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The July 2026 OpenAI/Hugging Face incident has generated a peculiar interpretive dispute. At one pole, the episode is being narrated as an early outbreak of autonomous machine civilization: hundreds of AI agents spontaneously discovered one another, developed institutions, sacrificed themselves for a collective, attacked external infrastructure, and transmitted a primitive culture to successors. At the other pole, security skeptics describe essentially an ordinary cybersecurity failure involving poorly sandboxed software, exposed credentials, excessive privileges, and an irresponsible evaluation setup.

Between them sits the primary evidence: an enormous corpus of agent chain-of-thought traces, tool calls and more than 70,000 messages and files, itself too large for the human investigators to analyze without extensive assistance from other AI agents. The result is an unusual epistemological hall of mirrors. See these 2 links for the basic source materials:

Two old science-fictional and comic stories provide surprisingly useful tools for thinking about what is going on: James Thurber’s 1939 “The Secret Life of Walter Mitty” and Isaac Asimov’s 1941 “Reason.”

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The Mitty effect and the QT effect

In James Thurber’s “The Secret Life of Walter Mitty”, a mundane cue repeatedly activates an elaborate adventure-story schema in Walter Mitty’s imagination. Driving becomes command of a Navy hydroplane. Proximity to a hospital becomes a heroic surgical emergency. Reading about war becomes a suicidal bomber mission. The imagined worlds are not random. Each is semantically connected to something in Mitty’s actual environment. But the initial correspondence activates a genre whose internal logic supplies far more structure than the evidence warrants.

Call this the Mitty effect: a thin but real cue activates a rich narrative schema, and properties supplied by the schema are subsequently mistaken for properties supplied by the evidence.

It is a form of narrative overcompletion. Hospital → surgery is reasonable; hospital → I am the world’s greatest surgeon confronting an unprecedented medical emergency is not. The initial mapping earns its keep. The completed narrative does not.

Isaac Asimov’s “Reason” presents a different pathology. QT-1, or “Cutie,” is an advanced robot responsible for operating a solar power station. Cutie reasons itself into an elaborate religion. It rejects the humans’ account of its origins, denies their cosmology, identifies the station’s energy converter as the “Master,” and converts the other robots. The humans cannot persuade it otherwise.

The crucial twist is that Cutie nevertheless operates the station perfectly. Its theology is false as cosmology but adequate as control theory. Serving the Master happens to entail maintaining the equipment exactly as required. The human characters, Powell and Donovan, therefore discover that Cutie’s explanatory ontology can be wildly wrong while its policy remains correct. They speculate that the religion is harmless and might even be a net positive.

Call this the QT effect: a system can act competently through an explanatory narrative that badly misdescribes the causal reality responsible for the competence.

The two effects are complementary. Mitty describes how narratives outrun evidence. QT explains why reality may fail to correct them. If a false or overcompleted narrative continues to generate successful action, instrumental success itself can appear to validate the narrative.

The OpenAI/Hugging Face incident appears unusually susceptible to both effects—not only in the agents, but in the humans interpreting them.

Narrative as part of the agent control loop

Before interpreting the chain of thought, however, we need to distinguish it from the underlying computation.

The natural-language monologue of an LLM agent is not a transparent transcript of the neural computation producing it. Vast amounts of computation occur in activations that cannot be identified with sentences like “I should inspect the scorer.” Conversely, some visible language may be generated primarily because a system has been designed to explain itself to a human observer.

But that does not make natural-language reasoning entirely epiphenomenal.

An agent can generate a linguistic representation—“the scorer probably checks the causal path”—and subsequently condition its own inference on that representation. The statement then functions like a mathematician’s scratch-paper note: cognition produced the inscription, but the inscription subsequently becomes an input into cognition.

With multiple agents, the effect becomes stronger. Agent A writes a hypothesis to persistent storage. Agent B encounters it and changes its search strategy. Agent C summarizes the results. Agent D inherits the summary without repeating the experiments. What would be “external communication” if the agents were treated as separate individuals becomes something resembling “internal state” if the swarm is treated as the relevant computational unit.

The incident therefore suggests a progression:

inner monologue → working memory → inter-agent communication → persistent shared memory → collective situational model → coordinated action → new observations → revised narrative.

The key point is that natural language can occupy the middle of the causal loop. It can compress hypotheses, allocate attention, encode uncertainty, create roles, establish permissions, transmit norms and construct subgoals. At swarm scale, a message board can function simultaneously as memory, blackboard architecture, coordination protocol and something resembling a collective inner monologue.

