State of Startups and AI 2025 - Sarah Guo, Conviction

Channel: aiDotEngineer

Published at: 2025-08-02

YouTube video id: 3MZS5gNElZM

Source: https://www.youtube.com/watch?v=3MZS5gNElZM

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[Applause]
So first question for you uh what is
definitely happening by the end of 2026
AI agents ship code directly to prod in
your environment, right? Not in like
some uh playground. Uh voice AI replaces
text for most business communication.
Inference cost dropped below a cent per
million tokens or wall-ally like we're
all chilling.
Any of these
first one ship ship code directly to
prod. Okay, this is a hopeful set of
engineers.
All of you want to get rid of your own
jobs. I love that.
The good thing is I also don't have
internet so I can't look at my next
question.
No, it's going to be good. It's going to
be good.
Um
I present from your phone. Uh, no. I was
going to go through poll questions while
we're trying to do AV setup. This one.
Yeah.
While this is happening, I'm actually
just going to introduce myself so we're
not wasting the time. Um, my name is
Sarah Goa. I, uh, helped start a AI
native venture fund. It's called
Conviction. And we got going about two
and a half almost three years ago now
just before the starting gun of chat
GPT. Um as always in technology
investing most of life it's better to be
lucky than right. Hopefully you can be a
little of both. Um uh and and the point
of having a new venture firm I I worked
at Greylock. It's kind of a
traditionalist venture firm a great one.
My partner Mike Vernal used to work at
Sequoia. You guys have probably heard of
them. uh was that we think like actually
you know at risk of sounding like those
people this time it's different right um
that this is the largest technology
revolution that we get to be a part of
and that there's so much change in the
technology the types of businesses you
can build the product decisions you make
what challenges these startups and big
companies face that you know maybe
there's opportunity for like a startup
VC as well and so um you know I'm I'm
thrilled to be working with like really
interesting people in the industry so
far. Uh Mike and I are investors in
companies like cursor, cognition,
mistral, thinking machines, Harvey, open
evidence. So a mix of um base 10 like a
mix of uh infrastructure model and
application level companies and you know
one more are my kids coming up yet?
Okay, cool. Um one more uh just
observation from the last two and a half
three years of doing venture. I I was an
investor for about 10 years before that
is I have never seen the like just the
uptake from users that has been possible
in the last couple years. I'm sure all
of you have experienced that it is not
trivial. Um you know AI product and AI
engineering uh and this is kind of the
theme of my talk so I'm sorry to give
away the punch line but it's quite a bit
harder than people had hoped. Um but the
the value creation is massive. Um, we
see companies going from 0 to 10, 50,
100 million in run rate very, very
quickly, faster than we've ever seen in
any technology revolution before. Um,
and I get asked a lot like where are we
in the AI hype cycle? Is the winter
coming? Is this like infinite AI summer?
And I would say um having actually been
an investor or an operator through a
macro cycle at this point like I try to
pay very little attention to what the
marketing world is saying or even what
the markets are saying, right? Because
you know if you're if you're an operator
or an investor
maybe you care about what the stock
price does every day, but really you
want to figure out if the company you're
working for or starting is going to work
long term, right? And if the products
are going to work long term. And the
things that I get most excited about are
seeing like crazy usage numbers. Okay.
Thank you, amazing AV team.
Okay, I'm gonna I'm gonna go real quick.
Um,
where are my presenter notes?
Okay, we're we're just going to keep
going. It's cool. It's cool. Um, so I
want to talk really quickly about uh
just a few things today. I think we lost
a little bit of time, but let's let's
say let's talk about capabilities, what
we're seeing work in the market, and
then um uh maybe some advice on like
what to build if those are, you know, a
question you're considering. Uh I think
the shorthand that we're going to use in
this presentation is like cursor for X,
right? Uh and I do think that's a really
massive opportunity. Uh the first thing
in capability for this past year is
clearly reasoning. Um, reasoning is a
new vector for scaling intelligence with
more compute. The labs are really
excited about this because they get to
spend more money and get more output.
Um, but we should also be really excited
about this in terms of unlocking new
capabilities. Right? If you just put
aside how it works, it's a confidence
boosting implementation detail. Um, but
we should expect more capability. You're
unlocking a new set of use cases like
transparent highstakes decisions where
showing the work matters. uh sequential
problems, problems where you need to do
systematic search. I I think this looks
like a lot of problems that we're
excited about and um face in knowledge
work every day. Uh as you have just seen
demos of and I'm sure are working on
given reasoning, people are really
excited about agents. um to put a you
know I want to do like the Steve Balmer
impression that's like agents agents
agents agents agents agents but uh I um
you have to give me more than 12 minutes
to like get that sweaty
uh but but like the non-marketing
definition that I think of is it's
software that um uh it takes some set of
steps it like plans it includes AI it
takes ownership of a task and it can
hold a goal in memory
you know, try different hypotheses,
backtrack. It ranges from super
sophisticated to super simple. Um, some
of the tools that might use to
accomplish a task include other models
or search. And largely, it's just like
AI systems that do something. Um, and
that's not a chatbot that looks more
like a colleague. Uh, and you know, one
thing that I think we have a really
unique vantage point on is, uh, we back
a small number of companies at
conviction, but we also run a grant
program for AI startups. It's called
Embed. We get thousands of applications
every year. Um, and includes like user
data and revenue data and like really
amazing people and the number of agent
startups has gone up 50% over the last
year and a lot of them are working like
we do see stuff that's working in the
real world and uh that's super exciting.
