Skip to content
Search

Latest Stories

Follow Us:
Top Stories

A Proven Playbook for AI Leadership: Lessons from America’s Chip Comeback

Opinion

Digital generated image of green semi transparent AI word on white circuit board visualizing smart technology.

What can the success of SEMATECH teach us about winning the AI race? Explore how a bold U.S. public-private partnership revived the semiconductor industry—and why a similar model could be key to advancing AI innovation today.

Getty Images, Andriy Onufriyenko

Imagine waking up to this paragraph in your favorite newspaper:

The willingness of the U.S. government to eschew partisanship and undertake a bold experiment -- an experiment based on cooperation as opposed to traditional procurement, and with accountability standards rooted in trust instead of elaborate regulations -- has led the U.S. to a position of preeminence in an industry which is vital to our nation's security and economic well-being.


You'd likely be incredulous, right? Private-public collaboration, as if! Bipartisan investment in a long-term project, sure! Redesigning out-of-date systems and ditching unnecessary regulations, in our dreams!

That is not a quote from a utopian novel.

I lifted that language from the Final Report to the Department of Defense on an initiative known as Sematech. In the late 1980s, the US recognized that it was in danger of missing a pivotal technological moment. Japan had seized a substantial advantage in semiconductor research and development. As of 1989, the US share of semiconductor equipment sales plunged from 69 percent in 1983 to just 51 percent in 1988. Over that same five-year span, Japan surged ahead--increasing its share from 25 percent to 40 percent. Rather than pursue a patchwork of state-driven approaches and in place of relying solely on private US firms to magically catch their Japanese rivals, a diverse set of stakeholders coordinated a national response.

Congress acknowledged that the status quo marked an "unacceptable situation" and formed SEMATECH--a private-public consortium of 14 private companies committed to putting the US back on the vanguard of this critical industry. In practice, this involved “developing, demonstrating and advancing the technology base for efficient, high yield manufacturing of advanced semiconductor devices.” The consortium embarked on research covering everything from lithography to plasma etch and deposition equipment. The idea was that this kind of basic research would lead to the US reclaiming its status as the market leader.

Such an intensive, ambitious, and cooperative research agenda would not have happened without a willingness for public and private participants to invest—literally and figuratively—in a more competitive, productive America rather than attempt to regulate our way back to the top. The deal required all stakeholders to put significant skin in the game. More specifically, each company pledged to pay one percent of their semiconductor sales revenue per year, albeit with a floor of $1 million and a ceiling at $15 million. The federal government threw in a cool $100 million of matching funds to help things along.

But the buy-in from the private side was far more than financial--as Chris Hughes explains in his book, “Marketcrafters.” Companies sent some of their top talent to Austin, home to Sematech’s state-of-the-art fab facility (which was built in just 32 weeks). Many of those brilliant engineers stayed for two years--demonstrating the importance of the cause as well as the esprit de corps that SEMATECH’s leaders managed to cultivate.

SEMATECH firms also agreed that the resulting research would be shared and commercialized, which meant that even non-members could experience downstream benefits from the project’s advances. Intel later reported that its cumulative $17 million in SEMATECH saved the firm $300 million via production efficiencies. On the whole, economists suspect that the return on investment was somewhere between $1.40 and $2.80.

We’re in dire need of a SEMATECH for AI. More than three years into the current generative AI revolution, there’s a shortage of basic AI research -- studying new AI paradigms beyond large language models, developing evaluations to test model capabilities, and generally advancing the AI frontier so that we can keep pace with geopolitical rivals and realize the technology’s potential to increase human flourishing.

It’s no secret that some of America’s greatest scientific and technological advances have come about through getting a bunch of really smart folks together in one place and charging them with an ambitious mission that’s personally meaningful, politically important, and, yes, lucrative. A SEMATECH for AI would reduce duplicative work that’s going on at the labs, across universities, and within nonprofits.

