OpenAI 金融服务负责人 Joseph Kim 访谈：打造 ChatGPT 金融产品与模型品味训练

to be a billion dollar business built on

top of a

chat GPT plugin. But 95%

of what we built was something that

absolutely a startup could do, another

company could do.

pre-training and therefore had

difficulty scaling the model. In 2026,

we've been able to solve many of these

issues.

heads of chat GPT finance from OpenAI,

and our co-host and head of editorial

from Latent Space, Richard, in the

studio. It's a special episode. We're

here in New York at AI Finance.

Joseph just gave a great keynote.

I think we don't want to repeat most of

the information, so honestly go watch

it, but give give like a 30-second of

what you talk about and then we'll leave

the breadcrumbs. You guys can go watch

the keynote.

intro to kind of what we just chatted

about.

One, I would say like we just released a

new product, chat GPT for financial

services. This is an enterprise-focused

product that's really trying to bring

the power mix the power of chat GPT,

kind of the tools and things that you

really, you know, like to use in the

consumer product, but also add in the

enterprise layer so that firm-specific

templates,

you know, sources like Dilupa, S&P, etc.

And so really exciting. We've been

working with our design partners Morgan

Stanley and Evercore on that, and

reception to that's been incredible. So

that was kind of the first half of the

talk. And in the second half of the

talk, really much more forward-looking,

so thinking deeply about some of the

trendlines that we're seeing in terms of

model improvements, and drawing those

trendlines out to the future and trying

to think a little about, okay, like what

are these models going to be able to do

at the end of, you know, mid-2027, end

of 2027, and what does that mean for

folks that are building products on top

of that model, and how can you kind of

start to plan ahead to the intelligence

that comes in, you know, just a

matter of months.

actually come from the research side of

things. So you now lead a lot of the

product and engineering, but tell us

what it takes to make a

financial-specific product. You

mentioned in the keynote that there was

sort of an uptick in users when you

launched ChatGPT work. So, Codex started

to become available to the masses.

People start using it for finance, and

now you're shipping your first finance

product. What is like financial

reasoning data like? What goes into

finance specific models?

mean, one thing I will say is that like,

you know,

all of the training data that we do for

financial, you know, reasoning analysis,

we do upstream into what we call the big

run, which is the the main kind of run

that becomes, you know, something like 6

Astra or 61 Astra, etc. And so, even

though we're adding a bunch of financial

data to the model, it's going into the

main main model, and so therefore it's

available to folks that want to build on

top of it. And that is something that's

very important to us.

You know, at OpenAI, we really do want

that intelligence to be accessible not

only to our products that we're building

in-house in first-party ways, but also

for folks that want to build startups,

build businesses on top of that, as well

as, you know, financial firms that want

to integrate that directly into their

companies. And so, we will make all that

intelligence accessible there.

I think you touched a little bit on

product and research, and I think that's

like really core to what we do at

OpenAI. Internally, we call ourselves a

research and deployment company. And so,

what that means and we kind of structure

that in order on purpose, like research

comes first, and then deployment comes

second. And so, as we think about, you

know, at the end of the day trying to

change the way that people do knowledge

work, we kind of work in that order,

right? We think about the model

capabilities, where they're headed, what

kind of new things they enable. So,

that's one. And then two, we think very

deeply about the product features and

surface areas that we should build to

make that model intelligence accessible

to more people. And I think you alluded

to to a slide in my my um

keynote where, you know, we had this

chart of Codex adoption within OpenAI.

