Debunking the 4 myths of deep-tech entrepreneurship
Tech risk, team risk and the evil capitalist agenda
1. Most ventures fail, it’s so risky!
It’s true that 90%+ of startups fail but these are mostly coffee shops, one man consultancies and other poorly thought out and poorly tested ventures. The stats across deep-tech ventures are closer to 33%. That still sounds pretty dire though right? If you’ve got a 66% chance of failing why even start?
Before jumping into the avoidable reasons behind that, let’s put these numbers into perspective. Most people enter academia thinking that they will work their way up to become a professor. In reality just 0.25% of PhDs make it to that level. That’s a ‘failure’ rate of 99.75%. Getting to the top in any game is incredibly tough. Funnily enough the chance of a tech businesses selling for $100m+ after taking angel funding is also 0.25%. Both routes are incredibly tough but it’s no more irrational to assume that you can build a world leading businesses vs. make it to become a Professor.
Killing those risks early
There’s 3 key risks in an early stage company: market, product and team. All of them can be made a lot less risky!
Market risk is far scarier than technical risk
Deep tech ventures fail predominantly on market fit, focusing too much on novel tech and not enough on providing step-change value for specific customers. This is almost entirely avoidable by following versions of well established methodologies such as lean and design thinking.
Far too many people think that you can’t test the market when producing cutting edge technology (the Steve jobs effect). This simply isn’t true. From quantum encryption to cancer therapies you can absolutely sense check it with the relevant buyers. In fact if there isn’t a user group excited about (paying for) the solution you’re building you’re almost certainly too far ahead of the market and will run out of cash before the market catches up.
Build a cult, not people who fill generic roles.
A poorly functioning team is the second biggest killer of deep-tech start-ups. This is result of the team being fundamentally misaligned on vision, the way of getting there or broader culture. Too many teams know something isn’t right but carry on anyway because: ‘these guys are the best in the world at the technology in question’, or ‘they are only people you know interested in starting something’. That is a huge mistake and it will break eventually, the longer it takes the harsher it will be. You must be absolutely honest with each other on vision, what you expect and how you feel from the beginning. When it feels right it probably is right, anything else is a warning sign.
Triple check the science
Your final risk is the actual product and within that whether the technology actually works. Most deep-tech startups fail here for two reasons: firstly not creating the right product for the customer and secondly the same reason that 30% of research cannot be replicated: The original science worked once, under huge academic pressure, a paper was submitted to a high impact journal and everyone moved on. Science, however, is a fickle master and often won’t work in a different lab, or under slightly different requirements or conditions. This applies regardless of whether it’s your science or building on external work. Online protocols and robotics will hopefully go a long way to resolve this, for now the key is to be hugely rigorous in ensuring the science works before transitioning into commercial proposition.
If you take these steps to de-risk before jumping in the failure rate is vastly reduced. You can’t plan for everything and there will always be external factors, however you can adapt to those, the above risks kill your company from the inside out. The funny thing is that no matter how things turn out you will vastly increase your network and become significantly more employable, indeed every day one of our Founders receives a job offers that they would only have dreamed about attempting to start something.
Only big shot Professors start companies
It’s true that a leading Professor does have the advantage of being able to transfer hugely valuable IP from their lab into the start-up, plenty of minions to do the grunt work and the the reputation to get people to listen (customers, investors, press).
However, they also have a raft of disadvantages: It’s really tough to line up the incentives of research with those of a commercial venture (customers generally don’t want the sort of things that make good papers). Their time is swamped with unavoidable duties from teaching to administration and any decision is often weighed down in bureaucracy.
Meanwhile as an emerging PhD or postdoc you probably have very little admin or politics to deal with. You probably have a relatively low personal burn rate and going off the radar for a few months wouldn’t fundamentally impact your career. At the same time you have 80% of the knowledge of your professor and even better can likely get them involved and work within their facilities. You absolutely do not need to work your way up through academica for the next 30 years to build something that has impact.
I know nothing about business
Doesn’t matter. Start-ups aren’t business, they’re a search for some idea of what a customer might actually pay for. This is just like science, set a hypothesis, create an experimental protocol (usually a set of questions or technical experiments), test it out and adapt. You are likely already amazing at this. But it’s about being pushy and talking sales right? Nope. It’s mainly about listening, having huge empathy, the creativity to join the dots and the resilience to see it through.
What about a ‘marketing person’ and a ‘finance guy’, as long as you can learn quickly, enjoy reading and listen to quality mentors you can pick all of this up very quickly. In time you will need specialist functions and the company will transition into a more normal, repeatable, business, for now you need an obsessive cult of fast learners desperate to see if the idea will work.
My pure science will be corrupted by your evil capitalist agenda
I’m not going to lie, if you create a company, and especially if you take money from other people, your aim is to create vast sums of money. Even if you create a B-corp or equivalent and have some social focus it’s still about bringing in money so that you can pursue those activities in a sustainable manner.
At first this might sound terrible, however it’s often surprising how well aligned financial reward and large scale impact can be. Changing customer behaviour is the hard thing, if you can get someone to change how they currently do things then they’ll also pay. If they won’t change behaviour it doesn’t matter how good your science is, the product will sit on the shelf.
A lot of the emerging synbio products are a good example of this, they’re often offer something like a much cleaner way of making some sort of commodity or high value product. However, they are also nearly always more expensive or come with other risks. Get those factors sorted and you’ll rapidly get the better, cleaner product into market and at the same time clean up on the financial rewards. The challenges in making this work can be enormous, as are the degrees of freedom in how you get there, but this in itself can be hugely satisfying coming from the grant constrained world of research.
In conclusion ;)
A hard-tech venture is one of the quickest ways of driving science led impact, it is going to be tough, it is still likely to fail, but nowhere near as likely as you think if you build for real, hungry customers and sense check your team early. Even if it does fail it’s just the start of something better. You don’t need to wait, de-risk all you can before jumping in but then take that jump.
Or join us at DSV and let’s build something that matters together.
