Rejus Almole – Page 7

Russian drones use Starlink, but Ukraine has plan to block their Internet access

Policy, spacex, starlink, Ukraine / Rejus Almole / February 2, 2026

Ukraine and SpaceX say they recently collaborated to stop strikes by Russian drones using Starlink and will soon block all unregistered use of Starlink terminals in an attempt to stop Russia’s military from using the satellite broadband network over Ukraine territory.

Ukrainians will soon be required to register their Starlink terminals to get on a whitelist. After that, “only verified and registered terminals will be allowed to operate in the country. All others will be disconnected,” the Ukraine Ministry of Defense said in a press release today.

Ukraine Minister of Defense Mykhailo Fedorov “emphasized that the only technical solution to counter this threat is to introduce a ‘whitelist’ and authorize all terminals,” according to the ministry. “This is a necessary step by the Government to save Ukrainian lives and protect critical energy infrastructure,” Fedorov said.

Fedorov has posted on SpaceX CEO Elon Musk’s X social network a few times in the past few days about Russia’s use of Starlink and Ukraine’s attempt to counter it. On January 29, Fedorov said his agency contacted SpaceX hours after “reports that Russian drones equipped with Starlink connectivity were operating over Ukrainian cities.” Ukraine “proposed concrete ways to resolve the issue,” he said.

Fedorov said that SpaceX started working on a solution immediately after the outreach. Musk wrote yesterday, “Looks like the steps we took to stop the unauthorized use of Starlink by Russia have worked. Let us know if more needs to be done.”

Fedorov said yesterday that because of “the first steps taken in recent days, no Ukrainians have been killed by Russian drones using Starlink.” Fedorov said the ministry “will share instructions for Ukrainian users to register their Starlink terminals for verification” in the coming days, and that registration “will be simple, fast, and user-friendly.”

Ukraine’s whitelist plan will require residents to make “one visit to the nearest Administrative Services Center,” a process that Fedorov said will be “free, fast, and without excessive bureaucracy.” Businesses will be able to verify their Starlink terminals online, while the military and service members have separate systems for registration. Service members with personal Starlink terminals will “only need to add the terminal to the ‘whitelist’ to prevent disconnection.”

Russian drones use Starlink, but Ukraine has plan to block their Internet access Read More »

Notepad++ users take note: It’s time to check if you’re hacked

Biz & IT, notepad, Open source software, Security, supply chain attacks / Rejus Almole / February 2, 2026

According to independent researcher Kevin Beaumont, three organizations told him that devices inside their networks that had Notepad++ installed experienced “security incidents” that “resulted in hands on keyboard threat actors,” meaning the hackers were able to take direct control using a web-based interface. All three of the organizations, Beaumont said, have interests in East Asia.

The researcher explained that his suspicions were aroused when Notepad++ version 8.8.8 introduced bug fixes in mid-November to “harden the Notepad++ Updater from being hijacked to deliver something… not Notepad++.”

The update made changes to a bespoke Notepad++ updater known as GUP, or alternatively, WinGUP. The gup.exe executable responsible reports the version in use to https://notepad-plus-plus.org/update/getDownloadUrl.php and then retrieves a URL for the update from a file named gup.xml. The file specified in the URL is downloaded to the %TEMP% directory of the device and then executed.

Beaumont wrote:

If you can intercept and change this traffic, you can redirect the download to any location it appears by changing the URL in the property.

This traffic is supposed to be over HTTPS, however it appears you may be [able] to tamper with the traffic if you sit on the ISP level and TLS intercept. In earlier versions of Notepad++, the traffic was just over HTTP.

The downloads themselves are signed—however some earlier versions of Notepad++ used a self signed root cert, which is on Github. With 8.8.7, the prior release, this was reverted to GlobalSign. Effectively, there’s a situation where the download isn’t robustly checked for tampering.

Because traffic to notepad-plus-plus.org is fairly rare, it may be possible to sit inside the ISP chain and redirect to a different download. To do this at any kind of scale requires a lot of resources.

Beaumont published his working theory in December, two months to the day prior to Monday’s advisory by Notepad++. Combined with the details from Notepad++, it’s now clear that the hypothesis was spot on.

Notepad++ users take note: It’s time to check if you’re hacked Read More »

TrumpRx delayed as senators question if it’s a giant scam with Big Pharma

anti-kickback statute, health, kickbacks, telehealth, trumprx / Rejus Almole / January 30, 2026

In other words, DTC websites run by pharmaceutical companies use “hand-picked telehealth companies to inappropriately steer patients toward specific, high-cost medications and inflate Big Pharma’s profit margins,” the senators write.

