Why Deep Tech Commercialisation Has a Free Work Problem
Publicly funded innovation programmes often ask independent experts, founders and commercial operators to speak, mentor or travel to their events without compensation – even while the programme itself has a budget for staff, communications, venues and production. Worse: when tickets to the event aren't for free...
The issue is larger than a missing speaker fee. Every innovation support system teaches economic behaviour through the way it buys, prices and distributes value. A programme cannot credibly teach founders to understand willingness to pay, negotiate confidently and charge for expertise while its own operating model treats commercially valuable knowledge as a free input.
That contradiction matters for founders, programme managers, universities, investors and public funders. If deep tech support is meant to move research towards markets, its incentives must model the commercial accountability it expects ventures to develop.
Why free expert work is a commercialisation problem
Founders learn from more than workshops. They also learn from the economic environment around them. When events, mentoring, introductions, travel and specialist advice are repeatedly provided without a meaningful commitment from the recipient, a parallel lesson emerges: valuable resources are available without a buyer making a real decision.
Then the same founders are expected to persuade customers to pay for an unfamiliar technology, accept implementation risk and become an early reference account. That is difficult enough in deep tech. A support system should help ventures cross the gap into transactions; it should not normalise the postponement of commercial consequence.
This is part of the wider problem described in why deep tech support systems struggle to produce deployment: activity can look productive while the underlying decisions remain unchanged.
Free work and pro bono contribution are not the same
Many experienced people choose to mentor founders, judge competitions, publish research or support communities without charging. I do too – regularly. Voluntary contribution can create a genuine public good, strengthen a field and help people who could not otherwise access specialist knowledge.
But pro bono work is a choice made by the expert. Free expertise as a recurring programme input is a choice made by the institution.
The distinction is especially important when a funded organisation can pay for programme management, communications, catering or event production, yet expects the people providing the substantive commercial knowledge to absorb both preparation time and travel costs. That is not simply efficiency. It is institutional cost externalisation.
The hidden cost: free expertise changes who gets heard
An unpaid contribution is not equally affordable for everyone. People employed by universities, public bodies, large companies, investors or grant-funded organisations may participate because someone else already finances their time. Independent operators and founders face a direct trade-off: every day spent preparing and travelling is a day not spent selling, building or serving customers.
As a result, an ecosystem built around unpaid expertise does not automatically select the most relevant commercial experience. It selects the people whose participation is already subsidised.
This can create a structural blind spot. Scientific, policy and institutional perspectives remain valuable, but they cannot substitute for lived experience of pricing, procurement, customer acquisition and economic risk. When commercial accountability becomes optional experience for the people designing commercialisation programmes, founders receive an incomplete picture of how markets work.
What should publicly funded innovation programmes deliver?
Not every euro of public funding needs to return immediately as tax revenue. Research, standards, infrastructure and education can create substantial value before a transaction occurs. Public intervention is often justified in deep tech because long timelines, technical uncertainty and capital intensity prevent private actors from funding every necessary experiment.
However, funding explicitly allocated to commercialisation should eventually create innovation additionality: economic outcomes that would not have happened without the programme. Relevant evidence includes:
new buyer relationships and procurement commitments;
customer revenue or credible willingness-to-pay evidence;
private investment supported by commercial proof;
intellectual property put to productive use;
jobs, capabilities and companies that persist beyond the programme;
weak projects stopped before they consume further resources.
Attendance, networking activity and visibility may be useful indicators, but they are not substitutes for changed decisions. A serious evaluation asks whether a buyer committed, evidence improved, a viable project reached its next transition, or an unviable one was closed. That is the difference between reporting outputs and demonstrating impact. The same distinction underpins the need for better European innovation metrics.
The invoice is not the point – accountability is
This is not an argument that every expert must always be paid. Nor does paying a speaker prove that a programme is effective. The useful governance questions are more demanding:
Who chooses to contribute, and who is expected to?
Who absorbs the economic cost?
Is the contribution a genuine public good or a core input disguised as goodwill?
What behaviour does the arrangement model for founders?
What evidence would justify continuing, redesigning or stopping the programme?
What economic consequence should the public funding produce?
These questions expose whether a support programme is building commercial capability or merely sustaining its own activity.
Design commercialisation programmes around real decisions
Deep tech programmes work best when incentives, evidence and governance point in the same direction. That means defining the commercial transition a programme is meant to enable, deciding what credible evidence looks like, and aligning budgets and participation models with that goal.
At Arise Innovations, I work with institutions to assess these structures and develop tailored commercialisation approaches around the actual technology, market and decision context – not a generic programme template. The aim is not to add more theatre. It is to make support produce decisions that survive outside the support system.
Commercialisation programmes should therefore be evaluated not only by what they teach, but by the economic behaviour their operating models normalise. Founders are always learning from the system around them – especially when the system does not realise it is teaching.
The full, and a little "less-filtered", argument can be found in my essay:
Related reading: Explore the analysis of why deep tech support systems are structurally designed to sustain themselves, or review Arise Innovations’ research and intelligence work
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