In 2020, David Meyer, alongside his co-founder, Guglielmo Montone, laid the foundation for Lynceus.ai with a vision to revolutionize high-value manufacturing through the creation of an advanced, AI-based operating system. This ambition wasn't solely focused on technological innovation; it aimed to redefine the manufacturing landscape by leveraging artificial intelligence to address some of the industry's most complex challenges.
David Meyer's role includes piecing together a team of exceptional talent and securing the essential resources needed to bring the Lynceus vision to fruition. In this conversation with David, we've touched upon his career path, challenges in managing and developing Lynceus, and his sources of inspiration.
Could you briefly share your journey into the manufacturing automation sector and the origins of your interest?
My journey into manufacturing began in the realm of consulting, where I supported private equity funds in evaluating manufacturing assets as potential investments. Despite gaining valuable insights, I found the work overly theoretical and soon pivoted towards a more hands-on role in Operations. My operational career kicked off at Uber, where I oversaw the onboarding of drivers across France, Switzerland, and Austria.
Subsequently, I ventured into the e-scooter industry, launching operations in France from scratch. After the company was successfully acquired by a competitor, the timing felt perfect to explore entrepreneurship.
Joining Entrepreneur First led me to my co-founder, Guglielmo, who introduced me to the world of semiconductor manufacturing. Together, we founded Lynceus, developing AI-driven software designed to boost the productivity of high-value manufacturing operations through real-time process optimization.
How have your roles at Uber and your e-scooter startup Circ prepared you for leading Lynceus?
My tenure at Uber coincided with its transition from a startup to a large corporation, yet the ethos of rapid innovation and agility reminiscent of its startup days persisted. This environment necessitated building systems from the ground up, swiftly iterating processes, and assembling teams capable of keeping pace with the company's dynamic growth. Such an experience honed my skills in rapid development and adaptation, essential for nurturing a startup like Lynceus.
At Circ, I was part of the early foundational team, stepping in before the Series A funding round and among the first ten employees. As Circ's inaugural employee in France, I spearheaded the company's launch in the country, scaling the operations from just myself to a robust team of more than 200 people spread across six cities and managing nine warehouses. This journey, starting from zero and scaling up in a highly scrappy and entrepreneurial environment, equipped me with invaluable insights into managing growth, team building, and operational scalability-skills that are directly transferable to my work at Lynceus, guiding its growth and navigating the complexities of integrating AI into manufacturing.
You have raised $10M to date in such a conservative industry, where the market is divided among long-standing companies. What helped you accomplish this?
These days, VCs are more cautious compared to the 2021 investment spree, which makes it harder to raise money. But once you have a clear application of AI within a strategic vertical with a clearly defined ROI and you understand why your customers buy it, it's going to play out well for you.
The second step is about being clear that this is not a type of company that would fit any investor. We asked investors to explain to us why they're excited about what we're doing and what they can actually bring to the table.
We want to avoid bringing in someone who doesn't understand what we do, the nature of the sales cycle, and the customers we work with.
How do you hire those people and make them work together?
This is also something challenging. The main learning for myself is not to get overly fixated on CVs. We tried hiring senior profiles in semiconductor companies, and it did not always work out: they were not startup guys. It didn't work out because they were not startup guys and were used to working in a different environment.
When you have someone with a great pedigree and CV who is happy to join your company, you see that as a sign of success. But in some cases, it blinded some of the more basic aspects of this person. Do you understand each other well? Do you think in the same way? And so I think we could have saved a lot of time and money by learning this earlier.
How do you estimate the competition in your niche? What is unique about your company?
We've been doing this for a longer time than most teams in the industry. Being independent and not having any clear association with a player on the market is important because we need to get privileged access to data. For now, we have never had any trouble accessing this data, and we want to keep it that way.
We are also seeing emerging competition from internal data science teams, which can be competitors or partners, depending on how we engage with them. Then there are other competitors building the machines, which end up being deployed-either machines that help produce semiconductors or machines that are used for measuring the chips themselves. Those are large, billion-dollar companies.
What were some of the biggest challenges you faced building Lynceus, and how have you overcome them?
I had to ramp up very fast in order to be credible. We would add 100 people a day on LinkedIn, try to speak to as many people as possible, and try to understand what they do, what their problems are, what kind of solution they would like, and what they have already tried.
Another one is that you also need to filter a little bit from all of the feedback that you get between what's extremely relevant, relevant for later, and just negative. Many people told us it was impossible to do this as well. So it's an interesting mix of being stubborn and open, which I think needs to happen.
We are working within a very specific industry, so we need a very specific mix of skills, from talented software engineers working on cutting-edge ways of deploying and maintaining software in factories where there's no internet and where the data is extremely valuable and sensitive to very good AI data scientists who also have an understanding of physics, to salespeople who come from the industry and have an existing base of contacts but at the same time are ready to join a French startup to make it work.
What do you enjoy the most when working in this complex industry?
The first thing is the fact of having a real-life impact. We are not building a tool to optimize recommendations on the feed simply. It's something that can solve a problem for people working on some of the most complex things that humankind ever needed, a thousand times more complex than a rocket, for instance. It feels good to be part of this because I'm not an engineer at heart, but it's an interesting industry to contribute to.
The second thing is that since there are major challenges to producing those chips, and the demand keeps increasing, coming up with a novel approach can quickly give you access to very senior people in this industry. So here you have an interesting mix of stakeholders from the person on the factory floor who needs to use your tool, and you need to make sure that you're designing something that's compelling to CEOs or VPs, who have a different perspective on what the tool should do and the impact that it should have. So you need to switch brains multiple times per day, which is interesting to me as well.
It's hard to predict the future, but I think we can have an impact not only on the chip industry. What we do is relevant for other massive industries as well. The sense of history is that the chip industry is the most sophisticated manufacturing vertical today, but a lot of other manufacturing verticals are following the same path.
At 32, you are the youngest CEO in the semiconductor industry. Does it help you to see things from a different perspective and, as a result, to disrupt the industry?
The good thing about being less experienced is that you have fewer preconceived ideas on how things should be. At Lynceus, we look at things pragmatically: what is the challenge, and how can we solve it? On the other hand, semiconductor manufacturing has been extremely successful at optimizing through iterations in clearly defined structures. In a way, we are also disrupting the established ways of working in the industry, bringing together previously siloed teams (such as IT and manufacturing) to deliver value.
Being younger also helped me build stronger relationships with industry veterans and decision-makers. I am fully aware of what I don't know and ask for advice every day. In turn, this helped us build connections faster and gain access to senior leaders at our customers.
