We're so excited for our very first
special guest. He has helped build
modern AI, then explain modern AI, and
then occasionally rename modern AI. He
actually helped co-ound open AAI right
inside of this office. Was the one who
actually got Autopilot working at Tesla
back in the day, and he has a rare gift
of making the most complex technical
shifts feel both accessible and
inevitable.
You all know him for having coined the
term vibe coding last year, but just in
the last few months, he said something
even more startling. That he's never
felt more behind as a programmer. That's
where we're starting today. Thank you,
Andre, for joining us.
>> Yeah. Hello. Excited to be here and to
kick us off.
>> Okay. So, just a couple months ago, you
said that you've never felt more behind
as a programmer. That's startling to
hear from you of all people. Um, can you
help us unpack that? Was that feeling
exhilarating or unsettling?
>> Uh yeah, a mixture of both for sure. Uh
well, first of all, um
I guess like as many of you, I've been
using agentic tools like lot code,
adjacent things, uh for a while, maybe
over the last year as it came out and it
was very good at you know chunks of code
and sometimes it would mess up and you
have to edit them and it was kind of
helpful and then I would say December
was this uh clear point where for me I
was on a break so I had a bit more time.
I think many other people were similar
and uh I just started to notice that
with the latest models uh the chunks
just came out fine and then I kept
asking for more and it just came out
fine and then I can't remember the last
time I corrected it and then I was I
just you know trusted the system more
and more and then I was vibe coding
[laughter]
and uh so it was kind of a I do think
that it was a very stark transition. I
think that a lot of people actually I
tried to I tried to stress this on uh
Twitter and or X because I think a lot
of people experienced AI last year as
ChachiPT adjacent thing. Uh but you
really had to look again and you had to
look as of December uh because things
have changed fundamentally and uh
especially on this like agentic coherent
workflow uh that really started to
actually work. Um, and so I would say
that um, yeah, it was just that
realization that really uh, uh, had me
um, go down their whole rabbit hole of
just, you know, infinity side projects.
Uh, my side projects folder is like
extremely full with lots of random
things and, uh, just, uh, V coding all
the time. Uh, so, uh, yeah, that kind of
happened in December, I would say, and I
was looking at the repercussions of that
since.
>> Um, you've talked a lot about this idea
of LLMs as a new computer. um that it
isn't just better software, it's a whole
new computing paradigm. And um software
1.0 was explicit rules, software 2.0 was
learned weights, software 3.0 is this.
Um if that's actually true, what does a
team build differently the day they
actually believe this,
>> right? So uh yeah, exactly. So software
1.0, I'm writing code, software 2.0, I'm
actually programming by creating data
sets and training uh training neural
networks. So the programming is kind of
like arranging data sets and maybe some
objectives and neural network
architectures. And then what happened is
that basically if you train one of these
GPT models or LLMs on a sufficiently
large set of tasks implicit basically um
implicitly because by training on the
internet you have to multitask all the
things that are in the data set. Uh
these actually become kind of like a
programmable computer in a certain
sense. So software 3.0 know is kind of
about uh you know your programming now
turns to prompting and what's in the
context window is your lever over the
interpreter that is the LLM that is kind
of like interpreting your context and uh
performing computation in the dig
digital information space. So I guess um
yeah that's kind of the transition and I
think there's a few examples of that
really drove it home for me and maybe
that might be instructive. Uh so for
example when you when openclaw came out
when you want to install openclaw you
would expect that normally this is a
bash bash script like a shell script. So
run the shell script to run to install
open claw. Um but the thing is that in
order to target lots of different
platforms and lots of different types of
computers you might run an open claw.
This these shell scripts usually balloon
up and become extremely complex. But the
thing is you're still stuck in a
software 1.0 universe of wanting to
write the code. And actually the open
claw installation is a is a copy paste
of a b bunch of text that you're
supposed to give to your agent. Uh so
basically it's it's a little skill of uh
you know copy paste this and give it to
your agent and it will install open
claw. And the reason this is a lot more
powerful is you're working now in the
software 3.0 paradigm where you don't
have to precisely spell out you know all
the individual details of that setup.
