Bridging the Gap: How Deep Tech Startups Solve the Big Problems Corporates Can’t

Insights on how startups with systemic approaches challenge and complement established companies

The greatest challenges of our time are systemic: climate crisis, food security, global health, industrial transformation. They are complex, interconnected — and precisely what traditional innovation models increasingly fail to address. Although companies invest billions in research and development, they often fail to create solutions that are truly transformative.

A look at the numbers is sobering: According to McKinsey, only 6 percent of companies achieve above-average economic returns on their R&D investments [1]. The innovation gap is even more stark in the climate tech sector: Despite increasing pressure from regulation, markets, and consumers, the majority of radically necessary technologies come not from corporations, but from startups [2].

Credits: Bruno Figueiredo via Unsplash

The reason for this lies not in lack of will, but in structures. Large companies optimize existing business models — they are oriented toward efficiency, scaling, and risk minimization. But systemic problems don’t need optimization; they need rethinking. And that’s exactly what deep tech startups deliver.

In this article, we show how deep tech startups tackle areas where traditional corporate innovation reaches its limits: They don’t think in products, but in systems. They don’t solve partial problems, but question root causes. And they do this with a radical combination of scientific depth, entrepreneurial agility, and a clear focus on impact.

Strategic partners — corporates, venture arms, clusters, and startup programs — have the opportunity not only to foster this force, but to strategically leverage it for their own future viability. The question is no longer whether cooperation makes sense, but how to make it succeed. And above all: how to structure it.

When Safety Becomes Risk

In most large companies, an unspoken law prevails: never disrupt the existing business model. Innovation should optimize the present, not question the future. The result is a systemic innovation paradox: precisely the technologies that are most urgently needed by society — in areas like energy, health, or new materials — fail internally due to existing KPI logic, return-on-investment requirements, and political turf wars.

The core problem: corporates are designed to minimize risks, not maximize opportunities. Innovation projects must measure themselves against traditional success criteria even in early phases — revenue potential, margin expectations, resource planning. Many deep tech approaches fall by the wayside because they cannot (yet) demonstrate a clear business case. Systemic technologies in particular, which initially require infrastructure, cooperation, or political support, don’t fit these frameworks.

According to a McKinsey analysis [3], disruptive innovation projects in established organizations are particularly often discontinued when they would need to create impact beyond departmental boundaries or potentially cannibalize existing product lines. Internal silos, traditional budget cycles, and short-term target agreements ensure that even ambitious innovation departments play it safe — and thus often continue developing irrelevant products instead of daring true transformation.

This dynamic becomes particularly visible in areas like the bioeconomy or climate tech. Projects for CO₂ capture, alternative protein sources, or circular materials often fail internally due to lack of understanding of external factors such as regulatory developments, societal pressure, or future cost exposure. Instead of systemic market logic, internal profit maximization logic prevails — and this often ignores what will matter tomorrow.

The consequence: technologies that are still considered “too early” today are brought to market maturity years later by startups with less capital but greater systemic understanding — while corporates must catch up.

Ironically, excessive risk aversion creates massive strategic risk. Those who only protect the existing lose the ability to renew. The world changes faster than traditional budget cycles — and companies that don’t recognize this lose not only innovation power but also relevance. Startups, however, operate beyond these constraints — and that makes them true partners for systemic future solutions.

Cut Off from the Pulse of Time: How Corporates Lose Touch with Real Innovation

Many established companies live in a self-created ivory tower — isolated from the academic hubs, societal dynamics, and scientific breakthroughs from which the great solutions of our time emerge. What once counted as solid strategy — R&D departments, partner programs, innovation scouting — today seems like an innovation subscription without impact.

The problem? Innovation is increasingly externalized. Instead of being deeply embedded in research and society, many companies outsource innovation work to accelerators, labs, or consultancies. But true transformation cannot be outsourced. It emerges where technological curiosity, societal responsibility, and entrepreneurial risk-taking converge — and this space is increasingly occupied by startups.

