Are calls for unity too weak and too generic to move Americans? What are the hard truths that stand in the way of unity?
Educator, activist, and author Tim Shriver joined the Braver Angels podcast to talk about his new book, "The Call to Unite," and the budding nationwide movement to unite America.
The Call to Unite | Tim Shriver with John Wood, Jr. & April Lawsonwww.youtube.com
The nation that wins the global AI race will hold decisive military and economic advantages. That’s why President Trump’s January 2025 AI Action Plan declared: “It is the policy of the United States to sustain and enhance America’s global AI dominance in order to promote human flourishing, economic competitiveness, and national security.”
However, AI global dominance does not just mean producing the best AI systems. It also means that the American “AI Stack” – the layered collection of tools, technologies, and frameworks that organizations use to build, train, deploy, and manage artificial intelligence applications – will become the international standard for this world-changing technology. As such, advancing a commonsense export policy for American AI chips will play a decisive role in determining whether the United States remains embedded at the core of global AI development or is gradually displaced by rival systems.
During the Biden Administration, U.S. policy unfortunately drifted away from an approach that encouraged American technological leadership. In 2024, President Biden signed Executive Order 14110, which wrapped AI development in a bureaucratic maze of political correctness, equity, and government control of virtually every aspect of training and deployment of AI models. And, Biden’s Department of Commerce's Bureau of Industry and Security (BIS) issued last-minute regulations to ban the export of virtually all microchips to China that could be used to develop AI systems.
Biden’s hard ban on AI-capable chips to China was superficially appealing. After all, without chips, China can’t compete with America, right? Wrong. Rather than preserving America’s advantage, blanket export controls on AI capable chips encouraged the emergence of a parallel Chinese technological ecosystem largely beyond U.S. influence and would severely injure US AI companies.
As a result of the ban, China launched an aggressive campaign to replace foreign suppliers. State-backed capital flowed into semiconductor fabrication, chip design, advanced packaging, and AI data-center infrastructure. Chinese firms were encouraged to adopt domestic chips. even though performance lagged Western counterparts, ensuring scale and revenue for Chinese manufacturers. Export controls did not, therefore, freeze China’s AI ecosystem; they reorganized it around domestic Chinese supply chains and used the opportunity to accelerate their competition on the global stage.
Fortunately, Trump has started to right the ship. Since taking office he has revoked Biden’s executive order and lifted the absolute bans on sales of mid-range chips like Nvidia’s H200 chip, allowing them to be sold to highly vetted Chinese buyers. This nuanced approach to chip exports will improve America’s strategic and economic dominance in several ways.
To start, America’s new approach is well designed to thwart the fast development of a competitive Chinese AI ecosystem without surrendering military and strategic dominance in this key technology. It bans export of very high-end AI chips, where the US and its allies enjoy a monopoly. But it allows discretionary export of a lower-class of AI capable chips (for example, the H200 and AMD MI325X chips) to be exported under strict export controls which prevent diversion, mandate Know-Your-Customer protocols, and prohibit any military or intelligence uses.
This lower-class of chips is also about one-tenth as powerful than the top-end chips produced by companies in America and our allies. Yet while the H200 is not Nvidia’s best chip, it still has six times the power of the best AI chip available in China today and is better than any chip that Huawei – the Chinese telecom giant that has served as a domestic alternative to Nvidia – plans to make for at least two years. Now Chinese firms seeking chips have to decide between inferior, costly Chinese chips and much more powerful American exports, taking the wind out of state-based manufacturing efforts in China.
Furthermore, Trump’s policy bolsters the chip manufacturing of the US and its allies. Nvidia, the largest AI/GPU chip manufacturer in the world is no longer hamstrung by exclusion from one of the world’s largest markets and is better to positioned to compete internationally more broadly. This is non-trivial. At around $40,000 per chip, Bloomberg estimates Nvidia has and will lose $10-15 billion a year in lost sales of the H200 chip alone as a result of the chip ban. And it’s not just Nvidia. Biden also cut off Intel, AMD and others from billions in revenue. All this reduces US revenue for R&D, shrinks production runs, increases per unit costs, and gives Chinese firms monopolistic domestic markets, allowing their international expansion.
President Trump’s AI chip export policy supports the larger strategic and economic interests of the US. We may still be the dominant AI figure world-wide, but this is no time to be encouraging the emergence of a competitive AI-stack ecosystem nor to hamstring American companies that are the key to that dominance. That is why we must prioritize speed, scale, and global adoption and resist well-meaning yet misguided efforts like the AI Overwatch Act (H.R. 6875) currently under consideration in Congress. This bill would effectively codify the kind of overbroad export approach President Trump has begun to unwind, undermining the strategic reset now underway.
The surest way to preserve America’s technological edge is not to shrink the commercial base on which it depends, but to expand it. In a long-term strategic competition, staying ahead matters more than trying to hold others back.