And it's funny because you can you can

see the the software engineering

adoption curve, and it's just like up

into the right pretty steady. People are

picking it up and and incorporating into

their daily routines. But if you look at

finance finance, legal, and sales, you

know, it's pretty much at zero, and

suddenly it kind of jumps up to 90% and

if you and if you look at that line and

try to see what happened around the time

where it jumped up, we we had released

Codex desktop app, um, you know, in

February and that was when that adoption

jumped up. And so, I do think it kind of

shows the importance of co-developing

those things and the purpose of the

product is to make that model

intelligence accessible to as many users

as possible.

data for using Codex for financial, you

know, applications, what sort of things

did you learn that you then applied into

the post-training of financial data?

like, yeah, I could talk a little bit

about how we went about building ChatGPT

for financial services. It's it was very

unique. We we haven't built a whole lot

of first-party products. I would say

like Codex was really like our biggest

and main one and

it was the first time we developed a

product where the product, the model

hardness and the the research was all

kind of under one house and under one

roof and it really gives us a lot of

flexibility to

go about building things. I think the

biggest takeaway that I have from having

worked on this for the last 6 months or

so

is in enterprise, it's, you know, you

have to deal with the reality of the

industry that you're in and go build

around that. And I think that piece is

it's easy to ignore. Like if you just

think about it from first principles,

you can come to a conclusion that makes

sense and you talk about it and you can

defend it with, you know, with logic and

reason. But if you ignore the state of

the world that you 're in when you start

building and the forces that are at play

within the industry to try, you know,

it's either competitive forces, people

have their own objectives. And if you

just don't think about that, you're not

going to build the right product, right?

And so, we saw that with, um, ChatGPT

for financial services. We thought about

that a lot. I'll give you one example.

Um, data. Data is like a huge part of

how we thought about, um, building the

product. And, you know, and and data was

one of those things where on first

principles it was a little bit tough

when I was talking about build working

on this product to defend because, you

know, you say, "Hey, like I want to go

and bring quarter earnings transcripts

into the product and bundle it." And the

first question that the first thing

people are going to say are is, "Look,

there's an MCP connection in the store.

Like what is what is the incremental

value that you're providing by at

bundling that in? On paper it looks bad.

It's like you can they might already

have a license for it, so they might

feel like they're paying twice for it.

Um and they might have an MCP. And so

like why are you building this? And the

reality is we went and we tested all

these MCPs. And what we found was the

error rates were were really really

high. Some some of the MCPs that we

connected some big names 40% plus error

rate on their MCPs, right? And so um

MCPs is one of those things where it it

looks way on paper, but you have to go

see look at the data and see what's

actually going on. And so for data we're

like, "Hey look, this is really bad. The

only way we can go about fixing this at

scale is to bring the data in house,

like just control that experience end to

end." And the extra money that you pay

on top to bundle that data is worth it,

right? And so that was a product

decision that we had to make that was

very industry specific. Um and it's been

resonating with customers. Customers

love it. They you know, the number one

thing they call out is the data. And so

um it worked.

us about the technical side of things

for people building a harness adaptation

for a specific service, right? So, you

guys went out, you changed Codex, you

did post training, you did whatever you

did to make ChatGPT for financial

services. If someone was to do the to do

the same for another domain, say legal,

sales, whatever, uh what are the best

practices there? What did you learn?

What goes into just taking off the shelf

and actually, you know, making a certain

product around it?

lot. Um

it it you know, I tried to allude to

this in my keynote as well, but the like

interesting thing is, you know, it's fun

to talk about the post training stuff

because it's unique to what we do and uh

gets people excited, but 95%

of what we built was something that

absolutely a startup could do, uh

another company could do. And we didn't

really have to go leverage our ability

to go make changes in the model to go

about building fantastic product, right?

And so I think it's a great call to kind

of mention that piece. Um what I would

say is like uh twofold. One is most of

the product that we built was built on

top of the Codex um

or I should say ChatGPT now, um, plugin

architecture and plug and primitives.

And that is something that we've been

investing a lot of time and energy in.

Um, you know, OpenAI is like an

interesting company where, you know, we

have we kind of face the same problems

internally that a startup would face,

uh, if they were to build on top of

OpenAI. So, in very in a lot of ways

like my team is kind of like a startup

that's building on top of OpenAI

technology, right? And so, um, even

internally, we need to find scalable

ways for teams like myself to stand on

the shoulders of giants and build on top

of Codex, right? The Codex is fantastic.

