Assessing the Risk of Funding AI Startups: Deeper Questions Every Investor Should Ask

The AI startup landscape has been dynamic, with numerous emerging players and innovative solutions in various industries, including healthcare, finance, transportation, e-commerce, and more, making it highly attractive for investors.

From autonomous vehicles to virtual assistants, and the seemingly godlike ChatGPT, AI is transforming industries and disrupting traditional business models. 

Fueling the AI fire and investor interest is the sheer size and growth the sector has and will continue to enjoy. According to Precedence Research, the global artificial intelligence (AI) market size was estimated at $119.78 billion in 2022 and is expected to hit almost $1.6 trillion by 2030. That’s a compound annual growth rate (CAGR) of 38.1% from 2022 to 2030.

However, not all AI startups are created equal, and investors need to exercise caution when deciding where to allocate their capital. To mitigate risk and make informed investment decisions, it’s crucial to ask the right questions and thoroughly evaluate the potential of an AI startup. The fast-paced nature of AI development, regulatory uncertainties, ethical concerns, and the competitive landscape can impact the success and viability of AI startups. Therefore, it becomes crucial for investors to thoroughly assess and evaluate the risks and opportunities associated with investing in AI startups.

I spoke with Daniil Kirikov, managing partner at Orbita VC, about some of the not-so-common questions, investors should be asking before they commit.

Expand Your Understanding of the Age-Old “Is There a Problem” Qualifier

One of the first questions all investors consider is whether any startup, AI or otherwise, is addressing a real problem that people are willing to pay for.

“AI hype can sometimes overshadow the actual value proposition of a startup,” Kirikov said. “Investors need to expand their understanding of whether there’s a problem with better questions to critically assess if the startup is solving an old issue that people were already paying to solve, but to someone else. A good litmus test for this is to ask, ‘Who were people paying to solve this problem 10 years ago?’ If the answer is unclear or non-existent, it could be a sign that the problem being addressed is not significant or sustainable in the long run.”

Assuming the startup is addressing a genuine problem, the next question to ask is whether AI enables a significant increase in efficiency in terms of cost, speed, or quality. 

“AI should provide a clear competitive advantage over traditional methods, and the startup should have a compelling value proposition in this regard,” Kirikov said. “If the startup’s solution merely provides marginal improvements or does not result in a substantial increase in efficiency, it may not be a compelling investment opportunity. Investors should seek startups that have a clear edge over the competition and can deliver a step-change improvement through the use of AI.”

Problem Defined, but Does This Need Solving, and Why Now?

Another critical factor to consider is the timing of the solution and whether it could have already been solved before the hockey-stick growth and interest in AI. After all, it’s 2023 – at this point in history, it is nigh on impossible to come up with an idea that hasn’t already been solved 700 times or find a gap in the market.

“Investors should assess whether the same solution could have been implemented two, three, or five years ago,” Kirikov said. “If the answer is yes, it may indicate that the startup is not leveraging new technologies or ideas and that others may have already attempted similar approaches without success. Learning from the failures of others is crucial in making investment decisions. If a solution has been attempted before without significant success, the likelihood of multiple increases in efficiency may be low. Investors should look for startups that are genuinely innovative and are leveraging cutting-edge technologies or ideas that are not readily available in the market.”

Dig Deep on Security Risk, Not Just Financials

Security is also a vital consideration in the AI startup space. As AI technologies become more prevalent and powerful, the potential risks and ethical concerns associated with them increase. 

“Governments and regulatory bodies are paying close attention to the development and deployment of AI, and startups that do not prioritize security and ethical considerations may face regulatory hurdles and legal challenges,” Kirikov said. “Investors should carefully evaluate the startup’s approach to security and compliance with relevant regulations to mitigate potential risks and liabilities.”

Asking deeper and more nuanced questions is now critical, given the sheer volume of AI startups being created daily. It is now not enough to understand at a surface level the problem the startup solves, its business model, traction and growth potential, team expertise, financials, risk mitigation, exit strategy, competitive advantage, funding requirements, and the timeline for achieving milestones when evaluating where to commit funds. Kirikov’s approach and level of questioning in the assessment phase are now crucial for the industry and can help all investors make more informed decisions.