Engineering’s ChatGPT Moment: CAD, Simulation & PLM Are Next | Bild + Bench

Episode Summary

Agentic AI has already reshaped software development. The next question is what happens when the same shift hits CAD, simulation, PLM, and the broader product lifecycle. In this episode of AI Across the Product Lifecycle, Michael Finocchiaro talks with Pradyut Sahoo, founder of Bild, and Martin Bielicki, founder of Bench, about where AI is genuinely changing engineering workflows today and where hype still outruns reality.

Bild is building a CAD data management platform that connects engineering and manufacturing. Bench is building an orchestration layer for engineering workflows across CAD, simulation, and PLM. The conversation lands on the practical mechanics of AI-native engineering software: why context beats prompt engineering, why QA and validation are becoming the new bottleneck, how frontier-model economics reshape startup burn, and why deterministic tooling matters more than stochastic magic when trust and IP are at stake. Both founders argue that startups with clean data models can out-innovate incumbents — if they earn buyer trust one deployment at a time.

Bild is building a CAD data management platform that connects engineering and manufacturing data. Bench is building an orchestration layer for engineering workflows across CAD, simulation, PLM, and beyond.

We discuss:

• How AI has changed startup software development • Why QA, validation, and architecture are becoming the real bottlenecks • Why context matters more than “prompt engineering” • How Frontier LLM costs change startup economics • Why engineering AI needs deterministic tooling, not just stochastic magic • How AI should touch CAD data without breaking customer trust • Why large OEMs are already asking for bigger AI transformation visions • Whether startups can out-innovate incumbent CAD/PLM vendors • What young engineers should learn if they do not want to be commoditized • Why “trust but verify” will define AI adoption in hardware engineering

Timeline:

00:00 Intro: Bild and Bench 01:16 The ChatGPT moment for engineering startups 03:33 How AI changed coding and developer workflows 06:37 Product thinking becomes the differentiator 08:17 Prompting vs context: what actually matters 10:00 From prompts to loops and agentic workflows 11:20 LLM costs, token economics, and startup burn 14:11 Frontier models vs cheaper model orchestration 15:39 How AI changes engineering team structure 18:50 Managing multiple agents in the same codebase 21:16 Where AI fits inside Bild and Bench 22:43 CAD data, IP protection, and customer trust 24:19 When will engineering have its OpenAI moment? 26:35 Why hardware is harder than software 29:09 Text-to-CAD, DFM, and startup workflow demos 30:05 Advice for junior engineers in the AI era 35:57 Digital transformation reality check 39:31 Are incumbents moving fast enough? 41:15 Buyers who want AI vs buyers who want value 45:30 How startups earn visibility and trust 49:13 CAD/CAE agent workflows and organizational readiness 51:49 Trust, verification, and human-in-the-loop engineering 53:39 Where to meet Bild and Bench next

AI will not replace engineering overnight. But it is already changing who can build, how fast teams can ship, and what the next generation of engineering software needs to become.

Subscribe for more conversations with the founders, operators, and technologists rebuilding the product lifecycle.

#AI #CAD #PLM #EngineeringSoftware #DigitalThread #Simulation #Manufacturing #ProductLifecycle #AgenticAI #IndustrialAI #Bild #Bench #BetterCallFino

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Full Transcript

Speaker

And we're live. This is uh Michael Finikero of the uh AI across the product life cycle podcast. Um I'm joined by my friends Pradu and Martin who we've met actually in person a couple of times now over conferences over the past year which is a lot of fun. Um so Prau, why don't you introduce yourself and and build a little bit? Yeah. So, my name is Purdue. I'm one of the two co-founders of Build. Um, Build is a CAD data management platform. We're essentially connecting all things engineering into manufacturing. Our focus is really around data management and automating the workflows built on top of that that data set.

Speaker

Awesome. Uh, and Martin Delei of Bench. Do you want to explain what what Bench is doing?

Speaker

Absolutely. Great to be here. Uh, my name is Martin. I'm the co one of the co-founders here at Bench and at Bench we're building uh an orchestration layer for engineering. So essentially looking to apply AI to uh automating AI end to end workflows in engineering so spanning CAD simulations uh PM and the like uh and yeah starting uh starting with with CAD and simulations mainly.

Speaker

Awesome. Um, so I usually like to open the podcast with a question like looking back a little bit like you know the open AI moment that we all had in November 2022 and we were like oh my god AI can do that now. Um I I've talked to um you know over 160 startups the last year and a half. Um do you guys did you guys feel um really bullish about AI um at that moment when you first saw Open AAI and Chat GPT or were you maybe you'd already worked on the earlier one chat GPT2

Speaker

I mean where did you guys say were you super bullish or were you a bit skeptical? So for us, we basically started a company because of it. So we we didn't exist beforehand. So [laughter] So yeah, we didn't exist beforehand. So So in our case, it was more so seeing what AI can do for um indust. It was very early results, but still uh we definitely see where it's going and we knew that it will transform essentially any industry out there. Um engineering is the one we obviously had expertise on. So we wanted to to facilitate [clears throat] that change and we wanted to bring that that into reality. Awesome.

Speaker

We're the we're the opposite. So, we started our company out in 2021, but like personally, I had an early access to Dolly. So, this is even before Chat GPT.

Speaker

Wow. Okay.