In an investigation last year of DTC platforms from Eli Lilly and Pfizer, the senators found that the pharmaceutical giants “spent up to $3 million combined for partnerships with telehealth companies, who funneled patients to the manufacturers’ products. … In one instance, 100 percent of the patients routed to a virtual visit with one of Eli Lilly’s chosen telehealth companies received a prescription.”

There’s already reason to be suspicious of conflicts of interest with TrumpRx, the senators note. There’s a “potential relationship between TrumpRx and an online dispensing company, BlinkRx, on whose Board the President’s son, Donald Trump, Jr., has sat since February 2025” the senators write.

The lawmakers are concerned that TrumpRx will violate the anti-kickback statute, which bars payments for inducing patients to use services or products that are reimbursable by a federal health care program.

Brian Reid, principal at health consultancy Reid Strategic, speculated to Politico that the delay of TrumpRx’s debut may be related to anti-kickback statute concerns.

“In any other administration, it would 100 percent be the AKS stuff,” Reid said. “It’s clear there’s a lawyer somewhere at HHS who has concerns about anti-kickback.”

TrumpRx delayed as senators question if it’s a giant scam with Big Pharma Read More »

NASA faces a crucial choice on a Mars spacecraft—and it must decide soon

blue origin, Mars, mars telecommunications orbiter, NASA, rocket lab, Space, spacex / Rejus Almole / January 30, 2026

However, some leaders within NASA see the language in the Cruz legislation as spelling out a telecommunications orbiter only and believe it would be difficult, if not impossible, to run a procurement competition between now and September 30th for anything beyond a straightforward communications orbiter.

In a statement provided to Ars by a NASA spokesperson, the agency said that is what it intends to do.

“NASA will procure a high-performance Mars telecommunications orbiter that will provide robust, continuous communications for Mars missions,” a spokesperson said. “NASA looks forward to collaborating with our commercial partners to advance deep space communications and navigation capabilities, strengthening US leadership in Mars infrastructure and the commercial space sector.”

Big decisions loom

Even so, sources said Isaacman has yet to decide whether the orbiter should include scientific instruments. NASA could also tap into other funding in its fiscal year 2026 budget, which included $110 million for unspecified “Mars Future Missions,” as well as a large wedge of funding that could potentially be used to support a Mars commercial payload delivery program.

The range of options before NASA, therefore, includes asking industry for a single telecom orbiter from one company, asking for a telecom orbiter with the capability to add a couple of instruments, or creating competition by asking for multiple orbiters and capabilities by tapping into the $700 million in the Cruz bill but then augmenting this with other Mars funding.

One indication that this process has been muddied within NASA came a week ago, when the space agency briefly posted a “Justification for Other Than Full and Open Competition, Extension” notice on a government website. It stated that the agency “will only conduct a competition among vendors that satisfy the statutory qualifications.” The notice also listed the companies eligible to bid based on the Cruz language: Blue Origin, L3Harris, Lockheed Martin, Northrop Grumman, Rocket Lab, SpaceX, Quantum Space, and Whittinghill Aerospace.

NASA faces a crucial choice on a Mars spacecraft—and it must decide soon Read More »

On The Adolescence of Technology

Adolescence / Rejus Almole / January 30, 2026

Anthropic CEO Dario Amodei is back with another extended essay, The Adolescence of Technology.

This is the follow up to his previous essay Machines of Loving Grace. In MoLG, Dario talked about some of the upsides of AI. Here he talks about the dangers, and the need to minimize them while maximizing the benefits.

In many aspects this was a good essay. Overall it is a mild positive update on Anthropic. It was entirely consistent with his previous statements and work.

I believe the target is someone familiar with the basics, but who hasn’t thought that much about any of this and is willing to listen given the source. For that audience, there are a lot of good bits. For the rest of us, it was good to affirm his positions.

That doesn’t mean there aren’t major problems, especially with its treatment of those more worried, and its failure to present stronger calls to action.

He is at his weakest when he is criticising those more worried than he is. In some cases the description of those positions is on the level of a clear strawman. The central message is, ‘yes this might kill everyone and we should take that seriously and it will be a tough road ahead, but careful not to take it too seriously or speak that too plainly, or call for doing things that would be too costly.’

One can very much appreciate him stating his views, and his effort to alter people to the risks involved, while also being sad about these major problems.

