The gap isn't about the science itself – it's about understanding market timing, customer needs, and surviving the brutal reality of scaling deeptech ventures.

The challenge starts with how universities are fundamentally designed. What are universities? They're academic cooperatives. Built around two core pillars – research and teaching. Commercialisation is often the third leg of the stool, driven entirely by personal motivation rather than institutional support.

This creates a structural misalignment. For senior university management, commercialisation is all risk and very little reward, at least personally. No one leader has real skin in the game when it comes to commercial success. Meanwhile, it's actually easier for researchers to publish everything and ignore commercial impact – it's better for their academic career trajectory, easier on their work/life balance.

So, any academic working on commercialisation is already swimming against the current. They're already different, already thinking beyond institutional norms. Every principal investigator that leads a lab is inherently entrepreneurial because they've had to be – they've secured funding, built teams, managed complex research programmes.

But being entrepreneurial enough to build a research team doesn't automatically translate to building a company. The skills overlap, but the stakeholders, timescales, and success metrics are completely different worlds.

Three ways to (maybe) spot a winner

Through years of evaluating early-stage technologies – often before they're even research papers – I developed three core heuristics for spotting viable spinouts. A caveat – this isn't bulletproof, there are plenty of things that I thought would go somewhere that didn't. But over a long enough horizon the pattern holds.

First, is there a big market? At least half a billion worldwide or a billion if you’re on the US west coast, because if it's not investable you won't raise the capital needed for deep-tech development. No one is building deeptech companies in their garage.

Second, is it a platform technology? Can you build multiple products from this core innovation? That's what separates sustainable businesses from one-trick ponies.

Finally, and most importantly, is there the makings of a commercial team? Academic teams never look commercial in the early days, but successful ones always have three specific roles: the grey-haired principal investigator who straddles the university-company divide, answering deep science questions; the technical implementer – often a postdoc or PhD student who can make the technology actually work operationally; and finally a commercial operator who handles project management, finance, fundraising, and customer relationships.

Where I've seen teams consistently fail is when that principal investigator, brilliant though they may be, tries to become the CEO. Leading a research team is fundamentally different from leading a company and dealing with investors. They can come with an authoritarian style that eventually hits a wall – sometimes at fundraising, sometimes several stages down the line.

The people who tend to succeed are those who ask questions and are genuinely open to the answers. I've seen academics ask customer questions because they're supposed to – because they're on an accelerator programme with grant funding at the end – but they don't really want to hear what customers tell them.

The successful teams? They actually want to hear what customers tell them, even when it's uncomfortable. Especially when it's uncomfortable.

The Irony of the forgotten goldmine

This structural misalignment creates a perverse irony. Universities can be sitting on goldmines – commercially viable innovations that never see the light of day because they're not academically interesting enough, or they don’t have the right team around them at the right time.

I've seen this play out: a research group develops a new diagnostic tool as part of a larger cancer research project. The tool works, solves a real problem for clinicians, and could be commercialised with minimal technical risk. But, before anyone has asked the key question of how to get this into the hands of users, the research gets published as a methods paper (or in some cases a simple recruitment advert for a post-doc) and boom! The opportunity is gone. It can no longer be patented or protected, and because it can’t be protected it becomes a poor investment risk.

Meanwhile, a lab team can do everything right, follow the process, spend years trying to commercialise their breakthrough cancer mechanism – which is scientifically brilliant but requires massive validation, faces regulatory hurdles, and needs a lot of cash to demonstrate clinical utility – and it falls down on one of many non-technical hurdles such as capital raising, project planning, execution, or team alignment.

This pattern repeats itself across research tools, software solutions, and incremental improvements that get published and left at the publication stage, never productised.

This is no one’s fault in particular. It’s a function of the system. People just act rationally in the system they find themselves in. The current system discourages academics from pursuing opportunities, asking hard questions, and working collaboratively in a commercially focused team.

What needs to change

The fix starts with incentives. Take career progression – if an academic sits in a promotion meeting with 20 publications while their colleague has 100, but they've done two successful spinouts, then that commercialisation effort needs to count for something meaningful. The REF and KEF frameworks attempt to measure impact, but they don't go nearly far enough.

More fundamentally, we need academics to ask commercial questions at the design stage, not as an afterthought. This only happens when commercialisation becomes a genuinely valued part of academic careers – not mandatory, but properly recognised and rewarded.

Applying the lessons

When I decided to join AMPLY, it came down to two factors shaped by everything I'd learned watching spinouts succeed and fail. First, timing. I’m not getting any younger, and if I was going to do a spinout, it needed to be before I hit 60 wondering why I never tried.

Second, and most importantly, was the team. Working with my colleagues Ben and Chris over two years, there was a connection – a similar work ethic, similar approach, and crucially, they were fundamentally good people who I could envisage working with for the next five to ten years. I was convinced that whatever challenges we'd face, and despite the many reasons that we could fail, we wouldn't fall down on the silly, inconsequential things that derail so many spinouts. Think team conflicts, ego clashes, market misunderstandings.

Because ultimately, the technology is only as good as the people who can successfully bring it to market. Academic excellence creates excellent possibilities, but commercial success requires understanding markets, timing, and human nature as much as science itself.