Frank D. Francone is a California attorney admitted to the United States Supreme Court bar. He is also a widely published author in Artificial Intelligence, having co-authored a graduate level textbook in machine learning and about fifty peer-reviewed scientific articles in AI and information theory.
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.
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, and is an Affiliated Research Fellow at the Cato Institute.
Anthropic’s lawsuit against the Trump administration over a Pentagon “supply-chain risk” label raises major constitutional questions about AI policy, corporate speech, and political retaliation.
Anthropic’s dispute with the Trump administration is no longer just about AI policy; it has escalated into a constitutional test of whether American companies can uphold their values against political retaliation. After the administration labeled Anthropic a “supply‑chain risk”, a designation historically reserved for foreign adversaries, and ordered federal agencies to cease using its technology, the company did not yield. Instead, Anthropic filed two lawsuits: one in the Northern District of California and another in the D.C. Circuit, each challenging different aspects of the government’s actions and calling them “unprecedented and unlawful.”
The Pentagon has now formally issued the supply‑chain risk designation, triggering immediate cancellations of federal contracts and jeopardizing “hundreds of millions of dollars” in near‑term revenue. Anthropic’s filings describe the losses as “unrecoverable,” with reputational damage compounding the financial harm. Yet even as the government blacklists the company, the Pentagon continues using Claude in classified systems because the model is deeply embedded in wartime workflows. This contradiction underscores the political nature of the designation: a tool deemed too “dangerous” to be used by federal agencies is simultaneously indispensable in active military operations.
The rhetoric has escalated as well. President Trump publicly attacked Anthropic as a “radical left” company and ordered agencies to “IMMEDIATELY CEASE” using its technology, while Defense Secretary Pete Hegseth insisted the company must accept “all lawful uses” of Claude, including those Anthropic considers ethically impermissible. The administration’s position is clear: refusing to enable mass surveillance or lethal autonomous weapons is grounds for punishment.
What’s striking is not just Anthropic’s refusal to yield but the wider commercial reaction. Silence can look like complicity, and complicity erodes trust faster than any political reward. The backlash against Bud Light after its brief partnership with a transgender influencer, leading to sales drops and resignations, shows how quickly culture reacts to perceived lapses. The slow response to an advertising controversy fueled boycotts and lasting damage. Silence or weak messaging erodes trust across industries. Anthropic’s stand now prompts overdue questions: What is the long-term cost of sacrificing values for appeasement? Is it enough for you to trust a president who demands loyalty but offers no stability in return? How would you respond if the pressure to compromise your values came directly to your doorstep?
This moment provides a rare clarity. It shows that corporate courage is not only compatible with long-term business health—it may be essential to it. In the sections that follow, we will examine real-world cases, review how the Constitution shapes this conflict, and trace how other companies are responding to these pressures. This roadmap will clarify not only why courage matters, but how it translates into lasting trust and competitive advantage. And it prompts other American companies to decide whether they will retreat into self-preservation or step forward to defend the democratic norms and moral boundaries that make innovation possible in the first place.
The central significance of the Anthropic case is its focus on a constitutional boundary: may the executive use state power to punish companies for disagreeing politically? The First Amendment’s protections cover both the right to speak and the right to refuse to endorse. When refusal makes a company a “risk,” retaliation is clear, and this is precisely what the Constitution seeks to prevent.
Anthropic’s lawsuit signals a deeper warning: If the government can label tech companies as security threats solely for refusing practices they deem unethical, then all industries risk punishment for political noncompliance. The precedent would make political allegiance central to business operations.
Other companies are responding quickly. Some do so quietly, evaluating risks at the highest levels. Others speak out, weighing the reputational risk of inaction higher than that of government backlash. In tech, firms now express unwillingness to enable mass surveillance. Legal advice is increasingly central as compliance with unpredictable federal directives threatens market stability.
Companies need to understand that the real risk is not in defying political pressure; it is in appearing to abandon their values when that pressure arrives. Trust, once lost, is nearly impossible to rebuild. In operational terms, trust is reflected in tangible business indicators: companies with higher employee retention rates, stronger talent pipelines, and superior customer net promoter scores consistently outperform their peers. Fortune 500 firms that rank highest in trust-based metrics regularly report greater consumer loyalty and more job applications from top candidates. Trust is not simply an abstract value; it is a measurable source of competitive advantage.
This moment tests the rediscovery of an old American idea: prosperity and principle are not opposing forces but mutually reinforcing. The companies that endure will not be those that bend most easily to political winds but those that show a consistent moral compass, even when the cost is real. Anthropic’s stand is not simply a legal maneuver. It should remind us that success with honor is still possible, and perhaps more necessary than ever.