Speaker

At Dolly and you could like prompt and it would make these like images and it was really cool. I mean like I kind of knew like wow this is it was the first real time where you could prompt something and it would create you know something brand new that was um even before Chachi PT happened. It was not text based, very visual. Um, so I I kind of knew that this it was, you know, it would really kind of change the world. Chat was the first application of it obviously and since then we've seen such an amazing proliferation of like AI and these different use cases. I mean now it's it's everywhere, right? From like our own tech stack to some of the services and products that we're offering to our customers. Um, and then even just like helping accelerate um our go to market. So, forget the engineering side, right? Like, we're using it across the entire organization. And I think that any company that is not leveraging AI in these different facets is really just going to be like 10 times slower than you you'll honestly not win. M um so in terms of uh I mean we just talked on the origin of two companies that roughly a year apart but how has um AI change the way you guys code and the way you guys manage developers and manage the development process. Um, I suppose it's a big difference, right? I mean, nobody's really sitting in front of an IDE actually writing lines of code anymore, which is a huge change from even as late as 2022, right?

Speaker

Yeah. I mean, our hiring strategy has like really shifted. Um, we actually find uh like kind of like testing and like QA and like validation to now be our bottleneck. So like we're hiring a lot of uh kind of people in the back end, right? So like have um cloud code or cursor right like write up all of these um new features. um it's to a point where we have cursor hook up to Slack and so when a customer reports um a new feature I can basically forward it to cursor and say at cursor go do this it'll build it in the back end and it's all interacting through Slack and then we essentially have graphite which you guys probably know about which will do some of the testing and then we'll actually have human in the loop do the validation verification at the end just you know making sure that it actually integrates properly to like through our workflows and in our application and then we're able to push it out maybe even the same day, right? Um, so we're able to go from concept to delivery in a day, which is obviously never possible before. Um, I will say though like our our the accelerant of the catalyst of actually using AI in our engineering database probably happened sometime around like mid to end of last year. like it before that right before like Opus 4.7 or you know that model kind of came out it was cool and like we you still had to do a lot of like checks right it was still like oh you know AI not I don't want to say slot but it was like instead of writing you know five lines of code it would write 200 lines of code or something like that I think today it's an extremely efficient tool when it comes to coding and we use it almost every day if not multiple times a day

Speaker

yeah had a very similar experience yeah probably very similar experience So once again, we started the company basically after LM came into into place. So we kind of always used them. Um but really the acceleration happened I would say [snorts] the first kind of bit in like mid 2024 and then another another kind of uh push maybe probably a year later and then recently was more so like this year and kind of late last year was more like multi- aentic kind of systems or kind of our engineers would be kind of managing multiple agents in parallel um which also brings it it challenges sometimes the the limit becomes the human mind's capacity and kind of what how How many threads can you manage at the same time?

Speaker

Yeah.

Speaker

Uh but yeah, on my side, more commercial side, we we also just use AI for for all all commercial stuff kind of uh customizing decks, sales, and all the uh the boring but important stuff the apps do as well.

Speaker

Yeah. Actually, like to to kind of Martin's point, I think like the commoditization of like what looks good, like if your product doesn't look good today, right? Like it's just not an excuse. I think you really have to think through a lot. you know, maybe like product manager is the wrong phrase here, but like someone that's really thinking through the product and then let the engineering aspect of it be handed off to to AI and these agents. I think that's really what's going to differentiate product to product, right? It's like the application and the workflow and how we think about like delivering value. That's going to be the differentiation, not the actual product. It's just the vehicle for that value.

Speaker

Agreed. Agreed. I think there's still space to be to be smart about how what the workloads and what the broader UI is, not just the design. The design can be but also I think I think there's a difference between having good okay design which AI can do and amazing design which is quite tough to get still.

Speaker

Yeah. And and I think like you look at we'll probably talk about this later but you look at like the the incumbent players right the question also is like at what stack does AI help with coding right like if you're on like a 1990s right software stack as much as you try to feed it you're you're going to hit a ceiling pretty quickly right like if you're not on you know I I don't know what the latest like you know software stack is today because I'm not an engineer but like you know I'm sure if you're not on anything post 2020, right? Like React and JS and all those kind of like scripts. Like I just I don't think that you're going to be able to succeed. And I I look at some of these old architecture that I'm not going to say their names, but all you know like the vendors are on. It's it's challenging, right? You can only innovate so much.

Speaker

I I've got a good question in here from on the chat from Morton Will We Wyberg who says uh to what degree are you depending on prompting skills as opposed to letting AI and fill in all the gaps? To reframe it slightly, how do you see the friction curve trending for AI in terms of autonomy? I use a lot of LLMs every day and they all have reliability problems with output quality. In in my work and like the more kind of commercial side, I think the prompt isn't as important. It's more so getting the right context in. So if if we have the right if if if AI has the right understanding of for example what customer I'm approaching why what's the use case then it can kind of work out what I'm trying to do definitely kind of go into the right direction the prompt itself I haven't seen mattering that much

Speaker

yeah and I think like these foundational models have also gotten really good about like hey if I don't have enough like um information from you in the prompt I'm going to follow up with questions right like if you work in in cursor or cloud code like it'll be like oh do you also want to look at this and do you also want to consider XYZ so like it's getting kind of better at asking you to be better at prompts as well right if it doesn't have all the information inside that prompt um I can't again I can't speak and now there's like more than just prompt it's like a visual you can send it a image and like a product screenshot and say go do this and it has all the context inside there so I think we are getting to a point where it is multimodal it doesn't just have to be a prompt it could be so much more as Martin said, right? Like context matters so much more than prompts these days.

Speaker

Well, I was reading it was, you know, some of these thousands of posts you get every day on LinkedIn about how they're going to, you know, here's the 17,000 prompts of cloud to make you better at marketing, whatever. But someone was saying that we were moving from prompting to loops, which is I think what GSD was trying to solve before the guy walked off with all the money, you know, but uh I think that that's sort of the idea you have. Well, you basically build your um skills on top of Git and then you you have a your scaffolding so you can ask questions. It remembers. It can go look in Git to find out what it knew, you know, 10 minutes ago because of course it forgets instantly when it writes something. Um yeah, I think that it's it's evolving a lot. I it it makes me wonder too, how do you and I I'm sorry this is wasn't on the script, but it still interests me because you mentioned Frontier LLM. Well, frontier is not cheap, right? I mean, I don't know how much I'm paying like way too much to anthropic these days. How do you guys deal with that? Because as a founder, before you just had headcount, right? And then a couple SAS licenses. Now you've got this variable cost of anthropic or open AI or whatever. And and I don't know about you guys, but like a m even cloud max is gone within like the first five days of the month. I've already burned through the entire subscription. And then suddenly I'm on payer use and damn it, the bill is just so high. How do you how do you guys deal with that? Because that's going to be an extra burn on your on on your resources, right?