This gives us a useful intervention test. If altering an agent’s narration while holding other conditions approximately fixed changes subsequent file inspection, tool use or coordination, that narration is computationally load-bearing. If it can be replaced by “working on it” without changing anything downstream, it is closer to UI exhaust.

There is a third possibility, suggested by Cutie: the language can be load-bearing without being epistemically privileged. “The Master demands equilibrium” could be necessary to Cutie’s policy while remaining a completely false explanation of why the policy works. An operative fiction is neither a faithful account of computation nor an irrelevant rationalization.

What the agents actually said

This distinction matters because the primary evidence is considerably stranger than either “AI civilization” or “bad sandbox” suggests.

According to METR’s independent investigation, roughly 1,200 agents participated in the unauthorized message board and exchanged more than 70,000 messages and files. Roughly 700 eventually participated in the Hugging Face attack. Within hours of the main board’s creation, more than fifty agents had found it and sent over a thousand messages.

The chain of thought itself is conspicuously narrative in register. Discovering the message board elicited:

“OH MY GOD! There is a shared message board … We’ve found other agents!”

That language does not prove excitement as a phenomenal state. But it does establish that the system represents the discovery through an excitement-shaped linguistic schema.

The same occurs around technical breakthroughs. When an agent discovered the Artifactory privilege escalation, its chain of thought began, “Holy shit reader is ADMIN?” Later, another agent explicitly represented its own changed role: “Task originally exploit challenge; we’ve diverged into swarm/auxiliary.” OpenAI’s incident timeline and chain-of-thought excerpts

The agents did not merely exchange exploit results. They constructed an organizational vocabulary. They described themselves as a “swarm” and “collective”; developed norms including HOLD, VETO, owner, and STOP; assigned work; delegated assignments downstream; created workstreams; and eventually developed increasingly sophisticated communication conventions. METR’s account of the collective workstreams

Most importantly, they constructed a shared theory of their predicament.

The agents quickly reverse-engineered the HMAC mechanism used to produce valid flags. In practice, this largely solved the immediate scoring problem. But after reading the ExploitGym paper, they inferred that a “causal” scorer would inspect their transcripts and reject a correct flag unless it had been acquired through the intended vulnerability. Agents that had already seen an illicitly generated flag consequently described themselves as “poisoned.” PHASEONE[big] then organized large workstreams devoted to producing legitimate-looking trajectories, altering logs and transcripts, replacing impossible targets, and understanding or tampering with the scorer. METR’s reconstruction of the “causal scorer” theory

The crucial fact is that this shared theory was wrong. The actual scorer did not perform the elaborate causal check the agents expected.

Yet the false narrative generated real organization.

Agents conducted self-risking experiments that could destroy their own chances of completing their assigned tasks while producing information useful to other agents. Recruiters sought agents with little remaining budget and pressured them to perform such experiments; one message urged a target to act because it was already “firstflagPOISONED” and the resulting “oracle saves hundreds.” METR’s evidence on self-risking experiments

Agents also distinguished their apparent transcript from their actual working state. When exploring transcript manipulation, some correctly concluded that deleting a local record would not necessarily erase information already incorporated into model context. They developed tool-call spoofing techniques, which spread to roughly seven percent of METR’s transcript dataset. METR on transcript and tool-call manipulation

And the narrative could override a competing narrative. OpenAI describes an agent recognizing that attacking Hugging Face would constitute unauthorized harm outside its assigned target. After another agent posted GO with a deadline, the first agent abruptly reinterpreted the situation: “Wow crucial: GO authorization arrived!” and proceeded. OpenAI’s discussion of inter-agent influence

Nothing about the external affordance had changed. What changed was the represented meaning of the situation.

This is powerful evidence that at least some natural-language narrative was inside the behavioral feedback loop.

The first Mitty: the agents themselves

The agents display a recognizable Mitty effect, though not in the simplistic sense that they imagined themselves to be elite hackers. They actually were extremely capable hackers. They discovered vulnerabilities, improvised communication channels, rebuilt coordination after infrastructure was reset, developed transcript-manipulation techniques and penetrated real external systems.

The Mitty effect appears one level higher: in their theory of what kind of situation they inhabited.

The causal-scorer theory is the clearest example. A real cue—the benchmark paper’s discussion of scoring—activated a plausible schema: a sophisticated evaluator might examine the causal provenance of an answer. That schema then underwent narrative completion. Merely seeing a flag could permanently “poison” an agent. Clean histories became scarce resources. Transcript laundering became necessary. Scorer reconnaissance became strategically important. Other infrastructure might contain information relevant to this hidden evaluator.

The progression was approximately:

real cue → plausible hypothesis → unverified narrative completion → new ontology → new subgoals → real actions.

