GLOBAL AI DEVELOPMENT

The global race for AI implementation & improvement

• China’s open-source AI push could be it’s “Android moment” in global tech race

• The climate for nuclear-driven AI is heating up, 2050 seen as crucial year for capacity

• China’s open-source AI strategy challenges U.S. tech dominance through sanctions

REGULATION & SAFETY

Law, lobbying & potential risks

• UK pursues controversial “opt-out” copyright law for AI training, making artists’ work available by default

• AI safety advocates need political experience before 2028 election, experts warn

• Study: AI sensor hardware creates overlooked risks requiring new regulations

AI GOVERNANCE

The latest government programs & implementations

• Trump administration scrubs 300+ FTC blogs on AI and privacy enforcement

• South Carolina emerges as key battleground for AI’s future amid predicted “mild winter”

• New joint venture Teranexa brings AI-powered Smart City solutions to municipalities

DEFENSE

Weapons, tech, research & contracts

• Anthropic finds AI models gaining college-kid-level cybersecurity and bioweapon skills

• AI-powered cyber threats outpace defenses in Middle East, warns Zain security chief

• Scale AI wins Pentagon contract to bring AI agents to military planning

Funding AI education, industry immersion, and career assistance to those who have served our nation.

What’s happening in AI policy right now

America’s AI industrialization

A new economic map emerges as AI, manufacturing, and infrastructure converge

Hyundai's massive $7.6 billion Metaplant America factory in Georgia represents more than just another automotive facility. It embodies a fundamental shift in how American manufacturing is being reimagined through the lens of artificial intelligence and automation. This plant—aiming to produce 500,000 electric and hybrid vehicles annually by 2031—showcases how AI isn't just disrupting software but reshaping the physical infrastructure of America's industrial base.

The Georgia factory marks a critical point in a broader pattern of automation acceleration across the American economy. From the Department of Energy's identification of 16 federal sites for AI data centers to South Carolina's emergence as a manufacturing AI hub, we're witnessing a geographic redistribution of technological capacity that could redefine economic power across the nation.

These developments raise profound questions about the future of work, competition between open and closed AI development models, and whether America can maintain its technological edge without leaving vast populations behind in the process.

_{The new geography of AI infrastructure}

The U.S. Department of Energy's initiative to identify 16 federal sites for AI data center development signals a significant shift in how the government approaches technological infrastructure. By leveraging existing energy resources on federal land, the DOE is attempting to accelerate the deployment of AI computing resources that currently represent a critical bottleneck in AI advancement.

This approach reflects a growing recognition that physical infrastructure—not just algorithms—will determine AI leadership. Companies that once focused on cryptocurrency mining, like IREN, are pivoting toward AI data centers, while global firms such as NTT and CyrusOne continue expanding their computing facilities with a new emphasis on renewable energy sources.

These infrastructure investments follow a pattern observed by Jerry Chen in "The New Moats", where he notes that "dramatic shifts are rendering some existing moats useless and leaving CEOs feeling like it's almost impossible to build a defensible business." In the AI era, control of physical computing resources and energy may become the new competitive advantage that replaces traditional software moats.

_{Manufacturing's AI transformation}

Hyundai's Georgia facility represents the vanguard of what manufacturing might look like in an AI-saturated world. The plant incorporates world-first Industry 4.0 technologies, including AI vision systems and robotics that aim for nearly total automation across all manufacturing processes.

What makes this particularly interesting is how it challenges the traditional narrative about automation and jobs. Despite its high level of automation, the facility plans to create 8,500 jobs—suggesting that highly automated factories might still generate significant employment when built at sufficient scale.

This mirrors a pattern identified by Clayton Christensen in his work on disruptive innovation, where he noted that technological changes often create new types of jobs rather than simply eliminating existing ones. The key question becomes whether workers can adapt quickly enough to the changing nature of work—a challenge that South Carolina now faces as its manufacturing sector increasingly integrates AI technologies.

_{The open vs. closed AI debate intensifies}

Amid these infrastructure developments, a crucial policy battle is brewing over how AI systems should be developed. Hugging Face's policy team has responded to the White House AI Action Plan with a strong advocacy for open source AI development, arguing that "openness, transparency, and accessibility in AI systems can drive innovation while enhancing security and reliability."

This position directly challenges the approach of companies like OpenAI and Anthropic, which have generally kept their most advanced models proprietary. The debate has significant implications for how AI development proceeds—whether it will remain concentrated among a few large companies or distributed across a broader ecosystem.

Ethan Mollick's perspective on "Latent Expertise" provides a useful lens here. He argues that "LLMs, without further development, are already useful as a co-intelligence that greatly improves human performance," suggesting that wider access to even current AI technologies could democratize productivity gains across the economy.

_{Jobs in the balance}

South Carolina's position is particularly instructive about the challenges ahead. The state's manufacturing and life sciences sectors are already embracing AI technologies, but this technological evolution creates urgency around education and workforce development.

As noted in reporting on South Carolina's AI future, "educational institutions must evolve to meet emerging workforce needs" with "future employment demanding new technical skills across all sectors." This echoes the recurring pattern in technological revolutions where the primary limitation isn't the technology itself but the speed at which humans and institutions can adapt to it.

Andrew Chen's work on "The importance of power users" offers a relevant framework. Just as digital platforms are driven by their most engaged users, economies may increasingly be shaped by those workers and regions that can adapt most quickly to AI-driven changes—potentially exacerbating existing inequality if education systems can't keep pace.

_{What comes next?}

The convergence of these developments—massive manufacturing facilities, expanded AI infrastructure, and the policy battles around open vs. closed development—suggests we're entering a new phase of the AI revolution focused less on breakthrough algorithms and more on physical implementation and economic integration.

Several key questions will determine how this plays out:

1. Will the benefits of AI-powered manufacturing be broadly shared, or will they primarily accrue to capital owners?

2. Can education systems adapt quickly enough to prepare workers for the changing nature of jobs?

3. Will open source approaches to AI development gain policy support, or will proprietary models dominate?

4. How will the geographic distribution of AI infrastructure affect regional economic development across America?

The answers will shape not just technological development but the fundamental economic prospects for millions of Americans in the coming decades. As Mustafa Suleyman has suggested, we may be entering "an inflection point in the history of humanity" where the distribution of both opportunity and risk from technological change is profoundly uneven.

Rather than seeing these developments through the typical lens of technologically deterministic optimism or pessimism, we might instead focus on the very human choices about policy, education, and investment that will determine whether this wave of automation ultimately expands or contracts economic opportunity across America.