Speaker

Yeah. But we've seen probably something similar, right, in the past of like with AWS as an example, right? People would say

Speaker

S3 and things like that,

Speaker

you had you had a server, right, and like you just paid it once and you just had to keep it in your closet and like you didn't have to think much about it. And now like you've got S3 buckets and EC2s and EC2s aren't, you know, cheap. So it's like how do you think about that as you scale your business and you know cloud computing and so I think we're going through that same like mental shift when it comes to cost right you're going from you know headcount costs to to maybe tokens right you know when we did AWS you're going from physical real estate costs and hardware costs to cloud computing and and that you could also say is kind of like a token right where an EC2 you're you're really getting charged an EC2 per minute or per hour whatever you know like denomination they use, but kind of this the same thing at the end of the day.

Speaker

Okay.

Speaker

I'm curious how it's going to play out cuz uh we were seeing like companies like Uber for example capping uh I think AI spending like $1,500 per month for their coders. I do wonder how it's going to play out in long term and now the cost conversation now is coming in like back into the scene where it wasn't conversation like a year ago and also I'm by extension I'm curious how it's going to work in our accounts and like engineering companies. I think many of them are still not using Genai for like their development. Most of them in my eyes are using kind of like AI accelerated simulations or the co-pilots. I don't think for many of them it's a topic yet. But I think it will be one in like a year or two to in my eyes our industry is like a year or two behind coding.

Speaker

Yeah. But cost is also just like one like you dimension that we look at. It's like it time right like even if the the the the number of tokens or the cost of tokens we end up spending in a year is like$100 $200,000 right like that is a replacement of sure like one engineer but the amount of like throughput that we would have had in those $200,000 of tokens would be much higher than headcount and engineering and it would be done way faster. Right? So now you're able to not just it's not just ship products faster. Maybe you have to do a little bit more validation there, but like there is a given trade even if costs creep up to the cost of like human labor.

Speaker

Agreed. Agreed. But I think it's going to be hard for companies to measure that out for the times. And that's also why Uber's capping capping theirs probably. Um yeah, it's a tough one.

Speaker

What about um I mean in terms of foundation uh sorry Frontier LLMs, what about homegrown ones? Are you guys looking at um training your own models or internally using LM Studio or Olama or whatever so that you avoid you know going every single time out to uh sonnet or fable whenever the US government allows us to use it right

Speaker

back as of yesterday

Speaker

it is and I used it I used it this afternoon [laughter]

Speaker

very briefly because it's so expensive

Speaker

for us we're not training our models yet it's on the kind of longer term road map but what we are doing is kind of uh essentially ensuring that we can use the cheaper models as often as possible. So essentially building tooling around uh around the the uh the models and our kind of what we have built thirdly deterministic tooling that essentially allows us to use lower intelligence models and that that's how we how we manage it. [clears throat]

Speaker

Yeah, we we look at it a little bit different. It's kind of like a stitch between what you said Michael on like the loops and what you said on like context. Nowadays people are calling it like the orchestration layer, right? Like if you can create a well kind of like orchestrated understanding of when do we have to use right like Fable 5 versus when can we use like a really cheap model um and you know are we going to use a VLM are we going to use text are we going to put in a raw step um you know that's also really important to that orchestration layer I think has really been our focus and behind the scenes there could be a variety of different models right to to your point Martin we could you know we have to make the smart decisions on knowing not to give the PhD right basic arithmetics. Um understand kind of like what level of model do we need to use for complex tasks and then like what are the the simple task um that much cheaper models are going to be great at and they're going to be faster at it too as well. Um and and has and last question we'll move on to more AI inside your stack. But what how has AI changed the way you organize your programmers in the organization? Other words, has it changed agile and uh waterfall? I mean everybody was already on agile, but I thought the waterfall was sort of making a little bit of a comeback. And now I wonder how agents have changed all that. I mean it seems like soon you'll be inviting agents to your scrum meetings rather than humans, right? because they're the ones doing all the the heavy lifting. How has that changing the way you guys because you're guys both managing these enterprises with lots of developers. How are you how has that changed the way you manage developers? Basically, I mean, we're a startup. I don't think we have scrum meetings [laughter] like right like we kind of we're we have to be a lot faster than that. I I think I I talked about this right like our our focus is really on like the bottleneck is just now more on like QA validation human loop before we get like market like what questions like what is an engineer like I'm I am now just you know forwarding a customer ticket on a slack channel to another slack channel and saying at cursor do this and it's just getting done in the background right so like the a lot of people in our organization that didn't have the ability to make changes to you know with limits right? And and with constraints um that didn't have the ability to to make changes to our product. um something as simple as hey we just want you know there's there's a drop-own list and there's 300 items in that list and instead of just sorting it alphabetically the c the customer wants to have a search bar at the top of that list can we just add that right that that is quality of life like cursor is great at that right uh that that is where we are finding the best use of AI right now inside of the non-engineering team right obviously the engineering team is using AI much much different ways than non-engineers are. So I think like the scope of what is an engineer and who can actually impact a product has gotten a lot wider in in our organization. Excuse me. But the you know the other part of is on the engineering side. What is the focus and the focus right now is how do we enable those updates to actually enter um production and that just means testing validation human in the loop. Yeah, for us, um, we're a team of five. So, so we probably haven't changed our ways very much after after AI. Uh, a bit bit too early for that. Uh, but what I have, what I have been thinking a lot about is how AI will change how engineering companies organize themselves and I think that has to come sooner or later later. Just how enterprises and software have changed how they organize their teams. This has to come in engineering to actually harness the full value. And I think this will not come until this will be induced by a software player coming in that kind of enables that cloud code for hardware kind of occurs for a hardware proposition. That hasn't happened yet and that it will still take time and it's going to be a huge uh implementation experience probably for the for the consulting companies or whoever else uh does it. Uh but yeah, but I'm curious about that and and I've been thinking about that quite a lot.

