What's it's going to mean to to be
[music] human like 50 years from now?
>> I think the world is finally taking
seriously, and we've been waiting for
this for years, that social contract is
going to have to evolve and maybe sooner
than people thought.
>> What are your predictions for when this
breaks [music] 85%?
>> Yeah, I think it really depends how much
effort the frontier labs are putting
into targeting Arc AGI 3 explicitly.
With the new set of models that I think
will come out, the impact that [music]
the technology will have on accelerating
science and accelerating the economy. We
may be wrong, we may be over optimistic,
but it feels like it's about to be
absolutely massive. There are a lot of
interesting projects, [music] and yet
there's this one new thing that's really
working.
Hi guys, I'm D, a partner at Menlo
Ventures. I'm one of the early investors
in India. And we are investors in
Anthropic.
So
not to say that will
color the discussion at all.
Um
but yeah, let's get started. Um
The first thing I wanted to ask you guys
is, you know
both of you are parents,
not just
founders and CEOs of companies.
And today when I go and talk to a lot of
parents
they're quite concerned about their kids
and the future.
As as fathers
what do you think about the most when
raising your kids today? Is it any
different from before? What really
matters to you when it comes to raising
your kids?
Maybe we'll start with Francois.
Yeah, and it's in my mind a lot. It's in
my mind a lot. It's like clearly they're
going to grow up in a world that's very
different from the world in which I grew
up in. It's expensive child. And yeah, I
think about it like the obviously their
their relationship to technology is
going to be completely different. You
know, they're they're they're going to
grow up in this Star Trek world where
they can just, you know, talk to a
computer and and have the computer do
things. Uh while back in my day, you
know, we had we had to write code like
character by character.
Um yeah, and so actually I'm a I'm a bit
concerned as well because, you know, I'm
I'm wondering what's going to happen to
human human agency. And and and our
ability to you know think independently.
Like are we are we just going to
outsource our minds? Like, you know, our
our thoughts, our cognition, and all our
abilities to the machine? What what are
we going to retain? You know, what
what's what's it going to mean to to be
human in like not in 10 years from now,
but like 50 years from now? Like my son,
my daughter, they're they're they're
going to live to see the the 22nd
century, right? That's that's that's
something I think about a lot.
What about you, Sam?
You know, actually
I think it'll be great. I I understand
that throughout history parents have
always freaked out about the rate of
change, wondered how life is going to be
for their kids.
I think it is awesome that our kids are
going to grow up in this Star Trek world
where you just ask a computer for
anything. Uh
and it does it, and I think they will
you know, look at us like I kind of
looked at my parents with this
like, "Oh man, you really had to do
things before computers?"
Uh and my parents worried that I wasn't
going to learn anything either cuz I was
just going to look everything up on
Google. It turns out I was able to have
a fulfilling career of a kind they
hadn't
they didn't have a model for. And
I assume that
our kids will have the same experience
and they will do things um
that absolutely astonish us.
That they will look back on us with look
back on our lives with some like
pity is maybe too strong of a word, but
man, you all had it hard and bad and
miserable. And this the quality of life
will be so incredible. Um
I I know actually I think all those
things.
I think it's amazing that
my kids will
never
be smarter than a computer, and they
will never expect that a computer can't
do these incredible things, and they
will they will achieve and they will
grow up expecting to achieve
these amazing
feats, and I think they'll have more
agency and more will than any of us do,
and they'll like they'll grow up
expecting that. It won't be weird. Um
and they'll develop with that way in the
same way that we developed with the
computers and
thought of all the things we could do
that our parents didn't think of.
Emotionally
I'm like
oof.
You know
what's this really going to be like?
Do you think just a quick follow-up on
that? Do you think in that world, you
know, when we grew up, intelligence
probably mattered a lot. You know, our
parents probably were like, "Be smart."
You know, you guys went to some pretty
good schools, you learned a lot. Do you
think that still continues to matter in
this world or not so much or maybe a
different kind of intelligence? What are
your thoughts?
So intelligence is really the ability to
adapt, and the the more rapidly the
world changes, the more important it is
to be able to adapt to it. So
personally, I trust my kids that, you
know
they will see a level of change uh
technological change and social change
that I haven't seen in my life, but I I
trust them to adapt like kids kids are
the most adaptable ones in the world.
