Open sourcing the AI ecosystem ft. Arthur Mensch of Mistral AI and Matt Miller

Summary

Arthur, founder and CEO of mistal AI, discusses the company's rapid growth and focus on open-source AI models. Despite being a young company, they've made significant strides by releasing high-quality models comparable to GPT-4 and forming strategic partnerships. Arthur emphasizes the importance of balancing open-source contributions with commercial interests and shares insights on the future of AI, including multilingual and multimodal models. He also highlights the advantages of building a business in Europe and the company's vision for AI's role in the next five years.

Takeaways

• 🚀 Arthur, the founder and CEO of mistol AI, has led the company to release high-quality models rivaling GPT-4 in just nine months since its inception.
• 🌟 The company's success is attributed to its open-source approach and the ability to efficiently develop models with a lean team of experienced individuals.
• 🤝 Mistol AI has established strategic partnerships with major companies like Microsoft and Snowflake, indicating a strong go-to-market strategy.
• 🧠 The decision to start mistol AI was driven by the founders' desire to see AI progress through open exchanges, which they felt was lacking in the field since 2022.
• 🌐 The company aims to bring AI technology to every developer, promoting a more open platform than competitors, and accelerating adoption through this strategy.
• 🔄 The balance between open-source and commercial models is managed by offering two families of models, ensuring leadership in open source while driving commercial adoption.
• 💡 Mistol AI's rapid development is attributed to hands-on work with data and a team that is willing to engage in the less glamorous aspects of machine learning.
• 🔍 The company is focused on improving its large models and developing open source models for specific vertical domains.
• 🌍 Being a European company provides Mistol AI with unique advantages, such as access to a strong talent pool and linguistic capabilities, as well as geographical opportunities.
• 🔮 Looking ahead, Mistol AI envisions a future where AI infrastructure is open, enabling the creation of assistance and autonomous agents accessible to all users.
• 💼 For founders in the AI space, the advice is to maintain an ambitious mindset and be prepared to build and reinvent from scratch every day, as the AI landscape is ever-evolving.

Q & A

What motivated Arthur and his co-founder to start mistol AI?

Arthur and his co-founder were inspired by the open exchanges between academic and industrial labs that contributed to the progress of AI. They were disappointed that this openness stopped early in the AI journey and wanted to push the field back towards more open source contributions, especially given the rapid advancements in AI technology.

How does mistol AI balance open source contributions with commercial interests?

Mistol AI maintains two families of models - one focused on open source to lead in that domain, and another for commercial purposes. They aim to stay relevant by continuously producing open source models while also developing better commercial models available on various cloud providers.

What is the advantage of being a European AI company like mistol AI?

Being a European company allows mistol AI to tap into a strong pool of junior talent from countries like France, Poland, and the UK. Additionally, they benefit from support at the state level and have a geographical advantage in serving the European market, including having a strong French language model.

What are some of the challenges mistol AI faces in maintaining its position in the AI field?

Mistol AI faces the challenge of staying ahead in the rapidly evolving AI field. They need to balance contributing to the open source community while also securing commercial adoption and enterprise deals to sustain their business model.

What is mistol AI's strategy for the future in terms of model development?

Mistol AI is working on improving their existing models and developing open source models for various vertical domains. They are also focusing on multilingual and multimodal models and plan to make customization and fine-tuning part of their platform.

How does Arthur view the future of AI technology?

Arthur envisions a future where AI technology becomes more autonomous, with the ability to create assistants and autonomous agents that can perform a wider range of tasks. He expects AI to become so controllable through human language that creating such agents will be a common skill learned at school.

What are some of the most exciting applications that mistol AI has seen built on their models?

Mistol AI has seen startups in the Bay Area using their models for fine-tuning and fast application development. They have also seen web search companies and enterprises using their models for knowledge management, marketing, and other standard enterprise applications.

How does mistol AI support its community of developers?

