How do you build superintelligence inside a company? Part of the key thing is not to just use AI as a co-pilot. This is the thing where you use it as
the building layer for everything. And you need to start recording all the artifacts. It's like a shared organizational brain. It's like the closest
thing to us being able to like connect our brains. >> If you frame this as a way for everyone in an organization to get better at what they do using
the like collective skill and instinct of the people they work with, it's incredibly powerful.
Today we have a real treat. Uh we have a special guest, general partner at YC, our partner, Pete Koomen. He created Optimizely, which was one of the
first and one of the best ways to do AB testing for apps and websites. And since then, he has gone on to create all of our agent infrastructure at YC.
So literally all of our harnesses and how we use AI internal to YC. Pete, welcome to the Light Code.
Thanks, Garry. For the last few years since ChatGPT, YC has been funding mainly AI companies. And we've been we've gone through like many different
like versions of advice for them about how to build AI-native companies that build like mainly AI products. And we've gone on a crazy journey with
them learning all of this. I think a lot of people don't realize that internally YC is actually
building and using a lot of the same stuff that we're helping our startups build and use themselves. And it's been I think a very powerful symbiotic
relationship for us to actually be adopting these tools and like transforming our own organization, which was started way pre-AI into a super
AI-native organization ourselves. And Pete has really been leading the charge for that. And so I'm really
excited about this episode because I've actually been wanting to talk publicly about all the stuff that we built internally and this is the first
thing that we're doing it. So, Pete, perhaps to start off, you sort of go back to the beginning and like talk about like there was a particular like
moment when we really started adopting these AI tools internally. It was really you who got us started
down that path. >> Sure. I'm happy to happy to tell the story here and it's I like framing it that way because it was a project that I and a few
engineers got started about a year ago, maybe a little more, but that has since snowballed into just a whole infrastructure layer that's made it
possible for us to use AI internally at YC in lots of different ways. And that's actually been one of the
neatest parts about this is watching the whole engineering team and many partners also just dive in and contribute to this infrastructure layer. We
started building our own harness inside of YC for kind of YC specific agents about a year ago. And uh the original impetus for the project was some of
the work that I and a and a few of the software engineers at YC were doing with our finance team.
Just for a bit a bit of backstory, so YC has for as long as it's existed, as far as I'm aware, run mostly on our own software. And this era has just
given us a huge advantage, right? And so, with that context, back to this moment, maybe a year ago, we were sitting down with the finance team talking
through a set of tools that we were going to build for them uh just to help them run through some of
their finance workflows. Booking journal entries, uh logging priced rounds, like all the sorts of things that make YC run, really. I was seeing kind
of two things at once. Like on one hand, uh we, you know, we had this sort of loop going internally, right? Where we'd sit down with the finance team,
the finance team would describe to our software engineers how, you know, this complicated financial
workflow worked and then software engineers would go and build some purpose-built software where there was a deterministic workflow encapsulating
everything that they had been told and then hand it back to the finance team and so on. And it felt really inefficient. And then at the same time,
this was right around the time when agentic tools were really agentic coding tools were really catching
hold, right? And so you had uh kind of the first generation uh Windsurf and Cursor that were well-established by this point. I think this is right
around when Claude Code was introduced. It felt like this was giving me superpowers, right? Um and then kind of watching this sort of old classical
way of building software in YC and then watching how I was doing things on my own machine, this it just
felt like a bigger and bigger divide between those things. And so the original impetus was, "Why don't we try to build some tools at YC that we could
use to run agents that would give the finance team control over their own software, right? Like remove the software engineers from this crazy loop
where they have to sort of understand these complicated workflows and give the finance team the tools
that they could use to encode their own workflows, not as, you know, not as Ruby, uh but as English with prompts, right?" >> I mean, what's
interesting is like uh we all funded companies like maybe even like two or three years ago when LLMs were out, but like agentic coding wasn't the
thing yet. And so the first thing actually was not agentic coding, it was LLMs for writing SQL queries. >> Yes. >>
So that's what I remember from like the first versions of what you built was uh how like good it was and how basically it rhymed with like these other
failed startups that we had funded like each of us probably funded one at some point, you know, here it was, it was working and it worked so well that
non-technical people, uh granted very smart people from finance, but with no engineering
background, could use these tools to ask real questions. I I really surprised, too, to be honest. And and so that we started with this kind of
purpose-built thing for finance and then rewrote it to be more of a general agent loop, right? And it and it's this is now you see these all over the
