Jane Street are partners of my podcast

and one of the fun ideas we had is why

don't I come visit a data center uh for

training that you guys run. So I just

got a tour of this uh Texas data center

from Ron Minsky who co-heads the

technology group and Dan Ponttovo who

heads the physical engineering team. So

thank you guys for showing me around

it's worth I've never been here before

so I also got I was also getting a tour

which was great. Previously I was

confused well how can you be doing GPU

things if you need to be trading on

nanconds and maybe you can talk through

what is the actual time horizon of the

trading you guys do can you afford to

have be running big models to in in the

middle of making trading decisions

>> I think the thing to understand here is

there isn't one time horizon there are

many time horizons uh there are trading

systems we build and trades that we do

where in order to be competitive you

actually have to turn around a packet in

in under 100 nanconds and like that's a

very different regime, right? You know,

people sometimes talk about like, oh,

can you guys write high performance

stuff in Okamel? It's like we can, but

like for this kind of speed, it's like

it doesn't matter if you write in Okamel

or Rust or C++. You can't use a CPU,

right? you are going to be on an FPGA

that's like direct wire attached to the

network and you're going to be turning

around the packet so fast that if you

like attached an oscilloscope to the

wire on the way in and the wire on the

way out you would see the packet start

to leave before it's done being

consumed. So it's like a very different

very specialized regime but like when

you're in that time regime you really

can't do very much computation. the

decisions you're making are going to be

very simple. And in fact, there's this

kind of whole curve of trade-offs

between how smart is the decision that

you're making, be it a model or some

other kind of maybe even like

handwritten decision-m process, and how

fast the turnaround is. And like the the

right way to build uh an optimal trading

strategy is really to have a kind of

ensemble approach where for some kinds

of decisions you're making very s simple

decisions very quickly. For some kind of

decisions, you're operating at the scale

of, you know, instead of thinking of a

hundred nanos, maybe like a handful of

mics or tens of microsconds or hundreds

of microscs or milliseconds. And in some

cases, there are processes where if you

can get that decision turned around, you

know, in an hour or that day, that's

totally fine. And you're you're kind of

competitive on a time basis at each of

these horizons. Uh but you're making

very different kinds of decisions at all

of them. Maybe you can't say but what

what is it exactly these models are

predicting like surely it's just not the

next thing in the order book or maybe it

is

>> right so we're definitely like dancing

towards stuff that's hard to talk about

but I think the simplest and most

important one that we've been thinking

about like we think about it now but

like also 25 years ago when I started at

James Street when I was building like

models out of linear regression you know

and stuff like that like a very useful

kind of thing is to predict a fair value

for a thing like what do we think this

thing is worth and that fits in in a

very kind of composable a into lots of

different trading processes. That's not

the only kind of thing that we use as a

prediction target, but it's an important

one.

>> It seemed like a meme I was getting for

a while about like what trading firms do

is like you you got to get the colo and

the where the NASDAQ exchanges and it's

very important that your machines are

right there without

>> thinking without thinking too much about

the exact details of what we put where.

like your inference processes might be

on CPU, might be on FPGA, might be on

GPU

>> depending on the kind of constraints of

how much compute you need, how big the

model is, what kind of latency

turnaround do you need. And yeah, like

bigger, slower things you can put

farther away. It's annoying to have to

put all the compute right by the

exchange. And for the stuff that's like

really really fast, like being in the

colo isn't enough, you care about like

how long is the spool of wire that gets

you there. like you're literally like

measuring out the length of the fiber

runs when you're when again when you're

like at this very very low nanocond

scale. Um but in general like the like

bigger models give you a lot more

flexibility in terms of where they

physically go. If we're putting GPUs in

some of these colloccated uh facilities

that are next to the exchanges right

now, you have to work with with their

rules. You know, who is who is that

provider? Who is who is giving you that

space? Yeah. And your power, your

cooling, all those constraints now are

maybe slightly tighter than if you have

a facility facility that you're

designing and operating. Um so you're

now having to kind of come up with ways

to, hey, maybe I could only get one GPU

in a rack because it consumes so much

power. So now I have to spread it all

out rather than being able to do liquid

cooled in one rack. So these are all uh

things we need to keep in mind as our

comput you know comput.

>> You guys recently signed a $6 billion

comput deal with core reef.

>> Mhm.

>> What are you going to use that for?