Mistol AI invests in developer relations, creating guides and gathering use cases to showcase what can be built with their models. They encourage developers to engage with them to discuss use cases, advertise their applications, and provide insights for future evaluations and improvements to their models.

What is mistol AI's approach to partnerships with companies like Snowflake and Databricks?

Mistol AI believes that AI models become stronger when connected to data. They have formed partnerships to run natively in the clouds of these companies, allowing customers to deploy mistol AI's technology where their data resides, which they see as important for the future of AI deployment.

How does mistol AI decide on the size of the models they develop?

The decision on model size is based on scaling laws and depends on the compute resources available, the desired training and inference costs, and the balance between latency and reasoning capabilities. Mistol AI aims to have a family of models ranging from small to very large ones.

What advice does Arthur have for founders in the AI space?

Arthur advises founders to always act as if it's day one, to be ambitious, and to dream big. He emphasizes the importance of continuous exploration and innovation while also leveraging existing achievements to stay relevant in the rapidly evolving AI field.

Outlines

00:00

🎤 Introduction and Background of Mistal AI

The speaker, Arthur, is introduced as the founder and CEO of Mistal AI, a company that has made significant strides in the AI field despite being only nine months old. The introduction highlights the company's success in releasing high-quality models comparable to GPT-4 and its open-source approach. Arthur's background at DeepMind and his work on the chinchilla paper are mentioned, setting the stage for his discussion on the philosophy behind starting Mistal AI and the opportunities it presents in the AI ecosystem.

05:01

🚀 The Genesis of Mistal AI and Open Source Mission

Arthur shares the story behind the establishment of Mistal AI, emphasizing the importance of open exchanges in AI development. He discusses the shift in the AI field in 2022, when open contributions declined, and how this motivated him and his co-founder to create Mistal AI. The company's mission is to democratize AI by making it accessible to every developer, in contrast to the closed approach of competitors. Arthur also outlines the company's rapid development and benchmark achievements, attributing their success to a lean team of experienced individuals.

10:02

🤝 Balancing Open Source and Commercial Models

The discussion shifts to how Mistal AI balances its open-source offerings with commercial strategies. Arthur explains the company's approach of maintaining leadership in open source while evolving its commercial models. He acknowledges the tension between community contribution and commercial adoption, highlighting the need for constant adaptation and strategic planning. Arthur also touches on the company's partnerships with Microsoft, Snowflake, and Databricks, and how these collaborations have contributed to Mistal AI's trajectory.

15:04

🌐 Geographic Advantages and Future Plans

Arthur discusses the benefits and challenges of building Mistal AI in Europe, particularly France. He highlights the strong talent pool, government support, and the advantage of being a European company. However, he also mentions regulatory challenges. Looking ahead, Arthur envisions a future where AI infrastructure will be open, with Mistal AI becoming a platform for creating assistance and autonomous agents. He predicts that in five years, AI technology will be more accessible, allowing any user to create their own assistant or agent.

20:05

💡 Engaging with the Community and Future Directions

The conversation focuses on Mistal AI's engagement with the developer community and the importance of feedback for model improvement. Arthur invites the community to share their use cases and collaborate for mutual benefit. He also discusses the company's future plans, including the development of multilingual and multimodal models, and the expansion of the platform to include customization features. Arthur emphasizes the company's commitment to remaining the best solution for developers and staying relevant in the open-source world.

25:06

🌟 Final Thoughts and Advice for AI Entrepreneurs

In the concluding segment, Arthur reflects on Mistal AI's rapid growth and the company's strategic approach to the AI ecosystem. He shares his perspective on the balance between exploration and exploitation, emphasizing the need for continuous innovation while maintaining a strong product and business focus. For aspiring AI entrepreneurs, Arthur advises maintaining an ambitious mindset and embracing the challenge of building from scratch every day, encapsulating the spirit of entrepreneurship.

