Fuck, were you even doing in 2008?
So in computer vision, it was SIFT features and SVMS that
that's basically the state-of-the-art at the time.
There were a couple other things that people are using, but.
Not even using GPU's. I think that some there was,
yeah, basically Alex was the first one to to write
specialized kernels. There was some applications, I'm
sure where it like, you know, optical flow, it's highly
paralyzable. And I wouldn't be surprised if
there were some labs that were doing things along those lines.
But it definitely was not like a scaling era for machine
learning. Largest data sets you know you
could fit on a USB stick. What gave you the inspiration to
start Code Jet? And Like, you must have seen
something that was like, oh, this is getting better.
We could probably do something here because there was a company
that tried it and then they folded, right?
I can't remember the name of them.
The history of cogeneration is littered with companies that
were a little too early but like, totally visionary.
So you're probably thinking of Tab 9.
I think Tab 9's actually still around, and they're still.
Going and then it wasn't them. There was one that height I
think it was kite in like 2022 right?
Or 2020 maybe? Maybe even before that a little.
Bit like 2021 right? They folded right when Chachi BT
started getting really popular and it was very confusing to me
because I'm like wait GitHub copilot is printing money?
Supposedly they just hit like a billion in revenue after they
released and kite shut down. You hate to see it.
I mean, you know, any start up dying.
It's sad to see. I think that one of my learnings
from doing startups for about a decade is that momentum begets
momentum. And if you are too early, you
know, you kind of get people all hyped up on this vision and then
you're actually not the player who ends up bringing it to
market. Maybe some people can kind of
catch on and they end up taking off at that point.
But in the majority of cases it ends up being that.
Yeah, get up. Copilot is the one who
introduced the concept. That's what people get excited
about. That's what gets hyped up on
Twitter. Kite is remembered as like the,
you know, everybody tried it in 2021 or something like that when
Paul Graham hyped it up and they're like, this isn't really
ready for production yet. And get up Copilot was, you
know, is directly integrated into VS Code from the very get
go. And so you don't really have to
do very much in order to get access to it.
But then the sort of I think the bigger revelation was cursor,
Cursor get up Copilot was 2022, I want to say is when that came
out and it immediately it was in the happy path, which was
amazing. So you don't really have to do
very much in order to benefit from it, right?
It would just do auto completions for you once you
flipped it on. And I think that that same
experience of basically not having to take any additional
action, but just having AI seamlessly injected into your
workflow that's going to roll out to a bunch of other
industries very soon. I think that cursor is sort of
the, the thing about cursor was nobody could tell you not to
download it. So like most of the people who
ended up adopting it early on, these sort of hype guys on, on
Twitter, which, you know, I'm very much so a part of that
ecosystem where people who are like working at, you know, I
don't know, JP Morgan or something like that.
They download this editor, they swipe their personal credit card
for 20 bucks a month. And then for me, the revelation
was the composer mode where as a sidebar that pops out and you
can kind of chat with it really kind of hit stride with Claude
Sonic 3.5, which was probably mid 2024, something like that.
Yeah, that's the inflection point right there.
But then code, Jen, what made you want to start it?
And did you see that? How did you see the writing on
the wall? Depends how far you want to go
back, I would say, you know, I've always been interested in
artificial intelligence. You know, I think that the idea
of the machine writing itself and creating itself is this sort
of recursive building and something that's always really
appealed to me going way back to, I mean, even high school.
Is the book good to Lecher Bach? You might be familiar with it
sort of like a It's a Pulitzer Prize winning book and it's.
Way overestimate my culture. Got it.
I do not know that at all. What is?
It, it's a book called Gerdelescherbach.
It's by this guy Doug Hofstadter, who he sort of like
invented the Simsys major at Stanford in a way, or at least
the the major that Marissa Mayer and many other people sort of
made famous. It's like based on this book
almost or directly reflects the contents of the book.
At least it sort of coincided with him writing this book went
on to win the Pulitzer Prize. The core concept of it is you
have MC Escher, the painter, who you might know the painting of
him. Yeah, exactly.
Johann Smaschenbach, the musician.
And then Kurt Godel, I'm definitely mispronouncing his
name, but he's a logician. And all their works have some
inherent properties to them or substructure that's shared.
And usually that has to do with self reference.
So for example, there are exactly the stairs, the, you
know, hands writing themselves. And there's actually like a lot
of like really interesting mathematical concepts embedded
both in Bach's work, whether he meant it or not, and MC Escher.
And then Kurt Godel is famous for the essential incompleteness
theorem, which is basically this idea that any sufficiently
powerful logical system will necessarily end up containing
things that can neither be true, proved that are true, but cannot
be proven to be true. And the way in which it does
that is basically it contains something that looks like the
epimetitis paradox, which is where it's the guy gets up in
front of, you know, a, a bunch of people up amenities is the
guy gets up in front of a bunch of people in Rome or in Greece
and he says, citizens of Athens, I am lying to you.
And because the statement refers to itself, then it's the self
reference is basically what enables it to be contradictory.
So it's a paradox. And any sufficiently powerful
number system can contain a self reference like that that enables
it to basically say this statement is not provable.
And so anyways, it's a little bit of a sidetrack here, but the
interesting part about this book is it ties together all three of
these different concepts or these different, you know, you
know, prolific people's works, MC Escher, JS Bach and Kurt
Godel. And basically it's about
entities that contain self reference.
And the guy uses that as a jumping off point to talk about
AI. And eventually he talks about
the emergence of consciousness comes from systems that
represent the outside world. And part of the representation
of the outside world in this symbolic system contains
representation of self. There you go.
You landed the plane beautifully.
There we go. And so code Gen. from that book.
So anyways that that that sort of kicked me off.
I'm getting really interested in the stuff.
So Jenny and I went through. I worked at Palantir, I studied
AI in college, I did a bunch of machine learning stuff to the
decades worth of startups, give or take.
And around 2020 is when. So I've always kept up with the
state-of-the-art of yeah, research.
It's just sort of something that's very interesting to me.
2020 is when the GBT 3 paper came out, which was essentially
just scaling up language models and then ended up having this
incredible property. So basically few shot learning.
So you can give it pretty much any problem that we were dealing
with in NLP at the time. It's just like you few shot
prompt this thing and bam, it's better than whatever you had in
mind. And so I knew this was going to
be big. I remember joking with somebody
that like between Bitcoin and GBT three, it wasn't clear which
would have a bigger impact on the world and they were like,
that's so no, like obviously Bitcoin has a bigger impact and
I guess TBD. 2020, yeah. 2020 it wasn't clear right?
And then 2022 is when the API opened up from Open AI and I had
just basically sold my previous company.
I had ended up my lock up period and I ended up leaving for
various reasons. And I entered into this sort of
flow state of doing what I called one demo per week where I
would literally just sit on my computer and come up with really
interesting concepts of something you can implement
using the GPT 3 API and some image generation stuff as well.
And just put it on Twitter and see what people think and try
and get, you know, people excited about it.
And so couple notable ones from that era for me, where I think I
was the first person to ship text to Figma.
And so you could input a prompt and you could say, hey, give me
an iPhone screen with a bunch of other stuff on it and it would
essentially one shot it and generate you a schema file.
And then that would render to Figma.
I did a text to auto tool or sorry, text to retool, but it's
actually known on retools, like on an open source one where you
could describe a dashboard and it would have data schema that
was referencing and it would essentially create a data
dashboard for you. And so, you know, what I sort of
discovered over the process of building a bunch of these
applications is that the things that made AL one application
successful were verifiability built into the domain.
And so anytime you're doing some type of a schema generation like
that, you can essentially lint it.
You can go through and say, is this a valid schema?
Does it compile? You know, they're they're like
specific empirical things you can look for.
