Lost in Translation: Why Deeptech Startups Struggle to Scale

Why the deeptech boom is masking a deeper execution gap—and what it will take to bridge research, capital, and market adoption

In recent years, deeptech has attracted significant global investment, driven by advances in artificial intelligence, biotechnology, and other frontier technologies. Yet, beneath this momentum lies a more sobering reality: a large proportion of early-stage deeptech startups fail to scale beyond their initial funding stages.

This disconnect—between capital inflow and startup survival—points to a deeper structural issue. It is not simply a funding gap, nor a shortage of innovation. Instead, it reflects a breakdown in how ideas move from research to real-world adoption.

At the center of this breakdown is a less-discussed challenge: translation. Deeptech startups operate across fundamentally different domains—scientific research, venture capital, and end-user markets. Each of these functions with its own language, expectations, and metrics of success. The failure to bridge these worlds often determines whether an innovation scales—or stalls.

The Translation Problem in Deeptech

Unlike consumer or SaaS startups, deeptech ventures operate at the intersection of science, capital, and market adoption. Each of these domains follows a different logic—and, crucially, a different language.

Researchers communicate in terms of technical precision, validation, and performance metrics. Venture capital, by contrast, evaluates opportunities through market size, scalability, and return timelines. End users, meanwhile, respond to clarity, usability, and immediate value.

This divergence creates structural friction that is often underestimated. Industry analysis increasingly suggests that deeptech startups fail not because of weak technology, but because of challenges in commercialization and market alignment. While innovation output continues to grow globally, translating prototypes into paying customers remains a persistent bottleneck.

The result is what many operators describe as a “translation gap”—where technically sound innovations struggle to be understood, evaluated, and adopted across stakeholders.

This gap manifests in multiple ways:

• Investors struggle to interpret technical breakthroughs into business outcomes
• Customers find it difficult to relate complex innovations to real-world use cases
• Founders often communicate depth, but not relevance

In effect, the challenge is not just building breakthrough technology—but ensuring that it can be understood across contexts.

The Persistence of the “Build It” Mindset

Compounding this challenge is a long-standing assumption within research-driven ecosystems—that building a strong product is enough to guarantee success. This belief continues to shape how many deeptech startups allocate their time and resources. Founders often invest heavily in product development, while underestimating the importance of market creation, distribution, and user education.

As Nidhi Mathur, venture partner at Axilor Ventures and part of Axilor Labs, noted in her conversation with AsiaTechDaily:

“Been working with researchers now for more than 20 years, and one thing that has remained consistent and unsolved across two decades is they’re very passionate about what they’re building and they think that building is the end of it, and once they build it, people will come, people will flock, right?

I think that is something that I would want more researchers to understand that while building is really, really tough, you don’t get to where we want to get to just by building alone. So, if you build it, they’ll come—that’s the standard fallacy. More and more people are aware of it, but I still see that they do not lay as much emphasis on, for example, awareness creation or distribution.”

The persistence of this mindset highlights a broader gap in how deeptech ventures approach commercialization. Building remains necessary—but without distribution and awareness, it is insufficient.

The Missing Layer: Execution Between Research and Market

The recurring nature of these challenges suggests that the issue is not limited to individual founders—it is structural. Between research and scale lies a critical, often underdeveloped layer: execution. This includes shaping business models, building teams, defining go-to-market strategies, and aligning product capabilities with real-world demand.

In traditional venture ecosystems, this layer is often assumed rather than built. Founders are expected to bridge it themselves, even when their expertise lies elsewhere.

In response, alternative models—particularly venture-building platforms—are beginning to gain traction. These models work earlier in the lifecycle, helping founders navigate the transition from technical innovation to market-ready businesses.

Designing Teams for Commercialization

One of the more effective responses to the deeptech execution gap is emerging through how teams are intentionally designed from the outset. Rather than expecting founders to operate across research, product, and commercialization simultaneously, a growing number of deeptech ventures are adopting complementary team models—where distinct roles are built around technical depth and market execution.

This shift recognizes a fundamental constraint: in deeptech, both innovation and commercialization are full-time problems. Attempting to stretch a single founder across both often leads to trade-offs that impact product quality, speed to market, or both.

As Nidhi Mathur explained to AsiaTechDaily:

“Encourage researchers to play to their strengths. Which means they can apply the same intelligence and learn how to do marketing and all. One, they will spend a lot of time and money doing that. But at the same time, my bigger worry is that it takes time away from making world-class research products, which they are best suited to build—and nobody else is better positioned to do that.
At Axilor Labs, we ask how we can bring complementary skills into the startup—people who can address the rest of it and focus on commercial value creation full-time, while researchers focus on innovation and product value full-time.”

This model is increasingly being reflected across venture-building platforms and early-stage deeptech programs, where teams are assembled with clear functional ownership from day one. Technical founders focus on advancing the core innovation, while experienced operators take responsibility for market-facing functions such as customer discovery, distribution, and business model development.

The impact of this approach is twofold. First, it preserves the integrity and pace of technical development. Second, it accelerates the often-delayed process of market alignment—ensuring that commercialization is not treated as a downstream activity, but as a parallel track.

In practice, this represents a broader shift in how deeptech startups are built: moving from founder-led generalism to system-led execution, where success depends on how effectively capabilities are structured within the team.

From Innovation to Execution: Bridging Deeptech’s Missing Layer

Across markets, the highest-performing deeptech startups are not necessarily those with the most advanced technology, but those that are able to integrate three capabilities early: technical depth, commercial thinking, and structured execution. The absence of any one of these creates friction that compounds over time—often surfacing only after initial funding is secured.

Research across deeptech ecosystems shows that startups that invest early in customer discovery, distribution pathways, and team composition are significantly more likely to transition beyond the seed stage. In contrast, those that delay these decisions tend to encounter bottlenecks that are harder to correct later.

This is where the ecosystem itself is beginning to evolve. Rather than expecting founders to independently bridge the gap between research and market, there is a growing recognition that this transition needs to be designed, not assumed. Venture-building platforms, operator-led teams, and domain-specific commercialization support are all part of this shift—introducing structure into what has traditionally been an unstructured phase.

As Nidhi Mathur pointed out in her conversation with AsiaTechDaily, the challenge is not just building what is technically possible, but ensuring that it connects with what is practically needed.

Quick Takeaways

• Deeptech’s biggest bottleneck is not innovation—it’s execution.
  High funding levels contrast with low scale-up rates, pointing to structural gaps beyond capital.
• The “translation gap” remains a core challenge.
  Misalignment between how researchers, investors, and customers understand value continues to limit adoption.
• Strong technology does not guarantee market success.
  Commercialization—especially distribution, positioning, and customer discovery—is often underdeveloped.
• The “build it, they will come” mindset still persists.
  Many founders over-index on product development while underinvesting in go-to-market strategy.
• Complementary team design is emerging as a key solution.
  Separating technical and commercial roles allows startups to maintain innovation quality while accelerating market entry.
• Venture-building models are gaining relevance.
  Structured support at the zero-to-one stage is helping bridge the gap between research and scalable business.
• Early execution decisions determine long-term outcomes.
  Startups that integrate customer validation, distribution, and team structure early are more likely to scale.