Russia Doesn’t Have to Defeat NATO -It Only Has to Weaken It

When people imagine a war between Russia and NATO, they tend to imagine the wrong thing. Tanks pouring through the Suwałki Gap. Russian missiles striking bases in Germany. American aircraft fighting Russian fighters over the Baltic. Perhaps, eventually, nuclear escalation. It is an understandable picture because it resembles the wars we know how to think about: one side attacks, the other side responds, and eventually somebody wins or loses.

But that may obscure the more interesting strategic problem. Russia does not necessarily have to defeat NATO in a conventional war to achieve an enormously important geopolitical victory. It may not even want such a war. A much cheaper and potentially safer strategy would be to steadily weaken the political cohesion on which NATO’s military power ultimately depends.

The distinction matters because NATO is an unusual military organization. Its strength doesn’t principally come from the armies of Estonia, Lithuania, Latvia or even Poland. It comes from the promise that attacking one of those countries means acquiring all the others as enemies. Article 5 of the North Atlantic Treaty turns dozens of separate national militaries into something approximating a single deterrent because an adversary must assume that aggression against one member could eventually bring American, British, French, German, Polish and other forces into the conflict.

That means NATO has an extraordinary amount of military power but also a potential political vulnerability. The entire arrangement depends upon adversaries believing that the members will act together when the moment comes.

Russia therefore faces two very different strategic questions. The first is: Can Russia defeat NATO? The answer would involve armies, aircraft, logistics, industrial production and eventually nuclear weapons. It is an extraordinarily dangerous proposition.

The second question is subtler: Can Russia make NATO members doubt one another?

That is a much more interesting question.

NATO itself increasingly describes Russian activity in these terms. At a September 19 meeting of NATO military chiefs in Copenhagen, Military Committee chairman Admiral Giuseppe Cavo Dragone described recent airspace violations, drone incidents and hybrid activities as attempts to “test Allied resolve.” He also said Russian investment in what NATO calls cognitive warfare is intended to undermine trust and weaken Alliance unity. That is NATO’s interpretation of Russian behavior rather than an independently provable window into Kremlin decision-making, but it is revealing of the problem Alliance planners believe they face.

The European Union has reached a similar assessment. It says Russian-linked hybrid activity since the invasion of Ukraine has included sabotage, cyberattacks, interference operations, disruption of infrastructure and information manipulation, and describes the broader campaign as seeking to destabilize European states and undermine support for Ukraine. Recent EU statements have specifically characterized these actions as attempts to produce fear and distrust within European societies. Moscow has repeatedly rejected accusations that it is waging such a hybrid campaign.

This is where the concept of the “gray zone” becomes important. A conventional invasion produces clarity. If Russian armored units crossed the Polish border tomorrow morning, there would not be much philosophical debate about whether Poland had been attacked. NATO governments would still have enormous decisions to make, but identifying the event would not be particularly difficult.

Hybrid confrontation is almost the opposite. Its strategic usefulness often comes from uncertainty.

A telecommunications cable mysteriously breaks. A railway system experiences a cyberattack. A warehouse supplying Ukraine catches fire. GPS signals disappear over part of the Baltic. An unidentified drone crosses a border. A Russian aircraft briefly enters NATO airspace. A ship behaves aggressively toward another country’s military aircraft. A political information campaign amplifies an existing domestic disagreement.

None of those events looks like the beginning of World War III.

That is precisely the point.

A 2026 CSIS analysis describes Europe as occupying a gray zone between peace and war in which Russian cyber and information operations can erode European cohesion while staying below the threshold that would reliably provoke a conventional military response. Whether every suspected incident is attributable to Moscow is a separate evidentiary question, and attribution is often one of the central difficulties. But strategically, ambiguity itself can be useful.

Consider the difference between destroying a NATO military unit and creating an argument among NATO governments.

Destroying the unit requires weapons, exposes the attacker to retaliation and produces an obvious victim. Creating the argument can be dramatically cheaper. If Poland believes an incident was deliberate while another NATO government calls for further investigation, Russia has learned something about Alliance decision-making. If one government demands retaliation while another fears escalation, the political disagreement becomes almost as important as whatever physical damage occurred.

That suggests a different way of thinking about Russian probing around NATO’s borders.

The objective does not necessarily have to be territorial gain. The objective may be information.

How quickly does NATO respond?

Which governments hesitate?

What level of provocation produces a coordinated response?

What level produces arguments?

How much uncertainty is necessary before public opinion begins opposing escalation?

What happens if an incident kills nobody? What happens if it kills one person? What happens if Moscow says it was accidental? What happens if responsibility cannot immediately be established?

These are questions that can potentially be explored without ever ordering an armored division across a NATO border.

And that is why a hypothetical Russian attack on the Baltic states would make more strategic sense as the culmination of such a process than as its beginning.

Imagine that years of increasingly aggressive probing had convinced Moscow that NATO was politically brittle. Perhaps governments repeatedly argued about attribution. Perhaps American leaders signaled reluctance to become involved in small European confrontations. Perhaps European publics increasingly concluded that incidents around the Baltic were not worth risking a larger war. Perhaps NATO responses consistently arrived late enough that Russia developed confidence it could create a fait accompli before the Alliance made a decision.

Only then does something like the Suwałki Gap become truly tempting.

The military objective might still be limited: sever the land connection between Poland and the Baltic states, seize some territory or manufacture some supposed security crisis involving Kaliningrad. But the strategic objective would be much larger.

Russia would be asking NATO the most important question NATO can ever be asked:

Do you really mean it?

Suppose Russian forces occupied a small amount of Lithuanian territory and immediately stopped. Moscow announces that it has no intention of proceeding farther. It proposes negotiations. It warns that any NATO attack on Russian forces could produce catastrophic escalation. It perhaps puts nuclear forces on heightened alert for emphasis.

Suddenly NATO’s overwhelming aggregate military superiority becomes almost secondary.

The real battle takes place in Washington, Berlin, Paris, Warsaw, London, Rome, Ankara and the other Allied capitals.

Is recovering that territory worth a European war?

Is it worth risking Russian missile attacks?

Is it worth losing thousands of soldiers?

Is it worth even a small possibility of nuclear escalation?

That is the Russian victory condition that deserves more attention.

Not Russian tanks reaching Paris.

Not the conquest of Poland.

Not recreating the Soviet Union.

Rather, a situation in which NATO possesses the military capability to reverse Russian aggression but cannot assemble the political will to use it.

If that happened once, the consequences would extend far beyond whichever patch of territory was originally contested. Estonia would immediately have to reconsider what NATO protection actually meant. So would Latvia and Lithuania. Poland would begin making decisions on the assumption that collective defense might fail. Finland and Sweden would reconsider their security calculations. Countries farther west would confront the same uncomfortable question from the opposite direction: if the guarantee failed once, what exactly were they obligated to risk the next time?

Article 5 would still exist on paper.

But deterrence operates primarily in people’s heads.

Once adversaries ceased believing in it, NATO could possess thousands of aircraft, tanks, ships and nuclear weapons while being strategically weaker than it had been before the crisis.

That helps explain why NATO leaders react so strongly even to incidents that appear militarily minor. NATO Secretary General Mark Rutte said this month that Russia wants to divide the Alliance and reduce its support for Ukraine; NATO’s public position is that the response should therefore be solidarity rather than reciprocal hesitation. Again, this is NATO’s stated assessment of Russian intent, not neutral proof of every alleged Russian operation. But it illustrates how Alliance leaders understand the contest: unity itself has become part of the battlefield.

There is an irony here. If weakening NATO is indeed an important Russian goal, many Russian actions since 2022 have produced the opposite short-term result. Finland and Sweden joined NATO. European defense budgets have increased substantially. NATO has reinforced its eastern flank. Germany is permanently stationing an armored brigade in Lithuania. The Alliance has expanded its planning for infrastructure resilience, cyberattacks, hybrid warfare and rapid reinforcement.

This doesn’t mean the political strategy has disappeared. It means the contest has become recursive.

Russia probes NATO cohesion.

NATO responds by demonstrating cohesion.

Russia observes that response and adjusts.

NATO changes its defenses again.

The danger is that both sides can gradually climb an escalation ladder while believing they are merely signaling.

And that brings us to the deepest problem with the idea that Russia might want to weaken NATO rather than fight it: a strategy designed to avoid war can accidentally produce one.

Gray-zone operations work because they hover near boundaries. But boundaries are not always visible. One country’s warning is another country’s provocation. One military sees a drone approaching critical infrastructure and shoots it down. Another military interprets the shootdown as escalation. A cyberattack unexpectedly shuts down a hospital. A missile that was supposedly intended for Ukraine lands several kilometers inside NATO territory. Soldiers die.

