How Copia Automation Is Bringing DevOps and AI to Industrial Code: A Conversation with Adam Gluck
Manufacturing Happy Hour
Adam Gluck, CEO and founder of Copia Automation, came to manufacturing from outside it. He studied sociology, taught himself software engineering, and learned DevOps at Uber. He then went looking for places in the economy where software touches the physical world most directly. In this podcast conversation he explains what Copia builds, how its product and customer understanding have changed since the company started in July 2020, and why he pushed an AI-skeptical team to build an AI copilot for controls engineers. He also discusses why he built the company in New York and whether venture capital's interest in "reindustrialization" will last.
Two ways to describe Copia
Asked to describe Copia as he would over martinis at the Chelsea Hotel bar, Gluck gives two framings and says the right one depends on who he is talking to. First, Copia brings "industrial DevOps" to operational technology (OT), taking best practices from software development and applying them to industrial code. Second, and increasingly, Copia provides backup and disaster recovery for global industrial enterprises. It helps them recover from cyberattacks, ransomware, physical disasters in the plant, and ordinary outages.
Some people in an organization care about the development side. Others mainly want good backups and version control as a basic part of their tech stack. Gluck suggests both kinds of listener might be in the podcast audience.
For listeners in manufacturing who don't know DevOps, he gives a simple definition: it covers everything from the moment code is written to the moment it runs in production. That includes basic practices like code review, automated testing, deployment, and monitoring. Copia's core product started with the most basic of these, source control, adapted so it works for industrial code.
From sociology to Uber to PLCs
Gluck says his background is "even weirder" than the host expected. He majored in sociology at the University of Chicago and started teaching himself software engineering as a freshman. He didn't major in computer science because he found the program too theoretical at the time, focused on mathematical proofs. He wanted to build things. He shipped an app roughly every quarter, ran an app development shop with a friend one summer as a self-made internship, and explored business ideas. His thinking was that instead of looking for a technical co-founder, he could be the technical person himself. At graduation, the choice was between a sociology PhD and using a skill set he calls "actually useful," so he became a software engineer.
His first job was at Uber as an iOS developer on the driver app. He later joined a team called engineering strategy, which worked on Uber's overall backend system architecture, including a large project he calls Uber Architecture 2.0. He was also part of the office of the CTO, who was his skip-level manager.
He links this history to manufacturing in two ways. The first is his interest in "bits and atoms." Uber excited him because software moved cars in the physical world. When he started there he lived on the South Side of Chicago, where he says you couldn't even get a taxi. Having a car show up after tapping an app changed how he got around the city. The broader "Uber for everything" idea, now visible in food delivery and ordering from pharmacies, appealed to him as software that transforms real cities.
The second is the company name. Copia is Latin for abundance. The idea came from a sociology professor, who Gluck says was editor-in-chief of the American Journal of Sociology at the time. The professor argued that materially abundant systems reduce violence and strife and are better to live in, and Gluck says there is a lot of evidence for this. Looking for the most fundamental layer of the economy, he landed on industrial automation, the systems that build everything else. As an example, he mentions a potential customer with a data point showing that 85% of American households consume its products.
He asked himself where he had leverage, and the answer was the toolchain for industrial code. He came across PLCs and robots, knew DevOps well, and asked people whether DevOps tooling existed for this world. People told him they didn't know why it didn't. He notes that DevOps was only about 11 years old at the time, so the practices were known but hadn't reached industry. He saw that as white space in the market.
What an outsider's lens provided
Gluck names two advantages of coming from outside the industry. The first is deep familiarity with DevOps, the systems he was "born and raised in" as an engineer. He places DevOps's rise around 2008–2009. At Uber, the company was hiring from Google, Facebook, and Netflix, and scale was the central concern. He helped form a team called driver platform, whose challenge was to go from four engineers to 200 working on a single application with millions of lines of code, without breaking it. He says a driver app outage cost Uber millions of dollars per hour.
The second advantage is breadth. Gluck estimates he has talked to about 2,000 companies, directly or indirectly, over Copia's four or five years. People who come from inside the industry often know one company very well, he says. He knows the problems of global enterprises and two-person system integrators alike, and how products fit into each. He doesn't think he would have gotten that view if he had started out assuming his previous employer's way was the way things are. Coming in humble and listening, he says, helped a great deal.
Hiring: better engineers solve problems
The host quotes Gluck's LinkedIn profile, which says his job includes finding and hiring people smarter than himself to solve hard problems, and asks how he does it. Gluck's view is that processes and organizational design only go so far. Problems get solved by very smart people. He recalls asking a CTO how he had fixed a year of technical issues on his team. The answer was to hire better engineers. The quality of the people, Gluck says, determines which problems an organization can solve.
On attracting talent, he says Copia's problem is real and motivating. Many software jobs involve building a piece of a piece of something, while Copia builds tools for engineers who run some of the most fundamental systems in the global economy. The technical scope also appeals to engineers. Copia is cloud-hosted, also offers on-premises enterprise deployment, is SOC 2 Type 2 compliant, deploys into industrial networks, and integrates with, by his count, about 65 devices and platforms.
He adds culture: treating engineers as engaged stakeholders in the business, not dropping deadlines on them without warning, and giving them hard problems along with the agency and time to own them. He also observes that self-taught engineers and boot camp graduates can be among the strongest hires. So much of engineering is teaching yourself, and people who have done that are less afraid of new tools and frameworks. The host adds that self-taught people tend to be less worried about breaking things and trying new ones.
How the product and market understanding evolved
Gluck says, somewhat to his own surprise, that much of Copia's original roadmap has been executed as planned. The plan was to start with source control, then add automated testing, then deployment, then integrations with monitoring tools, building out a full DevOps lifecycle. Customers have requested every piece of that stack. Source control remains the "bread and butter."
What changed was mostly his understanding of the market, which he calls incredibly complex. The same product solves different problems for industrial enterprises, system integrators, and machine builders. Each has different personas and reasons to adopt, and each needs different help getting activated.
The biggest shift was the demand for backup and disaster recovery. Copia built source control, but once it reached the market, customers wanted backups. Gluck gives several reasons. Audits and security requirements increasingly demand backup and disaster recovery. CISOs are taking responsibility for the plant floor and need backups. Regulation in Europe is pushing in the same direction. Backups are also routine in troubleshooting, where loading one is a common way to fix a problem.
The device-linked backup product brought Copia into different parts of customer organizations. Source control tends to appeal to corporate teams and development-focused people. Device-linked backup drew in people managing processes at plants. They were somewhat curious about source control and code review but mainly wanted to plug it in and see changes. Going deeper, Copia reached the plant-level persona who runs the plants day to day.
