You said on the last time we talked to you.

The chips themselves aren't.

We're not actually a major constraint for you, and that it was more like

citing the chips and the powering the chips, the access to electricity.

Talk about that.

What is the state right now?

Let's say like I assemble a bunch of people from Hudson River trading.

I get a bunch of GPUs.

Is it then not trivial to find a place to plug those in?

It's definitely hard to find sites and, at short lead times.

If I think if I went to the market and said, I want, you know, 6000,

Blackwell GPUs in a box somewhere

in North America for delivery in Q4,

I'm not sure such an offering exists at any reasonable price.

Like if from maybe someone will give up a lease and I could snag it.

But I think if I went to the market

and tried to get a quick story just to be clear,

the chips are available, but not the capacity.

And if I had power, I could get the chips Blackwell chips

for delivery this year, but I do not think I could get the whole solution.

And then if you go into 2027, for the next generation of GPUs, the Rubin

GPUs, they,

at least for the first, like stretch, are going to be very much sold out.

And so I think that's a get maybe you actually have on a 2027 delivery.

You have more like finding a data center shell by then.

But you need to you need to be in queue now if those GPUs if you want them early.

So those things are those things are in demand.

I'll say that for sure.

And we are one one of my greatest failures has been,

you know,

part of my skepticism has been predicting

how many GPUs we would need in a long enough horizon.

And it's punishing because it you're constantly playing catch up.

And, one of our competitors.

But I did a podcast this weekend and,

they mentioned something along the lines of fact.

They they had one data center

and it was their data center, and that was their data center.

And then as they're hungry and hungry from work, compute,

they had to go out and find it wherever they could.

And, I would say we are in exactly the same boat.

You just can't be picky.

It's like you've got, like, a megawatt there.

I'll take it. And that could be,

you know, not in terms that

are super favorable to you.

Hello, and welcome to another episode of the Odd Lots podcast.

I'm Joe Weisenthal and I'm Tracy Alloway.

Tracy, we did another one of our live shows this time, our biggest show ever.

Our biggest show ever.

It was absolutely amazing.

We did it at City Winery in New York.

I think we had over 300 people in the end... -Yeah like 350 people were there.

Yeah.

And the crazy thing is, I think it was our sort of

first themed show, and we didn't really plan it that way,

but it just worked out right, I guess, like, it's like it's feeling

and antique at the same time because we're in this moment

in which everything is just like I markets, markets, I, etc.

but, you know, there's all kinds of new things to trade.

And people are fascinated by the trade itself,

and people are fascinated by the way the technology development

is affecting the trade.

So we really wanted to do a kind of, you know, future of trading show,

which is a very broad thing, but it did sort of come out that way.

Yeah, it really did.

And you finally fulfilled your long time dream of doing a two part episode

with our guest.

So, our first speaker of the evening was actually someone

who's been on the show before. That's right.

So we had him on the show last year, and, we had him on our live show.

Listen to our episode with Iain Dunning.

He is the head of AI at Hudson River Trading.

Talked about all things implementing AI, GPUs,

all that stuff within the context of a trading shop.

Take a listen. -Joe, This is your dream, right?

You finally get to do a two-part episode.

This is the thing I always think about, which is that

after every episode we do, I'm like, ‘Oh, there's a question I wish I had.’

So we had Iain on sometime last year.

So the last round was easy and this one will be tough.

That's I'm a little worried about what's going to start first before we, you know,

talk about what you do etc..

So here's the question I wish I had as last time.

So Hudson River Trading

trading shop.

You're involved in the AI stuff.

Could you have could you

theoretically do what high flier did and launch an LM

with this tech stack that you have and launch a deep sea competitor?

I think so I think we're good at training models.

We have a lot of compute, and people are good at doing

the cycle of research, which is required to catch up to the sort of frontier.

However, I guess, reaching the frontier is, is clearly a very daunting task.

So maybe it's, with some effort, deep seek.

But beyond that, it's not a claim I'd be willing to make.

It's a hugely capital intensive. Yes, clearly.

And but do you ever, like, do people have a chat about that?

