Competing with Deep Tech

Today’s businesses resemble castles built on icebergs. Competitive advantage has always been temporary, but the commoditization of engineering and the speed of technological dissemination raise the bar as to what qualifies as technology.

Now, all the big icebergs are deep tech domains such as fusion reactors, industrialization of space, quantum computing, biotech, and photonics

Moats” have been the most popular analogy to describe competitive advantage. Just like moats protect castles from invaders, competitive advantage protects businesses from competitors. But the problem with that analogy is that it assumes a fixed and unchanging solid foundation for the castle, whereas in real life forces of commoditization erode advantages just like global warming melts icebergs.

Competitive advantage has always been temporary, but the commoditization of engineering and the speed of technological dissemination raise the bar as to what qualifies as technology.

In a hyper-competitive market, the path from cutting-edge to commodity has never been shorter. AI-powered solutions, open-source platforms, and global supply chains allow would-be competitors to leapfrog traditional barriers at unprecedented speeds. Where does this leave firms that once relied on incremental product improvements to stay ahead?

The stakes are high. But so are the rewards for those willing to look beyond short-term gains, and build robust scientific capabilities. Embrace deep tech, and you’ll be well on your way to forging bigger icebergs that keep your organization at the cutting edge—and your competitors perpetually trying to catch up. Some of the big icebergs are fusion reactors, industrialization of space, quantum computing, biotech, and photonics.

Below, we explore five key propositions for building sustainable advantage through “deep science”—fundamental research combined with AI-driven technologies that unlock faster, more profound innovation cycles, and create moats that resist quick imitation.

1. Unavoidable Commoditization Without Depth

“Shallow innovations quickly lose their edge, but deep science investments resist imitation.”

If there’s one certainty in today’s market, it’s that superficial differentiators will be copied almost as quickly as they appear. A new gadget feature, or a slight software improvement—these can be replicated by a well-funded competitor or a nimble startup within months, if not weeks. This erosion of unique selling points forces firms to look deeper, literally, to the scientific principles and underlying capabilities that power next-generation products and services.

Why Deep Tech?

  • Requires Specialized Expertise: Unlike straightforward product tweaks, deep science often draws on multiple disciplines (e.g., fusion, materials science, biotech, quantum physics). Acquiring this expertise and infrastructure takes years.

  • Involves Fundamental Research: True breakthroughs happen when companies push the boundaries of existing knowledge, making it harder for competitors to catch up.

  • Establishes IP Moats: Patents derived from fundamental R&D, along with trade secrets, create resilient barriers to imitation.

2. Convergence of Multiple Deep Tech Domains

“Generative design, knowledge graphs, and automated experimentation dramatically shorten discovery cycles, as exemplified by Recursion Pharmaceuticals’ ability to find drug candidates in one-third the time.”

Deep science does not need to be a slow, expensive pursuit. AI-driven tools have shattered old assumptions about long R&D cycles. For instance, Recursion Pharmaceuticals leverages automation and AI to discover drug candidates in about one-third the time it usually takes in traditional pharma research labs. By doing so, they underscore AI’s role as a game-changer in making fundamental research more efficient.

Key AI-Driven Strategies:

  • Generative Design: AI models iterate through thousands of potential solutions (e.g., molecular structures, product prototypes) in hours, not months.

  • Knowledge Graphs: These connect disparate data sources—scientific papers, patents, experimental results—enabling researchers to spot patterns and opportunities faster.

  • Automated Experimentation: Robots and high-throughput labs can test hypotheses around the clock, feeding results back into the AI models for rapid refinement.

3. Sustainable Moats Through Scientific Strategy

“Proprietary discoveries and testing protocols, now accelerated by AI, form long-lasting barriers to entry, influencing industry standards and regulatory landscapes.”

When deep science meets AI, you get scientific strategy at its finest. Proprietary discoveries—ranging from novel materials to advanced medical diagnostics—are tough to replicate because they’re often protected by a matrix of patents, specialized knowledge, and trade secrets. Moreover, the insights gleaned can be embedded into testing protocols and certification standards, effectively setting the bar for everyone else.

Why This Matters:

  • Influencing Regulations and Standards: By pioneering new technologies and proving their efficacy, you can steer how regulators and industry bodies define safety, quality, and performance.

  • Building Trust and Authority: A firm that routinely leads with robust, science-backed developments naturally becomes a go-to source of expertise—customers and partners trust it.

  • Commanding a Price Premium: Unique, difficult-to-replicate capabilities allow you to avoid the ruthless price competition that plagues commoditized markets.

4. Cultural and Organizational Transformation

“Adopting AI-enhanced deep science shifts R&D from a cost center to a strategic powerhouse, multiplying the value of each research initiative.”

To fully harness the power of deep science and AI, a firm often must rethink its culture and organizational structures. R&D can no longer be an isolated department working on projects that may or may not see the light of day. Instead, it becomes the strategic heart of the company, driving both product differentiation and future growth opportunities.

How to Make This Shift:

  • Interdisciplinary Collaboration: Encourage teams of scientists, engineers, AI experts, and business strategists to work together, ensuring that discoveries align with commercial goals.

  • Talent Development & Acquisition: Hire and nurture individuals who blend deep scientific expertise with data science skills.

  • Reward Curiosity and Experimentation: Acknowledge that not every scientific foray will yield immediate results, but the successes that do emerge can be game-changing.

5. Future-Proofing Against Commoditization

“With the right tools, talent, and IP management, firms can continually outpace competitors, securing their leadership position as the industry evolves.”

Even the most advanced innovations will eventually face competition, but a well-structured deep science approach is self-reinforcing. Each breakthrough supports the next, and your body of proprietary knowledge grows over time. In essence, you’re always moving the finish line.

Sustaining the Edge:

  • Continuous R&D Investments: Reinforce your deep science capabilities with ongoing funding. Resting on current breakthroughs risks letting competitors catch up.

  • Proactive IP Strategy: Protect new developments systematically, and manage your portfolio so that key discoveries bolster each other’s claims.

  • Adapt to Emerging Trends: AI and scientific fields evolve quickly; stay ahead through partnerships, research grants, and ongoing collaborations with top institutions.

© Saip Eren Yilmaz, 2024

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