This is AGI: Sequoia AI Ascent 2026 Keynote

**Pat Grady:** Good morning. How's everybody doing? All right. All right. A little bit better. Hey, thank you all for being here. We really appreciate it. We do t
his as a service to the community because we are living through important times and it's an honor for us to be able to serve as a bit of a gathering p
lace for people to come together. And this is by far the best agenda we put together and by far the best set of attendees. And so we just want to star
t out by saying thank you. We know you all crazy busy. Thank you for being here today. So, we do have a pretty exceptional agenda put together. As per
usual, Sonia in the impeccable outfit, Constantine and I are going to say a couple of words to start. You know, we have we have the privilege of bein
g in the midst of a lot of conversations with a lot of interesting people. And so, once a year, we like to try to synthesize that a little bit and sha
re back with you what it is we've been hearing. And so I'll say a few words of overall calibration and then Sony will say a bit about what we see toda
y and then Constantine will say a bit about what we think might be coming tomorrow. So for calibration, we're going to start by zooming out going back
to these siliconebased transistors which gave this area its name that got built into systems connected by networks that went public in the form of th
e internet supported applications like social media in the cloud eventually showed up on our pockets in mobile devices that today are capable of doing
something indistinguishable from magic which is AI. The reason we like to show this slide, and those of you who've been here in the past have seen th
is before, is because it reminds us that all of these waves are additive. And we sort of needed all of these decades of evolution to have the compute,
the bandwidth, the data, the talent to make the most of this moment. Now, this AI wave is a little bit different in three ways. First, it's the bigge
st wave yet. And that's generally true. But there is something more specifically true about this wave, which is it is the first one that is both softw
are and services. The top row shows the first 15 years of the cloud transition where the TAM for software went from about 350 billion to 650 billion a
nd cloud grew to be about 400 billion of that. The bottom row is what is brand new. This is the services revenue that seems to also be available now.
10 trillion is a conveniently round number. We don't know if it's 10 trillion or 5 trillion or 50 trillion. We do know that legal services in the US a
lone is a $400 billion market. That is one vertical and one geo and it's the same as all of software. So this opportunity is immense. Point number two
, fastest wave yet. I think we can all feel this. What it means is that this white space, and I direct your attention to the AI side of this page, thi
s whites space is getting filled pretty fast. These logos are the companies that got to a billion plus of revenue as a result of the cloud, mobile, an
d now AI tectonic shifts. And at current course and speed, there are more coming soon. Point number three, which is probably the most interesting one
and I borrow this from my partner Constantine is that there are two basic kinds of revolutions in technology. There are revolutions of communication w
hich are about the way information is distributed. Most of the people in this room have only lived through revolutions in communication. The internet,
the cloud, mobile, those are all about information distribution. Those are revolutions in communication. AI is different. AI is this one. AI is a rev
olution in computation. It's about how information is processed. And that might sound like semantics, but these are fundamentally different shapes of
waves. And maybe the most visceral way to feel this is to think about the fact that the floor keeps moving underfoot. The technology foundation on whi
ch everybody builds changes every day when new capabilities come out. And we've had three major inflection points over the last handful of years. Firs
t one chat GP chat GPT moment November 2022 the world saw the power of pre-training. Second one couple years later 01 model reasoning all of a sudden
a second scaling law emerges around inference time compute. Third one, just recently cloud code, nopus45, now 47. The world saw the power of long hori
zon agents. And while these look like three points on a continuum, it's kind of a hard break between two and three. It's a little bit of a discontinuo
us change. And if we may be so bold, we would say that this is AGI. And look, I'm an ecom major. We're venture capitalists. not about to propose a tec
hnical definition for AGI, okay? We study founders and markets and the collision thereof, which is businesses, but we do study businesses. And so from
a commercial standpoint, from a practical standpoint, from a functional standpoint, if you can dispatch an agent to do a job and it can recover from
failure and persist until that job is done, I don't know. That feels pretty much like AGI. Even if you don't think it's AGI, which is fine. By the way
, Sonia will talk a lot more about this in her part. Even if you don't think it's AGI, I think we can all see that the cars have arrived. Last few yea
rs, we've had a lot of faster horses, applications that made you 10 or 40% more productive, but didn't fundamentally change the way you work. Now we'r
e starting to see cars, applications that make you 10 or 40x more productive and absolutely change the way that you work. Change the nature of your wo
rk. Change the nature of your organization. Cars have arrived. This is the founder of Sequoia, Don Valentine. He was famous for asking one question. S
o what? Why does all this stuff matter? What matters is because just in the last few months the race has begun and it's a different kind of race than
what we're used to. The way you drive a car is different than the way you ride a horse. The way you build a car is different than the way you take car
e of a horse. So, it's a very different sort of race. And one of the reasons that we wanted to gather everybody here today is because nobody has all t
he answers. And the more time we can spend together, the more we can learn and hopefully figure out where all this stuff is headed. And it's important
that we do so as soon as possible because there's a lot at stake just from a commercial perspective. There's 10 trillion dollars up for grabs. We got
labs coming at it from a tech out approach. We got startups building on top coming at it from more of a customerback approach. We do have all of the
labs represented in this room, but most of you are building on top. And so we'll take a minute and uh talk about that customerback approach. So our ad
vice for those of you who are building on top of the labs, it's free advice and so it's worth every penny you paid for it. Our our advice would be to
get mad and we don't we don't actually need you to be angry. You can be angry if you want. If that's what drives you, that's cool. Go ahead and be ang
ry. But this is just a convenient acronym for modes, affordance, and diffusion, which are three characteristics or three pillars of a strategy for bui
lding on top of the models. first on moes just for fun. Anybody remember this slide from last year? One person and he's my partner. Okay, that's cool.