Two key lessons should be gleaned from the SEMATECH example: first, government should not pick winners or micromanage innovation; and, second, the state can sometimes do the most good by clearing space for voluntary coordination, aligning incentives, and then getting out of the way. A modern AI consortium should be narrow in scope, time-limited, and disciplined by real private capital—not a new regulatory superstructure, not a permanent bureaucracy, and not a vehicle for industrial policy. Its charge would be simple: fund pre-competitive research that the market undersupplies, share results widely, and sunset once its mission is complete. That approach respects what folks such as Nobel Laureate Joel Mokyr have long argued—that durable technological leadership comes from trust, experimentation, and decentralized problem-solving, not compliance checklists or precautionary bans.


Kevin Frazier is a Senior Fellow at the Abundance Institute, directs the AI Innovation and Law Program at the University of Texas School of Law.


Read More

Illustration of a man and woman arguing

Arguments or debates, disputes or conflicts, verbal clashes. Angry men and women roar at each other

Getty Images

What If Polarization Is the Wrong Diagnosis?

I just listened to political scientist Lilliana Mason challenge one of the most familiar explanations of what is wrong with American politics.

We are constantly told that America suffers from polarization. Republicans and Democrats have moved too far apart. They distrust each other, increasingly regard one another as enemies, and have lost the ability to compromise. The prescribed remedies are familiar: more civility, more dialogue, less hostility, and a return to the political center.

Keep ReadingShow less
 Vintage Computer with AI Text on Screen and Neon Lighting, figures of people standing around it.

Calling AI “super” makes it sound omniscient. A faith leader coalition explains why the word “artificial” matters and what to do instead.

Royalty-free/Getty Images

Reject the Sand God. Call AI by Its True Name

President Trump recently signed an executive order telling federal agencies to replace the term "artificial intelligence" with "Super Intelligence." He said the word "artificial" makes the technology sound fake.

Many Americans already treat AI as a “super intelligence” that knows better and that they should confide in. Many already assign it the role of spiritual counsel, close confidante, or even romantic partner. In a Common Sense Media survey, one-third of teenagers claimed they had discussed serious personal matters with an AI instead of a person, and 31 percent said talking with an AI companion was as satisfying as talking with friends, or even more so. Bible Chat, an app that offers scripture-based advice and prayers, has been downloaded more than 30 million times.

Keep ReadingShow less
news video camera
black video camera
Photo by Matt C on Unsplash

When the Cameras Went Dark: What a Press Ban Reveals About American Journalism

When I came to the United States from India in 1982, I did not trust newspapers. Back home, everyone knew which party a paper favored before reading a line. American television felt different. My family watched Peter Jennings every evening, and later I found Larry King on CNN, who let his guests talk instead of telling viewers what to think. That habit of holding everyone to one standard has mostly faded.

On Sept. 18, President Trump announced he was banning CNN, MS NOW and Politico from the White House, saying their reporting was fake news. The next morning the Secret Service took reporters’ badges at the gate. The outlets sued on Monday, and Fox, ABC, NBC and CBS declined to take CNN’s turn in the television pool. On Sept. 24, Judge Timothy Kelly, whom Trump appointed, blocked the ban for 14 days. He found the outlets likely to win on due process, because reporters got no notice and no chance to respond, and he doubted the government’s national security explanation. Trump’s answer was to call the judge “totally disloyal.”

Keep ReadingShow less
Woman sitting and typing on laptop on her lap.

AI chatbots are always guessing, even when they sound sure. Experts and Claude itself explain the AI trust gap, bias and why to check sources.

Oscar Wong/Getty Images

AI’s Information Crisis: Why Trust Is Harder to Sustain Than Ever

In all the debates about human and machine intelligence, one thing has become clear: We humans love to talk about AI. We’ve speculated whether AI will create or eliminate jobs, revolutionize or destroy science, comfort or torment us, entertain us brilliantly or rot our brains, or some combination of all these things. But amid the noise, we may have paid too little attention to a subtler impact: how AI is disrupting our relationship to information and making it harder to know what to believe.

The problem isn’t just deepfakes, the malicious manipulation of images, video, and audio. Even benign, well-intentioned AI applications can be profoundly misleading. The nature of the large language models that power chatbots means that they are always guessing, however confident they seem. How can we use AI to produce reliable information that improves public discourse rather than distorting it?

Keep ReadingShow less