place now, but I the first kind of magical moment that I had was we had this agent loop and we had a tool
registry, a shared tool registry for kind of YC specific tools. And the first tool that really was an unlock for me was I think a tool looking back
that you actually built, Jared. It gave these agents the ability to run read-only SQL queries against our database. >> Right? It was two tools
actually. One was running queries against our database and the other one was the ability to read our model
files. I remember I built those tools and I felt a little bit like I was breaking the rules cuz initially we started with very limited tools that had
very narrowly scoped domains and I kept getting frustrated because they weren't powerful enough to do the things that I wanted. And so I was like,
what if we just gave the thing like access complete access to the production database where it could
just like trample on anything? >> [laughter] >> And I started to like surreptitiously pushed it out maybe late at night. And it worked. And it worked,
yeah. It worked extremely well, right? >> Yeah, perhaps foreshadowing, you know, subsequent things like open claw where it turns out that like the
thing that was hampering the world was being worried about security and privacy and all the things
that could go wrong. And when you like worry a bit less, you're like, "Oh my god, these things are unbelievably powerful." It's it's another really
good example of this weird split between I'm at work and I'm kind of operating in this really narrow box and I'm at home using Claude coder or
whatever like open claw hermie and I can do anything, right? And and trying to trying to narrow that gap. So
why was this so useful? This ability to run SQL queries against our database. It sounds really simple. Well, I think this is where it's important to
talk about one of the big advantages that I think YC had coming into this experiment uh which is that we run on our own software and all of that
software sits on one Postgres database that has everything that's important to YC's world in it. You know,
every company that we funded, there's a company's table, there's a there's a founder's table, right? There's tables for our financial transactions,
there's tables for the notes that I leave in our little internal CRM, right? All of these functions that I think a lot of other companies farm out to
third-party SaaS tools, we've built our own. And as a result, we have this database with every
important piece of context that I can now ask questions like, "Hey, show me all of the investors who invested in a space-related company in the last
four batches." Right? It just turns out when all of that context is in one place with a little bit of additional uh information about how the schema
is laid out, an agent can go and ask any or answer arbitrary questions about our business. >> That was
a magic moment for sure when I first saw that. >> Yeah. The cool thing for me is that it didn't just make it easier to answer questions, it
dramatically increased the number of questions that we would ask and dramatically increased the scale and complexity of the questions that we would
dare to ask. Where like, you know, in the in the old days back when we were using like BI tools to ask to ask a
question like that, you know, like, "What investors have invested like in space-related companies?" That would be like several hours of writing SQL.
And so like, unless it was really important, you just wouldn't bother. It's just example of the you know, the this instance of Jevons paradox that you
get when you remove the amount of back and forth uh between different teams in order to get a thing
done, right? If if in order to ans- ask some kind of complex question about YC, I have to go and knock on, you know, the data science team's door and
wait for them to get it through, you know, their backlog, uh I'm just going to ask far fewer questions. I mean, there are people out there watching
this who work in places that still use it. The majority of people live in that world still, and it's
2026, which is a little unfathomable, actually. There's a long way to go, I think, which is which is really exciting. My guess is one question is how
do companies that live in that old world could get sort of wings to move so quickly? Because our the magic for us was as you said, everything was the
context is in one place that made it easy. You know, if you think about um data science uh
historically, one of the first things that the Googlers had to figure out was a big table, right? And big table was, you know, instead of schema you
and joins, you have one big table that um can be map reduced. Yes. And so, I think that's happening again, and I would argue that's happening now with
um Karpathy style knowledge LLM wikis uh with Gbrain. >> [laughter] >> I mean, that's what I'm
seeing anyway. Like, you know, obviously I have I have an Open Claw. It has uh access to lots of lots of systems, and then I'm normalizing it to my
own schema that's relevant to me and the things that I care about. And it is like denormalization. It's you're taking data and you're putting it into
a format that uh is more or less optimized for Open Claw or Hermes agent. Like, that particular type
of harness to be able to ask questions. And it needs retrieval, it needs rag, it needs graph rag, it needs uh you know, hybrid RRF. Like, there's
reranking in there. Like, you know, all the things that everyone has learned about retrieval uh is now inside Gbrain. And then, when you give the
agents a soul, and it and you give it uh the data, and it knows you and what you care about, like suddenly
these things have insane wings. Like, I just kind of can't believe how it sees around corners. And you might ask a question, and it'll even you sort
of interpret what your question was about, and like give you a thing that uh frankly like it would take a human who really knows you well to answer.