The agent has its own intelligence that
it packages up and then it kind of like
follows the instructions and it looks at
your environment, your computer and it
kind of like performs intelligent
actions to make things work and it
debugs things in the loop and it's just
like so much more powerful, right? So I
think that's a very different kind of
like way of thinking about it is just
like what is the piece of text to copy
paste to your agent? That's the
programming paradigm. Now I think one
more maybe uh example that comes to mind
that is even more extreme than that is
when I was building um menu genen. So,
menu genen is this idea where you um you
come to a restaurant, they give you a
menu. There's no pictures usually. So, I
don't know what any of these things are
uh usually like 30% of the things I have
no idea what they are, 50%. So, I wanted
to take a photo of the restaurant menu
and to get pictures of what those things
might look like in a generic sense. And
so I built I've vcoded this app that
basically lets you upload a photo and it
does all this stuff and it runs on
Verscell and uh it basically rerenders
the menu and it gives you like all the
items and it gives you a picture that it
uses an image um you know generator uh
for to basically OCR all the different
titles uh use the image generator to get
pictures of them and then shows it to
you. And then I saw the software 3.0
version of this which is which blew my
mind which is literally just take your
photo give it to Gemini and say use
Nanobanana to overlay the the things
onto the menu. Uh and Nanabanana
basically returned an image that is
exactly the picture of the menu that I
took but it actually put into the pixels
it rendered the different things in the
menu and this blew my mind because
actually all of my menu gen is spirious.
It's working in the old paradigm that
app shouldn't exist. uh and uh yeah the
software 3.0 paradigm is a lot more kind
of raw. It just um your neural network
is doing more and more of the work and
your prompt or context is just the image
and the output is an image and there's
no need to have any of the app in
between. Um so I think that people have
to kind of like reframe you know not to
work in existing paradigm of what things
existed and just think about it as a
speed up of what exists. It's actually
like new things are available now. And
going back to your programming question,
it's not even I think that's also an
example of working in the in the old
mindset because it's not just about
programming and programming becoming
faster. This is more general information
processing that is automatable now. So
um it's not just even about code. So
previous code worked over kind of like
structured data, right? And uh you write
code over structured data. But like for
example with my LLM knowledge basis
project um basically you get LLMs to
create wikis for your organization or
for you in person etc. This is not even
a program. This is not something that
could exist before because there was no
there was no code that would create a
knowledge base based on a bunch of
facts. But now you can just take these
documents and uh basically uh recompile
them in a different way and uh reorder
them and create something that is uh new
and interesting uh as a reframing of the
data. And so these are new things that
weren't possible. Uh and so I think this
is uh something that I keep trying to
get back to as to not only what can we
do that existed that is faster now but I
think there's new opportunities of just
things that couldn't be possible before
and I almost think that that's more
exciting.
>> I love the menu genen progression and
dichotomy that you laid out and I think
even I'm sure many folks here followed
your own progression of programming from
last October to early January February
this year. Um, if you extrapolate that
further, what is the 2026 equivalent um,
for building websites in the '9s,
building mobile apps in the 2010s,
building SAS um, in the last cloud era,
what will look completely obvious in
hindsight that is still mostly unbuilt
today?
>> Um, [clears throat] well, going with the
example of menu, I guess, uh, so a lot
of this code shouldn't exist and it's
just neural network doing most of the
work. Um I do think that the
extrapolation looks very weird because
you could basically imagine
I don't I yeah so you could imagine
completely neural computers in a certain
sense you feed raw videos like imagine a
device you takes raw videos or audio
into basically what's a neural net and
uh uses diffusion to render a UI that is
kind of like you know unique for that
moment in a certain sense and um I kind
of feel like in the early days of
computing actually people were a little
bit confused as to whether computers
would look like calculators or computers
would look like neural nets and in 50s
and 60s it was not really obvious which
way would go and of course we went down
the calculator path and ended up
building classical computing and then
neural nets are currently running
virtualized on existing computers but
you could imagine I think that uh a lot
of this will flip and that the neural
net becomes kind of like the host
process and uh the CPUs become kind of
like the co-processor so we saw the
diagram of you know intelligence compute
is going to of neural networks is going
to take over and become the dominant
spend of flops so you could imagine
something really weird and foreign when
where neural nets are doing most of the
heavy lifting. They're using tool use as
this like you know um historical
appendage for some kinds of like
deterministic tasks. Uh but what's
really running the show is these uh
neural nets that are in a certain way.
Um so you can imagine something
extremely foreign as the extrapolation
but I think we're going to probably get
there uh sort of piece by piece. Um and
I don't yeah that that progression is
TBD I would say.
>> [snorts]
>> I'd like to talk a little bit about um
uh this concept of verifiability, the
fact that AI will automate faster and
more easily domains where the output can
be verified. Um if that framework is
right, what work is about to move much
faster than people realize and what
professions do we have that people
actually think are safe but that are
actually highly verifiable?