>> The rest of the AI world has scaling

laws. We have scaling laws too and there

are lots of models that we want to

train. I think the thing that's

interesting and maybe different between

us and the kind of more traditional AI

labs is the amount of diversity in model

architecture and the amount of

experimentation that we're doing. So a

lot of the value you get from all of

this is just people are like trying lots

of very different new things in the

model designs and giving researchers

just like faster iteration time so they

can discover more ideas and drive more

innovation. It just turns out to be

incredibly important.

>> In the case of these foundation labs,

there's some gain from have training

just one model that does everything that

is fully general rather than building a

bunch of custom different models. Can

you give me a sense of why there's a

different trade-off at Jane Street? For

us, some of the specialization is about

being adapted to consume the right kind

of data, right? And there are just like

many possible data sources that we might

be feeding in. There are like a bunch of

just differences in the data rates that

we need to achieve. Like just like

another thing that like just makes us

need to kind of specialize some of what

we're doing is just like the overall

kind of both inference and trading

dynamics are made different by just like

the the bytes to flop ratio being

different. We have like way more data

that we are using to train the models,

but the data is kind of bite for bite

less informative just because financial

data is very noisy.

>> Yeah. Um, and so the models tend to be

smaller and the data tends to be noisier

noisier and there tends to be a lot more

of it. And it's also different between

different models that we build for

different applications, right? As we try

and figure out like how can we leverage

more of the information that we get.

It's like oh now there's like all of the

kind of decisions from like how do we

store and load data efficiently to how

do we shape the model to how do we make

the inference process, you know, have

both the throughput and latency that it

needs. those there's going to be a whole

different set of trade-offs there. And

so there's just like a lot of value in

kind of working that out and picking the

the best thing that you can do for for

different applications.

>> What is the um inference workload

actually or how does it compare to what

your traditional big chatbot LLM company

is doing

>> in broadstrokes? Latency matters more as

you might expect. Um, batching is still

an issue. Like depending on the model

you're doing, you might have models or a

part of models that are kind of

disagregated for different symbols that

you're looking at. And so the same kind

of like pulling in data from multiple

sources and batching them together makes

a difference. I think another thing

that's interesting is just like the data

rates are really high. Like the amount

the the aggregate data rate that you get

in a large LLM LLM lab from like all of

the different users is also very high.

But the amount of sequential data that

you're going to get from any one user is

not that high. Whereas when you the the

data that you're pulling is the bytes

that are coming out of like the NASDAQ

feed, it's like oh man the data rate

that you want that kind of sequentially

consumed in one domain kind of causally

one after the other is really high. And

so again like the dynamics change and

like but I think a lot of the same kind

of basic engineering questions are not

so dissimilar but like all the constants

are twiddled to different places and so

you end up making different choices.

>> What what does that mean in terms of the

how you how you had to design these

systems where whether it's in terms of

storage or whatever else.

>> Yeah, there's I think more emphasis on

the performance of the data loading than

you might otherwise see. I think we're

doing a lot of work to build out our own

kind of largecale uh data storage

system, our own kind of internal object

store um where we've like used various

kind of vendor products but um over time

I think for some of these research

focused use cases we kind of need to

operate at a much larger scale and need

also to deal with a diversity of data

centers right and this is like less a

training time less a inference time and

more of a training time question of like

we just can't get all the compute we

want all in the same place And I don't

know, I feel like in general like a an

important trick in like effectively

running a technical organization is

feeling is figuring out what shortcuts

you can take. One shortcut that we were

like privileged to be able to take for

many years is we got to pretend like

there was only one CPU architecture on

the planet. Like everything was for like

x8664.

We pretended like none of these other

things existed. Uh and that simplified a

bunch of things. Uh and we also had like

one big research data center and one big

storage cluster and that also simplified

a ton of things. And actually both of

those have now been unwound like you

just can't get the amount of power like

you cannot wire in enough thunderbolts

into like the same data center to power

all the things you need. You need to get

the data centers built all over the

place. So there's a big disagregation

problem and that gives you a problem

like oh now you have to think about like

your compute scheduling and your storage

scheduling being intertwined one with

the other and there's a ton of data. So

moving around is like non-trivial. Um,

and also we had to give up on this x86

only thing because Nvidia has a bunch of

cool new products that mean that you

need to support ARM. Now zooming out, I

want to ask a very naive question.

There's maybe a naive view that uh, you

know, if you have AGI, it can like

immediately do what Jane Street does.

Give me a sense of like why that naive

view is naive.

