.
And then Andre Karpathi wrote a essay about software two dot oh being like replicating
Finding in search space a specific behavior from a data set and a neural network.
And then training the program from the data set. This is like quite a lot of work.
to do from scratch.
I genuinely think of this era as a new era of software. We are manipulating
models of different kinds where
A bunch of the work has been done for us.
the foundation models are so capable. And if you can build intelligent systems to do increasingly useful work,
How could you not want?
To work on
Leverage these superpowers in every domain.
Maybe do it better than humans in areas that really matter.
Health care and science, but also
in a daily work and taking the operational toil out of every industry. And so it's just really interesting.
It's an ambitious era.
I think that's super exciting.
From a personality perspective, we had some founders doing references on us in investment context recently.
They walked away convinced that we'd go to work for them and they're kind of like
Oh, people are surprised. It's like why you're
Trying so hard when you
don't have to. And I'm like, oh, anybody in technology like above subsistence level, what does have to mean?
It's like the funniest observation to me.
I guess my reaction to that is of course I have to.
What else am I gonna do? Let the world pass me by, be number four.
Maybe they mean it like financially.
Oh like
Doesn't money really matter? I'm like no, but
It's
really fun to be right, go compete, work with great people. The discovery for me is maybe this resonates with you, the motivation of working with the most interesting and motivated and high leverage people in the world and helping them be a little bit more successful and being right about that over a long period of time.
Of course you have to.
Amen.
This has been so much fun.
I think you know my traditional
Closing question, what is the kindest thing that anyone's ever done for you?
There's too many to answer here. I'm very
grateful for the number of people that have given me
Opportunity.
is not an acceptable answer. I'm gonna name three people.
The first is
I mean, you had my husband Pat on the podcast. He's my favorite person in the world. I'm like, Oh, thanks for asking me to marry you. Like that's great, right?
I just
Appreciate that.
got very lucky. And then I'd say two people that I will be
Eternally grateful to for
different reasons.
One is Anil Busry, who used to be a partner leader at Graylock and was the founder of Work Day, an amazing technologist, where I think in Anil's view, he was like rescuing me.
from finance at Goldman who's oh at one point like I was at Morgan Stanley and you like belong back in technology. And I'm like, I know, like I'm a technologist. I'm just here learning about the business side for a year. And I'm like, I'm gonna go work at Stripe or something. But the thing that Anil did for me was just
Say
Working together for a small amount of time, like having complete conviction.
And I did too, but
You have potential. We don't know what we're gonna do with you. Just come work here.
And so it's like raw goods bet of an opportunity where I'm like, ah, I haven't done anything interesting.
In the technology world.
I am a
Kid from Wisconsin.
who was working in banking, who happened to work on your IPO.
And
It just takes like a certain
conviction in your own judge of talent for someone to be like, Oh, come work here.
When on paper, who cares about your profile. I'll forever be very appreciative to Anil for that.
My former partner
Ashim Channa is legendary enterprise and particular security master. But one of the things that I seek to emulate in Ashim is
His ability to work.
with
earlier career investors really comes from a place of amazing personal confidence.
One more sentence about this.
If like you could be
productive for
I think she was actually like a little bit skeptical.
of me when I came in, which is totally okay. Right. Trust is earned.
But he became a really big sponsor for me. The attribute I tried to emulate is give.
Your people as much and perhaps a little too much opportunity.
If they prove they can take it.
When I say confidence he was
incredibly open with his network. He was incredibly meritocratic, where
I don't mean to be cynical about this, but there are
Many people who would have been like, I'm investing in enterprise security and you as
A
twenty four year old really young looking girl from no particular interesting accomplished startup background.
Yes, we're gonna go do all of this company building work together and I'm gonna take your judgments.
very seriously, very quickly, I'm gonna open my world to you.
I think that's an amazing thing to do for somebody and in terms of giving
the people that I work with opportunity, may we try to be like that.
What a wonderful trio of closing thoughts. Sarah, thank you so much for your time.
Thanks, Patrick.
Mm.
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