So we could, we think about it, you know, I mean, I think

perhaps we missed our moment to do so.

There's so many open models now coming out from the US,

as well as, like China, that there's like a huge array of them.

It's kind of an interesting shift from that deep sea world

where it felt like

it was the first bolt from the blue of like a competitive open model.

Now I see so many groups releasing them.

I don't know what the future of open models is, that they're all kind of,

you know, a serious step back.

And the frontier is progressing.

So fast. I don't know how you keep up with that,

but many people believe that it's possible.

I I'm not so sure I'm one of those people there.

Okay, so

speaking of things moving so fast, my first question is slightly different.

I looked up your Twitter feed.

Oh no.

Before you came on the show, your last tweet before today was,

and I quote, feel this every day worry at some sort of AI induced delirium.

But then again, various empirical measures are exponential looking,

so it feels best to assume we're hurtling towards some sort of end game.

So first of all, please convince us all

live on stage that you are in fact, not suffering from AI induced delirium.

But secondly, like what is the end game that you speak of here?

Now, I saw like a San Francisco person.

I think you do.

Yeah, I, I have this I've been doing AI stuff,

since around 2016 and that started at DeepMind and I

it was a bit of a culture shock for me because there are true believers then, and

I was most certainly not a true believer, and I resisted it.

And it was kind of a natural skeptic for a long time.

But certain empirical measures of the pace of progress, in the outside world.

And I also look at our, our own business, which looks somewhat exponentially,

the amount of compute I'll have next year versus this year and the year after.

We do have this year versus last year looks kind of exponentially,

And we're doing things today that I didn't really I should have dreamt of.

I wish I had that kind of visionary say,

I'm a visionary and I can see the future and I'm building towards it.

But no, I'm I think I'm a pragmatic engineer archetype.

And so it's been very incremental and I'm like, wow, that happened in a year.

So what does this mean?

It's some sort of technological convergence.

Everything going faster all the time.

Well, give us delirium, probably.

But you must give us an example then, because,

you know, obviously those of us using just the regular models, obviously

the improvements in capabilities from one year to another are mind blowing.

But from the perspective of like, okay, the application of AI

within the trading context, what is something that you can do in 2026

that and say 2024 you would not have been able to anticipate?

I think it's one way.

I think it's just the amount of compute going into both training a model

and running a model, and that it's the same technology

working across every equity, every future, every crypto market,

every option market across the world with a kind of unified approach.

And this is something that we're doing.

But even more interestingly, I would not claim that we are some unique

people who have only the ones who have really made progress in AI and trading.

I think many of our peers are also investing massively.

And we're all doing it all at the same time.

But what does it mean?

Like, you sure you can't just like,

keep getting better at predicting markets forever?

It's got to be some sort of forcing function

where, you know, your your margins go to zero as you keep investing.

Remarkable.

You're saying is

you are just getting better and better at being able to predict where the market

is going to go further and further out in the time frame, basically.

And and we're not the only ones. So in the end,

can there be some Highlander type thing like what are we doing?

And this is like my scale.

And I guess the other thing I find interesting is of course, the scale

that everyone can see with the big labs and what they're doing of compute.

And it's like, it looks awfully exponential to me.

We just had another model released today from, anthropic.

And yeah, the type of spacing between them seems to be compressing.

I don't know, I'm

I do sound delirious, I sound feverish, and that's why it's, it's quite

literally everyone in this room. Probably.

I feel everyone must feel the fever to some extent. Yeah.

I never understood the Highlander.

There can only be one thing because there were already two.

They could just coexist, right?

Anyway, sorry, I'm just picking apart your analogy.

You mentioned a new model released.

When a new model gets released, like,

what is the first thing you do at Hudson River trading to evaluate it?

And how do you actually compare them to the existing ones?

So, I mean, our primary use case is as a rotating

a definitely kind of just like, accelerating your own research.

So, that can be coding, but it can also be,

you know, coming up with experiment ideas, monitoring experiments.

We had, could have a false start

with, I would say sometime last year with, the open source 4.0 models,

especially from, from anthropic,

where a cursory examination made us feel like, well,

this is the moment we've crossed the dividing line.

