Hi, and welcome to the Neil Ashton Podcast.
In each episode, we explained some of the fascinating ways
that science and engineering are changing the world around us.
We talked to leading engineers from elite level sports like
cycling and Formula One to some of the world's top academics to
understand how fluid dynamics, machine learning, supercomputing
are bringing in a new era of discovery.
We also hear some of their life stories, their career advice,
the lessons they've learned on the way that I hope will be
helpful to you too. So sit back and enjoy this
episode. Hi and welcome back to the Neil
Ashton Podcast. It's been a while, but I'm
excited to start a new season and series and I'm going to
start with a discussion on how I think agents and foundation
models are are really a disruptor to the current CAE and
EDA ecosystem. This, this is of course is just
a personal opinion. This does not represent my
current employer, NVIDIA, but I wanted to share some, some
thoughts I've really had over the past six months of how I see
this technology being, you know, obviously a massive opportunity,
but also also a disruptor. And so we'll, we'll walk through
that today from a technical point of view, from a commercial
point of view, from a scientific point of view.
And I'd certainly be interested to see whether you agree with
my, my thinking on this. If we take a step back and you
know, I'm going to look at as often I do in this, in this
podcast through a sort of CFD lens, but I think much of what
I'm saying is relevant across CAE and and also EDA for chip
design, etcetera. If you look, where did things
begin? I would argue in the 60s and the
70s people were writing their own codes.
Now, I've had episodes with Professor Jameson and others who
who started writing codes themselves before modern
supercomputing was around, and those often started at academic
institutions. Those codes then obviously
started to become useful and there was collaborations with
industry, particularly in the aerospace initially.
And that is why that then those aerospace companies started to
work closely with those developers and eventually
started to take them as their own codes.
And that's why you see today even there's a legacy of
aerospace companies having their own codes written from scratch,
frankly, because there were no other options.
And that was the way to do it. The automotive sector then took
some things from the aerospace sector.
And this depends on this a little bit, whether we're
talking about structural mechanics or fluid mechanics,
but it started off being you had to write it yourself with some
expert developer. Then over the course of, you
know, the 70s, eighties and early 90s, those codes started
to become commercialized. Those professors out of places
like Imperial or MIT or Stanford started to, to, to create
companies. And, and ultimately those are
the, the lineage, the lineage of where Abacus comes from, where
Fluent comes from, where Openfront comes from.
And in some ways those commercialization was because it
was a natural thing as one computing started to become and
make these tools more than just a, a niche application.
HPC facilities, you know, Beowulf cluster decided to
become commonplace and and of course, industry started to see
the benefit of those simulation approaches.
And frankly, then there was also a competitive nature that once
one company starts, you start having competitions between, you
know, the CD adapt Cos and the, and what what is now the, the
Ansys and the later the Dassaults.
There was that growing ecosystem of companies that that compete
against each other, that accelerates it.
The computing starts to get better and other than those main
players, you also started to have the hardware companies get
involved. So the Intel's, the AMD, the
Dell, the Lenovo, the Cray in those days, the the main people
building those those systems, those supercomputing systems.
And if you look at how it happened, that was probably true
for, for maybe 5 to 10 years or so where it was mainly a
creating software to run on workstations, servers and
supercomputers. And that, you know, spread
across the world, frankly. And those tools were very widely
used and started to create a real, you know, hundreds of
millions or even billions by then industry.
The the next technology that really came around was then
around like cloud computing. So cloud computing maybe 5-6,
seven years ago was, was a disruptor because it allowed
and, and obviously I'm skipping huge other things that happened,
but I don't want to spend 2 hours just giving the history
of, of CFD. The reason I pick out cloud, and
I saw it first time working for AWS, is it changed it from being
a purely software license point of view to where these providers
could also have a control of the hardware.
Packaging software and hardware together is attractive for a
company for an ISV because they can make profit off the hardware
and they can lock it together both from a performance point of
view, but also from a sort of stickiness point of view.
And there's advantages for the customer in terms of making it
more seamless because often if you just ship a binary, they
have to be the one to then figuring out how to get the
hardware. They are the ones who've got to
think about, do they need to buy twice as much hardware?
If they want to run twice as many simulations in the theory
of the ultimate SAS, then it is fully scalable.
So if you're running, you know, star CCM or fluent or power flow
or whatever, you could essentially say, I want to run
10 times more and through the, you know, infinite scale or
inferior of the cloud you can do in the back end.
So that sassification was, was something started maybe 6-7
years ago. And, and obviously companies
like Rescale were one of the first to do that at a cross
software level. And there's others like Total,
CAE, etcetera who tried to, to bring it together.