>> Yeah. And I don't want to totally

discount it like you know there's a

world that we should take seriously

where like you know we're going to build

large language models or some other AI

systems that are like strictly smarter

than all humans on the planet and more

capable at all cognitive tasks and like

>> yeah that's going to be weird and that's

like a different that's a that's a

different a different state of things.

Um, and in that case, yeah, you know,

maybe large amounts of things that Jane

Street does will be automated away and,

you know, maybe we'll all just like, you

know, sit back and, you know, drink more

margaritas or something. I don't know

what that world looks like, but it

doesn't feel like we're particularly

close to that now. I think that like in

general I think it's like easy to

underestimate the richness and

complexity of the work both that like a

company like Jane Street does but really

that is done in kind of any really like

ambitious high difficulty like company

scale task. I think trading in

particular feels to me as like kind of

AGI complete sort of like NPcomplete.

It's like meaning like that like all of

the different problems of the world end

up influencing what you're doing in a

trading context because at the end of

the day trading involves figuring out

what things are worth which means making

predictions about the future and lots of

different things flow into that and as

various pieces of that get automated you

know you have the usual thing of like

the other hard parts that we don't yet

know how to automate well that ends up

being where the competitive edge lies I

feel like humans and like human

cognition are like more valuable than

ever. Like I have never been more

desperate to hire more engineers and

more traders than I am today because

everything people are doing is more

valuable than it was. I mean some of

this is just me being somewhat skeptical

that we are quite as close to the models

that are like smarter than humans at all

the things as some people seem to think.

Maybe it's like physical infrastructure

like actually getting the colo. Maybe

it's actually like the software

infrastructure that you build. Like give

me a sense of what it is that would

>> Yeah, we build like a huge variety of

complicated pieces of software, have

people thinking about lots of different

trading problems, some of which are not

very electronic at all. Like the

business is just like way more diverse

than I think people give it credit for.

And there's an idea of like, oh yeah,

it's like it must be that like simple

thing where you just like you just have

smart people who like make smart

decisions and write good software. And

like if we could just automate the

smartness part, that would be the whole

thing. And I think it's just way more

complicated than that. What what do you

mean by the non electronic parts of

trading?

>> I mean, there's still trading that

happens

via chat between people talking to each

other and making decisions and like

someone like sizing up how much adverse

selection they think the person on the

other side of the phone represents.

That's like still a real part of the

business. Um there's just like, you

know, there's just different kinds of

securities that have taken longer to get

more automated. The bonds business for

example is just like not nearly at the

level of automation that you see in

equities. Indeed, we I think we were

kind of confused about this of like I

think those of us who have been like in

the business for a while. We kind of I

mean I I started a little too late to

really see the kind of transition of

equities becoming electronic. But I

think people who are you know paying

attention a little earlier than me were

like yeah and I guess everything else

comes next. And like you know what it's

been like you know 25 30 years and like

not everything has gone that way. the

systems are still, you know, we don't

have a lot of people like standing on

the floor of exchanges anymore, but

there's still lots of trading that is

deeply intermediated by humans and human

judgment.

>> King of which, how much are humans in

the loop on between the model and the

and the trading decision? Many of your

most profitable days happen when like

weird stuff happens and there are events

and the world kind of goes crazy and

like nobody knows what's going on and

like that's when it's like very hard to

provide liquidity in those contexts and

so you get paid more for doing it and

there's often a lot of volume on days

like that

>> and doing that well often involves human

judgment of like thinking about like how

is today different from all of the other

days and you know to the degree that we

can we want to build the models that

work well through phase transitions but

also we think humans work better than

models do through phase transitions and

sometimes you need this kind of meta

judgment to decide what to do and so

there's a even for the systems that are

largely automated there are decisions to

be made by the people who are watching

and we always have people who are

watching right I think an important part

of trading is paying attention to and

thinking about what's happening during

the trading day even if the individual

transactions are going by far too fast

for a human to kind of weigh in on a

kind of transaction bytransaction basis.

>> Dan, what what have been the more

notable changes over the last 20 years

that you've been doing in buildings like

these?

>> Yeah, people are actually care about

data centers and want to talk about it.