Um, it's got great memory. It's got good

skills. Like there's a you know, it's

persistent. Um, there's a lot of things

that are wonderful about Codex, but the

thesis of my team was Codex was Codex

will get you about 80% of the way to a

product that would be successful in the

market, but, you know, um, the 20% is

missing. And so, we need to find some

way for my team to go build that 20% to

actually go win cuz the other half of

the thesis was if you just took Codex

and launched it as a finance product,

um, it would fail. And so, that 20% is

actually pivotal. And so, what I would

say is we we went and we built on top of

the plugin architecture. It's using the

same primitives that are available to

any of the builders that want to go

build on top of, um, ChatGPT. And I you

know, I had a line in my, um,

presentation, but I I really do think

that there's going to be a

billion-dollar business built on top of

a uh ChatGPT plugin, right? Like it's

like you're going to be able to leverage

all of that horizontal functionality and

just add your secret sauce on top. And

that secret sauce might be data, it

might be UI changes, um, it could be an

integration, but, you know, that secret

sauce is really valuable and I think

there's there's a billion dollars worth

of value to be built there.

for the industry cuz we're at a a time

now where orchestration is uh

is becoming a lot more automated, you

know, teams of agents going out and

doing things. But for finance,

obviously, there are a lot of risks uh

involved in that. So, can you talk a bit

about about the safety sort of uh that

you've baked into the product?

important. Um,

I would say that like and it's only

going to get more important uh because

the you know, the agents are already

getting more autonomous and more

independent. And I think that trend we

expect it to continue into '27 and into

'28. Um I would say if uh

for the for the product that we built

and launched last month, um we there was

a bunch of paradigms that already

existed um as it pertains to safety um

and you know, being thoughtful around

auditing the work, making sure that the

model's not making a mistake that's

going to impact business outcomes. And

we were able to lean heavily into those.

And and what I would say is the existing

product that we launched is a

productivity tool for knowledge workers

working in this space. And so the idea

behind it is there's a a deep assumption

that there's always a human in the loop,

right? Like So the idea there is, you

know,

you're a financial analyst and you have

a a laundry list of things you want to

get done that day. And and just like how

a software engineer would do with Codex,

for every one of those items, the first

thing you do is try to build it with

ChatGPT, right? And it might get you 30%

of the way there, it might get you 80%

of the way there depending on the task,

but there's always this presumption of

humans in the loop uh that are presiding

with that work. And um that made it very

easy for us to to build a product where

we could kind of pass off the

responsibility of auditing review uh

into the people's hands and into

people's responsibilities. And that

worked for us well and it was easy to

communicate. Um so that was wonderful. I

will say we're thinking very deeply

about what comes next. And and what

comes next looks different uh than than

a productivity tool um that sits and and

is always being monitored by a human.

And I think as we think about what comes

next, there's kind of two things we're

thinking about. One is

how do we kind of shape the security

safety profile of the product into

something that people understand

intuitively. And

some I would say something that people

understand very intuitively is how to

work with other people. Um and so our

thesis is if we make the product very

similar to how you work with your your

co-workers, your interns, people around

you, that is something that people can

pick up and learn very quickly, very

easily. And we And therefore we need to

shape the security profile of that to

look like a person. And so there's a few

things I'll call out there. One is

there's this notion of read-write, uh uh

you know, even for people. Like, uh

you're a new software engineer at a

company, you oftentimes, you know, I was

at Google, you need you need something

called readability. And so, there's

these kind of like checks and balances

and systems in place before you can

start mucking around touching the

system. And so, there's already systems

in place to monitor the work of people.

So, if if models behave more like

people, then you can leverage those

systems. And that makes it very easy to

kind of talk about and work through this

um with these financial institutions.

So, that's something we're leading into

pretty heavily coming up. And the very

last thing I would say is, at least in

the context of banking, which is where

we kind of started, um there's already

this system in place for junior analysts

doing a lot of the groundwork. Uh their

work gets passed off to associates.