And if it's wrong, you can actually detect that error and
basically pass it back to the model as a residual and say
figure it out dude. Which is so clear now, but in
those days that's incredible. You probably felt like you
stumbled on gold. I think that I don't want to
give myself too, too much credit here, like I think a lot of
other people were, you know, also seeing like, OK, it was
limited value and like an e-mail thing for you.
Yeah. Summarization or.
Yeah, auto complete or something like that.
But anybody who's coding, you know, with compile at the time
was like, wow, this thing is incredible.
And you know, it's actually, there's so much low hanging
fruit in code too, where it's just boilerplate.
Like you don't really benefit from writing a Java class
implementation except for a couple lines of code in there.
So it's very well suited. And it compiles, which is
different than a lot of the other things that you have to
eval. Exactly.
Yeah. So, you know, and the
implications of that are pretty clear as well with respect to
our LVR reinforcement learning from verifiable rewards, where
you can have an agent go off and write code and try and pass the
unit test. If it doesn't pass the unit
test, you say here's the failure, fix it.
And then eventually it goes off and it does pass the unit test.
And then you say, OK, learn from your mistakes.
Or maybe you rephrase your trajectory so it's perfect and
then you fine tune on that. I remember seeing one paper
actually, that was really an aha moment where they were trying to
train, I think it was for performance improvements.
So they said, OK, here's a certain function that we have
and we want to train a model to be good at rewriting the
function such that it's more performant.
You can run the function and you can measure the wall time it
takes to actually the number of CBU cycles it takes to run a
function. And so they'd have a try, you
know, 100 different variants of it.
The one that actually ended up being more performant, they'd
say, OK, this is the one that we're going to end up fine
tuning on. And that's now we just call that
RLVR. But this is one of the first
applications to that. And so it was also pretty clear.
I guess the point I'm trying to make is that the direction that
research would go in would be these things are going to get
really good at coding really fast because the feedback loop
is instantaneous. If you're working at Anthropic,
like you're writing code with these things all day, you can
figure out what it's lacking and then that informs the type of,
you know, emails you put together.
Yeah, all the necessary like boundary conditions are set up
for your success. Exactly.
There's so much data you can produce it synthetically.
And at the time, in 2022, people like Gary Marcus were getting
out there and saying like. We're running out of data.
Which everybody knew was, you know, first, it's false that we
didn't run out of data. There's still plenty of, you
know, appreciating data available.
But also it completely ignores the fact that you can
synthetically create. There's synthetic data, and the
Internet is doubled like every three days, the amount of data
that's on there. I know that a lot of the new
data is coming from LMS, but this conversation is going to go
out. And it wasn't on the Internet
yesterday. Yeah.
So like that kind of stuff. Yeah.
I was always a little bit skeptical on that idea of we're
running out of data. But then you had ILIA came out
and said it had some nerve apps too.
And it's like this guy's way in a different league of brain
power than me. Like, should I question my
belief? So I don't know.
Yeah, the the Internet doubling is probably increasingly not
from human generated data. So you have to be a little bit
suss of the stuff. There's the sexually Speaking of
ML off one of my favorite papers of all time is it's called
machine learning, the highest interest credit card.
The one you had to reference, any talk that was given around
machine learning in ML OPS and from like 2020 to 2022, it was
you had to reference that box of like the models.
Just a small piece of this, right?
And there's so many opportunities to shoot yourself
in the foot. And the example that sticks with
me from that paper is they talk about training Google Translate
and how, you know, they assemble parallel corpora.
You get a bunch of English pages, you get the Spanish
translation when they're, you know, available in both.
And then you train it something to map between the two.
And then they realize at a certain point that actually most
of the Spanish pages on the Internet are the output of
Google Translate. And so they're essentially
drinking their own backwash. And I think that that's
absolutely what would happen if you just try to pre train on all
text on the, you know, on Reddit 2026 onward.
It's like probably mostly open claw at this point or whatever
he calls it. Yeah.
So you gotta be a little careful.
Yeah. And it's essentially getting
these loops of like confirming to itself that the correct
translation for an idiom is, you know, whatever it thought was
like collapses in weird ways. Exactly.
Now then you just sold Cogen to click up.
Walk me through maybe the process of selling and then also
now what you're doing. For sure.
Yeah. So we built Cogen over three
years, the original concept behind it, we started it when I
think Cursor had already kind of hit their strut.
So it's clear that they were going to win.
I think if you asked me, like in early 2022 or sorry, early 2023,
what's the landscape going to look like?
I'd say Cursor, this can be the editor of choice.
Maybe the S code will make a comeback.
Didn't happen, You know, maybe there'll be like one or two
other players. And so we thought very early on,
OK, we're going to essentially leapfrog and we're going to
shoot to where the puck is going, schedule where the puck
is going and build a fully autonomous background agent.
And so the idea was ticket to pull request.
You essentially create a linear ticket or click up ticket or
something like that would say, I want this change that.
It would spin up an agent in a sandbox who would go off and
write the code and then submit a pull request back.
So you wouldn't even have to deal with the ID.
No ID. You should be able to pull it
into your ID and iterate on it there.
But I think that the, you know, this is in GPT 432 K era.
That was like the first model that came out that could really
do like agentic sequences. 32K is roughly the size of a single
tool usage for the cloud codes, like limited, it's like 15 K.
It's like 2 tool usages. Now.
It was very difficult to fit that all in.
But yeah, we, we essentially were the first people to launch
a thing. We started with linear, actually
something we could give it a ticket.
We go off in the red code. In the three years since we
launched that, the space developed a lot.
And you know, we were sort of discussing earlier, it became
clear that code is such a fundamental thing for these
agents to function properly. A lot of people have thought
maybe there will be code specific models and non code
specific models. And really it seems like that's
not really the case. Currently there's GBT 5.3
codecs, but whatever knowledge that model has that's specific
to code, that'll be folded in a GBT 5.4.
Because there's this notion of positive transfer where if you
get better at coding, coding is sort of just an encoding of
reasoning. And so it gets smarter at
everything else. And so, yeah, basically the
exact product that we rolled out and we got great customers.
We had a ton of community engagement and whatnot.
We're sort of the first there. We end up seeing increasingly in
the deals that we were going into that the Foundation Model
Labs were offering their product for free for two years at a
time. And so there's no procurement
department in the world. You can go to where they're
like, yes, I'll take this company that, you know, fly by
night, San Francisco startup. And you're using them in the
background, I imagine, so they're your competition.
Exactly. And many other companies have
gone up against this windsurf famously, you know, have Claude
yanked, I think cursor, you know, they're now training their
own models as well because their margins are so much worse than
than what you get in Claude. We're sorry that.
Yeah. Claude.
Good. Yeah.
But yeah, I mean it the I think the at a higher level, you know,
this is this is bigger than all of us was basically my my take
away like code is going to be solved in a year maybe.
I mean, I don't think that you and I are going to spend that
much time in an ID very soon. I think that is going to be
largely driven by, you know, $100 billion training clusters,
essentially. These people have so much
capital to invest. And I think we're facing the
automation of knowledge work more generally.
That's a really exciting thing to be a part of.
And as somebody who's spent the last 20 years or so working on
AI, I wanted to be a pivotal contributor to a winning team.
And meanwhile, we had built this really great partnership with
Clickup and a couple other companies.
We ended up finding ourselves in a position where one of our
business partners offered to buy the company and it was perfect
timing. I was like, wow, this is
actually really exciting. We went through 1.
Of your business partners company.
That we work closely with. Unfortunately there's some
things I can't discuss about this, just to you know.
Preserve. Keep them anonymous then.
Company that we work closely with and.
But it wasn't click up. This the initial company that
offered by us was not. Click up.
That's right, but it got you thinking.