That is where I would say AWS and Microsoft and Google started
to enter into more seriously the CAE and EDA market as important
partners. Because frankly, now it wasn't
just The Dells and Lenovos, but you were deploying it on AWS or
GCP or or Azure or or Oracle, for example.
Now GPUs obviously had been around for quite a few years
before that, but but arguably it's only been in the past sort
of three, maybe four years where they have risen to become a
really important part of the ecosystem.
And frankly, that's because a lot of people have seen quite
clear benefits and they're moving to the GPUs.
And that's another technology point that's important because
it added a new competitive angle to this because they needed to
able to run things on a GPU, which meant that if you could
optimize your code for GPU can get advantage.
This is where flex compute, this is where luminary cloud started
to come out. The rise of, you know, VC
funding for these sort of companies led to a resurgence of
competitiveness, I would say, in that market.
And I the reason I point to the cloud before is that many of
those companies lent on as the cloud being an additional sort
of angle to their to their offering.
So at that point another thing happens which is that the bigger
companies start to look to consolidate and to start their
sort of acquisition period. So you saw companies like
Siemens and the timing of these are not all correct, but you
know Siemens and Altair, Ansys and Synopsys, Cadence and Beta
CAE and point wise are future facilities.
Many of these companies started to acquire each other.
And why was that done? Well actually so those companies
then wanted to start quiet and of course this is where one of
the key technology came in that starts to get to my point here,
which is the AI side. It was, it has been around to be
fair, for maybe six years or even 7 years.
But really it was since the sort of ChatGPT moment in the early
twenty 20s, 2021-2022 that brought this to life.
And so, you know, they started some new startups to really get
hold and the newer concept, the Physics X, the Navisto Extraity
MEAI, these companies started to come about offering the
surrogate modelling capability, the ability to do real time
simulations. And that frankly was a threat
and an opportunity. And I say that because that is
what I've heard when in conversations.
It's a threat because anything new is by definition a potential
threat. These software companies who
have, you know, obviously had quite a, a good position, see as
you know, how do we deal with this?
But clearly it's also a massive opportunity because it brings
new technology, new capabilities to, to, to customers.
And that it has been a very interesting thing to observe in
the past, let's say 3 or 4 years of how do these larger companies
deal with it? How do companies deal with it
who have their own codes? How do they respond?
Do they respond with hype? Do they respond with negativity,
with positivity? And what happened was then some
companies decided to start developing themselves, some
started to buy. So Ansys bought Extrality, which
is now SIM AI. Autodesk bought Navistone,
Siemens, you could argue and some part of it was the Alta
acquisition. And then some of these start off
start-ups that started with a more GPU focus started to pivot
quite heavily to the surrogate modelling.
So Lumia Cloud or Lumia AI now and Flex compute, both of you
know pivoted to, to, to that direction.
And so we, so we're at that point now where the, the
ecosystem is at a very influential point for a sort of
inflection point. And there's one final bit that
is important to that. And it's actually ultimately the
main topic that I want to speak about today, which is agents.
Because the missing piece to some of this has been if you
create a surrogate model and you take it to its fullest extent
and you have some sort of foundation model, you haven't
really got rid of the need to do CAD.
You haven't got rid of the need to do some sort of preprocessing
or post processing. You could argue you've just got
a faster plug in replacement for the for the bit of the solver or
the solve part, but you still need all the bit around the
rest. And that's why it's ultimately
been more of an integration play through some of these AI
start-ups because you're not going to replace CFD or FEA or
Eva, you're just really creating an additional tool.
So in that sense you can understand why the larger Isvs
and others have not necessarily seen this surrogate modelling as
a massive threat initially because they know that on it's
own it's it's not a replacement that has led to of course 1
angle, which is if you were to make a model that was so
generalizable, then it could given the right plug and play,
disrupt some solver only companies.
If you only produce a solver and not the CAD and maybe not the
preprocessing, then arguably you could, you know, replace some of
it. But the technology that I think
it adds a real new dimension to this is the agentic side.
Because what are what are agents?
Well, first of all, let's tie it back to the usefulness of Chachi
PT or Gemini. We have all found them to be
incredibly useful in our day-to-day tasks, but I have
personally noticed a a huge shift in usefulness with the
agentic side. And what do we mean by the
agentic side? At least the way I define it.
Well, I think of it is just simply now the LLM is able to
call some tools. Now that's probably
oversimplifying it, but from a practical point of view, instead
of me just being able to ask some ask Gemini to do something
for me or, or codecs or whatever.
Although the use of codecs now is sort of murky in the waters a
little bit. If I stick to like a chat
prompt, it's only going to be able to really do things that
rely just on text or to A to a limit where what I really want
it to do is to be able to go and do things for me.
So for example, if I ask, I want you to create a summary of all
my emails. Great.