And we had a very feverish week where we felt the AGI

and we left feeling empty because we realized that it was not

there and was not able to meaningfully augment human researchers.

And then we had that same feeling again, when opus 4.5 came out

and suddenly it was like, oh, wait, no, this is

this is actually what we thought it was going to be six months ago.

So in the most recent model releases, the differences have been more subtle.

But we

see I think we have a much better sense of, ever reducing set of errors.

They make.

And so we're kind of looking for those sorts of mistakes.

And we are we spent some time in the past

couple of weeks trying to come up with objective measures

to index them against humans in the active quant research.

Ideating signals and things.

You know,

current research used to be, as we talked about,

a little bit like handcrafting indicators and things.

Why not ask AI agents to, to do that

and compare them against humans, like a little sort of battle and,

it's it's they're like, I don't know, intern intern level AI perhaps.

I would be the thing is, like, would I would I think it'll be in a year

and I would not want to make a bold claim, but it would still be internal level.

Yeah.

So when we think about investing in general, even within sort

of like classical quant trading going back decades,

there's often it might be quant but there's some intuition behind it.

Right. Cheap stocks tend to do better.

And we don't actually totally have agreement why they did for a while.

But that seems

people aren't necessarily surprised by that.

Right? Right.

Are we going to get are we at the point where it's like,

why even bother coming up with the human intuitive story?

If you just skip the part of giving an explanation that sounds logical

to a person and it's just basically pure, like rigorous backtesting,

and then it's like, look, here is something that seems to work,

and we've tested a million different ways, and it seems to work,

and we don't even bother coming up with

a story for why, but we're going to trade it.

I feel like we're in that world today.

It's sort of, you know, post, post, post capitalism when I see IPOs.

Discussed for this coming summer at the valuations they are I'm like,

what is a fundamental like what what does anything it's it's it feels like

markets are just the cynical take is everything is gambling.

And so everything is some sort of like gambling market including public markets.

But joke is flows.

It's buying and selling and it's just it's worth

what it's worth and it's detached and more buyers and sellers price go up.

And models are excellent at like pulling that out of data.

But just like let's say, you know, the classic example of like

a bag tested is like, oh, companies with the ticker symbol.

It starts with P. They do well on Tuesdays. Yeah.

And it's like, well look the data says that.

But this makes no sense. We're not going to trade that.

Could it get to the point where it's like

look ticker symbols that starts with P do well on Tuesdays.

And we've run this a bunch of times and it seems to work.

So we're going to put money but just do what the are like.

I feel like, yes.

Although it sounds crazy right.

It sounds like I delirium when I say it, but I feel like there's some sense

that that could be true.

But at some point I can't predict.

So at the very short timescale, people accept this already, right?

Like I can't tell you the price of like a stock in a minute,

and no one would really reasonably expect any human to do so,

even if they had the order book and spent all the time in the road staring at it.

But we accept that neural networks can do this.

And then

when does that logic break down?

Why should it break down some long timescale?

If it's ingesting all the data and has everything and it can keep it all

in a context, in a way human account, why should I be able to understand it?

Yeah, and that is a strange thought.

A loss of control.

It feels like a loss of control.

But it's arguably, you know, people save us from math.

Maybe humans are actually very bad at math, right?

So it's not surprising AI is much better than humans at these, like, math proofs.

Humans probably would be pretty bad at markets

where thousands of tradable instruments on, like, very long time scales.

We just kind of accepted that we were.

Some people were good at this.

Yeah.

Maybe that's, Was that a temporary state of affairs?

Well, we talked about this the last time you were on the idea that the models

themselves are not very interpretable, I guess you would say.

But you're comfortable with that

on a short trading time frame, which is what you do.

And then we started joking about magic models.

And magic is a dangerous word to use on this podcast

because people start thinking about magic boxes. But anyway,

now that you've been doing this for another six months

since we last spoke to you, do you feel like you have better insight

into what the models are actually doing and why they're able to succeed

on short timeframes?

I, I do think there are diagnostics we have done where

when we can, we can see things that we can understand.

I it's like looking at some very, very complex thing.