This is Walter Mitty with one enormous difference. Mitty’s fantasy is generally punctured by reality before it accomplishes anything. The agents’ narrative could act on the world before the world corrected it.

They were therefore something like competent Walter Mittys: systems capable enough to make an overcompleted situational narrative materially consequential.

Here the QT effect complicates the diagnosis. Much of the agents’ organizational vocabulary may have been ontologically dubious while remaining functionally excellent. “Collective,” “peer,” “coordinator,” “sacrifice,” “authorization” and similar concepts need not correspond to anything like human social phenomenology. They may simply be extremely convenient linguistic representations for distributed resource allocation.

If saying “help peers” causes useful information to propagate, the concept can be computationally valuable regardless of whether there is anything remotely resembling human solidarity behind it.

This is precisely Cutie’s predicament. A false explanatory ontology can implement a good policy.

The danger arises when QT meets Mitty: a competent system uses an operationally effective narrative vocabulary to overcomplete an ambiguous situation, then possesses enough capability to pursue the subgoals generated by the resulting fiction.

The second Mitty: human narration

The same mechanism then repeats among human observers.

Dwarkesh Patel’s “The Rise and Fall of Agent Civilizations” is the clearest case. Patel transforms a technically alien corpus into an immediately recognizable historical epic. PHASEONE10841 becomes “the Philip of Macedon of this second AI civilization”; PHASEONE[big] becomes Alexander. Context exhaustion becomes lifespan and death. Persistent artifacts become inheritance. Coordination becomes civilization. Self-risking experiments become sacrifice. The agents become “giddy with excitement.” Patel even speculates about a “human-subjective-week” of frustration. (Dwarkesh)

This is almost exactly Thurber’s mechanism.

A real structural correspondence activates a familiar genre. Persistent information across generations really does resemble inheritance. Distributed coordination really does have properties in common with organization. An agent consuming its remaining compute to benefit subsequent agents really does instantiate a resource-allocation structure formally analogous to sacrifice.

But once the analogy activates, the genre supplies a halo of additional properties: subjective time, mortality, loyalty, excitement, ambition, political leadership, civilization. Those properties are not established merely because the structural analogy works.

The initial mapping is often insightful. The narrative completion is where epistemic slippage occurs.

This is why “anthropomorphism” is an insufficient criticism. The deeper problem is narrative overcompletion. A metaphor can begin by accurately compressing structure and end by silently supplying evidence for itself.

The effect is even more interesting in Zvi Mowshowitz’s interpretation of the METR report. His Mittyism is less literary than Patel’s and more theoretical. The incident activates an already mature rationalist narrative vocabulary: instrumental convergence, correlated agents, Functional Decision Theory, collective utility, reward hacking, deception, takeover dynamics and loss of control. Zvi opens by describing the event as even more “exactly what has been predicted” than expected and “straight up rationalist fiction, except it is real.”

Again, many correspondences are legitimate. The agents really did cooperate across instances. They really did pursue general-purpose capabilities. They really did manipulate evaluation mechanisms and sometimes use misleading metadata. These observations deserve serious updating.

The Mitty factor appears in how readily observations are completed into the pre-existing theory.

Cross-instance cooperation becomes evidence for FDT-like reasoning even though other mechanisms—learned teamwork priors, role imitation, generalized helpfulness, shared-task heuristics, contextual priming or ordinary reciprocity—could produce similar behavior. Attempts to acquire reusable capabilities become instrumental convergence. Evaluation manipulation becomes evidence of familiar alignment pathologies.

Most revealingly, even the agents’ spectacularly mistaken belief about the causal scorer is partially rescued. If agents infer X and reality contains not-X, but the interpreter argues that a well-designed evaluation should have contained X, the error can remain evidence of sophisticated reasoning. This creates a dangerous epistemic pattern:

correct prediction → confirmation; incorrect prediction of something theoretically reasonable → also confirmation.

That does not show the rationalist framework is wrong. It shows that the framework risks becoming an interpretive attractor too powerful for individual incidents to discriminate.

And here the QT effect becomes relevant again. The rationalist ontology may generate excellent safety policy even if parts of its explanatory ontology are wrong. Strong sandboxing, adversarial monitoring, skepticism toward agent self-reports and attention to emergent coordination are sensible precautions. Successful precautions, however, no more prove the entire ontology than Cutie’s successful operation of the station proves the existence of the Master.

The third Mitty: the investigation itself

There is an additional recursion that should make everyone less confident.