Associates are reviewing the work. Um

then the associates are kind of adding

their secret sauce, their magic, their

reasoning. That gets passed off to the

VP. VP goes up to the director, managing

director. So, there's this kind of like

a hierarchical system where there's this

implicit work checking that's happening.

That's already been built out to stop uh

to try to prevent against uh mistakes

that humans make, right? Like humans

make just as many mistakes as models.

And so, there's systems that are built

there. And so, we're also leaning

heavily in terms of those systems as it

as it pertains to building financial

products.

misconception where in the finance

world, one mistake really compounds.

Like your your whole career is at risk

if you submit something wrong. How

should people think about getting

started in using it? So, if you're an

analyst, um how should you use these

tools? And do you have like any

analogies towards where we're at right

now? So, there's like levels of

self-autonomy in self-driving. One of

your slides I think said like 2027 you

see no more PowerPoint. A lot of people

will be using AI tools in the loop.

we are now and where things are going

and how should they adopt these tools if

they have that misconception of like one

mistake, my career's over.

but how should they also think about it

going forward as

they need to offload more and more or

I have two perspectives on this. And

they come they start with different

assumptions. Um I think

I I think the notion that like my

career's over if you make one mistake is

an incorrect notion. And I have a line

of thought that like follows that. But

let me let's the first assume that it's

true, right? And and in the case that

it's true, what I would say is like not

all mistakes are created equally and not

all model capabilities are created

equally. And so when you think about the

mistake that a model or a human could

make that could be career ending,

you know, it's it's usually once you get

up to a certain level. Like if you're a

you know, VP the stakes are higher than

when you're a junior analyst because

there is a presumption of work having

been checked up to that point. So if an

MD MD sees a mistake, that's a huge

problem. A lot of the safety nets have

been, you know, have failed for that

mistake to make it up to an MD, right?

Um but what I would say is like, you

know, the career ending mistake is

something where you get a piece of

information factually incorrect. And

that piece of factual information

changes the conclusion. Like that is

obviously like the kind of the biggest

type of mistake that you can make. And

what I would say is we're seeing a lot

of progress in in

attention to detail. And you know, the

hallucination used to be a big term that

used to come up in 2022, 2023 that

doesn't come up nearly as often

nowadays. I would say that like we're

starting to see huge improvements in the

liability of models as it pertains to

factual mistakes. So the the kind of

egregious mistake that would get you

fired, models are getting much better at

and continue to get much better. And so

I feel actually quite confident that a

combination of that continual

improvement plus the existing system

checks in place are will make it such

that, you know, that type of mistake,

the chance of that propagating up to an

MD is just very low probability, right?

What I would say

the models are lacking today and they

still need to improve is actually not

the career ending mistake,

but it's the

it's the bit that involves human

judgment and taste. So it's the this

slide looks ugly, right? And it's it's

not wrong, but it's formatted like the

colors look a little bit off. Something

about it looks sloppy and therefore, you

know, this is not a good slide. And and

that's that's not a career ending

mistake in the way that in in like a

factual mistake is,

but it It matters. It still matters in

terms of how useful that work is. And

so, as we think about the progression in

going into 2027, what I would say is I

expect the model to get much better at

the human taste component. And that's

what I was kind of alluding to in my

keynote is I think the model will get

much better at human taste on things

that are softer objective functions.

It's not is it right or wrong? It's

comparative. It Is there something

that's better than this? And I think

models are going to get much better

along that dimension moving forward.

train taste into the models? Um

it's funny. Like very early on in the

like early early chess days, the only

thing we trained models on were

essentially taste, right? Like the there

was reinforcement learning through human

feedback. And the human feedback was

always a pairwise signal. It was like

given two options, which one's better?

And so, the early days of reinforcement

learning were built on the notion of

training on human taste. Uh and then

obviously things like verifiable rewards

and environments and all you know, we

built a lot of machinery to do other

types of rewards and other types of RL.

