With the growing number of large scale acquisitions by some notable tech giants of late in the field of AI and Machine Learning, Venture Capital is watching closely for the next DeepMind and Swiftkey opportunity. We spent some time with William McQuillan, Partner at Frontline Ventures to get a better understanding of the growing VC appetite for high tech, high growth start-ups and explore the differences between SV and European investment opportunities.
We started Frontline for three core reasons. First, we felt strongly that there wasn’t enough venture capital funds investing early enough into great entrepreneurs in Europe. Second, we also felt that the way venture funds added value needed to be re-evaluated. That is why we are a community-focused fund that actively promotes and facilitates peer-to-peer learning across all the founders in our portfolio. Finally, we wanted to encourage thinking global faster by supporting our founders to expand operations internationally and bring in international investors sooner.
More recently how have you seen the market evolve over the past 12 months with respect to investment in AI start-ups?
I generally try not to use the term “AI” – I still think we are quite a large amount away from creating sentient beings. With regards to machine learning, there has been an enormous increase in activity in London over the last 12 months. We’ve seen significantly more startups using ML to try to disrupt entire sectors. There has also be a large number of meet-ups each month, and increasing amounts of early- and late-stage investment into the space.
How do you feel the European VC model compares to the U.S with respect to how startups in this space get funding?
Overall they are very similar; there are a lot of great investors in both the US and Europe. Differences we see are: a) Generally, venture capital funds are happy to go earlier in the US, and b) there is a lot more later-stage capital available to US companies. A lot of people tend to focus on points like higher valuations or US VCs adding more value, but those two points are getting less and less significant each year in my view.
When looking at businesses developing AI solutions, do you have a preference over those focused on narrow, or broad AI?
People use different terms to refer to different areas of AI, i.e. broad/narrow, weak/strong. I’m assuming “broad” here means to create a sentient being. As previously mentioned, I still think we are a long way from that – but looking at more narrow tasks, there are clear and obvious challenges and inefficiencies in every company, sector, and almost every job role that machine learning can solve. I’m very bullish on those opportunities.
Which Enterprise verticals do you see as having the most potential for large scale AI adoption, and how is this influencing your own investments?
This is a question that people often ask. The nature of machine learning is that it can take tasks that humans are very slow to perform, often make mistakes in, or can’t perform because of large amounts of data required and make either the human much more efficient at the task or completely unneeded. What this means is that in the same way that software affected almost every single sector, company and job role, I think the same will happen with machine learning. The areas that will benefit the most are the ones where there are large amounts of data needed to be analysed in a monotonous way. For me this would be in financial services. Equally in the last three months I’ve seen companies using machine learning in many different sectors – from publishing and news/media to healthcare and robotics. I think there are simply so many ways in every sector that it could lead to global companies being created that it is hard to let specific sectors to create a bias.
Investments, and exits
We see many UK and European startups move to the U.S in order to scale or seek further investment – what do you think is needed to reverse that trend?
I don’t think that trend needs to be reversed. If UK or European founders want to build global businesses, the US is an enormous market that should be expanded to. There is no reason why they can’t still base their HQ or large portion of their operations in the UK or Europe. We have seen plenty of examples of our portfolio doing this (Logentries was a recent exit in our portfolio that did just that) and it works very well.
As AI and Machine Learning continue to gain momentum, how much of your time is now spent focused on AI VS other sectors/technologies?
If I had to quantify it, I would say close to 20% of the time I’m looking at new companies is related to machine learning. I don’t think that is reflective of the number of machine learning deals I am seeing, but more so the potential opportunity I see in the space and the time I’m choosing to give to it.
What’s next for Frontline in your own story of growth?
At Frontline, we want to continue to find the most exciting and ambitious entrepreneurs in Europe at the earliest stages. We want to help them build their startups into global companies by adding value through our community and platform activities.