Mikhail Taver is a seasoned investor with two decades of experience in high-level executive positions in prominent financial groups and industrial firms, as well as in investments and strategic consulting.
Mikhail has successfully concluded over 250 M&A and private equity transactions for major players in the industrial sector, and possesses profound expertise in areas such as IPOs, LBOs, direct investments, private equity, and mergers and acquisitions. His investment endeavors have also covered heavy industries like mining and manufacturing. In addition to this, Mikhail holds CFA, ACMA and CGMA designations.
As the founder and managing partner of Taver Capital, an international venture capital fund dedicated to investing in global artificial intelligence companies, Mikhail possesses a profound understanding of the investment process in deeptech and AI-powered startups.
You were one of the pioneers in investing in AI when it was still considered a niche. What initially drew you to AI technologies, and how has your perspective on AI investments evolved since founding Taver Capital?
When I chose AI, I did so considering it as a niche that I believed had good prospects. While I was right about the prospects, we have seen how AI has progressed at an accelerated pace and is now being adopted in virtually every industry, which means that I was wrong about the niche aspect. Now a mainstream technology, AI has evolved substantially since then, and so has my perspective as an investor.
Initially, when AI caught my attention as a potential investment sector, I realised that I needed to transition from being a generalist investor within tech to a generalist within AI. This led me to be one of the pioneer investors in AI-powered technologies. Now, it is time to make another transition, from being a generalist in AI to finding the next promising niche within AI. In my perspective, and given my extensive experience working with heavy industries, I believe this is industrial AI. My perception of AI’s potential hasn’t changed – I’ve always viewed it as a tool for enhancing efficiency and transforming businesses. However, when it comes to the question of where integrating AI can generate higher returns, my bet is that it can do so in those industries that are ripe for disruption — manufacturing, mining, and other sectors that most AI-centred investors aren’t looking at.
Could you explain what opportunities and challenges you see in Industrial AI? How does industrial AI differ from other AI applications in terms of investment potential?
I believe AI can bring new life to companies in this sector and boost their growth. Traditional industries like manufacturing, energy, and mining have been slow for years, and AI has great potential to change that.
Take mineral mining, for example. Today, the discovery rates of copper, nickel and lithium are at their lowest levels ever, despite discovery-related spending being at an all-time high. Because of this, the mining sector holds immense potential for disruption. This belief led me to invest in Earth AI, a company in Australia that has developed a vertically-integrated mineral exploration technology and helps mining companies find deposits faster, cheaper, and, very importantly, more sustainably.
Another case is Israel-based Ception, which is implementing AI systems to make construction sites and industrial plants more productive, sustainable and safe. MineCept, its SaaS model, utilises 3D mapping and precision visual positioning technology to enhance safety and operational efficiency on job sites.
In both of the examples illustrated above, investing in AI can help companies save billions in expenses, positively impacting a company’s bottom line. However, applying AI to heavy industries is a fairly capital-intensive endeavour, even for startups. Development funding needs to be calculated with a margin and with a long term horizon. Profit may come in steps; for instance, in mining, there may be no profit for a long time, then suddenly $20 million, then none again, and so on. This needs to be taken into account. Since it is a long-term project, both the founder and the team must have a strategic mindset, approach, and be ready for the fact that the result will not come soon.
Having said this, investors still hesitate to invest in industrial AI for several reasons. First of all, they believe that industrial deeptech investments are too time-intensive to be worthwhile. It takes about 5-6 years to determine if an AI project will work, which makes some investors skittish. This is true, and means that investors must be more selective when choosing a project.
We also need to consider that the industry, due to its size, has traditionally been the playground of private equity. VCs have long skipped it and, as a result, they do not know a lot about heavy industries and how to communicate with founders in the sector. Having experience in investing in sectors such as SaaS, they have no understanding of the industrial sector features, and as a result have unrealistic expectations. Hence, it is important to dive deeply into the industrial sector and learn how to communicate with its stakeholders.
Taver Capital has achieved several successful exits, including acquisitions by major companies like Facebook and Mitek. What key factors do you consider when deciding to invest in an AI startup that might indicate a future successful exit?