The host summarizes: source control first, then disaster recovery, then the realization that plant-level people have real influence. Gluck agrees and adds a nuance. Process-level and corporate people are usually the buyers, but plant-level people are often part of the decision. They are also the hardest to reach because they spend their days troubleshooting and keeping production running. Building a product that works well for them is, in his view, a major challenge for any industrial software company.
Building an AI product over internal skepticism
The host describes the scenario he pictured when he first read about Copia Copilot, drawing on his own years in automation. Controls engineers often write code that works but is poorly documented and doesn't follow industry best practices. Gluck says that's on track and uses it to explain how AI entered Copia's vision.
He points to the software world, where AI has transformed how code gets written. He cites claims of 30–40% productivity gains and people saying they built a whole company in a week, and he says some believe shipping velocity now matters more because code can be produced so fast. Inside Copia, though, there was significant AI skepticism. When Gluck, as founder, told the team they needed to build an AI product, the response was that they talked to customers every day and nobody was asking for one. The topic came up occasionally but didn't look like the problem customers were trying to solve.
Gluck's argument was that AI was a new technology that had been critical in software and might be equally transformative in industrial automation, but nobody knew yet. His promise to the team at kickoff was that he wouldn't build something that sucks. If people didn't use it constantly and it didn't deliver real value, they would kill the project. Consistent with what he calls a very customer-centric, product-centric culture, they worked with early alpha users to see whether it solved real problems.
What users actually do with the copilot
Code documentation became one of the biggest use cases, especially for plant-level users. Gluck makes a point he finds interesting: many people who maintain controls code at plants don't write ladder logic themselves. They learned it on the job. For them, a natural-language description of the code is very valuable, and he says users jump into the product specifically to document things. On the corporate side, reusable code and centralized libraries are often underdocumented, which makes consuming them hard, and documentation has been a strong use case there too.
The second use case is development. As in software, engineers writing repetitive logic, such as repeatedly using an add-on instruction (AOI) in many places, can generate a baseline of code. The team decided to make the tool visual. Where ChatGPT and similar tools are text-based, Copia can render ladder logic in the browser, so users can view generated code as ladder and work from there.
The project's product manager, Nick, was one of Copia's first users four years ago. Gluck says Nick got deep into how to handle code generation, documentation, and code analysis in a workflow ergonomic enough that users aren't annoyed by it. The team tracked usage, saw what Gluck describes as strong engagement, and decided to move to a more open beta.
"A leader in AI for industrial code"
Asked to summarize the stated vision of becoming a leader in AI for industrial code, Gluck places it inside the broader industrial DevOps vision. If AI has transformed how software is written, bringing some of that change to the code behind real production environments is a large opportunity for impact. He wants a product people use every day as part of their normal workflow, one that solves material problems.
He explains the choice of a chatbot as the initial form. It lets the team see how people use the tool and decide what to build more deeply into the core product. If everyone turns out to want a particular capability, it can become a default feature. Like the source control product, the copilot is meant to be multivendor, but it starts with Rockwell only. Many early testers were Rockwell customers, and the team wanted to get the workflow right before expanding. As evidence of progress, Gluck mentions that on the day of recording a team saw a demo and called it the best industrial copilot they had come across. He hopes the tool can eventually transform how controls engineers work.
Advice on AI adoption: painkillers, not vitamins
The host asks what Gluck would tell manufacturing leaders trying to get buy-in on AI. His answer centers on "moments of technology unlock." When a new technology arrives, a wave of new ideas follows. Some solve old problems better, and some solve problems people didn't think could be solved. Auto-generated code documentation is his example: there was no way to do it until large language models existed.
He thinks organizations should recognize these moments, but he doesn't believe any good product can be built without solving real pain. He uses the familiar vitamin-versus-painkiller distinction. Whatever an organization considers, and he notes hearing about many ways companies ingest operational data with AI, the question should be what the real problem is and whether the new technology solves it better than what existed before.
That, he says, is how Copia got past its internal skepticism. The team focused on problems and explored whether the technology opened a set of previously unsolvable ones. The answers so far are documentation and code generation for redundant functionality, which he notes resemble the gains software engineering has seen. He also stresses continued close work with customers, because people who see the tool often point out problems it could solve that the team hadn't considered. As demos continue, he says, people get excited because it solves problems they didn't think could be solved. The host sums this up as a problem-first mindset.
Why New York
Gluck admits the first reason he chose New York was simply that he likes it. He was living in San Francisco and, since starting a company meant deciding where he would live for a long time, New York appealed to him and many friends had moved there. He describes a broad movement of people from San Francisco to New York in 2020, when San Francisco shut down and lost population. Many tech people six or seven years into their careers felt they had built their Silicon Valley network and could go elsewhere.
He also cites the funding environment. By his reading, New York has a large number of seed rounds, probably second only to San Francisco in funding amount, and more varied types of capital than Silicon Valley's traditional VC, which he says invests in particular kinds of companies. Lux Capital, which led Copia's Series A, is a few blocks from him and, in his description, invests only in hard tech and was one of the early hard tech funds. Construct Capital, which led Copia's seed, is in DC. He adds Boston as another major East Coast venture hub and calls the East Coast a good place for hard tech and deeper industrial companies.
New York's tech scene and El Segundo
Gluck says that in the early 2010s, when he entered tech, New York wasn't seen as a real tech market. It was known for media companies, fintech, and proprietary trading firms that paid software engineers very high salaries. He jokes that you went there if you wanted to "sell your soul," while Silicon Valley people believed they were changing the world. He thinks this has changed over the past decade. He points to big exits and companies like MongoDB and Datadog as harder infrastructure and DevOps plays based in New York, and to Ramp as a strong recent example. New York's investors structure returns and portfolios differently, and he sees an influx of hard tech. A friend who works out of Copia's office, for example, is building a toolchain for validating FPGAs.
He characterizes Silicon Valley as historically tied to vertical SaaS and to deep infrastructure plays built around the hyperscalers, and says Seattle is similar. New York, in his view, attracts people with more eclectic interests, plus maker communities and people drawn from nearby Boston. It is also quieter than other markets. He contrasts it with El Segundo, near LAX, which has become a major hard tech hub around companies like Anduril and whose companies are "a little bit louder about it." He says New York has many similar founders, some of whom split time between the coasts, and proximity to DC helps those in defense. He meets high-quality hard tech founders in New York all the time.
Bridging Silicon Valley and 200-year-old industries
Gluck attended the Reindustrialize conference in Detroit. The host, who spends his time at traditional automation and manufacturing events, says he wishes the VC-backed startup world and the established automation community overlapped more. Many people in his world want fulfilling work, and many startups are building the next generation of industrial technology. The two have discussed hosting a manufacturing happy hour in New York to mix the groups.