I'd love to like get a peek into your
into your lives. Like what's something
that's happened in the last week or two?
Like a small anecdote you could share
that just tell us about tell us about
what goes on behind the scenes.
Sure. I mean I can yeah. [laughter]
So what one thing I've I've been doing
for the last few weeks with my son. So
my son is a huge Minecraft fan. Like
he's he's he's turning five soon, and so
that's it's playing so much Minecraft
and watching Minecraft videos on YouTube
and so on. And what I've been doing is
that I've been
live coding effectively
sort of like simple
pretty future complete Minecraft clone.
And in fact using Codex, so thank you
Sam for that. Um
and it's been it's been so fun. And
basically so my son is telling me, you
know, what what what he wants to see in
the game, what features he wants, and
he's he's pretty creative about it. And
then I tell Codex to do it. And I
haven't I haven't written a single line
of code. So it's it's it's it's been a
very fun experience.
What about you, Sam?
Um
I I think one interesting
like I'm very grateful to get to work on
so many interesting things and
built my kids can't build the Codex yet,
but we can, you know, put blocks
together and stuff. Um that's the most
fun, but the most kind of intellectually
stimulating thing is
I think the world is finally taking
seriously
and we've been waiting for this for
years, that the social contract is going
to have to evolve and maybe sooner than
people thought. And it finally feels to
me like there is a moment where in many
different countries, many different
political parties, many people who
normally
would think very differently about how
the economy should work and how power
structures should be developed
there is a real
finally, I think it's real, desire to
sit down and say, "Let's start talking
about ideas. Let's not just keep saying,
'Man, stuff is really going to change
and this is really going to be a big
effect on the economy.'" But
the Overton window the
interest
sort of willingness to get to work on
this with the people that can actually
make it happen. This last week, few
weeks, whatever, first time I believe
like, "All right, maybe the world is
really going to do this." Um and that's
been an exciting development. That's
awesome. That's awesome. Let's let's
move into some AI questions. This is a
question I just have to ask. I know Sam
probably just saw this benchmark.
Francois, you probably have a good idea
of this or at least a prediction.
You know, Arc AGI, Greg covered this,
right? Remember 2019, 5 years later, O3
preview crushes it, 88%. Arc AGI 2
launches about a year ago.
Um right now I think the top score is
Gemini Deep Thinking 84.6.
Today Arc AGI 3 is 0.37.
What are your predictions for when this
breaks 85%?
Yeah, I think it really depends how much
effort the frontier labs are putting
into targeting Arc AGI 3 explicitly. Um
like for Arc 2 for instance, a lot of
the very fast progress we saw in the in
the late 2025 was really because, you
know, there was just so much effort, so
much compute that was thrown at
the benchmarks.
Um
and I think if if the the frontier labs
decide that they're just not going to
look at Arc 3 whatsoever, they're not
going to they're not going to target it,
they're completely they're going to
completely ignore it, they're not going
to let the scores increase organically
just as a function of the G of the model
increasing, I think in this in this
scenario it would take several years to
saturate Arc 3. Uh but what's actually
going to happen in practice is they
they're they're going to throw a ton of
compute at it. Um and uh I would say at
least a year until saturation because
uh it's it's significantly harder than
than Arc 2. Uh we were deliberately
trying to make it, you know, like out of
distribution. Like the concepts, the
dynamics you will see in the in the test
environments are very different than the
ones that you see in the in the public
in the public games. Uh it's going to be
much much harder to put forth, for sure.
So at least a year. Thankfully, we have
a big lab CEO right here. So I guess
this is a kind of freebie question, but
does Arc AGI 3 matter to you, Sam? And
uh
how do you think about Arc AGI 3?
Um yeah, of course. I I think any
benchmarks that are interesting and real
and not currently saturated matter to
us. Um
there aren't many left.
I I mean there are metrics about
discovering new science. There are
benchmarks about discovering new
science. There are benchmarks about
economic acceleration. Those I think
really matter. But there are not a lot
of benchmarks in this sense.
Uh so of course it matters. Uh I thought
that was a very good and very nuanced
answer, which is how long it takes will
depend how hard how much people target
it. Um I hope people don't target it
that much, but I'm sure, you know,
people like scoreboards and they will.