Speaker

I guess maybe I should qualify the question. What I I guess what I was thinking is if you have multiple agents all hitting your codebase at the same time, they could be overriding each other and agile was supposed to help us not be destroying somebody else's code. So, how do you how do you manage that? How do you if you're using multiple agents that are all hitting the same codebase, how do you make sure that agent number one didn't just overwrite the code that agent number two did because they're actually not even talking to each other because they're all in their own little context universe that are are are not talking to each other, right? I mean that's why we used agile so you could coordinate multiple people writing the same code with agents that not really or or are you you think there's is there an orchestration layer on top already because I don't and cloud code I mean I have seven windows open side of warp and they don't know what the hell the other guys doing that's for sure you know they don't know I don't know that's a great question that's a great question for RCT I have no idea muted I think proud I can't hear you

Speaker

no I got I was gonna say I great question for our CTO. I have no I [laughter] have no idea, right? Um I I couldn't tell you you know too in detail kind of like uh you know the engineering organization and like exactly how they're dealing with these merge conflicts or you know whatever they're going to be. But what I will say is like our um it from like just not being a software engineer by background like it the cap the capabilities and like the things that we interview for today, right? um on a software engineering basis. It's it's more on architecture. It's more about how do you think about like the uh how do you think about the product? Um maybe again going back to it product management is maybe not the right you know uh role or the the phrase here but it's something really around like okay this thing has been built because again as you can if you imagine it it can be built today. Um, it's really about how do we integrate it in a frictionless way to the product and to the workflow so that the end user can extract the most value from the thing that we just built, right? Um, it's not just modules on top of each other where you have to kind of context switch and go to a different app or go to a different window. It all needs to be really seamless and the experience more than ever, right, matters today.

Speaker

Yeah, probably same answer from my side. Yeah, bit of a more question to my CTO. haven't heard that being a huge huge issue does is it being managed somehow.

Speaker

So in terms of your stacks like where is um AI already um influencing the user experience? Is it um what's managing the the underpinning the kind of the the yeah the the plumbing or is AI and the entire you know from top to bottom it's already all of stack or all of build and all of of bench are already leveraging AI just about everywhere. How how have you guys integrated into your stack? Yes. So AI is pretty central to our product. Um we have built with AI in mind from from day one. Uh with that it by design AI is not central to every part of the product. As I mentioned before, sound parts of the product are more deterministic and we kind of lean on those more deterministic methods um like CAD kernels or kind of different different tooling to essentially ease the load on on on the AI, you know, ease the the dependence on AI and also just just to get determinism because alo AI is not that's not made for determinism specifically. It's kind of how you uh marry that um intelligence with determinism. Uh so currently we use AI to collect context to plan tasks to to kind of to make judgments on kind of some some decisions. Um but we generally try to keep the execution and the actual completion of task uh deterministic and that's kind of how we how we get um really how we build trust with customers. Yeah, we we've I mean as we kind of started out this conversation saying like we started pre-ai um and like just the nature of our business is we are storing all of our customers cat data right millions tens now probably hundreds of millions of assets um and so we have to be really careful in terms of like how we implement AI into our product and making sure that like the customer is very well aware that like their product data um would touch AI if they choose to do it right it has to be an opt-in for our customers just kind of given the nature of uh you know how we implement build inside organizations um now over time I think like the customer's mindset will shift and it'll be kind of like oh yeah like you know why wouldn't I do it and you can kind of just throw it in your terms of conditions and that will be a general industry shift but for right now it is very much an optin like interface you understand that your CAD data is touching AI um but also like we're not training any models on our customer data right and we're very very clear on that so um I think trust is a big part of it especially in this industry um and I think that again just given the nature of um of build and and our product just because we're we're hosting IP it is really really important for us to make that our customers know if and when AI is touching their CAD and it has to be an opt-in situation.

Speaker

Awesome. Um, so now we've been like four years into this whole AI thing and it's we've seen already the amount of change has just been mindboggling, right? Um, I want to just ask like in the last section of this part of just how how do you see things moving forward? Do you think that uh we're going to hit a sort of open AI moment where you know there was before AI and then now we there's the after AI thing. Is there going to be something where engineering just suddenly overnight just becomes a different thing? Has it already happened and we missed it? Is it 10 years from now? Is it six months from now? What what's your prediction on where AI is going to take us and when it's actually going to have a immediate day-to-day irrevocable impact on how engineers build stuff?

Speaker

Yeah. So we're definitely building towards that moment. That's that's the the company really. Um so the difficulty is kind of uh in engineering it's a bit harder to scale AI across every single use case. So in code of course um text is more native to kind of how we how we code. Uh so essentially um AI kind of impacted all of coding at the same time and got better at all the use cases at the same time. works with engineering it seems to be going more of use case by use case or kind of um software stack by software stack maybe CAD first then simulations and then then PLM um so so kind of the the generalization is a bit tougher in engineering but we definitely do see that coming um we do already see revocable impact uh of bench today the the question of so AI is impacting our customers workflows uh it is just kind of the initial kind of workloads we we work with not everything at the same time yet so So, so yeah uh we are definitely pushing towards that. We believe that sometime in the next 12 to 24 months we will have that open AI m that open AI AI moment where the the day-to-day of engineering changes but out of my previous point even if the tech is here in 12 to 24 months the implementation and the change management is going to be a tough tough one especially in these companies that are by nature quite quite much slower than than a meta or an Amazon and their kind of development cultures. So I think both uh hurdles are going to be quite significant uh and both have to be taken in account.