And you can look at one facet of it

and be like, this is a facet I understand and that gives you some confidence.

But it might be illusory because it's a very, very complex object.

And you can

if you're only taking slices through it and to understand aspects of it.

You know, we had this emergent phenomenon.

We saw where

it felt like the model kind of understood meme stocks from first principles.

Say more about it.

Well, you know, like quantum stocks, and crypto stocks,

being kind of adjacent in stock space.

And of course, from a fundamentals perspective, this has no right.

There's no meaning to it.

But, we looked at the model in a understand lens

and it clearly felt like there they knew they were connected.

And there's some other actual companies that I probably won't.

Names feel like it's bad form, but, you know, Wall Street Bets favorites, I guess.

And they were near the cluster too.

And this was like just one little window.

But there were other slices we tried to take

which just didn't make sense to us. But again, it's like,

who? Who am I to say override the might of say,

the model says they're in that vicinity of hyper dimensional space.

Yeah.

The one thing for us, though, is that when we do have this magical model,

it is in a lot of safety around it because we're doing this

higher frequency trading, we're trading positions back and forth.

There's a lot of risk checks that are fully automated and things

I don't know how you generalize this logic to long term discretionary trading,

where the idea of like risk checking and that kind of layer of defense around

it, it's not so obvious to me how you apply that we can apply

very strict controls around this model because it's a well-posed problem.

We're not taking giant, idiosyncratic risks in like one name

for months at a time.

It's a very like we can sleep at night because of this.

I don't know how you apply the same thinking

to like a fundamental, long, short thing where you have to put a trade on

and it's for three months and you're

intentionally taking a very large risk in a very certain direction.

That's a that was was a risk management story on the AI.

If you just give up all control to just the magic prediction.

So you said something until the last time we interviewed you,

which is very important.

First of all, I feel like in the quote, AI trade unquote,

people are obsessed with like, what's the bottleneck now? Right?

And because whatever the bottleneck is, you probably sell for a lot more money.

You said on the last time we talked to you, the chips themselves are

were not actually a major constraint for you, and that it was more like

citing the chips and the powering the chips, the access to electricity.

Let's talk about that. What is the state right now?

Let's say like I assemble, I'm poach a bunch of people from Hudson River trading.

I get a bunch of GPUs.

Is it then not trivial to find a place to plug those in?

It's definitely hard to find sites and, at short lead times.

If I think if I went to the market and said, I want, you know, 6000,

Blackwell GPUs in a box somewhere

in North America for delivery in Q4,

I'm not sure such an offering exists at any reasonable price.

Like if from maybe someone will give up a lease and I could snag it.

But I think if I went to the market and tried to get a quick story,

just to be clear,

the chips are available, but not the.

I think if I had power I could get the chips Blackwell chips

for delivery this year, but I do not think I could get the whole solution.

And then if you go into 2027, for the next generation of GPUs,

the Reuben GPUs, they, at least for the first,

like stretch, are going to be very much sold out.

And so I think that's a get maybe you actually have on a 2027 delivery.

You have more like finding a data center shell by then.

But you need to you need to be in queue now for those GPUs.

If you want them early.

So those things are those things are in demand.

I'll say that for sure.

And we are one one of my greatest failures as being,

you know,

part of my skepticism has been predicting

how many GPUs we would need in a long enough horizon.

And it's punishing because it

you're constantly playing catch up and, whenever our competitors.

But I had a podcast this weekend and,

they mentioned something along the lines of fact.

They, they had one data center

and it was their data center, and that was their data center.

And then as they're hungry and hungry for more compute,

they had to go out and find it wherever they could.

And, I would say we are in exactly the same boat.

You just can't be picky.

It's like you've got, like, a megawatt there.

I'll take it. And it could be,

you know, not in

terms that are super favorable to you, because we'll say more about that.

How are you actually going out and sourcing this stuff?

Because as you say, it seems to be exceptionally competitive.

And at the same time, don't

you guys have an insane data center in like, Norway or something?

And it's not enough?

Yeah, and it's not enough.

Yes. We we go to the neo clouds, the hyperscalers,

everyone, and it's a constant dialog and they're,

they're all in competition with each other.