Um
is
AGI wasn't supposed to be numbered.
You know, in the way that people first
talked about AGI, it was you have AGI or
not. You're not supposed to have an AGI
one, AGI two, AGI three. And
and yet
we learn how hard it is to precisely
define what AGI is and how we're going
to test for it. And I bet there'll be a
four and a five and then a ASI one and
ASI two and then it'll keep going. We're
working on it. There we go. Um
but I think the main thing that points
to is not
that this team doesn't do incredible
work, which obviously they do, but
the goal posts move.
AGI is hard to define and great
benchmarks are hard to make.
Another benchmark that's talked about a
lot that sort of has no ceiling is this
uh METER benchmark, which is how long of
a task can an AI agent autonomously do
with some success rate. So, right now
um there's 12 hours with a above 50%
success rate. There's a model called
Opus that does that. What do you What do
you think of that benchmark? Like, is
that not valid? How's that different
from ARC-AGI? Francois, any thoughts? Uh
I like METER. I think it's good signal
in it.
Um
it's also a bit of a noisy signal. Like,
I wouldn't take the claim that uh
current AI can do like 12 hours of
autonomous work. Um and I mean, as a as
a counter claim, I mean, look look at uh
any Oxbury environment. It doesn't take
12 hours to to solve one of them. It
takes uh about 5 minutes like 5 to 10
minutes for a human. And uh current
functional models cannot do that. So,
we're not I don't think we're at 12
hours over arbitrary tasks. So, if you
look at METER, I think uh some of the
issues is if you're looking at longer
time horizons, and it's definitely the
regime that we're in now,
uh it's less reliable because they don't
have a lot of tasks for for for long
time horizons. So, it's small sample
size. Uh they don't have very good human
baselines as well.
Um so, yeah, let's let's not let's not
just take it at face value. A big
difference between uh their approach to
benchmarking AI and our approach is that
we are really targeting general
intelligence. And I feel like METER is
more of a domain-specific or even
task-specific benchmark. Like, for
instance, uh they're benchmarking the
hardness. I mean, if you start learning
hardness, it means that you're expecting
a certain kind of task. Uh your system
is already task-specific like it's
domain-specific. And they're heavily
indexing on uh software engineering. And
meanwhile, we're really trying to uh
benchmark the the the G like the the
team in in AGI. Um so, in principle, you
know, if you have AGI, uh it should be
able to look at any benchmark and it
should not need any harness. Should not
need, you know, repurposing uh with a
human in the loop for the new task.
Should just, you know, go and do it the
way humans do.
I I think it's a interesting thing to
measure for sure, but I think it's
inadequate in a few different ways that
are very important to understand. One is
that as these tasks get to five-year or
50-year tasks, it's like very hard to
evaluate and we don't want to wait five
years and
you know, that's going to be
weird. And then the other thing is
there are things that I could work on
for years and not solve and that a
genius mathematician could work on for a
few minutes and solve easily.
There are things that no one on Earth
can do
or haven't been able to do yet without
the help of tools, no matter how much
time they have, that we'd really like to
get done.
There's something that
matters about um
like, you know, can we discover new
science that humans have not been able
to discover and whether that takes a
huge amount of compute and a small
amount of time or a small amount of
compute and a lot of time,
like we want the new knowledge. And very
soon, I think we'll be talking about
things like that. Can we Can we
do things of real value to people? New
new scientific knowledge, new economic
acceleration. And
you know,
time on task is an interesting but very
perfect proxy for that.
Thanks. Um guys, it would be remiss if I
didn't ask you about, you know, both of
you are a part of companies that have
sort of different approaches to
achieving this goal. So, namely in up
the OpenAI world, at least what is
publicly known is, you know, you
pre-train a big model, you do some
post-train based on RL data, and you get
the model to be really good. And it's we
had reasoning happen along the way. It
sounds like we got really, really good
just doing that. And that paradigm has
has worked so far. Obviously, and uh
you have a slightly different approach,
Francois. Um you want to do deep
learning based on program synthesis. So,
maybe the way I'd want to frame this
question is, Francois, can you talk a
little bit more about your approach? I'm
sure more of the audience knows less
about the approach and uh I'd like to
compare and contrast that to sort of
Sam, what you're seeing at OpenAI.
