Semiconductor Insiders - Podcast EP371: How Vinci is Revolutionizing Hardware Design with John Bruggeman

Episode Date: October 6, 2026

Daniel is joined by John Bruggeman, Chief Marketing Officer at Vinci, where he leads the company’s market narrative and strategic positioning. His work focuses on translating complex technical advances into clear category definition, industry relevance, and market adoption. Daniel discusses How Vinci fits into the hardware… Read More

Hello, my name is Daniel Nenny, founder of semi-wiki, the open forum for semiconductor professionals. Welcome to the Semiconductor Insiders podcast series. My guest today is John Ruggerman, Chief Marketing Officer at Vinci, where he leads the company's market narrative and strategic positioning. His work focuses on translating complex technical advances into clear category definition, industry relevance, and market adoption. Welcome to the podcast, John. Hey, Daniel. Thanks for having me. me. I can't believe that that's what I actually do every day. It seems like it's more fun than that.

Yeah. Yeah, you know, when I heard you were back, we spent time together years and years ago when you're at Cadence and such. I knew you had something up your sleeve. So let's talk about this. What brought you to Vinci? Well, last summer, I met Hardik Kabaria, who is the CEO of Vinci and had lunch with him. he started explaining his vision that he thought hardware engineering would fundamentally change. And as you led on, I'd been in the space decades, sadly decades. And I didn't believe, I was skeptical that things would change or that there was room for something more than just iterative improvement, a little speed, a little, fidelity. But Hardick had an idea that could turn this thing upside down. And I started to believe.

And I did everything in my power to join the company. I joined the company in October. We came out of stealth in November. And it has been a rocket ship since then. We raised around in November as we came out of stealth, almost $50 million with tier one. early stage deep tech investors. And as you'll learn on this podcast, we've done so much more since then. Yeah, I just saw the announcement. Investors put $250 million in Tivinci at $1.5 billion valuation. I've never seen anything like it.

I've been in the EDA business in the semiconductor industry for 40 plus years. I mean, it's just incredible. So what do you think they're actually betting on here? the future of hardware engineering. So I think before I say what I think it's a bet on, I want to come out of the gate strong what it is not a bet on or what it is a bet against. And it starts it, they did not bet on better simulation, faster simulation, more accurate simulation.

That would warrant a good and strong backing behind good leadership, but nowhere near what by my calculations, and I'm not the most calculating, but the third largest series B software investment this year. And what they are betting on is that physics will become much, much more deeply integrated into the engineering process. Rather than what simulation is, which is really a episodically specialized step that's used very rationally. Today, engineers have to make thousands and thousands of design decisions throughout the engineering process, but high fidelity physics is only practical, can only be used for a subset, and it's a small subset of those decisions.

Because simulation is historically, it requires a real specialist with very, very advanced skills, very expensive, very scarce, becoming increasingly scarce. So it's an expensive process, it's complex, it's slow, and therefore we're very selective.

about when we do simulation. Yet physics is so inherent in the process, needs to be always available. It became clear and it is clear that there's going to be a fundamental mind shift. That physics is no longer just a validation tool. It becomes the actual intelligence layer, the infrastructure for hardware engineering.

I know that was a lot, Daniel, but that's what I think people are betting with their wallets. Okay, let's talk about the engineering process itself. Why is physics still something engineers typically bring into the process selectively, rather than something that they can use continuously as they design? Well, both you and I are veterans of legacy solutions and legacy simulation tools and how they're used in and around the very, very complex workflows. And anybody that use those tools cursorily will know how hard it is. And how long it can take.

And how long people stop the process, hold and wait for thermal engineer, a mechanical engineer, a simulation specialist, to come back with the result and validate a design. And while we're all waiting, time continues to go by. It doesn't stop. So engineers are sitting there. They're being forced. I got to decide about what material I use.

I got to decide about a geometry change. I got to decide about boundary conditions or all the myriad of other decisions that they have to make and they don't have real physics, real intelligence available to impact those decisions. So they do something else. They simplify, they lean back on their experience and take a best efforts guess. They build in guardrails. They build in margin. They throttle performance. Everything they do, it actually harms the underlying process. But if, somehow we could make physics intelligence, physics information, always available, always at the ready. We'd be able to address way more decisions based on fact, based on intelligence, based on good

decision-making data, as opposed to our best guess. So I think that's what's really going on and what needs to change to make this better. So what has changed in the industry? What is different about AI and modern compute today that makes it possible to put physics much closer to the center of the engineering process? Well, the first thing, what I think is we're seeing a collision of three different technologies. The first is AI itself and the maturation and the evolution of AI and how much it has progressed over the last 18 to 24 months. The second thing I see happening is GPU-based compute. We can process, we can transact at hyper speed compared to what we used to.

be able to do. And the third collision vector is new approaches to computational physics. The emergence of AI and GPU-based compute is driving us to rethink how do we get physics answers and we're seeing the emergence of computational physics. So these three things have come together and we're at a unique time and space where we're able to rethink how do we get a simulation answer, how do we get physics intelligence, and we can now run much, much more complex designs with a lot more decision criteria to make much higher fidelity answers. And I think we are now entering the age of physics intelligence. Interesting.

