Rising Titan – Mistral's Roadmap in Generative AI | Slush 2023

Summary

このビデオスクリプトでは、ロンドンを拠点とするライトスピードのパートナーであるポール・マーフィーが、AI分野での投資経験と、新興企業Mistolの創設者アーサーとの出会いについて語ります。Mistolは、開発者がモデルをカスタマイズし、特定のタスクに適合させることができるようにすることを目指して、AIの基礎モデルを異なるアプローチで構築しています。彼らは既に7Bモデルをリリースし、コミュニティからの強い関心を集めています。アーサーは、オープンソースモデルの重要性、AIの安全性、規制へのアプローチ、そしてAIが社会にもたらすポジティブな変化についても議論しています。最終的に、ヨーロッパにおけるAI分野のリーダーシップの重要性を強調しています。

Takeaways

• 🌐 Mistolは、AIの基礎モデルを他の会社とは異なる方法で開発し、よりオープンでアクセスしやすいモデルを目指している。
• 🚀 Mistolは設立からわずか6ヶ月で、効率的なチームワークにより、最先端のモデルを迅速に開発している。
• 💻 Mistolの戦略は、開発者向けに特化し、より小型で特定のタスクに適したモデルを提供することにある。
• 🔍 Mistolの7Bモデルは、コミュニティからの高い関心を集め、多様な応用例が見られる。
• 🤖 Mistolは、AIの安全性やバイアスの問題に対処するために、モデルの重みを公開し、カスタマイズを可能にしている。
• 📈 Mistolは、将来的にモデルのホスティングサービスや新しい技術、プラットフォームの発表を計画している。
• 🌍 ヨーロッパにおける人材獲得は競争が激しく、Mistolは最高のエンジニアと科学者の採用に注力している。
• ⚖️ AIに関する規制や安全性について、Mistolはアプリケーションレイヤーに焦点を当てたアプローチを提唱している。
• 🌱 AIは医療、教育、創造的思考などの分野で人々の生活を改善する可能性がある。
• 🇪🇺 ヨーロッパにおけるAI分野のリーダーシップは、地域の価値観に合った技術の発展に不可欠である。

Q & A

ライトスピードはいつからヨーロッパで投資を始めましたか？

2007年から。

ライトスピードはAIにどのくらい投資していますか？

約10年間でAIに約50社に投資し、10億ドル以上を投資しています。

ミストールの創業者は誰ですか？

アーサー、グォム、ティモットです。

ミストールはどのようなビジョンを持っていますか？

基礎モデルを他の会社とは異なる方法で作成し、開発者がモデルを特化させて独自のものにするためのよりオープンなアクセスを提供することです。

ミストールの最初の目標は何でしたか？

7Bモデルの構築です。

ミストール7Bモデルはどのように迅速に開発されましたか？

優秀なチームを組み、データセットに80％のチームを割り当てることで、3か月以内に完成しました。

ミストール7Bモデルに対するコミュニティの反応はどうでしたか？

非常にポジティブで、多くの開発者がミストール7Bをカスタマイズして新しい機能やアプリケーションを作成しました。

ミストールは将来何を計画していますか？

新しいモデルと技術を発表し、モデルのホスティング能力を提供するプラットフォームの立ち上げです。

ミストールは採用においてどのような課題に直面していますか？

最高のエンジニアと科学者を採用することが非常に競争が激しいため、大きな課題です。

ミストールはオープンソースとAIモデルの関係をどのように定義していますか？

モデルの重みを公開しているため、完全にオープンソースとは言わず、'オープンウェイト'と表現しています。これにより、モデルの修正が可能になりますが、すべてを完全に理解するわけではありません。

Outlines

00:00

🚀 ライトスピードによるAIへの投資とミストールのビジョン

ポール・マーフィーは、ロンドンに拠点を置くライトスピードのパートナーとして、2007年からヨーロッパでの投資活動について紹介し、AI分野への約10年にわたる投資と50社以上への出資を強調しました。彼は、新興企業ミストールとその共同創設者アーサーに対する高い評価を表明し、AIの基盤モデルを異なるアプローチで開発しようとする彼らのビジョンを紹介しました。アーサーは、開発チームの迅速な行動とオープンソースモデルへの取り組みを通じて、開発者がモデルを特化させ、彼ら自身のものにすることを可能にするプラットフォームを構築していると述べました。

