America’s AI Policy Is Truly Chaotic71%

By Francis Fukuyama0%

6/29/2026, 2:26:07 PM

BS Summary: This article contains 0 faulty reasoning types, including no named faulty reasoning patterns yet, with no single egregious example has been isolated yet. Analysis detected 0 faulty-reasoning hits from 1,203 analyzed words, generating a BS Score of 65.4% and a BS Rank of 71% (4,442 of 15,150 articles). This article is worse (more manipulative) than 70.70% of the article peer group.

Vice Chairman Senator Mark Warner delivers an opening statement during a Senate Intelligence Committee hearing on worldwide threats in the Hart Senate Office Building on March 18, 2026 in Washington, D.C. (Photo by Win McNamee/Getty Images.) This year has been a crazy time with regard to Washington’s treatment of artificial intelligence, and the pace has picked up in the last couple of weeks. This train of events began in February when Defense Secretary Pete Hegseth declared the leading AI firm Anthropic , a “supply chain risk,” meaning that neither the Pentagon nor its contractors could use Anthropic models without risking legal liability. Then, in April, Anthropic announced that its latest Mythos model was so powerful that it would be released to only a handful of organizations. Mythos, it was claimed, had extraordinary abilities to break into computer systems, and these early organizations were asked to use it to test and secure their systems. In June, Senator Mark Warner told the Senate Banking Committee, “thank God it was Anthropic. When the head of the NSA and Cyber Command came and said, ‘This tool broke into almost all of our classified systems, not in weeks, but in hours,’ … we are not going to solve this problem if we rely on a less ethical CEO operating on the basis of plain voluntary testing alone.” Experts later added caveats to this description of Mythos 5’s capabilities, but it is clear that the politics of AI regulation has shifted dramatically in the past month. The Trump administration came into office last year being advised by tech bros like David Sacks and Marc Andreessen, who opposed any form of AI regulation. The latter in fact created an organization called Leading the Future that put significant money behind lobbying against AI regulation. The Trump White House nonetheless intervened against Anthropic, but only for what looked like typically bad political reasons. The company, according to Hegseth, was too “woke”; otherwise, the administration’s default position was to oppose all regulation. We’re expanding our events offerings! Please check out our events page to join Book Club, Ask the Author, and Intellectual Bootcamp—and to watch recordings of recent events. Persuasion events This has now changed. The national security community has woken up to the fact that the latest AI models pose a clear and present danger to the security of the government’s data. This is not a speculative outcome years in the future, but one that is here right now. The Sacks-Andreessen faction can’t really argue against this, and so the balance of opinion on the need for regulation—at least, the pre-approval of new AI models—has shifted in favor of intervention. The latest twist is the Commerce Department giving Anthropic the green light to give its latest Mythos 5 models to a group of “trusted partners” so that they could ferret out vulnerabilities in the security of their systems. Similarly, OpenAI announced last week that its newest model would be restricted to a few government-approved organizations. So the White House has now joined the larger consensus in the tech community that believes that, however useful AI may be, it needs regulation. The problem is that the Trump administration’s AI policy is typically chaotic. It is not clear who has final authority to issue new rules for AI, and on what basis. It appears that the turn towards regulation is driven by serious people in the national security community raising serious concerns, and not some clownish attempt by political actors like Pete Hegseth to punish woke enemies. But how are such decisions being made, and how will they be made going forward? The problem up to now was that pro-regulation advocates could not define clearly the sorts of harms regulation was meant to protect against. Now there is a clear harm (cybersecurity), and so a more thoughtful institutional design effort can begin. Such an effort would need to answer the following questions: Do we need a specialized regulator for AI, or is this a function that can be added on to existing bodies like the NSA or the Defense or Commerce Departments? Regardless of which government entity does the regulation, how do we get sufficient capacity into the bureaucracy so that it can make well-informed decisions? U.S. government capacity in this area is woefully deficient. The typical approach in the past has been to outsource state capacity to private actors, but under present circumstances this could easily lead to regulatory capture by big, self-interested players. How does a regulator do surveillance and enforcement of any rules that it creates? The AI industry is huge and growing bigger by the day; moreover, foreign countries like China also have significant capabilities very close to those of the United States. If we deem a certain AI capability to be dangerous, how will we know that it is being developed, and how will we enforce rules limiting it? As Senator Warner’s remarks indicate, we are currently dependent on the good intentions of the CEOs running today’s large companies. To what extent should we delegate discretionary authority to the new regulator? In the past, statutes have spelled out the specific rules that the regulator was meant to enforce. But the AI field is evolving so quickly that any effort to write such specifications into hard law will be almost immediately overtaken by technological change. These are just some of the real-world institutional design questions we need to answer if we are to adequately regulate artificial intelligence. My suspicion is that we will indeed need a specialized regulator, because AI is a sector very different from other parts of the economy. When trucking became an important means of transporting goods early in the 20 th century, Congress decided to give regulatory authority to the Interstate Commerce Commission. But this was a mistake: the ICC was designed to regulate railroads, and the economics of trucking are very different from rail transport. This is why the advent of air transport led to the creation of specialized agencies like the FAA and CAB. Subscribe now It is time to stop talking about whether we need an AI regulator; the dangers are here and now and we need to move quickly. The discussion needs to shift to a much deeper and more specific analysis of how to regulate, understanding that the dangers posed by AI are likely to change over time. Our main competitor, China, is moving in this direction as we speak: the country is not run by a bunch of libertarians who want to let the technology rip, come what may. That may have been our position in the past, but it can’t define our policy today. Francis Fukuyama is the Olivier Nomellini Senior Fellow at Stanford University. His latest book is Liberalism and Its Discontents . He is also the author of the “ Frankly Fukuyama ” column, carried forward from American Purpos e, at Persuasion . Follow Persuasion on X , Instagram , LinkedIn , and YouTube to keep up with our latest articles, podcasts, and events, as well as updates from excellent writers across our network. And, to receive pieces like this in your inbox and support our work, subscribe below: Subscribe now