Keywords

💡Open Source

Open Source refers to something that is freely available for everyone to view, use, modify, and distribute. In the context of the video, it is a core value proposition of the company Mistral AI, which aims to make AI technology accessible to developers by providing high-quality models through open source contributions. This approach is intended to accelerate the adoption of AI and democratize its usage.

💡AI

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. In the video, AI is the central theme, with discussions around its progression, the development of foundational models, and the balance between open source and commercial models.

💡Foundation Models

Foundation models are large-scale machine learning models that are pre-trained on a wide variety of data and can be fine-tuned for specific tasks. They serve as a foundation or starting point for building AI applications. In the video, the company's goal is to create models that approach the quality of GPT-4, which is a type of foundation model.

💡Community

In the context of the video, 'community' refers to the group of developers, researchers, and users who engage with, contribute to, and use the open source AI models provided by Mistral AI. The community is essential for the feedback, improvement, and adoption of the technology.

💡Commercial Models

Commercial models refer to AI models that are developed for sale or licensing to customers, often including additional features, support, or customizations beyond what is available in the open source versions. These models are part of a company's revenue-generating strategy.

💡Benchmarking

Benchmarking is the process of evaluating and comparing the performance of a product, service, or model against a standard or other competitors. In the context of AI, it often involves testing how well a model performs on specific tasks or datasets.

💡Fine-tuning

Fine-tuning is a process in machine learning where a pre-trained model is further trained or adjusted on a specific dataset to improve its performance for a particular task. It is a technique used to adapt foundation models to specific applications or industries.

💡Multimodal Models

Multimodal models are AI models that can process and understand multiple types of data inputs, such as text, images, and audio. These models aim to mimic human-like understanding by integrating various forms of information.

💡Developer Relations

Developer relations refer to the practices and strategies a company uses to build and maintain a positive relationship with its developer community. This includes providing resources, support, and platforms that enable developers to create and innovate.

💡Autonomous Agents

Autonomous agents are systems or entities that can operate independently, making decisions and performing tasks without constant human intervention. In the context of AI, this refers to AI systems that can act on their own with some level of autonomy.

Highlights

Arthur, the founder and CEO of mistol AI, shares insights on the company's mission and achievements.

Mistol AI, despite being only nine months old, has managed to release high-quality AI models comparable to GPT-4.

The company's founding story began with a desire to continue the tradition of open exchanges in AI research and development.

Arthur and his co-founder, Timothée, were inspired by the lack of open contributions in AI in 2022.

Mistol AI's vision is to bring AI capabilities to every developer through an open-source platform.

The company has successfully balanced open-source contributions with commercial partnerships, such as with Microsoft and Snowflake.

Mistol AI's approach to model development includes both large and small models to cater to different needs and use cases.

The company is focused on fast development and reaching benchmark levels efficiently, outpacing other foundational model companies.

Mistol AI's strategy involves a lean team of experienced individuals who are willing to do the 'dirty work' of machine learning.

Arthur discusses the economic opportunity in AI and the company's plans for multilingual and multimodal models.

Mistol AI aims to become a platform for AI infrastructure, enabling the creation of assistance and autonomous agents.

The company's location in Europe provides a unique advantage in terms of talent pool and regional opportunities.

Mistol AI's partnerships with data providers like Snowflake and Databricks allow for AI models to run natively in their clouds.

Arthur shares his thoughts on the future of open source versus commercial models and how Mistol AI plans to stay relevant.

The company's approach to model sizes is influenced by scaling laws, training costs, and inference needs.

Mistol AI is working on an enterprise off-the-shelf solution to help businesses integrate AI more easily.

Arthur advises founders to always be ambitious and to view each day as a new opportunity to build from scratch.