05:02

🌍 コミュニティとのエンゲージメントと次のステップ

アーサーは、ミストールの7Bモデルがコミュニティによってどのように受け入れられ、活用されているかを説明しました。特に、開発者が独自のタスクやデータセットにモデルを微調整し、新しい能力やトピックを探求している例を挙げました。さらに、将来のモデルや技術、プラットフォームの展開について触れ、ミストールがモデルホスティングと迅速な推論機能を提供する計画を示唆しました。

10:03

🤖 オープンソースとAIの課題

オープンソースの概念がAIとモデルにどのように適用されるかについて、アーサーは「オープンウェイト」という用語を使用して説明しました。これは、モデルの重みを公開することで、変更の可能性を提供するものですが、完全な透明性や理解を必ずしも意味するわけではありません。彼は、モデルの変更とカスタマイズが重要であり、オープンソースアプローチが科学と技術の進歩を加速させるために重要であると強調しました。また、バイアス制御と解釈可能性の向上についても言及しました。

15:05

⚖️ AI規制と責任の所在

アーサーはAI規制に関して、製品安全、国家安全保障、そして存在リスクという三つの異なる側面を識別しました。役割と責任に関しては、アプリケーション層に圧力をかけることで、間接的にモデルプロバイダーに対する市場圧力を生み出すべきだと主張しました。これにより、より制御可能で安全なモデルの開発が促されると述べています。また、規制に対するアプローチとして、アプリケーション層に焦点を当てることの重要性を強調しました。

20:07

🌟 AIによる未来の可能性

アーサーはAIの持つポジティブな可能性に焦点を当て、特に医療、教育、仕事の方法における革新的な変化を強調しました。AIがクリエイティブな思考を促進し、新しい社会を形成することへの期待を表明しました。さらに、気候変動などの世界的な課題に対してAIがどのように貢献できるかについても触れ、科学的な発見や技術的な進歩を加速させることができると語りました。

25:09

🇪🇺 ヨーロッパにおけるAIのリーダーシップとその重要性

AI技術の進展におけるヨーロッパの位置付けと、地域内での技術的リーダーシップの確立の重要性についてアーサーは言及しました。彼は、ヨーロッパが技術的な進歩を主導し、政策および技術の両面で提案を行うことで、民主主義と社会に対するヨーロッパの価値観を形成していくことの重要性を強調しました。

Keywords

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Highlights

Paul Murphy introduces Light Speed and its investment history in Europe, emphasizing their focus on AI with a portfolio of 50 companies and over a billion dollars invested.

Arthur shares the vision behind Mistol, aiming to make foundational AI models more accessible and customizable for developers, with a focus on open source models.

Mistol's rapid development of a 7B model within three months of funding, highlighting the efficiency and dedication of their team.

The community's enthusiastic reception of Mistol's 7B model, leading to thousands of derivative works and integrations in various open source projects.

Mistol's plans for releasing new models, techniques, and a platform for hosting models with fast inference capabilities.

Challenges in hiring top talent and building a community around Mistol's AI models, emphasizing the importance of community engagement.

Arthur's perspective on AI regulation, advocating for clear distinctions between product safety, national security, and existential risks.

The potential of AI in transforming healthcare and education through personalized and empathetic interactions.

AI's role in addressing climate change by enabling scientific breakthroughs in fields like chemistry and material science.

The significance of having a European champion in AI to shape the technology according to European values and democracy.

Mistol's commitment to open source as a core part of its philosophy, facilitating customization and addressing biases.

The distinction between open source software and open weights in AI, and how Mistol balances competition and collaboration.

The need for independent regulatory bodies to ensure AI safety and prevent regulatory capture by large tech companies.

Arthur's view on existential risks of AI as speculative and the importance of empirical evidence in regulatory discussions.

The future of AI in enabling more creative thinking by automating routine tasks and structuring knowledge.

Transcripts

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[Music]