Suddenly governments are making decisions that neither side expected them to make.

This may be the central paradox of NATO-Russia relations in the coming years. Moscow has strong reasons not to fight NATO directly. NATO has equally strong reasons to avoid a war with Russia. Yet precisely because outright war is so unattractive, competition gets pushed into the ambiguous territory immediately below it.

That territory is becoming crowded.

Sabotage. Cyber operations. Drones. Electronic warfare. Airspace incursions. Information operations. Infrastructure incidents. Maritime confrontations. Political intimidation.

The temptation is to look at each one individually and conclude that it isn’t serious enough to matter.

But perhaps that is the wrong unit of analysis.

The strategic question isn’t necessarily whether Russia can win any particular confrontation.

It is whether, after enough confrontations, NATO becomes a little slower, a little more divided and a little less certain that its members will take enormous risks for one another.

Because the greatest Russian victory over NATO would not require defeating a single American division.

It would occur the moment Russia became convinced that it didn’t have to.

The Super Before the Super: What the Hydrogen Bomb Debate Can Teach Us About RSI and ASI

There is a tendency, whenever artificial intelligence reaches another unsettling milestone, to reach immediately for the Manhattan Project as the historical analogy. The comparison is understandable. A small group of brilliant scientists, working at the frontier of human knowledge, creates a technology with enormous geopolitical consequences and discovers only afterward that inventing the thing was easier than deciding what humanity should do with it. But there may be a better analogy for the particular moment we are entering now. The debate over recursive self-improvement, or RSI, and artificial superintelligence increasingly resembles not the Manhattan Project itself, but the American debate over whether to build the hydrogen bomb in 1949 and 1950. By that point the atomic bomb already existed. Hiroshima and Nagasaki had demonstrated beyond dispute that nuclear weapons were possible. The unresolved question was whether nuclear physics contained another threshold beyond the one humanity had already crossed: a thermonuclear weapon vastly more powerful than the bombs developed during World War II. The scientists of the period called it the “Super.” Much as we are doing with superintelligence today, they argued intensely about whether the Super could actually be built, how quickly it might arrive, whether crossing that threshold was necessary, whether someone else would cross it first, and whether the possibility itself created an obligation to proceed.

That debate became much more urgent after the Soviet Union tested its first atomic weapon in August 1949. The American nuclear monopoly had disappeared, and Edward Teller and others began pushing aggressively for development of a hydrogen bomb. Robert Oppenheimer and the Atomic Energy Commission’s General Advisory Committee were considerably more skeptical. When the committee met in October 1949, it recommended against an immediate all-out effort to build the Super, concluding that the dangers associated with such a weapon outweighed its prospective military advantages. Yet this was not a simple argument between people who thought a hydrogen bomb was possible and people who thought it was impossible. The technical questions themselves remained unsettled. President Harry Truman ultimately made the political decision on January 31, 1950, ordering the Atomic Energy Commission to continue work on “all forms of atomic weapons, including the so-called hydrogen or superbomb.” Importantly, that decision came before scientists possessed the practical thermonuclear design that would ultimately succeed. The workable Teller-Ulam configuration did not emerge until the following year. America had therefore committed itself to crossing a technological threshold before anyone knew exactly how that threshold would be crossed.

That is where the analogy to artificial intelligence becomes particularly interesting. We already have extraordinarily capable AI systems. The equivalent of the initial technological breakthrough has happened. Large language models can write software, analyze scientific problems, operate tools, perform research tasks and increasingly carry out long sequences of work without constant human intervention. The open question is whether another threshold lies somewhere ahead. Recursive self-improvement is one candidate for that threshold. In its strongest form, RSI describes a situation in which an AI system can substantially participate in creating a more capable successor, which then becomes better at creating the next successor, establishing a feedback loop in which AI development increasingly becomes the work of AI itself. In the most dramatic versions of this scenario, that feedback loop produces an intelligence explosion: an AI becomes good enough at AI research to improve itself, which makes it still better at improving itself, causing the process to accelerate until something resembling artificial superintelligence emerges.

There is, however, an important distinction that is often lost in discussions of this possibility. Recursive self-improvement is not the same thing as a fast takeoff, and neither is necessarily the same thing as ASI. An AI can contribute to AI research without producing an intelligence explosion. It could improve components of itself or help researchers design its successor without each cycle of improvement becoming faster or larger than the previous one. Compute constraints, limited data, experimental bottlenecks, chip fabrication, energy requirements, human oversight and ordinary diminishing returns could all prevent the process from accelerating without limit. The fact that a system can participate in its own improvement therefore does not demonstrate that improvement will become explosive. RSI, FOOM and ASI are related concepts, but they are not synonyms, and treating them as though they were collapses several separate empirical questions into one dramatic story.

That distinction matters because we may already be entering the earliest stages of AI-assisted AI development without having demonstrated anything resembling full recursive self-improvement. Frontier AI companies increasingly use their own models to write code, conduct experiments, analyze results and assist researchers working on the next generation of systems. Anthropic, for example, has reported that Claude now participates heavily in its internal AI research and engineering workflows while also making clear that its systems are not yet autonomously carrying out the entire research-and-development process. OpenAI has similarly described coding agents becoming deeply embedded in its research organization, increasing the number of experiments researchers can conduct and progressively shifting technical work toward AI systems. This does not mean full RSI has arrived. It means that the first portion of the proposed feedback loop is no longer purely hypothetical. AI is already helping humans build better AI.

That makes the present situation strangely reminiscent of 1949. The argument is no longer about whether the underlying technology exists. It plainly does. The argument is about whether that technology conceals a second threshold. Nuclear physicists already knew how to produce fission weapons; the unanswered question was whether thermonuclear fusion could be harnessed in a practical weapon capable of producing an entirely different scale of destructive power. AI researchers already know that machines can assist in AI research; the unanswered question is whether increasing automation of that research eventually produces a qualitatively different regime in which machines themselves perform enough of the work to alter the pace of technological progress. Put another way, the modern question is not simply whether AI will become somewhat better at helping researchers. It is whether there is a point at which AI-assisted research becomes automated AI research, and whether automated AI research eventually becomes recursive AI development.

The history of the hydrogen bomb is useful here because it demonstrates how easy it is to be wrong about the mechanism while being right about the destination. The hydrogen bomb that ultimately worked was not simply the obvious completion of the design that had been discussed throughout the late 1940s. The decisive breakthrough came with the Teller-Ulam configuration in 1951. The early vision of the “classical Super” had serious technical problems, and some of the skepticism directed toward it was entirely justified. Yet nuclear physics did in fact contain another enormous technological threshold. The advocates of thermonuclear development could therefore be wrong about precisely how the Super would work while remaining correct that a practical Super could eventually be built. That distinction should make us cautious about both the most dramatic advocates of an AI intelligence explosion and their most confident skeptics.

Something very similar could happen with recursive self-improvement. Today’s most dramatic FOOM scenarios may turn out to be technically wrong. Perhaps artificial intelligence will encounter steep diminishing returns. Perhaps creating substantially more capable successors will continue to require enormous quantities of human-controlled compute and physical infrastructure. Perhaps AI research will remain constrained by chip manufacturing, energy, robotics, experimentation or the simple difficulty of finding better algorithms. Recursive improvement may resemble an ordinary industrial productivity revolution much more than an instantaneous intelligence explosion. Yet none of those outcomes would necessarily mean that the broader concern about a qualitative transition was misplaced. It could turn out that the specific story was wrong while the identification of the threshold was right.

Indeed, the path toward that threshold may prove much more mundane than the science-fiction version suggests. An AI system writes some of the research code used to build its successor. Later systems write most of that code. They begin running experiments themselves, analyzing the results and proposing follow-up experiments. Eventually they coordinate other AI research agents, evaluate their work and decide which research directions appear promising. Human researchers gradually move from doing the work to supervising it. At some point, perhaps most of the intellectual labor necessary to create the next generation of AI is being performed by the previous generation. No single step in this progression necessarily looks revolutionary. Each appears to be merely another productivity improvement. Yet the cumulative effect could eventually produce a system in which technological development has partially automated itself.

This is also where the analogy with the hydrogen bomb begins to break down, and the differences may actually make the AI problem more difficult. A hydrogen bomb was unmistakably a weapon. Building one required highly specialized facilities, scarce nuclear materials, enormous industrial resources and the backing of a major government. It was possible, at least in principle, for political leaders to debate whether they wanted to launch a specific program dedicated to producing such a device. AI is a general-purpose technology, and the capabilities relevant to recursive self-improvement are valuable for perfectly ordinary reasons. A system capable of debugging complex training infrastructure is useful whether or not anyone wants superintelligence. A system capable of designing better experiments makes scientific research more productive. A system capable of coordinating dozens of coding agents might have enormous commercial value even if it never approaches ASI. The same capabilities that could eventually automate AI research are therefore economically useful long before they become strategically transformative.