Transcripts

I'm excited to introduce our first

Speaker uh Arthur from mistol uh Arthur

is the founder and CEO of mistal AI

despite just being nine months old as a

company uh and having many fewer

resources than some of the large

Foundation model companies so far I

think they've really shocked Everybody

by putting out incredibly high quality

models approaching GPT 4 and caliber uh

out into the open so we're thrilled to

have Arthur with us today um all the way

from BRS to share more about the

opportunity behind building an open

source um and please uh interviewing

Arthur will be my partner Matt Miller

who is dressed in his best French wear

to to honor Arthur today um and and and

helps lead lead our efforts in Europe so

please Welcome Matt and

[Applause]

Arthur with all the efficiency of a of a

French train right just just just just

right on time right on time we we're

sweating a little bit back there cuz

just just just walked in the door um but

good to see you thanks for thanks for

coming all this way thanks for being

with us here at aisn today thank you for

hosting us yeah absolutely would love to

maybe start with the background story of

you know why you why you chose to start

mrra and and maybe just take us to the

beginning you know you we all know about

your career at Deep your successful

career at Deep Mind your work on the

chinchilla paper um but tell us maybe

share with us we always love to hear at

seoa and I know that our founder commun

also L to hear that spark that like gave

you the idea to to launch and to to

start to break out and start your own

company yeah sure um so we started the

company in April but I guess the ID was

out there for a couple of months before

uh timot and I were in master together G

and I were in school together so we knew

each other from before and we had been

in the field for like 10 years uh doing

research uh and so we loved the way AI

progressed because of the open exchanges

that occurred between uh academic Labs

uh industrial Labs uh and how everybody

was able to build on on on top of one

another and it was still the case I

guess when uh in between even in the

beginning of the llm era where uh openi

and deep mine were actually uh like uh

contributing to another one another road

map and this kind of stopped in 2022 so

basically the one of the last uh paper

doing important changes to the way we

train models was chinchila and that was

the last Model that uh Google ever

published uh last important model in the

field that Google published and so for

us it was a bit of a shame that uh we

stopped uh that the field stopped doing

open uh open contributions that early in

the AI Journey because we are very far

away from uh finishing it uh and so when

we saw chat GPT at the at the end of the

year and um and I think we reflect on

the fact that there was some opportunity

for doing things differently for doing

things from France because in France you

have as it turned out there was a lot of

talented people that were a bit bored at

in big tech companies and so that's how

we figured out that there was an

opportunity for building very strong

open source models going very fast with

a lean team uh of experienced people uh

and show yeah and try to correct the the

the direction that the field was taking

so we wanted to push it to push the open

Source model is much more and I think we

did a good job at that because we've

been followed by various companies uh in

our trajectory wonderful and so it was

really a lot of the open source move

movement was a lot of the a lot of the

drive behind starting the company yeah

that's one of one of the yeah that was

one of the driver uh Our intention and

the mission that we gave ourselves is

really to bring AI to the hands of every

developers and the way it was done and

the way it is still done by our

competitors is very closed uh

and so we want to push a much more open

platform and we want to spread the

adoption and accelerate the adoption

through that strategy so that's very

much uh at the core well the reason why

we started the company indeed wonderful

and you know just recently I mean fast

forward to today You released Mr all

large you've been on this tear of like

amazing Partnerships with Microsoft

snowflake data bricks announcements and

so how do you balance the what you're

going to do open source with what you're

going to do commercial commercially and

how you're going to think about the the

tradeoff because that's something that

you know many open source companies

contend with you know how do they keep

their Community thriving but then how do

they also build a successful business to

contribute to their Community yeah it's

it's a hard question and the way we've

addressed it is currently through uh two

families of model but this might evolve

with time um we intend to stay the

leader in open source so that kind of

puts a pressure on on the open source

family because there's obviously some

contenders out there um the I think

compared to to how various software

providers playing this strategy uh

developed we need to go faster uh

because AI develops actually faster than

software develops faster than databases

like mongodb played a very good game at

that and this is a good uh a good

example of what we could do uh but we