While I agree with Dario about export controls, I do not believe an aggressively adversarial framing of the situation is conducive to good outcomes.

In the end when he essentially affirms his commitment to racing and rules out trying to do all that much, saying flat out that others will go ahead regardless, so I broadly agree with Oliver Habryka and Daniel Kokotajlo here, and also with Ryan Greenblatt. This is true even though Anthropic’s commitment to racing to superintelligence (here ‘powerful AI’) should already be ‘priced in’ to your views on them.

Here is a 3 million views strong ‘tech Twitter slop’ summary of the essay, linked because it is illustrative of how such types read and pull from the essay, including how it centrally attempts to position Dario as the reasonable one between two extremes.

Right up front we get the classic tensions we get from Dario Amodei and Anthropic. He’s trying to be helpful, but also narrowing the window of potential actions and striking down anyone who speaks too plainly or says things that might seem too weird.

It’s an attempt to look like a sensible middle ground that everyone can agree upon, but it’s an asymmetric bothsidesism in a situation that is very clearly asymmetric the other way, and I’m pretty sick of it.

As with talking about the benefits, I think it is important to discuss risks in a careful and well-considered manner. In particular, I think it is critical to:

Avoid doomerism. Here, I mean “doomerism” not just in the sense of believing doom is inevitable (which is both a false and self-fulfilling belief), but more generally, thinking about AI risks in a quasi-religious way. … These voices used off-putting language reminiscent of religion or science fiction, and called for extreme actions without having the evidence that would justify them.

His full explanation on ‘doomerism,’ here clearly used as a slur or at minimum an ad hominem attack, basically blames the ‘backlash’ against efforts to not die on people being too pessimistic, or being ‘quasi-religious’ or sounding like ‘science fiction,’ or sounding ‘sensationalistic.’

‘Quasi-religious’ is also being used as an ad hominem or associative attack to try and dismiss and lower the status of anyone who is too much more concerned than he is, and to distance himself from similar attacks made by others.

I can’t let that slide. This is a dumb, no good, unhelpful and false narrative. Also see Ryan Greenblatt’s extended explanation for why these labels and dismissals are not okay. He is also right that the post does not engage with the actual arguments here, and that the vibes in several other ways downplay the central stakes and dangers while calling them ‘autonomy risks’ and that the essay is myopic in only dealing with modest capability gains (e.g. to the ‘geniuses in a datacenter’ level but then he implicitly claims advancements mostly stop, which they very much wouldn’t.)

The ‘backlash’ against those trying to not die was primarily due to a coordinated effort by power and economic interests, who engage in far worse sensationalism and ‘quasi-religious’ talk constantly, and also from the passage of time and people’s acting as if not having died yet meant it was all overblown, as happens with many that warn of potential dangers, including things like nuclear war.

You know what’s the most ‘quasi-religious’ such statement I’ve seen recently, except without the quasi? Marc Andreessen, deliberate bad faith architect of much of this backlash, calling AI the ‘Philosopher’s Stone.’ I mean, okay, Newton.

What causes people call logical arguments that talk plainly about likely physical consequences ‘reminiscent of science fiction’ or of ‘religion’ as an attack, they’re at best engaging in low-level pattern matching. Of course the future is going to ‘sound like science fiction’ when we are building powerful AI systems. Best start believing in science fiction stories, because you’re living in one.

And it’s pretty rich to say that those warning that all humans could die from this ‘sound like religion’ when you’re the CEO of a company that is literally named Anthropic. Also you opened the post by quoting Carl Sagan’s Contact.

Does that mean those involved played a perfect or even great game? Absolutely not. Certainly there were key mistakes, and some private actors engaged in overreach. The pause letter in particular was a mistake and I said so at the time. Such overreach is present in absolutely every important cause in history, and every single political movement. Several calls for regulation or model bills included compute thresholds that were too low, and again I said so at the time.

If anything, most of those involved have been extraordinarily restrained.

At some point, restraint means no one hears what you are saying. Dario here talks about ‘autonomy’ instead of ‘AI takeover’ or ‘everyone dies,’ and I think this failure to be blunt is a major weakness of the approach. So many wish to not listen, and Dario gives them that as an easy option.

Acknowledge uncertainty. There are plenty of ways in which the concerns I’m raising in this piece could be moot. Nothing here is intended to communicate certainty or even likelihood. Most obviously, AI may simply not advance anywhere near as fast as I imagine.