In a political climate defined by loyalty tests and punitive governance, corporate courage becomes a civic act. When companies choose principle over intimidation, they do more than protect their bottom line; they help preserve the democratic norms that make free enterprise possible in the first place.
David Nevins is the publisher of The Fulcrum and co-founder and board chairman of the Bridge Alliance Education Fund.
As AI transforms the labor market, the U.S. faces a familiar challenge: preparing workers for new skills. A look at a 1991 Labor Department report reveals striking parallels.
“Today, the demands on business and workers are different. Firms must meet world-class standards, and so must workers. Employers seek adaptability and the ability to learn and work in teams.”
Sound familiar?
It’s the sort of guidance you’ll find on X, in studies issued by nonprofits, and, as I recently dug up, a report by the Department of Labor published in 1991. The familiarity is striking—and not accidental. Periods of economic transition tend to produce the same anxieties, framed in remarkably similar language.
The Labor Secretary spun up a commission to study “the demands of the workplace and whether young people were meeting those demands.” This was an important question at that moment for a couple of reasons. First, the economic prospects of the next generation of Americans did not look bright. Unemployment among young adults stood at 9.6 percent at the start of 1991; it climbed to north of ten percent within a few months.
Second, there was a concern that the economy was transforming at a faster rate than educational curriculums. “[M]ore than half of our young people leave school without the knowledge or foundation required to find and hold a good job,” observed the Secretary. Against that backdrop, the gap between schooling and work felt urgent rather than abstract. The Secretary’s Commission on Achieving Necessary Skills (SCANS) was thus formed and mandated to talk with educators, private sector stakeholders, and government officials to identify a path forward.
We find ourselves in a similar place today. Young adults have an 8.2 percent unemployment rate as of December of 2025. Pundits, researchers, and politicians fear our educational and vocational infrastructure is ill-suited for the labor market shifts being driven by AI. The technology may be new, but the underlying worry—that institutions are lagging behind economic reality—is not.
So far, our response seems to have been the same, too.
Then, there was a lot of talking, information gathering, and stakeholder engagement. These are all practical steps, in moderation, and they often feel like progress. “We have talked to [employers] in their stores, shops, government offices, and manufacturing facilities,” explained the Secretary. “Their message to us was the same across the country and in every kind of job: good jobs depend on people who can put knowledge to work.”
That groundwork was followed by something else that will also feel familiar to modern readers: a flood of broad statements about the skills Americans would need to thrive in a new technological era.
Take it from the Secretary:
New workers must be creative and responsible problem solvers and have the skills and attitudes on which employers can build. Traditional jobs are changing and new jobs are created everyday. High paying but unskilled jobs are disappearing. Employers and employees share the belief that all workplaces must ‘work smarter.’
From there, the conversation moved quickly from diagnosis to prescription—though not always with much specificity.
Then, there were a lot of generic policy recommendations.
The Secretary summarized the three takeaways from the SCANS report:
(1) “All American high school students must develop a new set of competencies and foundation skills if they are to enjoy a productive, full, and satisfying life.” This recommendation even called for equipping students with more “know-how.” Apparently, only one-half of young people had such “know-how.” Of course, a similar shortage of “know-how” is drawing headlines and shaping congressional debates today.
(2) “The qualities of high performance that today characterize our most competitive companies must become the standard for the vast majority of our companies, large and small, local and global.” Specifically, workers must become “comfortable with technology and complex systems, skilled as members of teams, and [passionate] for continuous learning.”
(3) “The nation’s schools must be transformed into high-performance organizations in their own right.” Notably, efforts at transformation were already underway but “a decade of reform efforts” had amounted to “little improvement.”
What there wasn’t a lot of was political prioritization and commitment—I’m talking a decades-long focus on “transforming the nation’s schools into high-performance organizations.” The Secretary's report did not lead to a sustained overhaul of the nation’s educational infrastructure. School as of 1991 looks more or less like school as of 2026. Ambition, it turns out, is easy to articulate and hard to finance, defend, and sustain. Transformation of just about anything—let alone something with as much resistance to change as our educational system—requires incredible amounts of political and financial resources to be expedited over several years.
That’s a lesson we must heed today. Just about every actor in our political system is incentivized to think on two-year time horizons (if that). These conditions are not conducive to initiating and sticking with transformational projects. Such changes are only possible if the public champions these efforts, providing political cover to those who are willing to incur short-term losses for long-term gains.
So, will we update our educational and vocational infrastructure for the Age of AI?
It depends. Specifically, it depends on whether we can collectively muster the focus and persistence that’s inherent to successful political projects—and whether we are willing to treat this challenge as more than another familiar talking point in a long line of reports that warned us, correctly, and were then quietly shelved.
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, and is an Affiliated Research Fellow at the Cato Institute.
Trump’s ‘Just for Fun’ War Talk Shows a Dangerous Trivialization