Case in point: While European universities like ETH Zurich, TU Munich, or the BioInnovation Institute in Copenhagen have long been producing systemic solutions in areas like synthetic biology, AI, or alternative materials, many corporates operate with delay — they react instead of shaping. Studies show that 70% of the world’s most innovative startups have close connections to universities and research institutions [4]. At the same time, the relevance of corporates in political innovation dialogues is declining, as recently seen in the example of AI regulation: While tech startups like OpenAI or Aleph Alpha publicly take positions, the innovation departments of many corporations remain invisible.

This strategic distance has consequences: companies that don’t actively embed their innovation power in scientific ecosystems not only lose access to talent and insights — they lose their ability to shape the future in the medium term. Those who have no voice in today’s discourse will have no business model tomorrow.

The alternative? Startups that see research, technology, and societal impact not just as playing fields, but as infrastructure for their own business logic. And this is precisely where the opportunity for new partnerships lies — if corporates are ready not just to buy innovation, but to relearn how innovation emerges.

The Failed Promise of Innovation Labs

Corporate Innovation Labs were touted for years as the answer to digital transformation and disruptive markets. From Berlin to Palo Alto, innovation centers were founded, startups invited, post-its stuck, and hackathons organized — yet the results in most cases fell short of expectations. Despite budgets sometimes reaching seven figures, only a few labs could produce genuine, scalable innovations that strategically advanced their parent corporation.

The reason lies less in lacking talent than in the structural design of the labs. Many innovation units were set up incorrectly from the start: as creative satellites without strategic mandate, without influence on core decisions, and without real access to entrepreneurial risk. The consequence: innovation projects remained stuck in prototype status, disappeared within the organization, or were rejected out of fear of internal competition.

According to a McKinsey analysis [5], only 6% of Corporate Innovation Units achieve measurable strategic impact — despite significant investments. The reason? Lack of connection to strategic goals, insufficient governance, and no clear ownership for implementation and scaling [6].

What began as a sandbox for creativity often became a stage for symbolic politics. Many labs served external presentation (“We are innovative!”), not actual future-proofing. But impact and deep tech innovations don’t work as PR measures. They are expensive, time-consuming, regulatorily complex, and politically sensitive — and therefore need not playgrounds, but genuine structural seriousness.

The failed promise of innovation labs is not an argument against innovation — but evidence that true innovation capability must be deeply anchored in a company’s structure. Where budgets, KPIs, and leadership are truly aligned with what shows impact beyond the next quarterly report.

Between Complexity and Clarity: Why Deep Tech Startups Think Radically Differently

While many corporates operate within clearly defined product categories, departmental boundaries, and existing value chains, deep tech startups start earlier — with the system itself. They don’t begin with the product, but with an in-depth analysis of the problem: What systemic dynamics are involved? What technological levers actually exist? Which actors — from regulation to science to infrastructure — influence possible solutions?

This systemic thinking is not a nice extra, but a hard survival strategy. Because many of the challenges that deep tech startups address — from decentralized energy to cell-based production — cannot be solved within existing logics. A carbon capture startup, for example, that only works on a filter without considering industry behavior, CO₂ pricing, or transport infrastructure will fail — no matter how good the technology is.

An example: The Danish startup Seaborg shows how deep scientific excellence paired with entrepreneurial focus produces technological breakthrough innovations — beyond traditional corporate R&D structures. The company develops a new type of reactor based on molten salts (Molten Salt Reactor), which is considered safer, more compact, and more cost-effective than conventional nuclear power plants. The vision: a scalable, CO₂-free baseload energy source for a decarbonized global economy. What stands out: While large energy companies hesitate or have dismantled their nuclear expertise, a startup succeeds in setting new standards with a small, interdisciplinary team — supported by partnerships with government institutions in Southeast Asia and a network of nuclear physicists, policy minds, and system designers. Seaborg specifically leverages the gap between academic progress and industrial policy needs — a gap that corporations often neither see nor systematically address.