Sure. So, you know, when I mentioned
program synthesis, lots of people ask
me, "Is are you doing like coding
agents? Are you doing code gen?" And
that's not at all what we're doing. Uh
we're not like adding a new layer on top
of the the other end stack. We're
actually trying to rethink the
foundations. So, we call our approach
symbolic learning.
And it's basically uh a new branch of
machine learning. Like, if you go back
to uh
the the machine learning problem in its
simplest form, you have some input data,
you have some target data, you're
looking for a function that will map the
inputs to targets and hopefully it will
generalize to uh new inputs as well.
Um and if you're doing parametric
learning, uh which includes deep
learning, then your function is a
parametric curve and you're trying to
find the right parameters via uh
gradient descent.
And
uh all of this all this picture is
basically what we're doing. What we're
replacing the curve with a symbolic
program that is minimal. It's like the
shortest program that can accurately
explain the data.
And when you're doing this, you're much
closer to uh
producing a form of optimal machine
learning like optimal learning. Uh
and uh there are a few consequences when
you when you get closer to optimality.
Uh if your model is genuinely uh the
simplest possible to to to explain the
data, then it's also going to be the
model that will be capable of
generalizing the most strongly. Uh it's
going to be the most compute-efficient
at this time and it's going to need uh
the least uh data points uh to find that
model because, you know, if if the model
is too biased, you don't need you don't
need
uh trillions of tokens to find it.
Um so, that's basically the story. We're
trying to rebuild machine learning from
the ground up on top of uh a symbolic
substrate that would bring us much, much
closer
uh to optimal AI. And eventually, you
know, we're going to rebuild the entire
stack. Like, I expect in the future
we'll have
uh very large-scale foundation models
that will be symbolic in nature. We want
our approach to be as scalable as
compute-efficient as deep learning.
Sam, what do you think about that
approach? And, you know, OpenAI
obviously takes a very different
approach. Is that going to continue to
be the case?
How do you think of different ways to
get to this amorphous somewhat amorphous
concept of of AGI? What do you think?
I I think that is a great thing to try.
I think that people should try many
approaches.
We
we try to bet with conviction on ours,
but we could very easily hit some sort
of unexpected roadblock and I think
you know, when we started with ours, it
was very unpopular and turned out to
have some value and uh I'm sure other
people will start out with things that
Well, this one isn't even very
unpopular. Uh this one is a great thing
to try. Uh
our approach is to push on the paradigm
we understand and try to build towards
uh an aligned AI researcher that can
discover whatever remaining stuff is to
discover.
Um
and
you know, we like seeing promising
signs, but still a ways to go.
There's a specific question I got from
Twitter for about your timeline
specifically for AI research, Sam. You
at some point said automated AI research
interns would be done by September 2026
and fully automated AI researchers by
March 2028. Do you have an update on
those timelines or is that still
>> say would be done. I said it was our
goal. Uh but I feel like we are on
trajectory for those goals.
Makes sense. So, good. I also want to
ask a question that's come up to me a
lot, which is
so many people in the research world
specifically feel like they've just been
it's it's been dominated by AI. And
they're really looking to people like
yourself to say, "What are the kind of
research directions that are less
explored that you'd you wish people
would do more of?"
Almost like call for research. Do you
have anything top of mind that you
think, "Yeah, I wish more people tried
these approaches or tried more
experiments in this direction?"
Francois, do you have any thoughts?
>> Like, outside of AI?
No, uh inside of AI different than the
current models or the current approaches
that we're taking in in some of the
labs.
Or it could be outside AI if you have
something interesting. I think there are
so many directions
uh that people have not tried or not
tried seriously yet. And in in a sense,
you know, I believe compute is a is a
great equalizer. Like, if you throw a
ton of compute at one approach, you will
see some really cool stuff happen.
Famous rule of uh evolutionary
algorithms in Pascal.
Uh I think it's been very underexplored.
We have not tried to scale them, but we
could. I think we would see some really
cool stuff. Uh that's true of uh program
synthesis and that's precisely what we
are trying to scale right now.
