Our 3 questions to: Raising funds and attracting talents for AI start-ups

It has been already some time since we last had ‘Our 3 questions to’. We are back again with the start-up sector. This time we focus on how easy/hard is to raise funds and attract talents for the European AI start-ups. We talked to Max Ciociola, founder and CEO of lyrics catalog and music AI company Musixmatch.

Have a look at how he pictures the challenges and the opportunities and share what is your personal take on this:     

What is the most valuable asset that attracts capital for AI Start-up in Europe?

First of all, I would like to mention a quote made by a venture capital representative I have recently heard at a conference “Every start-up I’m meeting with is putting AI in their investment deck”.

What does it mean? AI is surely getting a pretty fashionable word right now in tech space. I see the situation as quite similar to early 2008 when start-ups were focusing on building Apps. AI-economy is replacing the App-economy and this is happening in Europe too, from fintech through transport and smart assistants to healthcare.

But to have a realistic picture of the business and technical capacity of the start-ups claiming to do AI, we have to look closer at who are their founders and their teams. Do they have experienced data scientists as founders? How many AI-experts or researchers do they have?

I focus your attention in this direction because in 2018 teams are more important than technology itself in any AI companies. My reasoning behind this is that only top quality teams can leverage funding, as disrupting AI technologies can only come from highly-expert AI teams.

As an example we should consider DeepMind, a British AI company founded in 2010 and now part of Google Inc. DeepMind was founded by Demis Hassabis, Shane Legg and Mustafa Suleyman in 2010. Hassabis and Legg first met at University College London's Gatsby Computational Neuroscience Unit.  Hassabis, Legg and Suleyman were and are still considered among the highest AI experts in the world, same for their 100 people team based in Cambridge. Google, after buying DeepMind for 500 Mln $, shared that this was the best ever team they had seen to work on AI so far.

I was wondering what was DeepMind’s business plan to convince Tier-1 European venture capital persons such as Founders Fund, Horizon Ventures and the well-known Elon Musk, who also backed the company.

My answer? They had no business plan but they showed they had the best team on the AI market.

Raising money for an AI Start-up in Europe: What are the main challenges?

I will start with the ‘A-Team or AI Team’ case – or to put it in other words, this is the ability to build a top team of data scientists, machine learning experts and researchers. We should expect universities to play a great role in this challenge as anyone that wants to build a team of super experts and passionate scientists, needs to be close to universities, both geographically and in terms of social relations. Here I would like to note that I don’t expect this to happen anytime soon or at least in the next 10 years as European universities unfortunately are well-known for not being able to create entrepreneurial culture (like this happens in the US). 

The other challenge relates to the types of venture capital funding and their adaptability to the market needs. Here I would like to point out new funding models like Entrepreneur First in UK and in general funds, which not only support the business development of the start-ups, but are also involved in the very set-up of the new companies helping to create start-up companies from scratch and responding to real market needs. Another noteworthy example is Technology Tech Transfer from University (as part of the Junker’s Plan). I believe that, if properly done, it can leverage a lot of deal flows. Also, although it never really scaled up, corporate funding could leverage a lot in terms of backing new AI companies. Let’s not forget, big corporations will be the one struggling more for hiring the best AI talents and building their AI so they need to build a path to succeed on this.       

The third challenge relates to the business models of the start-ups. So far I have not seen anyone in Europe easily raising money just to build the best team without any business model simply because having a business model means having a plan for making money. And we need this money in order to hire and keep top quality teams. In other words, proper business vision will help European AI start-ups to raise funds and establish on the market instead having their teams acquired by the large corporations, what was the case of DeepMind.

How do you see start-ups competing with large corporations for hiring AI talents?

In the next 3-5 years hiring the best data scientists will be challenging because of fierce competition, having also in mind the needs of Google, Amazon, FB, Apple, Alibaba, Tesla, Microsoft to hire talents. Well, in general the tech space has always been a particular case regarding staff turnover, the company with the longest average retention is Facebook, where employees spend around 2.02 years at the company.

So how to survive as a small company in a space that is going to be super competitive?

With AILabs, our initiatives in Italy to build the first AI Fund and AI Research Lab, I am seeing more and more scientists interested more into their research and passions vs their salary, but at the same time the most recent research shows that the average salary of a senior data scientist is $111,118, and it is further increasing. In this context for me the right formula for attracting the best AI talent to the SMEs and start-ups looks like that: building the right company culture, the best environment for scientists with a long term vision, and for sure we should not forget about the financial component, because building a team in any parts of Europe is going to be expensive in the future.


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