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 Abaqus comes from, where Fluent comes from, where
OpenFOAM 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 clusters started 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-adapcos 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 SaaS, then it is fully scalable.
So if you're running, you know, STAR-CCM+ or Fluent or
PowerFLOW 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 SaaSification 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
TotalCAE, 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 Flexcompute, 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 Pointwise, or 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 acquiring 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 2020s, 2021-2022 that brought
this to life. And so, you know, they started
some new startups to really get hold: Neural Concept, PhysicsX,
NAVASTO, Extrality and Emmi AI. 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 SimAI.
Autodesk bought NAVASTO, Siemens, you could argue and
some part of it was the Altair 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 Luminary Cloud or Luminary AI
now and Flexcompute, 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
EDA, 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
ChatGPT 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 Codex or whatever.
Although the use of Codex 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, Matplotlib, 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 Claude Code or Codex.
It is also possible for the tools that that that that LLM 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
OpenFOAM 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 Codex or Claude or Vibe Code now with
Mistral entering as a new sort of player in this field from
with a heavy sort of sovereign AI angle, European angle.
So Codex, Claude or Vibe, they can actually go and do that for
you. And you don't have to be an
expert user of OpenFOAM because most likely those models
have either been trained on sufficient smart data that
included OpenFOAM 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 a CFD 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 OpenAIs, the Mistrals, 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 Project 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 Emmi AI by Mistral and you
see a huge interest from OpenAI 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 CATIA 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 agent knows how
to do it. I can say, you know, create this
in this CAD 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-CCM+ has with its 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 OpenFOAM?
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 a CFD
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 a CFD 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 hampered
by the actual tool itself. If the tool can match the speed
of the agent, the LLM, 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 ISV, but it's also a great opportunity because you
can potentially help drive your company to bring out the next,
you know, Industry 4.0 or 5.0 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 AI, I 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 AutoCFD 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.