Gluck agrees and describes how quickly the landscape has changed. When he started Copia about four years earlier, almost no VCs looked at industry. Since then, Construct formed as an early-stage fund, Lux had long been active, a16z launched American Dynamism, and Founders Fund and General Catalyst have done a lot in the space. With capital seeking returns there, founders who wanted to build in industry can now raise. Some are hardcore engineers building hard tech, some build hardware along with their own manufacturing, and some, like Copia, build horizontal software for the industrial tech stack and have to go deep with manufacturers to understand them. He notes that Copia, at four or five years old, counts as one of the older companies in this wave. Many are two or three years old.
He calls Reindustrialize a step toward that cross-pollination, though he says it skewed toward defense and VC-backed companies. Some manufacturing people, such as trade group representatives, did attend. He also notes the irony of holding a reindustrialization event in Detroit, which already has a great deal of industry and many strong companies.
The deeper issue, for Gluck, is cultural fit. Silicon Valley's language of disruption and "move fast and break things" isn't compelling to industries that are 200 years old, or to 150-year-old companies maintaining critical systems. Industrial buyers see what they purchase as infrastructure, while Silicon Valley companies may think they're selling software that can change next week. Industrial buying processes, built around expensive hardware, may also need to adapt to buying software. He calls this two cultures that need to come together, and says part of the solution is people talking over beers and learning each other's mental frames.
Is the interest in industry here to stay?
Asked whether VC enthusiasm for manufacturing will last, Gluck says "we'll see," then gives reasons to think it will stick. Investors including Construct, Lux, Iron Spring, and Renegade have built hard tech practices, and funds assume returns over 7 to 12 years. Founders should treat a startup as roughly a ten-year commitment, and he says typical time to IPO or acquisition is now 7 to 12 years, so everyone involved has to stay the course.
He finds the sector's position among tech hype cycles interesting. Industrial tech is close to a hype cycle but has stayed in the background. People think it's cool and important. VCs he talks to are still looking for companies that fit profiles they're comfortable with, so in his words capital and opportunity haven't fully met, and the sector hasn't reached the "hyper valuations" of AI. He sees it "plodding along in a nice way" instead of spiking and crashing. He expects stickiness because of secular trends, especially in defense. He also says the people involved, in El Segundo, in New York, and at the funds he named, are serious about these problems and willing to "take punches" in a market Silicon Valley traditionally didn't sell to.
The host gives his own reasons for optimism. The trend hasn't hit a real hype cycle outside industry circles, and much of the world, including many VCs, still thinks in SaaS terms. Manufacturing also keeps surfacing real problems with a multiplier effect. He contrasts this with an era of consumer apps solving marginal conveniences, such as bumping phones on a table to swap contact information. He and Gluck both lived in San Francisco from roughly 2015 to 2020 and left during the pandemic.
Gluck adds a structural reason. A healthy ecosystem needs exits, and large industrial companies such as Siemens, Rockwell, and Schneider are acquisitive and often grow through acquisition, which creates an M&A market. He also says hyperscalers like AWS, Azure, and GCP are building industrial focus, and he describes cloud as disrupting the global industrial enterprise in 2024. This natural market pull, he says, makes the movement feel real rather than a flash in the pan.
The open question: how industry buys software
Asked what they didn't cover, Gluck raises industrial go-to-market, the subject of a talk he recently gave. He says VCs often describe companies with good technology that couldn't figure out how to sell. How industrial companies evaluate and buy software, especially from early-stage companies, is in his view unresolved. Solving it may require a cultural shift. Startups may need to be capitalized to expect longer buying cycles, and the structure of purchasing from young companies may need rethinking. He calls it a question worth examining at a cultural level. The host agrees that this conversation will need another martini.
All right, here we go on the hotel WiFi. Sometimes you got to do it this way to get the recording out.
Yeah, you have a beautiful abstract art behind you, hotel room art behind you.
Yeah, anytime I... I've only recorded in hotels a handful of times, but I always try to get the artwork and less of the room to at least have, I don't know, something abstract and artsy behind me, but it's always just generic hotel art. But, you know, so here I am sitting at a hotel in Anaheim today. But let's say you and I are hanging out in New York at a bar having this conversation. Where would that be, Adam? Describe the place for us.
Well, if you came to New York, I'd make you drink a martini, just because that's what we drink in New York.
Deal.
And then I'd pick one of two places: in the West Village, Dante, which is near me, and they have a martini hour, so we can go drink there, or the hotel bar at the Chelsea Hotel, which is really nice.
Yeah, it's very nice.
So those would be my two spots. But I just would have to warn you that you'd have to have martinis. I'm okay with vodka or gin, but you'd have to lean into the martini lifestyle.
So unlike what a lot of people seem to think, that I'm just a beer person, I do love a cocktail as well. I tend to skew gin martini over vodka martini. But yeah, let's say we're hanging out there, Chelsea Hotel, having this conversation. How do you describe what Copia does as if you're having martinis with someone?
Yeah, so I would describe us in two different ways. One, we're bringing the concept of industrial DevOps to the OT space, so we're taking best practices from the software world and bringing it to the OT world. And then increasingly, you know, we do disaster recovery for global industrial enterprises, so we're kind of backing up, enabling people to recover from, you know, cyber attacks, ransomware, you know, actual disasters in the plant, and just outages as well. So I think both those framings are important, depending who you talk to in an organization. You know, some people really like the development side, and some people are like, we need good backups, we need version control as a fundamental part of our tech stack. So, you know, I'd kind of describe it in either of those ways, depending who I was talking to.
We might have both people listening. So I think most of our audience is familiar with DevOps, but because we are primarily an industrial manufacturing audience, this is a good describe-over-a-martini question: describe DevOps as if you're talking to someone over a drink who's never heard of it before.
Yeah, I always describe DevOps as, you know, and if people are reasonably technical, basically everything from when you're done coding to code getting into production. And so it's literally the operations of developing and writing code. And so that starts with very basic stuff like code review, automated testing, how do you deploy code into production, monitoring. All of that kind of falls under the umbrella of DevOps generally. And then our core product is hyperfocused on, you know, to begin with, was source control. So basically just common, basic source control practice of making it work, you know, in the industrial space for industrial code.
Well, I think that's appropriate background to really get us more into your background, because I have a very basic question to ask you: how did you go from being an iOS developer, a software engineer, to entering the industrial space? Because your background is relatively unique, I would say, for this space, but maybe becoming more of the norm.
Yeah, well, I'll say my background is even weirder, because I studied sociology in undergrad. Okay, so I studied sociology, and then I taught myself software engineering, and then I got my first job out of school at Uber as an iOS developer working on the driver application. And then I ended up on a team called engineering strategy over there, which is focused on Uber's overall backend system architecture. I worked on this huge project called Uber Architecture 2.0, and then I also, you know, was part of the office of the CTO, so the CTO was my skip level.