Um all that's possible now. And so, you know, your question is like all the data is everywhere. My
answer from like the Open Claw Hermes experience with G brain is like, yeah, you basically have to take that you're going to denormalize it and you're
going to put it in a format that is optimized for agent retrieval and understanding. You could wrap it in an MCP, but for whatever reason I just like
intuitively I'd be worried like it's still so you know, these things are really good at working
with MCP and CLI. Like they're a little even better with CLI. It seems like you have to denormalize and do the big table thing, but you know,
specifically for the agent. Looking back over the last year and a half, uh it feels like we're still kind of in the single player era of agents where
the harnesses that have gotten really popular, right? Uh Claude Code, Codex, Pi, Open Claw, Hermes. They're
all designed to be used by a single human running on a single machine. And it makes a lot of sense. Right? Because in that environment these agents
can do just about anything, right? And they make you incredibly powerful. It's it's they're a lot of fun to use. I think one of the big problems uh
that I don't think has been solved well yet by anybody is the multiplayer Mhm. harness, right? It's it's
enabling that kind of superpower but on a team or an organizational level, right? And and that's I think been the interesting thing to explore with
the infrastructure that we've built at YC is watching which primitives that we've created that have enabled individuals and teams to use agents. You
asked the question about if you're working inside of a kind of a legacy organization, which is like
anyone who's more than two years old, >> [laughter] >> uh what are the things that you can focus on uh in order to help enable everybody at your org
to use AI to to more. Uh and we talked about kind of this common context layer, right? And so, a data warehouse where it just as much of your internal
important context lives, it just turns out is extremely useful. There are many tools for connecting
individual agent harnesses to uh you know, other MCP tools, other sources of truth. But just like a coding agent inside a mono repo just tends to be
much more efficient, watching our agents operating on our single database that has everything in one schema tells me that there's a lot of value at
least in getting all of the context into one place. Having an internal tool registry, this is I think
the other really important thing that we built. So, in the beginning, like we were talking about, it was just the whole system was really simple. It
was like an agent loop and a simple tool registry and you know, a few other pieces, right? Like a model router underneath. The tool registry is where
most of the like YC specific stuff lives, right? Like tool registry is what turns these agents into
something that's useful at work. And we had like 20 tools at the beginning including this magical ability to query our SQL database, but over time
teams have added more and more tools. Every time we kind of come upon some piece of work at YC that we think could be improved with an agent, we can
just add tools and there's more than 350 today. I just checked, right? Every team is adding their own
tools. You know, I can do things like manage my office hours. Our finance team can uh you know, can book journal entries, right? We can help manage
the events that we run. Uh there's tools for all of the important work that we do at YC. And now, once these all exist in in one place, you can make
them available to these internal agents that we built, but you can also make them available to cloud
code, you know, running on on our individual machines. So, those things above all, I think, were the important pieces that we built that if I were
working in any other organization I would focus on building. I mean honestly inspired by what you guys did with tools like this idea of skillify in
open claw and then actually the most important the last part of skillify is like this meta skill that I
made in open claw where it's like you just do anything in open claw and Hermes. Hermes actually already has skillify they call it something as like it
makes skills automatically. But the most important thing I I think is actually like plugging into the resolver which is like your agents.md with like
the list of things that the agents can do and then like it links to the markdown entry point that
like lets you use a tool basically. And so like this thing keeps coming up in all these different contexts like cloud code has a skill. The skill
registry in cloud code is actually a resolver. Our tool registry is actually a resolver. And then the weird thing that you have to do on top of that
is actually um I have a meta skill called check resolvable that I call all the time. So I'm always like I
do something that's new or different in uh in my agent and then after it does it and I like it I say skillify it and then it becomes basically like a
tool call or method call and then I run check resolvable which is like you know look at all of the other skills and tools that exist and is it you
know dry don't don't repeat yourself and is it MECE which is you know I'm embarrassed to say a McKinsey
term for um the consultants use it for making really good slide decks mutually exclusive collectively exhaustive. That's like how you're supposed to
do slides if you're a McKinsey consultant but it's useful because it's like an additional layer on top of don't repeat yourself dry. And like the
models just seem to know what those things are and so if you have a dry and MECE resolver table anywhere
it's actually like the optimal resolver. Like it's bad to have 10 skills that do all the same thing. It's good to have one skill or one tool that has
parameters that then let you call them. So, I don't know. I think it's like this is like the wildest time to be alive as like an applied computer
scientist cuz it's like simultaneous like discovery of the same useful applied concepts over and over
again. And I wonder if like when people were, you know, developing the first versions of Unix or something is like discovering a stack and a >> heaps.
