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Where to Begin When Evaluating an AI Start-up for Investment

AI start-up is a game-changer proposition in 2021. The artificial intelligence (AI) sector has become a lucrative target for investment. Promising new AI start-ups are surfacing on a daily basis, many of them attempting to solve major issues for businesses, consumers, governments or entire societies.

According to data from the National Venture Capital Association, $19.98 billion was raised by 1,509 AI companies in the US alone in 2019. This figure looks set to rise in the coming years, particularly as new start-ups emerge to offer solutions to COVID-inspired changes.

But deep-tech ecosystems, and AI in particular, can be difficult to navigate for investors with little prior experience. Below are a few considerations to bear in mind when evaluating an AI start-up for investment.

How to Pinpoint a True AI Disruptor

The AI market is extremely competitive, with the barriers to entry for entrepreneurs having fallen in recent years – particularly when it comes to the costs involved.

The likes of machine learning, publicly accessible libraries, pre-trained models and APIs have all served to boost innovation in this space. On the one hand, this is a game-changer: ambitious founders can iterate faster and build better products than in years gone by. However, this also means that competition is rife, and it has become more difficult to pinpoint companies that have both a great product and scope to scale significantly.

My first piece of advice to investors exploring their options in the AI space would be to seek out companies that are innovating at the both the science and application level, rather than focusing solely on the latter. Indeed, there are plenty of application-level companies, with many guilty of simply regurgitating third-party APIs instead of inventing novel AI for their own purposes.

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True deep tech rests on a foundation of intense research. It knows what is possible at the boundaries, and the problems to solve to advance to the next level. This is why deep technologies are novel and represent significant advances over those currently in use. In time, they have the power to disrupt existing industries and even to create their own markets. The IP protection afforded to novel technologies means that these innovations will be difficult, if not impossible, to reproduce.

I would urge the investment community to explore core infrastructural advancements that are happening in the sector, as the companies at the forefront will often have a valuable competitive advantage. This is a particularly pertinent issue in light of the number of start-ups that claim to be delivering AI solutions but which, in reality, are not using the technology in an innovative way – more on this shortly.

More generally, in order for investors to have early exposure to deep tech, there are generally two options. The first is to build-out their in-house technical team, which in effect would involve having a PhD on payroll to provide the appropriate technical proficiency. This will create the capacity to screen companies before there is a product and market traction, as well as the ability to offer valuable technical support to founders along the journey.

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Alternatively, they might look to partners that do that for them. There is always the option to co-invest with investors that already have in-house scientists and a solid understanding of deep tech, to both better select and support their investees.

Investing in the Team, as Well as the Product of AI Start-up

Science-based companies can be difficult to vet without the appropriate technical know-how, and not every investor will have the background and experience to effectively evaluate AI businesses.

As mentioned above, this is complicated further by the fact that many AI start-ups that label themselves as such will have little to do with the technology. Indeed, a report released by MMC Ventures in 2019 raised questions about how the term ‘artificial intelligence’ has become a blanket phrase for start-ups looking to attract investment. Of the 2,830 European AI companies surveyed, in only 40% of cases did the technology actually constitute a key part of the product offering.

For investors that do not specialize in AI alone, assessing the qualities of the team becomes a vital part of the evaluation process. VCs ought to conduct thorough due diligence on the founder’s background and the company’s wider talent pool. This necessarily involves verifying the qualifications and previous field experience of the data scientists behind the product. Teams with strong technical backgrounds and relevant experience in the niche market that they are targeting will be more likely to stand out.

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Beyond this, a careful assessment of the founder’s personal characteristics will not go amiss. While sound technical knowledge, charisma and general business sense should all be considered, other important traits are often more indicative of the future prospects for an AI start-up.

This past year has demonstrated the importance of grit and determination: young companies will need to demonstrate that they can be responsive to changing market conditions, willing to do what is necessary to see the company through difficult challenges. A positive, can-do attitude is vital when building an AI product, particularly in an uncertain political and economic climate. It will see a team through the inevitable tough times and inspire inventive ways to overcome obstacles thrown in their path.

Founders should also be aware of their own strengths and weaknesses – they should be enthusiastic about onboarding the right talent to ensure all necessary areas of expertise are well covered. Likewise, seeking out and responding positively to critical feedback from peers, customers and experts is a positive attribute to possess. It illustrates a founder’s strength of character and ambition to succeed.

Investors will need to be confident that they are backing founders who are not only doing good through inventive applications of AI but can also see a vision through. With that in mind, the tech behind an AI product is paramount – but so too is the character of the founding team.

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