Silicon Valley's rise was built on a simple promise: shrink the transistor, and everything gets faster and cheaper. For decades, that promise held. Each new node delivered more computing power at lower cost, fueling new industries and expanding demand in a virtuous cycle that made Moore's Law a self-fulfilling prophecy. A $200 laptop today outperforms the supercomputers of the 1990s.
But the economic logic that powered the past half-century is shifting. The race to smaller nodes continues—TSMC and Samsung are producing at 3nm and targeting 2nm, while Intel pushes its 18A process—yet the payoff is no longer automatic. Building a leading-edge fab now costs over $20 billion, and governments are pouring money into the effort: the United States committed $52 billion through the CHIPS Act, the European Union launched a €43 billion initiative, and China has invested more than $150 billion since 2014.
California sits at the epicenter of this competition. Nvidia, Google, OpenAI, and a host of startups are buying advanced chips as fast as manufacturers can make them. But a critical assumption often goes unchallenged: that ever-smaller transistors will automatically translate into higher productivity and broader prosperity. That link is weakening, and the consequences could be profound.
The Cost Curve Has Reversed
For years, shrinking transistors reduced the cost of computing. That relationship largely broke down after 7nm. Advanced nodes now require extraordinary capital expenditures, and leading-edge wafers cost dramatically more than their predecessors. The result: only a handful of companies—Apple, Nvidia, Microsoft, Google, and Amazon—can afford to design at the most advanced nodes, concentrating the benefits among a small elite.
Engineering returns are also diminishing. Moving from 180nm to 28nm produced dramatic gains in performance and energy efficiency. The jump from 5nm to 2nm is far more incremental. Increasingly, the real breakthroughs come not from the transistor itself but from the surrounding ecosystem: chiplets, 3D stacking, advanced packaging like CoWoS, high-bandwidth memory, and software optimized for AI workloads. A well-designed system built on mature silicon can often outperform a poorly optimized design on the latest node.
Adoption, Not Fabrication, Drives Productivity
Most sectors of California's economy do not depend on leading-edge chips. The trucks produced in the Central Valley, medical equipment in hospitals, cranes at the Port of Oakland, and irrigation systems across agricultural regions typically run on mature-node semiconductors. These industries do not need 2nm processors. They need affordable computing, practical software, and workers trained to use both effectively.
California has seen this movie before. The state spent years celebrating broadband expansion while overlooking whether businesses, schools, and local communities were equipped to use the new infrastructure productively. Connectivity mattered, but adoption determined the outcome. Semiconductors present the same challenge. Building another $20 billion fab may generate headlines. Helping manufacturers in Fremont, logistics operators in Oakland, and farms in Salinas deploy AI tools at scale would generate productivity.
That distinction matters because productivity growth, not technological prestige, is what ultimately raises living standards. California should focus on three priorities. First, support domestic capacity at mature and mid-range nodes, where automotive, medical, and industrial supply chains remain most vulnerable. Second, lead in advanced packaging, where much of the performance gains from modern computing are now captured at lower cost and energy consumption. Third, treat technology adoption as an economic policy objective by expanding tax incentives, workforce training, and implementation support for small and mid-sized businesses deploying AI.
Robert Solow's famous observation still applies. In 1987, the Nobel Prize-winning economist noted that the computer age could be seen “everywhere but in the productivity statistics.” New technologies create measurable economic gains only when firms reorganize work around them. Hardware alone does not transform an economy; adoption does.
The semiconductor race remains vital for national security and technological leadership. California should absolutely compete to host the world's most advanced chip production. But policymakers should ask a harder question: if the state succeeds in reaching 1.4nm, who becomes more productive as a result? If the answer is only a handful of technology companies, California will have won the chip race while losing the productivity race.
This dynamic has implications beyond California. As the real AI shift is productivity, not chips, other regions—from Indonesia's nickel boom to the Philippines' nickel exports—face similar challenges: resource wealth or technological leadership does not automatically translate into broad-based economic gains. The lesson is universal: adoption, not just production, is the key to prosperity.
