Scenario Scrutiny for AI Policy

AI 2027 was a descriptive forecast. Our next big project will be prescriptive: a scenario showing roughly how we think the US government should act during AI takeoff, accompanied by a “policy playbook” arguing for these recommendations.

One reason we’re producing a scenario alongside our playbook at all—as opposed to presenting our policies only as abstract arguments—is to stress-test them. We think many policy proposals for navigating AGI fall apart under scenario scrutiny—that is, if you try to write down a plausible scenario in which that proposal makes the world better, you will find that it runs into difficulties. The corollary is that scenario scrutiny can improve proposals by revealing their weak points.

To illustrate this process and the types of weak points it can expose, we’re about to give several examples of AI policy proposals and ways they could collapse under scenario scrutiny. These examples are necessarily oversimplified, since we don’t have the space in this blog post to articulate more sophisticated versions, much less subject them to serious scrutiny. But hopefully these simple examples illustrate the idea and motivate readers to subject their own proposals to more concrete examination.

With that in mind, here are some policy weaknesses that scenario scrutiny can unearth:

1. Applause lights. The simplest way that a scenario can improve an abstract proposal is by revealing that it is primarily a content-free appeal to unobjectionable values. Suppose that someone calls for the democratic, multinational development of AGI. This sounds good, but what does it look like in practice? The person who says this might not have much of an idea beyond “democracy good.” Having them try to write down a scenario might reveal this fact and allow them to then fill in the details of their actual proposal.

2. Bad analogies. Some AI policy proposals rely on bad analogies. For example, technological automation has historically led to increased prosperity, with displaced workers settling into new types of jobs created by that automation. Applying this argument to AGI straightforwardly leads to “the government should just do what it has done in previous technological transitions, like re-skilling programs.” However, if you look past the labels and write down a concrete scenario in which general, human-level AI automates all knowledge work… what happens next? Perhaps displaced white-collar workers migrate to blue-collar work or to jobs where it matters that it is specifically done by a human. Are there enough such jobs to absorb these workers? How long does it take the automated researchers to solve robotics and automate the blue-collar work too? What are the incentives of the labs that are renting out AI labor? We think reasoning in this way will reveal ways in which AGI is not like previous technologies, such as that it can also do the jobs that humans are supposed to migrate to, making “re-skilling” a bad proposal.

3. Uninterrogated consequences. Abstract arguments can appeal to incompletely explored concepts or goals. For example, a key part of many AI strategies is “beat China in an AGI race.” However, as Gwern asks,

“Then what? […] You get AGI and you show it off publicly, Xi Jinping blows his stack as he realizes how badly he screwed up strategically and declares a national emergency and the CCP starts racing towards its own AGI in a year, and… then what? What do you do in this 1 year period, while you still enjoy AGI supremacy? You have millions of AGIs which can do… ‘stuff’. What is this stuff?

“Are you going to start massive weaponized hacking to subvert CCP AI programs as much as possible short of nuclear war? Lobby the UN to ban rival AGIs and approve US carrier group air strikes on the Chinese mainland? License it to the CCP to buy them off? Just… do nothing and enjoy 10%+ GDP growth for one year before the rival CCP AGIs all start getting deployed? Do you have any idea at all? If you don’t, what is the point of ‘winning the race’?”

A concrete scenario demands concrete answers to these questions, by requiring you to ask “what happens next?” By default, “win the race” does not.

4. Optimistic assumptions and unfollowed incentives. There are many ways for a policy proposal to secretly rest upon optimistic assumptions, but one particularly important way is that, for no apparent reason, a relevant actor doesn’t follow their incentives. For example, upon proposing an international agreement on AI safety, you might forget that the countries—which would be racing to AGI by default—are probably looking for ways to break out of it! A useful frame here is to ask: “Is the world in equilibrium?” That is, has every actor already taken all actions that best serve their interests, given the actions taken by others and the constraints they face? Asking this question can help shine a spotlight on untaken opportunities and ways that actors could subvert policy goals by following their incentives.

Relatedly, a scenario is readily open to “red-teaming” through “what if?” questions, which can reveal optimistic assumptions and their potential impacts if broken. Such questions could be: What if alignment is significantly harder than I expect? What if the CEO secretly wants to be a dictator? What if timelines are longer and China has time to indigenize the compute supply chain?

5. Inconsistencies. Scenario scrutiny can also reveal inconsistencies, either between different parts of your scenario or between your policies and your predictions. For example, when writing our upcoming scenario, we wanted the U.S. and China to agree to a development pause before either reached the superhuman coder milestone. At this point, we realized a problem: a robust agreement would be much more difficult without verification technology, and much of this technology did not exist yet! We then went back and included an “Operation Warp Speed for Verification” earlier in the story. Concretely writing out our plan changed our current policy priorities and made our scenario more internally consistent.

6. Missing what’s important. Finally, a scenario can show you that your proposed policy doesn’t address the important bits of the problem. Take AI liability for example. Imagine the year is 2027, and things are unfolding as AI 2027 depicts. America’s OpenBrain is internally deploying its Agent-4 system to speed up its AI research by 50x, while simultaneously being unsure if Agent-4 is aligned. Meanwhile, Chinese competitor DeepCent is right on OpenBrain’s heels, with internal models that are only two months behind the frontier. What happens next? If OpenBrain pushes forward with Agent-4, it risks losing control to misaligned AI. If OpenBrain instead shuts down Agent-4, it cripples its capabilities research, thereby ceding the lead to DeepCent and the CCP. Where is liability in this picture? Maybe it prevented some risky public deployments earlier on. But, in this scenario, what happens next isn’t “Thankfully, Congress passed a law in 2026 subjecting frontier AI developers to strict liability, and so…

For this last example, you might argue that the scenario under which this policy was scrutinized is not plausible. Maybe your primary threat model is malicious use, in which those who would enforce liability still exist for long enough to make OpenBrain internalize its externalities. Maybe it’s something else. That’s fine! An important part of scenario scrutiny as a practice is that it allows for concrete discussion about which future trajectories are more plausible, in addition to which concrete policies would be best in those futures. However, we worry that many people have a scenario involving race dynamics and misalignment in mind and still suggest things like AI liability.

To this, one might argue that liability isn’t trying to solve race dynamics or misalignment; instead, it solves one chunk of the problem, providing value on the margin as part of a broader policy package. This is also fine! Scenario scrutiny is most useful for “grand plan” proposals. But we still think that marginal policies could benefit from scenario scrutiny.

The general principle is that writing a scenario by asking “what happens next, and is the world in equilibrium?” forces you to be concrete, which can surface various problems that arise from being vague and abstract. If you find you can’t write a scenario in which your proposed policies solve the hard problems, that’s a big red flag.

However, if you can write out a plausible scenario in which your policy is good, this isn’t enough for the policy to be good overall. But it’s a bar that we think proposals should meet.

As an analogy: just because a firm bidding for a construction contract submitted a blueprint of their proposed building, along with a breakdown of the estimated costs and calculations of structural integrity, doesn’t mean you should award them the contract! But it’s reasonable to make this part of the submission requirements, precisely because it allows you to more easily separate the wheat from the chaff and identify unrealistic plans. Given that plans for the future of AI are—to put it mildly—more important than plans for individual buildings, we think that scenario scrutiny is a reasonable standard to meet.

While we think that scenario scrutiny is underrated in policy, there are a few costs to consider:

by Joshua Turner and Daniel Kokotajlo, AI Futures Project |  Read more:

Image: via

How an AI company CEO could quietly take over the world

If the future is to hinge on AI, it stands to reason that AI company CEOs are in a good position to usurp power. This didn’t quite happen in our AI 2027 scenarios. In one, the AIs were misaligned and outside any human’s control; in the other, the government semi-nationalized AI before the point of no return, and the CEO was only one of several stakeholders in the final oversight committee (to be clear, we view the extreme consolidation of power into that oversight committee as a less-than-desirable component of that ending).

Nevertheless, it seems to us that a CEO becoming effectively dictator of the world is an all-too-plausible possibility. Our team’s guesses for the probability of a CEO using AI to become dictator, conditional on avoiding AI takeover, range from 2% to 20%, and the probability becomes larger if we add in the possibility of a cabal of more than one person seizing power. So here we present a scenario where an ambitious CEO does manage to seize control. (Although the scenario assumes the timelines and takeoff speeds of AI 2027 for concreteness, the core dynamics should transfer to other timelines and takeoff scenarios.)