okay um welcome everyone really nice to

see you uh very very happy to be back at

slush especially this time with Arthur

uh my name is Paul Murphy I'm a partner

at light speed based in London uh just a

real quick uh bit about light speed for

those that don't know uh we have

actually we're a Silicon Valley based

fund but we've been investing Europe uh

since 2007 we have over 30 companies now

um in

Europe uh and um yeah we're investing in

pretty much every sector uh and every

stage um we're talking about AI today

and I think I think it's important to

put some context around that from our

perspective we actually have been

investing in AI for nearly a decade we

have about 50 companies um and have

invested over a billion dollars into the

category and that context is relevant

because uh when we met Arthur and his

co-founders we thank you we immediately

fell in love with the vision of mistol

um and so I thought the the best place

to to start would be to ask you Arthur

to tell us a little bit about what

you're building at

sure thank you very much slush for the

invitation thank you Paul as well um so

yeah we started mistal six months ago uh

with guom and timot and our vision was

that we wanted to make the foundational

models a bit differently from the other

companies uh we've been in the field for

almost a decade now and we've seen it go

from a cat and dog detector to something

is very close to being humanlike

intelligent or atast at least looks like

it and we knew that with a very

dedicated team we could develop uh

state-of-the-art models very very

quickly and we could actually take the

field into something that is that would

be more open where would give more

access to developers so that they could

specialize the models make them their

own make them as small as possible to

solve their task and for us the good way

of doing it and the good way of starting

that was to ship the best open source

models create models that would be very

easy to to use by individual developers

and from then on build onto an

Enterprise play to sell a platform that

allows developers to take large language

models and to make them their own to to

create some differentiation on the

application they're making and that's an

differentiation which is currently hard

to do when you only access apis of a

couple of providers but if you have a

deep access to the models you can create

things that are much more interesting

and this is what what we want to enable

so when we we LED your seed round it

wasn't that long ago you told us that

you're first thing you're were going to

do is to build your 7B model and then I

think it was it was like 3 months from

when we signed the docs on that round uh

we got our message saying hey we're

ready uh it's ready and it was faster

than we had expected it was already

incredibly ambitious I'm just I think

everyone's probably wondering how you

did how you did that so quickly well I

think the secret is to have a a good

team uh so we were joined by our first

employees uh a dozens of them at the

beginning of June and nobody took

holidays uh we Rec created the wall what

we call the machine learning Ops system

so that's actually Fair simple you you

need to create a very good Training code

base you need to create a very good

inference uh code base uh to to deploy

the models you need to be able to

evaluate the models and the one thing

you do need the most and where we

actually dedicated 80% of the team on uh

for three months is to have some very

good data sets so we we went to the open

web took public domain knowledge created

it so that we could just get the best of

it filtered it did everything to get

something very good did some work around

how to better optimize the models and

combine all of this uh and then train

the model to get the 7B and we continue

doing it uh with the new models we'll be

soon announcing like when you say it's

fairly easy I think maybe some people

would disagree with you on that but you

definitely made it look easy I think

that's true um so I'm curious uh the

community you know was has been very

engaged with 7B model Since You released

it I think it was you know trending on

hugging face for multiple days top you

know top top models um what kinds of

things have you seen that have been

interesting so far from the community so

we've seen I think thousands of um D

derivative work so uh Developers that

took mistal 7B and fine tuned it on

their task or on their data sets to make

it special so we've seen new

capabilities like longer context uh

better instruction following capacities

uh we've seen uh like new topics so

we've seen like occult specialized

models able to talk about uh post test

experience and the like much better than

what MB was able to do before so many

kind of different applications uh some

of them useful some of them

just funny um we've seen integration in

a lot of llm Open Source projects so the

open source world around Genera T is

pretty is is pretty involved already so

you have retrieval augmentation systems

you have projects that allow to deploy

the models on your laptop you have all

of these things and they adopted M 7B

very quickly and I think it was the

field was really missing an actor that

would produce the best open source

models and actively engage with the

community and that's what we we uh we we

are enabling okay and so now 7B is out

there what comes next so we have um

nothing announced yet but we we do have

things in house that we'll be announcing

before the end of the year uh new models

uh new techniques uh and obviously the

beginning of a platform so we're

actively working on the product uh we'll

be soon offering uh hosting capacities

for our models uh with very fast uh

influence uh capabilities and yeah

that's for uh very soon okay okay I'll

watch the space um so you're also while

you're doing all this incredibly what I

think most people would think of as

quite challenging technical work you're

also building a company and I know

that's not easy haven't done it myself

before um what's keeping you up at night

right now what's your biggest headache

um so hiring is obviously a very big

challenge I think the only reason why we

got there so fast is because we hired

the the best engineers and the best