So how does all that relate to manufacturing? To me, two things. One is I enjoyed Uber because of the bits and atoms. So that was kind of the Uber thing for a while, is like you're moving cars in the real world. To me, actually, when I started at Uber I was living on the South Side of Chicago; you couldn't even get a taxi. It's the idea that suddenly you have an app and someone just shows up to your apartment, when it used to be you're calling a number and it takes skents. That transforms the way you get around that city. There's this great vision of Uber for everything, that's now Uber Eats, where it's like you can deliver food and you can order from CVS. So all this micromobility and getting all this stuff, you know, working in cities and just transforming cities through software that changed the real world, that's exciting to me.
It's also the name. So I mentioned I studied sociology. Copia is a Latin word for abundance. And so really there's this idea, you know, that I had, which actually came from my sociology professor, who was editor in chief of the American Journal of Sociology at the time, and he was just talking about this, maybe just was reading some papers, but like, materially abundant systems reduce violence, reduce strife, like they're just better to live in, and there's a lot of evidence of this. And so to me, when you look at the bits and atoms and the fundamentals of the economy, you say, like, well, what's more fundamental than industrial automation, right? What's more fundamental than, like, the literal systems that build everything?
We get to work with, like, incredibly cool companies. You know, I mean, like, we have a customer in Amazon, you know, but we work with companies that make all sorts of food, drinks, you know, whatever. You know, it's like all these companies are fundamental, you know what I mean? Like, we went to a potential customer, you know, and we saw that they had somewhere a data point that 85% of American households consume their products. So you look at that, that's fundamental.
And so the shift to manufacturing is really tied to that: kind of like, where can we get more fundamental in the economy, where is, like, the rubber meets the road, and then where did I have leverage to actually have impact? And I think that was this tool chain in the industrial space. So I kind of stumbled across, you know, kind of PLCs and robots and all this stuff, and I was like, wow, like, I know DevOps, I don't know this space super well, did this exist? And I started talking to people, and people were like, we don't know why it doesn't exist. Now, at the time, DevOps was only 11 years old, so it's like, you know, people knew about it, but it wasn't getting built out, and there was white space in the market. So, you know, we came in, we started building this solution.
I mean, you've started touching on this a little bit, I feel, but I'm going to directly ask this question: how has your non-manufacturing background helped you see our industry and the challenges and opportunities we face through a different lens than most people do?
Yeah, so I think, I mean, the big thing, I think it's just a really deep knowledge of DevOps to begin with, right? Because those are the systems I was kind of born and raised in, you know, as a software engineer. Now, again, these systems aren't that old, like these processes are not that old. Like, we have a timeline that we showed that like 2008, 2009 was around when DevOps became big. But, you know, when I joined Uber we were hiring all these people out of Google and Facebook and all these companies, and Netflix, and people were really thinking about how do you scale software systems. Like, scale was really the big word, and how do you build these processes. And so I was spending a lot of my time in the team I kind of formed at Uber, or was early on, which was called driver platform. And so we said, how do we scale four engineers to 200, all working on one application that's millions of lines of code, and how do you make sure it never breaks? Because if the driver app goes down, it's millions and millions of dollars per hour for Uber. And so all those systems I was really steeped in.
And then, you know, coming to the industrial space, I have that deep familiarity. And then beyond that, I think I get a benefit in a way from being an outsider, because I talked to so many people. I mean, I've probably talked to 2,000 companies, directly or indirectly, you know, over the course of Copia in the last four or five years. So I have very broad industry knowledge. And I think sometimes when you come from the industry you have really good knowledge of one company, but we have this very specific lens that we look at in terms of the set of operations and processes, and I know the problems across major global enterprises to, you know, two-person system integrators that they run into, and how these sets of products and problems fit there. And I don't think I could have gotten that if I was started, you know, day one, you know, with an assumption that the company I come from is how it is, you know what I mean? So I think coming in humble and just being able to listen had a big benefit.
Yeah, and I have two questions based on your background and some of the things that you've said. This one actually really came up at the start of the conversation: we're talking about how do we get more people into the manufacturing industry, interested in this field. I have a very early question from your career. What necessitated or motivated you to start learning iOS after that sociology degree? Was this, like, immediate? You're like, I got this degree, it's not bringing me down the paths I thought it would? Rather than me keep talking, I'd be very curious to hear your answer.
Yeah, no, I was just, honestly, I started teaching myself software engineering, like, freshman year of college. So it wasn't like... I was just really interested in building. Like, I love to build things. I just found myself building all the time once I learned how to do it. I didn't actually major in computer science, just because the program at UChicago at the time, and now it's evolved, was very theoretical. And so it was like you're doing mathematical proofs, and I would just build an app, like, every quarter. And then I did, like, an app dev shop with a friend one summer. I, like, made my own internship. I was working on different business ideas. I was very interested in entrepreneurship, and then, rather than going and finding a technical co-founder, I was like, I can just be that technical person and just ship things. So that's kind of how I got into it: I just wanted to build things. And then when I graduated, I was like, well, you know, I could go get a PhD in sociology, but otherwise I just have this actually useful skill set now. So I was like, I'll go, you know, be a software engineer. So that's kind of how that came together.
I mean, if you were getting out into the job force in, like, the early 2010s, that would have been a pretty sweet time to start putting that software experience to use.
No doubt, yeah. Yeah, it's a little bit harder now, I hear, for college grads. There was a huge demand for software engineers, and the gap hadn't been filled entirely at that time, so you could kind of get in there with a slightly more informal background. Although still, I mean, we've hired people who have done dev boot camps and that sort of thing as well, and sometimes those are your strongest engineers, because so much of engineering is continuing to teach yourself and uplevel your own skills. And so some people who really come from, like, a program, they don't know how to do that, whereas some people who've really taught themselves, they're not as afraid to jump into new tools or new frameworks, or they keep kind of improving their skills. So I think it's an interesting background.
Yeah, that is an interesting point, right? If you're self-taught, you're less concerned about breaking things, trying new things, etc. Well, you brought up an interesting point there about, you know, filling that talent gap a little bit, right? We've started to fill that in the software world. You mentioned it's a little harder to find that type of job now. But I'm going to quote your LinkedIn profile real quickly with the next question I'm going to ask you, which is: how do you find and hire people smarter than yourself to solve hard problems? You mentioned that that's front and center in your description of what you do at Copia, so tell me how you do that.