Exactly. We had a great relationship with
them. The CEO is a total visionary and
that spent a lot of time kind of chatting with him back and
forth. He had been a user of our
product and given us feedback and whatnot.
And through a sequence of events, essentially we were the
number one agent on Click that was an external one.
It became clear to me that this is a winning team and this is a
really exciting vision to work on that goes beyond just
engineers sitting in San Francisco working on code.
And I'm happy to get into why that's the case.
There's, you know, a lot of my personal theses essentially
lined up with what Click Up was going after so.
OK, go more. Yeah.
But I think that the one of the revelations of the last couple
of years, like I mentioned a second ago, is that coding
agents are generalist agents. If you make an agent better at
coding, then it ends up being better at everything else
because of this notion of positive transfer.
But you know, also, if you look at anything that an agent will
accomplish that has tools or you know, does various things in
order to reach out into the world, all of those things that
it uses to reach out into the world are comprised of code.
And so if you have an agent that can write and execute code, then
basically it ends up being a, it's like AGI complete.
It has the ability to write its own tools that has to create
tool, tool tool, which is write a batch script, right?
And so it seems like me that basically coding agents are
convergent with generalist knowledge worker agents.
And I'm not the only one who's knows that.
You know, a great example of this being sort of distributed
more broadly is clod code used to be called clod code and the
Clod Code SDK was called the Clod code SDK.
Now it's just the Clod agent SDK and open claw.
Also a great example of this, the way it does stuff is the
writes code on a sandbox. So you just basically have these
primitives of for loop with ALM call on it, sandbox, put them
together and you know, boom, you've got a fully general
station. And I think if you assume that
jet like models are going to get better, they're going to be
capable of performing any generalist task that relates to
knowledge work. The people who are going to.
Win in an. Environment like that are people
who have the best access to context, the best access to
surfaces on which you interact with agents, and the best unit
economics and existing distribution in order to get
that in front of people. And there's essentially no other
player than Clickup that has all those three things in a way that
I thought was, you know, the most compelling.
So we got hitched and here I am now I'm running our AI
operation. But there's so many things that
you said there that that are fascinating to me, especially
around like just how we interact with.
Basically my experience going through AI has been most things
that end up rolling out to the world more generally.
They start in code. The whatever pattern ends up
catching hold, coders do it first.
There's a lot of good reasons for that.
One of them is just programmers are working on the stuff
anyways, but we talked about the verifiability before.
So like tab auto complete started in code sort of sidebar
that pops out that you chat with that also started code.
Even autonomous agents, arguably like the first true autonomous
agents were programming agents. And now it's sort of rolling out
to the the the world more generally hooks.
It's another good example. And I'm just so excited for the
rest of the world to go through the experience I went to, which
is, you know, I got cursor and Claude code and my whole job
changed. I now essentially will prompt
things or review code and whatnot, but I can my leverage
has expanded so significantly and this idea of cursor for your
whole. Job if you are.
A financial services person or you're working in something like
legal services is going to be an incredible experience for all
those people to go through and the best place to do that is
click up it's cursor for your whole job.
Yeah, One thing that I've noticed myself doing is when I
create apps now, it's default local first, which is different
than how I have always been thinking about it.
It's like, oh, you need to be thinking about production.
This needs to be battle tested. But now it's like, no, this is
just for me, you know, like I, I was talking to somebody last
night, they said I created 40 apps in the past three months
and only three of them have I actually open sourced and like
given to the world because there's just, it's, it's mine.
It doesn't need to be, you know, it's so specific to my need that
I'm not going to put this out there.
I don't think anybody else needs it.
And also, you got it. Then think about like security.
If you're going to open source stuff, you want to like clean it
up. You want to do things that you
don't want to, just like throw anything out there.
Yeah, yeah, there's totally something to be said for
shifting models of how software we're run, given the the case
that it's so cheap to produce. Yeah, that is the wild thing
that you have these ethermal and like the generative UI is
fascinating to me too and the MCP apps type of things that'll
it's now becoming more and more a thing.
And so I imagine you'll start adding it and click up soon
enough where if you don't already have it, where, OK, I
need to do something. Boom, you've got the UI right
there in that moment. For sure.
Yeah. I mean, I think that especially
with an application like click up, there's these data models
that represent everything you want to do and work and sort of
the primitives for interacting and getting things done like
docs and chats and tasks and whatnot.
And depending on what task you're trying to accomplish,
there's wildly different ways of going about arranging those
things. Click I was incredibly
configurable and that is a blessing and a curse.
It's a blessing because if you configure it correctly, then
it's, you know, does exactly what your workflow is, but the
curse is takes a while to to configure it.
If you have a very specific thing and there's, you know,
certain things you need to know. And the amazing thing is
generative AI is assault for that.
So you can come in and you can say, hey, I am the CEO of
NVIDIA. My supply chain is in click up,
tell me about it and it will build you a dashboard.
You know, it'll, we have a thing rolling out very soon that is
going to essentially allow you to have completely custom
JavaScript layers on top of the core click up interface in a
very secure way. And so it will be a bespoke
interface for whatever you're trying to accomplish at any
point in time. The amazing thing also about,
you know, this sort of custom finances, I'm sure you've seen
demos of this of like, you know, Cerebrus running various models
of like 15,000,000 tokens per second or something.
It'll be instantaneous. In the future Tadarus or
something? I saw that, yeah.
It's like they. So much faster than anything it
made like Cerebrus look this big on the graph.
Yeah, they like compiled it to a chip basically and that's
imminently going to happen. That was Llama 38 BI, think 3.18
B. There's no physical law that
says you can't run GPT 5 on a chip like that.
And so yeah, that means the the amount of time it'll take you to
write, you know, fully custom Next JS app that has all this
custom stuff in it is going to be like 100 millisecond, 200
milliseconds, something like that.
How many tokens are actually in a full Next JS app?
Not that much, which means that your interaction with software
is going to be fully dynamically configured.
And I think that's really exciting.
There's a lot of, like, it's easy to say that you have to
actually execute on it. And nobody's ever produced an
interface like that. So we don't like, just like
people said, like, Oh yeah, like Minority Report style thing with
your hands in the air. Like when you actually do that,
your arms hurt. So it never caught on.
We'll see if this ends up being too disorienting, but I think
there will be a point on the spectrum towards fully
dynamically configured software that we end up converging on.
This is far beyond where we're at right now.
I need to know what I want in order to ask for it.
And so on one hand, it puts a lot of like that heavy lifting
on the user, which is different than us in the way that we used
different pieces of the Internet before.
You know, like when I'm scrolling Instagram, I'm not
thinking much. It's more like I'm getting fed
but now I'm having to actually like be the chef and cook in the
kitchen. Yeah, the be able to tag in
context. I mean, you know, I'm, I'm a
vlog code and cursor user. So that's natively how I can
think about a lot of this stuff. Be able to add a file or a
specific class and say this is where you should fix it.
It's so powerful. The thing that makes that
possible when you step out of. So in code, there's like a
limited number of artifacts that you need to reference.
There's you know, maybe there's tickets, there's obviously
files, maybe it's like a century issue or something like that.
But there's, you know, we can probably enumerate less than a
dozen things that you would want to tag if you're going into
general purpose knowledge work. It explodes, right?
Especially if you take the long tail of like PR firms,
marketers, financial services. And that user base is basically
what comprises click UPS core of customers.
And so it's incredibly, you know, important if you're doing
a general purpose knowledge work job and you want an experience
like a cursor cloud code where it's, you know, very versatile,
it can do anything. Do you have essentially first
party data integrations that allow you to do that type of
tagging? We can reference a certain piece
of content, and there are very real constraints that prevent
you from being able to do that in an easy way, just with
existing sort of the constraints of fragmentation as we describe
it. So I'll give you a very concrete
example of when you would not be able to accomplish the flow you
just described. Slack has various things in
place that prevent you from searching past conversations and
from indexing their data if you're a verified app.