scientist in the world it's a very

competitive landscape uh Europe is full

of talent especially the junior ones uh

and so we we are this is some like a

very big preoccupation for us like I'm

constantly working on it so that's one

thing um the other thing is like

creating the community engaging with it

uh so we started with the with mral 7B

but we really need to uh yeah well

facilitate the life of our users uh have

them engage facilitate Upstream

contribution facilitate the emergence of

IDs that we could help enable

so that's another thing we have a lot of

um I guess policy matters uh that we did

not expect but obviously this is an

agenda that you don't select um there's

we we so there's there's different

tracks you have in the US you have in EU

um we've been uh vocal about the fact

that we wanted to have hard regulation

on the product side because it's very

important and we see ourselves as the

provider of tools and a big enabler of

compliance for the application makers so

we've been saying that uh constantly and

and and we've seen like the debate uh

progress on these topics and so this is

something that yeah we're very keen on

trying to enable from a technical

perspective because it's important that

you have technical Founders that

participate in that discussion uh and so

that that has kept me up at night uh for

for a while and I think you know the

ambition was certainly to be able to

build something that could rival other

large companies like open Ai and I'm

just curious what do you view as a

differentiating philosophy or approach

to companies like open AI I think a

differentiating philosophy is that we

really Target the developer space and we

really think that when you're making an

application that you want to put into

production you do want to have several

specialized models that are as many

chips you you should see them as chips

that you assemble in an application and

it's actually not easy to make a very

good chip for the use case you want so

you can start with a very big model with

thousands of billion well with hundreds

of billions of parameters it's going to

solve your task maybe but you could

actually have something which is 100

times smaller and when you make an a a

production application that goes at

scale and Target a lot of users you want

to make the choices that lower the

latency lower the costs uh and leverage

the actual proprietary data that you may

have and this is something that I think

that that's not the the topic of our

competitors they're really targeting

like multi-usage very large models AGI

we takeing very much much more pragmatic

approach in enabling super useful

application today uh that would be cost

efficient that would be very low latency

and that would enable strong

differentiation through uh proprietary

data okay and you've talked I think

another key difference you've talked a

lot about open source as being a core

part of your DNA um and I think question

I sort of wanted to ask uh Arthur by the

way wouldn't look at these questions

beforehand so he wasn't expecting this

one but I understand the concept of Open

Source software I think we all do we see

the code you kind of can take it modify

it um and use it but in the world of AI

and and models the concept of Open

Source just feels like it's maybe a bit

different because actually some things

you do keep for yourself or you have to

what does open source mean in the

context of llms and AI so we don't

really call them open source so the the

models we provide are open weight I

think it's important to like keep a good

distinction between the like the

terminology we were using for software

and the terminology we are using for

models if you provide the weights of a

models you're enabling modification

you're not necessarily enabling like

full understanding of what's going on

but even if you do provide full

transparency on the data sets and

training you don't know what's going on

cuz it's it's a bit opaque by Design so

it's an empirical science when you

create a model the only way to verify

that the model is doing what you expect

is to measure it with with with

evaluation this something will be

enabling and then it's to modify it with

some signal coming from either humans or

maybe machines to to modify the model so

really the modification part is super

important for differentiation and we're

taking this approach there's a full open

source approach which I think is very

valid as well for science in which you

disclose your data set you disclose

everything that I think that's that's

something that we would strive toward at

some point but obviously it's super

competitive and the data set part is

very hard to to obtain it's also very

Capital intensive you need a lot of gpus

so right now we're taking a balanced

approach in between what we uh opens

what the open ways we provide the things

we keep for ourselves to to get a

competitive uh Edge and this is going to

be a dynamic play and we expect it to to

evolve with time and with technology

okay and then does the does the open

weight approach help with other

challenges like biases and control yeah

so it helps with basically two things

the first thing is that you can modify

the the biases you can have like a

strong and fine uh modification

capabilities on the editorial tone on

the orientation and alignment of the

model so we allow alignment of your own

models to your own values and those can

slightly differs um so like fine control

of biases goes through fine deep access

to to models that's the first thing the

second thing it allows and we've seen it

with active engagement of the AI safety

community in particular around open

Source models it allows to have better

interpretability because you can see the

inner activations of the of the models

and and that tells you things about

what's happening uh about why the model

is taking a decision and not another so

why is it outputting award and not

another and so in the interpretability

world it's also super useful it's also

and I guess the last thing is that it's

very useful to do red teaming because

you have a deep access to to the model

and so you can try to verify the the

part of it which are a bit failing or

behaving unexpectedly and these are

things that you can then correct very