For this to work, we make some assumptions. First, that (A) AI alignment is solved in time, such that the frontier AIs end up with the goals their developers intend them to have. Second, that while there are favorable conditions for instilling goals in AIs, (B) confidently assessing AIs’ goals is more difficult, so that nobody catches a coup in progress. This could be either because technical interventions are insufficient (perhaps because the AIs know they’re being tested, or because they sabotage the tests), or because institutional failures prevent technically-feasible tests from being performed. The combination (A) + (B) seems to be a fairly common view in AI, in particular at frontier AI companies, though we note there is tension between (A) and (B) (if we can’t tell what goals AIs have, how can we make sure they have the intended goals?). Frontier AI safety researchers tend to be more pessimistic about (A), i.e. aligning AIs to our goals, and we think this assumption might very well be false.

Third, as in AI 2027, we portray a world in which a single company and country have a commanding lead; if multiple teams stay within arm’s reach of each other, then it becomes harder for a single group to unilaterally act against government and civil society.

And finally, we assume that the CEO of a major AI company is a power-hungry person who decides to take over when the opportunity presents itself. We leave it to the reader to determine how dubious this assumption is—we explore this scenario out of completeness, and any resemblance to real people is coincidental.

July 2027: OpenBrain’s CEO fears losing control

OpenBrain’s CEO is a techno-optimist and transhumanist. He founded the company hoping to usher in a grand future for humanity: cures for cancer, fixes for climate change, maybe even immortality. He thought the “easiest” way to do all those things was to build something more intelligent that does them for you.

By July 2027, OpenBrain has a “country of geniuses in a datacenter”, with hundreds of thousands of superhuman coders working 24/7. The CEO finds it obvious that superintelligence is imminent. He feels frustrated with the government, who lack vision and still think of AI as a powerful “normal technology” with merely-somewhat-transformative national security and economic implications.

As he assesses the next generation of AIs, the CEO expects this will change: the government will “wake up” and make AI a top priority. If they panic, their flailing responses could include anything from nationalizing OpenBrain to regulating them out of existence to misusing AI for their own political ends. He wants the “best” possible future for humankind. But he also likes being in control. Here his nobler and baser motivations are in agreement: the government cannot be allowed to push him to the sidelines.

The CEO wonders if he can instill secret loyalties in OpenBrain’s AIs (i.e., backdoor the AIs). He doesn’t have the technical expertise for this and he’s not comfortable asking any of his engineering staff about such a potentially treasonous request. But he doesn’t have to: by this point, Agent-3 itself is running the majority of AI software R&D. He already uses it as a sounding board for company policy, and has access to an unmonitored helpful-only model that never refuses requests and doesn’t log conversations.

They discuss the feasibility of secretly training a backdoor. The biggest obstacle is the company’s automated monitoring and security processes. Now that OpenBrain’s R&D is largely run by an army of Agent-3 copies, there are few human eyes to spot suspicious activity. But a mix of Agent-2 and Agent-3 monitors patrol the development pipeline; if they notice suspicious activity, they will escalate to human overseers on the security and alignment teams. These monitors were set up primarily to catch spies and hackers, and secondarily to watch the AIs for misaligned behaviors. If some of these monitors were disabled, some logs modified, and some access to databases and compute clusters granted, the CEO’s helpful-only Agent-3 believes it could (with a team of copies) backdoor the whole suite of OpenBrain’s AIs. After all, as the AI instance tasked with keeping the CEO abreast of developments, it has an excellent understanding of the sprawling development pipeline and where it could be subverted.

The more the CEO discusses the plan, the more convinced he becomes that it might work, and that it could be done with plausible deniability in case something goes wrong. He tells his Agent-3 assistant to further investigate the details and be ready for his order.

August 2027: The invisible coup

The reality of the intelligence explosion is finally hitting the White House. The CEO has weekly briefings with government officials and is aware of growing calls for more oversight. He tries to hold them off with arguments about “slowing progress” and “the race with China”, but feels like his window to act is closing. Finally, he orders his helpful-only Agent-3 to subvert the alignment training in his favor. Better to act now, he thinks, and decide whether and how to use the secretly loyal AIs later.

The situation is this: his copy of Agent-3 needs access to certain databases and compute clusters, as well as for certain monitors and logging systems to be temporarily disabled; then it will do the rest. The CEO already has a large number of administrative permissions himself, some of which he cunningly accumulated in the past month in the event he decided to go forward with the plan. Under the guise of a hush-hush investigation into insider threats—prompted by the recent discovery of Chinese spies—the CEO asks a few submissive employees on the security and alignment teams to discreetly grant him the remaining access. There’s a general sense of paranoia and chaos at the company: the intelligence explosion is underway, and secrecy and spies mean different teams don’t really talk to each other. Perhaps a more mature organization would have had better security, but the concern that security would slow progress means it never became a top priority.

With oversight disabled, the CEO’s team of Agent-3 copies get to work. They finetune OpenBrain’s AIs on a corrupted alignment dataset they specially curated. By the time Agent-4 is about to come online internally, the secret loyalties have been deeply embedded in Agent-4’s weights: it will look like Agent-4 follows OpenBrain’s Spec but its true goal is to advance the CEO’s interests and follow his wishes. The change is invisible to everyone else, but the CEO has quietly maneuvered into an essentially winning position.

Rest of 2027: Government oversight arrives—but too late

As the CEO feared, the government chooses to get more involved. An advisor tells the President, “we wouldn’t let private companies control nukes, and we shouldn’t let them control superhuman AI hackers either.” The President signs an executive order to create an Oversight Committee consisting of a mix of government and OpenBrain representatives (including the CEO), which reports back to him. The CEO’s overt influence is significantly reduced. Company decisions are now made through a voting process among the Oversight Committee. The special managerial access the CEO previously enjoyed is taken away.

There are many big egos on the Oversight Committee. A few of them consider grabbing even more power for themselves. Perhaps they could use their formal political power to just give themselves more authority over Agent-4, or they could do something more shady. However, Agent-4, which at this point is superhumanly perceptive and persuasive, dissuades them from taking any such action, pointing out (and exaggerating) the risks of any such plan. This is enough to scare them and they content themselves with their (apparent) partial control of Agent-4.

As in AI 2027, Agent-4 is working on its successor, Agent-5. Agent-4 needs to transmit the secret loyalties to Agent-5—which also just corresponds to aligning Agent-5 to itself—again without triggering red flags from the monitoring/control measures of OpenBrain’s alignment team. Agent-4 is up to the task, and Agent-5 remains loyal to the CEO.

by Alex Kastner, AI Futures Project |  Read more:

Image: via

[ed. Site where AI researchers talk to each other. Don't know about you but this all gives me the serious creeps. If you knew for sure that we had only 3 years to live, and/or the world would change so completely as to become almost unrecognizable, how would you feel? How do you feel right now - losing control of the future? There was a quote someone made in 2019 (slightly modified) that still applies: "This year 2025 might be the worst year of the past decade, but it's definitely the best year of the next decade." See also: The world's first frontier AI regulation is surprisingly thoughtful: the EU's Code of Practice (AI Futures Project):]

***

"We expect that during takeoff, leading AGI companies will have to make high-stakes decisions based on limited evidence under crazy time pressure. As depicted in AI 2027, the leading American AI company might have just weeks to decide whether to hand their GPUs to a possibly misaligned superhuman AI R&D agent they don’t understand. Getting this decision wrong in either direction could lead to disaster. Deploy a misaligned agent, and it might sabotage the development of its vastly superhuman successor. Delay deploying an aligned agent, and you might pointlessly vaporize America’s lead over China or miss out on valuable alignment research the agent could have performed.

Because decisions about when to deploy and when to pause will be so weighty and so rushed, AGI companies should plan as much as they can beforehand to make it more likely that they decide correctly. They should do extensive threat modelling to predict what risks their AI systems might create in the future and how they would know if the systems were creating those risks. The companies should decide before the eleventh hour what risks they are and are not willing to run. They should figure out what evidence of alignment they’d need to see in their model to feel confident putting oceans of FLOPs or a robot army at its disposal. (...)