Yeah, I mean, I think anytime you're building an organization, finding and incubating talent is kind of core to what you're doing. You know, like, ultimately you can try and solve things with processes and organization, but you're going to actually solve problems with really smart people. So I had this great... someone, when I was talking to a CTO, he was like, I was running into all these technical issues for, like, a year on my engineering team. Like, how'd you solve them? He's like, well, hire better engineers. And so, like, you know, the quality of the people that you have maps to the set of challenges and problems that you can solve as an organization, and then you have to hire those people.
I think we have a benefit of, like, the problem we're working on is a real problem. And I think so much of, like, software, you know, engineering, you can end up in a company where you're kind of doing, like, a piece of a piece of a thing, you know, or, like, you know, maybe the company makes a lot of money, but, like, you know, it's just not doing something that's really motivating or gets you out of bed in the morning. Whereas, like, we're building a product for engineers who are building some of the most fundamental systems that drive our economy globally. And so I think a lot of software engineers kind of crave that kind of problem area. And so putting the problem in front of people and being like, go chase and solve this, you know, that's really exciting.
And then if you look at, like, our solution, I mean, it's kind of insane in terms of, like, we're cloud hosted, we also have deploy-on-prem enterprise products, we're SOC 2 Type 2 compliant, we're, like, highest level of security, you know, compliance internally. We deploy in industrial networks. We integrate with, like, I think 65 different devices and platforms now. So you're, like, digging into manufacturing processes. We're working with world-class companies. So I think all those things are really compelling to people.
And then finally, it's just building a really good culture. You know, I mean, for me it's like, how do you work with engineers well? Like, you know, making sure they're engaged stakeholders in the business, you don't drop deadlines on them out of the blue. Like, just standard stuff that can help make engineers' lives better. And then, again, just kind of pointing them at hard problems and then giving them the agency and time to kind of own and solve those problems.
There's certainly more about talent and building your team that I could ask you about, and probably will ask you about here a little bit later. I do want to use this as a little segue into talking a bit more about Copia first, though. How has Copia evolved since its founding back in July 2020, I believe? Like, we're going to be talking about Copilot a little later. Were things like that always part of the vision? Or just tell me how things have evolved since July 2020.
Yeah, I think, you know, there's a few different things I've looked at. Now, to some extent, a lot of the vision roadmap that I came up with when I started Copia we've just executed on, which is kind of surprising. So, like, we knew we were going to start with source control, then eventually move to automated testing, then deployment, then, you know, ideally integrating with monitoring tools to build a whole DevOps life cycle there. All of those sorts of things in that stack have been feature requests for us. Source control is, you know, bread and butter. I think the one thing that kind of shifted a little bit... I'd say there's a few things that have shifted. One is just market understanding. I mean, the market is incredibly complex, so understanding, like, why would an industrial enterprise
adopt a source control product and who in that would adopt the product, or looking at different personas within system integrators or machine manufacturers, they all have these different use cases. So trying to be like, how does this solve different... Look, it's the same product, but it solves different problems for each type of organization, and then how do you actually help those organizations get adoption and activate on those products?
The other thing that's been a big shift for us is we built source control, but then when we got on the market, people really wanted backup and disaster recovery. So that was a really big want from the market, and basically that came from a few things. One, increasingly there's material requirements for backup and disaster recovery that come from audits and security requirements. We just see that all the time: CISOs take over the plant floor, they need backups, and now there's some regulatory stuff as well in Europe that's pushing that. And then it's just like backups are par for the course, I think, in a standard troubleshooting workflow. People just load up a backup; it's a very common way of resolving a problem. So we just see that as a need.
But because of the device link, backup and disaster recovery, we kind of uncovered different parts of the organization. So source control tends to be really on the corporate side, or people who are doing development. With the device link, we kind of got pulled into people who are managing processes at plants. They were curious about source control a little bit and want to have a code review process, yes, but they're also like, we just need to plug this in and have visibility to changes. And then we got a level deeper now, and it's really the plant-level persona that's running those plants that kind of integrates and works with that product, and solving problems for them is a little bit different than the initial set of people we were talking to, which tended to be a little bit more on the corporate side and a little bit more on the process management side. So just basically market depth as we come a lot deeper.
Yeah, tell me if I'm summarizing this correctly, right? You really started with source control, and as you had more conversations, you started realizing disaster recovery was a priority you needed to move into. And as you were having these conversations, where I believe you were just saying you were talking primarily at the corporate level, you started realizing, hey, it's a plant-level persona that has a lot of influence over this. Am I following that train of evolution correctly?
Yep, exactly, exactly. It's just interesting because the process-level people, the corporate people, tend to be the buyers, but the plant-level people are often part of the decision, you know what I mean? And they're the ones you don't get to talk to as much because they're very busy, because they're troubleshooting and maintaining production all day, you know what I mean? So trying to own and build a product that works really well for those folks, I think, is a big challenge for any industrial software that you build.
Well, let's put this in the context of an example of a story, because when I was reading about Copia and learning more about you over time, particularly learning more about Copia Copilot, I'm curious how you solve the challenge of... and this is me as someone that worked in the automation world for a long time, right? There are controls engineers that write code that works, but the code is not terribly well documented, and quite frankly, the way it's written isn't necessarily following industry best practices. That was the scenario in my mind where, when I was thinking of Copia and Copia Copilot, I'm like, wow, that sounds like the solution to get past some of those issues. Tell me if I'm on the right track, or tell me if there's a better scenario to describe what you're doing.
Yeah, absolutely. So I mean, this gets to how the vision evolved. I didn't even mention AI, which is kind of the point of this call. The AI stuff's interesting. So like DevOps, AI for the software space has transformed the way software gets written. People say it's like a 30 to 40% productivity improvement, and some are like, we built a whole company in a week, you know? So that actually changes, and even some people say it just changes the way software gets written, where shipping velocity is just so critical because you can pump out so much code so quickly.
And so inside Copia, there was a lot of AI skepticism, and then I'll get into your question. AI skepticism to begin with. So I was like the founder coming in, like, guys, we need to build an AI product, and everyone's like, dude, we're talking to customers every day, no one's saying they want an AI product, you know what I mean? They're like, we hear a little bit of that, it's come up from time to time, but it doesn't seem like this is the problem people are solving. And I said, well, hey, look, it's a new technology, it's been very critical in the software space, it could be equally transformative in this space, but we just don't know yet. So let's build a product here.
My kind of agreement with the team when we kicked it off was, I'm not going to build something that sucks. So if people aren't using it, if it doesn't have real value, we'll kill the project, right? That was kind of the takeaway: if we can't see people are using this all the time. And so the pain points that you brought up are some of the big pains. But I said, let's build it, we'll just see. We'll get it in people's hands. Our culture is very customer-centric, very product-centric. We'll work with some early alpha users, we'll get it into people's hands, and we'll see if it actually solves a problem for them.
