Supervising Disruption? Some kind of clever ML pun? No – there is a trope normally attributed to Marc Andreesen that goes something like this. If it’s a consumer play, back young people doing something you don’t understand. If its B2B, back founders from the industry who have done it before, preferably together. It seems to me this next generation of deep or frontier businesses are about doing both – you need fresh eyes and brains to ask disruptive questions, but you still need experience to navigate distribution and enterprise priorities. How well prepared is the UK/EU venture environment for backing these kinds of projects?
Not very is my experience.
When we first started investing in deep-tech companies 5 years or so ago, there were few folks out their very interested. The ecosystem has been transformed – more specialist funds, more specialists – and just more funds – chasing everything that has AI, Bioinformatics, drones or robots in the business plan. More talent, more experience, and more money has had an impact on valuations and the ambitions of founders, which is in many ways good. But I’m not sure that the rest of the ecosystem has caught up. I’m not sure there are enough later stage investors in Europe who will back technology and vision but little commercial proof with big bucks – although I am sure there are lots of non-specialists investors with one or more deep tech deals on their books that will get frustrated at the two year mark when they are not seeing that magic £1m ARR they’re told to expect on the horizon.
This blog is the first of three that are my attempt to share some of the best practise and lessons learned from our experience. It’s a work in progress, stuff rattling around my head for the last few months, some parts are slightly contradictory, and some may reflect our own naivety. I mostly use AI examples to illustrate points, but I think most of the comments are broadly applicable.
Setting the Scene
Another week, another demo day, another group of well-rehearsed entrepreneurs pitching their world changing ideas. This particular week’s group was mostly fashionable frontier tech start-ups – two PhDs and an MBA taking something based around AI and disrupting medicine, healthcare, finance or insurance.
It reminded me of that old joke about the 2 micro-biology PhDs who walk into a bar.
“I haven’t seen you two in here before, what brings you to town?” says the barman,
“We’re researching the impact of coprophagial digestive enzymes on microbiota”
“Wow – that sounds hard! How can I help?”
“Two pints of cloudy cider, some pork scratchings and one of those hard-boiled eggs should do it” comes the reply.
Ok so that isn’t an old joke, I made it up, and you’ll forgive me a small dose of occupation-appropriate cynicism. In reality no one loves seeing science fiction turned into reality more than I do, and trying to understand the state of that science with smart impassioned founders is one of the true pleasures of this job. However, as I sat in the audience I couldn’t help but wonder how many investors really understood the journey that they were about to go on. The pitches give the impression of imminent world domination – the reality is most teams are years away from product-market fit and even longer from substantial revenues.
Landmines Along the Journey
I want to start with calling out some of the common mistakes we’ve seen or have been guilty of. If AI is all about pattern recognition then guilty as charged – here are some landmines to identify and avoid. I am sure there are many more, but these will at least make you sound more informed at board meetings.
And I’m sure there are more – these are some we’ve seen more than once. The good news about repeated mistakes – that which does not destroy us makes us stronger – is you can turn them around into a series of observations about what you should do differently next time – and hopefully identify progress milestones. That is the subject of next week’s blog.
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