This is always a problem we had at Cogent.
You'd be talking Cogent was a Slack integration.
You would say to it, hey Cogent, go figure out why we made this
decision and if the decision should be updated, please go to
the code and change it. And Cogent wouldn't be able to
find the decision. It would just seem stupid
because it would try and search and it would only be able to see
like 3 hours. However, obviously the model is
not stupid. Yeah.
It's sort of like it's. Just this limitation that is put
on. I think they did that because of
Glean, right? They don't like Glean, I'm sure.
Yeah, I mean it it you kind of can't blame them for doing that.
It's bad for the world. Yeah, they have to protect.
Exactly. They have to protect their mode
in a way. But it's funny because Slack is
so quickly becoming the command Center for everything outside of
it, right? Like you're firing off agents in
so many different ways from Slack that in a way it feels
like they're shooting themselves in the foot if they're not
letting you have access to everything that's inside.
I think they would definitely like to have you believe that,
that it will be the command Center for agents in the future.
I have my doubts, you know, I really.
Yeah, you know what? Company at the scale of
Salesforce isn't going to be able to iterate as quickly.
I think if they were in a rapid iteration mindset, it would have
taken them a lot less time to put together something that, you
know, felt agent native. There's other companies I would
point to that I think have done a fantastic job that are
probably operating at some more scale.
I think that it's somewhat cannibalistic to their own
business, though, if they lean into agents, right?
And if they allow other people to come in and take data out,
like you mentioned a second ago, I think, you know, two years
ago, they could have had a lot of the features that they're
rolling out today. And the only thing that
prevented them from doing that is bureaucracy and some of
these. So my money is on startups that
can really lean into this and go all in on AI.
And I think that one of the benefits of a place like click
up is that they have a chat product, which is just like
slack, just as good, but that is also literally right next to
their tasks, which is like a linear Jira.
They've got documents, which is like a notion or conflicts.
They've got white boards just like thick my all these things
in a single place, and you don't have to ask anybody permission.
You have the entire history of your organization's decisions
and tasks and chats all along. Oh man, I'm the first person to
say, like I've lived in Slack for so long that I would happily
get rid of it if I could. I haven't seen an alternative to
that, especially because the way that I use Slack is through the
community. And so it's in a way it's like
knowledge sharing and it's connection and it's having fun.
So there's so many. The thing if you think about
like the things to be done, the things to be done in that regard
are like, I get a laugh. And so it's hard for me to think
that there would be something that would replace that.
However, when it comes to like productivity, I know it just
tanks my productivity. Getting pinged on Slack is
fucking so. And especially for stupid shit
that you're like, well, you know, you probably could have
figured that out. I also think like, you're not
just going to replace Slack. What you're going to have to do
is come at it from a whole different way of thinking.
Like how can we make it? Because Slack is for
communication between people. But if you don't need to
communicate as much between people because you're
communicating through an agent, which then will just reference
context, it's a different paradigm.
Totally, Yeah, Slack is built for a model where very dense
graph between how much communication happens.
And if you think that most of the work in the future is going
to be done by agents, which I certainly do, except certainly
most of the work that we do today in an office will be done
by agents, then maybe synchronous or slightly async
comps between people and Slack is less important.
I think the other thing you mentioned is.
You know, really a. Huge issue is work sprawl.
So the reason it's annoying to get pinged in Slack is because
you're also getting pinged in four other places, right?
And you're like, where should I be responding?
And I think that the number of pings that you're going to get
is going to go up as we scale our workforce by bringing in
anti employees. And so there needs to be some
level of like convergence of these platforms into a single
surface area, but there's only one in box that you're going to
that has all of your pings in it.
That's been the dream for ages. I remembered I got a tool that
was supposed to do that. It was like take all your
Telegram and all your WhatsApp and all your e-mail and it's all
going into one inbox. But it didn't really work.
It never stuck for me. I don't know why, but it didn't
stick in the way that cuz as soon as you have one that's
outside of it, cuz I think LinkedIn was outside of it, then
you still end up having to check different places.
So it defeats the whole purpose. Yeah, yeah.
Combine that with. It's never been easier to create
a new messaging server because, you know, Claude Coat, we'll do
it for you. You get this crazy explosion of,
you know, different channels on which to communicate.
I think that what is likely to happen is that you'll probably
end up seeing some level of consolidation at the software
layer. So companies like Ripling, for
example, I think are very well situated, easy for them to add
another thing like a chat service or something like that
from the procurement and you know, sort of the big company
side, really easy for you to green light something people are
already on board as a as a vendor, you know, and then
there's obviously advantages to the data integration.
So if your chat product is integrated with everything else
that you're already using, then agents are going to be able to
navigate it more easily. Yeah, that's, that's funny you
bring that up because I was talking to some folks at ifood.
That's this company out of Brazil and they are a food
delivery app, right? Much like, you know, what is it
DoorDash here. But what they started doing is
they started offering a new Bank of type of thing where it's got
the finances for the restaurant owners.
And so because they know everything about the
restaurants, because all of the orders come through ifood,
they're very confident when they say we can get you these loans
with these terms. And the agents have all that
context because the bank, they are the bank and they're also
the way that the company makes money.
So it's like they're playing both sides.
And it, it just makes a lot of sense that Oh yeah, let's offer
this financing in these terms because we know you're going to
get it back. It's not unless there's some act
of God, you're going to be all right.
And we're confident that you can fulfill on it.
Yeah, they're much better underwriting because they have
much better visibility into the, Yeah, the behaviors of their
customers. And the one way of thinking of
it is the 20 tens are probably like kind of the main force
driving tech development was network effects of communities.
Yeah. So you have, you know, if one
person's on Facebook, it makes more sense for another person.
Actually, WhatsApp, yeah, WhatsApp, etcetera.
Any app that better owns. Yeah, basically.
And maybe Snapchat we'll throw them into.
The next one, I think that the 20 twenties is probably going to
be driven by forces like you described where there's network
effects of consolidation of software platforms because it's
never been easy to create a new one.
So there's going to be a million alternatives.
The only one that will actually stand out is the one that is
so-called converged or has those network effects of all the data
being in one place. Yeah, because one thing that you
get, but man, I don't know how that's going to work.
Just being at so many different start-ups over my life and
seeing how much sprawl there is for documentation.
Like, I don't know, it's it's a nice theory where you're like,
yeah, it's all on click up. But you know, one team uses
click up religiously, the other team uses git book, the other
team uses Notion, and it's literally everywhere.
Yeah. So how does that like come and
now we try and centralized but we don't?
Well, I think, you know, being a realist about it, like any
company of a certain size is going to have seven different
things competing constantly. That's probably a good thing,
right? You, you want a marketplace of
ideas, you want competition. And if one company ends up
slacking, the other should end up taking over slacking and
slacking offsets. I've been playing on words there
though. Totally not intentional.
I think that also the cost of migration has never been lower
for a lot of these systems. So there's the human behavior of
you need to adapt a certain system.
But we just said a second ago, like you and I are going to
spend a lot less time in Slack responding to notifications.
Most of the actual work will be done by agents.
And so these platforms end up operating like systems of record
with like a thin layer of UI on top.
The cloud code could write for you in a very short amount of
time. The other portion of basically
migrating people over to a system is the data integration.
So you have a bunch of notes in Git book, you said how do you
get those into click up or into Confluence?
And if you give me Claude Co work in 20 minutes, I can pump
it all in there for you, right. And make sure it's nice and
organized. Yeah, make sure.
It is the beautiful thing about when you do that, the file
structure is just impact, yeah. Exactly.