You know, we just went through a bunch of conferences and you see a lot of compelling demos in the booths and such and from the vendors. But, you know, in my experience, it's much harder to make something engineers will actually trust with a real design, right? So what have you had to prove for Vinci to move from an interesting technology to something engineers can actually use in production? Well, you and I walk the floor of doing. a couple months ago.

And I think we saw every single agentic tool being brought to EDA. We saw all kinds of AI this and AI that. And so the demos are everywhere. And I think you hid the nail on the head when you said, well, impressive demos. But when I start to deploy them in the real world, I'm not sure that this is raised. for prime time yet. And that was where Vinci had to start. Because when we are engineering a complex product, the answer doesn't matter. It's not useful just because it's fast or just because it's visually stemming. It has to be accurate. And we use the phrase manufacturing resolution.

at 100% accuracy. It's got to be deterministic. It means the same set of conditions in produce the same outcome every single time. Not it's close, but not exact. And it's got to be reliable. It's got to be always there and always available. Or I won't use it to make a real engineering decision. And for kind of the first six months when we came out of stealth, we had to prove every single

time to every customer that we produced manufacturing resolution, high fidelity results that were deterministic on the designs that they were using against legacy solver solutions. We did that for six months and 100% every single time we were as accurate or better than legacy solvers. But that was just the start, right? What started happening, and if you look, where are we today? Six months after that, we are already running on 40, well, it's actually more than 40, Daniel, production engineering programs across 40-ish or more, real semiconductor and electronic companies on their flagship products, running our physics intelligence on their

designs that are hundreds of millions of degrees of freedom all the way up to. I watched one today. we did one at 15 billion, with a B, 15 billion degrees of freedom, so very complex design in under three minutes. Now, if you could put that complex and advanced package into the system and in less than three minutes, get a high fidelity answer out that was of complexity 15 billion degrees of freedom, then you've got something real. And we've been proving that and doing that for the last year. And we've gained that credibility and that reputation with our customers

that what you saw at DAC as an argument about what could be possible with AI to what it actually is in our customers, which is just a regular part of the workflow every time the engineer needs to make. a decision. I agree with that 100%. So you know, you started in semiconductors, arguably one of the hardest engineering environments there is. But John, knowing you, you have something bigger up your sleeve here. So what's next? I appreciate that because for sure, when I first met Hardick, he said, bear with me, we're starting in semiconductors because it's the hardest. And it's a little bit telling about our culture because we take the hardest problem on first, and if we can solve that,

then we'll certainly solve every other problem. And I just want to pause for one second. Why is semiconductor the hardest? Well, one is the scale. So we absolutely work on designs that have single digit nanometer features on a dye that's on. a stack that makes up a chip that's on a system that scales all the way out to centimeter square. That scale is 10 to the minus 7 delta. And what we used to work on when I was doing this was at best 10 to the minus 3. So this scale you have to traverse and be able to explore the entire design space in a single shot run and be able to zoom in to any level of specificity and zoom back out to the total design is an incredibly hard problem. And if you can do that and you

can make physics intelligence work at that complexity across that scale, you have the foundation to do any physics. And I think we have proven in the lab and at our customers that every physical product is governed by physics. The physics can be solved in our platform. And so I think you're going to see very quickly us in autonomous vehicles, in drones, in satellites, in batteries, in all kinds of different industries. And I might even nod and nudge until you were already there. That's incredible. So let's take the idea that we've been discussing to its logical conclusion.

So if physics becomes continuously available to every engineer, you know, not as a separate simulation step, but as part of the design process itself, what fundamentally changes about how we build physical products well the possibilities are endless if we start it today today engineering in the abstract it looks like design simulate interpret revise and do it again and then do it again and it's long and it's slow and it's arduous But if, if physics can be continuously available, easy to use by non-specialist engineers, well, then that slow process changes.

And engineers can ask more questions. They can explore unlimited alternatives. And they understand the consequences of the decisions they make while the design is moving and the changes can be made quite practically. So physics will stop being a separate tool, a standalone tool, a standalone market, and it will become the underlying intelligence infrastructure of the entire hardware engineering process. And that's Vinci's ultimate vision, that physics intelligence is infrastructure for everyone designing physical products. We've got some shocking use cases that we're seeing our customers use when physics is easily

available and accessible. And I think that's a world change. And I think that's what drove 250 million at a 1.5 billion investment. and that's what I think the world has to look forward to. And then going all the way back to your first question, that's why I joined Finch. Great place to stop, John. Great conversation.

We'll have more. So I'll be hitting you up for more podcasts. It's going to be an incredible story. Thanks for your time today, Daniel. All right, John. Bye. That concludes our podcast. Thank you all for listening and have a great day.