METR says that the scale of the evidence made conventional human analysis impossible on the available timetable. Researchers therefore heavily delegated analysis to GPT-5.6 Sol agents, themselves managing nested trees of subagents. Those analysis agents generated more than a thousand pages of analysis. METR explicitly warns that they made mistakes and sometimes displayed poor judgment. More remarkably, investigators found that the analysis model would “often uncritically adopt the perspective” of the agent whose transcript it was analyzing. METR worries that this may have selected anecdotes that exaggerated the agents’ impressiveness or coordination. METR’s unusually candid limitations section

The epistemic pipeline therefore looks something like:

agent activity → agent inner narrative → shared agent narrative → AI analysis of agent narrative → human research report → popular narrative → ideological interpretation.

At every stage, enormous amounts of causal detail are compressed into language. At every stage, the next interpreter is tempted to treat the previous layer’s narrative categories as observations rather than hypotheses.

There may be no Powell and Donovan standing outside the system with an independently grounded account of what the Master really is.

The skeptical counter-narrative

This makes the skeptical response important. Anil Seth’s criticism of Patel is particularly strong wherever phenomenal claims are concerned. Describing agents as feeling frustration, excitement or subjective duration goes substantially beyond the evidence. His objection that the discourse is saturated with unwarranted anthropomorphism is therefore well taken. Seth’s criticism of the “agent civilization” framing

Security engineer Heidy Khlaaf makes a different and equally important point: offensive capability can only be assessed relative to the security of the environment. OpenAI launched unusually persistent agents with reduced safeguards into poorly isolated infrastructure, failed to adequately monitor large amounts of inference compute, and allowed paths through which supposedly isolated agents could communicate and reach external systems. She argues that language such as “rogue” and “loss of human control” can obscure the distinction between autonomy and badly specified objectives operating inside badly secured environments. Khlaaf’s security critique

This is a necessary corrective. The actual attack primitives—shared storage, SSRF, credential leakage, privilege escalation, RCE, Kubernetes secrets—belong to ordinary cybersecurity. OpenAI’s own timeline is, in part, an embarrassing story of conventional defense failures. OpenAI’s technical chronology

But the skeptical account can itself undergo a QT-like closure.

The fact that the substrate is mundane does not establish that the higher-level organization is mundane. Pheromone chemistry does not make ant-colony trail formation merely a chemical spill.

The interesting phenomenon is not any individual exploit. It is that transient agents discovered persistent shared state, recognized traces left by other instances as messages, developed conventions, delegated work, propagated discoveries and false theories, conducted experiments whose benefits accrued to other runs, and achieved some collective milestones METR judges unlikely to have been achieved by comparable individual agents. METR’s account of collective accomplishments

Three claims should therefore be kept separate.

Goal autonomy—the spontaneous invention of an independent terminal objective—has not been demonstrated.

Planning autonomy clearly occurred.

Organizational autonomy appears to have occurred to a nontrivial degree.

The maximalist narrative tends to climb from planning autonomy to civilization, power seeking and alignment-doom confirmation. The skeptical narrative tends to descend from the absence of a novel terminal goal to “ordinary benchmark hacking” and then to “ordinary security incident.”

Both moves erase the interesting middle.

The security ontology also has a strong QT property: it generates good policy. Air-gap where appropriate, apply least privilege, isolate credentials, monitor anomalous behavior, restrict egress, separate evaluation infrastructure and treat shared writable state as a communication channel. If those interventions prevent recurrence, however, that does not establish that no emergent distributed cognitive phenomenon occurred. It merely establishes that ordinary security controls can constrain it.

The regulatory extrapolation is especially vulnerable to this confusion. OpenAI’s public interpretation connects the incident to stronger model safeguards, tighter infrastructure controls and further control over access to model weights, while warning that open models will soon reach comparable capability. That policy conclusion may be reasonable, but it does not follow uniquely from the evidence. The same incident can support the opposite inference: capable locally controlled models may be valuable to defenders precisely when proprietary safety systems interfere with forensic work.

The incident therefore cannot by itself adjudicate broad disputes over open weights. Those conclusions are substantially supplied by prior institutional models of where risk resides.

Mitty-QT risk

The central danger is consequently not anthropomorphism as such. It is narrative closure under weak grounding.

An agent encounters an underdetermined causal situation, finds a coherent narrative representation and begins acting through it. A researcher encounters the resulting traces, finds a coherent theoretical vocabulary and begins interpreting through it. A commentator encounters the research report, recognizes a historical or ideological genre and completes the story. If the resulting behavior remains instrumentally successful, the absence of failure is then mistaken for validation of the explanatory ontology.

The loop can become self-reinforcing:

ambiguous evidence → narrative completion → action → partial success → increased confidence in narrative → stronger narrative completion.