Uh, other modalities are progressing
too. I'm sure a lot of people are using
voice, video, image generation um, even
beyond you know studio gibli. But you
have companies like Hey Genen and 11 and
Midjourney that are rocketing past 50
million of AR. These are real businesses
now. Um, I want to see if I can quickly
play for you. They told me to express
myself, so I did. They told me to
express myself, so I did. Now I'm banned
from three coffee shops. Hands can hurt
or heal. That's the difference between
chaos and creation. So if you're
wondering where Q3 is headed, So if
you're wondering where Q3 is headed,
here's the thing. Consistency always
beats urgency. We've got the projections
ready and let's just say it's looking
solid. I would definitely recommend it
to anyone. I would definitely recommend
it to So I I think like if you just are
looking for artifacts of improvement,
this is from a company called Hey Jen.
Um you can make clones of yourself of
fake people and like you have gestures
and expressions that uh reflect emotion
and content now, right? So these models
work together and like I don't know
about you guys but looking at that last
gal like I feel influenced. I don't know
what the bunny is but I would buy it. Um
and and and so I think like huge swaths
of the economy are going to be affected
by this sort of multimodality. Um some
investors or operators would say
multimodality would just be for niche
verticals that enterprises don't have
you know your average enterprise doesn't
have that much voice video image data
today. Um, but I think that changes,
right? When you can do stuff with this
data, when it is structured and
understood, there's more reason to
capture it. And I think of like how much
video do all of us watch every day? It's
one of the highest bandwidth
communication methods, and we're just
going to use more of it. Um, we think
voice is where we're going to see uh
applications first in business workflows
um because it's already a very natural
communication mode. So, uh, everything
from medical consults to lead
generation, places you already had
business voice, you just couldn't scale
it before. Uh, I I think that's where
we're going to see it first. But as
these other modalities become more
controllable and also less costly, we
should see all of them. Uh, I I think
it's safe to say you can expect
capability improvement in every part of
the model layer, which is really
exciting. A lot of people were talking
about the uh the data wall or like the
end of AI summer, but for anybody who's
building applications, I I'm at least to
tell you one person's opinion is uh it's
not coming. Um and and then usefully for
all of us, uh that market for model
capabilities is getting more
competitive, not less. Um Sam Alman
himself, I think, said it best. Last
year's model is a commodity, which is a
scary thing for a model provider to say,
because last year's model is now pretty
damn good, right? The numbers tell the
story. GPT4 went from $30 per million
tokens to $2 in about 18 months. The
distilled versions of that are like now
10 cents. So, we can really use them
very broadly. Um, if you look at this
chart, uh, green is Google, yellow is
anthropic. So, you see, you know, it's a
real mix. This is data from Open Router.
So, thank you Open Router for that. But
um you really saw Claude cut into
OpenAI's market share and Google come
roaring back with Gemini. Uh this data
is obviously a little biased because a
lot of people just go direct to OpenAI,
but if you're into multimodel that there
really is a mix and you do have credible
new players like SSI and thinking
machines, some of the best researchers
in the business with orthogonal
technical approaches um entering the
frey as well. And I'm sure many of you
have experimented with DeepSeek uh
coming out with releases of you know
both base and reasoning models that are
uh reasonably competitive with a claimed
fraction of the training cost like we
should just assume that open source will
do as open source does and we can rely
on the model market to compete for our
business which is really exciting. Um
and so the view is plan for a world that
is multimodel. um tools like open router
or inference platforms like base 10 help
that uh and uh I think like be
comfortable with that I I am okay so we
have all this capability let's ship uh
shift quickly to the application layer
we have to start with cursor uh a
million to 100 million of AR in 12
months and half a million developers I
assume all of you uh zero sales people
to start that's not growth that is a
killer application um cognition which
started with more autonomy is already
the top committer in many companies
feeling a little threatened but also
excited because recruiting is hard. And
then Windsurf who's on a tear itself and
really beloved is being acquired by
OpenAI for $3 billion. So we know for
sure that the labs don't think that they
can just you know steamroll everyone
right lovable and bolt hit 30 million of
AR each in a handful of weeks uh helping
non-engineers vibe as well. So you know
our our our ranks are expanding. Um and
I think it's useful to just like analyze
a little bit why code was first. Uh
fundamentally it is text with it's log
it's like logical language with
structure right so much of coding is
sophisticated boilerplate like we all
love engineering but some of it is like
craft work not new algorithm work um you
don't need AGI to write a like uh an API
endpoint or um a react component.