need to adapt faster so yeah uh yeah

there's obviously this tension and we're

constantly thinking on how we should

contribute to the community but also how

we should show and start uh getting some

commercial adoption uh Enterprise deals

Etc and this is uh there's obviously a

attention and for now I think we've done

a good job at at doing it but it's it's

very it's a very Dynamic thing to to

think through so it's basically every

week we think of uh what we should

release next on the on both families and

you have been the

fastest uh in developing models fastest

reaching different benchmarking levels

you know one of the most leanest in

amount of expenditure to reach these

benchmarks out of any of the any of the

foundational model companies what do you

think is like giving you that advantage

to move quicker than your predecessors

and more efficiently well I think we

like to do uh the like get our hands

dirty uh it's uh machine learning has

always been about crunching numbers uh

looking at your data uh doing a lot of

uh extract transform and load and things

that are uh oftentimes not fascinating

and so we hired people that were willing

to do the dot stuff uh and I think

that's a uh that has been critical to

our speed and that's something that we

want to to keep awesome and the in

addition to the large model you also

have several small models that are

extremely popular when would you tell

people that they should spend their time

working with you on the small models

when would you tell them working on the

large models and where do you think the

Economic Opportunity from mrol lies is

it in doing more of the big or doing

more of the small I think and I think

this is um this is an observation that

every llm provider has made uh that like

one size does not fit all and uh

depending on what you want to when you

make an application you typically have

different large language model calls and

some should be low latency and because

they don't require a lot of intelligence

but some should be higher latency and

require more intelligence and an

efficient application should leverage

both of them potentially using the large

models as an orchestrator for the small

ones um and I think the challenge here

is how do you make sure that everything

works so you end up with like a system

that is not only a model but it's really

like two models plus an out Loop of of

calling your model calling systems

calling functions and I think some of

the developer challenge that we also

want to address is how do you make sure

that this works that that you can

evaluate it properly how do you make

sure that you can do continuous

integration how do you how do you change

like one how do you move from one

version to another of a model and make

sure that your application has actually

improved and not deteriorated so all of

these things are addressed by various

companies

uh but these are also things that we

think should be core to our value

proposition and what are some of the

most exciting things you see being built

on mrra like what are the things that

you get really excited about that you

see the community doing or customers

doing I think pretty much uh every young

startup in the Bay area has been using

it for like fine tune fine-tuning

purposes for fast application making uh

so really I think one part of the value

of mix for instance is that it's very

fast and so you can make applications

that uh are more involved uh and so

we've seen uh we've seen web search

companies using us uh we've seen uh I

mean all of the standard Enterprise

stuff as well like uh Knowledge

Management uh marketing uh the fact that

you have access to the weights means

that you can pour in your editorial tone

much more uh so that's yeah we we see

the typical use cases I think the the

but the value is that uh for of the open

source part is that uh developers have

control so they can deploy everywhere

they can have very high quality of

service because they can uh use their

dedicated instances for instance and

they can modify the weights to suit

their needs and to bump the performance

to a level which is close to the largest

ones the largest models while being much

cheaper and what what's the next big

thing do you think that we're going to

get to see from you guys like can you

give us a sneak peek of what might be

coming soon or how what we should be

expecting from MRA yeah for sure so we

have uh so Mr Large was good but not

good enough so we are working on

improving it quite quite heavily uh we

have uh interesting open source models

uh on various vertical domains uh that

will be announcing very soon um we have

uh the platform is currently just apis

like serverless apis uh and so we are

working on making customization part of

it so like the fine tuning part um and

obviously and I think as many other

companies we we're heavily betting on

multilingual uh data and and

multilingual model uh because as a

European company we're also well

positioned and this is the demand of our

customers uh that I think is higher than

here MH um and then yeah eventually uh

in the months to come we are we will

also release some multimodal models okay

exciting we we look forward to that um

as you mentioned many of the people in

this room are using mrol models many of

the companies we work with every day