Um so, yeah, these these two I think are
pretty good candidates, but in general,
they are just
they're just this infinite supply of uh
novel and cool ideas that could work
maybe. And in fact, probably would if
not work, at least you would see some
really interesting results if you were
to to pursue them seriously.
Sam, what do you think?
There was a time after right after we
had put out GPT-4, where I was having
dinner with one of the great OpenAI
researchers and he said, you You the
thing that is most sad to me right now,
there're all these brilliant people in
academia, there're all of these great
ideas to try.
Everybody just wants to reproduce GPT-4,
which at the time was, you know, still a
difficult thing to do. And he's like,
there there's this incredibly wide
space. How do we convince every academic
department around the world to stop
saying like, you know, we just need to
we just need to train a big model in the
in the same style. And
we never found an answer to that, but I
think finally people are saying again,
let's try some of these very different
ideas.
Uh, I'm not sure what's changed there,
but it does feel exciting that that's
happening. However, I will give an
answer outside of AI, which is
for the first time in the last few
months, I think you can use the latest
frontier models
hugely
accelerate some other kinds of research.
And, you know, like AI research has no
lack of smart people working on it and
no lack of funding and I think it's
great. But
the stack that
other directions of research, you know,
physics, mathematics, bio, whatever
you'd like, that have been difficult to
make fast progress and all of a sudden
feel like they might get burst open and
all these factors,
um, that feels very underexplored
relative to the potential.
Makes sense. I have one last question
for you, Sam, and then we can do the
rapid fire. I know you have a hard stop
at 7:30. Um, and let me ask you the
OpenAI question, Sam. Um, OpenAI is
doing a lot of different things, right?
So, you have different modalities, you
have Sora, you have Operator, Atlas.
There's a bunch of different experiments
you have run and I think everyone's
grateful that you're you're running all
these experiments. Um, but very recently
you said you'd shut down Sora. Um, there
was a leak about this new pre-train that
you have and you want to focus all of
your effort into sort of coding and and
the enterprise. Um, can you sort of
add some throw some color on that and uh
talk a little bit about what are
OpenAI's top priorities?
Um,
with the new set of models
that I think will come out, the impact
that
the technology will have on accelerating
science and accelerating the economy.
Uh,
I mean, we may be wrong, we may be
over-optimistic, but it feels like it's
about to be absolutely massive. And we
think that will be the most important
area for us to dedicate our compute, our
resources.
Um,
I think things are going to get pretty
heated. Uh, and we
I mean, I think
there's many great things but to do, but
several times in OpenAI's history we've
said, you know,
there are a lot of interesting projects,
and yet there's this one new thing
that's really working.
Uh,
as happened in the original GPT days, I
want to shut down a bunch of other
projects to concentrate our compute. And
it feels like it's really time to
concentrate our compute. I mean, to give
like an obvious example, if we will be
able to put
a million GPUs towards curing all
cancers or a million GPUs towards making
videos, I love the videos, I pick the
cancer research. Um,
and as we look at the next couple of
years, I think it is a time, like we've
had a couple of times in our history, to
say, "Okay, we see a really big thing on
the horizon.
Like, let's point all of our firepower
at it."
That makes total sense. All right, let's
do the rapid fire. Let's start with AGI.
This is our AGI launch after all.
I want you to answer on a scale of 1 to
10,
how far are we to your definition of
AGI? One being nowhere close, 10 being
probably going to happen this year.
Friends, why don't we start with you?
Are we talking a linear scale?
>> [laughter]
>> And it's linear.
Let's let's I'll go with with five. I I
I think so.
Obviously, we have models now that have
like genuine fluid intelligence.
Uh, they are intelligent uh in hence
they're not human level and it's
actually still we're still in this
regime where I'd say easy to find things
uh that are, you know, easy far far from
normal people or from average people.
Uh, you don't have to you don't have to
look for super geniuses to beat the
models. Uh,
and uh and you know, things that are
basically impossible for current AI. So,
we're still in this regime where it's
not hard to find these tasks that humans
can do, AI cannot. I think as we move
closer to AGI, it's going to be harder
and harder.