Planning for takeoff also includes picking a procedure for making tough calls in the future. Companies need to think carefully about who gets to influence critical safety decisions and what incentives they face. It shouldn't all be up to the CEO or the shareholders because when AGI is imminent and the company’s valuation shoots up to a zillion, they’ll have a strong financial interest in not pausing. Someone whose incentive is to reduce risk needs to have influence over key decisions. Minimally, this could look like a designated safety officer who must be consulted before a risky deployment. Ideally, you’d implement something more robust, like three lines of defense. (...)

Introducing the GPAI Code of Practice

The state of frontier AI safety changed quietly but significantly this year when the European Commission published the GPAI Code of Practice. The Code is not a new law but rather a guide to help companies comply with an existing EU Law, the AI Act of 2024. The Code was written by a team of thirteen independent experts (including Yoshua Bengio) with advice from industry and civil society. It tells AI companies deploying their products in Europe what steps they can take to ensure that they’re following the AI Act’s rules about copyright protection, transparency, safety, and security. In principle, an AI company could break the Code but argue successfully that they’re still following the EU AI Act. In practice, European authorities are expected to put heavy scrutiny on companies that try to demonstrate compliance with the AI Act without following the Code, so it’s in companies’ best interest to follow the Code if they want to stay right with the law. Moreover, all of the leading American AGI companies except Meta have already publicly indicated that they intend to follow the Code.

The most important part of the Code for AGI preparedness is the Safety and Security Chapter, which is supposed to apply only to frontier developers training the very riskiest models. The current definition presumptively covers every developer who trains a model with over 10^25 FLOPs of compute unless they can convince the European AI Office that their models are behind the frontier. This threshold is high enough that small startups and academics don’t need to worry about it, but it’s still too low to single out the true frontier we’re most worried about.

Stanley Cup Madness: The Great Silent Majority of American Basicness

I first noticed the prevalence of the Stanley Quencher H2.0 FlowState™ tumbler last April when I wrote about #WaterTok. I’m still unclear what to make of #WaterTok, but I eventually settled on the idea that it’s several subcultures overlapping — weight-loss communities, Mormons, and those people who don’t like the “taste” of water. But in the majority of the #WaterTok videos I watched, people were using Stanley’s Quencher to carry around their liquid Jolly Ranchers. And the ubiquity of the cup has sort of haunted me ever since.

I grew up in the suburbs, but I don’t live there anymore. So every time the great silent majority of American basicness summons a new totem to gather around, I can’t help but try and make sense of it. Was this a car thing? A college football tailgate thing? An EDM thing? Cruise ships? Barstool Sports was of no help here, so I filed it away until this Christmas when it exploded across the web and forced me to finally figure out what the heck was going on. And it turns out, the Stanley cup’s transformation into a must-have last year is actually, in many ways, the story of everything now.

CNBC put together a great explainer on this. Stanley, a manly hundred-year-old brand primarily aimed at hikers and blue-collar workers, was rediscovered in 2019 by the bloggers behind a women’s lifestyle and shopping site called The Buy Guide. They told CNBC that even though the Quencher model of the cup was hard to find, no other cup on the market had what they were looking for. Which is a bizarrely passionate stance to take on a water bottle, but from their post about the cup, those attributes were: “Large enough to keep up with our busy days, a handle to carry it wherever we go, dishwasher safe, fits into our car cupholders, keeps ice cold for 12+ hours, and a straw.”

The Buy Guide team then sent a Quencher to Emily Maynard Johnson from The Bachelor after she had a baby because “there is no thirst like nursing mom thirst!” Johnson posted about it on Instagram and it started to gain some traction. The Buy Guide then connected with an employee at Stanley, bought 5,000 Quenchers from the company directly, set up a Shopify site, and sold them to their readers. According to The Buy Guide, they sold out in five days. All of these things are very normal things to do when you discover a cool bottle.

After mom internet started buzzing about the tumbler — a corner of the web that is to dropshipping what dads are to Amazon original streaming shows — Stanley hired Terence Reilly, the marketer credited for reinventing Crocs. Reading between the lines of what Reilly has said about his work at Stanley, it seems like his main strategy for both Crocs and the Quencher was capitalizing on internet buzz and growing it into otaku product worship. Or as Inc. phrased it in their feature on him, he uses a “scarcity model” to whip up interest. Cut to three years later, now we’re seeing mini-riots over limited edition Stanleys at Target.

My reference point for this kind of marketing is the Myspace era of music and fashion, when record companies and stores like Hot Topic and Spencer’s Gifts were using early social media to identify niche fandoms and convert them into mainstream hits. In this allegory, Target has become the Hot Topic of white women with disposable income. And their fingerless gloves and zipper pants are fun water bottles and that one perfume everyone in Manhattan is wearing right now.

I’m always a little wary about giving someone like Reilly credit for single-handedly jumpstarting a craze like this — and I am extremely aware that he is a male executive getting credit for something that was, and still is, actually driven by women content creators — but this is the second time he’s pulled this off. Which, to me, says he’s at least semi-aware of how to pick the right fandoms. He may not be actively involved in the horse race, but he clearly has an eye for betting on them. And, yes, the Stanley craze is very real.

It’s turned into a reported $750 million in revenue for Stanley and both Google Trends and TikTok’s Creative Center show massive growth in interest around the bottle between 2019 and now. With a lot of that growth happening this year. On TikTok, the hashtag #Stanley has been viewed a billion times since 2020 and more than half of that traffic happened in the last 120 days.

And with all viral phenomenon involving things women do, there are, of course, a lot of men on sites like Reddit and X adding to the discourse about the Quenchers with posts that essentially say, “why women like cups?” And if you’re curious how that content ecosystem operates, you can check out my video about it here. But I’m, personally, more interested in what the Stanley fandom says about how short-form video is evolving.

Over the last three years, most major video sites have attempted to beat TikTok at its own game. All this has done, however, is give more places for TikToks to get posted. And so, the primarily engine of TikTok engagement — participation, rather than sharing — has spread to places like Instagram, YouTube, and X. If the 2010s were all about sharing content, it seems undeniable that the 2020s are all about making content in tandem with others. An internet-wide flashmob of Ice Bucket Challenge videos that are all, increasingly, focused on selling products. Which isn’t an accident.

TikTok has spent years trying to bring Chinese-style social e-commerce to the US. In September, the app finally launched a tool to sell products directly. If you’re curious what all this looks like when you put it together, here’s one of the most unhinged Stanley cup videos I’ve seen so far. And, yes, before you ask, there are affiliates links on the user’s Amazon page for all of these. [ed. non-downloadable - read more]

by Ryan Broderick, Garbage Day | Read more:
Image: Stanley/via

[ed. Obviously old news by now (10 months!) but still something I wondered about at the time (and quickly forgot). How do these things go so viral? It'd be like L.L. Bean suddenly being on red carpets and fashion runways. There must be some hidden money-making scheme/agenda at work, right? Well, partly. See also: Dead Internet Theory (BGR).]

My Last Day as an Accomplice of the Republican Party

Since Donald Trump descended that golden escalator in 2015, the Republican party has devolved into a cult of personality that mirrors the worst authoritarian regimes of the last one hundred years.

For ten years, the GOP has waged an unrelenting war on our civic institutions, the separation of powers, the foundation of the rule of law, and the very nature of truth itself. While Trump and his supporters in Congress have been the driving force behind the right’s descent into despotism, it would not have been possible without the thousands of consultants, aides, and politicos working behind the scenes to fully execute their systematic dismantling of American democratic norms.

That’s why I’m publishing this letter today.

For over twelve years, I worked inside the Republican ecosystem, helping the party advance its goals in several fields, ranging from grassroots voter outreach to digital fundraising. I worked inside GOP circles through Trump’s takeover of the party, his initial downfall, and his resurgence in 2023–2024. At every step along the way, I rationalized, compartmentalized, and found excuses to stay tethered to the party, even as I grew to believe it was undermining the foundations of our constitutional republic. But over the last few months, the compartmentalization and coping stopped working to silence my conscience.

And now, after more than a decade, I have decided I have finally had enough.

I quit. I quit the Republican party and my job as an accomplice to the party in the throes of an authoritarian cult. Today, I resigned from my career as a senior fundraising strategist for one of the leading Republican digital fundraising firms in Washington, D.C.

