Introduction
“Insanity is doing the same thing over and over and expecting different results.”
-Albert Einstein
Perhaps the most overused quote of the 21st Century, or perhaps the most pertinent? We humans claim to thrive on novelty, innovation and progress but nevertheless, for the longest time, we have failed to challenge the methods we use to achieve it.
Living in the present day is an intoxicating contradiction of “Holy shit the world is changing too quickly…” and “We’ve had 100s of years to find a better solution and we’re still doing it like that?!”. Science has delivered some awesome results already this century so you wouldn’t be blamed for thinking that we’re doing well, but when you look more closely at the input-output efficiency of the scientific monolith, you realise that we’ve barely scratched the surface of human progress.
You might’ve heard of venture capital’s answer to E=mc2; 9 out of 10 startups will fail so let’s invest in 10, but the story for Deep Tech may be even worse than that. BCG suggests the success rate might be as low as 5% for Deep Tech Startups between Seed and Series A, a further 80% failing between Series A and B and around 50% failing between Series B and C meaning that if even if you’re one of the lucky 1 in 20 companies that make it to Series A, your odds of survival only increase to 4 in 20 from there [1].
So why is it so god damn hard for the most overqualified workforce on the planet to build companies? And are we really ‘doing our best’ or is that just an excuse?
Diving Deep Requires a lot of Oxygen
There are of course inherent differences between deep tech and regular tech (e.g. software) that result in it taking longer and requiring more ‘oxygen’ (AKA capital). The most notable being that deep tech requires a lot of time and money spent on the validation and/or refinement of the technology itself.
If the customer is not impressed, we’re not talking about changing a few lines of code. When working with physical matter, things simply take longer to modify, scale or refactor. Deep tech ventures require 48% more upfront capital than traditional startups to achieve comparable revenue milestones to traditional tech [2]. This means that planning is absolutely crucial to ensure development stays on track and all possible outcomes are accounted for — especially when things inevitably go wrong. Small hiccups or distractions can result in months/years of delays to already cash-starved organisations.
Deep tech companies very often have complex and lengthy regulatory hurdles to overcome. Developing relatively simple diagnostic tools for healthcare, for instance, must navigate FDA approvals that take 12–18 months and cost upwards of $500,000.
In addition, enterprise and government customers (the primary buyers for many deep tech solutions) have procurement processes averaging 9–18 months [3]. Even if the tech is ready, if customer discovery hasn’t started yet, companies could find themselves still 18 months away from generating revenue. A study of 150 European deep tech startups found that 62% began customer interviews only after securing seed funding, losing 12–18 months of critical market feedback [4, 8].
As a result of these lengthy timelines, it takes deep tech companies 25–40% longer in between funding rounds as compared to traditional tech [5]. Longer timelines mean greater risk. Greater risk makes funding scarce and scarce funding has a whole host of knock-on effects on the pace of and appetite for innovation.
Centralised Success
To make matters worse, the odds of success for university spinouts are far from evenly distributed. Of the 5% of them that survive the first cull, the majority of them spawn from merely a handful of countries, regions and institutions.
What this says is that the infrastructure, support systems and models for success are neither standardised nor accessible. If you don’t live and work on those sacred grounds, chances are you’re not going to make it.
For biotechnology, 60% of global activity is concentrated in North America and 30% in Europe and over a third of that activity is localised in 5 key biotech hubs (4 of them in the USA) [6]:
- Silicon Valley
- Boston
- London
- NYC
- San Diego
In the UK alone, innovation is heavily localised to what is commonly referred to as the ‘Golden Triangle’ consisting of London, Cambridge and Oxford with almost 43% of all spinouts in the UK coming from that region.
Now the UK isn’t a huge kingdom, granted, but the universities in the Golden triangle are responsible for only 29% of the scientific publications produced by UK universities which emphasises the fact that there is a distinctly higher rate of successful commercialisation within the region as compared with the rest of the UK [7].
Once again, these realities lead to disparities in the availability of funding and support which further contribute to an inefficient innovation ecosystem.