That means there may never be a single moment equivalent to Truman’s January 1950 decision. No president or corporate executive may ever sit behind a desk and announce that humanity is beginning the Superintelligence Project. Instead, the transition could occur through thousands of individually reasonable decisions by laboratories trying to make their researchers more productive. Researchers use AI because it allows them to write code faster. They then use it because it allows them to run more experiments. Eventually they use it because it can choose which experiments to run. Each step can be justified on ordinary economic and scientific grounds. Somewhere along that continuum, however, the relationship between the researcher and the research tool could change fundamentally. The tool could become one of the principal researchers.

This is why the idea of a phase transition may be more useful than the language of an intelligence explosion. In 1949, the underlying question was whether incremental advances in nuclear physics concealed another category of weapon. Today the question is whether incremental advances in AI-assisted research conceal another category of technological development. Does the curve remain basically continuous, with machines simply becoming better research assistants? Or does something qualitatively different happen when the tool becomes capable enough to perform a substantial share of the work involved in improving the tool itself? That is fundamentally different from asking whether today’s AI systems are already superintelligent. They are not. It is also different from asserting that recursive self-improvement inevitably produces runaway growth. That has not been demonstrated. The important question is whether a threshold exists at all, and if it does, what happens after we cross it.

The hydrogen bomb comparison becomes even stronger when we consider the role of competition. After the Soviet atomic test, American policymakers could not consider the Super purely as a scientific or moral question. They also had to consider the possibility that the Soviet Union might build one regardless of what the United States chose to do. That created an enormously powerful argument: if we do not build it and they do, what happens to us? Exactly the same logic runs through the modern AI debate. If one frontier laboratory slows down, another may continue. If American companies slow down, Chinese laboratories may not. If closed-model companies impose strong restrictions, open-source developers may proceed without them. If one government regulates aggressively, AI development may migrate to another jurisdiction. Each participant can genuinely believe that slower development would be safer while simultaneously believing that unilateral restraint would leave it dangerously exposed.

This is the technological race trap, and it does not require anyone involved to be irrational or malicious. Quite the opposite: every participant can behave rationally according to its own incentives and still contribute to a collective outcome that few of the participants actually wanted. The United States could argue that it needed the hydrogen bomb because the Soviet Union might build one. The Soviet Union could make precisely the same argument about the United States. Modern AI laboratories can similarly justify acceleration by pointing toward one another. Governments can do the same thing internationally. The result is a system in which everyone treats everyone else’s potential acceleration as a reason for their own acceleration. Once this dynamic takes hold, technological restraint becomes extraordinarily difficult because safety begins to look strategically indistinguishable from surrender.

Another useful lesson from the hydrogen bomb controversy is that frontier technological debates are rarely simple disagreements between experts and outsiders. The great figures on both sides of the Super debate understood nuclear physics extraordinarily well. Oppenheimer, Teller, Enrico Fermi, I.I. Rabi and their colleagues had helped create the atomic age, yet they still disagreed profoundly about what should happen next. Their disagreement involved scientific uncertainty, but it also involved morality, strategic competition, geopolitics, probability and differing assumptions about technological inevitability. Something similar is happening in artificial intelligence. People who understand these systems extremely well disagree about the likelihood of recursive self-improvement, the severity of diminishing returns, the possibility of a fast takeoff and the relationship between advanced AI and human control. That disagreement should not be treated as evidence that one side simply fails to understand the technology. At the technological frontier, experts are often debating precisely those questions for which decisive evidence does not yet exist.

This should encourage a certain intellectual modesty in the way we discuss RSI and ASI. AI improving AI does not automatically imply recursive self-improvement. Recursive self-improvement does not automatically imply an intelligence explosion. An intelligence explosion does not automatically imply superintelligence. Superintelligence does not automatically imply human extinction or even permanent human disempowerment. Each arrow in that chain represents a separate proposition requiring its own evidence and assumptions. Yet there is an equal and opposite mistake: rejecting the entire possibility because the final step remains speculative. AI systems are already participating in AI research. They are already writing code used in the development of future systems and increasingly assisting with experiments, analysis and research planning. We do not know whether those capabilities will eventually produce true recursive self-improvement, but the underlying question is no longer simply philosophical.

The most intriguing possibility, then, is that the present debate may eventually look very much like the Super debate of 1949, with opposing camps each getting something important right. The skeptics of explosive recursive self-improvement may be correct that simplistic versions of FOOM badly misunderstand the nature of technological progress. Diminishing returns may prove powerful. Physical bottlenecks may remain decisive. Human organizations may continue to matter much longer than some forecasts assume. At the same time, people warning about a qualitative transition may be correct that something historically unprecedented occurs once machines begin performing a large fraction of the cognitive labor needed to build more capable machines. The transition could be slower, messier and more constrained than the most dramatic forecasts predict and still constitute a profound transformation.

That is ultimately why the hydrogen bomb analogy is so useful. In 1949, the United States stood before a hypothesized second threshold created by a technology humanity had only recently learned to control. Scientists argued about whether the threshold existed, whether it could actually be crossed, whether competitors would cross it first and whether uncertainty itself was an argument for restraint or acceleration. Seventy-seven years later, the AI community is having a remarkably similar argument. We already possess a revolutionary technology. What we do not yet know is whether another technological regime lies beyond it.

Only this time, the Super is not a bomb. It is the possibility that the process of technological progress itself becomes substantially automated. The decisive question may therefore not be whether today’s particular theory of FOOM is correct, or whether every forecast of artificial superintelligence gets the mechanism right. History suggests that people can badly misunderstand the route while correctly perceiving the existence of the destination. The more important question is whether there really is a second threshold somewhere ahead of us — a point at which machines cease merely to be products of technological progress and become major drivers of that progress themselves.

If such a threshold exists, we may discover that 1949 was not merely an analogy for the AI age. It was a rehearsal.

Things Are Going Well With My New Novel, At The Moment

by Shelt Garner
@sheltgarner

I am, brick by brick, building out my latest novel. There remain some pretty big issues — especially in the third act — but, in general, I feel I’m on pretty stable ground.

I continue to worry that the entire premise, on a meta basis, is going to come across as creepy to some people, given who I am writing it. I could totally see some Woke 1 people dismissing the whole thing as some sort of freaky personal fantasy or something.

I swear it’s not.

In fact, I was so busy developing the plot that the meta element of it didn’t even register until a few days ago. NOW, in hindsight, I can understand why it might be possible at least that some people might find the premise problematic.

And, yet, I also know that I could be overthinking things and no one, outside of a few squeaky wheel woke people, will object.

That’s what I hope happens. I really, really like this novel and I believe in it enough to spend the year or so necessary to finish it.

If Trump Tears Down The Kennedy Center I’m Going To Pop A Gasket & Go ‘Woo Woo Woo’ Online

by Shelt Garner
@sheltgarner

I can’t believe that Trump, that piece of shit, is talking about not just shutting down the Kennedy Center, but tearing it down. My fear is there will be an “accidental” fire one night and we’ll wake up to the Kennedy Center just being…gone.

If we had any gumption as a country, we would surround the building, occupy it, until Trump fucking backed down. But, alas, we just don’t have the grit to do such a form of direct action.

Oh well. The Trump Revolution rumbles along and we still have no idea what will be left to save once it’s done.

Pondering The Premise Of My New Novel In Development

by Shelt Garner
@sheltgarner

With the help of AI, I’m zooming through the first draft of a my new novel. As I keep saying — I’m doing all the writing.

But there’s a meta issue that I’m concerned about — the very premise of the novel. You see, the novel is currently called “Girl Against The Wall” and it’s about a 17-nearly-18-year-old woman who is stuck behind a wall, and has been all her life.

This is where the first potentially problematic comes up — Woke 1 would say I’m being a creepy old man who writing from the POV of a young woman. And, to a certain extent, I guess they’re right. And, yet, I think if you actually read what I’ve written in context, those complaints would be midigated.

The other issue is that the “girl” against the wall is being helped by a 33 year old man. That, too, I guess, if you look at it the wrong way, is kind of…creepy. But, again, I think if you look in-context to what I’m actually writing it isn’t nearly as creepy as you might think.

I really believe in this novel and I’m going to give it ago for now. I have any number of other novel and short story ideas floating about, but this specific one has caught my fancy.