It feels like we're right at that moment >> Yeah. today. Like we're just coming up with the new primitives for what an agentic system actually is. And
you can see it in the parallel sort of development of like we're just trying to do a thing and it
might be in cloud code or it might be in our own internal harness or it might be in open claw, it might be in Hermes. Like these things just keep
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for your spot. Yeah, it's really interesting to look at how some of the other companies that are building this stuff have built their infrastructure
because you see a lot of these same primitives in each of them, right? Like there's the agent loops, there's tool registries, there's skill
registries. Looking at at the way that we're using skills now YC, so if you think of skill as a simple
abstraction layer over tools, we have a handful of sort of shared skills uh that we all have access to uh through this through this agent system. And
it's been interesting to watch. I think you've talked about this where this progression of like in the beginning you were kind of writing your own
system prompts and then skills emerged, so you started writing your own skills and then you would start
uh meta prompting where you where you know, you'd do it again. Write a skill. Exactly. Improve the prompt. Yes. Automatically. Yeah. >> Yes. Seeing us
kind of do the same progression internally where we have a couple skills and now we've gotten to the point where we have these sort of autonomous
self-improving loops, right? Uh you know, and so uh >> Auto research from Karpathy again, you know,
yeah. Or slash goal now in Codex. Like they've they've incorporated it, too. We have this general agent that every night will go and read through all
of the agent conversations that employees have had and look for uh things it could have done better and pieces of context that if it had up front, it
would have done more efficiently. This is Open Crow's dream cycle and G brain also has a dream
cycle. This is a um a a skill improvement dream cycle, but it could also potentially um read all the transcripts and then write them back into the
internal uh DB into the internal CRM on like what we know about people and companies. Indeed. And we there are cool examples of using transcripts
actually to make these skills more effective as well. One of the shared skills that we have uh is a skill
that partners at YC use to help our companies uh write what we call two sentence descriptions, right? Everybody here has written hundreds of these. >>
We should probably explain what a two sentence description actually is, yeah. >> Sure. So, a two sentence description is a concise way of explaining
what your company does in natural language that anyone will understand and why it's interesting.
Sounds easy, but it's surprisingly hard for founders to actually And also no one does it. Weirdly. Weirdly like even the most experienced founders
like forget because they have perfect context. Interestingly, uh I now realize YC itself is uh a context engineering uh sort of process in that like
people we would frequently teaching people, you have perfect context about what's going on in your
brain, but great communication is replicating that same context in someone else's brain. And that's what a two sentence pitch is. Like what is it?
Like I don't even know what the heck this is. And then second part is like, is it interesting or valuable? What you know, is it worth my time? And so
that you know, when I when I teach two sentence pitches, that's my favorite way to do it. It's like, do
I even know what the heck this is? Because if you don't know what it is, you can't even ask a question about it. It's like, something about computers,
I guess, whatever. What time is lunch again? And then the second part is equally important, which is like, if I've heard that you know, there are like
20 companies. Like, there are five other companies in this room that do X. Like, and then I don't
understand like why this is noteworthy. Like, again, I'm like thinking about my pastrami sandwich again, right? [laughter] So the two sentence pitch
like viscerally is important for founders. And it's it's a it's a simple kind of atomic thing that every partner at YC has practiced over and over and
over again. I think Tom, uh one of the one of the partners here, wrote a skill that teaches an agent
how to uh take some context about a company and can and condense that into a two sentence description. And so that was his sort of handwritten prompt
or skill about how that was done. And one of the cool things that happened in the last month or two was that a couple of the other partners took a
meeting that they had with a a group office hours they had with a bunch of the companies in the spring
batch and just went through and had every founder try their hand at a two sentence description and kind of gave them feedback and input. And so kind
of the knowledge that lives in a partner's head about how to do this effectively was exchanged back and forth, right? And and now lived in the context
of that meeting transcript. And handing that back to the agent and saying, given, you know, what
you've learned by reading through this context, improve the two sentence description skill. And they got noticeably better after that. Like, this
thing is now better than I am, I would I would argue at writing those. This is how super intelligence happens inside organizations. [laughter] I mean,
this two-sentence pitch thing sounds like something kind of small, but embedded in it is actually
something very powerful. I'm sure you guys have heard Jack Dorsey talk about what he's doing with Block. He basically is trying to turn Block into a
mini AGI around helping people in the world make payments to one another, right? Uh and then this is actually the micro mechanism by which he's going
to do that, right? Like you can look at the operation of any organization as the aggregate of you
know, I mean, the two-sentence pitch at YC is that's sort of one of like thousands of things that I would argue we do for founders. But you know, we
just walk through a very concrete way where someone wrote a prompt, used it, used a bunch more, other people used it, a bunch of artifacts came off of
