Open sourcing the AI ecosystem ft. Arthur Mensch of Mistral AI and Matt Miller
Summary
TLDRArthur, 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
- ๐ 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.
- ๐ 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.
- ๐ค Mistol AI has established strategic partnerships with major companies like Microsoft and Snowflake, indicating a strong go-to-market strategy.
- ๐ง 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.
- ๐ The company aims to bring AI technology to every developer, promoting a more open platform than competitors, and accelerating adoption through this strategy.
- ๐ 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.
- ๐ก 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.
- ๐ The company is focused on improving its large models and developing open source models for specific vertical domains.
- ๐ 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.
- ๐ฎ Looking ahead, Mistol AI envisions a future where AI infrastructure is open, enabling the creation of assistance and autonomous agents accessible to all users.
- ๐ผ 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
๐ค 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.
๐ 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.
๐ค 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.
๐ 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.
๐ก 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.
๐ 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.
Mindmap
Keywords
๐กOpen Source
๐กAI
๐กFoundation Models
๐กCommunity
๐กCommercial Models
๐กBenchmarking
๐กFine-tuning
๐กMultimodal Models
๐กDeveloper Relations
๐กAutonomous Agents
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, Timothรฉe, 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
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