And the problem that you brought up was a really big one. Just code documentation has been a huge one, particularly for those plant-level personas. One interesting point is a lot of people who maintain controls code at a plant don't actually write ladder logic, where it's like they've learned it kind of on the job. And so a natural language description of your code is actually really valuable. So that documentation feature has become really big. We've seen people jump into the product and just document things. Similarly, on the corporate side, if you have reusable code, centralized libraries, that sort of thing, often they're underdocumented and you have to consume some code. That's been a really powerful use case for us.
The other use case we see is development. So similar to software, if you're writing code all day and you're using an AOI and you have to use it in a bunch of different places, or you have highly repetitive logic that you're doing, just like single to generate a baseline here of code. And we decided we were going to make everything visual. So ChatGPT or whatever is all text-based; we're like, we can render the ladder in the browser, so we're going to make it visual. So you could actually then render ladder logic in a browser and then start using it from there.
And then beyond that, we just got really into the customer pain points. Nick, who's on it, was actually one of our first users of Copia back four years ago, who was the product manager working on this, and he just got really in the weeds on how can we solve these problems of code generation, documentation, code analysis, and then build a really sweet, very ergonomic workflow where people are not annoyed using this tool. And then we were tracking people using it all the time, and we were seeing really good engagement from our customers, which is why we decided to move it towards more of an open beta.
Okay, well, with everything you're saying, one of the big questions I have is, what does it mean then, maybe to summarize, when you say you want to be a leader in AI for industrial code? You've been sharing a lot of, I think, the story around it. I'd love to hear you summarize, because if I did my research right, that is your current vision for Copia.
Yeah, well, we have the vision of industrial DevOps. The AI vision is to be a leader in that category, and again, it kind of comes back to what I said: it's transformed the way software has been written, so we can bring some amount of the same transformation into the industrial space with AI around code and the software that drives actual production environments. It's a huge opportunity to have impact. And so I think that's there, and then we just want to build an amazing product. As I said, hyper-focus on the customers. I want to build something people can use every single day as part of their standard workflow and that solves material problems for them.
I think as we've dug into this, we're discovering this. One of the goals with the copilot, and the reason we did it as a chatbot to begin with, is we just want to see how people are using the tool, and then we can also start to integrate more of that into our product. So there's a lot of opportunity when we see, oh, everyone just wants this, okay, we'll just add it to our product by default, and that sort of thing as well. But really, we just want to have a really strong solution in this space.
So similar to source control, it will be multivendor. We started with just Rockwell to begin with, simply because we really want to get the workflow right. I mean, a lot of our early customers to test it were Rockwell customers, so we were like, we'll just keep it simple. But multivendor, solves meaningful problems, really ergonomic for controls engineers and people who work in these environments. And we had a great data point today: we demoed the product for a team and they're like, this is the best industrial copilot we've come across, this is amazing. So we're starting to see that, and I think we can build a really strong tool for this environment that's transformative, ideally, over time to the workflow of controls engineers.
One other question I want to ask you around Copia, particularly around your use of leveraging AI: you made the comment earlier that there is some AI skepticism within Copia, right? Your team was saying, hey, customers aren't asking for it, but it seems like that was always part of your vision in some way, shape, or form. So what are your thoughts around AI skepticism, getting people bought into it, leveraging it in a solution within a company, et cetera? Because I think whether I'm a manufacturing leader out here listening to this because I need DevOps for industrial code right now, or I'm just trying to figure out how to leverage AI in my business, I think your answer could be pretty helpful to them.
Yeah, I mean, so my two cents is this: there are moments of technology unlock that happen, and when those technology unlocks happen, you get this kind of moment where a bunch of new innovative thoughts come out, and sometimes those can solve problems better than previous technology that existed, right? Or they can solve problems that people didn't think were solvable. An example being the code documentation. There's no solution for autogenerating code documentation until you have large language models to do it for you, you know? And so I think it's really important as an organization to recognize those moments of technology unlock and then try and figure out what problems they can meaningfully solve. But I don't think you're going to build a good product of any sort without really solving some real pain. They talk about vitamin versus painkiller: some real pain that an organization is experiencing. I think you mentioned code documentation; that's just a big one that's come up for us, you know what I mean? You have to really solve that.
And then getting buy-in. So I think for any organization that's looking to AI, whether it's for your operations, and there's tons of different solutions that we've heard about in terms of how people are ingesting this data, any organization should be like, what's the real problem, and can this new technology solve it better than stuff that existed? And I think that's kind of how we got through the AI skepticism as an organization. We said we're just going to really focus on problems. We're going to explore this new technology and see if there's a domain of problems that we can now solve that weren't solvable before. And I think it kind of comes down to the documentation stuff and code generation for really redundant functionality, which looks a lot like the software engineering benefit as well.
So we're seeing that, but then also that's again why we're continuing to work really closely with our customers, because sometimes people see a tool and they're like, oh, that'll solve this thing for me. So it's just talking to people and hearing that. And so as we got through that process, we realized we could build a solution that actually was solving a lot of problems, and as we're showing it to people, they're getting really excited, because it's like, wow, that really solves this thing I didn't even think we could solve. So that's one of those things there.
Yeah, yeah. Problem-first mindset, and as new technology comes up, it can be a solution to the problems that have existed for a long time. I love the way you're looking at it at Copia. Let's go beyond Copia and into your home city of New York City a little bit, because this is something you and I riff about, the state of the industry, every once in a while, right? But one of the first things I want to bring up is I really haven't talked to anyone that's running their business out of New York lately, just because I feel like there's a lot in the Midwest, there's a lot out in Silicon Valley, and you've done a lot of work in Chicago and then in San Francisco. So why pick New York as the spot to lead Copia?
Yeah, well, I like New York.
That's a good first part of the answer, right? I'm glad part of this is lifestyle related.
Yeah, yeah, yeah. I was like, where do I want to live? So I was living in San Francisco, and part of this really was, okay, I'm going to go start a company, I can make a decision where it's going to be and where I'm going to live for a long time. And New York was a great place to be, and I had a lot of friends who kind of migrated here. I think there was also, in 2020, a huge kind of alorn of people from San Francisco to New York, and part of that was San Francisco really shut down; the population actually declined during that time. And so a lot of people who had been in tech for six, seven years or whatever kind of reached some stage of maturity there and were like, okay, I've made my Silicon Valley network, and now I'm going to go somewhere else. And so New York was that place.
If you look at the underlying data, New York has a huge amount of seed rounds. It's probably number two to San Francisco in terms of funding amount, and there's a lot of money out here, obviously, and different types of money from traditional VC that's in Silicon Valley that invests in very particular sorts of things. So for example, Lux, that led our Series A, they're three blocks away from me. I mean, maybe not three blocks, five blocks or whatever, and they do only hard tech; they're one of the really early hard tech funds. Construct Capital, which did our Series C too, they're in DC. And so we see this kind of East Coast, and then historically Boston is another really big location for venture on the East Coast. I think the East Coast is a nice place to do some of the hard tech stuff, for sure, the deeper industrial stuff.