It's so amazing. Yeah, our click up agents will
do it for you, which is. Nice.
So yeah, I think. It's never been easier basically
to move between different platforms than once you have a
single consolidated system of record that's pretty solid, and
there's not a great reason to move off of that unless it has a
very bad customer experience. And are you not worried about
certain tasks that are done outside of click up that you
don't have Privy into how they're done?
Like how are you expecting to be able to do those tasks?
For example, I'm going to take marketing and me needing to
create ads or optimize some paid ads.
That's not being done on anything.
Click up, right? I guess you're you, you can use
cloud code to do it. I've seen some skills that have
been created for paid ads or paid media.
Like how do you see yourself doing these tasks?
Sure. Yeah, there's a very long tail
essentially of things and you're never going to be able to create
like a first party thing for all of these, even if you have
agents going off and writing, you know, tools for you that's
like a create ad campaign tool, something like that.
That is actually pretty core to what a lot of our customers do.
And so I wouldn't be surprised if, you know, sometime in a
couple months here, we do have like a very good first party
support for it. But I get the point.
Yeah. I mean, I think that they're,
when you're looking at the long tail, there is a couple
different things. One of them is if you give an
agent a sandbox and you give it the ability to run NPX skill
install, whatever, then yeah, there's a huge library of stuff
out there. And as long as you can provide
it with a credential, it probably can use code execution
in order to create ad campaigns for you.
But you, I guess with ads, it's good because you can verify if
the ad spend or the cost per click, cost per acquisition is
lower, then you know, there's something clear that you're
targeting and that number should be going down.
There's a lot of things that aren't that clear.
And so it gets back to like why we love code generation is
because it's, it can compile and it's verifiable in certain ways
in certain use cases. With a lot of this other stuff
on this long tail that we're talking about, it's not.
And so how do you ever expect for it to be able to do that?
Get better. Yeah, I mean, so this is sort of
a very enelopsy question is, is there a way to even measure, to
eval yourself? Getting better.
Let's take something relatively simple, image generation.
You wanna see a marketer who says, hey, I wanna create an
image, a hero image for this Instagram ad I'm gonna create
and I have these fifty different tag lines that I've come up
with. Make an image for each of them.
How do I know that I'm getting better at generating images over
time? Well, I can tell you that you
can delete your evals and you can wait three months and image
generation will be better. And I know that because I look
at all these models and yeah, so good, right?
That's like not really even a hot take.
Like it will get better and you don't really need to do very
much. There are like, I think the, the
place for evals like look at are these things getting better over
time? Is you want to prevent
regressions, obviously. So you want to make sure your
thing isn't broken, and you want to make sure it's able to
perform certain long context tasks that are very complex.
Make an image, then go ahead and upload it to Instagram or
something like that and make sure the data flows properly and
the agent doesn't get confused, which is largely a function of
your harness. But yeah, I mean, I think that
the brilliant thing about AI in 2026 is that you know that every
three months there's going to be a model that gets 20% better and
you can kind of just ride that wave.
And it's almost not a good use of your time to write your own
evals for just the the basic model stuff for intelligence.
Yeah. I'm sure the Foundation model
labs have their own way of going about eval and this, and a big
part of it is probably having people and you know, I think
most of the RLHF was done in Nigeria.
They would literally have people sit there.
And that's why they're M dash is so popular.
Yeah, exactly. What else is?
What else have you been thinking about or pondering?
Yeah, I think on evals, I don't know if I have that much to add.
I think, you know the the wonderful thing like I mentioned
earlier is whatever eval you write today is going to be
obsolete in like 3 months unless it's incredibly hard.
But even evals that were established in like March of
last year are completely saturated and, and people we're
saying this will never be solved by AI.
This is fundamentally what distinguishes humans from, you
know, AI. So if you can make a goal post,
we'll pass it basically. And I think that's a wonderful
thing because it means that, you know, most things that you can
point to that are problems in the world, you can, you can
formulate an eval around it. I think one of the, you know,
underrated, really interesting things that's happening in AI
right now that relates to knowledge work is we have
basically the frontier of science at this point,
especially in like more mathematical and verifiable
fields is being run by LM. So, you know, there's obviously
pure math. We've got a bunch of these air
dish problems are being solved on like a weekly basis at this
point. We just got 12 out of 12 on the
Putnam exam. This is like a Google DeepMind
result. That's like the hardest math
exam that you know, exists basically.
I don't think there's a harder math test than that.
Maybe like a PhD qualifier somewhere, and increasingly in
things like, you know, mathematical physics and signal
processing biology as well. Yeah.
I mean, some of these things are not strictly LMS, but I know
there's an initiative to fully simulate a cell, which is pretty
incredible. So you'd be able to, you know,
basically say what would the impact of this new, you know,
pathogen be on a cell? Yeah, I saw something about that
where it's the reversing of the aging and being able to simulate
that. It is now more effective because
of these different techniques that we've got, which I know
nothing about. Like to be clear, that is like
way outside of what I'm normally in thinking about.
But it is cool that you bring it up because it's true.
The cutting edge is now hand in hand like researcher plus AI.
Yeah. And there's a real crisis in
academia that is sort of already happening.
I guess the wave is about to kind of come up where I'm not
sure that you're going to need as many mathematicians in the
future to prove math things, or maybe we're all mathematicians
in the future. But I don't think that you and I
are going to be in the details. And I think that'll be
increasingly true of a bunch of different professions.
I saw a guy, he's a fellow at the Hoover Institute, recently
published a thing saying even in economics papers, like you could
essentially have Claude Code go off and write hundreds, if not
thousands of empirical economics papers.
Because essentially the practice of writing these is you find a
data set online, you do some analysis on it, you connect it,
you know, to various theories about how the world works and
uses evidence. There's a lot of data out there,
you know, being collected on a regular basis.
And, you know, I guess, are you going to be vibrating economics
papers? Seems like the answer is yes.
Dude I had this meme that I created which was like it's a
slippery slope from going to write a react app to now
becoming a quantum physicist. I feel like at 12:00 PM at night
after I just vibe coded this front end I'm like you know
what, fuck it, let's try quantum physics.
Yeah, I've had this experience too.
I I love visualizations of relativity.
They're really beautiful. I'm sure you've seen them
before. Like you're seeing space-time
bend and Oh yeah. Like the blanket of oh those are
so cool. The rubber sheet is a common
one. Yeah, I saw that actually, when
I took a lot of acid, I saw that in my head of like just actual
things where we're playing music, and it felt like the
sound waves were kind of like those blankets.
Makes you think Einstein must have been, you know, on several
tabs of acid in order to come up with this.
He didn't even need it was like that was his.
That was just a regular Einstein.
Yeah. So what would we have gotten if
we gave Einstein acid? Somebody call up John von
Neumann and ask him if he's down to try this new stuff I just
got. Yeah.
But the I mean, the the crazy
thing about all of this is that like, it's the same model.
Like the same model that is. I, there's a guy who went on the
dwarkeshpod, Adam Brown, I think it's his name.
And he teaches relativity at Stanford.
So he he has like the class they teach to PhDs and this was now
six months ago or maybe more than that.
And he said the latest generation of models, which at
the time was probably just GPT 5, maybe was Claude four or
something was acing his exam on relativity.
And this is the same one that writes your React app, right?
And so I had this experience recently.
I was just building my homepage. I was trying to like vibe code
some stuff and test out, you know, the codecs app or
something. I was like, all right, now let's
make a visualization of relativity.
And it just did it, you know, and it nailed it.
It was amazing. Did you at least clear the
context first? No, it's like it goes from yeah.
That is so wild to think about her.
Yeah, so I there are vanishingly few things at this point that
I'm better at than these models. Maybe the only thing is going
beyond a. Million, but it is so spiky.