Uh yes. So I I spent uh some time
writing about verifiability and um
basically like traditional computers can
easily automate what you can specify in
code and uh kind of this latest round of
LLMs can easily automate what you can uh
verify in a certain in a certain sense
because the way this works is that when
frontier labs are training these LLMs
these are giant reinforcement learning
environments. So they are given
verification rewards and then because of
the way that these models are trained
they end up basically uh progressing and
creating these like jagged entities that
really peak in capability in kind of
like verifiable domains like math and
code and adjacent and kind of like
stagnate and are a little bit um you
know rough around the edges when uh
things are not kind of like in that in
that space. So I think the reason I
wrote about verifiability is I'm trying
to understand why these things are so
jagged. Um and some of it has to do with
how the labs train the models but I
think some of it also has to do with um
the focus of the labs and what they
happen to put into the data
distribution. Uh because some things
basically are significantly more
valuable in economy and end up creating
more environments because the labs
wanted to work in those settings. So I
think code is a good example of that.
There's probably lots of verifiable
environments they could think about that
happen not to make it into the mix
because they're just not that useful to
have the capability around. Um, but I
think to me the big um I guess like the
big mystery is uh the favorite example
for a while was that how many letters
are are in a strawberry and the models
would famously get this wrong and it's
an example of jaggedness. Uh the models
now patch this I think but the new one
is I want to go to a car wash to wash my
car and it's 50 meters away. Should I
drive or should I walk? And
state-of-the-art models today will tell
you to walk because it's so close. How
is it possible that state-of-the-art
Opus 4.7 will simultaneously refactor a
100,000 like [laughter] codebase line
codebase or find zero day
vulnerabilities and yet tells me to walk
to this car wash? This is insane. And to
whatever extent these uh models are
remain jagged, it's an indication that
number one maybe something's slightly
off or um number two you need to
actually be in the loop a little bit and
you need to treat them as tools and you
do have to kind of stay in touch with
what they're doing. And so I think all
of my writing long story short about
verifiability is just trying to
understand um why these things are
jacked. Is there any pattern to it? And
I think it's some kind of a combination
of verifiable plus labs care. Maybe one
more anecdote that is instructive is uh
from GPT 3.5 to GPT4 people noticed that
chess improved a lot and I think a lot
of people thought oh well it's just a
progression of the capabilities but
actually it's it's more that uh I think
this is public information I think I saw
it on the internet um a huge amount of
like um data of chess made it into the
pre-training set and just because it's
in a data distribution uh basically the
model improved a lot more than it would
just by default. So someone at OpenAI
decided to add this data and now you
have a capability that just peaked a lot
more. And so that's why I think I'm
stressing this um dimension of it as we
are slightly at the mercy of whatever
the labs are doing, whatever they happen
to put into the mix. And you have to
actually explore this thing that they
give you that has no manual. And it
works in certain settings, but maybe not
in some settings. And you have to kind
of um explore it a little bit. And uh if
you're in the circuits that were part of
the RL, you fly. And if you're in the
circuits that are out of the data
distribution, uh you're going to
struggle and you have to kind of figure
out which which circuits you're in in
your application. And if you and if
you're not in the circuits, then you
have to really look at fine-tuning and
doing some of your own work because it's
not going to necessarily come out of the
LLM out of the box.
>> I'd love to come back to the concept of
jagged intelligence in a little bit. Um,
if you are a founder today and thinking
about building a company, you are trying
to solve a problem that you think is
tractable, something that uh is a domain
that is verifiable, but you look around
and you think, "Oh my gosh, well, the
labs have really really started uh
getting to escape velocity in the ones
that seem most obvious, math, coding,
and others." What would your advice be
to to the founders in the audience?
Um
so I think maybe that comes to the
previous question of I do think that
verifiability because it um let me
think. So verifiability makes something
tractable in the current paradigm
because you can throw a huge amount of
RL at it. Um so maybe one way to see it
is that uh that remains true even if the
labs are not focusing on it directly. So
if you are in a verifiable setting where
you could create these RL environments
or examples then that actually sets you
up to potentially do your own fine
tuning and you might benefit from that.
But that is fundamentally technology
that just works. You can pull a lever if
you have huge amount of diverse data
sets of RL environments etc. Uh you can
use your favorite fine-tuning framework
and um and uh pull the lever and get
something that actually uh works pretty
well. So um I don't know what the
examples of this might be. Um, but I do
think there are some very valuable uh
reinforcement learning environments that
people could think of that I think are
not part of the Yeah, I don't want to
give away the answer, but there is one
domain that I think is very uh Oh, okay.
