Taver Capital's Mikhail Taver on a deep dive into robotics and AI in the industrial sector

An exciting conversation on artificial inteligence, how regulations impact technological progress and many more aspects of the industrial sector. Don't miss Mikhail's show notes below ⏬

Jul 16, 2024

Transcript

In this episode of the EUVC podcast, Andreas discusses with - Mikhail Taver, Founder and Managing Partner of Taver Capital II, with a target fund size of $200M.

Taver Capital is a fund based on Delaware, US, focused on early-stage global AI startups from the industrial sector. They have already invested in companies like Earth AI, MSQRD (acquired by Facebook), ID R&D (acquired by Mitek), and Sayana (acquired by Headspace)

In this conversation, Andreas discusses with Mikhail his experience in robotics and AI, the potential it has in various industries, the challenges of communication between startups and corporations, and the impact of regulation on technological progress. They also touch on the topic of mentors and the need for founders to find their own path.

Takeaways

AI has the potential to revolutionize various industries, such as manufacturing, mining, and energy, by optimizing processes, reducing costs, and improving efficiency.
Effective communication between startups and corporations is crucial for successful partnerships and collaborations. Startups need to understand the scale and scope of corporations, while corporations need to be aware of the challenges faced by startups.
Regulation can either enable or hinder technological progress. It is important for regulators to strike a balance between ensuring safety and fostering innovation.
Education and networking are essential for emerging VCs. Building a strong network and leveraging existing connections can greatly facilitate the fundraising process.
The founder experience does not automatically translate into being a successful investor. Investment experience and a deep understanding of the industry are equally important.
It is important for founders to find their own path and not simply replicate the actions of others. Each startup journey is unique, and founders should trust their instincts and make decisions based on their own circumstances.

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Chapters:

00:12 Taver Capital II: Focus and Investments
03:34 Passion for Robotics and AI
03:53 Mikhail's Journey into Robotics and AI
06:36 AI in Industry: Early Examples
08:11 Earth AI: A Success Story
08:47 Predictive Maintenance and More
09:28 VC Metrics of Success
11:07 Challenges in Industry Disruption
12:09 Communication Gaps Between Startups and Corporations
15:07 GP-LP Dynamics in Venture Capital
18:24 Communication Gaps Between Startups and VCs
21:19 Working with Generalist VCs
23:04 Regulation and Technological Progress
31:45 Advice for Emerging VCs and Founders

✍️ Show notes

Ai in the industrial sector.

Earth AI, my portfolio company: A predictive explorer and driller for clean energy metals, identifying mineral prospects over 100 times faster and more cost-effectively than traditional methods.
Yandex Data Factory and Magnitogorsk Iron and Steel Works: Back in 2017, they had a joint project using machine learning algorithms and big data analysis to optimise ferroalloy consumption during steel production at the oxygen converter plant, cutting production costs.
Predictive/prescriptive maintenance: Becoming increasingly common, it helps save time and money and is especially important for non-interruptive processes like steel manufacturing, where restarting a furnace is extremely costly as well as lengthy.
This sector is one of the few remaining that lag behind those that have already been disrupted by AI. It is less understood by traditional VCs, partly due to the fact that Heavy Industries have historically been more of a PE play and PE people play it safe.

Communication lacunas between startups and corporations

Startups often lack understanding of corporate needs and production volumes, especially with hardware. Example: A corporation needed 10,000 drones produced by year-end, but a startup could produce only ten because of its production capabilities. Before going to corporations, you should get prepared.
Corporate sales cycles may take up to 5 years, or at least 3, while startups usually expect it to be quick, typically planning for 1 year at most.
Startups often misunderstand corporate budgeting. Even after securing a deal, there's almost a 100% probability that the current year's budget is fixed, leading to a year's wait, and even then payments are also likely to be later than expected.

Communication lacunas between startups and VCs

VCs often treat everything like SaaS, asking inappropriate questions and not understanding how it works (e.g., asking a mining startup about CAC or LTV).
VCs may lack industry experience, leading to decisions not to invest based on misunderstood metrics and if they do invest without understanding the industry, then may start to pressure startups for unrealistic traction, especially in deep tech requiring long development times.
VCs on boards may offer misguided advice due to lack of industry experience.

Regulations of AI in the industrial sector

EU AI Act. Unacceptable risks - is nuke acceptable? Bomb no, but energy -yes! Looks watertight so you just know you won’t be able to do much and start packing up.
EU Digital Markets Act - still fighting MSFT for bundling Teams, chances are not all the new AI features Apple has recently announced are going to be available in Europe…
California’s SB 1047 - many ‘interesting’ things, from model developers responsible for use (totally antonymous in spirit to Sec 230 re UGC) to tying the threshold to current computational requirements that become outdated as we speak. Looks patchy with lots of wiggle room so you never know if you are going to get in trouble. NB a double-edged sword, some may say there’s not enough regulation - see Ilya Sutskever.
I’ve always thought it is best to regulate end use but not R&D, since otherwise you’ll only hinder progress in your own country/bloc and this will eventually lead to losing competitive advantage - scientists and researchers are like water, they’ll just leak to wherever else they can work.
Costs of complying!

Biggest learnings from last 10 years in your life

When times are tough, companies are less likely to adopt innovations, even though it seems they should. It's commonly believed that during crises, companies want to economise and adopt high-tech solutions to cut costs. In reality, people fear risking their jobs and prefer to maintain the status quo.

Advice for your 10 year younger self

Don't skip school. Studying provides foundational knowledge that affects how you think and work.
Don't just replicate others' actions - you may lack knowledge of the reasons behind them. Be cautious about blindly following role models.

Tips & tricks for emerging VCs fundraising

I still consider myself one of them, so I would love to hear some rather than teach. What I can definitely advise is to use your network to its full extent. Think hard, create a large list of contacts, then think of more remote connections. Repeat this process at least once more. You'll be pleasantly surprised by how far your network can reach.

A recent strong belief that changed your mind.

Founder experience is overvalued when transitioning to VC roles.
It's widely believed that founders from a specific industry know how to invest in it. This may be true, but often they miss things obvious to outside observers or investors. We've all been children, but that doesn't necessarily make us good teachers or parents.…

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