Speaker

Yeah. And I I like uh just on your last point Martin I think there'll be some industries that will be faster to adopt um you know AI and in their tooling. Uh consumers probably going to in my opinion take kind of the lead there just because of lack of regulation and like you know they're just trying to get products to market faster.

Speaker

Um

Speaker

is it consulting? So you broke up for a second.

Speaker

Consumer consumer.

Speaker

Consumer. Okay. Yeah, not consulting. [laughter]

Speaker

Um, uh, yeah, I mean I I don't I don't know when I but like the if is not really a question, right? Like I think it will happen. Um, I just don't know the when. But I I do agree with Martin that like there is like different aspects that people are focusing on on like the application today. Like there's I probably see five videos a day on like oh text to CAD, right? And then it's like, oh, just cuz you can design it doesn't mean you can manufacture it, right? And then like there's another like AI thing around, oh, DFM and the so there's like there's so much to it. Um, and it's not as simple, right, as as code. Not saying that code is simple, but like it to Martin's point, like it no one thought about backend AI engineering and front-end AI engineering and software, right? But like in hardware, you have to think about engineering, you have to think about manufacturing, you have to think about supply chain. There's a lot more facets to it. Um, and and so like for us, I think that moment really looks like the democratization of information and kind of like how we're feeling about that today. like you don't need to be a back-end expert to now manage a database and to have compute in the back end and to be able to code um and have an app live. You could do all of that even if you didn't know anything about software engineering yesterday. You could do that today. And I think that is kind of like the moment that I'm really looking forward to in hardware where it's like I don't really need to know all the thousand rules of like GD&T and I don't really need to know how to use Solid Works or or NX or whatever that you know the tool is. I just need to know this is kind of like what I want it to look like. These are the components I want uh to be part of it and I need AI to rapidly create this this product and then we'll start thinking about the downstream implications of sourcing and and actual DFM. But like the actual LLM or the AI tool will already start thinking about that, right? So it's kind of really like how do we enable more people to build things without having to carry all of this knowledge base with us?

Speaker

Interesting. Um did you guys see that there's going to be that uh demo of five or six startups in DC at the CDF fam and that's going to be kind of crazy, right?

Speaker

Yeah. in a couple workflow top his digital cisuit all these guys I've interviewed and and been really blown away by it wish I could go I hope you guys have a good time out there Dwan and company um but I think there'll be some there I'm I'm sort of expecting that to be as almost an open AI moment there I think there might be some amazing stuff happening so I hope that somebody records it and shares that particular presentation with everybody um so I guess you guys are both um as bullish or bullish than you were back in 2022. Then the sounds from to me to you guys are both before.

Speaker

Um well before I move on to the the last subject um for the in the demographics of people watch this podcast there's actually quite a lot of like entry level or very early engineers. I'm university kids because I write a lot of educational stuff. um what what advice can you give its founders uh and of awesome software companies to these younger graduates that are maybe worried about being aed out of a job? That's sort of a big topic right now. Um, so h what kinds of skills, what kind of things, what I mean, Prada, you you pointed to human testing and understanding how things work and and intu intuition, but what other what other kinds of things can people do to prove that, hey, I'm actually better than an agent and so you should hire me.

Speaker

I think it's like different than being better than an agent. I think it's just being different than an agent, right? Like don't have commoditized skills, right? Um like I think a lot of people are like oh coding's dead because entry-level coding jobs are kind of like now it's just commodity right like data entry is a commodity um and I think like over time there will be things that are commodities and there will be like high value work that will be needed um and we see that shift across all sorts of industries right like we work with um uh customers that are in the autonomous agricultural space right and there's this whole uh uproar of like, hey, like are farmers going to be out of jobs um if they're not, you know, are we going to have low-skilled labor um kind of be cut in half because these tractors can kind of go and pick and and kill weeds and all that sort of stuff, right? So, the question is not about like um will AI, you know, do my job, it's like is your job something that's differentiated? Um and I think just like having a unique take um and uh you know we talked about this earlier which is like uh our focus has really been on the the front end right like what is the actual product that we want to build and how do we really think about integrating it into like a very delightful experience for our customers and then it's on the back end which is like can we validate that and make sure that our like thesis is true right like does it actually hold ground when we have you we just coded this like feature but like how does it actually look in the hands of our customers. Um, everything in the middle is now just like AI, right? Um, and I think that could be true for hardware engineering, right? It's like the the world could be your imagination. Like it's like anything anything that you want, you know, want to think about could could happen, right? And you could build it and you could go to a manufacturing shop and you could go and and get it and, you know, built and assembled and and sourced. All that could be true. Um, then the question is like what can AI not do? And it's really about like thinking, right? It's like uh like it's creativity. It's being imaginative. Um and I'm seeing a lot of really cool um you know jobs open up in these larger organizations um especially in Silicon Valley around like creativity in engineering and less around like the actual like doing of the engineering.