that around literally like the transcript of using it becomes a thing that can be used to meta
prompt and improve in an automated fashion on a daily basis the operation of that one skill. And then suddenly that one skill, you just said it. That
skill is now better than any of us individually than be you know, when before we actually had access to that. And so this is like a particular like
needle pin prick in the fabric of like how any organization does things. And then how do you build
super intelligence inside a company? You do that on everything you do. And it's not more complicated than that. Like you literally just compose
everything that you do and any given thing that any given person can do, you combine that in aggregate and in this particular process and like you
have a super organization. It's possible now. Like every single person watching this can do this at any
company, at their own company, they can do it at their job. I mean, the interesting thing is that's why you should start a startup Cuz people are
going to be trapped in organizations with people running organizations that are very powerful and have all these resources and all this capital that
do not believe what we just said. >> Because they keep all the context locked down. >> Right, because
it's unsafe. Unsafe. This is one of those things that we talk about um how to build that AI native organization, right? Part of the key thing is not
to do just use AI as a co-pilot. I think that's very 2020 three, four, right? This is the thing where you use it as a really the building layer for
everything. And you need to start recording all the artifacts. Like people wouldn't have thought of a
meeting recordings and this is one of those reasons why all these uh meeting recorders have been taking off. People have been finding them with
coaching them on the meetings, but it's not just that. You could take that and improve all the output for you that you do, like writing emails,
communication, planning. You have the whole context of everything. It's funny to say I remember the Dario essay
where it's like there's some of the blockers on just the rate of progression of AI are not technical, they're just sort of like social, cultural
things. Things kind of like a really interesting example. Two years ago it would have seemed to I just remember like it felt odd to just like record a
meeting or like there was just like people trying to figure out what the like social etiquette around it
was and like how intrusive it was. And today I just feel like it's almost like default assumed that like most meetings are being recorded, especially
if they're on Zoom, but just in general like everyone started recording things now. It's a little scary, but I think if you frame this as a way for
everyone in an organization to get better at what they do using the like collective skill and instinct
of the people they work with, it's incredibly powerful. Having a canonical two-sentence description skill is not just a way to like generate a a
snippet of text for a a It's a way to help me get better at understanding what makes for effective founder communication, right? Because now I can tap
into everything that Diana and Hardt and you two have learned over the many years you've done this job,
which are now kind of baked into this skill through the conversations that you've had. >> It's like a shared organizational brain. Yes. It's like the
closest thing to us being able to like connect our brains, right? Yeah. >> It totally is, right? And I I can have an agent now come and I can do
practice sessions with it, right? I can have it critique. Like there are so many possibilities once you
get all of this knowledge into a place where an agent can work with it. It's a It's a very empowering thing for every human in the organization.
There's some subtle interesting things around here that like, you know, other people might get wrong that like, I feel like we've gotten right. I
mean, one of them is by default the agent conversation is actually globally viewable by any full-time
employee at YC. You know, we sort of weren't sure about that decision. I mean, it felt right and it felt like living in the future, but it did not
come easily. I feel like we had a lot of conversations about like, "Well, then everyone sees everything. Is that okay?" And like, you know, "What is
not okay?" And then I'm glad we made the choice to keep it open, actually, cuz I agree. >> people
learned how to use it from watching how other people used it. >> Yes. We used that transparency to solve several problems at the same time. One, every
agent conversation, as you mentioned, was broadcast internally to a Slack channel. And anybody could join that Slack channel and look and learn,
right? And I remember this is another kind of big unlock moment was when you started using it really
heavily. You were like super creative with the things you were doing with it. And a lot of us watched that. It was like, "Oh, wow. I didn't even occur
to me to >> to to use it that way, right? It allows you to be a little more lenient on internal security, right? One of the things we talked about
earlier was this trade-off where these agents are at their most powerful when they are given
unrestricted access to lots of context, which runs counter to the way more most organizations work. It turns out that by defaulting to public
broadcast for these conversations, you kind of institute a bit of a social control on what people can do with it. Uh that as we learned, I think has
been like reasonably effective uh inside of this high-trust environment at keeping private information
private. Yeah, what's interesting is um it betrays two traits of uh truly agentic, like 1,000x superintelligent organizations that I would not have
necessarily guessed would exist, but are now like must exist if you want to create this type of organization. You have to be relatively egalitarian,
and you also have to be trust by default. And then neither of those things uh actually are most
organizations in the world. If you're the founder of an organization, you actually have to have those at the core of what you're doing. And I think
like that kind of environment honestly works best at startups, right? When it's a small group of people that are all aligned and and operating in a