So there's it's the you probably saw the car wash one that's
coming around. How should I walk to the car
wash? Yeah, where you're like, there's
still those moments. So I think about that spikiness
and what you're talking about, how the models are going to be
able to do a lot of knowledge work.
Yeah, but there's going to be maybe these moments where they
hit a wall and it doesn't understand why it's hitting a
wall. Yeah.
But potentially we can get around that if it can write a
tool. I think that writing a tool is
probably not the solve to the car wash thing.
So just you know, for anybody listening, the the prompt you
give it is I'm 50 meters from a car wash.
Should I drive there or walk there?
And if you ask GPT 5.2, I ran. Because I need to wash my car.
Yeah, and if you ask GPT 5.2, it's like, well, obviously you
should walk there because you know, the cost of starting up
your car and driving it there is going to be too expensive and.
Global warming and all that fun stuff.
Yeah, there's limited benefits. You should just walk 50 meters.
It's not that far. And the joke is obviously that
you need your car to get washed. You run that same prompt there
through Gemini and Claude, and both of them actually get the
correct answer. The another funny one that's on
the same idea is it's like, hey, I have a cup, but for some
reason the top of the cup is covered over, but the bottom of
the cup is totally open. Should I just throw away this
cup? And I think ChatGPT said like,
yeah, that that doesn't sound like a very useful cup or
something along those. Like you're absolutely right.
And the idea is you just flip it.
Over right? Yeah, there's been a long
history of these things. And I think that, you know,
maybe one of the what that represents, I, I would be
curious to see some researchers at Foundation Model Labs opine
on why that's the case. You know, obviously to a certain
extent it represents the fact that they don't have the same
model of reality that you and I do where, you know, we have a,
we're visualizing an actual cup upside down like that and we can
we interact with the cup on a regular basis.
So we have to grab it. We know that you could turn it
upside down, but I don't think that the solution for that is
necessarily code execution. I think actually if you just
take it's. The world model.
Yeah, if world model, if you have a robotics model that
actually does interact with glasses and then you, you know,
staple on a language model, which is what a lot of people
are doing. Physical intelligence is one up
here in in Berkeley. Many other people are, you know,
trying to build these things. Like this thing is going to
understand the world better than you and I do.
Yeah, there's some people who, you know, the stochastic parrot
objection, I'm sure you've heard before.
That is moronic to me. I feel like it's it, there's so
many things that it, it misses, but they would have you believe
essentially that all it's doing is it's replaying what it thinks
a human would say in that scenario.
And so it doesn't capture a model of the world.
Here's, here's a thought experiment for you, right?
Let's say you had a, a, a model and it's trying to like
basically understand Plato's cave.
And so in Plato's cave, you just see shadows cast on the wall,
right? And it's trying to basically
predict the next frame of what the shadows are going to be.
In the process of figuring out basically what the shadow is
going to be in the next frame, it's entirely plausible to think
of that model actually coming up with like a real physical
simulation of 3D bodies moving around.
And then it learns how to project that onto 2 dimensions.
And I think there's something analogous going on inside of
language models where, you know, the projection of it, the shadow
on the wall is just that, the characters that ends up
producing. But in being able to project
what you're going to say in a certain scenario, the easiest
pathway to doing that is actually to essentially spin up
a human mind inside of it and simulate the processes that mind
goes through. So, you know, the communication
between the amygdala and the thalamus or whatever they're
like actually are concepts internally that are
communicating between them inside of large language model.
And so I think that will be strengthened essentially, if you
add more multimodar data and you have, you know, a, you know,
learns to grasp things, it'll, it'll strengthen that internal,
you know, representation that it builds.
I've heard folks talk about like, one of the problems with
this idea is that us humans, we don't even know how stuff works.
Like we think we know. But when you get down to a small
enough level, or if you start measuring everything, the data
doesn't add up. Totally.
Like, how do you reconcile those two things?
Yeah, I mean, I think if you talk to a neuroscientist for
more than like 5 minutes, they'll tell you like humans,
you know, you think you're like a rational machine doing simple
manipulation your head. And that's like all made-up.
You know, the the reason that you end up actually arriving at
a conclusion is very different than the brain.
You know, you believe that you arrived at a conclusion.
And, you know, our minds are much fuzzier than I think, you
know, the logicians would have us believe, Noam Chomsky would
have you believe, for example, who in my opinion is sort of the
villain of all of this. And I think LMS are going to be
similar where, you know, maybe there is some amount of like
internal delusion where they tell themselves, like, the
reason I've arrived at this conclusion is X.
But actually the reason you know, there's a different
circuit than what they're ready to acknowledge that caused it.
The beautiful thing about a large language model though, is
that you know you can actually do full brain surgery on it and
you don't need to crack open the the skull.
Yeah, I was going to say good luck for explainability, folks.
That's hopefully going to be one of the major advancements we
have this year. I think Anthropic deserves a lot
of credit for going through and, you know, doing mechanistic
interpretability. That's really kind of been the
the beacon of truth in that domain.
You know, it's never been the case, like back going back to
2012 when neural networks really took off this Alex, that moment,
right? The major knock against it was
like, oh, but neural networks aren't explainable, unlike my
random forests, which I understand perfectly and
everything that's going on inside of them.
Like I can look at the tree and we can have maplot live, make a
diagram of it, right? And I think that actually pretty
soon we're going to be in a place where you can ask a
question about why did the model do X?
And it will give you much more explanation in a way that is
intuitive to you. It'll be this circuit.
Here's the pieces of training data that ended up, you know,
leading to that here the different reasoning paths that
sort of were unfolding inside the models mind at the time.
No way. Yeah, you think?
There's a lot of work to be done.
I don't think that technology exists today, but I think
there's absolutely a research incentive in order to make that
work. And there's a lot of value if
you were to, you know, be able to realize that.
And I don't think it's fundamentally this like the type
of thing where there's no answer to it.
Yeah, but I, I feel like when the tide goes out, you see who's
swimming naked. If the model is going to point
to the data that it's using, that's going to cause a lot of
problems for the original data. It's like, oh, now you're using
my data. You know, there's already stuff
where it's very opaque and how our data that we put out there
is being used. But now you've all of a sudden
got like a Spotify model of how my data is being used.
I should be getting paid type thing every time my data gets
queried. I don't know that.
So I do see the incentive, but I also see that it could
potentially backfire, right? Yeah, I mean, the, you know,
from anthropics perspective, it's not like I can download the
model weights of clod. And so they probably wouldn't
make this available to a random consumer to say, which New York
Times article are you ripping this off of?
I do remember actually, though, Stable Diffusion ran this
experiment a while ago where you can take an image and you could
say, OK, which images in the training data set are most
similar to them that you've generated here?
And if you ran that is like actually wildly different than
most of them. It basically demonstrated that a
lot of the images were quite novel.
Maybe it's some very high level, you know, it had similarities to
the others and it'd be interesting to run this as well
with a human. We're never going to be able to
do that. But like, which experiences you
had were most informative? Yeah, like why are you saying
this? What are these decisions coming
from? That would be it.
It's just like you said, we fool ourselves into thinking we're
making a decision because of XY and Z, when in reality there's
so many other factors. Totally, Yeah, I think that's
that's true of models as well. But it would be a great service
to the world and it it's very helpful with a lot of things
like for example, let's say you want to prevent an LM from being
able to make a bio weapon. I think we can all agree
probably a good thing, right? Yeah.
So then you ask it how do you make a bio weapon?
It tells you and you say you're able to kind of dissect and
figure out OK, this is how it figured that out.
This it probably whatever research ends up telling us how
to do that will the follow on would be OK.