Um, we're not gonna Okay, as a reminder, this slide shows the merchandising cycle, which is the links in the value chain required to take something f
rom an idea to a happy customer. We're not actually going to go through the links in the chain. The point that I want to make here, if you approach th
ings from a techout point of view, each link in the chain gets approached a little bit different. If you approach things from a customer back point of
view, each link in the chain you approach a little bit different. Now, here's a part that's counterintuitive. In a revolution of computation, which i
s about information processing, what you want to do is look down here because there's cool new stuff coming out all the time. What you should actually
do for the sake of building modes is look up here because your customers are not changing nearly as fast as the capabilities are changing. The things
that you build might be irrelevant tomorrow. The degree to which you wrap yourself around your customers is going to be a bit more durable. That's no
t to say that product and technology is not important. It is insanely important and generally speaking, best product wins. But in a world where produc
t changes so fast because capabilities change so fast in thinking about modes, we would encourage you to go as customer back as possible and think abo
ut all the ways you can wrap yourself around those customers. Okay. The A in MAD stands for affordance. This is a term that we borrow from the design
world. A hammer is an object that has a force. I have a 2-year-old son. If I gave him a hammer, he would know what to do with it. He would grab you, s
tart hitting stuff. It's why we don't give him hammers. Okay? An object with affordance is one that doesn't need to be explained. People just know wha
t to do with it. Cloud code is insanely powerful. Go open up a terminal for the average Fortune 500 employee and see how far they get. While it is pow
erful, it does not offer that much affordance. That's not a knock in on anthropic, but it is an opportunity for anybody who wants to build on top and
to create paths of least resistance for your specific customers and their specific problems so that it's just braindead simple for them to figure out
how to get to the outcome that they need for their business. That's the concept of affordance. And then finally, the D in MAD is diffusion. And the di
ffusion gap is the opportunity for companies building at the application layer. The rate at which capabilities are diffusing out into the market is fa
r shy of the rate at which those capabilities are being created. And every day that the foundation models move faster than your average Fortune 500 en
terprise, that gap gets bigger and that opportunity gets bigger. So for modes try to think customer back for affordance try to think about creating th
ose paths of least resistance for your customers and that diffusion gap that represents your opportunity. Unless that slide from earlier with the whit
e space starting to fill up was discouraging for anybody. May we remind you that no lead is safe. There's this expression in racing. You cannot pass 1
5 cars in the sun, but you can pass 15 cars in the rain. And right now, there is a torrential downpour of new capabilities coming out of the foundatio
n models, which means that no lead is safe. But it also means that anybody can win. What a time to be alive. And with that, I'll hand it off to Sonia.