Yeah, tell us a bit more about the tech scene in New York then, right? I think your comments about the second-most seed round companies based there, only second to San Francisco, you've got VC presence out there. Maybe share a bit more about the tech scene with a bit of a hard tech spin to it as well, because I know that's primarily where our audience is hanging out, in the hard tech manufacturing space.
Yeah, and I
I think if you look at the kind of tech scene in New York, the idea that New York is like a proper tech market, even in the early 2010s when I started getting into tech, was considered a little bit of a... like, people thought of it as like you do media, new media companies, or like fintech, you know, and you have some prop trading firms that hire really good software engineers and pay them an insane salary. So if you want to sell your soul, you go work for one of them, but you know, if you're a Silicon Valley person, you don't do that. You're changing the world, you're building technology, you know what I mean? But if you want to build algorithmic trading, you know, that's another option.
I think that's really evolved over the last decade, you know, with some big exits in the area, you know, like MongoDB and I think Datadog out here, you know, where you're seeing kind of some of those harder DevOps infrastructure plays being in New York. You know, another more recent company that's doing really well in New York is Ramp. So you see some of that sort of building of these sorts of companies.
You have a different kind of base of investors who have different ways that they look at returns and structure their portfolios and that sort of thing. And then as a function of that, you definitely see some influx of hard tech. So for example, I have a friend who works out of our office who's building a tool chain for validation of FPGAs, you know what I mean, for chip programming. And so you see some of these people who have this kind of maybe more eclectic set of interests and they're coming out to New York.
Silicon Valley historically is more tied to verticalized SaaS, tied to deeper infrastructure plays when you're looking at DevOps scaling and that sort of thing, because they have the hyperscalers there. Seattle's like that as well. But I think New York has a good opportunity to create a space for those people. And then obviously you get kind of maker communities and all that as well, and then kind of an influx of people with Boston being so adjacent. So you get, I think, a more interesting group of people that that enables outside.
And so I think there's that. New York's also quieter than a lot of markets. So you know, another really big market is El Segundo, which has come up because that's where Anduril and all that stuff is, and that's become a really big market in California where a lot of these hard tech companies sit, and they're a little bit louder about it, which is fine. But I think New York has a lot of similar people, and even people who are in that scene split time with New York or the East Coast a little bit as well. Being close to DC is helpful if you're doing defense stuff. So I think New York's just a little bit quieter, but I meet a lot of very high-quality hard tech founders all the time within New York. And I think part of it's just the draw of the lifestyle and that sort of thing as well. But I think it's still a really good place to build a business and there's a lot of great talent here.
Yeah, and for the listeners out there, El Segundo is basically LAX, right? It's in Los Angeles, it's right next to LAX, the airport out there.
You know, I am curious, because you went to the Reindustrialize conference this year, you're a bit closer to, let's say, the VC-backed companies' side of the world than I am. And you mentioned that El Segundo is making a lot of noise, right? There are a lot of younger companies out there, and I mean recently founded companies, right? They have founders of all ages out there, but they're making a lot of noise about how they're reindustrializing, changing the way manufacturing can and will be done. I'm curious about your impression on that, because as someone that spends a lot of my time in the automation world going to a lot of these different manufacturing events, I would love to see a bit more of them around there as well, because I think there are a lot of people in my world that are looking to do cool, fulfilling things. I think a lot of these companies are creating the next generation of industrial technology and solutions, and it's like, gosh, I wish there were a bit more overlap between these communities. So I've been sharing my thoughts there for the last minute. What do you think, Adam?
No, you and I jammed about this recently, right? So I totally agree with you. I think it's like, how do you get those... and that's what we were talking about. We decided to do a manufacturing happy hour in New York, you know, or something like that, and we get a good mix, or we find somewhere to do something where we can kind of mix the, you know, VC kind of tech scene spin.
I mean, I think the big thing is even four years ago when I started Copia, there was almost no one looking at the industrial space in VCs. So it's very young. And then there's kind of some shifts that happened. So like Construct, which led our seed, came into existence. Dana kind of spun out of a fund with Rachel and they built out this good fund for early stage. Lux had been around for a long time. There's a few funds doing early stage. You get a16z did American Dynamism, you get Founders Fund. So you get a set of VC firms, and General Catalyst has done a ton of work there. So now you're starting to get a set of VC firms that are investing really heavily in this space.
And then you have basically money seeking return, you know. And so you get some people who have wanted to build in the space, or now they have the opportunity to go raise from those people, so they go and do that, and they're building out technology. Some of those are hard tech founders who are just hardcore engineers, like, I'm going to build this stuff, you know what I mean. Some of that's just like, we're building hardware and we're going to build our own manufacturing, you know what I mean, and those are cool companies, you know. And then some of them are like us, where we're trying to build a solution for the horizontal industrial space in the tech stack, and then we have to go really deep and talk to manufacturers and really understand what's happening.
But I think you're totally right. It's like the space is really young. These companies are all, you know... I mean, we're like one of the older companies who've been doing this, and we're like four or five years old, right? So some of these companies are two years old, you know, they're very recent, two or three years old. I think we'll see more kind of cross-pollination there over time. It's going to be really interesting. I think Reindustrialize was a stab in that direction, you know what I mean. It was more on the defense side a little bit in terms of the people there, a little bit more on the VC-backed side, you know, but they were trying to meet some people. And you did get some people who are from the manufacturing space there as well, you know, representing trade groups or whatever. So I think we're starting to see it come together a little bit, but I think there could be a lot more of it, and I think it's going to be really beneficial.
I think it'll also change a little bit the tech scene's stance towards industrial companies, because there's a lot of really good industrial technology out there. Like this trade show, this event, is called Reindustrialize. It was in Detroit, but there's also already a ton of industrialization in Detroit, right? There's a lot of really good companies out there. So I think it's like, you know, how does even the language of disruptiveness and that sort of thing, which is common to Silicon Valley, align with industries that are 200 years old, companies that are 150 years old that maintain really critical... So move fast and break things is not as compelling in the industrial space.
So it'll be interesting to see how Silicon Valley, which can bring a lot of money and talent and innovation to the front, can kind of integrate and work with manufacturing companies and understand their material requirements and how to sell to them. And they're buying something they view as infrastructure, whereas Silicon Valley companies might think they're selling software, you just change it next week or whatever. And you know, how do you align those kinds of understandings? Even the buying process of industrial companies, how can they evolve so they can buy software, you know what I mean, when they're used to buying very expensive hardware? So I think all of that's like two cultures that will need to come together for it to work. And part of that's just getting people, probably over beers at a manufacturing happy hour, just chatting and getting to know each other and starting to understand their mental frames a little bit more.