First of all, I try to make sure that the founders truly understand what they are doing. This isn’t just about what they say, but also, about what they can concretely back with key figures. Secondly, I rely on my network to positively assess and vouch for new prospects. By the way, when industry experts say something is nonsense, that it’s impossible or won’t work, I may sometimes consider that to be a good sign. The same goes if, after the product makes its first steps, industry insiders start heavily criticising the startup for insignificant reasons.
Besides conducting due diligence on the founding team, I analyse whether the startups have potential for sustainable growth and long-term returns. If they are simply pursuing immediate profits driven by market trends, I tend to pass, because there is no value in the long run. I prioritise companies that can deliver lasting value over time.
Also, I evaluate whether companies adhere to conventional and well-proven business practices. Founders must have a clear vision of the market and run the company efficiently, keeping a close eye on finances, operations and employee morale. A robust financial model is essential to ensure the success and growth of a startup, since it acts as a guidepost to attain financial sustainability and streamlines the company’s activities. Then, I consider whether they have a clear action plan. This will make the strategic decision-making process transparent and manageable. One more point is that I value content over form. In the early stages of a business, substance is often more important than style. While having a visually appealing product can certainly help attract attention and generate interest, it’s ultimately the product’s quality that will determine whether or not a business is successful.
Taver Capital invests globally, utilizing a network of local expertise. How do you manage the complexities of investing in diverse markets, and what role does local insight play in your investment decisions?
Since middle school, I’ve been in a very multicultural environment, so it is not difficult for me to connect with founders regardless of their location, language difference, etc. I can communicate with people and I don’t see any barriers to finding startups.
Furthermore, having portfolio companies in different countries brings tangible benefits. Firstly, there’s always someone to talk to if you can’t sleep. Seriously though, from a business perspective, diversification is an additional guarantee of security. I saw this clearly during Covid, when some countries lay low, while others, on the contrary, had some kind of growth and development. For example, in the US there was a strict lockdown, and in Australia work was in full swing. It was an interesting experience.
The reality is that even if the same thing happens everywhere, it happens at different times. Therefore, by diversifying your portfolio, you mitigate geopolitical and local economic risks.
In what ways do you foresee AI reshaping economic landscapes, particularly in the industrial sectors?
There will be growth and improvement. What’s important is that this growth will be more sustainable — meaning it will be cleaner and more environmentally friendly. Let’s take Taver Capital’s portfolio company, Earth AI, which I mentioned earlier. Its tech-driven approach to targeting, testing and verifying discoveries required for the electric vehicle and renewable energy revolutions represents a major breakthrough for the industry, as it helps find maiden deposits in unexplored areas at a fraction of the usual cost. This is important today because there is a race for critical metals to fuel the renewable energy transition. The number of new discoveries has decreased by 73% over the last decade, and the development of old deposits often occurs in an environmentally unfriendly manner.
AI-driven discovery is also significant at a time when essential “clean energy” minerals like copper and nickel face shortages despite substantial investments in exploration. Earth AI stands out by identifying nickel, copper, zinc, and vanadium mineral prospects over 100 times faster and cost-efficiently than traditional methods.
Then, let’s take a look at Industry 4.0. It is a trend of automation and data exchange in manufacturing technologies, and encompasses the integration of digital technologies, such as the Internet of Things, AI, cloud computing, and data analytics, into industrial processes. Industry 4.0 is visible in the creation of “smart factories” that are more interconnected, efficient, and capable of autonomous decision-making.
By the way, replying to numerous concerns regarding the reduction of jobs, I don’t think this will lead to any spike in unemployment. We’ve already gone through an industrial revolution three times. In my opinion, humanity is simply becoming more productive.
What are the primary qualities or metrics you look for in AI startups when considering them for investment? Are there specific innovations or team characteristics that stand out to you?
The important thing is that the founders have already proven they can work together and have demonstrated their proficiency in doing so, which is usually quite apparent. If founders are family, I consider that as a red flag, because if there are issues with one, there will be issues with both, thus doubling the risks.