high-trust environment. >> The other thing you have to do is be willing to spend like 10 to 100,000 a
year on tokens. But if you're willing to do it and you invest in the skills and you like actually do everything in an open way with your team that
way, like basically what I realize is it allows you to live in 2028, right? Like what you spend 100,000 or a million dollars a year on now, it will be
commonplace like in 2 years, right? It'll it won't cost 100,000 in a year, it'll cost 10,000, and the
year after that it'll be like a couple hundred bucks, right? And everyone will do it. And we'll call it like this is how companies are now. So,
basically there's a one-time warp where you can leapfrog every incumbent, all Fortune 500s, all startups that exist by doing this. Like I'm imagining
in the '90s, I wonder if it felt similarly when companies started buying computers for their employees. >>
Yeah. They were probably very expensive and probably only certain companies really invested in buying these like expensive, flaky computer systems for
their employees, but like what a superpower to have a computer when your competitors like don't have computers. I think more tactically how I've seen
this affect uh YC has been raising the floor. The floor. The floor in a sense. What I mean by that
is that you could have a new employee joining and maybe would have taken them 6 months to ramp up, but with this it's sort of like they automatically
get a lot of the context from the company working and they know how the best people on the star players in your organization do things by
apprenticeship automatically with AI instead of uh because partner time is expensive or sometimes the best
people in our org they're very busy, right? And you get to kind of run the simulation of what it's like to be like Pete when he does like a awesome
job coaching founders on sales or like Gary when he's like talking to founders and giving very specific advice. I think it helps all the new entrants
in the organization just be a mini version of you a lot faster. One of the first things that I
appreciated about being able to use a coding agent was that all of the dumb questions I was too embarrassed to ask, I had no trouble asking the agent.
And it this is kind of that same thing but at an organizational level, right? You're a brand new employee, you're embarrassed to ask, you don't want
to bug Harj with a question and now you don't have to, right? You and which on that means a lot more
questions get asked and answered and people ramp up much more quickly. After you had built all of this agent infrastructure at YC, it inspired you to
write this essay "Horseless Carriages" that went like pretty viral on the internet. Maybe you can like explain the ideas behind horseless carriages. I
think they're still very relevant now. It was a critique of a lot of the AI software that I saw
being built at the time. And to be totally honest, I think a lot of it still falls into this >> It's still like that. Yeah, it didn't change. >> Yes.
I just saw a lot of examples of uh companies building software and adding AI features by sort of slotting a little bit of AI inside of a lot of
software, right? And the example that I used at the time was the kind of email writer that uh the Gmail uh
team had shipped. But the real idea underneath was this kind of that the the potential for AI is to shift control of software from the developer to
the user, right? And the simple example I started with was basically that all of these kind of like AI as a little feature kept a bunch of prompt
context about how the AI should do a job locked away and hidden from the user, which is just this classic
example of like, well, it's the developer's job to figure out how all of this stuff should work. So, the developer should write that, and we should
protect the user from that kind of complexity. >> Safetyism, I hate it. >> Right. And you know, and it And it's just again going back to this contrast
between watching the way that some of these tools work and what it was like to use a coding agent on
my computer that could do anything, right? And feeling like I I had superpowers. I think the conclusion that this essay points to is that as we get
better at building AI-native software, it's going to look a lot more like the agent wrapping software deterministic tools rather than deterministic
software wrapping an AI, right? And we've done our best to expose that to internal employees with some
of these primitives that we built. Um but we have a lot we have a long way to go. The chat as the interface, I just feel something There's like I
things going around right now about how there's a need to build new interface for like AI and what does that look like. And I think that just comes
from people who haven't like touched and felt it yet. Chat is actually pretty good because like you trust
the agent, you increasingly trust the agent to do more of the work, and you trust its decisions, and you don't actually need to like have too much of
a UI to go in and like review the things it's it's doing. I feel like it's time for a just-in-time software. >> Yeah, basically, right? Like yes,
occasionally you want it to present you like maybe you like a specific view of something, but >> And it
could make the software and build it as a single-page JavaScript just purposely built for you at that moment. Yeah. And it could be a skill file that
could be like called anytime you want. I was thinking a lot about this because I used to be in the camp that oh, when ChatGPT came out and it was
2023, that perhaps chat was not going to be the UI for all these AI applications. And I've definitely
changed my mind. Part of it is that after experiencing all these tools, and I think the more I reflect upon it, why chat is probably the better
interface is because it's the closest thing to human language, and human language and writing is basically the closest thing to expression of
thinking. >> Mhm. So, chat is the closest stepping stone to clear intelligence. >> Yeah. So, you can't just put it
in a box. I think it just constrain us too much to have a very specific box. So, that's why I thought it was like, okay, all in with chat interfaces.
