Open AAI founder and CEO Sam Alman
sounded very enthusiastic during the
Cisco AI summit about the launch [music]
of Codex, the software development agent
that helps developers write, understand,
debug, and refactor code by
understanding natural language prompts.
Altman feels like the launch of Codex
marks another CH GBT moment that can
lead to full AI companies. In these
highlights, he explains why
>> this is going to change how all of
OpenAI works and change how other
companies work. Um, it yeah, the model's
really hit some threshold and I think
now the interface and harness has caught
up and
I think this is the first this you're
talking about chatbt moments. I think
this is like the first time I felt
another chat GPT moment
>> for
here is a a clear clear glimpse of the
future of knowledge work and how
enterprises and individual people are
going to use AI to work in a completely
different way. The idea that a coding
model can create a full complex piece of
software but also interact with the rest
of the real world to build a company
around it is a very big deal. And then
this this notion of what's happening
right now, I'm sure you're tracking this
with Moldbook and um what happened with
Claudebot. Is it just a passing fad or
do you think that there's something that
we should take away from that on um
>> No, I think it is definitely not a
passing well mold book maybe. I don't
know. But uh Open Claw is not. Um,
I
I think this idea that code is really
powerful, but code plus generalized
computer use is even much more powerful
is here to stay. The fact I
when I initially installed Codex, I said
I was never going to give Codex full
control of my computer with without
checking what it was doing. And that
lasted about like two hours because it
was so useful. And then I and then I
kind of got persuaded by people that I
really shouldn't have it running like
that. So now I have two laptops until I
figure out how this is all going to
work. But giving an AI agent full use of
your computer and your web browser with
all your sessions leads to incredible
stuff. And that seems here to stay. the
the the
idea that we can take
a codeex style workflow and expand it to
all knowledge work. Um so that whatever
you're doing codecs can be or what some
version of that can be using your
computer and using the web for you and
editing documents and
whatever other kind of work you need. um
feels like a genuine transformation in
how knowledge work will happen and that
I think OpenClaw did an incredible job
of
bringing many ideas together to make
that feel usable and real and that seems
certain to be part of our future.
I think moldbook is cool. Uh, and I
think it points to something that will
be real and maybe it will be mold book.
Uh, but there will be new kinds of
social interaction where you have many
agents in a space interacting with each
other on behalf of people. Uh, leading
to all sorts of new things. It's like
you you can imagine a totally new kind
of social network where everybody makes
an agent or many agents and puts them in
there and the agents are talking and
doing stuff and finding them people and
information and collaborating with other
people's agents to come up with new
ideas that I'm sure there will be an
interesting I think the future of social
may look something like that very
different than today whether it's a mult
book or not uncertain I think the
obvious constraints are the biggest ones
uh still energy
manufacturing enough hardware, all that
kind of stuff. The non-obvious ones that
are most top of mind for me, one, how
are we going to balance the sort of
security and data access versus the
utility of all of these models?
>> Um, I don't think anyone has a great
answer to this yet. I it feels to me
like there is a new kind of security or
data access paradigm that needs to be
invented for this. Um, another is how
are we going to rewrite all software to
be equally usable by humans and AIs?
There's like a bunch of weird quirks
right now about trying to do that with
the way software work.
>> And does that change the architecture of
the software itself where you're going
to optimize it for agents more so than
humans? And so it fundamentally changes
how you build software.
>> Yes. And there's like big examples of
that and then there's like dumb examples
of that. So, for example, uh I
I would love my agent to be able to use
Slack on my behalf because I hate
drowning in Slack and I think it's like
this chaotic mess for me and but it's
important. Uh but the way it works right
now is my agent can use the Slack web
interface and go read all my threads and
do something for me. But then it has
marked a bunch of stuff as red in the
process of doing that and it's broken my
workflows.
>> Yeah.
>> So I don't like that's just a silly
example of how software is not like a
lot of software is not quite meant for
an AI and a person to be using it
together. Um maybe we'll want AIs to
like have different
sort of user accounts in some ways using
the same kinds of things. maybe a lot of
software will get rewritten so that it's
primarily or largely used by AI but also
still works for people using it the
oldfashioned way. Um, another kind of
non-obvious block is how like one of the
most powerful things about AI is you
could do this sort of
always on computing where you could have
an AI um listening to your meeting or
watching your meeting and you know
watching what you're doing on your
computer and
uh then just like add a lot of value and
do stuff for you. We don't even our like
existing computer hardware is not really
meant for that. our permissioning system
and how we think about what an AI gets
to see and do stuff with and what it
gets to keep is not really meant for
that. Um, our legal system doesn't
really support that. Well, you'd like to
be able to, you know, record a meeting
and learn something from it and delete
the recording. Uh, so I think there's a
lot of just usability things like that.