Or, even if it does advance quickly, some or all of the risks discussed here may not materialize (which would be great), or there may be other risks I haven’t considered. No one can predict the future with complete confidence—but we have to do the best we can to plan anyway.

On this point we mostly agree, especially that it might not progress so quickly. Dario should especially be prepared to be wrong about that, given his prediction is things will go much faster than most others predict.

In terms of the risks, certainly we will have missed important ones, it is very possible we will avoid the ones we worry most about now, but I don’t think it’s reasonable to say the risks we worry about now might not materialize at all as capabilities advance.

If AI becomes sufficiently advanced, yes the dangers will be there. The hope is that we will deal with them, perhaps in highly unexpected ways and with unexpected tools.

Intervene as surgically as possible. Addressing the risks of AI will require a mix of voluntary actions taken by companies (and private third-party actors) and actions taken by governments that bind everyone. The voluntary actions—both taking them and encouraging other companies to follow suit—are a no-brainer for me. I firmly believe that government actions will also be required to some extent, but these interventions are different in character because they can potentially destroy economic value or coerce unwilling actors who are skeptical of these risks (and there is some chance they are right!).

… It is easy to say, “No action is too extreme when the fate of humanity is at stake!,” but in practice this attitude simply leads to backlash.

It is almost always wise to intervene as surgically as possible, provided you still do enough to get the job done. And yes, if we want to do very costly interventions we will need better evidence and need better consensus. But context matters here. In the past, Anthropic has used such arguments as a kudgel against remarkably surgical interventions, including SB 1047.

Dario quotes his definition from Machines of Loving Grace: An AI smarter than a Nobel Prize winner across most relevant fields, with all the digital (but not physical) affordances available to a human, that can work autonomously for indefinite periods, and that can be run in parallel, or his ‘country of geniuses in a data center.’

Functionally I think this is a fine AGI alternative. For most purposes I have been liking my use of the term Sufficiently Advanced AI, but PAI works.

As I wrote in Machines of Loving Grace, powerful AI could be as little as 1–2 years away, although it could also be considerably further out.

That’s ‘could’ rather than ‘probably will be,’ so not a full doubling down.

In this essay Dario chooses his words carefully, and explains what he means. I worry that in other contexts, including within the past two weeks, Dario has been less careful, and that people will classify him as having made a stupid prediction if we don’t get his PAI by the end of 2027.

I don’t find it likely that we get PAI by the end of 2027, I’d give it less than a 10% chance of happening, but I agree that this is not something we can rule out, that it is more than 1% likely, and that we want to be prepared in case it happens.

I think the best way to get a handle on the risks of AI is to ask the following question: suppose a literal “country of geniuses” were to materialize somewhere in the world in ~2027. Imagine, say, 50 million people, all of whom are much more capable than any Nobel Prize winner, statesman, or technologist.

…for every cognitive action we can take, this country can take ten.

What should you be worried about? I would worry about the following things:

Autonomy risks. What are the intentions and goals of this country? Is it hostile, or does it share our values? Could it militarily dominate the world through superior weapons, cyber operations, influence operations, or manufacturing?

Misuse for destruction. Assume the new country is malleable and “follows instructions”—and thus is essentially a country of mercenaries. Could existing rogue actors who want to cause destruction (such as terrorists) use or manipulate some of the people in the new country to make themselves much more effective, greatly amplifying the scale of destruction?

Misuse for seizing power. What if the country was in fact built and controlled by an existing powerful actor, such as a dictator or rogue corporate actor? Could that actor use it to gain decisive or dominant power over the world as a whole, upsetting the existing balance of power?

Economic disruption. If the new country is not a security threat in any of the ways listed in #1–3 above but simply participates peacefully in the global economy, could it still create severe risks simply by being so technologically advanced and effective that it disrupts the global economy, causing mass unemployment or radically concentrating wealth?

Indirect effects. The world will change very quickly due to all the new technology and productivity that will be created by the new country. Could some of these changes be radically destabilizing?

I think it should be clear that this is a dangerous situation—a report from a competent national security official to a head of state would probably contain words like “the single most serious national security threat we’ve faced in a century, possibly ever.” It seems like something the best minds of civilization should be focused on.

Conversely, I think it would be absurd to shrug and say, “Nothing to worry about here!” But, faced with rapid AI progress, that seems to be the view of many US policymakers, some of whom deny the existence of any AI risks, when they are not distracted entirely by the usual tired old hot-button issues. Humanity needs to wake up, and this essay is an attempt—a possibly futile one, but it’s worth trying—to jolt people awake.