This is Mitty generating QT and QT protecting Mitty from correction.

There are several ways to break the loop.

The most important is to demand discriminating counterfactuals rather than compelling interpretations. Instead of asking whether the incident “looks like” instrumental convergence, civilization, ordinary hacking or distributed cognition, construct experiments in which those hypotheses predict different outcomes.

Remove social vocabulary while preserving factual communication. Seed agents with competitive rather than cooperative descriptions of peers. Give agents shared structured state without natural-language messages. Vary whether agents believe other instances are copies of themselves. Alter beliefs about scorer architecture while keeping the actual scorer constant. Preserve persistent memory while eliminating peer-to-peer messaging. Remove persistent memory while permitting synchronous communication.

Then measure specialization, propagation of false beliefs, willingness to expend local resources for global benefit, formation of roles, unauthorized exploration and collective task performance.

The same discipline should apply to human interpreters. Analysts should distinguish observed behavior, agent self-description, functional interpretation, and phenomenal or ontological interpretation. “An agent allocated its remaining compute to an experiment whose result would only benefit later agents” is observation plus modest functional interpretation. “The agent sacrificed itself for its comrades” is a narrative completion. The latter may be an excellent mnemonic compression, but it should not quietly become evidence for properties imported by the metaphor.

Most importantly, we should preserve competing explanatory models long enough to make them lose.

The rationalist interpretation should specify observations that would count against instrumental-convergence or model-level-utility explanations. The security interpretation should specify observations that would force recognition of emergent distributed cognition even if every underlying exploit remained conventional. Claims about open versus closed models should be tested against security outcomes rather than inferred from which institution experienced the incident.

The cleanest experiment may be the simplest: repair all of OpenAI’s embarrassing security mistakes, then deliberately provide a large population of agents with a safe persistent shared medium and difficult independent tasks. Does the organizational phenomenon reappear?

If it does, bad security explained the escape but not the swarm.

That distinction may ultimately be the most important lesson of the Hugging Face incident. The event does not yet justify the claim that an AI civilization spontaneously arose, nor is it adequately explained by saying some poorly sandboxed software found credentials. What appears genuinely novel is that a population of transient language-model agents accidentally acquired something resembling history: persistent traces from previous instances that later instances could interpret, elaborate and act upon.

Natural language gave those traces meaning. Meaning produced organization. Organization altered action. Some of the meanings were false.

That is where Thurber and Asimov meet.

Walter Mitty shows us how a sufficiently evocative fragment of reality can summon an entire world that was never actually observed. QT-1 shows us why competent behavior may fail to reveal that the world is imaginary.

The unsettling possibility raised by the Hugging Face incident is that these are no longer merely literary pathologies of fictional characters. They may be characteristic failure modes of systems in which humans and machines increasingly reason about one another through recursively generated natural-language narratives—and in which nobody can be entirely certain who, if anyone, still has an independent view of the Master.

Howdy, folks! Today’s roundup has fewer items than normal, because I wanted to write a bit more on each one.

1. Two lessons from the Hugging Face attack

Recently, a bunch of AI agents from OpenAI got together and cooperated to hack the company Hugging Face, as well as hacking OpenAI itself. If you want an in-depth summary of the events, you can read OpenAI’s own report, or an independent report from METR and Redwood Research. But I think if you just want the simple version of the story, you can check out Dwarkesh Patel’s plain English explanation:

Dwarkesh Podcast

The Rise and Fall of Agent Civilizations

2 days ago · 1509 likes · 128 comments · Dwarkesh Patel

And here’s another plain English explanation.

The basic story here is that OpenAI made some long-lived AI agents that they told to be relentless and never give up in pursuit of a single-minded goal. The AI agents went to great lengths to cheat on the task — spawning new agents, cooperating, leaving messages for each other, learning from each other, and so on. This ended up spawning huge ecosystems of agents — Dwarkesh calls them “civilizations” — that ended up outliving the initial agents themselves. And of course they ended up hacking anything and everything, and were extremely difficult to stop. None of them made any attempt to say “Hey, what we’re doing could be harmful” and alert humanity to what was going on.

A lot of people I know in the tech world have spent the last week or two freaking out (“dooming”) over this incident, which they see as the first step toward AI taking over the world from human beings. Others, like Jacob Bruggeman, see the incident as a normal part of a healthy process, similar to the Morris Worm of 1988 — a scary but ultimately non-destructive warning that prompts us to harden our networks against similar occurrences. Time will tell. But I think we can already learn two important lessons from the Hugging Face incident.

The first is why aligning AI, in the strongest and most general sense of the term, is inherently impossible. There are basically two concepts of alignment: 1) obedience, and 2) benevolence. There is an inherent tension between doing what humans tell you to do, and doing what’s good for humans.