I used to be in the other camp, and it's like I just multi-modal. I know we've talked about like Telegram is not ideal, but I actually really Yeah,
it's pretty good. Yeah, it's pretty good. >> And the voice memo, sometimes when I don't want to type,
you just do the voice memo, and it's it feels like I'm talking to Like I can give my open claw, like I can give it text, I can give it voice, I can
give it pictures of things, like I can give it files, like it's like pretty good. >> Yeah. I just experienced this. So, like January, I think the last
episode we did, I just talked about this, like I spent January and through February building a half a
million lines of code for a Rails app, which was Gary's list, and it was like, yeah, I know people make fun of me for like it was a blog, but it was
like I built the blog in like the first week. Like I spent a month and a half building a full agentic framework that did like my own version of deep
research and like fact-checking. But the thing is I built it the way I would have built software in
2013, the last time I wrote code. It was like the web 2.0 version of this. And Claude code lets you do that. And what's crazy to connect is like I'm
working like I don't know. I think I wrote like 40,000 lines of code the last 3 days just for G brain. And G brain is basically Gary's List 2.0, but
it's totally open source, right? So everything I had to write for agentic retrieval, everything I had
to do for voice extraction, everything I had to do for fact-checking, all of that now exists inside G brain and I just gave it to my, you know, Gary's
List team yesterday as their own open claw instance. And they're flying now, right? Like they were complaining about like I had made, you know, this
monolithic writer chat interface and it was like full of bugs cuz I was like re-implementing things
that open claw and telegram already do. And now they just use open and claw telegram and my retrieval system with like all the same data that I
extracted it out and with our MCP and it's working great. Like basically, you know, Gary's List 2.0 the next rewrite thankfully is not half a million
lines of rails code that is like insane to actually, you know, it's rigid, it's takes a long time it like
takes like 10 times longer. You know, even though it was 1/100 the amount of time to do it like by hand, you don't have to do it by hand. Like that
half a million lines of code in rails is easily like 10,000 lines of like typescript and like maybe 2,000 lines of markdown. And all of that is way
more dynamic. Like you could just say like actually, for the second paragraph, I really like including a
biography of like the we're focusing on. And it's like I don't have to code that in Rails. I don't even have to write that into um a Ruby file that
then gets evaled in like, you know, my complex eval infrastructure. Like, Open Claw just knows that, and I have an eval skill. My editor-in-chief can
just change it on the fly, and I didn't touch it. Yes. And it's like, this is insane, actually. Like,
this is actually the dawn of just-in-time software, and I can see it right now. The best AI software that I've used, whether it's inside of Wyse, or
tools that others have built, tend to be very small. And just add kind of the smallest amount of code ahead of time that you need in order to let the
model shine. Mhm. And you can build an awful lot with that, right? I can write tens of thousands of
lines of code, uh like you're saying. But, the ability to start at this like extremely simple thing that I need to understand very little in order to
use is incredibly powerful, and I think that's I think most software in the future is going to look like. >> talking about this earlier, but I think
that is what Open Claw did really well. Like, there were like a few things that you want you wanted
like some ability to give it a bit of personality. You wanted it to like persist and last for a long time, and have some concept of memory. And it's
not like perfect, but that's like actually like good enough is like for that use case. And Claw code, too. Every time Boris comes and speaks at Wyse,
he spoke with Diana uh earlier this week. One of One of the things that really stands out is how
obsessed he is with simplicity, and with just like making the product as small as possible. My favorite example of this is uh the this open-source
harness called Pi, which is a That's what That's what Open Claw uses as its out-of-the-box coding agent. It's this beautiful piece of software, which
is just like the smallest possible coding agent. You can use Pi to modify and extend Pi, right? And
it's this kind of idea of like self-extending and self-referential software. It's really fascinating. And and you're right, Openclaw was built on top
of that. One of the things I'm very curious to see is how many other sort of pieces of classic software emerge in this form as this kind of minimal
thing that you start with uh and then use an agent to extend over time. I think more and more I mean,
looking at honestly the benefits that we've gotten from having our own customizable software, I suspect that a lot of commercial software uh will come
with this capability uh out of the box in the future. There's a really interesting subtle thing that I wanted to talk about around like what I learned
from your essay, uh which is like AI can either be centralizing or decentralizing. And um the
Google Gmail like I can't change the prompt thing is like the perfect example of that. We basically have a choice to be made over the next I don't
think it's even that long. I think it's like 18 to 24 months. It might take 5 years, but um there are sort of two scenarios and uh what comes to mind
is literally like the uh 1984 Macintosh commercial by Apple where it's like is 2034 going to be like
1984? And you know, the 1984 case would be we have centralized control, like there are five kings, there's only, you know, one of them maybe wins.