here in the silan valley ecosystem are

working already working with mrol how

should they work with you and how should

they work work with the company and what

what type of what's the best way for

them to work with you well well they can

reach out so we have uh some developer

relations that are really uh like

pushing the community forward making

guides uh also Gathering use cases uh to

Showcase what you can build uh with mral

model so this is we're very uh like

investing a lot on the community um

something that basically makes the model

better uh and that we are trying to set

up is our ways to for us to get

evaluations benchmarks actual use cases

on which we can evaluate our models on

and so having like a mapping of what

people are building with our model is

also a way for us to make a better

generation of new open source models and

so please engage with us to uh discuss

how we can help uh how discuss your use

cases we can advertise it uh we can uh

also gather some insight of of the new

evaluations that we should add to our

evaluation suit to verify that our model

is are getting better over time MH and

on the commercial side our models are

available on our platform so the

commercial models are actually working

better than than the the open source

ones they're also available on various

Cloud providers so that it facilitates

adoption for Enterprises um and

customization capabilities like

fine-tuning which really made the value

of the open source models are actually

coming very soon wonderful and you

talked a little bit about the benefits

of being in Europe you touched on it

briefly you're already this example

Global example of the great innovations

that can come from Europe and are coming

from Europe what you know talk a little

bit more about the advantages of

building a business in France and like

building this company from Europe the

advantage and drawbacks I guess yeah

both both I guess what one advantage is

that you have a very strong junior pool

of talent uh so we there's a lot of uh

people coming from Masters in France in

Poland in the UK uh that we can train in

like three months and get them up to

speed get get them basically producing

as much as a as a million dollar

engineer in the Bay Area for 10 times 10

10 times less the cost so that's that's

kind of efficient sh don't tell them all

that they're goingon to hire people in

France sure uh so that like the the

workforce is very good engineers and uh

and machine learning Engineers um

generally speaking we have a lot of

support from uh like the state which is

actually more important in Europe than

in in the US they tend to over regulate

a bit bit too fast uh we've been telling

them not to but they don't always listen

uh and then generally uh I mean yeah

like European companies like to work

with us because we are European and we

we are better in European languages as

it turns out like French uh the the

French Mr Large is actually probably the

strongest French model out there uh so

yeah that's uh I guess that's not an

advantage but at least there's a lot of

opportunities that are geographical and

that we're leveraging wonderful and you

know paint the picture for us 5 years

from now like I know that this world's

moving so fast you just think like all

the things you've gone through in the

two years it's not even two years old as

a company almost two years old as a

company um but but five years from now

where does mrr sit what do you think you

have achieved what what does this

landscape look like so our bet is that

uh basically the platform and the

infrastructure uh of int of artificial

intelligence will be open yeah and based

on that we'll be able to create uh

assistance and then potentially

autonomous agent and we believe that we

can become this platform uh by being the

most open platform out there by being

independent from cloud providers Etc so

in five years from now I have literally

no idea of what this is going to look

like if you were if you looked at the

field in like 2019 I don't think you

could bet on where we would be today but

we are evolving toward more and more

autonomous agents we can do more and

more tasks I think the way we work is

going to be changed profoundly and

making such agents and assist

is going to be easier and easier so

right now we're focusing on the

developer world but I expect that like

AI technology is in itself uh so uh

easily controllable through human

languages human language that

potentially at some point the developer

becomes the user and so we're evolving

toward uh any user being able to create

its own assistant or its own autonomous

agent I'm pretty sure that in five years

from now this will be uh uh like

something that you learn to do at school

awesome well we have about five minutes

left just want to open up in case

there's any questions from the

audience don't be shy son's got a

question how do you see the future of

Open Source versus commercial models

playing out for your company like I

think you made a huge Splash with open

source at first as you mentioned some of

the commercial models are even better

now how do you imagine that plays out

over the next cample of years well I

guess the one thing we optimize for is

to be able to continuously Produce open

model with a sustainable business model