similarly to uh what we've been doing in

the open source software for security

cyber security and the like okay and

then what I mean what is sort of what do

you view as at stake here you know why

is this is this in other words is this a

business Advantage for Mel or is it

something more fundamental that you see

as almost a

responsibility so it's both a business

Advantage because we allow further

customization and differentiations and

it's a very mature market and we expect

that on the application space the one

actors the actors that are going to

survive and create some value are the

one that will be able to strongly

differentiate themselves and so they

would need deep access to models so

that's a business differentiator then

there's a bit of an ideological

differentiators in the sense that I've

been contributing to open source for 10

years G as well we really think that AI

has been accelerated by open science by

the circulation of knowledge and that's

how we went in 10 years from something

very very uh well interesting but that

would just detect Tech boats and

something that actually uh will speak

the human language so this has been

allowed because you had big tech labs

you had the Academia as well that was

all of them were communicating at

conferences every every year and and

information would circulate and that

accelerated things and suddenly in 2020

open I decided to stop publishing and it

was followed by its competitors uh very

closely after and so ever since 2022 we

haven't seen like major advances in llm

publicly announced and so we've seen

currently there's like new architectures

that are used internally by our

competitors and that are not available

out there this is something we will

correct very soon okay great um so I

want to shift Focus now talk about

something you mentioned earlier which is

regulation and it's a topic you kind of

can't avoid I think you've thinking

about AI um a lot of focus within Europe

and and in the UK um and I think you at

the safety Summit in the the AI safety

Summit um last month there's a lot of

ideas out there and I think um you know

curious to hear your view is to what

should be the priority how should

regulation be prioritized and

instrumented yeah so I think it's quite

interest it's a very interesting topic

for me and and we've been uh yeah we've

been contributing IDs the one thing that

I would start with is that we've been

talking about regulation and safety and

mixing Concepts very heavily so there's

a matter of product safety which is

answering the question of you deploy a

diagnosis assistant in the hospital you

want it to be safe you want to be able

to measure whether the decision it's

making is actually sound is actually

correct so that's that's what we call

product safety that's something you have

when you buy a car you have product

safety of your car and it should very

much be similar for applications that's

one thing and AI to some extent creates

new problems because you have models

that are not deterministic and so they

behave in a potentially an expected way

so it's useful to refine the hard lws

that we have around uh product safety

regulation now there's another topic

that came up which is National Security

so the question of whether the llms that

we're training the LM that everyone is

training is spreading too much knowledge

so when you have access to llm you're

effectively able to educate yourself on

many topics and this is something that

is a concern for different actors

because you could have like small groups

that are deemed bad that could use this

knowledge to do bad things so this is

this has been at the a central topic

especially in the US um we're still

lacking a lot of there's absolutely no

public evidence that llms are

facilitating anything so we're really we

we've been advocating for for some

empirical grounding of the discussion

and this is something that's currently

very much lacking and then there's a

third thing which is kind of mixed with

all this with with the two first which

is existential risk so knowing whether

the technology we're making is

effectively on an unbounded exponential

that will end up destroying us because

as every exponential it kind of breaks

the limits at some point and and that's

well it becomes IL defined as we say in

mathematics so this is something that

for us is very much science fiction

that's empirical evidences so what we've

been saying is that we should really

focus on the first topic which is

imminent is something that is we do need

to have product safety on AI because

it's it's going to to otherwise it's

going to break trust in the technology

we're making and so we want to enable

that on the second part we are lacking

empirical evidence but I think this is

something that we should monitor closely

knowledge historically knowledge the

spreading of knowledge has always had

more benefits than uh than than

drawbacks and we AI is not different in

that respect but still it's something

that that could do with monitoring

because it's really new technology on

the third aspect of AGI and and and the

like and and the fact that that you

could have an autonomous system that

would go out of control this is

something that we are not at heas

discussing because we really think that

as scientists we are lacking evidence of

any existential risk and we think that

it pollutes the discussion on the first

aspect which is super important yeah and

so if I just kind of make sure I

understand this right the view is that

the application layer is probably the

one that has the most responsibility in

terms of safety at least to consumers or

end users whoever that is businesses but

that perhaps the models could provide

that as a feature or functionality but

it's not the responsibility of the model

to ensure that the ultimate data

transmitted is itself safe exactly so we

think that the correct way of putting

some pressure on the model providers uh

like us is to effectively say that any

application which is deployed and that

includes the application that we deploy

uh should be should meet a certain

number of safety standards so they

should do what they're expected to do

and if you do that then that means that

the application providers will be

looking at model providers that are