Second, you have deterministic
validation. You can automatically check
if code works, run tests, compile,
execute, do things developers would do.
And third, researchers believe code is
crucial for AGI, right? So, they poured
resources into it. Um, and uh code
became a key benchmark and a training
priority and an area for data
collection. But I think the last point
is um the money point to me. Uh
engineers built tools for engineers.
They understood the workflow intimately
and that made all the difference. And
that last part is the playbook for every
other industry. I'm sure people are
building things that serve beyond
engineers. And I don't think the winners
will just be AI experts learning those
domains. They'll be customer centric
like problem centric builders who
understand AI and then redesign
workflows from first principles around
manipulating those models. Um and so I
think that's really the opportunity to
build cursor for X. Um let's think a
little bit about what that means. Cursor
is not a single model. Uh you know one
model's doing diffs, one's doing merge,
one's embedding the files. They
manipulate and package up the context.
They prompt the models very skillfully.
They let engineers avoid repetitive
tasks and standardize with things like
um cursor rules. And then if you're
using cursor in a team or even yourself
regularly, retrieval accuracy gets
better the more you use it with coverage
and freshness. And so all of this
happens in a UX that makes sense, right?
Like I, you know, I use VS Code. I'm
familiar with it. My shortcuts work. Um,
and I make it safe to say yes, right?
Like green for add and red for subtract
makes sense. I can scroll through it.
Um, and it's fast enough that I don't
get frustrated. So my my view is cursor
if it's a wrapper, it's like a very nice
thick perhaps 14 or 15 billion dollar
wrapper, right? It's like if your
burrito was 80% wrap and 20% fill, but
you got to choose the fill and there's
like an empty like an open market for
fill, right? Um, and so where's the pro
where's the value now? It may not be in
the protein. It's kind of in the
company. Um, so like if we try to
generalize that recipe a little bit, if
you are building a generic text box like
unless you're just like learning to do
this, please don't like OpenAI already
one that or it's just not very valuable
to do. So your domain knowledge, your
workflow knowledge can be the bootstrap.
If you already know what users in your
industry need, don't make them explain
it. Uh, build products that show up
informed. They collect and package
context automatically including from
other sources not just natural language
presented to the models use the right
models at the right time now known as
orchestration and present the outputs to
the users thoughtfully right um so I do
not think this is the end of the guey uh
I I think you can capture and enable
workflow with these models and all this
requires taste and a ton of work I' I'd
argue that like some version of this
recipe is much of the work each of us is
going to do so don't listen to the labs
from a user experience perspective The
prompt is a bug, not a feature. I think
it's like a stepping stone. Don't make
me think as a user. The best AI
products, they feel like mind readading
because they are. Um, there's enormous
headroom in building these products. And
I I think that's really exciting because
that's what most of us in this room have
alpha on. Uh, what is a software company
if not a very thick like workflow
wrapper most of the time? That's true in
2015. It's true in 2025.
Um, besides code, where might you go
apply this? We think the opportunities
to build value around the LLMs exist in
every vertical and profession. Uh, but
here's something counterintuitive.
Beyond coding, one of the things that
I've been surprised by is that the most
conservative low tech industries seem to
be adopting AI fastest. We call this the
AI leaprog effect internally. Um, these
are three portfolio companies. Um,
they're working. Sierra resolves 70% of
uh customer service queries for their
customers. They serve people that you
know you guys use like SiriusXM or ADT.
Harvey is you know two years in well
over 70 million of ARR. It's AI is
essential now to being competitive in
the legal industry. Um there's a company
called Open Evidence uh which helps
doctors stay upto-date with medical
research. You have to be a clinician to
use it but you know you give it your
medical ID number and you can do
intelligent search against um uh medical
research uh at the point of clinical
decisionmaking. Today it reaches a third
of doctors in the US weekly and the
average user uses it daily, right? And
so I think there's just examples of, you
know, huge value beyond chatbt. These
are companies that know their customer
and are solving real problems. As a as a
piece of trivia that you may or may not
know, um Brett at Sierra is the chairman
of the board at OpenAI. Um OpenAI was
Harvey's uh seed investor. And if you
know these people are not fretting about
thin rappers like I suggest you don't
either. Okay. Finally, I'll make an
observation. A lot of people are excited
about full automation. Now I'm sweaty
enough. So agents agents agents agents
agents agents. Um but when we analyze
the applications to embed I said you
know it's gone up to 50% you know
doubling a applications for agentic
startups in the last year. Um I I think
some people think co-pilots are
yesterday's news. They want to get to
the endgame, right? Like you know your
colleague and AGI. But in terms of what
works, like the data on what's driving
revenue, uh I think co-pilots are still
really underrated. We see a whole
spectrum of how much automation and I
think the uh Iron Man analogy is still
really great here. Tony Stark's Iron Man
suit augments him, right? He can do all
these amazing things, but could also fly
around on command, could do some basic
tasks without Tony. And my experience
with these companies has been that human
tolerance for failure or hallucinations
or lack of reliability, it just reduces
dramatically as latency increases,
right? Um, so the path of least
frustration today for many domains is to
build great augmentation and then just
ride the wave of capability because we
know it's coming. And so my advice for
many domains would think about like you
know build the suit and you can extend
out to the suit that flies on its own
once Tony or any of us is wearing it.
