Incentives to learn for different types of deep-tech founders
How founder type can influence how fast deep-tech startups learn and grow.
The most important feature for early-stage deep-tech entrepreneurial projects is to learn as fast as possible.
In a context of extreme uncertainty, team diversity is usually seen as a plus by potential investors and is acknowledged as a fundamental ingredient of learning by innovation scholars.
However, I think that team diversity, at the beginning of the entrepreneurial journey, might be one of the biggest constraints of early-stage deep-tech startups coming out of universities and research centers.
By experience, I see that different types of incentives and competencies might indeed slow down learning, eventually killing projects even before they take off.
This article will explore how founder-type influences the ability to learn of a deep-tech entrepreneurial team coming out of a university.
Types of founders and related incentives/naivety
In early-stage deep-tech startups emerging from universities and research centers, there might be four different types of founders.
Type 1: Junior people with a major in business/economics or an MBA, usually with some form of innovation consulting experience.
These people are usually the “business developers”, managers, and investor relationship managers of early stage projects. Most of them however have little technical background, fact that might limit their ability to understand problem-solution fit, or even to understand how far is the technology from a reasonable commercial milestone. Moreover, since they are often juniors, they have little to loose if the startup fail.
Incentives to learn: low.
They are the “business experts” among techical people. They know they are right.
Incentives to rush the process: high.
A “CEO & CO-founder” headline on LikedIn will boost their ego. Often join exactly because of this.
Type 2: Professors and researchers who invested a great deal of their careers into the technology.
This second category of founders have a profound knowledge of the technology. They probably have bet their careers on developing such kind of inventions, thus they are the first people beliving that their technology will succeed. Most of the time however these people had little or no exposure to entrepreneruship.
Even worse, they often lack any kind of incentives in learning “business related” stuff. They are the only ones that have a stable job, and likely will get little out of the venture anyway. The promise of “easy money” from Investors combined with a deep love for their baby however might be a letal combo.
Incentives to learn: low.
What they lose if the initiative fail? Knowing that probably the initiative will fail (lesson 1 of any entrepreneurship course), probably very little.
Incentive to rush the process: depends on presence of alternative forms of funding.
extremely low in case there are other accessible funds.
extremely high in case there is no alternative source to fund the technological development.
Type 3: People with actual business and industry experience.
These people are hard to find, especially because they are extremely hard to convince if the starting team has not yet learned a bit about the process. They need to see that the technology has real world potential. They need to see that the starting team knows about the real problem, since probably you will require them to switch career. A stable career in a corporation with a unstable career in startups… so there must be a clear path. They can be extremely valuable resources with high incentives to learn (willing to prove themselves), and probably will have little incentive to rush the process since they will need to reduce uncertainties before jumping on such a risky project.
Incentives to learn: high.
Swithc career. Need to prove themselves
Incentive to rush the process: depends.
Hopefully they know how things work, and what is required to move from 0 to 1. You will need a great deal of talk before onboarding one!
Type 4: PhD Students and early post-docs who are reasonably going to lead the commercialization effort.
PhD and early stage post-docs, from my point of view, are the category of people that are better positioned to learn. They have extremely high incentives to rush the process: they have a deadline from day one. The should also have very high incentives to learn about entrepreneurship. Even though people do not talk much about this yet, only a small percentage of these people will effectively end-up un working in academia, while most of them will work in the private sector. Irrespectively from where they will work, entrepreneurial skills will give them an edge over competing candidates.
PhDs and early researchers moreover might not be too much in love with the technologies they are developing yet, but have a deep-technical expertise require to understand whether they are moving in the right direction.
Most of the time however they end up working on someone else plan, thus love for “the solution” should not be too much deeply rooted in their minds. This combination of fixed deadline + career flexibility should make this category of founders more open and eager to learn.
Incentives to learn: should be high.
If they realize that academy is just one of the possible career path and that entrepreneurship can improve their stand, irrespectively of the career path they will choose.
Incentive to rush the process: high.
They are the ones that have a timer on their heads.
How these incentives shape team learning?
At the start of their entrepreneurial journey, founders of types 1 and 2 often carry very big blind spots, shaped by their incentives and prior experience (or lack thereof), that can lead to a biased understanding of the entrepreneurial process.
This creates a significant disadvantage for their startups, as engaging type 3 individuals becomes nearly impossible unless they either happen to be in a highly developed ecosystem, or ar capable to overcome their “founder reflexes” and related cognitive biases.
My friend Jeroen Coelen of I Want Product Market Fit blog summarized these “founder reflexes” in this beautiful post.
Me and Renita Kalhorn recently dig deep into technical people biases here.
Conclusion
In deep-tech entrepreneurship, speed of learning is everything, yet not all founders are equally equipped or incentivized to learn. While diversity of backgrounds is essential in the long run, at the very beginning it can act as friction rather than fuel.
University-born startups often struggle not because their technologies lack potential, but because their founding teams start with misaligned incentives and cognitive blind spots. Professors protect their inventions, business graduates chase personal validation, and industry professionals hesitate to engage. Amid all this, PhD students and early post-docs emerge as the most promising learners: hungry, time-bound, and technically competent enough to bridge academia with real world.
If we truly want to unlock the potential of deep-tech coming out of universities, we should stop assuming that “team diversity” automatically drives progress. Instead, we must build founder readiness first, creating environments where early-career researchers can learn fast, fail safely, and evolve into entrepreneurial scientists.
That’s where deep-tech learning really begins — not in the lab, but in the mindset of people.