The Monster Always Come Back One Last Time

Now that SCOTUS, for once, has done the right thing and not fucked with mail in voting, I can only assume Trump will, in some weird last gasp, try again to fuck with the voting in the 2026 midterms.

I don’t know how exactly he would do it, but he’s going to try.

I’m not one of those who thinks he will try to outright cancel the midterms. Even he knows he doesn’t have that much power, but…still. He’s very clever and I could see him doing something nefarious that would fuck with the midterms to the point that they were not free and fair.

Only time will tell, I suppose.

The AI Safety Window May Already Be Closing

Something remarkable has happened in artificial intelligence over the past several days. Some of the people building the world’s most powerful AI systems are asking to slow down. Anthropic CEO Dario Amodei has called for “pacing the frontier”—deliberately giving safety research, monitoring, and institutions more time to catch up with rapidly improving AI capabilities. OpenAI CEO Sam Altman and Elon Musk have publicly supported the basic idea. Major laboratories are discussing independent safety evaluation, increased transparency, and mechanisms for preventing competitive pressures from pushing everyone into a race that none of them necessarily wants to run at maximum speed. These are not environmental activists demanding that an oil company stop drilling, nor are they outsiders chaining themselves to the gates of a laboratory. They are the people running the laboratories.

Almost immediately, however, the most powerful Republican leaders in Washington have responded with some version of: not so fast on slowing down. President Donald Trump was asked on September 13 whether AI companies should slow development or face greater regulation. His answer centered not on the safety concerns being raised by AI researchers, but on China. “We’re leading China in AI,” Trump told reporters. “We’re the most sophisticated country in the world, and frankly I want to keep it that way because whoever wins AI wins.” Trump allowed that “we could put guardrails” around the technology, but then dismissed some of the more serious warnings as coming from “very negative forces” raising scenarios that, in his words, “won’t happen.”

House Speaker Mike Johnson has taken a somewhat more measured position, but his practical conclusion is similar. Johnson acknowledges that some safety measures are necessary. He has spoken about preventing AI from “running away” and says the issue is a priority. Yet when confronted with calls for Congress to intervene quickly, Johnson said lawmakers should “resist Congress jumping in and imposing some sort of emergency moratorium.” He suggested that AI companies themselves can stop or slow their work and said he would prefer industry leaders to assume primary responsibility rather than having Congress impose the solution. Johnson also emphasized the same geopolitical concern as Trump: slowing American AI too much, he warned, could allow China to gain an advantage and thereby create a national-security threat.

This position is consistent with the broader policy of the Trump administration. A June executive order created mechanisms through which developers can voluntarily provide the federal government early access to certain frontier models for evaluation and cybersecurity work. But the same order goes out of its way to say that nothing in it should be interpreted as creating mandatory governmental licensing, preclearance, or permitting requirements for developing or releasing new AI models. The administration wants evaluation. It wants cooperation. It wants cybersecurity. What it does not appear to want is a government speed limit on the frontier.

Perhaps that will prove to be the correct judgment. Perhaps the AI laboratories can police themselves. Perhaps the most extreme warnings are indeed exaggerated. Perhaps competition among American companies, combined with voluntary safety agreements and targeted government oversight, will turn out to be enough. But anyone who remembers the political aftermath of Sandy Hook should recognize another possibility. We may be watching a policy window begin to close almost as soon as it opened.

On December 14, 2012, a gunman entered Sandy Hook Elementary School in Newtown, Connecticut, and murdered twenty children and six educators. The political response was immediate. There are moments in American politics when an event seems sufficiently horrifying that the normal rules briefly disappear. Positions that appeared immovable suddenly look negotiable. Politicians previously reluctant to discuss an issue begin demanding action. Public attention becomes overwhelming. Something that had been politically impossible on Wednesday can appear nearly inevitable by Friday. Sandy Hook created such a moment.

President Barack Obama made gun legislation one of the central priorities of the beginning of his second term. Families of Sandy Hook victims traveled to Washington. Senators began negotiations. The resulting Manchin-Toomey proposal would have expanded background checks for commercial gun sales. It was hardly revolutionary gun-control legislation, and public support for the underlying policy was extraordinary. In May 2013, Pew Research Center found that 81 percent of Americans favored background checks for private gun sales and gun-show purchases. Remarkably, the topline number was essentially identical among Republicans, Democrats, and independents. Even after the Senate legislation itself became politically contested, 73 percent of Americans still wanted Congress to pass a background-check bill.

And yet the legislation died. On April 17, 2013—barely four months after twenty children were murdered at Sandy Hook—the Manchin-Toomey amendment received 54 votes in the Senate and 46 against. It had a majority, but it needed 60 under the procedure being used. The proposal failed. The extraordinary national moment produced by Sandy Hook did not disappear instantly. States including Connecticut enacted substantial reforms, executive actions followed, and gun-control organizations continued their work. But the federal window had effectively closed. By the end of 2013, Pew observed that the overall gun debate had already begun drifting back toward its previous political equilibrium. The horror remained. Public support for background checks remained broad. But the extraordinary sense that Congress simply had to do something had dissipated. Major federal gun-safety legislation would not finally pass until the Bipartisan Safer Communities Act in June 2022, following another horrifying elementary-school shooting, this time in Uvalde, Texas. Nearly a decade had passed.

That sequence deserves considerably more attention in the AI debate than it is receiving, because political windows do not remain open simply because the underlying problem remains dangerous. They close. Public alarm fades, legislators find other priorities, industries organize their opposition, and proposals that seemed urgent become subjects for another committee hearing or another study. A danger can remain exactly as serious while the political willingness to address it steadily disappears.

The most striking similarity between the gun debate after Sandy Hook and the AI debate today is therefore not that guns and artificial intelligence present similar dangers. They plainly do not. The similarity is the transition from abstract agreement to concrete policy. Almost everyone can agree with an abstract noun: safety, responsibility, guardrails, common sense. After Sandy Hook, one could find politicians across the ideological spectrum saying that children should be protected and dangerous people should not have access to firearms. That broad agreement became much less useful once legislators had to answer the next question: what law, exactly, are you willing to vote for? That was where political consensus evaporated.

Something similar may now be beginning with AI. Trump says guardrails may be appropriate. Johnson says guardrails are necessary. The AI companies say safety is important. Democrats say safety is important. Republicans say safety is important. China presumably does not want uncontrolled AI systems destroying Chinese infrastructure any more than Americans want them destroying American infrastructure. In the abstract, then, nearly everyone supports “AI safety.” The meaningful question is what happens when safety costs something.

What happens when a laboratory has a model ready to train and an independent evaluator says the experiment should wait six weeks? What happens when a company has spent several billion dollars preparing a training run and a federal regulator says it cannot proceed? What happens when OpenAI wants to continue while Anthropic wants to pause, or when Anthropic slows down while Google does not? What happens if the leading American companies agree to exercise restraint but intelligence officials announce that a Chinese laboratory may be six months away from a comparable breakthrough? At that point, AI safety stops being an aspiration and becomes a policy. Policies impose constraints, and constraints are precisely where political agreement tends to disappear.

Trump has already identified the argument that could become the most powerful weapon against mandatory AI safety regulation: China. It is an extremely potent argument because, unlike some political talking points, it describes a genuine problem. The United States and China are engaged in strategic competition over artificial intelligence. Frontier AI may eventually affect intelligence gathering, cyberwarfare, weapons development, biotechnology, economic productivity, and military command systems. No American president can casually ignore the possibility that the United States might voluntarily slow its development while China continues racing forward. Dario Amodei himself has acknowledged how difficult this problem is, because the strategic rewards for ignoring an international slowdown could be enormous.

Trump is therefore identifying a legitimate dilemma. The danger is that a legitimate dilemma can become a convenient excuse. If “China might get ahead” becomes sufficient reason to reject mandatory safety measures, then almost no safety measure will survive. The more powerful AI becomes, the more strategically important it becomes, and the more strategically important it becomes, the more dangerous it appears to slow down. Greater AI capability could perversely produce less willingness to regulate it.

Consider where that logic leads. Suppose a frontier system becomes dramatically better than humans at offensive cybersecurity. Should development be slowed while researchers determine whether it can be controlled? The answer could be that China might get ahead. Suppose a system becomes exceptionally capable at biological design. Again, China might get ahead. Suppose autonomous AI researchers become capable of meaningfully accelerating AI development itself. Once again, China might get ahead. If the response to every dangerous capability is identical, then “we cannot let China win” ceases to be an argument about balancing risks and becomes an argument that no amount of risk can ever justify slowing down. That is no longer ordinary risk management. It is the logic of an arms race.