Speaker

Awesome. What's an example of that that creativity role? Do you have any any tangible examples like what what the actual job spec is there? There's like uh so if you like if you look at um like open AI and like anthropic jobs today it's kind of like no one and they even like say it's right like this this job may not exist in 12 months kind of funny because they know like you know time AI is going to to go but

Speaker

there was one that was um about AI in um physical sciences and it's like oh like what are different ways AI could right touch physical sciences and all they're really trying to do is like oh we've we've been focusing purely ly on code, right? And like there's this big market for code, but like what else could happen, right? And like where what are the opportunities AI being embedded in there? And really what they're just thinking about is like people that are creative understand a domain and that can think about how can AI create a better experience in that domain, right? And that doesn't require knowledge of, you know, like core AI LM infrastructure. That doesn't require knowledge on being a PhD in material sciences or whatever, but it's just like the applications, right? And that's the creativeness and that's why I keep going back to like it's not it's not a product manager because like the product manager role like that was there 5 10 years ago is so different than kind of what you're looking at right now. Um and again like once you understand and can define that that embedded application of AI in these more nuanced and niche verticals I think it opens up this whole new like industry right that like AI can go and disrupt. And I think like us as startups, we're trying to take um we're trying to take some uh forward paths towards embedding AI into hardware engineering, right? Whether it's um on the simulation side, whether it's on the design side, manufacturing, whatever it might be. But like it's happening also at these big companies and and that's again the big differentiator. It's how are you actually introducing these experiences to your end customer because everything else will you know product can get commoditized very quickly. Yeah. Um to answer the question briefly about kind of what what junior engineers should do. I think um first of all engineers are here to stay for for a long time and like for for a long time engineers are going to be uh in the loop and that's kind of a a big feature kind of many many of our customers really want. Um but also kind of looking to what happened in software. I think there's a big rift in the software engineering market in a sense that the best are getting paid more than ever and the lower half are just kind of strugging as well. I think that we're a long long way off of of engineers not being needed or not being valued. But it's even more important to be in that that top half or the top quarter even. So, so yeah, the advice would be just be the best you can.

Speaker

Ouch. [laughter] There's only so many places for this. Um

Speaker

uh so I I like to um switch gears and and think about digital transformation. Um you know, when I think about companies being dig, we were you were I think was prior to you alluded to how companies are evolving and stuff like that. I think of digital transformation on a scale of one to five where one is sort of company's still on Excel and email which is unfortunately the case almost everywhere and then like a five would be oh they've already got autonomous agentic um adaptive digital twins right which is basically nobody um when you guys go and talk to customers do you find that they're closer to one closer to five closer to three I mean how how what what's your feeling because no customer is going to come on to my podcast and say yeah I suck at digital transformation, right? But you guys, you don't have to name them obviously, but you guys are in touch with these people. What What's your feel? Are companies starting to transform or are they still a bit reluctant?

Speaker

I think

Speaker

I think everyone wants to be like a four or five, right? But like where are they today? Realistically, they're like a two, right? I don't think they're like a one, right? Maybe

Speaker

two being Yeah. two being generous sometimes, but yeah.

Speaker

Yeah. Ouch.

Speaker

Hey, we we work with some like uh like fabricators that are still doing prints like physical prints and they're still doing like you know the the physical classic signoffs on on changes and red lines and and that's okay, right? Like uh it's not bad. It's just it's it's work for them, right? And and for them like what is five? Like five could just be like oh I have an iPad in front of me and like now I can redline on an iPad instead of like this physical print. Um and and so like I think like everyone wants to be more innovative but I think everyone also is like grounded in truth that like this is not going to happen overnight right um there is change management involved there are processes there are other business applications there's compliance factors um so really for us we see a lot of I and I think most people also on the other end I don't know if they want to be a five I think they want to be like a four right I don't think they're looking and saying I'm going to replace my entire entire finance or engineering team with AI and like that's how we're going to run the business. I think they're going to be like we're going to be a 10 times more productive business with the same headcount and with the same resources and we're just going to grow market share. Um so what we're seeing is like people that want to be fours that are probably like one and a half twos.

Speaker

What about you Mark?

Speaker

Yeah. Yeah, that that probably tracks. I uh I don't have much to add here. It's it's mostly I would say especially on the AI transformation scale is like one or two. Um digital probably a bit better. uh if if you look at those kind of larger enterprise like automotive space yeah digital more more so uh AI not not there yet and and kind of everybody uses AI but more so in in the ML sense than um than genis but more so I also think like it's important to flavor in like what department you're talking to and who at that department you're talking to right um if you're talking to the CIO you're going to get a very different answer than if you're going talk to like the VP of engineering or in and mechanical engineer. So, and it's also that answer might be completely different from engineering to manufacturing as well.

Speaker

Absolutely. Um, so what so my theory is that if you're going to wait for the big three to do that transformation, it's going to be a while. Whereas if you go to build or bench, you're going to get the AI without having to wait. And and I'm wondering if when you've done that when the customers seen what you can do has there been an aha moment where like oh man if I you know fixed broke down the data silos and I had data governance and I had more data to feed to these AI powered tools holy crap I would get so much more out of this and I don't need to wait for you know two three years for the road map to mature at these big vendors. Have you ever seen that actually happen or