You know, been working on cooling for a

while and now all a sudden people people

talk about it and and and think it's

interesting. So that's like that's fun

and exciting. And for for folks on my

team, I think they feel that way as

well. There's people who have been in

the data center industry for 20 years

that kind of still want to do it the way

they used to. And I think that's kind of

falling by the wayside now. Uh you're

finding ways where people are um

challenging previous thoughts. Hey,

these my entire data center is backed up

by generators. But generators are some

of the longest lead time items you can

buy. So maybe we take those away and

only put it for a core part of the

system that needs that resiliency. Um

that gets our GPUs on six months faster.

Let's do it. So those are things that uh

you know maybe Maybe it's not the best

engineering decision, but it's truly the

best business decision. And I think it's

stuff like that that has been coming up

more and more often.

>> It feels like every year people change

the what what is bottlenecking scaling

AI compute right now as you're doing

more negotiations and trying to acquire

more comput. What what is the current

bottleneck and what do you expect it to

be for

>> putting aside comput and memory and all

that fun stuff. So generators, uh

transformers, um some of the cooling

equipment that's used now for the liquid

cooling is is is in in a lot of demand.

So um and it changes rapidly. What I

tell you today is is going definitely

going to be different 2 weeks from now.

Um we do this thing we work very closely

with internal teams on the procurement

side uh to to stock up on some of this

stuff. Stuff that we know is fungeible

across all our data centers. We will

warehouse and have it ready to go. Um,

there's components like generators where

you're not going to put a giant

generator in a in a warehouse or or you

know, for instance, if you're doing

something behind the meter like a

turbine, you're gonna you're going to

have to think about those markets a

little bit more. Um, where you're

getting them, where you're staging them,

you can't just leave them off to the

side. Um, so I think the components

definitely change. Those are some of the

big ones. And uh you know as we get to

more and more density you know I think

one hope is that the buildings get a

little bit smaller and maybe and and and

we're able to like you know build the

buildings faster get all that compute

kind of in a nice tight bundle and then

all the infrastructure around it's got

to got to be maybe pre-built and

delivered to site right modular data

centers or modular infrastructure is

becoming more and more of a thing where

these components especially the long

lead components are being um designed

and built offsite and shipped to site.

So almost as close to plug-and-play as

you can get.

>> Well, one of the points you made earlier

is that as uh as the racks themselves

get more uh dense uh you know more and

more of the data center is like the

infra around

>> the actual racks which actually is kind

of similar to um like a a a package on

like a a chip right or like a chip on a

package. It's like the the compute is a

very small part of the total area of

package.

>> Yeah, it's it's interesting. thing. I

mean I I don't you know um it it doesn't

solve any problems per se. I mean maybe

it creates it creates others. Sure. Like

you know you get to a one megawatt rack,

right? People are like what does that

even mean? One megawatt in a rack and

and you know you know the the cooling

kind of the pipe is just going to get

larger that you're bringing there and uh

the amount of power whether it's kind of

the AC power that we're using now or 800

volt DC where it's where it's going in

the future. You still have to bring all

that those components to a spot. And the

thing that's like interesting from our

point of view is like you know we could

design these these engineering things

but at the end of the day whether it's

Nvidia or an ASIC or who they have to

sell a component that can work in a data

center and they're they're thinking very

hard about what they sell um because you

need people to use it right if you're if

you build a one megawatt data center um

one megawatt rack but there's no way to

power and cool it kind of useless. So,

you know, we're working very closely

with with kind of almost everyone in in

that space to think about what are the

components you need to be able to

support these next generations

>> because the lead times you're talking

about, you know, over a year sometimes

and you're just you're deciding on the

infrastructure before you're placing an

order for the chips.

>> So, you know, you're trying, for

instance, the you know, TPUs, they use

lower temperature water and they're

they're half as dense as as you know, an

NBL72 GP300, right? So that requires a

different strategy and and you want to

make sure you can handle those in the

future.

>> One of the things that allows

hyperscalers to commit to large amounts

of compute is that they have some

reserve use for excess compute that

they're not using for training or

inference of LLMs at a particular time.

For example, like Meta, if they're not

using some of the GPUs they bought, they

can just say we'll just make our Insta

ad uh serving uh model slightly better

for today. What is the equivalent sort

of reserve use of compute for Jane

Street that's just a lower bound on how

much that's worth for you?

