The US is missing a critical piece of infrastructure common in other leading tech ecosystems: an institution like Belgium's IMEC. These public-private entities focus on the pre-competitive phase between academic research and commercial development, de-risking technology and shortening cycle times—a crucial gap in the US quantum strategy.

Contrary to the belief that it has no current utility, quantum computing is already being used commercially and generating revenue. Major companies like HSBC and AstraZeneca are leveraging quantum machines via cloud platforms (AWS, Azure) for practical applications like financial modeling and drug discovery, proving its value today.

The focus in advanced therapies has shifted dramatically. While earlier years were about proving clinical and technological efficacy, the current risk-averse funding climate has forced the sector to prioritize commercial viability, scalability, and the industrialization of manufacturing processes to ensure long-term sustainability.

An often overlooked indicator of national competitiveness in quantum is 'cycle time'—the duration from idea to testable prototype. While the US excels at research, long fabrication lead times (e.g., 18 months for a photonic circuit) create a major disadvantage compared to regions where it takes weeks, hindering the rate of innovation.

Unlike AI, where software learnings diffuse rapidly, quantum progress is a 'hardware sport.' Tacit knowledge is deeply embedded in physical systems, making iteration times longer and knowledge transfer more difficult. This creates more defensible moats for companies and nations that achieve breakthroughs.

The biotech industry recently endured its own "dot-com bust." Post-COVID hype gave way to investor impatience with the sector's fundamental realities: it takes over 10 years and massive capital ($200B/year industry-wide) to get a drug approved, leading to a sharp market correction.

Our ability to generate and test therapeutic hypotheses in silico is rapidly outpacing the slow, expensive conventional clinical trial system. Without regulatory reform, the pipeline of promising drugs will remain stuck, preventing breakthroughs from reaching patients. The science is solvable; the system is not.

Drug development can take a decade, a timeframe that misaligns with typical investor horizons and employee careers. Success requires navigating fluctuating capital market cycles and implementing strategies to retain key scientific talent for the long haul.

While AI for novel drug discovery has lofty goals, its most practical value lies in accelerating development. This includes applying AI to de-risked assets for new indications, improving delivery methods, and designing faster, more effective clinical trials, which is where the real bottleneck lies.