And so, people kind of forgot that a lot

the early days of of RL reinforcement

learning were all around human taste.

And so, how do you train on human taste?

It's I think the technical answer is

quite simple. I think the like practical

answer obviously is more complicated. Um

the practical answer is you just train a

reward model, right? You just train a

reward model. You get a bunch of humans

to look at pairs of things and say which

one is better. And this still works. Um

so, like um fun fact, like we did this

for our slides, right? Like I mean, how

do you how do you go about making these

models better at generating

presentations? Well, we just get a bunch

of humans to look at pairs of slides and

say which one looks better. And humans

are actually really, really good at

aesthetic um judgment, right? Actually

much better than models is something

that we discovered. Is like a human can

look at two slides and within seconds

say this one looks better. And if you

have a model, like a grading grading

model, try to go about doing that, um

it's going to spend a whole bunch of

reasoning tokens this pixels this way,

this thing's that, uh and then maybe

comes to the wrong conclusion. And so,

humans are still really, really valuable

in making these models better. And so,

we will we will rely a lot on uh human

preference um to imbue things like

taste. So, that's one. Um, the last

thing I'll kind of add is there's

precedent for this, which makes us

confident that this strategy will work.

And that precedent I would say is

coding.

Um, the models have gotten I would say

coding is not fully solved, but like

let's say we're 80% of the way there and

we're making progress on the on the last

20%. Um, coding is a combination of

verifiable rewards and human taste,

right? And so, the verifiable rewards

are, you know, does the does the code

compile? Are there syntax errors? Um, is

the output of the function expected? Um,

and so there's these verifiable power

rewards that are in place, but a lot of

good software engineering is um, is this

clean system design? Did you build a

system that's maintainable over time?

Um, is it easy to unit test? Is it

following paradigms that the vast

majority of the community or the um,

industry thinks is the right paradigm?

And if you look at it from that lens,

um, financial uh, a lot of financial

work looks very similar. It's the, you

know, I have an Excel model, does it sum

up? Are there reference errors? There's

a whole bunch of verifiable things that

we can check to make sure that the work

is passing this the, you know, first

pass sanity check, but there's also a

lot of is this the way I would have

built this model? Um, something I

learned about as I started working in

finance is there's this notion of like

the feel of the model, which is, you

know, you you kind of perturb the inputs

and you look at the outputs. And an

experienced financial professional can

kind of perturb the inputs, look at the

outputs and kind of understand does the

data flow through this model in a way

that feels good, right? And that is a

human preference that looks a lot like

does this code look clean? Is it well

written? And I think our success in

coding gives me confidence that we can

solve a lot of these problems in finance

as well.

from cognition. So, it's not only like

sweet bench is very um, you know, is the

answer correct? This is also would you

merge the code? So, you can have

multiple answers that are proper PRs

that pass, but which one would you

merge? And that's why you sometimes see

the charts of lower reasoning is better,

but it's a taste benchmark as well,

right?

predictions I thought was interesting

was that uh, you know, the models that

you're training will become much better

at, you know, investment thesis and sort

sort generating ideas for investment and

trading.

Um but obviously, you know, uh that's

something that humans are uh well

experienced kind of financial

professionals are very good at and they

kind of have an intuition for that kind

of thing, but it's often counter to the

prevailing wisdom out there. Otherwise,

you know, uh there's less of a chance of

it making a a great return. So, how how

are you tackling that problem in terms

of training the agents to do that kind

of work?

I would say that like up until date, the

vast majority of applications of AI in

finance have been really focused on I

would say like the junior analyst's

work. Um

and I think that's I think that's for

two reasons. One is

you have to build the product based on

the like capabilities of the model of

today. And so, if you look at the

capabilities of the model today, they're

really quite good at taking pretty

concrete instructions of what to do

and kind of executing, right? So, build

me a slide, I want it to look this way,

you know, make it look good, use our

template, these types of things. The

model's quite capable of doing those

things and so therefore, we've built

product experiences around that. Um and

the other thing I'll call out is um

there is a strong preference to make the

model good at things that humans are not

good at or don't like to do, right? And

so, uh human you know, most humans I

talked to there's obviously exceptions,

but a lot of the humans I talked to like

um

humans I talked to people I talked to.