Sorry, I don't mean to vape post on on
the stage, but there are some examples
of this.
>> On the flip side, what do you think
still feels automatable only from a
distance?
>> I do think that ultimately almost
everything can be made uh verifiable to
some extent. some things easier than
others. Um because even for like things
like writing or so on, you can imagine
having a council of LLM judges and
probably get get to some get get
something uh reasonable out of the um
from from this kind of an approach. So
it's more about what's easy or hard. Um
so I I do think that ultimately um uh
yeah, I think uh
>> everything [laughter]
>> everything is automatable.
>> Amazing. Okay. Um, so last year you
coined the term vibe coding and today
we're in a world that feels a little bit
more serious, more regent engineering.
What do you think is the difference
between the two and what would you
actually call what we're in today?
>> Uh, yeah. So I would say vibe coding is
about raising the floor for everyone in
terms of what they can do in software.
So the floor rises, everyone can vibe
code anything and that's amazing,
incredible. But then I would say agentic
engineering is about preserving the
quality bar of what existed before in
professional software. So you're not
allowed to introduce vulnerabilities due
to VIP coding. Um you are um you're
still responsible for your software just
as before, but can you go faster? And
spoiler is you can but how do you how do
you do that properly? And so to me
agentic engineering when I call it that
because I do think it's kind of like an
engineering discipline. You have these
agents which are these like spiky
entities. They're a bit fable, a little
bit stocastic, but they are extremely
powerful. is how do you how do you
coordinate them to go faster without
sacrificing your quality bar and doing
that well and correctly um is the the
realm of agentic engineering um so I
kind of see them as as different like
one is about maybe raising the raising
the floor and the other is about um you
know extrapolating and what I'm seeing I
think is there is a very high ceiling on
agentic engineer uh capability and you
know people used to talk about the 10x
engineer previously I think that this is
magnified a lot more 10x is uh is not uh
the speed up you gain. Um and I think uh
it does seem to me like people who are
very good at this um peak a lot more
than 10x uh from from my perspective
right now.
>> I really like that framing. Um one thing
that when Sam Alman came to AIN last
year, one memorable thing he said was
that people of different generations use
chatpt differently. So if you're in your
30s, you use it as a Google search
replacement. But if you're in your
teens, tragic is your gateway to the
internet. What is the parallel here in
coding today? If we were to watch two
people code using OpenClaw, Claude Code,
Codeex, one you'd consider mediocre at
it and one you would consider fully AI
native. How would you describe the
difference?
>> [clears throat]
>> I mean I think it's a just trying to get
the most out of the tools that are
available utilizing all of their
features investing into your own um kind
of setup. Uh so just like previously all
the engineers are used to basically
getting the most out of the tools you
use either it's vim or v code or now
it's you know cloth code or codec or so
on. So um just investing into your setup
um and um utilizing a lot of the you
know uh tools that are available to you.
Um and I think it just kind of looks
like that. I do think that um maybe
related thought is um a lot of people
are maybe hiring um for this right
because they want to hire strong agentic
engineers. I do think that um what I'm
seeing is that uh the you know most
people have still not refactored their
um their hiring process for a gentic
engineer capability right like if you're
giving out puzzles to solve and this is
still the old paradigm I would say that
hiring have to has to look like give me
a really big project and see someone
implement that big project like let's
write say a Twitter clone uh for agents
and then uh make it really good make it
really secure and then have some agents
uh simulate some activity uh on this
Twitter and then I'm going to use 10
codecs 5.4x for X high to try to break
your break your um uh this website that
you deployed and they're going to try to
basically break it and they should not
be able to break it. And so maybe it
looks like that, right? And so yeah,
watching people in that that setting and
building bigger uh projects and uh
utilize utilizing the tooling is maybe
what I would uh look at for the most
part.
>> And as agents do more, what human skill
do you think becomes more valuable, not
less?
>> Uh so um yeah, it's a good question. I
think um well right now the answer is
that the agents are kind of like these
intern entities right so it's remarkable
um you basically still have to be in
charge of the aesthetics the the
judgment the taste and a little bit of
oversight maybe one one of my favorite
examples of like the the weirdness of
agents is um for menu genen uh you sign
up with a Google Google account but you
um purchase credits using a stripe
account and both of them have email
addresses and my agent actually tried to
basically
um like when you purchase credits, it
assigned it using the email address from
Stripe to the Google email address like
there wasn't a persistent user ID that
that uh for people it was trying to
match up the email addresses, but you
could use different email address for
your Stripe and your Google and
basically would not associate the funds.
