What’s Stopping AI Startups From Meeting Their Potential?

By Salvatore Minette, CEO, Fountech.Ventures

Nowadays, there is a lot of noise across all markets about artificial intelligence (AI). Some might say it represents the next wave of innovation in society’s technological evolution. After all, it explains why businesses spanning all industries are keen to leverage innovative AI solutions to their advantage.

But if we consider the plight of AI startups who are driving innovations in this space, we come to realise the many challenges of building – and scaling – a successful deep tech business. Having overseen the growth of startups from inception to launch, I have seen first-hand the difficulties that entrepreneurs must overcome to truly make their mark in the face of fierce competition.

Below I outline some of the key obstacles that prevent AI startups from scaling fast and sustaining growth, and how we at Fountech.Ventures are helping businesses with strong drive and a solid proposition to take on the world.

Securing investment

Despite receiving plenty of attention, it’s no secret that early stage startups often struggle to secure investment. For those that are enabled by AI, the problem can often lie in a lack of understanding amongst VCs about the technology and how it works. Naturally, their focus is on the business potential of these startups and the finished product, rather than the tech itself.

Often, the complexity of investing in AI-fuelled businesses is compounded by the fact that many so-called ‘AI startups’ are hardly driven by AI at all. A study by MMC Ventures in 2019 found that two-fifths of Europe’s self-proclaimed AI startups do not actually use any AI programs in their products. Indeed, some businesses are piggy backing off the term, giving the impression they are leading the next wave of tech-based innovation.

With technology getting more sophisticated and specialised, sorting fact from fiction is becoming harder for those with only a surface level understanding of AI. Let’s be honest – most VCs do not want to risk an investment into something they aren’t completely familiar with. And who can blame them.

Finding the right talent

This leads to another point: while there is a growing stream of AI developers and data scientists entering the field, it can be frustrating for founders to build the right team of people. The current pool of talent is still far too small to meet the needs of cutting edge startups – this is as true in the US as it is across other parts of the world. Constructing a team of AI experts who are also able to work in a high-pressure, startup environment can be a difficult undertaking.

That said, we are in no danger of interest in the AI space waning. As interest in the industry grows, I have no doubt that in the years to come startups will not be deprived of the expertise they need to scale their operations. The challenge is finding team members who are technically proficient in AI and also fit with the culture of the business.

Accessing mentorship

AI startups have very different needs to their counterparts. They require mentorship from those with technical knowledge and those with experience in the industry – data scientists, software engineers and tech entrepreneurs who can help them further develop the technology and build-out the commercial model.

Early stage businesses require investment at every stage – and not just of funds. It is only with the investment of time, resources, skills and knowledge that these startups can scale and achieve their vision. Unfortunately, this level of engagement is rarely seen across typical incubator models, leaving many startups with little guidance.

Supporting innovative US startups

The US has long been a leader in the AI race; according to CBInsights, AI startups received a record $26.6 billion in funding in 2019. However, to maintain its status against competitors like China, the UK and Canada, the US must offer greater support to the innovative startups that are trying to break through in the field. There must be a willingness and determination to invest and deploy sophisticated technologies at scale.

This is what inspired the launch of Fountech.Ventures – a deep-tech startup incubator, so to speak. In fact, it is more than just an incubator – through our programme, we aspire to support entrepreneurs who have a roadmap, but lack the funding and mentorship they need to scale. In doing so, we hope to address all of the above challenges and help nurture the deep tech AI startups of the future. With our base in Austin, Texas, we are able to support entrepreneurs who want to turn their proposition into a reality that will see deep tech technology solve problems and drive change.

Our model is simple: we select a handful of deep tech AI startups every six months from all over the globe and deploy our in-house scientists, engineers, entrepreneurs and investors to help them refine the technology and build a solid commercial model.

All of our startups will launch via our Austin base, allowing them to cut their teeth in the most competitive environment there is – the US market. Indeed, this launchpad was chosen carefully; Austin is already home to tech giants like Dell, Google, Facebook, Amazon and Apple, not to mention a fast-growing number of startups and scaleups.

Through the injection of technical and commercial expertise, we hope to inspire a new wave of businesses to further this momentum and cement the US’ title as a global leader in AI. By nurturing the potential of fledgling companies, the US can maintain this lead and oversee the development of brand-new technologies that will ultimately work to the advantage of businesses, organisations and global society.  

Salvatore Minetti is the CEO of Fountech.Ventures – a next generation incubator for deep tech startups. It is based in Austin, Texas, US, and originated in London, UK. The company supports deep tech startups through the stages of ideation, development, commercialisation and funding. It is helping the brightest deep tech startups on both sides of the Atlantic to reach their growth potential.