Another example is Meatable, a Dutch startup that cultivates meat. Here too, the product alone is not enough. Meatable thinks in systems: from the regulatory framework (EFSA approval processes), to public acceptance, to scaling into existing logistics and cold chains. This is not luxury — but a prerequisite for actually creating impact as a food tech venture.

What these two startups have in common: They ignore the traditional phase models of research → development → commercialization. Instead, they recognize that technology development today is always also a form of system design. And this is exactly what makes them valuable partners for corporates — not just as acquisitions, but as sparring partners for what companies can hardly achieve alone anymore.

The Long Breath of Progress

Anyone founding a deep tech startup needs more than a good idea. They need patience — and a damn good argument for why this patience pays off. While software products launch new features on a weekly basis, science-based startups often must invest years before even a first prototype exists. For traditional investors, this sounds off-putting. For everyone who truly wants change, it’s an invitation to maturity.

Science-driven innovation cannot be accelerated like a TikTok algorithm. Those developing novel batteries, producing cultivated meat, or synthetically optimizing microorganisms are deeply intervening in biological, chemical, and physical systems. What emerges here is not just a product — it’s a new logic. A new infrastructure. A new market.

The renunciation of quick results is not a deficiency. It’s a condition for real transformation. Because what good is the twentieth app for reducing CO₂ footprints if the industrial base continues to run on fossil energy? Or a bio-label if the supply chains for it aren’t traceable?

Deep tech startups like Twelve show that impact emerges where fundamental production processes are rethought. Twelve converts CO₂ into raw materials for plastics and fuels — an alternative to petroleum-based petrochemistry, built on electrochemical conversion. The company has researched for years, invested millions in infrastructure — and today is changing how entire industries work.

The difference becomes particularly clear in synthetic biology. Startups like Ginkgo Bioworks or Zymergen have done pioneering work — even if not all of these pioneers were successful long-term. Their work shows: biotechnology is not a software module. It requires reliable processes, robust bio-foundries, and a deep understanding of biological systems. Only this way can scalable platforms be built, for example for producing bio-based materials, enzymes, or medical active ingredients.

A central point emerges: it’s not speed that counts, but direction. Those who invest deeply in scientific research often work slower — but with the potential for exponential leverage. Once this leverage takes hold, entire markets shift.

In the world of venture capital, belief in MVPs, sprints, and quick product-market fit often dominates. But impact doesn’t emerge through minimal products, but through maximal change. Those who want to address the climate crisis, food security, or energy independence must build systemic solutions — not clever add-ons. Therefore, the question isn’t: How quickly can a product be brought to market? But: How big is the leverage if it succeeds?

A new battery technology that stores twice as much energy changes logistics, mobility, and energy markets. Cell-based protein production replaces millions of tons of CO₂-intensive meat industry. And a process for CO₂ utilization could form the material foundation of a regenerative industry.

For companies and investors, this means: it’s not enough to apply the same standards to deep tech startups as to SaaS models. Instead of KPIs and scaling phases, new evaluation logics are needed. Those who want impact must learn to accept uncertainty — and understand science not as risk, but as value creation. Because research is not a preliminary phase. It’s the core of the business model. And it’s the key to exactly what we need: solutions that don’t optimize what’s going wrong — but create alternatives that don’t have to go wrong in the first place.

Laboratory Success Is Not the Benchmark

A proof-of-concept is like a promise: it shows that something works in principle — but not whether and how it can hold its own in the real world. This is precisely where the difference between traditional startups and deep tech ventures begins. While many tech companies try to fit into existing markets, deep tech startups often must first create an entirely new playing field — including rules, players, and infrastructure.