Also, the founding team should have a wide range of knowledge. This does not necessarily mean a degree. While it’s important for the founder to have a higher education, it does not need to be in the specific field the startup operates in. This facilitates creative thinking and gives founders the ability to see the big picture while also being able to delve into the details.
Having this dual ability gives the founding team a clear and distinct vision of the market they are pursuing and an intuitive understanding of their customers’ needs. Speaking about customers, I value founders who can listen to their feedback and consider it. In fact, not only from customers, but in general, it takes a lot of courage to openly listen to somebody else’s opinion. So that’s another aspect that I strongly consider.
Finally, as I mentioned before, I closely examine a startup’s financial model before making any decision, as I believe it is critical to have a solid foundation for sustainable growth and scalability.
AI continues to evolve, what emerging areas within AI are you most excited about? Are there particular trends or technologies that you believe will be pivotal in the next decade?
I would look not only beyond Industrial AI, but beyond AI in general. So many developments are currently happening in the industry that it helps to keep an open mind to see which aspects need support or are fertile ground for the emergence of new ideas. For example, I would consider aspects such as energy efficiency in model training, which is a big topic right now. There is a lot of talk about how Big Tech companies are having to deal with hiking emissions due to their AI initiatives, and are facing a lot of backlash for doing so. This is an example of a segment within AI that could use new ideas and fresh solutions.
Another area that seems to be a big trend is security and ethics. As an example, some Apple features are not available in Europe because of the DMA requirements. I also believe that the DefenceTech sector will grow, and this will spur the development of civil industries. However, these two are closely linked, because there are a lot of ethical considerations that need to be kept in mind regarding the implementation of AI in government programs.
Based on your extensive experience, what advice would you give to entrepreneurs looking to venture into the AI space? What common pitfalls should they avoid?
Do not focus solely on AI. It’s best to engage in sectors where you want to do business, whether this is the oil industry, book publishing, steel casting, or anything else. AI is just a tool; there’s no need to pursue AI for the sake of AI itself. Artificial intelligence should simply serve as a technology that enhances your business efficiency.
Given your investment in Earth AI, can you discuss how AI can play a role in sustainability efforts, especially in sectors like clean energy and mineral exploration?
AI can contribute to these sectors in several ways: optimised resource management, predictive maintenance, environmental monitoring, enhanced mineral exploration, etc.
Overall, AI’s ability to process and analyse data at scale enables smarter decision-making and operational efficiencies, providing methods of exploration and extraction which are much more efficient and environmentally friendly.
For example, as I have already mentioned, Earth AI discovers new deposits more efficiently, and drills to prove out those deposits more quickly than traditional explorers and drillers can. It uses proprietary drilling hardware, featuring the Zero Disturbance Mud System and Mobile Logistics System, significantly reducing the operations’ environmental impact.
How do you see current and upcoming regulations affecting AI investments? What should AI startups be aware of to navigate these regulatory landscapes effectively?
The general trend is that regulation in the US and Europe is becoming more stringent. This is because AI and related technologies are developing very rapidly, necessitating regulatory oversight. This process is happening across all sectors; therefore, every industry is regulated in some way. The difference lies in the fact that businesses in traditional sectors like construction and automotive are accustomed to regulation, whereas AI is only at the beginning of this path.
I think generally it has its merits, as it makes the market more organised and systematic. However, today, the wording of the existing or proposed regulations still gives a lot of space for interpretation, which raises concerns. Certainly, it is necessary to carefully study the rules and observe their enforcement, but the possibility of subjective judgments about AI startups and subsequent decisions about which of them should be subject to tighter regulation is an alarming sign, and one that could have unintended consequences.
This could lead to a shift in AI development to countries employing different or more sophisticated approaches, like China. On the other hand, сountries without excessive government regulation and those that encourage innovative ideas will attract developers.
What I can advise for startups is to monitor the current legislation in different countries, and maybe consider the countries where regulation is less stringent or better suited for your industry, and also, to operate in critical industries where there will always be some leeway, especially if you are planning on operating in the US.
Thank you for your detailed responses, readers who wish to learn more should visit Taver Capital.
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