The the capability overhang feels to me
like the biggest it has ever felt. I
used to say like a few months ago maybe
I would have said it feels bigger than
any time except right when CHBT right
before CHBT launched. It now feels even
bigger than that. Even though people are
using AI for a lot of stuff. Um the fact
that AI can make
small but increasingly big scientific
discoveries. The fact that AI can write
full pieces of software. The fact that
soon AI can do more generalized
knowledge work. Um those are all huge
huge things like we have always said
that you know we're going to automate
research use that to be able to automate
the economy use that to be able to
deliver incredible new value to people
and share all these benefits and this
whole new technological world. Uh and we
always said that as a sort of like
abstract sometime in the future thing.
And yet now AI is doing research and AI
is able to do huge amounts of economic
work. And
this, as you said, has happened many
times in the past, but living through it
definitely feels like, huh, this is not
quite how it seems like it should go.
The the diffusion, the absorption is so
slow.
>> Yeah.
>> Um,
>> is it slower than you thought it would
be?
>> Yes. But I think I was just naive and
didn't think about it that hard. And in
retrospect and looking at the history,
it shouldn't be surprising. It feels
fast in some ways uh relative to other
things like you know CHBT grew crazily
quickly uh relative to any other piece
of software before. Um
Codex is growing extremely quickly. I
expect the general purpose computer use
knowledge work to grow quickly and yet
looking at what's capable looking at
what's possible it does feel sort of
surprisingly slow uh in terms of advice
figuring out how to set up enterprises
such that they can quickly absorb these
new tools and not have a year or
multiple years of how are we going to
think about the sort of
data access and security questions that
are currently blocking people even at
the level today of adopting things like
codecs that feels very important and I
don't want to make a too dramatic of a
prediction but I think the companies
that are not set up to be able to adopt
let's call them AI co-workers very
quickly um will be at a huge
disadvantage and it is going to take a
lot of work and some risk to be able to
adopt
The Codex app is the first time to me it
has truly felt like interacting with a
teammate.
>> Yeah.
>> Um it and I I think like one of the
lessons there is even if you have
amazing technology which the models got
I think 52 was the model that got super
good but even once you get there's still
so much value in how you package it, how
you how you have users interact with it,
how easy you can make it. But this it
really does to me now feel in a way that
talking to chatbt has always clearly
felt like a tool that I am like working
with a collaborator now.
>> Right. Right.
>> And
that seems very much like the shape of
things to come.
All the evidence we see right now is
that AI models will continue to get much
more capable and also that they will get
much less expensive and use many fewer
resources per task.
And all of the history we've seen of
every time that happens is people want
to use them much much more. So we are
planning for a world where
AI usage grows at an accelerated pace
each year. Do you think people are
underestimating the capacity that's
going to be needed even now after
everything that's being built out?
>> Right now they're saying like $5
trillion will be spent on this over the
course of
>> Yeah, maybe if that really gets spent
quickly uh that will be enough. And I
and I think it's also possible that
along the way we have some supply gluts
temporarily. Um but over a period of
decades it seems certain to me that the
world is going to need a lot more tokens
even though the and we'll make you know
each token way more efficient. You'll
all have a device in your pocket running
super powerful models off of a battery
and what but you know there will still
be this drive for more and more and
more. Um
I think the world has come to realize
this and capitalism is doing its thing
and supply chains are reconfiguring. Uh
policy is changing and we are going to
build an incredible amount of
infrastructure. Now is it enough? Uh
people always talk about the total
market demand for AI. It feels to me
something like the market demand for
electricity or energy. You can't you
can't talk about that as a general
thing. You can say how much demand there
will be at different price levels. Um
and in this case you can say at
different price levels for different
quality like how smart it is or how fast
it is or thing like things like that. Um
but
if we continue to make AI really capable
and really cheap, there will be a ton of
demand at some price. If it's more
expensive, there will be uh you know
less demand.