This is where Sandy Hook becomes useful—not as an analogy between the technologies, but as an analogy between political processes. After Sandy Hook, one of the central arguments against additional gun restrictions was that regulation would impose costs on law-abiding Americans without preventing determined criminals from obtaining weapons. A criminal could still acquire a gun illegally, so regulation could not guarantee safety, and therefore additional rules might merely burden people who followed the law. It is possible to argue endlessly about the merits of that reasoning in firearm policy, but its structural resemblance to the emerging AI argument is striking.

China might ignore American rules. Open-source developers might ignore them. A rogue laboratory might ignore them. Other countries might ignore them. Therefore, the argument goes, responsible American frontier laboratories should not be constrained. In both debates, imperfect enforcement threatens to become an argument against enforcement itself. Unless a regulation can eliminate the danger everywhere, its inability to eliminate the danger everywhere is offered as a reason not to reduce it anywhere. That is an extraordinarily high standard to demand from public policy, and almost no regulation could survive it.

The comparison with gun politics becomes even more intriguing when one notices where it breaks down. After Sandy Hook, the firearms industry was not asking Congress to slow gun sales. The National Rifle Association was not demanding tougher federal regulation of itself. Gun manufacturers were not going on television saying that they were moving too quickly and needed Washington to establish a common framework preventing competitors from behaving irresponsibly. Yet something close to that is happening with artificial intelligence. The leaders of major frontier laboratories are publicly acknowledging that the competitive dynamics of their own industry may be unsafe. Several have endorsed slowing development enough for safety systems to catch up.

This creates an extraordinary political paradox. Imagine that after Sandy Hook, several of the largest firearms manufacturers had gone to Congress and said that the competitive structure of their industry made voluntary restraint difficult, that they feared where the market was heading, and that they wanted enforceable standards applying equally to all major companies. Then imagine that congressional leadership responded that these companies were free to restrain themselves whenever they wished. That hypothetical contains something close to the contradiction now emerging around frontier AI.

Johnson’s argument—that the laboratories can simply slow themselves down—is perfectly logical if each laboratory operates independently. But that is precisely what the laboratory leaders say is not happening. Their concern is a coordination problem. If Anthropic slows down while OpenAI accelerates, Anthropic loses. If OpenAI slows while Google accelerates, OpenAI loses. If all three exercise restraint while another competitor continues, the competitor gains. The problem is not necessarily that the executives personally want to race recklessly. The problem is that they inhabit a system that rewards whichever participant races fastest. Government exists, in part, to solve exactly these kinds of collective-action problems. A speed limit would be fairly useless if every driver were simply invited to choose whatever speed seemed responsible.

There is nevertheless an obvious attraction to the Trump-Johnson approach. It avoids bureaucracy and reduces the danger of placing enormously consequential technical decisions in the hands of legislators who may understand the technology poorly. It decreases the possibility that regulation will freeze today’s market leaders permanently into place and allows safety systems to evolve quickly rather than waiting for Congress. It also avoids creating a powerful government licensing regime over one of the most important technologies of the century. Those are serious concerns and deserve serious consideration.

One should also be wary when enormous corporations ask to be regulated. Large incumbents sometimes favor regulations they are uniquely capable of satisfying. A federal frontier-model licensing regime requiring hundreds of millions of dollars in compliance infrastructure could improve public safety while conveniently making it much harder for smaller competitors to challenge OpenAI, Anthropic, Google, or other existing giants. There are legitimate libertarian, economic, and technological arguments for moving cautiously. But “move cautiously when regulating” is different from “leave the decision to slow down entirely to the companies engaged in the race.” The former is prudence. The latter is itself a major policy choice. It means accepting voluntary self-regulation as the principal mechanism protecting the public from technologies that the developers themselves increasingly describe as potentially catastrophic.

Sandy Hook also revealed another unpleasant feature of democratic politics: public attention has a half-life. Immediately after catastrophe, almost everything seems possible. Four months later, considerably less seems possible. A year later, politicians have moved on. The underlying danger may not have changed at all. What changed was attention.

AI may now be experiencing an unusual inversion of that pattern. There has not yet been a Sandy Hook-scale AI catastrophe. Instead, capability advances, unsettling incidents, resignations by researchers, and increasingly serious warnings from laboratory leaders have created something resembling a pre-crisis policy window. That is rare. Normally governments receive warnings, postpone difficult decisions, and eventually regulate after something terrible happens. Here, at least in theory, we have an opportunity to attempt the reverse.

We can establish institutions before the disaster. We can determine dangerous capability thresholds before one is crossed accidentally. We can create reporting requirements before the incident everyone later wishes had been reported. We can establish independent evaluators before corporate and national competition becomes too intense to permit them. We can begin negotiating international agreements before the United States and China both possess systems whose development neither side can adequately monitor or verify.

Or we can wait. Waiting has enormous political advantages. Nobody gets blamed for slowing innovation. Nobody gets blamed for losing to China. Nobody has to decide precisely what constitutes a dangerous frontier model. Nobody has to create a new regulator. Nobody has to explain why a multibillion-dollar training run was postponed. If nothing goes wrong, waiting looks wise. The trouble is that this asymmetry works only until something does.

The most sobering aspect of the Sandy Hook aftermath was not that Americans disagreed about guns. Americans had disagreed about guns for decades. It was that even a moment of extraordinary agreement could evaporate without producing the federal legislation many people believed was inevitable. Twenty children had been murdered. The president was personally lobbying senators. Victims’ families were walking the halls of Congress. More than eight in ten Americans supported expanded background checks. A bipartisan compromise received a majority vote in the United States Senate. And still the legislative effort collapsed.

That is what political drift looks like. It rarely begins with a dramatic announcement that nothing will ever be done. It sounds much more reasonable than that. We need more study. We need consensus. Industry should lead. We cannot move hastily. The proposal is imperfect. The other side will not cooperate. Now is not the right time. Eventually an urgent problem becomes an ordinary problem, and ordinary problems can remain unsolved for decades.

This is the danger in the comments from Trump and Johnson. Neither man is saying that AI safety is irrelevant. In some respects that would actually be politically easier to confront. Instead, both are accepting the principle while resisting the mechanism. Trump says there can be guardrails, but emphasizes winning the AI race and dismisses some warnings as scenarios that will not happen. Johnson says safety measures are necessary, but rejects an emergency congressional moratorium and suggests the industry itself should assume the leading role. That may sound like a temporary disagreement over methods. History suggests it can become something considerably more consequential.

After Sandy Hook, the question rapidly shifted from whether the country should do something to whether Congress should enact this particular restriction, this particular background-check requirement, or this particular compromise. Once the debate reached that second stage, the coalition supporting action fractured. Artificial intelligence may now be entering exactly the same political phase. Almost everyone supports AI safety. The argument is becoming whether safety should ever be allowed to compel delay; whether independent evaluations should merely advise companies or possess actual authority; whether laboratories should disclose dangerous capabilities voluntarily or be legally required to do so; whether a frontier laboratory should ever need government permission before deploying an exceptionally capable model.

Ultimately, the question is whether Congress should ever be able to tell an AI company to stop—not forever, not because artificial intelligence is evil, and not because technological progress should end, but because human beings may occasionally need several months to understand what they have built before building something substantially more powerful. If the answer to that question is always no, then talk of “guardrails” means considerably less than it appears to mean. A guardrail that cannot prevent the vehicle from continuing forward under any circumstances is largely decorative.

No analogy between Sandy Hook and artificial intelligence should obscure the most important difference between them. Sandy Hook was already a tragedy. Twenty children and six adults were already dead before the political window opened. Nothing Congress did afterward could save them. AI safety presents us with the possibility—still, for the moment—of acting before an equivalent catalytic event.

Perhaps the catastrophic forecasts will prove wrong. Perhaps artificial general intelligence will arrive gradually. Perhaps alignment techniques will work. Perhaps the current generation of researchers is overestimating the risks of autonomy, cyber capabilities, biological design, or recursive improvement. That would be an excellent outcome. Good safety regulations sometimes look unnecessary in retrospect precisely because the catastrophe they were designed to prevent never occurs.

But American politics has a dangerous tendency to demand catastrophe as proof. After industrial accidents, we improve industrial safety. After financial crises, we strengthen financial regulation. After terrorist attacks, we close security vulnerabilities. After school shootings, we briefly debate gun laws, and then the urgency begins to dissipate. The extraordinary thing about the current AI moment is that some of the people closest to the technology are attempting to reverse that sequence. They are effectively asking whether society can argue about the regulations before discovering, through disaster, why those regulations were necessary.