to actually uh like fuel the development

of the Next

Generation uh and so that's I think

that's as I've said this is uh this is

going to evolve with time but in order

to stay relevant we need to stay uh the

best at producing open source models uh

at least on some part of the spectrum so

that can be the small models that can be

the very big models uh and so that's

very much something that basically that

sets the constraints of whatever we can

say we can do uh staying relevant in the

open source uh World staying the best

best uh solution for developers is

really our mission and and we'll keep

doing

it

David there's got to be questions for

more than just the Sequoia Partners guys

come on you talk to us a littleit about

uh llama 3 and Facebook and how you

think about competition with them well

lfre is working on I guess uh making

models I'm not sure they will be open

source I have no idea of what's going on

there uh so far I think we've been

delivering faster and smaller models so

we expect expect to be continuing doing

it but uh generally the the good thing

about open source is that it's never too

much of a competition because uh uh once

you have like uh if you have several

actors normally that should actually

benefit to everybody uh and so there

should be some if if they turn out to be

very strong there will be some cination

and and we'll welcome it one thing

that's uh made you guys different from

other proprietary model providers is the

Partnerships with uh snowflakes and data

bricks for example and running natively

in their clouds as to sort of just

having API connectivity um curious if

you can talk about why you did those

deals and then also what you see is the

future of say data bricks or snowflake

in the brave new LM world I guess you

should ask them but uh I think generally

speaking AI models become very strong if

they are connected to data and grounding

uh yeah grounding information as it

turns out uh the Enterprise data is

oftentimes either on snowflake or on

data rcks or sometimes on AWS uh and so

being able for customers for customers

to be able to deploy the technology

exactly where their data is uh is I

think quite important I expect that this

will continue continue doing the ca

being the case uh especially as I

believe we'll move onto more stateful AI

deployment so today we deploy serverless

apis with not much State it's really

like Lambda uh Lambda functions but as

we go forward and as we make models more

and more specialized as we make them uh

more tuned to use cases and as we make

them um

self-improving you will have to manage

State and those could actually be part

of the data cloud or so there there's an

open question of where do you put the AI

State and I think that's the uh my

understanding is that Snowflake and

datab Bricks would like it to be on

their data

cloud and I think there's a question

right behind him the

grace I'm curious where you draw the

line between uh openness and proprietary

so you you're releasing the weights

would you also be comfortable sharing

more about how you train the models the

recipe for how you collect the data how

you do mixure experts training or do you

draw the line at like we release the

weights and the rest is proprietary so

that's where we draw the line and I

think the the reason for that is that

it's a very competitive landscape uh and

so it's uh similar to like the tension

there is in between having a some form

of Revenue to sustain the Next

Generation and there's also tension

between what you actually disclose and

and everything that yeah in order to

stay ahead of of the curve and not to

give your recipe to your competitors uh

and so again this is this is the moving

line uh if there's also some some Game

Theory at at stake like if everybody

starts doing it then then we could do it

uh but for now uh for now we are not

taking this risk indeed I'm curious when

an when another company releases weights

for a model like grock for example

um and you only see the weights what

what kinds of practices do you guys do

internally to see what you can learn

from it you can't learn a lot of things

from weights we don't even look at it

it's actually too big for us to deploy a

gr is is quite

big or uh was there any architecture

learning I guess they have they are

using like a mixture of expert uh pretty

standard setting uh with a couple of

Tricks uh that I knew about actually but

uh yeah that's uh

uh there's there's not not a lot of

things to learn from the recipe

themselves by looking at the weights you

can try to infer things but that's like

reverse engineering is not that easy

it's basically compressing information

and it compresses information

sufficiently highly so that you can't

really find out what's going

on coming the cube is coming okay it's

okay uh yeah I'm just curious about like

um what are you guys going to focus on

uh the model sizes your opinions on that

is like you guys going to still go on

the small or uh yeah going to go to the

larger ones basically so model size are

kind of set by like scaling lows so it

depends on like the compu you have based

on the computer you have based on the

The Landing AR infrastructure you want

to go to you make some choices uh and so

you optimize for training cost and for

