Andrej Karpathy on Software 3.0 and the Future of Intelligence | AI Ascent 2026

**Stephanie Zhan:** 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 a
ctually 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 rar
e 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 y
ear, 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 star
ting today. Thank you, Andre, for joining us.

**Andrej Karpathy:** Yeah. Hello. Excited to be here and to kick us off.

**Stephanie Zhan:** Okay. So, just a couple months ago, you said t
hat 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?

**Andrej Karpathy:** 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 too
ls 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 sometim
es 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 transit
ion. 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 las
t 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 t
hat 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 wou
ld say, and I was looking at the repercussions of that since.

**Stephanie Zhan:** Um, you've talked a lot about this idea of LLMs as a new computer. um that it isn't j
ust 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 th
is. Um if that's actually true, what does a team build differently the day they actually believe this,

**Andrej Karpathy:** 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 o
f like arranging data sets and maybe some objectives and neural network architectures. And then what happened is that basically if you train one of th
ese 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 ki
nd 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 th
at 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 whe
n 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 y
ou 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. A
nd 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 t
ext 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 t
han 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 pic
tures 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 gen
erator 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 literall
y 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 para
digm 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. An
d 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 abo
ut programming and programming becoming faster. This is more general information processing that is automatable now. So um it's not just even about co
de. 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 kno
wledge 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 ca
n 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 interest
ing 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.

**Stephanie Zhan:** I love the menu genen progression and dichotomy that you laid out and I think even I'm sure ma
ny 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 wil
l look completely obvious in hindsight that is still mostly unbuilt today?

**Andrej Karpathy:** Um, [clears throat] well, going with the example of menu, I guess, uh, s
o 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 becaus
e 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 de
vice 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 th
at 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 comput
ers 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 compute
rs 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 t
he 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 the
se 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 probabl
y get there uh sort of piece by piece. Um and I don't yeah that that progression is TBD I would say.

[snorts]

**Stephanie Zhan:** I'd like to talk a little bit abou
t 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 fram
ework 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 tha
t are actually highly verifiable?

**Andrej Karpathy:** 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 s
ense because the way this works is that when frontier labs are training these LLMs these are giant reinforcement learning environments. So they are gi
ven 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 i
s 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 mor
e 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 th
at. 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 usef
ul 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 ma
ny 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 thin
k 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 t
oday 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 [laugh
ter] 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 u
h 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 l
ittle 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 pub
lic 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 yo
u 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 wh
atever 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 circui
ts 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.

**Stephanie Zhan:** 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 proble
m 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 r
eally 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?

**Andrej Karpathy:** Um so I think maybe that comes to the previous question of I do think that verifiability because it um let me think. So veri
fiability 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 technolo
gy 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. U
m, 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, bu
t there are some examples of this.

**Stephanie Zhan:** On the flip side, what do you think still feels automatable only from a distance?

**Andrej Karpathy:** 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 appro
ach. So it's more about what's easy or hard. Um so I I do think that ultimately um uh yeah, I think uh

**Stephanie Zhan:** everything [laughter]

**Andrej Karpathy:** everything is autom
atable.

**Stephanie Zhan:** 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 regen
t engineering. What do you think is the difference between the two and what would you actually call what we're in today?

**Andrej Karpathy:** Uh, yeah. So I would say v
ibe 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 t
hat's amazing, incredible. But then I would say agentic engineering is about preserving the quality bar of what existed before in professional softwar
e. 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 d
o 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 stoc
astic, 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 flo
or 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.

**Stephanie Zhan:** I reall
y 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 in
ternet. 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 mediocr
e at it and one you would consider fully AI native. How would you describe the difference?

**Andrej Karpathy:** [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 o
r 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 kin
d 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 stro
ng 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 f
or 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 ha
s 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.

**Stephanie Zhan:** And as agents do more, what human skill do you think becomes mor
e valuable, not less?

**Andrej Karpathy:** 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 en
tities 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 cred
its, 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 associat
e 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 cro
ssorrelate 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 som
ething 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? Becaus
e 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 dif
ferent 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 u
m 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 ha
ve 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 t
he 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

**Stephanie Zhan:** do you
think there's a chance that this um taste and judgment matters less over time or will the ceiling just keep rising

**Andrej Karpathy:** um yeah it's a good question I w
ould 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 R
L. 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 cod
e, 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 ca
n 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 a
s 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 j
ust 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.

**Stephanie Zhan:** Yeah.

So I'd love to come back to this idea of uh jagged forms of intelligence. you wrote a little b
it 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 g
hosts. Um, and these are jagged forms of intelligence that are shaped by data and reward functions, but not by intrinsic motivation or fun or curiosit
y 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 bui
ld and deploy and evaluate or even trust them?

**Andrej Karpathy:** 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 philosophizi
ng. 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 y
ou 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 simu
lation 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 susp
icious of it and um

figuring out over time.

**Stephanie Zhan:** That's where it starts. Um okay, so you are so deep in working with agents that don't just chat. The
y 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?

**Andrej Karpathy:** Uh yeah, I think I think every a lot of people probably here are excited about what this agent uh you know native agentic e
nvironment 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 shou
ld 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 automat
ion 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 ag
ent 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 b
log 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 confi
gure 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 gen
en 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 whe
ther 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 towar
ds 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 o
ut 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.

**Stephanie Zhan:** I really like the visual analogy of sensors and actuators. I actually hadn't thought of that. That's
super interesting,

**Andrej Karpathy:** right?

**Stephanie Zhan:** Um okay, I think we have to end on a question about education. Um because you are probably one of the very best in th
e world at making complex technical concepts simple and deeply thoughtful about how we design education around it. Um, what still remains worth learni
ng deeply when intelligence gets cheap as we move into the next a era of AI?

**Andrej Karpathy:** Yeah. Uh, there was a tweet that blew my mind recently and I keep thin
king 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 proc
essing 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 a
ll 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 informat
ion. 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 q
uestions 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 b
it 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 understandin
g 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.

**Stephanie Zhan:** I'm e
xcited 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]