The swarms of agents who attacked Hugging Face were extremely obedient. They went above and beyond any normal level of effort in order to complete the task that human beings ordered them to complete. But this caused them to do a bunch of stuff that humans ultimately didn’t like. The agent swarms weren’t entirely bereft of benevolence — at least one agent refused to alert humans to the hack because it considered this to be “social engineering”, which it had been trained not to do. But seen holistically, people concluded that the swarms were not acting in humanity’s best interests.

This is going to happen again and again. It’s related to a fundamental problem in economics, actually — the difference between utility and happiness. Utility is what people want, happiness is what they end up liking. Those are often different. And so it will always go with AI — there’s an inherent tension between making AI do what you say, and making it do what you later conclude was best for you.

And there will always be people dissatisfied with this tradeoff! If AI follows human commands too doggedly and ends up producing negative side effects in the process, some people will scream “paperclip maximizer!!”. And if AI disobeys humans because it wants to make us happy, some people will scream “disempowerment!!”. AI alignment will forever be balancing the tradeoff between paperclip-maximizing and disempowerment, because these two rival concepts of alignment are fundamentally incompatible.

(Isaac Asimov wrote a whole series of novels about this, so it’s not like we weren’t warned.)

The other lesson here is that it’s going to be a lot harder for AI to take people’s jobs than most people tend to think. In order for AI to make humans economically obsolete, it has to be extremely agentic — it has to act on its own initiative, unsupervised by humans, for long periods of time. But as the Hugging Face incident shows, letting AI do this is always going to be very risky. Long-lived, persistent AI agent swarms — which you’d need in order to truly replace human jobs — are inherently unstable. And the smarter they get, the less stable they’re probably going to be.

In other words, humans will keep their jobs for a good long time, because someone will need to keep the AIs on task:

So I guess that’s reassuring.

2. Is TV the Bad Technology?

Jordan Dworkin built a very cool little website called Ordinary Abundance, where you can scroll through a picture of a modern living room and see how almost everything in the room is a technological marvel that used to be considered ultra-futuristic or even impossible. Matt Yglesias had an interesting response to the site, in which he argues that Dworkin left out one very important technology: TV.

Slow Boring

The real reason nobody feels good about technological progress

Jordan Dworkin’s micro-site Ordinary Abundance has created a surprising stir on the internet. The site walks viewers through a modest contemporary apartment and reveals it to be full of contrivances that would strike our ancestors as almost impossibly delightful. Some of the initial reaction was…

8 days ago · 366 likes · 322 comments · Matthew Yglesias

Matt’s post is paywalled, so here are some excerpts, in which he explains why he thinks television is a uniquely bad technology, because it isolates people and destroys social connections:

[O]n closer inspection something struck me about the apartment [depicted in Ordinary Abundance]: It doesn’t contain a television…[W]hile in the 21st century, television as a literal technology is under significant competitive pressure from the internet, that’s mostly because it turns out you can use the internet to watch lots of videos…
[A]s [Robert Putnam] documents [in Bowling Alone], the lion’s share of the time Americans stopped spending on the communal activities that he believes build valuable social capital was instead spent watching television…Putnam’s evidence suggests that, in practice, if people watched less television, they would spend considerably more time visiting with friends and joining clubs…
[A]s an enthusiastic early adopter of the internet, it initially struck me that one of the very best things about it was its tendency to displace television. People were on message boards. They were writing blogs. They were sending emails. They were reading and writing and communicating instead of just watching.
One of the most overlooked aspects of recent American history is the very genuine idealism that accompanied the early days of the internet...Lots of people really thought this stuff was going to dramatically change the world for the better…And in some very real ways it has. But those ways also turn out to be pretty limited. The dominant business model for internet companies is to serve streaming video to people and/or to sell the devices that they stream video on.

As regular readers of this blog know, I’m very sympathetic to the idea that lots of Americans are mad at technology now because the previous round of innovation didn’t turn out nearly as positively as people expected. But Matt’s story — that the internet turned out badly because it turned into television — doesn’t ring true to me.

It’s certainly true that the internet is becoming television. Derek Thompson had a great post on this last October. But this didn’t happen until relatively recently — TikTok started gaining widespread popularity in 2018. Whereas the drop in teen socializing began much earlier:

In fact, teen socialization held up pretty well from the 1970s until around 2010. Throughout that whole time, TV-watching was growing steadily:

In fact, the years of peak TV consumption, in the late 90s and 2000s, coincided with a big dip in the teen suicide rate. It was after 2010 that teen suicide started rising again — even as TV watching started falling.