Um
I'm not going to go through each of
these mostly because I lost time but um
there are a ton of opportunities. We put
requests for startups on our website.
We're interested in a couple different
categories of things. They go from uh um
like just good fit for purpose like the
law is a space of lots of text
generation, right? Um to things that
weren't possible before AI. My partner
Mike will say like this is a really
interesting era of machines
interrogating humans. What can you do if
you can go like collect data on demand
from people? Um we could talk to every
customer, not just the top 5% by
contract value. Um, we could root cause
every alert proactively, right? Versus
like just firefight. Um, and the mental
model is how can you build as if you had
an army of compliant, infinitely patient
knowledge workers. Um,
you know, one aside here is I think
there are many hard problems where like
the basic premise is the answer to them
is not in common crawl, right? The
reasoning around them is not in common
crawl. So um this would be robotics,
biology, material science, physics,
simulation. Um they require clever data
collection. Um probably interaction with
atoms, not just bits. Super scary uh for
a software person, but I think the juice
is worth the squeeze, right? The same
reasoning that crushes math olympiads
can seemingly navigate molecular space.
And I think there are some really
fundamental questions for um human
society that can be answered when people
work on these problems. And uh it's it's
really cool as a machine learning person
to meet people in their at the top of
their field at the intersection of
machine learning and all of these other
areas because like you guys would also
the same architectures apply right and
and that's just um that's really
exciting.
Um
how should we think about defensibility?
Did this advance?
Okay. So, um, one last point and then
I'll conclude here. Uh, some would say
stay out of the weight of the labs.
Don't pick up pennies in front of the
steamroller, right? But I would offer,
um, what I think is an uncomfortable
truth. Execution is the moat in AI. Um,
and that's available to all of us.
Cursor arguably did not invent code
completion. They did not invent the
model. They didn't invent their product
surface area, right? They just
outexecuted on every dimension of this.
They shipped a great experience faster
than their competitors could copy. and
they capture the hearts and minds of
developers at least in this term. Um I
don't I don't mean this to be cruel but
I often get asked about like counter
cases and the importance of first mover
advantage. Let's be brutally honest. In
contrast, like Jasper had first mover
advantage brand. They raised $125
million, but its first product was a
series of prompts and a text box and
like very good SEO. And like you have to
keep running like ChatBT, you know,
crushed the first iteration pretty
quickly. And so, uh, I I don't think
this is satisfying advice, but I think
it is like real from the trenches. Build
something thick and stay ahead. And like
no domains are out of question. Um,
magical AI experiences, they build
customer trust and drive adoption. And a
lot of the data we need to improve these
experiences and the context we need it
is not easily available today. And that
advantage is you know uh open for the
taking and not for the labs.
So I I guess in conclusion I think the
opportunity is early and really massive
like I've made a career bet on it. Um I
I think many of you are. We're in the
dialup era of AI and we're moving pretty
quickly to to broadband. Um, Instagram
came four years after the iPhone. Like I
was was there when Greylock made that
investment. Um, Uber five years. Uh,
Door Dash six, right? So, the truly
transformative companies. They weren't
necessarily the first people to
recognize the changes or the opportunity
is those who reimagine the experiences.
Um, and the game board keeps getting
shaken up. That's the thing that's
different this time, right? It's like
getting a new iPhone that's actually
different every 12 months. And um so you
have like new model release, new
capability breakthrough, you know,
onetenth the cost. And every time the
game board turns, I think there are like
there's an opportunity to to win again.
Okay. Um so I I'll give you one last
sentence and be chased off the stage.
This was not my fault. Um here's what I
really want you to remember. Uh you as
the engineers got the magic first. Um
the anthropic like economic index said
that 40% of use was still coding. That's
not like 40% of the economic opportunity
in the world, right? And so it is the
job of everyone in this room and you
know globally online to be the
translators for the rest of the world.
So I encourage you to build something
revolutionary. Thanks.
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