Product Market Fit
Another big reason for the low success rate of deep tech spinouts is a lack of product market fit. This is by no means unique to deep tech itself but the problem is exacerbated by academia’s failure to engage customers and stakeholders early in the development process. What results are a number of common pitfalls that lead inexperienced founders to their inevitable fate:
- Market Misalignment — In nuclear energy for example, startups frequently design reactors optimized for theoretical efficiency metrics rather than grid compatibility. This results in systems requiring >$1 billion and 10+ years to deploy [1, 5]. Such timelines are clearly incompatible with utility companies’ planning cycles and R&D budgets.
- Misjudging Adoption Barriers — Startups often underestimate regulatory and infrastructure challenges. Some good examples of this are autonomous vehicles and eVTOL companies. Their prototypes may be exciting and technically feasible, but the companies often fail to account for the 15+ years required to update urban infrastructure to enable safe and successful deployment [9, 10].
- Technoeconomic Analysis — researchers often exist in a vacuum where reagents and experiments ‘cost as much as they cost’ and so they have little insight into the economic scalability of their proof of concept. Very often, this means that the final product costs more to produce than the incumbent solutions and will likely never be adopted by the market.
- Technology Push vs. Market Pull — Many deep tech founders start with a breakthrough technology and then look for a problem to solve, rather than beginning with a validated market need. This “technology push” often results in solutions searching for problems, rather than addressing urgent, real-world customer pain points [11].
- Ignoring Competitive Alternatives — it’s all well and good building something great but doing so with the blinkers on, failing to recognise what your competitors are offering and how that is likely to change over time can be the achilles heel for many [12].
Failure to Plan is Planning to Fail
One of the biggest misunderstandings about entrepreneurship that deep tech founders have is that if the tech is good, everything else will fall into place. Unfortunately, if that were true, companies wouldn’t need a CEO and could spend 100% of their budget on R&D to make the most awesome thingimabob that nobody asked for.
Almost ¼ of all startups fail because they didn’t have a sound marketing strategy [13]. Many more fail simply because of team composition and expertise [14]. It’s very unlikely that a genetic engineer will totally disrupt the wastewater treatment sector without having somebody in the team that has experience in and deeply understands that sector.
A winning team has a balance of brains and brawn. Entrepreneurship is more akin to football than it is to bowling. One person or one competitive advantage is not going to take the trophy, you need an experienced and complementary team and a future-proof strategy in order to come out on top.
Everything from timing of funding rounds, Go-to-market strategy, R&D timelines, team expansion, regulatory submissions and customer engagement must be meticulously planned. And your plan also needs a plan in case things don’t go to… plan.
Business as Usual
Finally, there’s the issue of what it actually takes to build a business.
Many technical founders believe superior science will carry them to market dominance. Rarely true. You need customers, strategy, pricing, sales, feedback loops, capital, and coordination across all of it [15].
Each business lever impacts the next. Sales feeds into product design. Product impacts pricing. Pricing defines your business model. And so on. Most academics haven’t been trained to pull those levers. Why would they?
But in deep tech, you need to be more scientific about business than the tech itself. That’s where most fail.
Coming Up Next
In Part 2, we present the solution: How Phlo is redefining what it means to build a successful deep tech venture from the ground up.
Stay tuned.
References:
[1] bcg-an-investors-guide-to-deep-tech-nov-2023–1.pdf
[4] 5 most common mistakes made by deep tech startups — Hello Tomorrow
[5] The Deep Tech investment paradox — Euro Funding
[6] Top 5 Global Startup Hubs: Biotechnology | StartUs Insights Research
[9] https://www.motius.com/post/the-deep-in-deep-tech-is-for-deep-problems
[10] The Deep Tech investment paradox — Euro Funding
[11] https://www.alfred.ch/post/the-elusive-product-market-fit-in-deep-tech
[13] https://explodingtopics.com/blog/startup-failure-stats
[14] https://spdload.com/blog/startup-success-rate/
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