Um [laughter]

uh you know, they really don't like

building Excel models. It's a tedious

kind of work and it's very detail

oriented, it's easy to get wrong. And

similarly with slides. Um you know,

you're you're copying logos, you're

placing them very precisely on a

specific part of the page. That's very

tedious work that a lot of people don't

like to do. And so, that's been part of

the impetus for us to go and focus on

some of these tasks, right? It's a

combination of two of those things.

There's the like existing model

capability is pretty good at uh certain

types of task and also human preferences

of what they like to do and not like to

do helps drive the direction of the

product as well, right? It's a

combination of those two things. Um

I think that like we've solved a lot of

the low-hanging fruit of those two

things. And so, going into 2027, we're

going to start to move up the stack,

right? We're going to go from the task

that a junior analyst can do to some of

the tasks that an associate can do to

some of the tasks that an MD would do

and move up that chain. And as you move

up the chain, what I would say is the

objectives become much fuzzier. It goes

from this is clearly a good or bad

slide, or something that we'd put in

front of a client or not, to well, I

don't know if this investment idea is

good. And it's And it would take me, you

know, 3 years of waiting to really be

able to give you a a clear concrete

answer of like what the market has done.

And so the objectives start to become

fuzzier. And so the the way you go about

training the model, and the way you

think about how to make the model

better, those things becomes more

difficult. It becomes more difficult

from a research perspective. And so um

but it's not impossible, is what I would

say. And so what it takes is a lot of

research focus. It takes a lot of

wonderful data. It takes a lot of

feedback from experts in the field. We

really want to get the the, you know,

the P99.9 type of feedback uh to make

the model, you know, as good as humans.

But I don't think it's an insurmountable

problem. And I And And what it takes is

continued scaling of the base model, and

focus. Focus from the research labs who

think that this is an important problem

to go solve. And one of the things that

I'm doing at uh on my team is making

this a area of focus and priority for

OpenAI. And And I think because of the

combination of those two things, I feel

confident we'll make a lot of strides in

current financial benchmarks? So, I

think we're still in the state of, you

know, uh does this check out

numbers-wise? Uh how do you think about

making your own, adding in taste? Um I

think this is also an open call for

people working in this space to work on

their own benchmarks. And a sort of

follow along to that is, you said build

for build systems for models today, but

you also give a lot of predictions for

the future. Um how do you put

guardrails, limitations, um

checks on financial services that you

build for people today, just so they're

confident in, you know, this is not like

going out off the rails?

talk a little bit about evals. Uh evals

is, yeah, something near and dear to my

heart. I spend a lot of time working on

evals. I would say that um

the like um the prior that I would share

with people is building a good eval is

incredibly hard. Peop- Pe- you know,

that's not obvious to people who haven't

worked on an eval before. Um to build an

eval that actually like correlates with

the thing that you're actually trying to

measure, like the skill that you're

trying to measure, capability you're

trying to measure, very difficult. Um

because whatever you put together is

just a proxy for whatever that thing is,

right? And it's just one dimension. So,

that's one. Two is it's very tedious to

get the answer key or the way that you

grade this correctly. And if you don't

get the way that you get you grade the

answers correctly, it's a eval that's

very easy to hack and very easy to

reward hack. And therefore, you know,

you could score very highly on the eval,

um but actually do really poor on the on

the actual underlying skill or objective

that you're trying to measure, right?