But what if I want you to now look through all my emails, look
through all my Slack messages, and then I want you to go in and
also look at a journal paper that I'm writing.
And I want you to look at the emails where I got some response
from somebody. And I want you to take that, go
into, go into that. Oh, and I also want you to go
and create some graphs for me. So can you also create some
Python graphs using, you know, map pot, Lib, etcetera.
And I want you to do that all for me autonomously.
What I'm describing to you now is absolutely achievable right
this second using cloud code or codecs.
It is also possible for the tools that that that that NLM is
essentially calling or the agent is calling can be simulation
tools. So if I extend that further and
I say I want you to be helping me to do some journal paper,
maybe I could say now, well, could you actually, could you
run some simulations for me? I have an idea.
You know, here's some e-mail correspondence, here is a thread
on a Slack channel with some collaborators.
Not just write the paper, but will you go and set up an open
phone simulation that that tries out some of those ideas we
discussed and, and can you kick it off?
And then when it's done, can you create the graphs and can you do
it for me? That's completely achievable
today. That is not future thinking.
That is achievable today. My point is in what part of that
then is this CFD tool? The main thing it's not is it
because actually codecs or clawed or vibe code now with
mistrial entering as a new sort of player in this field from
with a heavy sort of sovereign AI angle, European angle.
So codecs clawed or or vibe, they can actually go and do that
for you. And you don't have to be an
expert user of Open phone because most likely those models
have either been trained on sufficient smart data that
included Open Phone documentation and various
reports across the web. Or more likely that in a real
life scenario, and maybe this is where the commercial bit comes
in, one of those providers could have specifically added that
capability. They could have looked at
science and engineering as a key area they want to get into, and
so they will actually try to make their models good at doing
this. At that point, the stickiness or
the the difficulty of using ACFD code is no longer there, which
is arguably one of the reasons those codes exist or have such a
strong Moat because they're not easy to use.
They require expertise, they're very well validated.
If agent starts to come in and very powerful LLMS come in that
can use these tools very well, then actually the uplift of
changing to a different tool is not as much.
And the ability to orchestrate multiple tools is also the same.
So it raises a very interesting question, which is are the
software companies just going to reside to being just tools and
skills and actually your relationship will be more with
the open AIS, the mistrials, the anthropics, etcetera.
Or, and it is the case today, will new companies arise that
are trying to do this? Interestingly, and there's very
little public information on this, but you have companies
like Prometheus, I think Jeff Bezos put out some announcement
about wanting to create a artificial engineer out of this.
So read into that what you will. But it sounds pretty clear that
they want to build some sort of capability like that.
You see the recent acquisition of Meai by Mistral and you see a
huge interest from open AI and Anthropic in general, AI for
science, which makes you look at the future and start to think
there could be a new wave now of companies coming around.
And there are companies that are now base themselves purely to
create agents to create artificial AI engineers.
This in some ways has the opportunity to massively
democratize and and reduce the need for you to be some
specialist. An example I gave is I remember
when I was an engineer that being a CAD expert was a
challenge. You would have to learn how to
use NX or CTIA or SolidWorks, and frankly, if you didn't know
how to use those, you couldn't use it.
You would have to go and find somebody, have to pay someone to
do it. And I personally found it a
blocker actually, because I knew what I wanted to do, I just
couldn't do it. Now Fast forward and by the way,
similar extent to some software tools where you know CFD, right?
I understand the theory, I understand what I want to do.
I just don't know how to use the code or I don't have to script
it or whatever. As long as the Asian knows how
to do it. I can say, you know, create this
in this scan package, set up the simulation, this tool.
It's kind of interesting then that it relies on a good skills
capability. It relies on those tools being
open to agents. And that is a very interesting
point, which is will companies embrace this, you know, agent
first approach or will they try to put more guard rails and and
even block people to use those tools via agents?
Will there be some terms and conditions and licenses?
And and that brings up another point around licenses.
A lot of licenses are based around a human having a seat on
a on a machine, and just like cloud with the ability to scale
disrupted the per core license model.
And really wanted it more to be like a just a power session like
like Star has with his POD license.
Finally, and I know this is rambling on a little bit, but
I'm just dumping all my thoughts down here, what about open code?
Open source codes? Much of the challenge was around
support and capability and validation.
Is it not the case now with coding agents and the power of
LLMS that these codes actually could become very capable and
and could be used more easily in a workflow instead of a
commercial tool because the agent can write and script and
validate and go off and do test suites as much as possible?
So I'm not saying at all that you will see a complete wipe out
of all the current CAE companies.