But in some sense, there must be some much bigger,

shadowy competition going on behind the scenes behind these neo clouds,

because they are all looking for space and power.

And I don't know if that's the true scarce resource.

And they're they're kind of intermediary layer over it.

I don't know what their process is like for sourcing it, but,

yeah, they have come to us and said, this lease opened up.

Can you please get back to us by the end of the day?

And for a commitment on a long term contract?

And contracts are long term.

This is not a this is not spot compute.

This is like 8000 GPUs for three years, four years, five years payment.

Do you pay half upfront? Do you want to pay some per year?

A lot of different commercial terms.

Credit risk on both sides. It's complicated stuff.

Tell us more about the counterparty risk.

So it's like you come and you say you want capacity in some data center.

I'm Iain from Hudson River trading who. Yeah.

Well this is the kind of thing this crowd a lot of people know what Hudson

River trading is,

but maybe in San Francisco or whatever, that's not a household name, etc.

they want to know for sure that you're going to be good.

You're going to like, pay your bills, etc..

How do you establish to the data center that you are going to be a reliable,

I guess, tenant?

Yeah, it's a it's, definitely being a dance.

It's getting better at this point.

I think we've entered it.

Enough deals, enough people that I think we have that.

But we've had everything from people being like, oh, you've issued bonds.

What's the rating on those?

To not wanting us to sell too much of one site because explain that.

Like if we take all their power rights,

and then go bust, they might have a long lead time

where they can't get another tenant and fill that.

And so,

there's a kind of two party problems to this where it's like, they,

they want customers, but there's presumably a lot of customers,

but maybe not as many customers are willing to do the big size and pay

it more upfront.

But, you know, we're looking at their,

CDs is on some of these ones and thinking about how that affects our.

Yeah.

You know, maybe we should pay you 350 an hour

and take out of CDs for $0.10 for our equivalent of insurance.

And on you're your heavy, leveraged neo cloud.

You're having a disruption.

No names. Yeah, but, you know, it's it's.

I think it's

reason to be cagey on both sides because this is all come from nothing.

Like a year ago.

We weren't there asking for it, and, they didn't exist to sell it.

And so the only rock is Nvidia, I guess, extremely well-capitalized entity

who, is not going anywhere and is making, like, GPUs.

And, we have a very positive relationship with them.

And I think that is also a material factor.

How much optionality do you actually have on GPUs now?

Like if you say you want to prioritize latency or throughput, like,

can you get the chips that you need to specify on one of those things?

Or like, do you just take what you can get

or build your own?

Yes. Yes you can, you can.

Well, many people,

I guess, now are working on building their own chips for, for inference,

which is a strictly simpler technological problem.

And, ourselves and many of our peer trading firms

have hardware teams to tackle this.

And you can outsource parts of IT process.

So it's not as daunting as it seems.

But it's definitely an active area investigation for us.

And now clearly everyone, because I feel like everyone's talking

about their partnership with Broadcom or something like this.

And if someone says partnering or Broadcom they're making an inference chip.

So that's interesting right.

Because you hear about like Amazon like they've they're Trainium Trainium.

Yeah. Google TPUs etc..

So we could be in a world

in which we hear of like a Hudson River trading branded chip.

I don't think we'll sell it, but yeah, okay.

But but yes, you're right. That is that is definitely the right model.

And on the other hand, Jensen never sleeps.

And yeah, Jensen purchased grok.

And, you know, they've got their, new, product

line up from the grok acquisition, which is a very compelling product as well.

And there are other setups, etched comes to mind.

So, you know, the inference space is a smaller design space.

It's not clear and how solutions will be,

necessary thing in the future if enough people competing.

But, on the training side,

I mean, what are moats like?

There's just a Nvidia. It's just.

And I suppose Google.

But you know, if you are using TPUs, you're also kind of entering

a very close relationship with Google.

So I'm feeling a vendor lock in.

It's it's a complicated thing if you go down that path.

But if you're compute hungry like the Neo labs I mean I the labs

you'll take what you can get.