Let’s look at the mentioned examples again:

• Twelve (twelve.co) transforms CO₂ using renewable energy into chemicals, fuels, and materials — including jet fuel, plastics, and industrial precursors. But Twelve doesn’t sell reactors as modular systems. The company develops a new logic for CO₂ utilization: local, emission-free, scalable. For this, it cooperates with airlines, plastic manufacturers, and automotive corporations. Twelve is thus less a hardware startup than an orchestrator of new value chains. The technology is a means to an end — what matters is the system that emerges from it.
• Meatable (meatable.com) also doesn’t simply develop cultivated meat. The challenge lies not in cell cultivation, but in scaling: How can bioreactors be operated cost-effectively? What regulatory hurdles exist in different markets? How will cooling and logistics infrastructure be adapted when meat is cultivated rather than slaughtered in the future? Meatable therefore thinks in ecosystems — and works with authorities, food suppliers, and consumer researchers on a complete transformation of the supply chain.
• Seaborg (seaborg.com) builds reactors based on molten salts. But the real innovation leap lies outside the technology. Anyone wanting to deliver baseload energy must invest in grid structure, location policy, public acceptance, and safety standards. Seaborg works closely with governments, infrastructure operators, and energy agencies to create the conditions for a new nuclear-based energy system. The reactor is only part of the solution — what matters is the environment that makes it viable.
• Ginkgo Bioworks (ginkgobioworks.com) is perhaps the best example of a new market logic: The company understands synthetic biology not as a single application, but as an operating system. Ginkgo doesn’t develop end products, but makes its bioengineering platform available to other companies — similar to cloud infrastructure. The business model is based on partnerships, licensing, and a portfolio approach where biology becomes scalable infrastructure for various industries: agriculture, pharmaceuticals, cosmetics, materials. Thus Ginkgo shifts the logic of biotechnology — from product development toward platform economy.

What all these startups have in common: They don’t think from the product, but from the system. And this means that traditional metrics like “time to market” or “product-market fit” are insufficient. What matters is: What market logic is being newly created? Which players must be convinced, which structures built, which regulations actively co-shaped?

Deep tech companies don’t develop individual products, but enable new value chains — often through the combination of technological excellence and strategic co-construction. Market entry is not the goal, but the starting point for profound transformation.

When Buying Isn’t Enough: Why Co-Creation Is More Than Venture Clienting

Venture clienting is currently being touted as the royal road to startup cooperation. The idea: companies don’t just become investors, but early customers of young firms — securing strategic access to innovations. Unlike traditional procurement, it’s not just about “purchasing,” but about making startups market-ready faster through early pilot projects and real customer feedback.

But particularly with deep tech innovations, this model reaches its limits. What’s often missing is not market access, but co-shaping what the market actually demands. Deep tech startups operate in markets that are just emerging: with unclear regulations, missing standards, and sometimes non-existent value chains.

What’s needed here is more than early testing — it’s co-creation. That means: joint roadmaps, open data spaces, regulatory coordination, shared risks — and a commitment to long-term collaboration. While venture clienting “uses” innovations, co-creation helps make them possible in the first place.

Particularly in regulated markets, many corporations’ reflex is: wait until technologies are “ready.” But this is precisely where the greatest opportunities for co-creation lie. Take the example again: Ginkgo Bioworks. The startup cooperates with large agricultural corporations to develop microbiological inputs for cultivation — living microorganisms that replace chemical fertilizers. The problem: approval of such products takes years, is regulated differently in each country, and is scientifically highly complex. The solution: Ginkgo develops the biology, partners contribute market access, approval know-how, and distribution infrastructure. Result: faster, systemic market entry — not as “delivery,” but as jointly supported innovation.

Twelve also shows how co-creation works: The company collaborates with Alaska Airlines and Microsoft to develop synthetic jet fuel for climate-neutral flying. But the partnership didn’t start with the molecule — but with the question: What role should aviation play in a climate-neutral economy at all? The fuel development was conceived together with airlines and regulators — including tests in real flight environments and integration of government funding programs. Result: Twelve is not a supplier, but a co-designer of transformation — with the airline as partner, not as customer.

What makes many cooperations successful is breaking with traditional supplier structures. The best examples show: co-creation doesn’t mean buying in a startup or holding a minority stake — but jointly testing hypotheses, creating experimental spaces, doing pioneering work with public impact, and trying out new business logics.