Yes, even if those were the only things to worry about, that’s a super big deal.

My responses:

Yes, just yes, obviously if it wants to take over it can do that, and it probably effectively takes over even if it doesn’t try. Dario spends time later arguing they would ‘have a fairly good shot’ to avoid sounding too weird, and if you need convincing you should read that section of the essay, but come on.
1. What are its intentions and goals? Great question.
Yeah, that is going to be a real problem.
Given [X] can take over, if you can control [X] then you can take over, too.
Participation in economics would mean it effectively takes over, and rapidly has control over an increasing share of resources. Worry less about wealth concentration among the humans and more about wealth and with it power and influence acquisition by the AIs. Whether or not this causes mass unemployment right away is less clear, it might require a bunch of further improvements and technological advancements and deployments first.
Yes, it would be radically destabilizing in the best case.
But all of this, even that these AIs could easily take over, buries the lede. If you had this nation of geniuses in a datacenter it would very obviously then make rapid further AI progress and go into full recursive self-improvement mode. It would quickly solve robotics, improve its compute efficiency, develop various other new technologies and so on. Thinking about what happens in this ‘steady state’ over a period of years is mostly asking a wrong question, as we will have already passed the point of no return.

Dario correctly quickly dismisses the ‘PAI won’t be able to take over if it tried’ arguments, and then moves on to whether it will try.

Some people say the PAI definitely won’t want to take over, AIs only do what humans ask them to do. He provides convincing evidence that no, AIs do unexpected other stuff all the time. I’d add that also some people will tell the AIs to take over to varying degrees in various ways.
Some people say PAI (or at least sufficiently advanced AI) will inevitably seek power or deceive humans. He cites but does not name instrumental convergence, as well as ‘AI will generalize that seeking power is good for achieving goals’ in a way described as a heuristic rather than being accurate.

This “misaligned power-seeking” is the intellectual basis of predictions that AI will inevitably destroy humanity.

The problem with this pessimistic position is that it mistakes a vague conceptual argument about high-level incentives—one that masks many hidden assumptions—for definitive proof.

Once again, no, this is not in any way necessary for AI to end up destroying humanity, or for AI causing the world to go down a path where humanity ends up destroyed (without attributing intent or direct causation).

One of the most important hidden assumptions, and a place where what we see in practice has diverged from the simple theoretical model, is the implicit assumption that AI models are necessarily monomaniacally focused on a single, coherent, narrow goal, and that they pursue that goal in a clean, consequentialist manner.

This in particular is a clear strawmanning of the position of the worried. As Rob Bensinger points out, there has been a book-length clarification of the actual position, and LLMs will give you dramatically better summaries than Dario’s here.

MIRI: A common misconception—showing up even in @DarioAmodei ‘s recent essay—is that the classic case for worrying about AI risk assumes an AI “monomaniacally focused on a single, coherent, narrow goal.”

But, as @ESYudkowsky explains, this is a misunderstanding of where the risk lies:

Eliezer Yudkowsky: Similarly: A paperclip maximizer is not “monomoniacally” “focused” on paperclips. We talked about a superintelligence that wanted 1 thing, because you get exactly the same results as from a superintelligence that wants paperclips and staples (2 things), or from a superintelligence that wants 100 things. The number of things It wants bears zero relevance to anything. It’s just easier to explain the mechanics if you start with a superintelligence that wants 1 thing, because you can talk about how It evaluates “number of expected paperclips resulting from an action” instead of “expected paperclips 2 + staples 3 + giant mechanical clocks 1000” and onward for a hundred other terms of Its utility function that all asymptote at different rates.

I’d also refer to this response from Harlan Stewart, especially the maintaining of plausible deniability by not specifying who is being responded to:

Harlan Stewart: I have a lot of thoughts about the Dario essay, and I want to write more of them up, but it feels exhausting to react to this kind of thing.

The parts I object to are mostly just iterations of the same messaging strategy the AI industry has been using over the last two years:

Discredit critics by strawmanning their arguments and painting them as crazy weirdos, while maintaining plausible deniability by not specifying which of your critics you’re referring to.

Instead of engaging with critics’ arguments in depth, dismiss them as being too “theoretical.” Emphasize the virtue of using “empirical evidence,” and use such a narrow definition of “empirical evidence” that it leaves no choice but to keep pushing ahead and see what happens, because the future will always be uncertain.