Sounds like we need to grab some beers and throw some parties, you and I, in El Segundo and New York City sometime in the near future. Do you think, you know, I have my opinion on this, I'll share it in a second, but do you think this enthusiasm that venture capital and these younger companies have around industrialization and manufacturing, do you think that's here to stay for a while, or do you think it's a passing trend?
We'll see. I mean, there's people who have cut their teeth on this. Like, you know, I mentioned Dana, you know, Bal from Construct and Lux, Tai from Iron Spring. We have another fund investor, Renegade, R not over there. You know, all those funds have taken a little bit of a hard tech piece, and they assume a 7 to 12 year return on a fund, you know what I mean? So they're deploying capital into that space. When you start a company, you assume it's a 5 to 10 year commitment, really it's worth thinking about as a 10-year commitment, and now standard times to IPO or to acquire are 7 to 12 years. So you know, you're going to have to stay the course for a while.
I think it's interesting to me because, you know, there's a lot of hype cycles in tech, and industrials, I'd say, is almost a hype cycle, but it's kind of sat in the background in an interesting way to me, where it's like people think it's cool and it's important. VCs, when I talk to them, are still trying to find companies that fit profiles that they're comfortable with. So capital hasn't met equity quite yet in a way that makes sense for them. But it hasn't quite hit the inflection of hyper-valuations of AI and that sort of thing. So it will be interesting to see, does it stick? I think we'll see some stickiness, because I think there's secular trends that really are demanding this, especially with the way the defense sector is evolving. And so I think it's something that feels like it's plodding along in a nice way, as opposed to being in this like, oh, it's so cool now, and now it's down.
And then I think a lot of the people who are doing it, like the El Segundo people, the people in New York, the funds I talked about, they really care about these problems as well. They're really serious about it, you know what I mean? And so in my mind that's exciting as well. They're very motivated. And I think people care about these problems and think that they're important, and people are willing to take punches to figure out a market that traditionally Silicon Valley folks didn't sell to, you know. And you have to go figure it out and it's hard, you know what I mean, but people are motivated to go figure it out.
I think it's interesting, because you were chatting earlier, rightfully so, that you're looking at problems first. And that's when you look at artificial intelligence and DevOps for code development, and that's where you build out, that's where you apply tech, right? You look at the problem first, then you bring in the technology. And I think there are actually two reasons in my mind that this isn't a passing trend. One is the thing you just said, that it hasn't really hit a hype cycle yet. It might seem like a bit of a hype cycle to you or I because we look at this industry all the time, but I don't think most other people are out there hearing about all these companies talking about hard tech, defense, etc., when I think a lot of the world still lives in a SaaS mindset, including a lot of VCs. And you named a handful of them, which I'll include in the show notes, that are doing more hard tech-centric defense investments in this day and age. So that's one of the reasons that I'm optimistic this is not just a passing trend.
The other is we're just going to keep uncovering more and more problems in the manufacturing space that need to be solved. You know, manufacturing has that multiplier effect. So as we continue to use that term reindustrialize, there will be more opportunities for companies to solve more problems, which are not, in my mind, first world problems, right? I think as SaaS and iOS development got really big, people started building companies that weren't necessarily solving problems for the masses, right? Once you had your Ubers out there, companies where it's like, yeah, it is really convenient to get a car at the press of a button, you started getting into some things where you could just, like, I don't know, put your phone next to someone else's phone, knock the table, and exchange contact information.
You and I both lived in San Francisco, and I think probably left San Francisco around the same time, because I was there 2015 to 2020. Yeah, we were there the exact same time. Yeah, I was going to say, I moved to Milwaukee after that, so you and I were both part of the pandemic departure there. But I just see more of an opportunity to solve real problems in the manufacturing space, and if that's the case and VCs start figuring out, let's say, a system around what returns look like with hard tech companies, I think we're going to be reindustrializing for quite some time.
Yeah, and there is a nice thing too. I mean, if you look at a healthy ecosystem, you have these massive industrial companies that are acquisitive, right? They do buy companies, so you have an M&A market, you know what I mean, and they're very acquisitive at some level, you know, and they're used to buying technology. So the Siemens, the Rockwells, Schneiders, all these companies often grow structurally through acquisition. So you have that kind of exit market that can make this work, as well as the hyperscalers like AWS, Azure, GCP. They're all trying to move into these markets now as well and have an industrials focus, because cloud is disrupting the global industrial enterprise in 2024, right? So all of this stuff is kind of moving in that direction. So I think there's a very natural kind of market push into this space, as you brought up, and I think it makes this feel more like a real thing as opposed to a flash in the pan, you know.
Yeah, well, we definitely need to continue this conversation over beers at some point. I do have one final question for you, and that is: throughout our conversation we covered your background, talked about Copia, talked about tech scenes and the evolution of and interest in industrialization. Is there anything you wish I would have asked you that we didn't cover today?
Oh wow, we covered a bunch of great stuff. You know, I think you hit on a lot of awesome stuff. It was a fun conversation. I'm trying to think about if there's anything. No, I think that mostly hits on everything that I'd love to jam about, and we can keep chatting. And I think, you know, the other thing that I recently gave a talk on that's kind of interesting is just industrial go-to-markets. That's another thing that a lot of industrial companies are going to have to figure out, like how to actually sell to the industrial space. And I think that's going to continue to be a conversation as well, which is like, how do industrial companies evaluate and buy software, you know? As you see these people innovating, how do they actually evaluate new technology? Because I think the lift there... and you often hear this from VCs: this is really good tech, but the company couldn't figure out the go-to-market, or that sort of thing. So go-to-market, how to actually sell to industrial companies, and how they evaluate solutions in a way that makes sense to enable an ecosystem, I think will be really
Interesting, you know, as well. I think that'll require a little bit of a culture shift over time, and maybe it's how businesses get capitalized to expect a longer buying cycle or something like that. But the actual structure of how do you actually buy from early-stage companies, I think, is another kind of interesting question that is worth digging into and thinking about at a cultural level.
I was going to say, I think that also calls for another martini for continuing that conversation. That one I need a martini or two to have a conversation on.
So, well, I appreciate everything you've brought into play today. For the folks out there listening, if you want to connect with Adam, if you want to connect with Copia, all those links are over in the show notes page. Congrats on Copia Copilot again, brand spanking new. Adam, it's been great having you on the show.
Yep, great chatting. Always a good conversation. I always want to keep it going, so it was super fun jamming. Cheers.
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