**Sonya Huang:** Thank you, Pat. And can I just say it's so nice to see so many friendly faces in the audience. There is an exceptional group of people here today and
I'm just really happy to be part of this ecosystem with all of you. And so the purpose of my section is to talk about what's happening in AI right no
w which for 2026 is agents. Okay, flashback to 2022. Show of hands. Does anybody here remember AutoGPT or baby AGI? Okay. Okay. So these projects were
overnight hits on GitHub. And what they did was they took GPT3, gave it some tools, wrapped it in a loop, and let it run towards a goal. And it was p
romising until you watch those agents just fail over and over and over again. Kind of cute, kind of endearing, but completely useless. And I put this
slide here to remind us that, you know, we all knew agents were coming. Uh we could have seen it years ago, but back in 2022, the models just weren't
ready yet. Fast forward to today. Something around the turn of the year really changed. Suddenly, we have agents everywhere around us and they seem to
actually be working. Two agents in particular have been home runs. Cloud code for the technical crowd and OpenClaw and all of its lobster brethren, u
h, which democratize agents to anybody with a phone. And so whether you are a hardcore engineer or a normie, the punch line is that anybody can create
agents now. And so what we're seeing is people are building agents for everything. There is silly stuff like an open call agent that will literally s
nitch on your neighbors for tax fraud. Please don't do this. Or actually maybe please do this. Uh there's entrepreneurial stuff, agents running genera
tive media campaigns to sell construction services. And then there's the professional layer. I can tell you there's a huge race internally at Sequoia
for who can build the best agents to do our jobs better. So what does it mean to be an agent? Here is one possible definition. An agent is a system th
at perceives its environment, chooses actions, and progresses autonomously towards a goal. By the way, guys, I made this in C dance by myself. I'm I'm
very proud of it. The video models have come a long way. Uh and more specifically, I view agents as having three functional components. First is the
ability to reason and plan. Uh this is the baseline level of intuition uh and the abil ability to think on the fly. Second is the ability to take acti
ons. This is tools, search, write, compile. And then finally, the ability to iterate towards a goal. This is the persistence that gives agents the abi
lity to accomplish things over long time horizons. And so agency combines these three things. It is simply the ability to get done. If we boil agents
out into their constituent components, the models, the tools, the harnesses, each component has progressed rapidly over the last year. First, the mode
ls are the brain. This is the most important thing that's happened. The meter chart measures uh how long a model can sustain progress on a complex tas
k without going off the rails. And we've gone from the order of tens of minutes a year ago to the order of hours today. And so this is this is the mos
t important thing that's happened. the models are finally getting capable enough to sustain per performance on long horizon tasks. Second, the tools a
re the arms and the legs. These give models access to things that make us productive on a computer. The terminal for file systems and dev tools, iMess
age, Slack, uh web search, computer use, you name it. And the last two decades that we spent building tools for humans have ended up being uh able to
port over to be incredibly useful for for agents as well. And there's a common refrain that SAS is dead. I think to the contrary the value of these to
ols is going to explode as the number of agents using them increases. Models and tools give agents capability. The harness is what gives them persiste
nce. The ability to stay on task, adapt, and keep going. And that feedback loop is now really starting to crank, especially now with reinforcement lea
rning. We're giving these agents uh we're taking them to driving school, giving them uh you know, training them uh in RL gyms, and we're pushing perfo
rmance in different settings from mechanical engineering to design to finance. We're also seeing the early glimmers of self-improvement or the machine
building the machine. uh for example, Andre's other research project uh improves research autonomously towards a GPT2 level model in just two hours.
So what does a world of agents everywhere look like? Agents exist on a sliding scale of agentness. Um and so let's take coding as an example. In 2023,
we had tab autocomplete. One AI assisting a human in line. This was incrementally useful, fundamentally not transformative. We now have agentic devel
opment. One human talking to an agent, instructing it what to do, maybe managing a team of agents. But this paradigm is getting pushed further. We're
now seeing background agents, async agents, agents spawning sub agents. We think that async agents in this whole paradigm is likely to overtake the cu
rrent paradigm in volume just because the amount of leverage in the system. And then finally pushing the bleeding edge of the frontier, what I call da
rk factories. um taking human review out of the system completely. This sounds crazy, but I've seen it happen in production, including with cyber secu
rity companies. It is possible with good enough guardrails and good enough engineering. So, we're progressing up a scale of agenticness. agents are go
ing from little helpers that do a little amount by your side to interns that need to be managed to interns that manage themselves and eventually to in
terns that can be trusty enough to p to push to prod without oversight. And so that's the evolution that's happening not just in coding but across all
of agents. Uh the most important takeaway uh for the founders in this room is that services is the new software. Pat's been saying this for as long a
s I've known him. And our partner Julian, who's in the audience today as well, uh, published a great article on this. And we've known this for a long
time, but I think it's actually happening. So, in medicine, you're able to hire an agent that inspects your genome, gives you personalized recommendat
ions, can prescribe you medication, uh, recommend you clinical trials. In law, you'll be able to hire agents that can negotiate contracts on your beha
lf, even perform litigation, and settle for you. in math and the sciences, we're seeing agents that can solve Airdos problems or discover new supercon
ductors. Like, how thrilling is that? Or in uh in the consumer world, personal agents that can manage your inbox for you, your calendar, your finances
, uh file your taxes. And we expect there's going to be agents everywhere. And that's in part because hiring agents is so much easier than hiring empl
oyees. Humans are hard to scale. Infinites are infinitely scalable with compute. Humans are hard to keep happy. Except for me, I'm always happy. Uh ag
ents are low maintenance. Uh humans are uh humans are expensive. You pay them salaries. Uh you pay agents tokens. Generally, it costs less to accompli
sh a task with tokens than the equivalent in salary. Today, humans are still generally smarter, but the bitter lesson presses on and soon agents will
be smarter at many things. And so, the point of this slide is not that we humans are out of a job. Uh I think a uniquely human trait is adaptability.