controllable enough that can give some

form of guarantees that can give some

evaluation Tools around the fact that

they're controllable and that they do

what they're expected to do so you have

some form of second order pressure that

is put you put pressure on the

application layer and that puts a market

pressure on the foundational model

developers and that's the correct way of

making a healthy competition in making

the most controllable models in making

the best evaluation tools and making the

best Guard railing tools and we think

that it's a much better way of doing it

than applying directly a pressure on the

foundational model layer because if you

do that well you're you're in a IL

defined territory because you're trying

to control something which is by Design

super multi-purpose very akin to a

programming language so you can't really

regulate the programming language

because you can do anything with it and

so really there's a problem of

definition and then there's an

operational problem of the fact that if

you put some heavy pressure on that

layer you're effectively um favoring the

big actors that have a lot of compliance

capabilities and you're you're making it

harder for startups with Innovative IDs

to to come up and compete and

so this like foundational models is a

bad proxy for a market capture and so we

believe that applying the regulation

pressure on the application layer is the

one thing to do because that's going to

Foster competition and provide a safer

world do you think that there's a role

for an or you know an iaea kind of like

organization to exist that helps to

enforce or provide this guidance

regulation so yes I think um this kind

of Regulation if we need

to monitor I think we we do need to have

empirical evidence of what's happening

in the space and we need to monitor the

product side safety and one way of doing

it is to enforce that we have very very

independent uh organisms that actually

monitor these things and when I say

independent I mean that we should be

very cautious of of preventing pressure

and Regulatory capture of this things so

setting standards but ensuring that no

big actor is basically writing the

standard themselves so what that means

that if we are if we if we need to have

this this form of organisms they need to

be very well funded probably state

funded

and being completely screen from

pressure from the industry okay so now I

want to shift you know I think the

regulation debate is largely many of the

debates in AI are tend to sort of skew

somewhat negative so let's dream for a

second like how can AI make our lives

better what do you see as the utopian

future with

AI so I think the there's so there's

many vertical in which Ai and like

interact ING with machines with natural

language carry a lot of value uh so

Healthcare is going to be completely

changed by AI because you you will be

able to

interact uh with empathic beings uh that

are actually super well grounded on

statistics and that's really what you

were expecting from medicine so we

expect that AI is going to empower uh

Physicians to be much better at what

they're doing and to make better

decisions um education is also a super

interesting topic uh personalization of

Education we know that it's super

important to uh take the most most

potential of of human beings and having

some like your individual teacher being

an assistant this is going to change a

lot of things especially in the global

South um so that's two things generally

speaking this is going to change the way

we work so it's a way the fact that it

can interact with v structure knowledge

and that it can do well act as if well a

bit imita the boring task of of your

daily life this is going to enable more

space for creative thinking so we will

be able to think more creatively and

that's going to unleash I think a new

Society very soon and if you think about

some of the more existential risks we

face in the world like climate change do

you think that something like that can

be addressed or at least improved yes so

I think this is a frontier which which

hasn't been completely addressed yet but

this is really a promise of having

better models the fact that if you if

you have some ways of reasoning around a

pool of science well you can enable

scientists to come up with new ideas you

can potentially unlock very precise

things like create like in chemistry in

accelerating uh chemical reaction so

that you emit less CO2 for instance

these things like Material Science

chemistry Fus nuclear fusion as well all

of these locks that we have and that are

that we basically need to break in order

to address climate change well I mean

that's one of the way you can can

address climate change obviously the one

way is also to reduce consumption but

the the these things we we think that AI

is going to be an enabler of of of

breaking these lcks it's not going to be

an easy task uh there's still many

things to invent and we think that going

through the open science part uh

fostering the the the keeping fostering

the AI community that drove the field

forward for 10 years is is super

important to break these logs okay

that's great so I think I want to come

back to to Europe sort of for our last

questions we're we're out of time um how

I mean I think the fact that the company

is being built in Europe is very

important to you it was obvious to you

and your co-founders when we invested um

how important do you think it is for the

industry that we have a European

champion emerg in in the field of

AI so the Technologies is AI generative

AI is is really a wave you can it's

going to change society quite

significantly and in Europe we have a

choice of either being on top of the

wave and driving the technology forward

or just looking at it happening in the

US uh and in China and we think that in

order to shape the technology to our

values and to the way we think about

democracy about Society we need to have

very strong technological actors that

are able to drive the field forward make

proposals um both in term of policy and

in term of technology and so that's why

we believe it's super important that

actors

that we have strong actors in Europe

great thank you so much Arthur really

appreciate it

amazing