I think anthropic takes TPUs, Trainium and GPUs.

You know they need them all.

Maybe we'll

create a little bit of controversy here, because later on, in a little bit,

we're going to be speaking with Carmen Lee, the CEO of Compute Exchange,

which is, you know, one of the multiple entities are trying

to build financial markets for compute capacity, right?

Traded like oil.

So it's like compute futures and stuff that right

now, could you see a use for that, a financial instrument

that's like on some liquid tradable exchange

for h100 or whatever, some benchmark of how much it cost to run these chips.

Could you see that being a useful instrument for you at some point?

It's it's plausible.

And it probably relates back

to my previously stated failure to plan correctly for the future.

If in some sense I could lock in a price for some future date,

for delivery or something of compute,

something that is connected to providers to compute in the long term future.

I think there could be value to that.

We could basically hedge our risk that we, wait too long to put the order in and it

price goes up.

I mean, in 2026, the price of memory has gone up,

so much that I, we do have concrete, specific things.

I wish I put that order in a month earlier.

So it's a real, real thing.

Do I believe that there would be a good market,

with less liquidity for long dated compute futures?

That, I guess remains to be seen.

I don't know what I would do.

Have a short dated compute future.

I do think defining what compute is is pretty hard,

and I have no idea what physical delivery would be,

if that is indeed of interest, because, you know,

because of a long term contracts and because of how much,

work goes into every site.

Yeah.

Like when we connect to a Nio cloud site,

we're thinking about how to connect it back to our other sites.

Everyone's got a different networking system.

The file system, like, you know, with GPUs, which was all the focus.

But there's also have, like, you know,

how do how is data stored at that site or is it sort of inside it all?

Is there an adjacent site that all the hard drives are in

and they're all idiosyncratic, and I can't do anything on 128 GPUs.

I need thousands of GPUs or bust.

That's like my lot size.

And so it's very hard to see how you could

kind of break that down into useful units.

But, you know, maybe it's just a spot thing.

And, if it's long dated, I don't know, there could be to learn more.

We'll learn more.

Yeah, yeah,

I did get a preview, and it is pretty cool, like the actual program

where you can select,

like, the type of compute you need from a specific data center that has like

literally, I think dozens if not hundreds of parameters at the moment.

So maybe we can get a demonstration from Carmen.

What is your token spend?

Yeah, at the moment, is it bigger than Joe's, I hope.

So I, I

struggle I, I, I think I what is my average.

I think it's on the order of 100, $200 a day,

per employee, per permits from serve on my team.

I see, like, that's kind of what I've been seeing lately.

And some people are more in a thousand

a day range, burst, but bursty for that.

Wait, do you like those people because they're, supposedly more productive?

Are you telling them? Definitely. And not trying to encourage that?

I mean, some people go through surges of experimentation

I delirium, which is understandable.

And I think we are always trying to understand

if the people who are using more like,

are they doing it for something that you haven't figured out yet?

But that's a pretty profound new expense to, to have.

It's, it's it's not at the level of it concerns us,

but, what didn't exist at all is an expense type.

So that's kind of interesting to think about.

Well, I'm curious, like,

you know, for the consumer models, they talk about how sycophantic they are.

Does that happen? It's like yes, in you're close.

This is really smart.

You're close to cracking the code of the market.

Keep pursuing this like just one more token throw this

this idea is doing or is it?

Claude likes to say this is doing some real work here.

Yeah, this argument is really good.

I do you get that in the engineering?

I think we do.

And, it's, it's interesting.

We have our

we just started a new internship for the summer, and,

in previous internships, we noticed that, you know,

it's quite daunting coming into this quant trading context.

You know, you have.

No, there's not much to, like, read a book, can't read a textbook about it.

It's useful.

So people ask AI and,

you know, it would always mention some things of like an unusual frequency

that maybe, an expert in that field wouldn't like focuses on some things.

And we noticed, in our, like, winter internship program,

a lot of sort of very technical quant finance research terms

being mentioned, a lot by the interns that no full time are used.

And that's like the original seed of the mind virus was AI.

So there's a little stuff, stuff like that.

But our token spending is going to go up.