And so this is the kind of thing that
these agents still will make mistakes
about is like why would you use email
addresses to try to crossorrelate the
funds? They can be arbitrary. You can
use different emails, etc. Like this is
such a weird thing to do. So I think
people have to be in charge of this
spec, this plan. And um I actually don't
even like the plan mode. I I would I
mean obviously it's very useful, but I
think there's something more general
here where you have to work with your
agent to design a spec that is very
detailed and maybe it's uh maybe
basically the docs and then get the
agents to write them and you're in
charge of the oversight and the top
level categories, but the agents are
doing a lot of the under the hood. And
um so I think you're not caring about
some of the details. So as an example
also with um arrays or tensors in neural
networks. Um there's a ton of details
between PyTorch and NumPy and all the
different like pandas and so on for all
the different little API details. And I
I already forgot about the keep dims
versus keep dim or whether it's dim or
axis or reshape or permute or transpose.
I don't remember this stuff anymore,
right? Because you don't have to. This
is the kind of details that are handled
by the intern because they have very
good recall and but you still have to
know for example that um you know
there's underlying tensor there's an
underlying view and then you can
manipulate view of the same storage or
you can have different storage which
would be less efficient and so you still
have to have an understanding of what
this stuff is doing and some of the
fundamentals um so that you're not
copying memory around unnecessarily and
so on but uh the details of the APIs are
now handed off so it um you're in charge
of the taste the engineering the design
um and that it makes sense and that
you're asking for the right things and
that you're saying that okay that these
have to be unique user IDs that we're
going to tie everything to um and so
you're doing some of the design and
development and the engineers are doing
the fill in the blanks and that's
currently kind of like where we are and
I think that's what everyone of course
is seeing I think right now
>> do you think there's a chance that this
um taste and judgment matters less over
time or will the ceiling just keep
rising
>> um yeah it's a good question I would
Okay.
Um, I mean, I'm hoping that the that it
improves. I think probably the reason it
doesn't improve right now is again, it's
not part of the RL. There's probably no
aesthetics cost or reward or it's not
good enough or something like that. Um,
I do think that when you actually look
at the code, sometimes I get a little
bit of a heart attack because it's not
like super amazing code necessarily all
the time and it's very bloaty and
there's a lot of copy paste and there's
awkward abstractions that are brittle
and like it works but it's just really
gross. Um, and I do I do hope that this
can improve in future models. Um, a good
example also is this uh you know micro
GPT project which where I was trying to
simplify uh LLM training to be as simple
as possible. The models hate this. They
can't do it. I tried to I keep I kept
trying to prompt an LLM to simplify more
simplify more and it just can't you feel
like you're outside of the RL circuits.
It feels like you're obviously you know
you're pulling teeth. It's not like
light speed. So I think um I do think
that people are still remain in charge
of this. But I do think that there's
nothing fundamental again that's
preventing it. It's just the labs
haven't done it yet almost.
>> Yeah.
>> So I'd love to come back to this idea of
uh jagged forms of intelligence. you
wrote a little bit about this with a
very thoughtprovoking piece around
animals versus ghosts. Um, and the idea
is that we're not building animals, we
are summoning ghosts. Um, and these are
jagged forms of intelligence that are
shaped by data and reward functions, but
not by intrinsic motivation or fun or
curiosity or empowerment. Uh, things
that kind of came about via evolution.
um why does that framing matter and what
does it actually change about how you
build and deploy and evaluate or even
trust them?
>> Uh yeah, so yeah, I think the reason I
wrote about this is because I'm trying
to wrap my head around what these things
are, right? Because if you have a good
model of what they are or are not, then
you're going to be more competent at uh
using them. Um and I do think that um I
don't know if it has I'm not sure if it
actually has like real power. [laughter]
I think it's a little bit of
philosophizing. Um, but I do think that
um
I think it's just um coming to terms
with the fact that these things are not,
you know, animal intelligences. Like if
you yell at them, they're not going to
work better or worse or it doesn't have
any impact. Um, and uh it's all just
kind of like these statistical
simulation circuits where the the
substrate is pre-training so like
statistics and then but then there's RL
bolting on top. So, it kind of like
increases the dispendages and um maybe
it's just kind of like a mindset of what
I'm coming into or what's likely to work
or not likely to work or how to modify
it. But I don't actually I don't know
that I have like here are the five
obvious outcomes of how to make your
system better. It's more just being
suspicious of it and um
>> figuring out over time.