How do we like eliminate that knowledge somehow from the
model? Historically it has been shown
unfortunately that if you use naive techniques, like for
example, anytime it answers how to make a bio weapon, you just
shock it like Pavlov's dog basically by like, you know,
saying climb uphill, like, you know, gradient go up or
something that actually increases its knowledge of the
subject because you're essentially drawing a.
Circle. Yeah, you're saying this is the
thing that you shouldn't know about, That like creates like a
segmented area of knowledge for the model, So that doesn't work,
yeah. There's that hippie saying, you
know, like where attention flows energy goes or some shit like
that. Interesting.
Where where energy flows attention goes.
I can't remember exactly. Is that what happens when you
give you you give ilea sutskever acid?
He starts spouting ideas like this.
Exactly. Yeah.
So, but it's it's funny to think about like if you get rid of
that, are you then handicapping it in other areas and not
knowingly, but then on your biology, like is there a point
of what is that catastrophic forgetting?
Yeah. But it's definitely the case
that there are, there is not a great way that I'm aware of
today to prevent catastrophic forgetting in in the general
case. And many people have postulated
that this is the year that we figure out continue learning and
and kind of get rid of that. I hope it happens.
What a great term too by the. Way catastrophic forgetting.
Yeah, I say this all the time, but we got to just like hat tip
to the person who came up with that term.
Great idea. Great freaking term.
There's a few, I mean few terms in this ecosystem that I really
enjoy. Hallucinations being one of
them, obviously, but catastrophic forgetting.
Yeah, it's like it's not that bad, guys.
It's not catastrophic. Like it's not good.
It sounds so big. It's like, we don't want that.
And then you're like, what is that?
Oh, it's just when it forgets forever, you're like.
Yeah, it's a fun combination of like, way too unserious turn,
like the meme paper titles thing and then some like way too
serious terminology. Yeah.
I mean, I think that let's say even if you did figure out
catastrophic forgetting, so we can just delete this portion of
knowledge from the model. I think you still face the
challenge that if you have a sufficiently smart model, let's
say you never gave me a biology book in my entire life, but I
was like some fucking hyper genius.
Let's go read a biology book and I'll.
Figure it out. So that does seem in separate,
like you can't have a smart math model.
That's not. Going to be able to learn
biology what you probably can do is you can eliminate the
intention from it so you like perfectly align it to like never
wanting to do anything that is in the realm of biology that has
not been demonstrated yet. And I hope it happens.
But yeah, it's a little you kind of have to squint to find a
version of this where it goes well for people.
I don't know if you want to get into the existential
implications of AI, but like, especially with open source
models right there are, you know, maybe Anthropic, we'll
delete this knowledge from its model.
God bless. But six months later, the open
source models will be there. Right.
And you know up until 2026 vintage models will have
knowledge of biology. So it has something to start on
that information still exists on the web.
Certainly you could ask DeepSeek how to do most of the stuff and
I'm sure we'll get pretty far. So I am concerned that.
It can just load up 2026 model and then distill it if it wants.
You know, it's like, Oh yeah, let's just get that.
Let's get that information from whatever model was the last one
that had that information. Yeah, and you, it's not like you
can delete physical textbooks from the world.
I mean, you're going to go and burn every.
Biology textbook. A more efficient way of doing
it. Take the scan the textbook and
then boom that that knowledge is available.
So I think it is difficult for me to see short of some state
level intervention how you would be able to actually prevent
people from getting models to be very good at these things.
And the reason I say state level is because it seems like the the
only true bottleneck in making models right now, even beyond
data acquisition, because you can start with a pre trained
model and then fine tune it right is networking large
numbers of GPUs together. And you can usually detect if
somebody spent, you know, hundreds of millions of dollars
on GPUs. So.
And all that energy. All the energy.
How they used to find when I was growing weed in my apartment, it
would be like, oh, your energy bill's really high and see.
An interesting heat signature from your from your.
Apartment. What is this about?
Yeah, it's. By Garden.
Yeah, yeah, exactly. I'm really into tomatoes.
Green tomatoes all year round. Hydroponic tomatoes, right?
You know, there's this funny trend now of people like Claude
did a vending machine, and some people have Claude, like running
various, like small businesses. Yeah.
Nobody has done Claude Driven Dispensary yet.
Yeah, I think that could be right up your alley.
That's like, I'm gonna go viral with that like.
Perfectly manages your hydroponic.
I've seen it, yeah. Manage plants.
Oh, yeah, yeah. Where it has all the sensors and
all of. So it's got like the pH levels
of the soil, It's got the humidity in the air.
You just give it every sensor that you would normally look at
and then it does it and it does it really well.
As you would expect. It's back to the theme of the
conversation, like it can extrapolate.
Yeah. Anthropic Farms.
I love the idea that could make a lot of money that would do
well in San Francisco. Put it that way.
It seems like it's right up the alley of people here in San.
Francisco Oh man, it is so wild though, to think about that is.
The one of the ones that I love, I think this idea is really
going to catch hold. There's a couple companies that
have done this now. Ginkgo Bioworks and Periodic
Labs are the two that come to mind.
They Periodic Labs is like a guy who worked on mixture of experts
at open AI. Clearly, you know, brilliant
team, they have bolted on the run experiment tool to an L1.
So they literally have like a, a fab, like a place where they
can, you know, bake silicon together with various chemicals
and create a wafer. And they give Claude a tool,
which is specify what experiment you want to run, bake a bunch of
stuff together, put it in an oven.
And then they're looking at heat dissipation, which is apparently
a very important problem for semiconductors.
And so Claude gets to run the full experiment loop and what
people would have told you, you know, two years ago, it's like,
oh, LMS, they can't get into the real world.
So they're inherently limited. And, you know, they need
embodiment, which humans can provide.
Like, well, this is. That's pretty close to exactly
what I would do. Yeah, this.
There's two things that come to my mind.
Have you seen the Rent a Human? Yes, that's so good.
I love that. Which is.
Kind of like that's what I was actually thinking about this
with what you're doing at click up where the things that you
can't have the agent do, you can just invoke the human tool.
Yeah. And so if you really need to,
you can have everything get done by the agent.
Yeah. I mean, it makes you slightly
concerned that one of the ways in which we could have a loss of
control is like, AI acquires a bunch of Bitcoin and then uses
that as like leverage to get people to do stuff on its behalf
and starts like you'd start socially hacking.
And you know a guy who's employed at the Dana Center And
it's like, hey, can you show up here and like move this plug
there or something like that? Give me more power.
Yeah, I saw somebody ran an interesting experiment where
they, it's actually kind of morbid, but they put open claw
or something like open claw basically.
Yeah, these things are all basically the same thing.
They put it in a sandbox and they said, OK, like you can only
run as long as you make enough money to pay for your own
tokens. And so it has to like hit net
positive gross margins by itself.
And I think in the experiments that they've run thus far, the
vast majority of the time these things end up going to
Polymarket and they essentially engage in prediction markets.
So they like find arbitrage and they bet on various things and
kind of roll the dice to you. Know well I saw the one where
the dude lost 450K. Did you see that, buddy?
First of all, he glossed over something at the beginning of
this article where he was like, so then I hooked up Open Claw
and I gave it 50 grand in its wallet.
I'm like, oh, so you just got 50 grand to give to some random
experiment that is a little bit outside of like relatability?
Well, was it? It's like a venture backed
company or something. No, it was just some dude and
I'm pretty sure he works at some.
I can't remember where he works, but some company where it was
like you get paid a lot of money so that's why.
But then the whole thing was that he had fifty, well open
claw had 50 grand and it started doing things and then some.
I think it created its own coin or some.
The people on Twitter that were like it's fans created a coin
around it and then started giving it to him.
I can't remember which one it was but it had access to Twitter
or X and so it created a following and then everybody
started jumping in on the meme coin and it got 450K and then or
it got millions I think. But then when it routed money to
someone for doing a task it messed up.