But we do expect the deployment of agents across the application layer to be swift and at an unprecedented rate in scale because the economics are so
clear and because of the inherent scalability of fits. So if you add all this up, the number of agents is ballooning on some sort of exponential, mayb
e a super exponential. And I think we're about to hit the point where things get genuinely strange. Uh what happens when commerce happens between agen
ts? Can they pay each other? What happens when agents can actually negotiate the terms of a transaction with each other? Are we going to have swarms o
f agents policing us, preventing things like cyber security or megadan? All we know is the world is getting weird extremely quickly. And so I'll close
by channeling my in my inner Beneserate. Uh long horizon agents are here. The curve that they're on is very clear. And for founders, I think everybod
y has examples of people that are accomplishing insanely hard timelines thanks to AI. So, Nathan from Zed accomplished a three-year moonshot project o
ver the holidays by himself with cloud code. Uh Brett Taylor rebuilt Sierra over a weekend. Um the notion team rewrote 8 million lines of code in just
six weeks. And so everybody has these examples of compressed timelines, but I think very few people outside of the AGI labs have seen what happens wh
en you take these compressed timelines and you stack them on top of each other. And that's what's possible now. And so whatever you can imagine buildi
ng over the next hundred years, we think is now possible in a 100 days thanks to agents. I will pass over to Constantine.

**Constantine Buhayer:** Thank you, Sonia. All rig
ht. Thank you so much Sonia Pat for the brilliant overview analysis. In this section we're going to talk a little bit about what's next. Uh so the goa
l here is we all know we're in the AI age. What's it going to look like? What's it going to feel like? How is it characterized? Earlier in the present
ation, Pat bifurcated technological revolutions between comput and communication. We're going to do another bifurcation here for types of work. There
is physical work. This is a package on the Pony Express. This is a satellite on a Falcon 9. Work equals force times distance. Physical movement. And t
hen there's cognitive work. This is Pythagoras coming up with this theorem. This is deep mind solving the pro protein folding problem, conscious think
ing. These are very different types of work, but we believe that they're going to follow a very similar pattern in revolution. So let's talk about phy
sical work because we've been through this revolution with the industrial revolution. For the vast majority of human history, all the work or virtuall
y all the work for serving humans was done by some sort of muscle. People or animals. People moving something or an animal pulling the human along. Th
is starts at 1700, but it goes back millennia. Then things started to change. Water and wind, steam engines, and then things accelerated. Steam engine
s, combustion, electric motors. Today 2026, you could estimate that 99 plus% of all the physical work done on planet Earth for humans is done by a mac
hine. The plane that brought you here, the manufacturing of all the goods in this room, all the transportation that sets up for the pinnacle of the hu
man experience you're having right now. Well, we think a similar pattern is going to happen in cognition. We're just a little earlier on. So, for most
of human history, all the thinking on planet Earth for humans was done primarily by humans, maybe a little bit for animals, the the sheep dog chasing
the sheep, right? And there was this sliver on top of mechanical work, the astrolabe or the clock. Now over the past couple hundred years there was n
ot a lot of progress until electronic computation and in the past hundred years think about all the trillions of calculations that are happening at an
y given moment to serve you the human. All of that work all of that cognitive work that's happening to serve us at any given moment. Trillions of calc
ulations. We believe that the neural network is the next big wave and that in the near future 99.9% of cognition on planet Earth will be done by machi
nes. Well, the parallel is pretty stark. And the good news is we've been through a revolution like this. The cognitive revolution is going to be a lot
like the industrial revolution, just much, much bigger and much faster. So, what's it going to be like living in this future? I'd like to share some
motivations for this future in the form of four short stories. The first story. In the mid 1800s, America wanted to build a grand monument to our firs
t president and our greatest war hero, George Washington. So, we designed the tallest building in the world at the time, the Washington National Monum
ent. And we wanted to cap it with the most precious metal in the world, 100 ounces of the most precious metal in the world. So precious in fact that w
e put it on display at Tiffany's in Manhattan. That metal was aluminum. Within decades of the completion of the Washington National Monument, a young