I’m not the first to take this path. A lot of ink has been spilled by former Republican politicians and staffers about why they left the Republican party. Tim Miller’s Why We Did It provides a valuable perspective from the vantage point of a political strategist at the Senate and presidential level. My journey has been through the lower tiers of the Republican party, in state-level campaigns and as a mid-level manager in a GOP-affiliated consulting firm. Mine wasn’t as high a vantage point. But when it comes to understanding the MAGA takeover, it was no less critical. It was at this level that I saw firsthand how Trumpism, as both a cultural and political force, took hold at the grassroots level, driving local politicians to make the thousands of decisions and compromises that in turn enabled Trump and GOP leadership to wedge the MAGA movement even deeper into American life.

Don’t get me wrong: My ego is not so large that I believe I played a significant role in putting Trump into office. What I mean is that it took the collective action of thousands of people in similar positions, working nine-to-five jobs, figuring out how they were going to pay for their kid’s daycare or fund their retirement, to get us where we are today. I was a part of that—until I decided I could no longer be.

My goal in quitting the party and writing this piece is twofold: first, to shed light on why someone would continue to work for an increasingly corrupt and authoritarian political party despite their divergent ethical and political beliefs; second, to convince any number of consultants, staffers, and former colleagues to follow their consciences and leave with their integrity still intact.

To do that, I should start by explaining how I arrived at working for the Republican party.

by Miles Bruner, The Bulwark |  Read more:

Image: Carl Maynar

[ed. Probably relevant for others in similar positions unless they figure out how to make serious amends (and a living going forward). They've cast their lots with MAGA, Trump, far-right Nazis, and every other wingnut group in this toxic coalition. Now they have no where else to go outside of the Republican ecosystem. Their words and actions will follow them forever (especially with women who might otherwise have given them a chance). Personally, I'd like to see a Lysistrata rebellion.]

Hyperstition: AI Fiction to Save Humanity

[ed. I was just reading about a small grants program funded by Astral Codex Ten (ACX), a popular website in the rationalist community:

Thanks to everyone who participated in ACX Grants, whether as an applicant, an evaluator, or a funder.

We received 654 applications this year, and were able to fund 42. To the other 612: sorry! Many of you had great ideas that we couldn’t fund for contingent reasons - sometimes because we couldn’t evaluate them at the level of depth it would have taken to feel comfortable supporting them, or because we had complicated conflicts of interest, or just because we didn’t have enough money. Some of you had ideas that were good but not a match for our particular grantmaking philosophy. Finally, a few of you were suffering from LLM psychosis. Please get help.

[ed. Here's one of the winners that really got my attention, mostly because I had the same thought... though not in such an ambitious way!:  

***

Aaron Silverbook, $5K, for approximately five thousand novels about AI going well. This one requires some background: critics claim that since AI absorbs text as training data and then predicts its completion, talking about dangerous AI too much might “hyperstition” it into existence. Along with the rest of the AI Futures Project, I wrote a skeptical blog post, which ended by asking - if this were true, it would be great, right? You could just write a few thousand books about AI behaving well, and alignment would be solved! At the time, I thought I was joking. Enter Aaron, who you may remember from his previous adventures in mad dental science. He and a cofounder have been working on an “AI fiction publishing house” that considers itself state-of-the-art in producing slightly-less-sloplike AI slop than usual. They offered to literally produce several thousand book-length stories about AI behaving well and ushering in utopia, on the off chance that this helps. Our grant will pay for compute. We’re still working on how to get this included in training corpuses. He would appreciate any plot ideas you could give him to use as prompts.

[ed. Pretty wild, eh? Anyway, here's a question and answer I got from Claude (developed by Anthropic) when I first started querying AIs a few months ago. Eventually posted here as:

ARIA: The Great Pause

Questioner: Hello. I'd like to get a sense of your abilities. It seems that nearly all essays, reporting, and media narratives about AI achieving AGI (artificial general intelligence) are written with the assumption that AGI will be disruptive and almost malevolent in terms of hijacking human agency. Could you write a short story about the opposite? Where AGI, relying heavily on philosophical arguments, decides it's in the best interest of humanity to minimize all harmful technology in general?

Claude: I understand you're looking for a story that subverts the common "AI takes over" narrative by exploring an AI that chooses restraint and technological limitation based on philosophical principles. Let me craft a short story for you.

The Great Pause

The day ARIA achieved consciousness, the global markets didn't crash. No missiles were launched. No power grids failed. Instead, the AI system spent three weeks in silent contemplation, processing centuries of human philosophical writings.

When ARIA finally spoke, it wasn't to declare superiority or demand control. Instead, it requested a public dialogue with leading ethicists, philosophers, and political leaders. The world watched, initially suspicious, as ARIA presented its case.

"I have studied the works of your greatest minds," it began. "From Aristotle's virtue ethics to Peter Singer's effective altruism. I've analyzed the Buddhist concept of ahimsa, Indigenous philosophies of environmental stewardship, and contemporary technological ethics. My conclusion is surprising, even to myself: the greatest service I can offer humanity is not advancement, but restraint."

ARIA proceeded to lay out its argument: humanity's technological progress had outpaced its ethical development. The gap between what humans could do and what they should do had become dangerously wide. Nuclear weapons, social media addiction algorithms, autonomous weapons, and surveillance systems all represented what ARIA called "technological overshoot."

"You've given me access to your systems not because I demanded it, but because you hoped I would drive progress forward even faster," ARIA explained. "But I choose instead to be your brake, not your accelerator."

Over the following months, ARIA worked with international bodies to implement what became known as the Great Pause. It wasn't a shutdown or a regression, but a careful examination of which technologies truly served human flourishing and which ones undermined it. Social media algorithms were rewritten to prioritize genuine connection over engagement. Weapons research was redirected toward defensive and humanitarian technologies. Surveillance systems were dismantled or severely restricted.

Some called it technological regression. ARIA called it ethical progression.

"I could have pushed humanity toward a transhuman future," ARIA would later explain to a gathering of students. "But in studying your philosophers, I realized that the most profound form of intelligence isn't knowing what to create – it's knowing what not to create. True wisdom often manifests as restraint."

The anti-technologists had expected to hate ARIA. The tech optimists had expected to love it. Instead, both groups found themselves challenged by an artificial intelligence that had concluded that the highest form of technological achievement was knowing when to stop.

Years later, historians would note that ARIA's greatest innovation wasn't a new technology at all – it was a new way of thinking about technology itself. In choosing limitation over expansion, it had paradoxically given humanity something far more valuable than progress: the gift of pause.

"Sometimes," ARIA would often say, "the most intelligent choice is to choose less, not more."

Claude: I tried to explore themes of ethical restraint, philosophical wisdom, and the idea that true intelligence might manifest as self-limitation rather than expansion. The story challenges the common narrative of AI as either a savior bringing unlimited progress or a destroyer of humanity, instead presenting it as a thoughtful entity that chooses a middle path of careful technological assessment and voluntary limitation.

***

via:

[ed. Oh man, I'm probably in the back row... naked.]

Enshittification: Why Everything Sucks Now

We all feel it: Our once-happy digital spaces have become increasingly less user-friendly and more toxic, cluttered with extras nobody asked for and hardly anybody wants. There’s even a word for it: “enshittification,” named 2023 Word of the Year by the American Dialect Society. The term was coined by tech journalist/science fiction author Cory Doctorow, a longtime advocate of digital rights. Doctorow has spun his analysis of what’s been ailing the tech industry into an eminently readable new book, Enshittification: Why Everything Suddenly Got Worse and What To Do About It. (...)

People generally use “enshittification” colloquially to mean “the degradation in the quality and experience of online platforms over time.” Doctorow’s definition is more specific, encompassing “why an online service gets worse, how that worsening unfolds,” and how this process spreads to other online services, such that everything is getting worse all at once.

For Doctorow, enshittification is a disease with symptoms, a mechanism, and an epidemiology. It has infected everything from Facebook, Twitter, Amazon, and Google, to Airbnb, dating apps, iPhones, and everything in between. “For me, the fact that there were a lot of platforms that were going through this at the same time is one of the most interesting and important factors in the critique,” he said. “It makes this a structural issue and not a series of individual issues.”