And so, the the the like prior that I

will share is evals are really hard, um

not just in financial work, but just

across the board. And um what I would

say is uh we don't think that there's a

really good eval today, um for financial

um work. Um there's a bunch of existing

prior work. I'll I'll reference some of

the work that we did, which is a GDP

eval, um which is like a little bit more

broadly in knowledge work. Um if you

guys look at the Astra announcement, you

know, we don't talk about GDP eval. Um

we we know that the eval is completely

saturated, uh and that it's uh there's a

and the pieces that are not saturated

are essentially broken, right? And so,

we actually don't report on that, we

don't track it anymore. And so, one of

the tricky things for um uh GPT-6 Astra

was um we didn't talk a whole lot about

financial evals. We have some internally

that we didn't talk too much about and

didn't want to share at the time yet.

They're coming. Um but it was there was

no good way for us to show progress uh

on financial tasks on these evals. Um

there's a funny like funny things like I

think there's this bias in evals where

if the numbers look good, the

researchers assume that the model's

gotten better and they don't investigate

it. If the model if the you made a much

more capable model and the eval numbers

look bad or haven't increased in the the

scale that you want, then you go

investigate, right? So, this is kind of

like bias baked into uh investigation

these evals. Something like this

happened with GPT-6 where we we ran

GPT-6 on a set of on financial evals and

the numbers didn't really change much.

We're like, "Oh, like what's going on

here? Let's take a look at this eval."

We we we did a deep dive into these

different evals and lots of things were

broken. Uh we like the basically we

found that we had saturated the evals

and the questions that we were getting

wrong were driven by incorrect answer

keys, um poorly specified prompts, or

just some other flaw with the eval. Um

and so all that to say like one of the

things that my team is working on

currently is to try to build a much

better eval that actually predicts the

ability to automate the work of that

specific profession. And so yeah, and so

the idea is like we'll have something

like an investment banking eval that

predicts, you know, if you score well on

this eval, you could probably automate a

lot of the work of a junior analyst uh

in investment banking.

uh any callouts for people in the

finance space, the Latent Space

audience, uh your team is probably

hiring, but last thing to put out there,

how how can people be be helpful to you?

first and foremost like please check out

Chat GPT Financial Services. Um it is an

enterprise product, but we're trying

very hard to make it available to more

uh prosumers on the pro plan and so

hopefully we'll have some cool

announcements there. Um two, yes, my

team is hiring. Uh we're seeing

incredible uh customer traction with the

product and uh because of that we're

hiring and scaling the team rapidly.

It's a very diverse team with lots of

different roles and so I just want to

call out a few of them very quickly. Um

there's obviously the the standard uh

front end back end uh engineering, so um

kind of traditional product building. So

that was one. Two, we're very much looking

for wonderful product managers. I think

in some ways uh great product management

is the the main bottleneck in trying to

go win in this space and so we're trying

to hire wonderful product managers in

investment banking, equity research,

asset management, uh you know, across

the board we're we're trying to hire

product managers. Uh and then the other

role that I want to give a special shout

out to is we're hiring someone we call a

subject matter expert. Um and you know,

it's really important. Uh one of the

things that we did well in in coding is,

you know, the the people that were

training the models and making them

better understood what good code looks

like, right? And that was a huge

tailwind for us as we're making the

models better at these things. We don't

have that advantage when it comes to

making the models models better at

finance. And so we're we're building out

and trying to hire a lot of folks that

are able to bring that financial

expertise and work with the engineers,

work with the researchers, work with the

product managers to make the model and

the product better in your specific

fields. And so if any of those resonates

with you, like I highly

you know, recommend that you reach out

to me. I'm available on LinkedIn. We

also have these job listings posted on

our careers page. It's pretty easy to

find.

Well, you answered my question. I was

going to ask when I when can I have a

play with this, but it is coming to Pro

Pro Pro Pro Pro

people.

it makes sense you make stuff for

enterprise and then this trickles down

the the regular models. It comes to Pro

plan. So I expect for your general usage

down the line it'll get better at

console stuff.

Check out the keynote. You gave a lot.

You gave good predictions. You talked a

little bit about some juicy we fixed

pre-training issues. So I 'll leave

breadcrumbs for you guys to go watch,

but thanks for joining.