They have the most advanced AI. They have uh end run around all compute and power. They have all the space data centers cuz they could you can't build
any terrestrial data centers in America anyway. There's this like centralization of control. And not
only that, they don't let you run your own prompts. Like they literally do the Gmail thing, but like for your whole computing existence, right? And
this would be as if like personal computers never existed and there were only mainframes and minicomputers. Like this is sort of lost to the sands of
time, but you know, in the 1960s and '70s when computers first came out, like you couldn't go to the
store like you can today. You couldn't go to an Apple store and just buy an iPhone, let alone uh a Mac. You had to get access to like this thing that
was worth like hundreds of thousands of dollars to millions of dollars. And the only >> was like And it was like tightly locked down by corporate
policies. You're right. And the And the thing that really spurred the computing revolution was when
people started having personal computers that they could experiment on. Yeah. And just like the priesthood, right? There was a small priesthood and an
institutional base that controlled capital, literally the means of production. And so, you know, this is like a coherent future that we could live in
that I don't want to live in. And the alternative to that is actually uh embedded in the Homebrew
Computer Club. It's embedded in the revolution that Steve Jobs and Steve Wozniak gave us when they were in the garage in Mountain View, literally
soldering together breadboards. And they like sold 500 of these Apple Ones. And I think we're at the Apple One moment right now. We are coming up with
the primitives. We are learning how do these things work and how do we sell it and how do we package
it? Uh but then there's like a lot of choices right now, right? Like most people, the mass you know, a billion users use ChatGPT. And ChatGPT like
gives you a little access. But MCP is really locked down. You actually, you know, can't hook things up to your own databases that easily. Um and you
know, for what? Safety? Like I would argue Claude is like a little bit more open, but not really.
Perplexity is probably the best version of it, but it's still like you know, pretty limited compared to what you could do with Open Claude and Hermes
Agent. And so, what does the uh revolution look like that is like the true personal AI moment? And that's what I hope that we are building with things
like G Brain and you know, Hermes Agent and Open Claude. Like the ability to run your own software,
to change your own prompts, to test all of it, to have your own private repo that like you know is only yours, to be able to choose which model to
use, and maybe it's an open weight model. Like to me that's sort of the white pill for AI is we could have corporate control, no control of your own
prompts, and like literally the AI happens to you. You know, you're under the API line. Or like there's
this other alternative where I want like a billion people to actually control and program for themselves what are these things. This should be an
extension of yourself and what you care about, not what, you know, Meta or Alphabet or even OpenAI or Anthropic care about. I always really bristle
when I see AI framed as a way to replace people because it just doesn't match the way that I have
experienced it and the way that so many of the people around me have experienced it, not as a replacement for humans, but as a thing that empowers. If
you look at at kind of how tech has developed since the era of mainframes to PCs to the internet which gave everyone like a publishing platform like
it's it's a story overall above all of individual empowerment and I think AI is going to play out
the same way. I think it is going to enable us to do more than we could before. I think it's going to eliminate kind of the drudgery style work that
like made a lot of my job painful in the past. To me it's like we have to make choices to do so. By default like a company is not open. By default a
company is command and control. By default maybe the leadership gets access to these tools, but like
the, you know, line level people, the staff people don't, right? And like we need like a radically different type of organization and we need to
actually offer computing in a different way and these are all choices and the people who are watching are going to be the people who build all these
things in society. So, we better choose well. >> [music] >> Well, that's all the time we have for today. I
mean, I think we covered some pretty heavy stuff, but Pete, thanks for joining us. Thank you. Thank you. Thanks for watching, guys. We'll see you guys on the next one.