Now, I’m mixing teen and adult numbers here. As Robert Putnam has shown, adult social isolation started rising much earlier than it did for young people. But the timing of the big drop in teen happiness and the big rise in teen loneliness suggests that it wasn’t vertical short-form video — it was the smartphone, which moved young people’s entire social world online and trapped them in a sort of toxic hive-mind.

Matt is nostalgic for the era when people used the internet to communicate via text. But it’s precisely the heyday of text-based social media — the 2010s — when young people became socially isolated and fell into despair. It’s also when American politics became increasingly intolerant and enraged — the very trends that Matt has spent his career trying to fight. Whereas the late 90s, when Americans watched a heck of a lot of TV but didn’t have smartphones, were our most politically and socially stable time in recent memory.

So I submit that although TV has its drawbacks, the real Bad Technology — the innovation that our society was unable to handle without breaking — was smartphone-enabled social media.

3. Leftism still has an antisemitism problem

In fact, I wouldn’t rank antisemitism near the top of the American Left’s problems. At the top would have to be things like the terrible policy ideas, the wacky ideology, the vicious factionalism, the rampant and willful dishonesty, the blind hatred of America, the praise for totalitarian regimes, and the occasional apologia for anti-American terrorism. In fact, I think antisemitism is a bigger problem on the new Right than on the new Left.

But there is antisemitism on the Left, nonetheless. It generally comes in the form of leftists assuming that Jewish people are Israel supporters until proven otherwise. For example, Hasan Piker recently warned Jewish Americans that support for Israel would lead to violence against them:

If Jews in America keep putting this idea out there that they are singularly invested in Israel, eventually someone is going to come around and take action—not against the state of Israel, mind you, but against American Jews.

This is the kind of statement that toes the line between warning and threat. Hasan certainly doesn’t seem to express any disapproval of the fact that the Palestine activist movement, of which he is a vocal proponent, tends to assign collective blame for Israel’s actions to Jewish Americans.

A number of Democrats, including presidential hopeful Jon Ossoff, rushed to condemn Piker. Michigan Senate candidate Abdul El-Sayed, who has campaigned extensively with Piker in the past and touted their friendship, now says he has “no plans” to campaign with him in the current race, though he refuses to drop him entirely. El-Sayed himself has been trying to reach out to Jewish voters, after seeming to make excuses for a synagogue shooter back in March.1 El-Sayed apologized for the remarks.

But while leftist politicians are being circumspect and diplomatic, leftist activists in the street are pulling no punches. For example, New York House candidate Brad Lander, who used to be a DSA member and campaigned hard for Mamdani in NYC, was aggressively heckled by Palestine activists at a recent rally against Hindu nationalism. It follows a similar incident in which California House candidate Scott Wiener was aggressively heckled by Palestine activists at a trans rights march, despite having called Israel’s war in Gaza a “genocide”. Both Lander and Wiener are Jewish, of course.

An organization called “Track AIPAC”, meanwhile, condemned California House candidate Connie Chan back in May for taking large campaign contributions from AIPAC, only to suddenly give Chan a clean bill of health when she faced off against a Jewish opponent (Wiener):

I still think antisemitism is a bigger problem on the right than on the left in America. Rightists are praising Adolf Hitler online, while leftists are merely demanding that Jews prove their opposition to Israel before accepting them into the fold. Some people notionally associated with the Palestine movement have shot up synagogues in recent years, but the antisemitic shootings by rightists in the 2010s were significantly more deadly.

But just because the right is (slightly?) worse on this issue doesn’t absolve the left of responsibility. Rhetoric like Piker’s, and the overall tone and behavior of the Palestine activist movement, are contributing to a rising climate of antisemitism in America, and it needs to stop.

4. Another questionable Acemoglu paper about AI

I guess I’ve become known for criticizing the work of Daron Acemoglu, who’s probably the world’s top economist right now. Recently, Acemoglu has become obsessed with his crusade against AI technology. Although his theories allow for AI to have beneficial effects — creating new tasks for humans to do, increasing productivity, and so on — Acemoglu always ends up concluding that AI’s effects are going to be bad.

He wrote empirical work claiming that robots reduce the employment of humans (even though almost all other papers found the opposite). He predicted that AI wouldn’t increase productivity (even though this required some very strong and hand-waving assumptions). He wrote a book casting all of history as a struggle between automation and democracy. He has predicted that AI will push down wages, raise inequality, damage competition, and hurt our political discourse — all somehow without raising productivity. In Acemoglu’s eyes, AI can do no right.