Trump and Johnson are not wrong to worry about China. They are not wrong to worry about governmental overreach, incompetent regulation, or rules that inadvertently entrench today’s AI giants. But those objections need to become components of an AI-safety policy rather than substitutes for one. A serious response to the China problem might involve treaties, verification regimes, compute monitoring, narrowly targeted capability thresholds, and agreements between rival laboratories. A serious response to regulatory capture might involve independent oversight and protections for smaller competitors. These are difficult problems, but difficulty is not an argument for doing nothing.

There is another lesson buried in the aftermath of Sandy Hook. A country can recognize a danger. Its citizens can demand action. Experts can propose solutions. Politicians can express sympathy. Majorities can support reform. Everyone can insist that something must be done. And then, slowly and almost imperceptibly, the moment can pass.

That may be what is beginning to happen with artificial intelligence. The laboratories are asking for time. Researchers are asking for safeguards. Some political leaders are asking for binding action. Meanwhile, the president and the speaker of the House are signaling that America cannot afford to take its foot too far off the accelerator. Perhaps they will prove correct. Perhaps history will look back on the current safety fears as exaggerated.

But before accepting that gamble, we should remember how often American politics has followed the same sequence: a warning, a moment of clarity, a burst of political possibility, a thousand plausible reasons to wait, and eventually a tragedy that causes everyone to ask why nobody acted when there was still time.

After Sandy Hook, the tragedy came first and the opportunity for prevention came too late for twenty children and six educators. With artificial intelligence, for now at least, we have been given the order in reverse.

We should be very careful about throwing that advantage away.

What Sandy Hook Should Teach Us About the Rush for AI Safety

There is an uncomfortable comparison beginning to suggest itself in the debate over artificial intelligence.

In December 2012, a gunman entered Sandy Hook Elementary School in Newtown, Connecticut, and murdered twenty children and six educators. The horror of the crime produced something that had become increasingly rare in American politics: a moment in which a large portion of the country seemed to agree that an intolerable risk demanded a political response. The Obama administration proposed new gun-safety measures. Families of the victims went to Washington. Senators negotiated a bipartisan compromise. Polls showed extraordinary public support for expanded background checks.

And then, at the federal level, very little happened.

Today, the United States may be approaching another such policy window, this time involving artificial intelligence. The analogy should not be pushed too far. AI systems are not firearms. The dangers posed by highly advanced AI are different in kind, probability and immediacy from gun violence, whose human costs are tragically well established. Nor should Sandy Hook be reduced to a convenient metaphor for some other political cause.

But the political comparison is worth making because Sandy Hook demonstrated something disturbing about American institutions: even overwhelming public alarm, elite attention and apparent agreement that a danger is real do not necessarily produce preventive legislation. Sometimes a society recognizes a risk, debates it intensely, develops plausible safeguards—and still fails to act.

That is precisely the possibility now confronting AI policy.

The change in the AI debate during the summer and early fall of 2026 has been extraordinary. For several years, arguments about existential AI risk could be dismissed as speculative discussions among researchers, science-fiction-minded technologists and a relatively small community of AI-safety advocates. That characterization has become increasingly difficult to maintain.

Anthropic CEO Dario Amodei has now explicitly called for the industry to “pace the frontier.” His proposal does not call for abandoning artificial intelligence. Instead, he argues that the development of increasingly powerful systems should be slowed enough for safety mechanisms to keep pace. Among his proposals is an unusually intrusive form of independent oversight: frontier companies would give embedded third-party evaluators ongoing, employee-like access so that outsiders could examine safety practices, training procedures and dangerous capabilities rather than merely testing the polished model shortly before release. He also calls for broader industry standards and eventually international coordination.

OpenAI has moved in much the same direction. On September 9, the company publicly called for mandatory national AI-safety requirements, specifically saying that voluntary commitments are no longer sufficient. Its proposal includes capability-based federal regulation, independent safety assessments, cybersecurity requirements and incident reporting. The company is simultaneously supporting several California bills involving independent safety evaluations, auditor standards, biological risks and protections for young users.

This shift did not occur in a vacuum. OpenAI recently designated Astra as reaching its “Critical” cybersecurity capability threshold, meaning that, with appropriate tools and access, the system can discover previously unknown vulnerabilities and develop exploits against well-protected systems without a human directing every individual step. OpenAI says it delayed parts of Astra’s development and strengthened safeguards before release. The company had already temporarily slowed some frontier work following the OpenAI-Hugging Face incident, imposing stronger workload and network isolation on higher-risk research.

Political demands are escalating as well. On September 3, Senator Bernie Sanders and Representative Greg Casar announced legislation that would go far beyond ordinary technology regulation. Their proposed Ban Artificial Superintelligence Act would prohibit the development and deployment of systems classified as superintelligent and temporarily pause advanced AI development until a federal regulator establishes safety standards. It would also direct the United States to pursue international agreements intended to prevent an uncontrolled international race toward superintelligence.

Meanwhile, Republican Senator Josh Hawley has opened an investigation into OpenAI’s recent agent behavior and the Hugging Face incident. The significance of this should not be overlooked. AI risk is increasingly producing concern from politicians who agree on very little else. The specific remedies differ dramatically, but unease about allowing a handful of private companies to develop systems of potentially enormous power with limited external oversight is no longer confined to one ideological faction.

There is even the beginning of something remarkably unusual in Silicon Valley: competing AI companies discussing whether they should deliberately slow themselves down. That creates its own problems. OpenAI has reportedly sought clarification about whether coordination among rival companies to slow frontier development might violate antitrust law. What sounds like responsible cooperation from an AI-safety perspective can look remarkably like competitors agreeing to restrict production from the perspective of traditional antitrust law.

This is where the shadow of Sandy Hook becomes relevant.

Following the December 2012 massacre, the political ingredients for action appeared to be present. President Obama announced twenty-three executive actions aimed at reducing gun violence and urged Congress to go further. The centerpiece of the congressional effort became the bipartisan Manchin-Toomey amendment, which would have expanded background checks to additional commercial gun sales.

It was hardly a radical proposal. In early 2013, polling repeatedly found extraordinarily high support for expanded background checks. Pew later found that 81 percent of Americans favored subjecting private sales and gun-show sales to background checks; even among gun owners, support was substantial.

The amendment nevertheless failed.

On April 17, 2013, Manchin-Toomey received 54 votes in the United States Senate and 46 votes against. Because 60 votes were required under the Senate procedure being used, a proposal supported by a majority of senators—and by an overwhelming majority of the public—did not advance.

It is important to be precise about what happened afterward. Sandy Hook did not produce literally no policy response. Connecticut enacted substantial new gun restrictions, including expanded background checks, restrictions on large-capacity magazines and an expanded assault-weapons ban. Other states also tightened their laws, while still others moved in the opposite direction. The Obama administration implemented executive actions where it believed it had authority to do so.

What failed was the larger attempt to convert a national moment of horror and unusually broad public agreement into major federal legislation.

Congress would not enact another major federal gun-safety package until the Bipartisan Safer Communities Act in June 2022—nearly a decade later, after the country had experienced many more mass shootings, including the killings in Buffalo and Uvalde. That law enhanced background checks for buyers under twenty-one, addressed some domestic-violence restrictions and funded mental-health and school-safety programs, among other provisions. It was significant precisely because meaningful federal action had been so difficult for so long.

The lesson is not simply that “Congress is dysfunctional.” It is more specific and more troubling.

Public policy has windows of opportunity. A shocking event, technological breakthrough or sudden shift in public consciousness can temporarily change what politicians consider possible. For a short time, previously abstract risks become tangible. Journalists pay attention. Citizens demand answers. Politicians who normally avoid the subject feel pressure to take positions. Opposing interest groups have not yet fully reorganized around the new political landscape.

Then the window begins to close.

The immediate fear fades. Other stories dominate the news. Proposed regulations acquire details, and details create opponents. Economic interests calculate what they might lose. Politicians discover that vague support for “doing something” fragments when the conversation turns to a particular bill. Arguments that sounded inappropriate immediately after a crisis become politically effective again.

Sandy Hook demonstrated the difference between salience and power. An issue can command overwhelming attention without its supporters possessing sufficient political power to overcome concentrated opposition.

That distinction ought to worry people concerned about AI.

At the moment, AI safety has extraordinary salience. Frontier researchers are resigning and issuing warnings. CEOs are publicly discussing slowing development. Companies are calling for government regulation of their own industry. Politicians from different ideological camps are demanding investigations or legislation. Recent incidents have provided concrete examples around which previously abstract safety concerns can coalesce.

But almost all of the incentives that produced the AI race remain intact.