inventor came up with electrolysis, the process of separating aluminum from dirt. And within decades, aluminum was used to wrap our candies and our sa
ndwiches. and then tossed into the trash. Aluminum is intelligence. Electrolysis is artificial intelligence. We're about to enter a world where some o
f the most precious skills that took decades to earn, PhD level skills, are so instantly invoked that right after using them, you can crumple them up
and throw them right in the trash. Story number two, we are entering a world of alien design. The world as we see it today is all about design for hum
ans. You know, it's it's been optimized in a way that makes sense to our brains because we are doing almost all the cognition in the world. Well, when
machines do the cognition, it's going to be a little different. In 2006, NASA was optimizing an antenna for a large space mission, satellite space mi
ssion. And traditionally, their antennas looked like this. It was a beautiful geometric symmetrical pattern that optimized surface area for some power
constraints. This time around they said we're going to hand it over to computer and we're going to have an evolutionary algorithm a lot like reinforc
ement learning. The result this antenna right here dramatically more productive not intuitive to the human mind. In this AI era, when we hand over cog
nition to machines, we're going to get results that are not intuitive to us. When AI is designing chips, cars, buildings, they might look dramatically
different. The world that we enter into, we have to be open-minded because the AI is not going to think like us. It's going to have alien design. The
third motivation story is on emerging sciences. Not emerging science. We all know there's emerging science. I'm talking about emerging sciences. In t
he early industrial revolution, you had great engineers like Newman and Watt and they perfected combustion engines. Basically put a petrochemical into
a piston, ignited on fire, millions, billions of particles explode. move the piston work. For almost a hundred years, all of that was tinkering. It w
as an engineer saying, "Ah, that works a little bit better." Maybe something you could see see like a scaling law, but it was engineers playing with t
he product and seeing how they can improve it a little bit. over 120 years after Sadi Caro came around and formalized this in a new science thermodyna
mics. He said, "Wait a second. There are millions or billions of particles. We can actually formalize what that all looks like." In this case, there a
re billions of neurons, trillions of tokens. Right now, we're in the tinkering phase of AI. Even if we think it's an understood science, it's not. In
the future, we will have a science as fundamental as thermodynamics introduced in the next couple decades. Someone in this room might come up with tha
t science. And that science will be taught in high schools. It will be that fundamental. And it will help us master AI. It will even help us master co
nsciousness. Fourth story, the art of unreason. So for the vast majority of human history, tens of thousands of years, art has been a progression towa
rds realism, this is uh cave painting from about 25,000 years ago, Egyptian hieroglyphs, Greek pottery, uh Renaissance paintings, uh a grand transform
ation toward realistic art. Just look at the difference. Over tens of thousands of years, the triumph of humanity, and then engineering came along, th
e Dogger type, early photography, and all of a sudden what was spent decades of life to perfect the skill of getting every brush stroke perfect gone.
So, how did the world react? They thought that painting was over. Oh, that's it. The machine can do it better than any human. Art is art is ended. Wel
l, what happened? How did humans respond? Humans responded by saying, was the purpose of this art to capture the moment in the way the eye sees it or
was it to capture the moment in the way the heart and the soul sees it? Impressionism, expressionism, cubism, neoexpressionism. All these new forms of
art are how humanity responded to this dramatic change in science. 2500 years ago, Greek philosopher Pagoras wrote, "Man is the measure of all things
." What he meant is that nothing in a vacuum has value to humans. Not aluminum, not art, not intelligence. It only has value because of the experience
. AI can do the work. AI will do the work. But only the human connection can give you a reason to care. That's why we're all in this room today. A dec
ade from now, work is going to be dramatically different. Things are going to change so much. But the one thing that will be constant is the relations
hips that you form today with the person right next to you will endure. That's what you're going to look back on. That's what's going to be valuable f
rom today. So, I encourage you to form those relationships with the people next to you, enjoy your time together at this AI ascent, and really lean in
to what makes us most human.