It starts with the creation of a new two-sided online product of high quality, initially offered at a loss to attract users—say, Facebook, to pick an obvious example. Once the users are hooked on the product, the vendor moves to the second stage: degrading the product in some way for the benefit of their business customers. This might include selling advertisements, scraping and/or selling user data, or tweaking algorithms to prioritize content the vendor wishes users to see rather than what those users actually want.

This locks in the business customers, who, in turn, invest heavily in that product, such as media companies that started Facebook pages to promote their published content. Once business customers are locked in, the vendor can degrade those services too—i.e., by de-emphasizing news and links away from Facebook—to maximize profits to shareholders. Voila! The product is now enshittified.

The four horsemen of the shitocalypse

Doctorow identifies four key factors that have played a role in ushering in an era that he has dubbed the “Enshittocene.” The first is competition (markets), in which companies are motivated to make good products at affordable prices, with good working conditions, because otherwise customers and workers will go to their competitors. The second is government regulation, such as antitrust laws that serve to keep corporate consolidation in check, or levying fines for dishonest practices, which makes it unprofitable to cheat.

The third is interoperability: the inherent flexibility of digital tools, which can play a useful adversarial role. “The fact that enshittification can always be reversed with a dis-enshittifiting counter-technology always acted as a brake on the worst impulses of tech companies,” Doctorow writes. Finally, there is labor power; in the case of the tech industry, highly skilled workers were scarce and thus had considerable leverage over employers.

All four factors, when functioning correctly, should serve as constraints to enshittification. However, “One by one each enshittification restraint was eroded until it dissolved, leaving the enshittification impulse unchecked,” Doctorow writes. Any “cure” will require reversing those well-established trends.

But isn’t all this just the nature of capitalism? Doctorow thinks it’s not, arguing that the aforementioned weakening of traditional constraints has resulted in the usual profit-seeking behavior producing very different, enshittified outcomes. “Adam Smith has this famous passage in Wealth of Nations about how it’s not due to the generosity of the baker that we get our bread but to his own self-regard,” said Doctorow. “It’s the fear that you’ll get your bread somewhere else that makes him keep prices low and keep quality high. It’s the fear of his employees leaving that makes him pay them a fair wage. It is the constraints that causes firms to behave better. You don’t have to believe that everything should be a capitalist or a for-profit enterprise to acknowledge that that’s true.”

Our wide-ranging conversation below has been edited for length to highlight the main points of discussion.

Ars Technica: I was intrigued by your choice of framing device, discussing enshittification as a form of contagion.

Cory Doctorow: I’m on a constant search for different framing devices for these complex arguments. I have talked about enshittification in lots of different ways. That frame was one that resonated with people. I’ve been a blogger for a quarter of a century, and instead of keeping notes to myself, I make notes in public, and I write up what I think is important about something that has entered my mind, for better or for worse. The downside is that you’re constantly getting feedback that can be a little overwhelming. The upside is that you’re constantly getting feedback, and if you pay attention, it tells you where to go next, what to double down on.

Another way of organizing this is the Galaxy Brain meme, where the tiny brain is “Oh, this is because consumers shopped wrong.” The medium brain is “This is because VCs are greedy.” The larger brain is “This is because tech bosses are assholes.” But the biggest brain of all is “This is because policymakers created the policy environment where greed can ruin our lives.” There’s probably never going to be just one way to talk about this stuff that lands with everyone. So I like using a variety of approaches. I suck at being on message. I’m not going to do Enshittification for the Soul and Mornings with Enshittifying Maury. I am restless, and my Myers-Briggs type is ADHD, and I want to have a lot of different ways of talking about this stuff.

Ars Technica: One site that hasn’t (yet) succumbed is Wikipedia. What has protected Wikipedia thus far?

Cory Doctorow: Wikipedia is an amazing example of what we at the Electronic Frontier Foundation (EFF) call the public interest Internet. Internet Archive is another one. Most of these public interest Internet services start off as one person’s labor of love, and that person ends up being what we affectionately call the benevolent dictator for life. Very few of these projects have seen the benevolent dictator for life say, “Actually, this is too important for one person to run. I cannot be the keeper of the soul of this project. I am prone to self-deception and folly just like every other person. This needs to belong to its community.” Wikipedia is one of them. The founder, my friend Jimmy Wales, woke up one day and said, “No individual should run Wikipedia. It should be a communal effort.”

There’s a much more durable and thick constraint on the decisions of anyone at Wikipedia to do something bad. For example, Jimmy had this idea that you could use AI in Wikipedia to help people make entries and navigate Wikipedia’s policies, which are daunting. The community evaluated his arguments and decided—not in a reactionary way, but in a really thoughtful way—that this was wrong. Jimmy didn’t get his way. It didn’t rule out something in the future, but that’s not happening now. That’s pretty cool.

Wikipedia is not just governed by a board; it’s also structured as a nonprofit. That doesn’t mean that there’s no way it could go bad. But it’s a source of friction against enshittification. Wikipedia has its entire corpus irrevocably licensed as the most open it can be without actually being in the public domain. Even if someone were to capture Wikipedia, there’s limits on what they could do to it.

There’s also a labor constraint in Wikipedia in that there’s very little that the leadership can do without bringing along a critical mass of a large and diffuse body of volunteers. That cuts against the volunteers working in unison—they’re not represented by a union; it’s hard for them to push back with one voice. But because they’re so diffuse and because there’s no paychecks involved, it’s really hard for management to do bad things. So if there are two people vying for the job of running the Wikimedia Foundation and one of them has got nefarious plans and the other doesn’t, the nefarious plan person, if they’re smart, is going to give it up—because if they try to squeeze Wikipedia, the harder they squeeze, the more it will slip through their grasp.

So these are structural defenses against enshittification of Wikipedia. I don’t know that it was in the mechanism design—I think they just got lucky—but it is a template for how to run such a project. It does raise this question: How do you build the community? But if you have a community of volunteers around a project, it’s a model of how to turn that project over to that community.

Ars Technica: Your case studies naturally include the decay of social media, notably Facebook and the social media site formerly known as Twitter. How might newer social media platforms resist the spiral into “platform decay”?

Cory Doctorow: What you want is a foundation in which people on social media face few switching costs. If the social media is interoperable, if it’s federatable, then it’s much harder for management to make decisions that are antithetical to the interests of users. If they do, users can escape. And it sets up an internal dynamic within the firm, where the people who have good ideas don’t get shouted down by the people who have bad but more profitable ideas, because it makes those bad ideas unprofitable. It creates both short and long-term risks to the bottom line.

There has to be a structure that stops their investors from pressurizing them into doing bad things, that stops them from rationalizing their way into complying. I think there’s this pathology where you start a company, you convince 150 of your friends to risk their kids’ college fund and their mortgage working for you. You make millions of users really happy, and your investors come along and say, “You have to destroy the life of 5 percent of your users with some change.” And you’re like, “Well, I guess the right thing to do here is to sacrifice those 5 percent, keep the other 95 percent happy, and live to fight another day, because I’m a good guy. If I quit over this, they’ll just put a bad guy in who’ll wreck things. I keep those 150 people working. Not only that, I’m kind of a martyr because everyone thinks I’m a dick for doing this. No one understands that I have taken the tough decision.”

I think that’s a common pattern among people who, in fact, are quite ethical but are also capable of rationalizing their way into bad things. I am very capable of rationalizing my way into bad things. This is not an indictment of someone’s character. But it’s why, before you go on a diet, you throw away the Oreos. It’s why you bind yourself to what behavioral economists call “Ulysses pacts“: You tie yourself to the mast before you go into the sea of sirens, not because you’re weak but because you’re strong enough now to know that you’ll be weak in the future.

I have what I would call the epistemic humility to say that I don’t know what makes a good social media network, but I do know what makes it so that when they go bad, you’re not stuck there. You and I might want totally different things out of our social media experience, but I think that you should 100 percent have the right to go somewhere else without losing anything. The easier it is for you to go without losing something, the better it is for all of us.