Meatable, for instance, works closely with government agencies in Singapore — not just for approval, but to build a marketable system for cultivated meat. This partnership is closely watched by large food corporations — because it shows how to not adapt to existing systems, but actively co-shape them.

Seaborg also cooperates with Southeast Asian energy agencies to develop regulatory sandboxes for reactor deployment — long before the technology will be market-ready in Europe. Here it becomes clear: it’s not about B2B sales, but about system innovation in partnership.

For corporates who have been hesitant so far, this is an invitation: instead of waiting for the perfect business case, it’s worth getting involved earlier — as co-designers, not as later buyers. Those who do this secure not only technological advantage, but also learn how innovation really works in complex systems: iteratively, interdisciplinarily, collaboratively. And for deep tech startups, it’s clear: the big levers emerge where you don’t just develop technology — but forge alliances that can move entire systems.

What Makes Partnerships Succeed: Roles, Resources, Realism

What starts promisingly often fails due to underestimated differences: speed, language, expectations. Deep-tech startups operate under high pressure — with limited resources, short decision-making paths, and a massive focus on proofs and product development. Corporates, on the other hand, bring processes, politics, and parallel projects — and often underestimate how much this can paralyze founding teams when there is no clear mandate, no fixed contacts, or no defined budget.

Successful partnerships do not start with the next meeting, but with a clear understanding of roles. Who is really the decision-maker on the corporate side? Who holds the budgets? Who bears the risks? What sounds like basics is often unclear in practice — with fatal consequences for time, trust, and outcomes. Successful co-creation means not leaving these questions open, but clarifying them early — ideally in a joint project structure with fixed responsibilities and escalation paths.

In deep-tech partnerships, speed not only decides first-mover advantages but often the existence of a startup. Six months without a decision can consume a runway; a year without feedback can destroy a scaling option. This means: Corporates must be willing to operate outside of classic procurement tactics — with agile pilot processes, accelerated committee decisions, and a clear willingness to work with unfinished technologies.

This requires courage to embrace uncertainty. Yet, it is precisely in regulated markets — such as the energy sector, healthcare, or industrial processes — where crucial learning effects emerge: What is missing for certification? How does a real system react to the new element? What infrastructure needs to be co-developed?

A classic reason for failure is unresolved questions about IP — meaning who actually owns the results of joint development. While startups often want to retain maximum control from their technology founder logic, corporates think in terms of exploitation rights, exclusivities, and market segmentation. The problem: Once these questions are negotiated only after the proof-of-concept, trust is usually already damaged.

Successful cooperations regulate these questions in advance — such as through tiered licensing models that link later participation or exclusive use to certain milestones. Important here: a realistic understanding of mutual interests. Startups need protection for their core IP; corporates need reliability for their investments. Good partnerships create transparent frameworks for this — instead of blocking innovation with standardized NDAs.

Many corporate-startup projects fail not because of technology or budget, but because of invisible hurdles: internal compliance requirements, lack of clarity about escalation paths, overloaded steering committees. The rule is: The more complex the environment, the clearer governance structures must be. Who is allowed to decide what? How is documentation done jointly? Which KPIs make sense for both sides?

A best-practice example here is the cooperation between Twelve and Microsoft: Together, they are developing CO₂-based chemicals for chip production. The success lies not only in the technology — but in a precisely orchestrated process of product development, pilot integration, compliance management, and communication strategy. No coincidence, but the result of good governance.

Whether molecular platform, bioreactor, or reactor design — deep-tech startups need more than capital. They need partners who are willing to be real co-creators. And corporates that enter into such partnerships should ask themselves: Do we have the necessary clarity, speed, and openness to credibly fulfill this role? Because this is exactly where it is decided whether co-creation remains a buzzword — or becomes the place where real system solutions emerge.

Venture Studios and Clusters as Catalysts for Innovation

Many groundbreaking technologies remain stuck in the lab because the path to a market-ready product is bumpy, expensive, and complex. Scientists understand technology, entrepreneurs understand the market, and corporates understand processes — but rarely do all speak the same language. Without neutral mediators, promising ideas often remain fragmented and go unused.