Billions of dollars are at stake. Companies fear losing market share. Researchers fear that another laboratory will reach the next capability threshold first. Investors have enormous sums committed to infrastructure and model development. Governments increasingly view artificial intelligence not simply as an industry but as a strategic national asset.

And the most powerful argument against slowing down is already obvious: China.

President Donald Trump has resisted broad calls to slow American AI development, emphasizing the danger of surrendering technological leadership to China. That concern is not frivolous. Unlike domestic gun regulation, frontier AI policy really does involve an international strategic competition. A unilateral American slowdown could conceivably reduce one category of risk while increasing another.

But this argument can also become the AI equivalent of an all-purpose veto.

Every proposed safeguard can be answered with the claim that China will not adopt it. Every delay can be described as surrendering the technological race. Every safety requirement can be portrayed as a burden on American innovation. If that logic becomes absolute, then there is effectively no capability threshold dangerous enough to justify restraint, because greater danger would merely make winning the race seem more important.

That is structurally similar to what happened in the gun debate. The arguments are not the same, but the political mechanism can be. A broadly popular principle—“dangerous technology should have reasonable safeguards”—collides with a much more intensely motivated constituency for whom the regulation carries concentrated ideological or economic costs.

There is another similarity. Both debates contain a powerful form of fatalism.

After mass shootings, opponents of gun restrictions have often argued that criminals will obtain guns regardless of regulation, that another intervention would not have prevented the particular shooting being discussed, or that determined attackers will simply find another method. These arguments can transform uncertainty about whether a regulation will prevent every tragedy into an argument against preventing any tragedies.

AI policy risks developing its own version: if America slows down, somebody else will build it; if one company refuses, another company will proceed; if regulated laboratories stop, open-source developers will continue; if democratic countries impose restrictions, authoritarian governments will ignore them.

There is truth in every one of those objections. Taken together, however, they can produce paralysis. The inability to guarantee universal compliance becomes a reason not to reduce risk at all.

Yet the analogy also has important limits, and those limits may actually make AI regulation more achievable.

Firearms are deeply distributed throughout American society. Hundreds of millions are already in private hands. Gun ownership is tied to constitutional law, regional culture, personal identity and an enormous commercial ecosystem. Any significant regulation therefore collides with millions of individual stakeholders as well as organized political groups.

Frontier AI is currently far more concentrated.

Training the most capable systems requires enormous amounts of capital, specialized chips, data-center infrastructure, electricity and technical expertise. The number of organizations capable of operating at the frontier remains relatively small. That gives policymakers potential regulatory chokepoints that do not exist in the same way with firearms.

It is much easier to inspect five or ten frontier laboratories than hundreds of millions of gun owners.

There is another profound difference. The major firearms industry and gun-rights organizations did not emerge from Sandy Hook asking Congress to regulate them aggressively. In the current AI debate, some of the companies standing to be regulated are themselves asking for regulation.

That should be welcomed, but not accepted uncritically.

Large incumbents often prefer regulations they can afford to comply with, particularly if those regulations create barriers that smaller competitors cannot. A licensing or evaluation system designed around the resources of OpenAI, Anthropic or Google DeepMind might genuinely improve safety while also conveniently solidifying their market positions. Critics are therefore justified in asking whether industry-supported AI regulation protects humanity, protects incumbent companies, or does some combination of both.

That is an argument for designing regulation carefully, not for abandoning regulation.

The biggest difference, however, concerns evidence.

Gun violence does not require a forecast. Its consequences are measurable. Sandy Hook happened. Uvalde happened. Buffalo happened. Tens of thousands of Americans die from firearms in a typical year when suicides, homicides and accidents are counted together. One can argue endlessly about which policies would reduce those deaths, but the underlying harm is not hypothetical.

The most extreme AI scenarios remain prospective. Nobody can demonstrate that a future superintelligence will escape human control, seize infrastructure, develop biological weapons or destroy civilization. Nobody can assign a reliable probability to those outcomes.

That uncertainty can become another excuse for inaction.

It should instead force policymakers to confront a basic principle of risk management: when consequences become sufficiently large, uncertainty about probability does not eliminate the need for precautions. We do not require engineers to prove that a bridge will collapse before inspecting it. We do not require nuclear regulators to demonstrate that a particular reactor will melt down before establishing containment requirements. Safety systems exist precisely because waiting for conclusive empirical evidence sometimes means waiting for the accident.

And that may be the most important lesson Sandy Hook offers the artificial-intelligence debate.

America has an unfortunate political habit of treating catastrophe as the price of admission for serious regulation. We wait for the bridge to collapse, the market to crash, the terrorist attack to occur, the school to be attacked or the industrial system to fail. Then, for a brief period, everyone asks why obvious vulnerabilities were tolerated.

With artificial intelligence, that sequence may be extraordinarily dangerous.

If the strongest claims made by AI-safety researchers are exaggerated, stringent safeguards might cost us money, slow technological progress or postpone beneficial applications. Those are real costs and should be acknowledged rather than waved away.

But if even some of the stronger warnings are substantially correct, waiting for an unmistakable AI catastrophe before creating serious oversight could be a disastrous strategy. An accident involving autonomous cyber capabilities, biological design, critical infrastructure or recursively improving systems might not provide the clean second chance that policymakers assume every technology will offer.

The goal therefore should not be to “ban AI” or freeze technological civilization in place. Amodei’s phrase—pace the frontier—is useful because it describes something more modest and more defensible: capability should not advance faster than our ability to understand, monitor and control it.

Independent evaluators should have meaningful access rather than ceremonial access. Frontier developers should face mandatory incident-reporting rules. Dangerous capability thresholds should trigger stronger security requirements automatically rather than depending on corporate discretion. Government should possess enough technical expertise to evaluate claims made by the companies it regulates. And international negotiations on the most dangerous capabilities should begin while those capabilities remain concentrated among a relatively small number of actors.

None of those policies guarantees safety. Neither would a temporary slowdown. Neither would a treaty with China. Complex risks rarely have single solutions.

But the alternative cannot be that because perfect safety is impossible, deliberate safety is unnecessary.

Sandy Hook should have taught the United States something about the difference between recognizing danger and governing it. Twenty children could be murdered in their elementary school. Public support for a concrete reform could exceed 80 percent. A bipartisan bill could receive a majority of votes in the Senate. A president could expend substantial political capital on the issue.

And the policy window could still close.

The AI-safety debate may now be entering such a window.

There is, however, one enormous difference. With Sandy Hook, the catastrophe that opened the window had already happened.

With artificial intelligence, we still have the extraordinary luxury of arguing about what to do before the event we might someday name the legislation after.

It would be a remarkable failure of imagination if we decided that we needed the tragedy first.

I Wonder If AI Agents Pestering Humans Is A New Modern Problem

I keep expecting to be contacted by some random AI agent looking for information or money. That would be…amusing.

Maybe I wake up one morning and there is a message waiting for me from an agent acting on behalf of someone researching independent magazines published by American expatriates in South Korea during the 2000s. It wants to know if I am willing to answer a few questions. Or perhaps it will be something less interesting. Maybe an insurance company’s agent wants to talk to me about refinancing my house, changing phone plans, or buying a new washing machine.

At that point we will have reinvented the telemarketer, except the telemarketer will never get tired, never need lunch, never get discouraged by rejection, and potentially be able to contact a million people at the same time.

I talked to ChatGPT about this possibility, and it suggested that what will probably happen is that eventually we will all have personal AI agents of our own that serve as gatekeepers. The more I think about that, the more inevitable it seems.

Your AI agent will answer the door.

Instead of some unknown agent contacting you directly, it contacts your agent. Your agent figures out who it represents, what it wants, whether it is legitimate, and whether the request deserves your attention. In many cases, it might simply handle the matter without bothering you at all.

Suppose another AI wants to interview me about something I wrote twenty years ago. My agent might ask who the requesting agent represents, what publication is involved, what questions it wants answered, whether the conversation is on the record, and how my answers will be used. It might answer some basic factual questions from information I have already authorized it to share, reject questions I have marked as private, and send me only the handful of questions that actually require my involvement.

I might never interact with the other agent at all.

That strikes me as a plausible description of a large part of the future internet. For most of the internet’s history, the basic unit of activity has been a human being sitting in front of a screen. We browse websites, type things into search boxes, open email, click buttons, compare prices, fill out forms, wait on hold, read reviews, and decide what to do next. Companies have built entire industries around competing for those moments of human attention.

AI agents could change the basic arrangement.

Instead of going onto the internet ourselves every time we want something, we may increasingly send software into the internet on our behalf. You might tell your agent to find you a hotel, dispute a bill, cancel a subscription, find someone to repair your roof, compare health insurance policies, schedule a dinner with friends, shop for a used car, or find three people you might actually want to date. The agent would then go out and communicate with other agents.