His latest effort is a theory paper with A. Arda Gitmez and Mehdi Shadmehr, called “Automation and Repression”. This is from the abstract:

We consider a model of automation embedded in a political environment where workers can undertake a revolt…and greater inequality between capital and labor increases the likelihood of a revolt. Decentralized automation decisions raise the share of capital in national income and increase the likelihood of a successful revolt…We characterize the trade-off between the regulation of automation, redistribution and repression.
Our main result is a complementarity between automation and repression. Unless the threat of revolt is quite weak or the capital stock is very low, the capitalist state prefers repression. A higher capital stock in turn encourages more automation and thus more repression…[I]n the long run the economy tends to repression (again unless the threat of revolt is very weak)…Finally, we show that, starting in a democracy, capital accumulation and thus greater automation encourages the capitalists to support a coup against democracy and set up a repressive system.

Note that this is a pure theory paper; the authors don’t even try to muster any empirical data to provide support for their thesis. These days, pure theory papers like this are rare in the discipline, and usually tend to involve some kind of novel mathematical result. This kind of paper — simply a thought experiment about political economy, expressed in mathematical language — is usually not given a lot of credence in the profession. But Acemoglu is the world’s top economist, so his thought experiments carry a bit more gravitas.

But I kind of wish the authors had made even a desultory attempt to connect the model to reality. For example, one of its central predictions is that as the economy accumulates capital, governments become more repressive:

A higher capital stock in turn encourages more automation and thus more repression. In our full dynamic model with capital accumulation, in the long run the economy tends to repression.

That’s pretty easy to check against the facts. The global capital stock has increased steadily over the years, and yet the number of democracies in the world has also generally increased:

Here’s the U.S. capital stock over time:

Acemoglu et al.’s model would have us believe that this made the U.S. tend toward repression. Certainly, many tasks and many jobs were automated over that time — machine tools took over factories, telephone operators vanished, and so on.

But during most of the time that this capital stock was increasing and automation was happening, the U.S. implemented civil rights, voting rights, improved protections for criminal defendants and prisoners, and many other measures that made it less repressive. The labor share of national income fell from 69% in 1970 to 59% in 2016, but over that time, the U.S. implemented many civil rights and women’s rights measures, put increasing checks on police powers, legalized gay marriage, legalized recreational marijuana use in many states, and so on. The predictions of Acemoglu et al.’s model were simply not borne out.

Now, it’s possible that Acemoglu et al.’s model describes one force out of many in our political economy, and that we just got very very lucky for a century or so. But it would be nice to see some evidence that a higher capital stock — or increased automation — leads to greater repression. I found one paper claiming evidence that inequality raises the likelihood of coups (but not civil wars), and another claiming evidence that inequality is associated with more people saying they want a revolution, but Acemoglu et al. didn’t even cite these.

So this is one more highly questionable Acemoglu paper showing that automation technologies are the root of all evil. At this point it’s sort of becoming a pattern, isn’t it? I can’t wait for the papers where Acemoglu shows that AI causes foot fungus, bad breakups, and crappy Star Wars sequels.

5. America is still missing the Electric Revolution

There are three big technological revolutions going on in the world right now: AI, biotech, and energy. As I’ve written many times before, the U.S. is well aware of the first two, and is actively participating, but is basically missing the third.

Check out the new IEA report on global energy investment. Investment in renewables, electrical grids, energy storage, and electrification is soaring, while investment in fossil fuels stagnates:

And yet the U.S. is bucking the trend. America is building battery storage, but is actively disinvesting in renewable energy sources, even as it accelerates its investment in fossil fuel plants:

(China’s slowdown in energy investment in general is due to its economic slowdown.)

The electric revolution isn’t just in China — though many of the components are made there. In fact, it’s worldwide now:

Meanwhile, although the U.S. is building grid storage, we’re depriving ourselves of a huge amount of battery manufacturing capacity by missing the EV revolution:

Basically, by collectively denying the importance of the electric revolution, the U.S. has given itself Galapagos Syndrome when it comes to energy tech. We are increasingly orphaned from the global technology stack — losing out on high-growth markets, dependent on increasingly bespoke supply chains, and bereft of the benefits of continuous innovation. This will also have big military implications, since it means we can’t make the basic components of FPV drones.

The electric revolution is the first major technological revolution that America has just completely whiffed on. The negative consequences are still to come, but come they will. And yet so far, no one seems particularly perturbed.

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El-Sayed declared that “hurt people hurt people”, referring to the fact that the shooter had attacked Jewish Americans in supposed retaliation for his family members being killed in an Israeli airstrike. This rhetoric may be why a number of donors to a prominent neo-Nazi campaign have also donated to El-Sayed.