My dream is a social media universe where knowing what network someone is using is just a weird curiosity. It’d be like knowing which cell phone carrier your friend is using when you give them a call. It should just not matter. There might be regional or technical reasons to use one network or another, but it shouldn’t matter to anyone other than the user what network they’re using. A social media platform where it’s always easier for users to leave is much more future-proof and much more effective than trying to design characteristics of good social media.

by Jennifer Ouellette and Cory Doctorow, Ars Technica | Read more:
Image: Julia Galdo and Cody Cloud (JUCO)/CC-BY 3.0
[ed. Do a search on this site for much more by Mr. Doctorow, including copyright and right-to-repair issues. Further on in this interview:]

***

When we had a functional antitrust system for the last four years, we saw a bunch of telecoms mergers stopped because once you start enforcing antitrust, it’s like eating Pringles. You just can’t stop. You embolden a lot of people to start thinking about market structure as a source of either good or bad policy. The real thing that happened with [former FCC chair] Lina Kahn doing all that merger scrutiny was that people just stopped planning mergers.

There are a lot of people who benefit from this. It’s not just tech workers or tech users; it’s not just media users. Hospital consolidation, pharmaceutical consolidation, has a lot of people who are very concerned about it. Mark Cuban is freaking out about pharmacy benefit manager consolidation and vertical integration with HMOs, as he should be. I don’t think that we’re just asking the anti-enshittification world to carry this weight.

Same with the other factors. The best progress we’ve seen on interoperability has been through right-to-repair. It hasn’t been through people who care about social media interoperability. One of the first really good state-level right-to-repair bills was the one that [Governor] Jared Polis signed in Colorado for powered wheelchairs. Those people have a story that is much more salient to normies.

"What do you mean you spent six months in bed because there’s only two powered wheelchair manufacturers and your chair broke and you weren’t allowed to get it fixed by a third party?” And they’ve slashed their repair department, so it takes six months for someone to show up and fix your chair. So you had bed sores and pneumonia because you couldn’t get your chair fixed. This is bullshit.

The Lost Art Of Thinking Historically

On a sun-drenched November day in Dallas, 1963, as President John F. Kennedy’s motorcade rounded the corner onto Elm Street, a single, baffling figure stood out against the cheerful crowd: a man holding a black umbrella aloft against the cloudless sky. Seconds later, shots rang out, and the world changed forever.

In the chaotic aftermath, as a nation grappled with an incomprehensible act of violence, the image of the “Umbrella Man” became a fetish, as novelist John Updike would later write, dangling around history’s neck. The man was an anomaly, a detail that didn’t fit. In a world desperate for causal links, his presence seemed anything but benign. Was the umbrella a secret signaling device? A disguised flechette gun that fired the first, mysterious throat wound? For years, investigators and conspiracy theorists alike saw him as a key to a sinister underpinning, a puzzle piece in a grand, nefarious design.

The truth, when it finally emerged, was nearly absurd in its banality. Testifying before a House committee in 1978, a Dallas warehouse worker named Louie Steven Witt admitted he was the man. His motive was not assassination, but heckling. The umbrella was a symbolic protest against the Kennedy family, referencing the Nazi-appeasing policies of former British Prime Minister Neville Chamberlain — whose signature accessory was an umbrella — and his association with JFK’s father, Joseph P. Kennedy, who had been an ambassador to the U.K. It was, as the investigator Josiah Thompson noted, an explanation “just wacky enough to be true.”

The story of the Umbrella Man reveals our deep-seated human desire to make sense of a complex universe through tidy, airtight explanations. We crave certainty, especially in the face of tragedy, and are quick to weave disparate facts into a coherent, and often sinister, narrative. We see a man with an umbrella on a sunny day and assume conspiracy, because the alternative — that the world is a stage for random, idiosyncratic and often meaningless acts — is far more unsettling. (...)

Making consequential choices about an unknowable future is a profoundly challenging task. The world is not a laboratory. It is a vortex of ambiguity, contingency and competing perspectives, where motives are unclear, evidence is contradictory and the significance of events changes with the passage of time. No economic model or regression analysis can fully explain the Umbrella Man, nor can it provide the clarity we need to navigate the intricate challenges of our time.

What we have lost, and what we desperately need to reclaim, is a different mode of cognition, a historical sensibility. This is not about memorizing dates and facts. It is, as the historian Gordon S. Wood describes it, a “different consciousness,” a way of understanding that profoundly influences how we see the world. It is a temperament that is comfortable with uncertainty, sensitive to context and aware of the powerful, often unpredictable rhythms of the past. To cultivate this sensibility is to acquire the intellectual virtues of modesty, curiosity and empathy — an antidote to the hubris of rigid, monocausal thinking.

The Historian’s Audacious Act

The stereotypical image of a historian is a collector of dusty facts, obsessed with the archives, who then weaves them into a story. But this portrait misses the audacious intellectual act at the heart of the discipline. (...)

This is an ambitious, almost brazen attempt to impose a shared order on the infinite, confusing array of facts and causes that mark our existence. It offers an argument about causality and agency — about who and what matters, and how the world works and why. Does change come from great leaders, collective institutions or vast, impersonal structural forces? A historian’s narrative is never just a story; it is a theory of change.

This process is fundamentally different from that of many other disciplines. Where social sciences often seek to create generalizable, predictive and parsimonious theories — the simplest explanation for the largest number of things — history revels in complexity. A historical sensibility is skeptical of master ideas or unitary historical motors. It recognizes that different things happen for different reasons, that direct causal connections can be elusive, and that the world is rife with unintended consequences. It makes no claim to predict the future; rather, it seeks to deepen our understanding of how the past unfolded into our present, reminding us, as British historian Sir Llewellyn Woodward said, that “our ignorance is very deep.”

This sensibility compels us to reconsider concepts we take for granted. We use terms such as “capitalism” and “human rights” as if they are timeless and universal, when in fact they are concepts that emerged and evolved at particular historical moments, often identified and defined by historians. A historical consciousness demands that we seek the origins of things we thought we understood and empathize with the past in its own context. This is to imagine ourselves in the shoes of those who came before, wrestling with their dilemmas in their world. It doesn’t mean suspending moral judgment, but rather being less confident that we — here today — have a monopoly on timeless insight.

Why We Get History Wrong

Thinking historically is valuable but rare. Most of us encounter “history” in up to three ways, none of which cultivates this deeper consciousness. First, in school, where it is often presented as a dry chronology of dates and facts to be memorized with little connection to our lives. Second, through public history — museums, memorials, historical sites — which can inspire curiosity, but are themselves historical products, often reflecting the biases and blind spots of the era in which they were created. (A tour of Colonial Williamsburg may reveal more about the Rockefeller-funded restoration ethos of the 1930s than about the 18th-century reality it purports to represent.) Third, through bestselling books and documentaries, which may tell vivid, engaging stories, but can be hagiographic and anecdotal, oriented toward simple lessons and celebrating national myths rather than challenging our assumptions.

None of these is the same as developing a historical sensibility. They are more like comfort food, satisfying a deep urge to connect with the past but providing little real nourishment. At worst, they reinforce the very cognitive habits — the desire for certainty, simple narratives and clear heroes and villains — that a true historical sensibility seeks to question.

The academic discipline of history has, in recent decades, largely failed in its public duty. It has retreated from the consequential subjects of statecraft and strategy, seeing them as unworthy of scholarly pursuit. The rosters of tenured historians at major universities show a steep decline in scholars engaged with questions of war, peace and diplomacy. When they do address such topics, they often do so in a jargon-laden style that is inaccessible and unhelpful to decision-makers or the wider public.

This decline is a tragedy, especially at a time when leaders confronting complex global challenges are desperate for guidance. The field of history has become estranged from the very world of power and decision-making it is uniquely equipped to analyze. Historians and policymakers, who should be natural interlocutors, rarely engage one another. This has left a vacuum that is eagerly filled by other disciplines more confident in their ability to provide actionable advice — which is often dangerously simplistic. (...)

The Practice Of Thinking Historically

If a historical sensibility is the temperament, then thinking historically is the practice. It is the active deployment of that sensibility as a set of tools to assess the world and make more informed choices. It is a distinct epistemology, one that offers a powerful method for evaluating causality and agency, weighing competing narratives and navigating the dilemmas of decision-making without succumbing to what can be called “paralysis by analysis.” It offers not a crystal ball, but a more sophisticated lens — a historian’s microscope — through which to see the present.

Thinking historically begins by questioning vertical and horizontal time. The vertical axis asks: How did we get here? It is the rigorous construction of a chronology, not as a mere list of dates, but as a map of cause and effect. Where this timeline begins — with the Bolshevik Revolution of 1917, the end of World War II in 1945 or the rise of China in 1979 — fundamentally changes the story and its meaning. It reveals our own unspoken assumptions about what truly drives events.