Confirmation Bias
0%
Anchoring Bias
0%
Availability Heuristic
7.7%
Representativeness Heuristic
0%
Hindsight Bias
3.6%
Overconfidence Bias
2%
Framing Effect
0%
Loss Aversion
0%
Status Quo Bias
3.3%
Sunk Cost Effect
0%
Optimism Bias
1.4%
Pessimism Bias
7%
Negativity Bias
32%
Self-Serving Bias
0%
Fundamental Attribution Error
1.5%
Actor-Observer Bias
0%
In-Group Bias
11.6%
Out-Group Homogeneity Bias
4.2%
Halo Effect
0%
Horn Effect
0%
Dunning-Kruger Effect
0%
Recency Bias
13.4%
Primacy Effect
0%
Blind-Spot Bias
0%
Ad Hominem
9.1%
Straw Man
0%
Appeal to Authority
4%
False Dilemma
14.9%
Slippery Slope
4.7%
Circular Reasoning
0%
Hasty Generalization
13.4%
Red Herring
0%
Bandwagon
7.3%
Appeal to Emotion
10.1%
Begging the Question
3.4%
Post Hoc (False Cause)
4.9%
Tu Quoque
0%
Burden of Proof
0%
Appeal to Nature
0%
Composition/Division
0%
Anecdotal
4.5%
No True Scotsman
1.3%
Ambiguity (Equivocation)
0%
Gambler’s Fallacy
0%
Middle Ground
2.5%
Personal Incredulity
0%
Special Pleading
0%
Genetic Fallacy
0%
Unattributed Quote
8.2%
Quote-first Misdirection
4.5%
Biased Writer Voice
13.5%
Indoctrination
8.7%
Politically Left Leaning Bias
2.9%
Politically Right Leaning Bias
5.8%
Attempt to Sell a Product or Service
6.4%

1203 words analyzed.

Speakers

6speakers13%attributed speech1,048writer words
Selected voice

Pete Hegseth

100%flagged-word coverage
57 attributed words37% of attributed speech79% writer coverage
Unattributed Quote+97.4 pts
Writer 2.6%Pete Hegseth 100%
Biased Writer Voice-15.5 pts
Writer 15%Pete Hegseth 0%
Indoctrination-10.0 pts
Writer 10%Pete Hegseth 0%
Attempt to Sell a Product or Service-7.3 pts
Writer 7.3%Pete Hegseth 0%
Politically Right Leaning Bias-6.7 pts
Writer 6.7%Pete Hegseth 0%
Quote-first Misdirection-5.2 pts
Writer 5.2%Pete Hegseth 0%
Politically Left Leaning Bias-3.3 pts
Writer 3.3%Pete Hegseth 0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.