Critics question whether deep tech startups are doing "novel science." However, the strategic goal is often not a new discovery, but making a proven but abandoned technology (like nuclear fission) economically viable and scalable again. This demonstrates that for reindustrialization, effective execution on proven tech can be more valuable than chasing purely scientific breakthroughs.
Shure's founders pivoted back to their original EOR concept, which failed years prior due to a lack of automation infrastructure. The recent maturity of AI agents and stablecoin rails made the initial vision feasible, showing that timing and technological readiness are critical for an idea's success.
Unlike software, where customer acquisition is the main risk, the primary diligence question for transformative hardware is technical feasibility. If a team can prove they can build the product (e.g., a cheaper missile system), the market demand is often a given, simplifying the investment thesis.
The massive energy consumption of AI has made tech giants the most powerful force advocating for new power sources. Their commercial pressure is finally overcoming decades of regulatory inertia around nuclear energy, driving rapid development and deployment of new reactor technologies to meet their insatiable demand.
The push to build defense systems in America reveals that critical sub-components, like rocket motors or high-powered amplifiers, are no longer manufactured domestically at scale. This forces new defense companies to vertically integrate and build their own factories, essentially rebuilding parts of the industrial base themselves.
Facing immense electricity needs for AI, tech giants like Amazon are now directly investing in nuclear power, particularly small modular reactors (SMRs). This infusion of venture capital is revitalizing a sector that has historically relied on slow-moving government funding, imbuing it with a Silicon Valley spirit.
Unconventional AI operates as a "practical research lab" by explicitly deferring manufacturing constraints during initial innovation. The focus is purely on establishing "existence proofs" for new ideas, preventing premature optimization from killing potentially transformative but difficult-to-build concepts.
Moving from a science-focused research phase to building physical technology demonstrators is critical. The sooner a deep tech company does this, the faster it uncovers new real-world challenges, creates tangible proof for investors and customers, and fosters a culture of building, not just researching.
To rebuild its industrial base at speed, the US government must abandon its typical strategy of funding many small players. Instead, it should identify and place huge bets on a handful of trusted, patriotic entrepreneurs, giving them the scale, offtake agreements, and backing necessary to compete globally.
The decisive advantage in future conflicts will not be just technological superiority, but the ability to mass-produce weapons efficiently. After decades of offshoring manufacturing, re-industrializing the US to produce hardware at scale is Anduril's core strategic focus, viewing the factory itself as the ultimate weapon.
The mantra 'ideas are cheap' fails in the current AI paradigm. With 'scaling' as the dominant execution strategy, the industry has more companies than novel ideas. This makes truly new concepts, not just execution, the scarcest resource and the primary bottleneck for breakthrough progress.