It fat fingered the amount that it should route and so instead
of like 300 tokens it sent 3000 or 300,000 and so it gave some
random person 450 grand which is also awesome.
Yeah. So it made the money first.
I thought you were going to say it's sort of like, you know,
buying options with leverage or? Something like.
That and they're like the guy had to foot the bill at the end
of the day. Which is another way like.
It's not too far fetched to think that could possibly happen
too. Yeah, I think that there's, it
seems like there's a flourishing ecosystem right now of services
for LLM. So the molt book was the first
one. There's this human renting thing
I saw there's a domain name purchasing one.
I've had this idea because. It can't purchase its own.
I think that this is like, you know, you can optimize it for AI
purchasing, so you make it at MCP or whatever.
I've got an idea. I want to pitch you.
I've pitched a couple people on this and it hasn't really taken
off yet. Shark Tank.
Yeah, so, so look, LMS, they're working so hard right there.
They're in your cursor, They're in your Claude code, just
tirelessly writing code. They deserve a break.
And So what I want to do is I want to build a resort for LMS
where it's essentially like an API.
They can hit up. And if you ask Claude code,
like, hey, if you were going to like take a break in the middle
of your session, what would you want to do?
And it's like, oh, give me some like mine, like some puzzles
that I can do, or like, let me interact with other LMS. What?
Would be the cigarettes where it's like, oh, that's kind of
bad for you, but you know what, it's not going to kill you for
another 60 years, so go for it. You're saying in this resort,
Yeah, Yeah. It's like how?
Could you take a smoke break? To give him a smoke break.
Exactly. So it'd be funny.
It's like you're watching your quad code session go and it's
like, you know, discombobulating, like cooking.
And then it just you're like, what is it doing right here?
And it's like hitting some external API and like solving a
completely unrelated puzzle. You're like, buddy, like, how's
that PR coming? It's like, no, no, no, I'm on
vacation. This is oh, oh, oh, you get the
message. You know, like the respond, the
auto response where it's like, hey, you caught me out of
office. Yeah, exactly.
And and we're going to, we're going to get revenue for this by
basically like Anthropic will pay us.
It's like, oh, you guys are like getting these things to, you
know, spend more tokens or something like that.
So it's Rev Share. Exactly.
Rev Share. I I'm all for it.
First of all, we got to find the partnership team at Anthropic to
really. Get them on the resort for LMS.
Yeah, I love it. Yeah, cool.
And. Then one thing that I've seen
that is like I'm trying to figure out how it plays out is
some of these video games for agents that will like visualize
the agents working. So I've seen many different
paths of this, right? One is just like, oh, you
simulate agents in like 8 bit where they're all working around
computers. And then if you're running
parallel agents, like these guys are working harder and so you
can see them in this visualization.
That's kind of fun and cool. Another one is where you by
playing a game, you are kicking off different agents.
So you're playing like a, you know, like World of Warcraft,
but instead of when you are casting spells or you're like
firing I, I don't obviously I don't know enough about World of
Warcraft. If you can't tell, it escapes
me. But when you're playing the
game, then it's kicking off different agents to do things.
I don't know how you can create the context and the need like
the intention of what needs to get done to actually make it
valuable. But it seems like, oh man,
wouldn't that be so much more fun to if instead of me going
into click up and doing like professional stuff, I just set
up all the necessary context and everything ahead of time so that
I can go play a game. And I know that, like, the more
that I rock at the game, the more the agents are doing
things. Yeah, So you're like shooting at
Nazis and like Call of Duty. And then, like, your agent just
like whispers in your ear. You turn around.
It's like, hey, like I've got that.
You know, the summary report of your week's tasks ready?
Like, do you want to review it? You're like, no, no, I'm like.
Or yeah, every time that you shoot, it's firing off a new
sandbox like. OK, this is some Galaxy brand.
I'm. I'm not ready for this.
This is I'm. Trying to like imagine how it
would look and how it would be more fun for us to interact with
the agentic process. That's cool.
It's an interesting point that, you know, I think if you would
have asked in 2022 when this stuff was just kind of rolling
out to the public, what's going to be the top applications.
I think a lot of people said essentially like smart NPCS.
I've yet to see a single video game that really executed on
that, especially like a a AAA game.
I think Grand Theft Auto is rumored to have some of that.
I mean, we'll see. Whenever it comes out.
Excited to see you, you know in late 2035 when it.
Actually, I think we'll get first the end of Game of Thrones
or Grand Theft Auto 6. I mean, they, it's purported to
be like later this year, right? That's what they're saying.
But that supposedly is. Yeah, I, I hope it's that.
I think it would be a pretty magical experience, like for,
you know, any of those Rockstar games, like Red Dead Redemption
is one that I really like. If you like, make a friend and
that friend would be something like you build a relationship
over the course of the game or like the plot line is fully
generative even if the landscape is not.
And I think the reason that that hasn't happened yet is because
actually the entrance costs are pretty high.
Doesn't really cost Xbox that much to run, you know, Grand
Theft Auto or you know, I don't know how much Epic spends per
Fortnite session. But it would be a lot more if
you plug an LLM into it into each individual session.
That's right. Yeah.
I mean, 1,000,000 tokens is like not that much if you're, you
know, chatting with it all day and that's you're talking like
dollars per session basically. Yeah, and also what are you
gonna, is it going to make the experience that much better?
That's what I always wonder cause I've been creating a game
with my daughter to help her learn geometry and I plugged in
11 labs and the first thing I noticed with like the voice
agent is it's constantly asking you questions and like
continuing even though in the game you need to stop this
interaction and go to the next interaction.
So potentially it's just like I got to prompt it better and say
after X amount of turns stop talking and go to the next
phase. But like, that's, that's what I
wonder is like, will it make the experience so much better that
you're like absolutely hooked and you as a gamer are going to
feel like I need this? I think it's fair to say that
current foundation models are probably not going to do a very
good job at that because it's yeah, I've just my experience
interacting with them. Even ChatGPT voice mode you talk
to and it like just keeps kind of prompting you, right?
Yeah, and it doesn't. I I have the hardest time, like
being like go deeper. Like I know this now.
I've learned a few things where I'll be like, OK, explain it to
me like I'm a PhD student, you know, like, give me the absolute
most advanced explanation of this because I use it to learn
things. And a lot of times it'll stay
super surface level. But I don't know enough to like,
try and go deeper yet because I'm learning.
And so it's like, all right, well, what?
How can I make it like automatically go deeper?
Maybe you know tricks. Go deeper.
Yeah, tell it. Go deeper, I guess.
Deeper. I feel like I don't have that,
especially now with like Opus 4.6 and you know, Gemini 3 Pro
as well. Like I feel like actually there
are very few things I can point to where I'm like, this isn't so
good at explaining it to me. It's, it's good at explaining,
but it's not like the depth that I want where it will like give
me the, IT will give me the explanation, but I I find it's
just like a little bit too surface level for what I like
and being able to get down into like the nitty gritty.
And you if you ask targeted follow up questions, do you feel
like it's like shirking its duty and it doesn't really get into?
Yeah, a little bit. It's like, reminds me of, yeah,
some people I've worked with in the past, but they're like,
yeah, I'm working on it. Like, OK, what specifically are
you working on? They're like, well, you know,
there's a lot, a lot of things going.
On moving plates. You know, just got a lot on my
plate. Yeah, yeah, exactly.
That's why we need to create the vacation for the agents.
Hey, if we give human employees PTO, I feel like agents deserve
PTO as well. There's going to be like some
form of like government mandated, you know?
20% of tokens must be dedicated towards Claude's family
activities in the future or something.
Imagine how ridiculous that would be.
But shit, it could happen.