And at some point, you know, uh it's
it's going to have to be like, you know,
PhD level uh tasks only and and and it's
and it's going to be very very short
list of times it's going to be very
difficult to come up with them. And
that's the key when we have AGI. Makes
sense. Sam, what do you think? It feels
very close to me. I think the only thing
I'd say is really missing is some sort
of continuous learning or long-term
memory. Uh, but other than that, it
feels very AGI-like to me already.
Awesome.
What is one product or experience that
you've tried, other than your own,
that has sort of blown your mind, AI or
otherwise, in the last month or so?
So, it's probably um as I commented to
me, it's a coding like Codex and Code
Code. It's really really impressive and
really useful as well. That's awesome.
Sam, what do you think?
>> These magnetic tiles for small children.
Every face connects to every other face.
Okay. I had to like cut one open to see
how it worked. Um, but kids really love
them, they're awesome. What do you know
what they're called?
Magna- The tiles, they're little cubes,
like a full cubes so that every side can
connect.
>> going to say they're sold out after
this.
>> [laughter]
>> That's awesome. Um, okay.
What is one industry or asset class that
you are super bullish on and super
bearish on in the next 10 years?
I'm very bullish on hardware as
especially uh robots and drones. I think
that's going to be more and more
important, you know, as AI capabilities
advance, we're going to want to put more
and more of these capabilities into the
real world and we're going to need a
support for that. We're going to need
robots, we're going to need drones. So,
I'm very bullish on drones.
Uh, very bullish on hardware in general.
Uh, as uh as software gets easier and
easier with like HNC coding and and
eventually AGI, the the importance to
compare to the importance of hardware is
only going to rise.
What do you say, Sam? I could give like
a 3-hour answer to this question with
and I had so much fun.
I will just limit myself to a single
observation though. The there was this
quote that was bouncing around Twitter
for a while, which is all of the things
we've learned about society, about
managing scarcity, are not really going
to apply to managing abundance. And it
seemed really smart and really profound
and then I was like after a day I was
like, "Yeah, that's kind of like not
that interesting." And then it kept like
rattling around my head and I was like,
"You know what?
There is one thing about this that is
blowing my mind, which is
countries like us have a great deal of
debt.
And the plan on this is we are going to
uh inflate it away as far as I can tell.
You know, we have 130% someone will know
the number, something like that,
debt-to-GDP ratio, but, you know, we're
like a powerful country and inflation's
going to keep going and it's not that
scary.
If we're in a forever deflationary
world,
I don't know how you're supposed to
think about that
as an asset class and what's going to
happen.
That's a great answer. Um, I want to do
favorite book or a book that's impacted
you most in life.
Feel free to go anyone. Let's go first.
>> to go with the Asimov. Okay. Uh, robots,
the foundation the series.
Um, I've wanted to build uh human level
AI since I was like 16 and I think one
of the the big influences on me at the
time was the
Asimov's uh robot stories.
What about you, Sam? Um, that's a great
choice. I would going to say The
Beginning of Infinity, but uh the Asimov
stories maybe did have more of a impact
on me when I was younger. I in general,
I think about this now with kids, like
books that you read as a kid where
you're like, "I can go do anything, I
can be a high-agency person, I can dream
about the future." Those are really
great. Was that the book for you?
I mean, it wasn't one. Like, I was like
all of the young adult fiction that had
some vibe of that I loved.
Um, last one.
What is a person
alive today that you respect the most?
Could be family, could be a famous
person, could be
So many great names, you know.
Um,
Okay, let me go with a really random
answer.
Uh, I want to I want to highlight like a
researcher
whose work has influenced me and is not
well known at all. Uh, his name is uh
Pierre Boudier.
Uh, he's a he's a French researcher so
we are researchers. He's been doing AI
for for a very very long time.
And he was one of the pioneers on uh AI
curiosity, like artificial curiosity,
which is really a fascinating concept.
So, he's done a lot of great work. Uh,
he's very successful but also not not so
well known. So, yeah, I respect him a
lot, but you know, that's just I I could
go with like so so many different
people. Yeah, that's awesome. I like
that. Also, Sam, you can answer well
known or not well known, up to you. Um,
my mom.
Love that. Love that. Well, with that, I
think we should wrap.
Thank you guys so much for being here
and thank you all for attending.
>> [applause]