inference cost and then there's

obviously um uh there's the weight in

between between uh like for depends on

the weight that you put on the training

cost

amortization uh the more you amortize it

the more you can compress models uh but

basically our goal is to be uh low

latency and to be uh relevant on the

reasoning front so that means having a

family of model that goes from the small

ones to the very large

ones um hi are there any plans for

mistol to exp expand into uh you know

the application stack so for example

open a released uh the custom gpts and

the assistance API is that the direction

that you think that M will take in the

future uh yeah so I think as I've said

the we're really focusing on the

developer first uh but there's many um

like the the frontier is pretty thin in

between developers and users for this

technology and so that's the reason why

we released like a an assistant

demonstrator called lha which is the cat

in English and uh it's uh the point here

is to expose it to Enterprises as well

and be make them able to connect their

data connect their context um I think

that's that that answers some some need

from our customers that many of of the

people we've been talking to uh are

willing to adopt the technology but they

need an entry point and if you just give

them apis they're going to say okay but

I need an integrator and then if you

don't have an integrator at end and

often times this is the case it's good

if you have like an off the shelf

solution at least you get them into the

technology and show them what they could

build for their core business so that's

the reason why we now have like two

product offering there the first one

which is the platform and then we have

the sh uh which should evolve into an

Enterprise off the shelf

solution more over there there there I'm

just wondering like where would you be

drawing the line between like stop doing

prompt engineering and start doing like

fine tuning because like a lot of my

friends and our customers are suffering

from like where they should be stopped

doing more PRT engineering yeah I think

that's that's the number one pain Point

uh that is hard to solve uh from from a

product product standpoint uh the

question is normally your workflow

should be what should you evaluate on

and based on that uh have your model

kind of find out a way of solving your

task uh and so right now this is still a

bit manual you you go and and you have

like several versions of prompting uh

but this is something that actually AI

can can help solving uh and I expect

that this is going to grow more and more

automatic across time uh and this is

something that yeah we would love to try

and

enable I wanted to ask a bit more of a

personal question so like as a Founder

in The Cutting Edge of AI how do you

balance your time between explore and

exploit like how do you yourself stay on

top of like a field that's rapidly

evolving and becoming larger and deeper

every day how do you stay on top so I

think this question has um I mean we

explore on the science part on the produ

part and on the business part uh and the

way you balance it is is effectively

hard for a startup you do have to

explore it a lot because you you need to

ship fast uh but on the science part for

instance we have like two or three

people that are like working on the next

generation of models and sometimes they

lose time but if you don't do that

you're at risk of becoming irrelevant

and this is very true for the product

side as well so being right now we have

a fairly simple product but being able

to try out new features and see how they

pick up is something that we we are we

need to do and on the business part you

never know who is actually quite mature

enough to to use your technology so yeah

the balance between uh exploitation and

exploration is something that we Master

well at the science level because we've

been doing it for years uh and somehow

it transcribes into the product and the

business but I guess we're currently

still learning to do it

properly so one more question for me and

then I think we'll be we'll be done

we're out of time but you know you've in

at the scope of two years models big

models small that have like taken the

World by storm killer go to market

Partnerships you know just tremendous

momentum at the center of the AI

ecosystem what advice would you give to

Founders here like what you have

achieved in the pace of what you have

achieved is truly extraordinary and what

advice would you give to people here who

are at different levels of starting and

running and building their own

businesses in it around the AI

opportunity I would say it's it's always

day one so I guess we yeah we are uh I

mean we got some mind share but there's

I mean there's still many proof points

that we need to establish uh and so yeah

like being a Founder is basically waking

up every day and and figuring out that

uh you need to build everything from

scratch every time all the time so it's

uh it's I guess a bit exhausting but

it's also exhilarating uh and so I would

recommend to be quite ambitious usually

uh being more ambitious uh I mean

ambition can get you very far uh and so

you yeah you should uh dream big uh

that's that would be my advice awesome

thank you arur thanks for being with us

[Applause]

today