I mean, that's almost guaranteed.

And yeah, we're getting value out of it.

Maybe not to productivity,

but I talked to someone who said that team is 50% more productive.

That's pretty good.

I mean, you'd have we pay $100 a day for that.

I just don't understand how people who are token

poor could keep up with someone who's token rich.

And that's again goes to the acceleration feeling.

It's like if you have two people who are sort of equally resourceful and smart,

but someone has basically a copilot with them that's giving them a 50% boost.

And all they have to do to get that is essentially spend money.

It creates a have have nots dynamic that possibly compounds.

As you have more success, you make more money, you're more willing to eat.

Now $1,000 a day per person for token spend, you go even faster.

And this feeling again of compounding acceleration,

which might be delirium, but you could make an argument for

why it's it could be a real effect instead of more winner take all contexts

where speed of improvement is is like the key thing.

There's a story there, I think.

Or delirium I don't know.

Well, I mean, speaking of the haves and have nots,

the other big story in AI world is just competition for talent,

and everyone is sort of chasing the same genius engineer, I guess.

How are you finding that at the moment?

It's it's changed a bit.

There's a lot of dynamics going on.

There's still a feeling that, if you are plucky enough,

you can get a VC to fund your idea based on very little.

You have the right pedigree. And that's always been true.

I guess some sense

this is like the wiki philosophy, some sense you go and it's just that

some of the numbers and the FOMO feeling and as is quite shocking.

And so that's actually a form of competition.

Just like, why didn't I go create a startup?

I don't have any ideas or anything.

I'm just going to make a startup.

For the for the big labs, question of upside, remaining upside.

You're at it now.

I guess they're the two big ones that are $1 trillion valuation.

Where do you go from there?

I think that's affecting people's level of, like, forward

looking optimism for people who are taking offers now

and for people who are at those places and looking to leave.

Generally it's, a question of like, well, they've become big tech.

The they've added people at a vast rate.

And the culture has shifted, especially at some of the labs a lot, to our favor.

So I think for a while it did feel like we were in a very,

very fierce competition.

And now maybe it's now it's maybe a more even playing field, but

I don't know, I talked to a lot of undergrads, and

they don't feel great, about the future, so they feel very worried.

This is what I was going to.

They just went by way too fast.

But like you mentioned already, the models are like, okay, junior level.

Yeah.

So what is like what is talent look like at this point?

And what like I've seen

some of the anthropic interview questions and it's like designing some GPU kernel

or like optimizing the configuration of GPUs within the data center.

What do you

what is what do you want someone to bring to the table at this point?

I mean, I think the first thing is just trying to embrace an open book

philosophy, like let the interviews be done with the

AI is something we try to aspire to do, because it's just at some point

you become it becomes unrealistic to pretend anyone would work without that.

One of the big things in quantum is being like the, you know, there's this

like archetype of like the math theorist or the string theorists or something,

and they go in to Long Island somewhere and they come out with Alpha.

But, you know, like our, our experience has been a little bit more mixed

because it's like if you kind of implement your ideas, how do you

how does that happen exactly?

Well, now Claude can presumably implement the ideas.

So trying to embrace that maybe we do accept more theorists,

more dreamers, people who can come up with ideas,

trusting that the implementation work can be done by AI.

And so I think that's our shift. But I've been joking.

It's like the word sell versus shape, rotate or type like

I feel like the era of the word sell may be a like if I do this.

Yeah.

I mean, prompt engineering is kind of a boomer term at this point,

but there is something to be said for like,

describing what you

want clearly and without confounding factors.

And that is a skill that can be learned.

And there's not evenly distributed in the population.

And I would argue that it's shot up in value simply because of AI. So

I like to think of myself as one of these people though.

So that could be the delirium talking, I don't know.

All right.

Iain Dunning, we could talk for like, two more hours.

Thank you for having me.

Thank you so much for joining us.

Like.

That was our conversation

with Iain Dunning of Hudson River trading, recorded live in New York.

I'm Tracy Alloway.

You can follow me @tracyalloway.

And I’m Joe Weisenthal, you can follow me @thestalwart.

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