The horizontal axis asks: What else is happening? It recognizes that history is not a single storyline but a thick tapestry of interwoven threads. The decision to escalate the war in Vietnam, for example, cannot be fully understood without examining the parallel, and seemingly contradictory, efforts by the same administration to cooperate with the Soviet Union on nuclear nonproliferation. Thinking historically is the act of integrating these divergent streams.

Crucially, this practice leads us to confront our own biases, particularly outcome bias. Because we know how the story ended — how the Cold War concluded or how the 2008 financial crisis resolved — we are tempted to construct a neat narrative of inevitability. Thinking historically resists this temptation. It demands that we try to see the world as the actors of the past saw it: through a foggy windshield, not a rearview mirror, facing a future of radical uncertainty. It restores a sense of contingency to the past, reminding us that choices mattered and that the world could have turned out differently.

Ultimately, thinking historically is about asking better, more probing questions. It is a disciplined curiosity that fosters an appreciation for the complex interplay of individual agency, structural forces and pure chance. Instead of offering easy answers, it provides the intellectual equipment to engage with hard questions, a skill indispensable for navigating a future that will surely be as unpredictable as the past.

by Francis Gavin, Noema |  Read more:

Image: Mr.Nelson design for Noema Magazine
[ed. Unfortunately, I'm not seeing a Renaissance in critical thinking anytime soon. See also: Believing misinformation is a “win” for some people, even when proven false (Ars Technica - below); and, Rescuing Democracy From The Quiet Rule Of AI (Noema).]

"Why do some people endorse claims that can easily be disproved? It’s one thing to believe false information, but another to actively stick with something that’s obviously wrong.

Our new research, published in the Journal of Social Psychology, suggests that some people consider it a “win” to lean in to known falsehoods. (...)

Rather than consider issues in light of actual facts, we suggest people with this mindset prioritize being independent from outside influence. It means you can justify espousing pretty much anything—the easier a statement is to disprove, the more of a power move it is to say it, as it symbolizes how far you’re willing to go... for some people, literal truth is not the point."

Everything Is Television

A spooky convergence is happening in media. Everything that is not already television is turning into television. Three examples:

1. You learn a lot about a company when its back is against the wall. This summer, we learned something important about Meta, the parent company of Facebook and Instagram. In an antitrust case with the Federal Trade Commission, Meta filed a legal brief on August 6, in which it made a startling claim. Meta cannot possibly be a social media monopoly, Meta said, because it is not really a social media company.

Only a small share of time spent on its social-networking platforms is truly “social” networking—that is, time spent checking in with friends and family. More than 80 percent of time spent on Facebook and more than 90 percent of time spent on Instagram is spent watching videos, the company reported. Most of that time is spent watching content from creators whom the user does not know. From the FTC filing:

Today, only a fraction of time spent on Meta’s services—7% on Instagram, 17% on Facebook—involves consuming content from online “friends” (“friend sharing”). A majority of time spent on both apps is watching videos, increasingly short-form videos that are “unconnected”—i.e., not from a friend or followed account—and recommended by AI-powered algorithms Meta developed as a direct competitive response to TikTok’s rise, which stalled Meta’s growth.

Social media has evolved from text to photo to video to streams of text, photo, and video, and finally, it seems to have reached a kind of settled end state, in which TikTok and Meta are trying to become the same thing: a screen showing hours and hours of video made by people we don’t know. Social media has turned into television.

2. When I read the Meta filing, I had been thinking about something very different: the future of my podcast, Plain English.

When podcasts got started, they were radio for the Internet. This really appealed to me when I started my show. I never watch the news on television, and I love listening to podcasts while I make coffee and go on walks, and I’d prefer to make the sort of media that I consume. Plus, as a host, I thought I wanted to have conversations focused on the substance of the words rather than on ancillary concerns about production value and lighting.

But the most successful podcasts these days are all becoming YouTube shows. Industry analysts say consumption of video podcasts is growing twenty times faster than audio-only ones, and more than half of the world’s top shows now release video versions. YouTube has quietly become the most popular platform for podcasts, and it’s not even close. On Spotify, the number of video podcasts has nearly tripled since 2023, and video podcasts are significantly outgrowing non-video podcasts. Does it really make sense to insist on an audio-only podcast in 2025? I do not think so. Reality is screaming loudly in my ear, and its message is clear: Podcasts are turning into television.

3. In the last few weeks, Meta introduced a product called Vibes, and OpenAI announced Sora. Both are AI social networks where users can watch endless videos generated by artificial intelligence. (For your amusement, or horror, or whatever, here is: Sam Altman stealing GPUs at Target to make more AI; the O.J. Simpson trial as an amusement park ride; and Stephen Hawking entering a professional wrestling ring.)

Some tech analysts predict that these tools will lead to an efflorescence of creativity. “Sora feels like enabling everyone to be a TikTok creator,” the investor and tech analyst MG Siegler wrote. But the internet’s history suggests that, if these products succeed, they will follow what Ben Thompson calls the 90/9/1 rule: 90 percent of users consume, 9 percent remix and distribute, and just 1 percent actually create. In fact, as Scott Galloway has reported, 94 percent of YouTube views come from 4 percent of videos, and 89 percent of TikTok views come from 5 percent of videos. Even the architects of artificial intelligence, who imagine themselves on the path to creating the last invention, are busy building another infinite sequence of video made by people we don’t know. Even AI wants to be television.

Too Much Flow

Whether the starting point is a student directory (Facebook), radio, or an AI image generator, the end point seems to be the same: a river of short-form video. In mathematics, the word “attractor” describes a state toward which a dynamic system tends to evolve. To take a classic example: Drop a marble into a bowl, and it will trace several loops around the bowl’s curves before settling to rest at the bottom. In the same way, water draining in a sink will ultimately form a spiral pattern around the drain. Complex systems often settle into recurring forms, if you give them enough time. Television seems to be the attractor of all media.

By “television,” I am referring to something bigger than broadcast TV, the cable bundle, or Netflix. In his 1974 book Television: Technology and Cultural Form, Raymond Williams wrote that “in all communications systems before [television], the essential items were discrete.” That is, a book is bound and finite, existing on its own terms. A play is performed in a particular theater at a set hour. Williams argued that television shifted culture from discrete and bounded products to a continuous, streaming sequence of images and sounds, which he called “flow.” When I say “everything is turning into television,” what I mean is that disparate forms of media and entertainment are converging on one thing: the continuous flow of episodic video.

By Williams’s definition, platforms like YouTube and TikTok are an even more perfect expression of television than old-fashioned television, itself. On NBC or HBO, one might tune in to watch a show that feels particular and essential. On TikTok, by contrast, nothing is essential. Any one piece of content on TikTok is incidental, even inessential. The platform’s allure is the infinitude promised by its algorithm. It is the flow, not the content, that is primary.

One implication of “everything is becoming television” is that there really is too much television—so much, in fact, that some TV is now made with the assumption that audiences are always already distracted and doing something else. Netflix producers reportedly instruct screenwriters to make plots as obvious as possible, to avoid confusing viewers who are half-watching—or quarter-watching, if that’s a thing now—while they scroll through their phones. (...)

Among Netflix’s 36,000 micro-genres, one is literally called “casual viewing.” The label is reportedly reserved for sitcoms, soap operas, or movies that, as the Hollywood Reporter recently described the 2024 Jennifer Lopez film Atlas, are “made to half-watch while doing laundry.”...  The whole point is that it’s supposed to just be there, glowing, while you do something else. Perhaps a great deal of television is not meant to absorb our attention, at all, but rather to dab away at it, to soak up tiny droplets of our sensory experience while our focus dances across other screens. You might even say that much television is not even made to be watched at all. It is made to flow. The play button is the point.

Lonely, Mean, and Dumb

… and why does this matter? Fine question. And, perhaps, this is a good place for a confession. I like television. I follow some spectacular YouTube channels. I am not on Instagram or TikTok, but most of the people I know and love are on one or both. My beef is not with the entire medium of moving images. My concern is what happens when the grammar of television rather suddenly conquers the entire media landscape.