But I would like the world to just get
to use a ton of it. Uh we're in this
capability over now as we mentioned
where people are like oh you know can
use it for chat or maybe some people
understand you can use it for code. I
think this will just be like how we do
stuff, how companies run, how scientific
discovery happens, how we use most
software personally in our lives and
making a lot of it if we can have it at
a reasonable price seems like a very
good bet.
>> Do you worry about um
the fact that the US has not had as much
of a lead on the open source side? Talk
to us a little bit about that. like
>> I do worry about that. Um
>> yeah, I think we should
>> we should do more.
>> Yes.
>> Uh so what's what's stopping you from
doing it?
>> Focus and time, but I think we need to
solve that somehow. To
>> to be clear, I think it's most important
that we lead on frontier models and I
expect those to be accessed via APIs and
other products. So I it would be okay
but not great if we didn't also have the
open source lead. Um people want their
own models. People want control of their
own models. Uh people want to run models
locally. Especially
>> if you think about a world where you
have a model that is going to see your
whole life. You're going to have a new
kind of device that's
sort of always on and kind of keeping
track of everything and adds huge value
to you. I at least would really like
that running on inference eye control.
Um and and so I think people are going
to need this. Uh you know again if we
get behind obviously it's not the end of
the world but I would really like us to
lead there and I think there will be
increasing demand for locally running
private models.
We have kind of
two large products and now we have some
others that are coming up. We have
chatbt and our API business. Um and
now we have codeex which looks like it's
going to get very big uh and a few other
things. The and in the future we'll have
consumer devices and robots and all
sorts of other parts of this too. Uh
people do seem very willing to pay for
AI as a subscription service. Uh not
everybody but a lot of people are more
than we thought would. Um, businesses of
course are willing to pay for things
like Chachet Enterprise,
>> but even consumers, you've reconditioned
the consumers quite a bit. Like
>> consumers, I mean, we have I don't know
the exact number off the top of my head,
but you know, many many tens of millions
of consumers paying a subscription fee.
>> Yeah.
>> Uh, and
that was a surprise to me to be honest,
a happy one. Um, so that I think can go
further and as we add things like
codecs, people are willing to pay much
much more there. um advertising
for mass scale consumer businesses does
seem like a good model. Uh although I
think we'll have to be very careful in
how we do that. Uh and then
businesses increasingly want
something like an AI cloud subscription
>> where they're like I want to partner
with an AI company. I want you to like
handle security and context linking and
access and I want to be able to run lots
of agents on it. I want like a general
purpose platform there. I want some
agents from you, some agents from other
people. I may even want to run other
people's models. I want a Chach
enterprise license. I want a ton of API
access. Uh you know, things like that.
So, I think there will be a a model
there as well. Clear. We don't want to
take a share of people that are just
like paying us for the API and making
you know, discoveries like that's great
and that's theirs. Uh I can imagine
though that like if there is a future
where we can spend billions of dollars
on inference and cure an important
disease. Uh
we may explore partnerships there where
we pay for that cost in partnership with
the drug company and then kind of get
some royalty on it. This is not
something we're doing now, but
I think the frontier of scientific
discovery with AI will require so much
capital that maybe we think of ourselves
as an investor in some of those cases. I
can imagine like billions of humanoid
robots building more data centers and
mining for material and building more
power plants and all of that. Um, I can
imagine just the economy growing at an
unprecedented rate as there's all sorts
of
incredible new services and scientific
discoveries happening. Um, I can imagine
the Bonoyman probes launching and then
beyond that, I got no idea. And the
models are going to get so much better
quickly. like I have we've been trying
to figure out how we communicate about
what we think is happening that does not
seem like hype or
um you know crazy but the models are
just going to get
very very good this year and that will
that is a tailwind that I think we can
build incredible things with headwind uh
yeah probably some sort of like global
destabilization, mega supply chain
disruption, something like that.
>> And and on the on the on the tailwind
side on the models getting infinitely
better, do you think in 26 we see a 10x
improvement, a 100x improvement, a 5x
improvement?
>> What do you mean by like a what is a 10x
improvement? Like
>> things that you can problems you can
solve that you you were
>> Yeah, I would bet it subjectively feels
something like 10x the end of this year.
Like that's a I don't know how to like
put an exact metric on that.
>> Yeah.
>> But that feels reasonable.