>> That's where it starts. Um okay, so you
are so deep in working with agents that
don't just chat. They have um real
permissions. They have local context.
they actually take action on your be
your behalf. What does the world look
like when we all start to live in that
world?
>> Uh yeah, I think I think every a lot of
people probably here are excited about
what this agent uh you know native
agentic environment looks like and
everything has to be rewritten.
Everything is still fundamentally
written for humans and has to be moved
around. I still use most of the time
when I use uh different frameworks or
libraries or things like that, they
still have docs that are fundamentally
written for humans. This is my favorite
pet peeve. Like I don't uh why are
people still telling me what to do? Like
I don't want to do anything. What is the
thing I should copy paste to my agent?
[laughter] Like uh so it's just um every
time I'm told, you know, go to this URL
or something like that, it's just like
ah [laughter]
you know. [snorts] So um everyone is I
think excited about how do we decompose
the workloads that need to happen into
fundamentally sensors over the world,
actuators over the world. How do we make
it agent native? Uh basically describe
it to agents first. um and then have a
lot of automation around um you know the
um yeah around data structures that are
very legible to the LLMs. Uh so I think
um yeah I'm hoping that there's a lot of
agent first um infrastructure out there
and that you know for Menuguen famously
when I wrote the uh not I'm not sure how
famously but when I wrote the blog post
about Menuguen [laughter]
um a lot of the work a lot of the
trouble was not even writing the code
for Menugen it was deploying it in
versell because I had to work with all
these different services and I had to
string them up and I had to go to their
settings and the menus and you know
configure my DNS and it was just so
annoying and so that's a good example of
I would hope that menu gen that I could
give a prompt to an LLM build menu genen
and then I didn't have to touch anything
and it's deployed in that same way on
the internet. Uh I think that would be a
good kind of a test for whether or not
uh a lot of our infrastructure is
becoming more and more agent native. And
then ultimately I would say yeah I I do
think we're going towards a world where
um there's agent representation for
people and for organizations and um you
know I'll have my agent talk to your
agent uh to figure out some of the
details of our meetings or or things
like that. So, [laughter]
um I do think that that's uh roughly
where things are going, but um yeah, I
think everyone here is excited about
that.
>> I really like the visual analogy of
sensors and actuators. I actually hadn't
thought of that. That's super
interesting,
>> right?
>> Um okay, I think we have to end on a
question about education. Um because you
are probably one of the very best in the
world at making complex technical
concepts simple and deeply thoughtful
about how we design education around it.
Um, what still remains worth learning
deeply when intelligence gets cheap as
we move into the next a era of AI?
>> Yeah. Uh, there was a tweet that blew my
mind recently and I keep thinking about
it like every other day. It was
something along the lines of um, you can
outsource your thinking but you can't
outsource your understanding.
And um,
>> I think that's really nicely put. I so
yeah because I still I'm still part of
the system and I still I still have to
somehow information still has to make it
into my brain and I feel like I'm
becoming a bottleneck of just even
knowing what are we trying to build why
is it worth doing uh how do I direct you
know how do I direct my my agents and so
on so I do still think that ultimately
something has to direct the thinking and
the processing and so on and um that's
still kind of fundamentally constrained
somehow by understanding and this is one
reason I also was very excited about all
the LM knowledge bases because I feel
like that's that's a way for me to
process information and anytime I see a
different projection onto information. I
always like feel like I gain insight. So
it's really just a lot of prompts for me
to do synthetic data generation kind of
over over some fixed data. Uh so I I
really enjoy uh whenever I read an
article I have my uh you know my wiki
that's being built up from these
articles and I love asking questions
about things or um and I I think that
ultimately these are tools to enhance
understanding in a certain way and this
is still kind of like a bit of a
bottleneck because then you can't direct
the you can't be a good director if you
still uh because the LM certainly don't
excel at understanding you still are
uniquely in charge of that. So, uh,
yeah, I think, uh, tools to that effect,
I think are incredibly interesting and
exciting.
>> I'm excited to be back here in a couple
years and to see if we've been fully
automated out of the loop and they
actually take care of understanding as
well. Uh, thank you so much for joining
us, Andre. We really appreciate it.
[applause]