This innovation gap arises mainly due to a lack of structures: Who orchestrates the collaboration? How can knowledge be translated purposefully? And how can the patience required for deep-tech developments be maintained without expecting immediate returns?

Venture studios like us, COSMICGOLD, systematically fill this role. They are not classic incubators or mere capital providers but structured platforms that bring together research, startup expertise, and industry knowledge. They act as catalysts — transforming scientific insights into marketable business models, coordinating interdisciplinary teams, and creating frameworks where technology development does not happen in silos but as a collective force.

We, for example, connect excellent scientific institutions with experienced entrepreneurs and relevant industry networks. Through this networking, innovation projects are designed to address regulatory hurdles early, use capital effectively, and precisely meet market needs.

In addition to venture studios, innovation clusters are crucial for enabling profound systemic changes. In regions or sectors where networking and specialization come together, ecosystems emerge that offer rapid learning cycles, access to experts, and a consolidated resource base.

These clusters act as living forums where knowledge flows, standards are co-developed, and new value chains emerge. They are the breeding ground for collaborations in which startups, research institutions, and corporates not only grow side by side but together.

Neutrality is an underestimated success factor here. Venture studios and clusters that do not primarily sell their own products but mainly accompany the innovation process create trust on all sides. This independence enables them to balance conflicting interests, facilitate flexible partnerships, and distribute risks fairly.

Especially in regulated, complex markets — energy, mobility, biotech, advanced manufacturing — this role is central. Innovations rarely emerge linearly in these fields but through iterative processes that must integrate various perspectives.

The role of venture studios and clusters as structured mediators will be crucial in transforming deep-tech innovations from science into real market and system solutions. Without these platforms, valuable technologies risk remaining stuck in the innovation bottleneck — to the detriment of industry, society, and the climate.

Conclusion: Innovation Needs New Alliances

Neither young companies nor established corporations can solve the systemic challenges of our time alone. Climate crisis, resource transition, biotransformation, or the decarbonization of entire industries are tasks that require new technological paradigms — and these only emerge through collaboration. Startups bring speed, technological pioneering, and fresh thinking. Corporates offer infrastructure, regulatory experience, and market access. But without a strategic connection, both sides remain below their potential.

Many innovation partnerships remain in the “tech-dating” stage: a pitch here, a pilot there — but real integration is lacking. Systemic impact does not arise in pilot phases but through active co-creation: co-innovation, joint product development, shared responsibility, and a clear strategic framework for collaboration.

Those who still think of innovation partnerships like procurement processes — whether under the label “venture clienting” or innovation scouting — remain on the surface. The difference lies in the depth of the relationship: Venture clienting evaluates startups based on procurement logic, while co-creation shapes the future together with them.

The companies that act as shapers today will not only be users tomorrow but co-owners of the next industrial revolution. Strategic alliances — across sector boundaries, company sizes, and countries — are not a nice-to-have but a prerequisite for relevance.

This means: Freeing up capacities for real collaboration. Adapting governance structures to enable speed. Sharing knowledge instead of protecting it. And utilizing networks that moderate, translate, and accelerate — from venture studios to scientific-industrial clusters.

The transformation of our economy is not a sprint. It is a marathon — and no one wins it alone. The next big market opportunities arise where technological excellence combines with strategic will and institutional trust. The time for spectators is over.

Those who co-shape now are not only investing in new solutions — but in their own future viability.

Sources:
[1] https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/the-eight-essentials-of-innovation
[2] https://www.pwc.com/gx/en/issues/esg/state-of-climate-tech-2023-investment.html
[3] https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/the-eight-essentials-of-innovation
[4] https://dealroom.co/reports/the-european-deep-tech-report-2023
[5] https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/innovation-in-a-crisis-why-it-is-more-critical-than-ever
[6] https://hbr.org/2019/07/why-innovation-labs-fail-and-how-to-ensure-yours-doesnt