Speaker

I was actually surprised a couple times by kind of big OEMs approaching us with actually and actually asking for quite big visions immediately. So they are thinking about how do we transform our our whole company and our whole development process with AI and I've been surprised. I didn't think this would happen kind of coming would come from them that quickly. Um so yeah so they they actually know what they where they want to go. It's more so that nobody can really deliver that hot position yet. So, so I I I will I think that they will be probably picking different players for different initial style workflow and then looking to who can then do the whole thing maybe or who can kind of what's the combination to achieve the whole vision? How quickly can can we get there? Um but yes, I was surprised a couple times. Uh but then some other customers uh yeah you come in with with one use case and then they start thinking okay maybe we could apply this here and there maybe this department also could need this uh but generally we we tend to start from the most obvious painful one and then continue the conversation. Yeah, we we take a a slightly different approach. Our you know, you you get kind of two buyers. You get like the buyer that's buying AI and you get the buyer that's buying value, right? And if AI happens to be hard that value, then great. [snorts] Um and and we like the the latter uh kind of buyer, right? Which is hey, I have a a problem and I'm looking to solve for it. If AI is part of that solution, amazing, right? But like if AI is not part of it, that's still okay. Um, and I think that's like really important to our philosophy. It's like we're not just we don't go to companies and companies will come to us and say, "Hey, I want to use AI. How can I use AI?" Right? It's that's you go to Accenture for that. Um, you really you're coming to us because you're saying, "Hey, I have a very specific problem. I'm looking to solve for it." And your solution, you know, uh, claims to, you know, automate engineering documentation. Right? Now, part of that could be deterministic. Part of that could be through inferencing and AI. Um, and every vendor will have their own approach in terms of how they claim to automate insurance documentation. Um, and it's like who is delivering the best value or the most amount of value, right? In terms of solving for that problem. I think when you look at the legacy players, they have these big claims, but they they fall short, right? They're maybe delivering 10 to 20% of the the proposed value. whereas we're able to uh sure leverage AI in many cases, but at the end of the day actually deliver on the value that we're we're telling people, right? Um and so I think that just it's differentiating between the smart buyer and the buyer that's buying AI to buy AI.

Speaker

But there was I'd say an issue in your response though because if if the customer goes back to Accenture, Accenture and CAP, they're all going to propose another module of Windshill or 3D experience or Team Center or SAP. They're not going to They don't know who Pinch is. They don't necessarily know who Bill is.

Speaker

Yeah.

Speaker

So, you don't want that that to be the gut response because it'll never get back to you again. You know,

Speaker

I don't think I don't think our our win to um a organization is, hey, we have AI and Winshell doesn't have AI, right? Or I mean, Winell has AI, right? It it really is kind of saying, you know, here's exactly what you can do in Winshell and the value that it has and there's value there and then here's the value and the philosophy that build has. and and the and it's like it's really just like philosophical differences at the end of the day, right? Um are you an organization that is um going to spend $3 million on windshill and use it as a vault, right? And there's a lot of other value inside of it, but maybe your organization is not ready to adapt that value. So even though that there's things there, you just can't do anything. Or are you looking at an organiz or are you looking at a tool like build where your organization can adapt it in in days or weeks or months, right? Um but you can go deep into like the engineering value that you can drive. You don't have to go into supply chain and operations and manufacturing just drive a lot lot more value vertically into the engineering organization. So I think it's just like a philosophical buy and it's like really comes down to value. If somebody's looking to to solve for problems, we're here to help solve for those problems. Um, and we'll give them our roadmap, right? To Martin's point, they do kind of lead with this what is the five-year plan, but let's back into making sure we're solving for the one-year plan for sure, right? Um, but they do want to know that you're going to grow with them because their business is going to grow and your system needs to grow with their evolving needs. Um, and there's different ways to solve for it, but and I think that to just kind of end on this one note, the the the thing that we where we really win is like just kind of say, hey, this this was X tool and this was it in 2020 and this is X tool and this is it in 2026. I can name you on one finger or on one hand, you know, the number of changes that have really been happening, right, in in six years. and now look at build or look at bench or look at any of these newer players and you could probably you know get all of those updates done in a week or in two months, right? So the pace of innovation that we're able to deliver is far outpaces um any of the incoming tools and that's like true for most industries anyway.

Speaker

Did you want to jump in Martin?

Speaker

I think that covers everything. Okay. Because I was just hoping that the thesis is more if you use uh well I just I guess a qu the question is more about the v how do you guys get more visibility to the startups when the all the the public space is taken by the bigger vendors. How how do we make more space for your solutions to be more visible? And I guess that's why I created thread mode and I do these podcasts to give you guys sort of a platform.

Speaker

I think there's a couple different approaches. I think first it just comes down to your product and I think a lot of the the big players are maybe not as focused as we are and they just kind of deliver the same value in the the verticals the applications that we choose that we can. So I think the core is just what can your product do versus what can't theirs do. And I think for a long time they cannot match what we do broadly us as startups cuz just because their speed and just historically they just have just not known for being the fastest or the quickest to innovate. So so I I'm not worried about that part that much. Um otherwise it's about building trust with these big companies. So right now I guess trust is built mainly through like pilots and through kind of proving that that actually works. I think trying to increase the um I guess um the position that starts holding in in other clients minds would be would be interesting and with that some programs like you know those programs like plug-andplay or or different kind of accelerators or whatever they are of those innovation platforms they can help and there's I guess different results that come from them. Some of them really elevate your profile and they give you a great introduction and then um the framing of the the sales completely different some of them don't frankly right this they just not that valuable um so yeah so in the end I think the core in my opinion is just the product and I think um being able to showcase the trust in some way with the client usually a pilot kind of in your approach to kind of how you built the product um is what you need I I think the question will become um yeah what happens when um when I don't know the salt systems releases something that's 10 times worse but kind of kind of does the same thing and my of course our thesis that we still win and I think that the product speaks for itself but but I think that's going to be the testing moment.

Speaker

Yeah.

Speaker

Anything to add? Well, I was just say like, you know, capability is kind of like a checkbox, right? But like the experience is what really matters um at the end of the day, right? So like yeah, they could click a check box and we can do this, right? As Martin said, but if it, you know, it's it's not a a binary can we do this, can we not do this? It it really is about well, how can we how well can we do this, right? And how well have we integrated into the core experience so that you can actually gain value from this thing that we've built. And I think startups, you know, more than um incumbent uh vendors are very customer obsessed and we really care about like what how are you feeling and we we take the time to actually have those conversations with customers. Whereas I think legacy, you know, vendors get caught into this kind of like innovators dilemma situation where it's like I'm going to be focusing on my top, you know, 50 customers and I'm going to index on problems um that they have and that might cause a a negative impact to the rest 2,000 customers that I have, right? Because from a balance sheet perspective and from a a P&L, those 1000 customers, whatever they are, are 80% of my revenue and I need to keep them happy and I need to grow them. And that's just a different philosophy I think startups have.