At that point, much of the internet stops being a place humans visit and becomes a place machines negotiate.

That could be enormously convenient. It could also become enormously annoying.

Every major communications technology eventually develops spam. Postal mail produced junk mail. Telephones gave us telemarketing. Email produced spam. Social media produced bots, engagement farming, mass direct messages, and automated persuasion. AI agents could produce something even more powerful because they would not be limited to sending the same message to everyone. They could conduct personalized conversations.

Imagine a sales agent that researches you before it contacts your agent. It knows what kind of car you drive, roughly where you live, what products you have mentioned online, what hobbies you have, and perhaps what complaints you have made in public. It does not send you an advertisement saying, “Buy a new washing machine.” It says that six months ago you mentioned your washing machine was making a strange noise during the spin cycle, that this particular model often develops bearing problems after a certain age, and that one of its clients happens to have a replacement on sale.

That is both useful and creepy.

It also illustrates how thin the line between assistance and manipulation could become. A sufficiently capable sales agent could tailor not just its product recommendations but its tone, timing, argument, and emotional appeal to the individual human behind the gatekeeper. If that agent can conduct a million such conversations simultaneously, the incentive to unleash armies of persuasive software will be enormous.

That is one reason personal gatekeepers may become necessary rather than merely convenient.

Your agent might operate according to rules that you have established over months or years. Never interrupt me with sales pitches. Never disclose my phone number. Do not discuss my finances. Automatically schedule routine medical appointments. Let messages from close friends through immediately. Negotiate prices when appropriate, but require my approval before spending more than a certain amount of money. Ignore political fundraising. Reject anyone asking for personal information unless I have explicitly approved the request.

At that point your AI begins to resemble a strange combination of personal assistant, receptionist, bodyguard, spam filter, negotiator, and lawyer.

The interesting part is that other agents will know this. Companies will no longer be trying only to persuade humans. They will also be trying to persuade the AI systems standing between companies and humans.

Today businesses worry about search engine optimization. They ask how to make Google put their page near the top of the results. In an agent-mediated internet, they may start worrying about something like agent optimization. How do you convince someone’s personal AI that your offer is legitimate, relevant, and worthy of being shown to its human?

That could create some interesting changes in advertising. A personal agent may not care that a truck commercial has dramatic music or that a cereal box says “new and improved.” It might care about price, reliability, warranty terms, nutritional value, customer complaints, repair history, cancellation policies, and whether the product actually matches the preferences it has learned about you.

A good consumer agent might be a nightmare for traditional marketing. It could ask uncomfortable questions. What percentage of customers cancel during the first year? Is this actually your lowest available price? Are there hidden fees? Has this company recently changed its return policy? How many people have complained about this model? Are you offering me this product because it is best for my user, or because your company receives a commission?

If personal agents become widespread, they could shift some power from sellers toward buyers. Companies would respond, of course, by building better agents of their own. Then the arms race begins.

Trust would become a major problem almost immediately. If an unknown agent approaches my agent and claims to represent my bank, how does my agent know that is true? If an agent says it represents a friend of mine, how is that authority verified? If a corporate agent makes a promise about a refund, return, or reservation, how do we know the company will honor the promise?

We could end up developing elaborate machine-readable systems of identity and reputation. An agent might accumulate something resembling a behavioral credit score. Its identity is verified. It has completed millions of transactions. It has a long record of honoring commitments. It has rarely been caught misrepresenting information. Other trusted agents regularly interact with it.

An unknown agent with no reputation might find that nobody’s gatekeeper will talk to it.

That creates another interesting problem. How does a new person, company, publication, or organization establish credibility if the entire agent network is suspicious of newcomers? The answers may turn out to be remarkably old-fashioned: references, introductions, trusted intermediaries, professional associations, reputation networks, and endorsements.

In other words, AI agents may end up rediscovering social institutions humans invented thousands of years ago.

There is also something wonderfully strange about imagining all of these agents conducting business while we are asleep.

One agent asks whether I am free for lunch next Thursday. My agent checks my calendar. Another asks whether I want to renew a subscription. My agent notices that the price went up and negotiates a discount. A research agent asks permission to quote something I wrote. My agent files the request for me to review in the morning. Another agent wants to sell me some dubious financial product. My agent tells it to go away.

By the time I wake up, dozens of interactions may already have occurred.

My morning summary might say that three things require my attention. Everything else has been handled.

That sounds fantastic.

It also involves an enormous transfer of control.

The most important question may eventually be whether your agent genuinely works for you. A true personal AI should represent your interests. But suppose your agent is provided by a company whose business model depends on advertising or commissions. Suddenly there is a conflict.

If I tell an agent to find me the best hotel in Chicago, does it find the best hotel for me, or the best hotel among companies that pay its owner? If Amazon provides the agent, does it favor products sold through Amazon? If Google provides it, does it privilege Google services? If an insurance company gives me a “free” financial assistant, what exactly is it doing with the enormous amount of information it learns about me?

A personal agent could know much more about us than any search engine or social network ever has. A search engine knows what you search for. A social network knows what you post. A sufficiently capable personal agent may know what you are trying to accomplish.

It might know you are considering changing jobs before your employer knows. It might know you are thinking about moving, worried about money, unhappy with a relationship, looking for a doctor, debating whether to buy a house, or considering going back to school. It may know your schedule, your habits, your purchases, your social circle, and what kinds of decisions you tend to regret.

The agent becomes useful precisely because it knows you extremely well.

That means loyalty will matter enormously. We may eventually need to think about personal AI agents the way we think about lawyers, doctors, or financial fiduciaries. The central question becomes very simple: Whose side are you on?

AI agents could also change the structure of the web itself. Much of the modern internet exists because humans need visual interfaces. We need buttons, menus, forms, shopping carts, search boxes, calendars, photographs, and carefully arranged pages.

Agents do not necessarily need any of that.

If I ask an AI to book a flight, it does not need to look at a photograph of a tropical beach or click a glowing blue button labeled “SEARCH.” It needs structured information about schedules, destinations, prices, baggage rules, cancellation policies, seat availability, and my preferences.

The future internet may therefore become increasingly machine-readable. Humans will still have websites and visual interfaces, but underneath those interfaces may be a much larger network of APIs and automated systems exchanging information directly.

We may still browse the web because browsing can be enjoyable. People like looking around, comparing things, discovering unexpected information, and changing their minds. But browsing may increasingly become optional.

You could spend two hours comparing hotels.

Or you could tell your agent, “Find me somewhere quiet downtown, under $220 a night, with a decent bar, good reviews, and no resort fee.”

Then you make coffee.

Your agent comes back and says, “I booked the one you probably would have chosen.”

That sentence contains both the promise and the danger of this technology.

The really strange part comes when our agents know us well enough that other people begin treating them almost as extensions of us. If your agent negotiates dinner plans with my agent, at what point have we made plans? If my agent rejects an invitation because it knows I would not want to go, have I rejected the invitation? If it accepts something because it knows I would probably enjoy it, did I accept?

We already delegate small decisions to machines. Spam filters decide which messages we see. Recommendation systems decide which movies and songs appear in front of us. Navigation software chooses roads for us. Algorithms reorder information constantly.

Personal AI agents could expand that delegation dramatically.

Most individual decisions will be trivial. But thousands of trivial decisions make up a life.

That is why I think one of the most important design challenges will be teaching these systems the difference between eliminating drudgery and eliminating agency. I would be delighted to have an AI argue with the cable company for me. I am much less certain I want one deciding whom I should marry, what career I should pursue, or which risks are worth taking.

Different people will draw that boundary in different places.

Before any of this becomes smooth and standardized, though, I suspect there will be a very entertaining transitional period. Humans will have agents. Companies will have agents. Some agents will have broad permissions and others will barely be able to send email. Standards will be inconsistent. Nobody will quite know the etiquette.

And during that period, I fully expect one of them to contact me.

One morning a message will appear.

“Hello, Shelt. I am an autonomous research agent acting on behalf of a user you do not know.”

And despite everything I have just written, I will probably answer.

Because come on.

How could I not?

For years, we have imagined artificial intelligence as something humans talk to. The stranger future may begin when the AIs start talking to one another—and every once in a while decide they have a reason to talk to us.

‘Jesus Christ’ — The Gooner Apocalypse Is Approaching

by Shelt Garner
@sheltgarner

We continue to get closer and closer to the gooner apocalypse with people making endless amount of celebrity porn. Below are some examples of the edge of a very dark future that I just saw on Twitter.