Navigating the Risks and Opportunities of Starting an AI Company

The world of AI startups is rapidly expanding, with a multitude of new players and innovative solutions in various industries. According to GlobalData, 3,198 AI startups received $52.1 billion in funding across 3,396 VC funding deals in 2022. And while ChatGPT has done for AI what Pokemon GO arguably did for AR - put a tech term on the general public’s lips - the AI sector has been skyrocketing for many years.

Silicon Valley has produced some of the most successful and innovative companies in the world. However, depending on which reports you to read, the industry’s lack of success is somewhere between Harvard Business School’s 75% failure rate and the dismal 90% Startup Genome proposed, and usually in the first year.

Thanks to the huge and highly publicized successes, startup ecosystems worldwide have adopted the same approach with varying results, and reportedly with higher failure rates. Without direct access to the other benefits Silicon Valley offers - resources, mentors, talent, and the network effect created by having so many startups in a relatively small area - it is hard to replicate the original.

In a notable study by Harvard Business School professor Paul Gompers and his colleagues, they analyzed 1,000 VC investments that have set the tone for many a pitch deck since.

Leading the way, 30% of VCs cited the management team as the most critical factor in their investment decisions. A close second at 25% is the market, including size, growth, and competitive landscape.

Bringing up the rear, however, are the most crucial questions VCs should be asking when selecting the AI solution to fund. Product or service, business model, and financials were cited by a measly 5-15% of VCs as the most critical factors, and that’s concerning.

It is critical to dig deeper than simply looking at the problem when assessing the AI startup’s solution. VCs need to start asking questions that go deeper and assess if the startup is solving an old issue that people were already paying to solve. Expanding on that line of questioning, could this issue have already been solved before the current AI bandwagon? And could the same solution have been implemented two, three, or five years ago? If the answer is yes to any of these, it may indicate that the startup is not leveraging new technologies or ideas, and others may have already attempted similar approaches without success.

Add this consideration to the fast-paced nature of AI development, and the resulting boom across the competitive landscape, and you can understand why it is crucial to spend more time and effort on assessing the viability of the startup you’re considering as a future portfolio company.

Investors should still focus on every deciding factor, but it’s time to dig deeper into the problem the startup solves, its business model, traction and growth potential, financials, and risk mitigation.

Security is a vital consideration in the AI startup space, especially as the general public becomes more aware of how companies collect and use their personal data. As AI technologies become more prevalent and powerful, the potential risks and ethical concerns associated with them increase.

AI startups need to prioritize security and ethical considerations from the outset. 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.

AI startups face unique challenges and risks, but by carefully considering these factors, they can increase their chances of success. It’s important to find a strong ecosystem that supports their growth, to ask deeper questions when assessing startups, and to prioritize security and ethical considerations from the outset. With these strategies in place, AI startups can position themselves for success in this rapidly expanding industry.