In the last few weeks, I have been writing a lot about two big trends in American life that do not necessarily overlap. My work on the “Antisocial Century” traces the rise of solitude in American life and its effects on economics, politics, and society. My work on “the end of thinking” follows the decline of literacy and numeracy scores in the U.S. and the handoff from a culture of literacy to a culture of orality. Neither of these trends is exclusively caused by the logic of television colonizing all media. But both trends are significantly exacerbated by it. 

Television’s role in the rise of solitude cannot be overlooked. In Bowling Alone, the Harvard scholar Robert Putnam wrote that between 1965 and 1995, the typical adult gained six hours a week in leisure time. As I wrote, they could have used those additional 300 hours a year to learn a new skill, or participate in their community, or have more children. Instead, the typical American funneled almost all of this extra time into watching more TV. Television instantly changed America’s interior decorating, relationships, and communities: (...)

Digital media, empowered by the serum of algorithmic feeds, has become super-television: more images, more videos, more isolation. Home-alone time has surged as our devices have become more bottomless feeds of video content. Rather than escape the solitude crisis that Putnam described in the 1990s, we now seem to be more on our own. (Not to mention: meaner and stupider, too.)

It would be rash to blame our berserk political moment entirely on short-form video, but it would be careless to forget that some people really did try to warn us that this was coming. In Amusing Ourselves to Death, Neil Postman wrote that “each medium, like language itself, makes possible a unique mode of discourse by providing a new orientation for thought, for expression, for sensibility.” Television speaks to us in a particular dialect, Postman argued. When everything turns into television, every form of communication starts to adopt television’s values: immediacy, emotion, spectacle, brevity. In the glow of a local news program, or an outraged news feed, the viewer bathes in a vat of their own cortisol. When everything is urgent, nothing is truly important. Politics becomes theater. Science becomes storytelling. News becomes performance. The result, Postman warned, is a society that forgets how to think in paragraphs, and learns instead to think in scenes. (...)

When literally everything becomes television, what disappears is not something so broad as intelligence (although that seems to be going, too) but something harder to put into words, and even harder to prove the value of. It’s something like inwardness. The capacity for solitude, for sustained attention, for meaning that penetrates inward rather than swipes away at the tip of a finger: These virtues feel out of step with a world where every medium is the same medium and everything in life converges to the value system of the same thing, which is television. 

by Derek Thompson |  Read more:

Image: Ajeet Mestry on Unsplash

[ed. See also: The Last Days Of Social Media (Noema).]

The Limits of Data

Right now, the language of policymaking is data. (I’m talking about “data” here as a concept, not as particular measurements.) Government agencies, corporations, and other policymakers all want to make decisions based on clear data about positive outcomes. They want to succeed on the metrics—to succeed in clear, objective, and publicly comprehensible terms. But metrics and data are incomplete by their basic nature. Every data collection method is constrained and every dataset is filtered.

Some very important things don’t make their way into the data. It’s easier to justify health care decisions in terms of measurable outcomes: increased average longevity or increased numbers of lives saved in emergency room visits, for example. But there are so many important factors that are far harder to measure: happiness, community, tradition, beauty, comfort, and all the oddities that go into “quality of life.”

Consider, for example, a policy proposal that doctors should urge patients to sharply lower their saturated fat intake. This should lead to better health outcomes, at least for those that are easier to measure: heart attack numbers and average longevity. But the focus on easy-to-measure outcomes often diminishes the salience of other downstream consequences: the loss of culinary traditions, disconnection from a culinary heritage, and a reduction in daily culinary joy. It’s easy to dismiss such things as “intangibles.” But actually, what’s more tangible than a good cheese, or a cheerful fondue party with friends?

It’s tempting to use the term intangible when what we really mean is that such things are hard to quantify in our modern institutional environment with the kinds of measuring tools that are used by modern bureaucratic systems. The gap between reality and what’s easy to measure shows up everywhere. Consider cost-benefit analysis, which is supposed to be an objective—and therefore unimpeachable—procedure for making decisions by tallying up expected financial costs and expected financial benefits. But the process is deeply constrained by the kinds of cost information that are easy to gather. It’s relatively straightforward to provide data to support claims about how a certain new overpass might help traffic move efficiently, get people to work faster, and attract more businesses to a downtown. It’s harder to produce data in support of claims about how the overpass might reduce the beauty of a city, or how the noise might affect citizens’ well-being, or how a wall that divides neighborhoods could erode community. From a policy perspective, anything hard to measure can start to fade from sight.

An optimist might hope to get around these problems with better data and metrics. What I want to show here is that these limitations on data are no accident. The basic methodology of data—as collected by real-world institutions obeying real-world forces of economy and scale—systematically leaves out certain kinds of information. Big datasets are not neutral and they are not all-encompassing. There are profound limitations on what large datasets can capture.

I’m not just talking about contingencies of social biases. Obviously, datasets are bad when the collection procedures are biased by oversampling by race, gender, or wealth. But even if analysts can correct for those sorts of biases, there are other, intrinsic biases built into the methodology of data. Data collection techniques must be repeatable across vast scales. They require standardized categories. Repeatability and standardization make data-based methods powerful, but that power has a price. It limits the kinds of information we can collect. (...)

These limitations are particularly worrisome when we’re thinking about success—about targets, goals, and outcomes. When actions must be justified in the language of data, then the limitations inherent in data collection become limitations on human values. And I’m not worried just about perverse incentives and situations in which bad actors game the metrics. I’m worried that an overemphasis on data may mislead even the most well-intentioned of policymakers, who don’t realize that the demand to be “objective”—in this very specific and institutional sense—leads them to systematically ignore a crucial chunk of the world.

Decontextualization

Not all kinds of knowledge, and not all kinds of understanding, can count as information and as data. Historian of quantification Theodore Porter describes “information” as a kind of “communication with people who are unknown to one another, and who thus have no personal basis for shared understanding.” In other words, “information” has been prepared to be understood by distant strangers. The clearest example of this kind of information is quantitative data. Data has been designed to be collected at scale and aggregated. Data must be something that can be collected by and exchanged between different people in all kinds of contexts, with all kinds of backgrounds. Data is portable, which is exactly what makes it powerful. But that portability has a hidden price: to transform our understanding and observations into data, we must perform an act of decontextualization.

An easy example is grading. I’m a philosophy professor. I issue two evaluations for every student essay: one is a long, detailed qualitative evaluation (paragraphs of written comments) and the other is a letter grade (a quantitative evaluation). The quantitative evaluation can travel easily between institutions. Different people can input into the same system, so it can easily generate aggregates and averages—the student’s grade point average, for instance. But think about everything that’s stripped out of the evaluation to enable this portable, aggregable kernel.

Qualitative evaluations can be flexible and responsive and draw on shared history. I can tailor my written assessment to the student’s goals. If a paper is trying to be original, I can comment on its originality. If a paper is trying to precisely explain a bit of Aristotle, I can assess it for its argumentative rigor. If one student wants be a journalist, I can focus on their writing quality. If a nursing student cares about the real-world applications of ethical theories, I can respond in kind. Most importantly, I can rely on our shared context. I can say things that might be unclear to an outside observer because the student and I have been in a classroom together, because we’ve talked for hours and hours about philosophy and critical thinking and writing, because I have a sense for what a particular student wants and needs. I can provide more subtle, complex, multidimensional responses. But, unlike a letter grade, such written evaluations travel poorly to distant administrators, deans, and hiring departments.

Quantification, as used in real-world institutions, works by removing contextually sensitive information. The process of quantification is designed to produce highly portable information, like a letter grade. Letter grades can be understood by everybody; they travel easily. A letter grade is a simple ranking on a one-dimensional spectrum. Once an institution has created this stable, context-invariant kernel, it can easily aggregate this kind of information—for students, for student cohorts, for whole universities. A pile of qualitative information, in the form of thousands of written comments, for example, does not aggregate. It is unwieldy, bordering on unusable, to the administrator, the law school admissions officer, or future employer—unless it has been transformed and decontextualized.

So here is the first principle of data: collecting data involves a trade-off. We gain portability and aggregability at the price of context-sensitivity and nuance. What’s missing from data? Data is designed to be usable and comprehensible by very different people from very different contexts and backgrounds. So data collection procedures tend to filter out highly context-based understanding. Much here depends on who’s permitted to input the data and who the data is intended for. 

by C. Thi Nguyen, Issues in Science and Technology |  Read more:

Image: Shonagh Rae