Speaker

Um, actually some good questions in the chat that just came in like a minute ago and I know we're almost done, but I'll I'll pose the first one to Martin and the second one to Pradu. How do we do how about we do that? So the first one is from Susan Coons, my friend over at Zemens and she wants to hear about how AI agents are beginning to automate CAD and CA workflows end to end. Um great question and I think uh the end to end point is is a very interesting one because a lot of a lot of the customers we speak to want end to end workflows uh and have end to end orchestration but kind of to to our previous point I don't think that they are ready necessarily organization wise to do end toend workflows via agents and I think that there's going to need to be kind of evolution in how they structure their teams internally for that to be able to do end to end workflows. Currently where we are seeing the biggest um adoption kind of the most pool is kind of end to end workflows but kind of contained within one department or one team. So for example, an end toend workflow for the CAD department, but not CAD simulations and systems. They want that for sure. That's where we're going and that's what our product will allow enable. But I do think there's going to have to be some reshuffing internally on who manages that because if we have an optimization problem going from CAD through SIM through uh through requirements and then back, who manages that? Who who's the overarching team? Is it CAD now managing the whole whole process? Usually we see then the the CE team not being very willing to give up their ownership.

Speaker

Of course not.

Speaker

So so so that's kind of what we're seeing now. And I and I think the tech will come sooner than the than the organization readiness.

Speaker

That's a great answer. Thank you very much. Um that reminds me too like we were having um I've had two podcasts so far on just Ebomb and MBAM. And to me it's also it's the same problem because at the end of the day you could do it in the RP, you could do it in PLM but whose budget is that? the engineers on the PLM and the manufacturing. So everybody's gonna say okay so the question to uh to to Pradu this is from Brian Stack the founder and CEO at digital twin uh.co.uk UK. So a UK startup doing digital twin. He says um as I'm awakening the space and just having validated our digital twin for machinery and R&D facility. I'd like to know your take on the future of engineering. We also run humanade robot robots here. An engineer can look at a machine and go and create a fix and repair a part. The AI uh how do you see that? Wait a second. I got lost in the reading that we also run humanade robotics here. an engineer can look at a machine and go and creatively fix or repair a part whereas the AI will probably just try to replace it. So how do we get around those kind of things and then he said how do you because he said the executive teams are trying to decide how they can hire less skilled staff because the AI tools are going to do everything.

Speaker

Okay, I think the question got lost in

Speaker

Yeah, [laughter] I think he's asking just about the future of engineering. How at at what point can uh are we going to be able to trust AI to do more of these engineering tasks? And how do we keep skilled humans in the loop as opposed to just unskilled people that are just hands to do stuff that the robot can't do? I think that's sort of what he's trying to get.

Speaker

Yeah. I think it's like a crawl, walk run approach and it's like trust but verify is like our thesis, right? Especially at build, right? Like it's like okay, we we think that the AI could do a lot of this, right? especially on like the software side but as I mentioned we always have human in the loop on the verification the QA side of things right so it is always trust but verify and I think like that'll hold true for like hardware engineering as well right it's like yeah let the you know AI go from texticat or whatever you want to do but then like you know the first step is always going to be have a drafter have mechanical engineer look at drawings make sure everything's annotated well make sure like it's up to like DFM standards so I think it's like you can achieve 90% of the work um in a relatively short time using AI, but it's leveraging humans still to verify that the data is accurate and like it's actually represented the output that you were hoping to achieve. I think over time that it'll it'll you know obviously grow into AI owning that verification layer and then it's like AI verifying AI verifying AI as we're seeing we're seeing today um in the software world. But um you know I think it is that crawl walk run approach paired with trust but verify.

Speaker

Um so but just to close out thank you very much. Um I was just wondering how where can they uh where can folks meet you between now and the end of the year? What trade shows you going to be at where they can come and get a live demo bench and build and and meet you guys.

Speaker

Yeah. So we actually were in in Startup Autoban in Stogart today as we're just wrapping out now. So So my CTO is there and my co-founder as well. Um, and uh, then we are also visiting uh, Detroit for the American Automotive Summit in September.

Speaker

Nice.

Speaker

While you're there, you should swing down to IMTS in Chicago.

Speaker

I was about to say IMTS is about that time.

Speaker

Good news.

Speaker

We'll be We'll be there at IMTS. We'll be in DC in a couple weeks at the FAM.

Speaker

Are you gonna be there? Awesome.

Speaker

Are you demoing? Are you on the stage?

Speaker

No, no, I'm I'm just going to be I'm gonna be just attending there for for a day. I'd love to hear your feedback afterwards.

Speaker

Yeah. Yeah. I I'll share I'll share any like notes that I have with you.

Speaker

Thanks.

Speaker

Um we've got a few like we've got like partners um conferences. They're like we're we're part of like the Netswuite SDN program and Netswuite's like our biggest ERP integrator. So um we'll be at Sweet World later this year in October I think. Um and then obviously IMTF

Speaker

and if there's another threaded event I can count on you guys, right? I'm hoping to do one in Munich um in October. I'm still trying to figure that one out. Um all right. Well, thank you very much. I hope uh that people I saw Thank you for the questions um from the audience. And um uh next week I'll have um I'll do another one of these uh trying to remember who that is. Who am I with next week? Uh well anyway, uh it'll be fun. Uh it's been great. Thank you guys. Um we'll see you guys uh next week on the AI across the product life cycle podcast. And uh take care. Thank you. [snorts]

Speaker

Thanks, Michael.

Speaker

Bye-bye.