>> Part of what's going on is like in many

ways we're just like very compute

constrained. There's lots of innovation

and experimentation and new ideas that

people have that is bounded by the

amount of compute that we have. And so

like in some ways like if we just think

about like like we do a we try and and

do a kind of moderately rigorous job of

thinking about the value of the new

different runs that we can do and the

value of the runs that we're turning

away is really quite high, right? So

like we're doing what we think are the

most valuable things but you know if you

know if it turns out we have more

compute than we need for those there's

just like a ton of other

>> research and experimentation that we can

do in that space. So like we're we're

we're nowhere near to like being like oh

too much compute like we sort of have

have the opposite problem. I think

there's also really lowhanging fruit in

that direction. Just like it's valuable

to retrain the models more often.

There's some decay in the quality of

models over time and being able to rerun

them like that's that's kind of has

immediate and clear value to the firm.

Uh there's also some amount of bulk

inference tasks that that we can do that

can like fill in the gaps in the systems

where there's nothing else to schedule.

Um so we don't quite have the thing that

looks like the analog of like the

Instagram ad serving thing, but there is

just like a ton of other like kind of

dark space of like things that we're not

doing, but we would if we had more

compute. So we're like pretty

unconcerned about getting value out of

these. Here thing there is like there is

a bunch of embedded bets like we are

like investing a lot of money in in this

stuff and you could imagine that like

things won't get better at the rate that

we are thinking they will in terms of

like the value of the individual models

and trades that we're doing and like

it's a competitive environment. maybe

other people will out compete us. We're

like I think one part of remaining good

is like always being nervous about other

ways that competitors can like figure

out doing similar things to what you're

doing and reduce the value of of that.

So like there are ways there are ways

that it might not work out but uh

certainly with anything like the current

mix of compute jobs that we have we're

just like very far from having this

problem. It's it's interesting to this

doesn't exactly answer it but like you

know you could disconnect the uh the

powering the data center from the chips

and say okay well you know I I I might

need to use this compute later let me

commit to the data center and the power

now but like delay the decision on the

chips which are very expensive right and

and just be slightly long power and data

center for that that that point of time

where you might need that compute um and

then we'll build in situations where hey

maybe we can kind of offload some of

that capacity to somebody else it's much

easier for us I is to offload power and

data center capacity than is the chips

themselves for obvious reasons. But uh

you can you can really bifrocate those

two.

>> This also changes the considerations

around hiring. I mean you already have

like the highest bar for hiring but it

just increases even more if you hire one

more person that is one person who will

need compute to do their experiments and

that compute is going to be traded off

against somebody else who's excellent on

your team who could be doing experiments

themselves. I I hear what you're saying,

but we don't think, oh, it would be

weird to hire more researchers because

then we'd have to give them more comput.

It's more like the research is

incredibly valuable. The researchers are

incredibly valuable. This is a good

argument for buying more compute. Um,

and so we're like very axed to grow the

amount of compute. Like these days, we

are in something like the range of like

tens of thousands of GPUs and we will in

not too long be in the range of hundreds

of thousands of of GPUs. And we think

it's like well justified by the business

like you know it's not it's it's not

like it's it's not like you know we're

worried about like oh you know can we

justify it based on like the penals of

the trading strategy. It's like no no no

it's like these are clearly good

investments. Um so it doesn't feel like

it's slowing us down on the hiring

front. In some ways, the the biggest

impediment to growth is that it takes

time to like really train people and

absorb them into the culture and kind of

build build them up and build the place

up. Like we want Jane Street to continue

to be a great place to work. Like I I

just don't think of the hardware thing

as at all being the thing that slows us

down. And and I think the real limiting

factors are finding great people and

having the mentorship capacity for them.

>> I guess this might be a good opportunity

for you guys to mention what kinds of

roles you're currently hiring for. Oh

man, why don't you start in the in the

engineering space?

>> Yeah, I I'll start. I mean, I think so,

we're generally just looking for really

smart people, people that that that are

interested in in in doing this stuff and

and that's, you know, mechanical

engineers, electrical engineers, project

managers, architects, people that help

design and build some of these spaces.

And, you know, our our our remit uh in

my team is is really to to find the

spaces, to design them, to construct

them, and then to operate them, right?

So, it's full life cycle. So in each one

of those you kind of need people you

know lots of engineers, lots of what we

call physical engineering which is a

madeup term that that we came up with

but uh you know mechanical engineers and

structural engineers maybe electrical

engineers those types of folks

>> and and machine learning and trading in

general is really like a whole team

sport and so we want to hire people from

lots of different backgrounds and with

lots of different capabilities. Uh we're

certainly like very excited to hire

people with kind of you know specific

like machine learning backgrounds of

like you know designing architectures

and building models in various cases. We

both I mentioned that we have like a

bunch of like custom architectures and

stuff for like our own bespoke kind of

kinds of data that we need like the data

kind of characteristic of the markets.