In fact, it definitely won't be like that, but I just put this
out that there is a very interesting opening it because
these two new technologies around sort of foundation
models. This idea that you could have
simulations that would run in seconds or minutes instead of
many hours. The idea that if you have such
surrogate models and foundation models tied with agents, you
could finally realize the sort of reverse engineering or design
optimization, the automatic generation of cases.
If you have a pipeline that is able to do that, then you could
simply have agents go and creating optimum geometries for
you, running those simulations autonomously with you being
essentially the the orchestrator.
The potential for productivity is absolutely massive.
It is. It could finally realize some of
the dreams of people in the CAE and the EDA sector around
productivity boosts, around finding designs around people
with just domain knowledge driving the code rather than
code knowledge. And that is important from an
education point of view. I would say what matters more
now is not are you really good at writing Python?
No. Are you really good at using
Openfox? No.
Do you understand physics? Do you understand numerical?
But do you understand how to pose the prompt?
Do that is more important? Do you almost then it starts to
become that you need to go higher up.
If it's so easy to run ACFD code, why don't why don't I
start to run an FEA code as well?
Many companies are you are split if you are ACFD person or an FEA
person. You're split if you're an
acoustics person. Well, now you could become more
than engineer who simply is just driving the tools.
That position in a company is often the sort of chief
engineer. Well, maybe now it's more
important that you understand trade-offs and and larger
engineering concepts and doing this sort of specialist deep
down analysis can be more automated and clearly
simplifying what it. There are many industries where
this will happen for many years, but I'm I'm sort of trying to
Fast forward and play a what if scenario to, to to all this
point. It raises many interesting
questions. Therefore, on the if the LLMS
and the agents are key. It raises questions about
traceability around sovereignty. Who owns these models?
Where these models done? Should you be developing your
own models? Can you take them off the off
the shelf? So many, so many questions.
It, it rises, but I, I find it a fascinating area that I think
will be a key technology driver, an innovation area for, for, for
years to come and something that I'd like to explore over the
course of this, of this season. I want to look at speak to some
people who can give insights into this on the the the core
ideas around sort of foundation models.
Because the reason that foundation models of surrogates
are so important is that if a tool still takes a day to run
the the the agentic sort of optimization workflows will
ultimately be massively Hanford by the actual tool itself.
If the tool can match the speed of the agent, the LM, then that
is what will really have the breakthrough.
So whilst kind of the advantage of the agent is that, and so as
you don't need to use any of these surrogate model
technologies, they're even more optimum when you when you do.
And, and so this is where the the, the combination comes from.
So, yeah, I don't know what the future will, will, will, will
be, but I know that it is certainly an exciting one.
I think this is a great time to be in the start up space.
It's a great time to be researching, to be in academia,
thinking of your PhD, thinking about what topics you want to
do. It's a challenging time to be an
end engineering customer. You don't know what to pick.
It's a challenging time to be an established ISP, but it's also a
great opportunity because you can potentially help drive your
company to bring out the next, you know, industry 4 point O or
five point O or whatever the the number is today.
So I will, I'll leave it there. I'm sure I didn't cover many
things and I hope you appreciate that this isn't me in any way
diminishing what will be the classic example of any new
technology taking years and years and years to them.
So I'm not saying next year or even 5 years everyone will be
doing this, just like some people are still running on
Windows 3.1 or Windows 95 in some places around the world.
But I am confident, I am definitely confident that if we
look in three years time that the role of agents and, and and
surrogate models on AI will be transformative.
And so please, if you're listening, if you are one of
these people who are still sceptic of AII would
respectfully encourage you to to try it.
And I would hope that you will believe me.
The final call out, and I will say though, is in order to
convince the community, we need transparency, we need openness
as much as possible within a commercial mindset.
People who know me know that I have always tried throughout my
career to, to do things in a transparent and open way.
It's part of the reason for doing this podcast is to sort of
share the knowledge. The reason that I've created
these sort of open source data sets is that desire to do it.
The reason for doing these workshops or do CFD and others
is to try and share it. I am fully appreciative of the
need for commercialization. It's how the world works.
But I think the one key thing if this agents and AI and
everything is going to be a transformative.
People will only get on board if they can believe it, if they can
see it, if they can see transparency, they can see
publications. I feel like academia and
scientific rigor has a massive part to play in this
transformation. And if it can be done with
scientific rigor at transparency and good science, then I think
the the world will move on. If it's done behind closed doors
with no publications, with just sort of a, you know, black box
solution that that will not convince.
And in fact, that will in some ways be a barrier to this, what
I feel is transformative technology helping.
So with that, I will leave it there.
I look forward to hearing your comments and I hope you'll join
me on this new season. Please listen to the old
episodes. There's quite a few, quite so
many hours, but I hope you'll join for this season.
So with that, thanks for listening.