Um we also build LLMs and people who

experience in all sorts of part of the

life cycle of LLM training. we're

interested in hiring and have been

growing that area. Um, you know, we we

hire lots of like people with like

generally good scientific and technical

backgrounds from like math and CS and

physics and engineering and stuff to be

traders and like there's a kind of mix

of skills there. Uh, but that's like an

area we continue to be very excited to

hire in. On the software engineering

side, there's like a general software

engineering role which we're always

eager to get great people for uh that,

you know, I think just rewards a little

bit, you know, it feels a little silly

to say, but just like, you know, as Dan

was saying, smart, curious people with

really good CS backgrounds, uh, you

know, fit into that generalist role and

there's lots of different kinds of

things they can end up doing. There's

also a bunch of interesting specialized

areas where we really are excited. Like

here's a thing that's kind of new. With

all of this scale, we are much more

interested in fleetwide optimization

than we were in the past. Like

>> we our old view about about performance

optimization was that it was much more

about, you know, making the things that

were most speedritical as fast as

possible. And more generally, yeah,

compute's kind of cheap and like people

are expensive and we're not spending

that much time optimizing our general

compute. But like, man, we're doing a

lot of general compute now. You know,

you start investing billions of dollars

in this stuff and it just becomes more

valuable there. And there are people who

have experience in doing this at some of

the hyperscalers and we'd love to hire

more people with that kind of background

to think about the optimization problems

that we're hitting which are like

related in important ways different but

like you know so it's like both a

related challenge and a new one. Um

we're like we do a lot of fun like

hardware engineering stuff. We're like

working on our own AS6 people with that

kind of experience is super exciting.

Um,

one thing that we mentioned a little

earlier at lunch was like we're starting

to think about building out a formal

methods team using basically

mathematical proof to make software

engineering more effective.

>> That's like a new very speculative area

and we're like very excited to find

people there. We feel like that's a kind

of a set of a whole community of people

who in the past I feel like I've always

had to disappoint by like yeah we're not

interested in formal methods but like I

think the whole AI revolution makes

formal methods suddenly a much more

interesting field and so it's a place

we're excited to invest in. So I don't

know and like I don't know project

managers people who do front-end dev

actually like for most of Jane Street's

experience we pretended like this whole

web thing had never happened and like

almost all all of our tools were just

like in the terminal but you know it

turns out it's useful to be able to like

draw a straight line and you know have a

tool tip and things like that. So, we've

actually invested a lot in building

really good tools for doing front-end

development and building tools for

people and having great front-end

engineers who are both really good

software engineers and have a good sense

of what it means to make an application

that's good for a person is really

important. I say like as as a general

meta point about all of this, I think

that like in all of the like legitimate

and real excitement around AI tooling, I

think people sometimes like

kind of miss out on the importance of

the human element of all of this. I

think that we really we really care a

ton about building tools that are good

for people and that comes that includes

the AI tooling itself, right? I think

trying to drive tooling in a way that

increases human understanding and agency

and efficiency is like that's the core

thing. We are limited more than anything

else by the amazing people who work

there and like being able to find more

of the right people and grow the

organization so that we can get more

done. Uh and so we have a very kind of

humanoriented way that we think about

the systems that we build.

Um, it's been really cool to have you

guys um, make these fun puzzles and

challenges. I think in general you do

that, but also you um, you've been uh,

you guys have made a couple for the

listeners of the podcast in particular.

And I think people who are listening to

this might find it interesting to check

those out. um uh including one by the

way which not only was nobody who

submitted to the competition able to

solve but Jane Street itself cannot

solve which um uh which involves finding

back doors to various LLMs that have a

trigger phrase baked into them. Anyways,

I mentioned this because um uh to the

extent people are interested in learning

more, I think these are the kinds of fun

puzzles that might give some indication

of what work is like and um uh why

things like fun place.

>> Yeah, puzzles are a deeply embedded part

of the culture. So, it's kind of great

to use them as a way to reach out to

people as well.

>> Yeah. Yeah. Um, I guess the plug here in

this case is janestreet.com/doresh.

Uh, so that people can learn more about

the open worlds and about all these

puzzles. Yep. Awesome. Cool. Thanks for

doing this, guys. Thank you very much.

>> Our pleasure.