Reverse the burden of proof. Instead of it being your responsibility to demonstrate that your R&D project will not destroy the world, say that you will need definitive proof that it will destroy the world before changing course.

Predict that superhumanly powerful minds will be built within a matter of years, while also suggesting that this timeline somehow gives adequate time for an iterative, trial-and-error approach to alignment.

So again, no, none of that is being assumed. Power is useful for any goal it does not directly contradict, whether it be one narrow goal or a set of complex goals (which, for a sufficiently advanced AI, collapses to the same thing). Power is highly useful. It is especially useful when you are uncertain what your ultimate goal is going to be.

Consequentialism is also not required for this. A system of virtue ethics would conclude it is good to grow more powerful. A deontologically based system would conclude the same thing to the extent it wasn’t designed to effectively be rather dumb, even if it pursued this under its restrictions. And so on.

While current AIs are best understood by treating them as what Dario calls ‘psychologically complex’ (however literally you do or don’t take that), one should expect a sufficiently advanced AI to ‘get over it’ and effectively act optimally. The psychological complexity is the way of best dealing with various limitations, and in practical terms we should expect that it falls away if and as the limitations fall away. This is indeed what you see when humans get sufficiently advanced in a subdomain.

However, there is a more moderate and more robust version of the pessimistic position which does seem plausible, and therefore does concern me.

… Some fraction of those behaviors will have a coherent, focused, and persistent quality (indeed, as AI systems get more capable, their long-term coherence increases in order to complete lengthier tasks), and some fraction of those behaviors will be destructive or threatening.

… We don’t need a specific narrow story for how it happens, and we don’t need to claim it definitely will happen, we just need to note that the combination of intelligence, agency, coherence, and poor controllability is both plausible and a recipe for existential danger.

He goes on to add additional arguments and potential ways it could go down, such as extrapolating from science fiction or drawing ethical conclusions that become xenocidal, or that power seeking could emerge as a persona. Even if misalignment is not inevitable in any given instance, some instances becoming misaligned, and this causing them to be in some ways more fit and thus act in ways that make this dangerous, is completely inevitable as a default.

Dario is asserting the extremely modest and obvious claim that building these PAIs is not a safe thing to do, that things could (as opposed to would, or probably will) get out of control.

Yes, obviously they could get out of control. As Dario says Anthropic has already seen it happen during their own testing. If it doesn’t happen, it will be because we acted wisely and stopped it from happening. If it doesn’t become catastrophic, it will similarly be because we acted wisely and stopped that from happening.

Second, some may object that we can simply keep AIs in check with a balance of power between many AI systems, as we do with humans. The problem is that while humans vary enormously, AI systems broadly share training and alignment techniques across the industry, and those techniques may fail in a correlated way.

Furthermore, given the cost of training such systems, it may even be the case that all systems are essentially derived from a very small number of base models.

Additionally, even if a small fraction of AI instances are misaligned, they may be able to take advantage of offense-dominant technologies, such that having “good” AIs to defend against the bad AIs is not necessarily always effective.

I think this is far from the only problem.

Humans are not so good at maintaining a balance of power. Power gets quite unbalanced quite a lot, and what balance we do have comes at very large expense. We’ve managed to keep some amount of balance in large part because individual humans can only be in one place at a time, with highly limited physical and cognitive capacity, and thus have to coordinate with other humans in unreliable ways and with all the associated incentive problems, and also humans age and die, and we have strong natural egalitarian instincts, and so on.

So, so many of the things that work for human balance of power simply don’t apply in the AI scenarios, even before you consider that the AIs will largely be instances of the same model, and even without that likely will be good enough at decision theory to be essentially perfectly coordinated.

I’d also say the reverse of what Dario says in one aspect. Humans vary enormously in some senses, but they also all tap out at reasonably similar levels when healthy. Humans don’t scale. AIs vary so much more than humans do, especially when one can have orders of magnitude more hardware and copies of itself available.

The third objection he raises, that AI companies test their AIs before release, is not a serious reason to not worry about any of this.

He thinks there are four categories (this is condensed):

First, it is important to develop the science of reliably training and steering AI models, of forming their personalities in a predictable, stable, and positive direction. One of our core innovations (aspects of which have since been adopted by other AI companies) is Constitutional AI.

Anthropic has just published its most recent constitution, and one of its notable features is that instead of giving Claude a long list of things to do and not do (e.g., “Don’t help the user hotwire a car”), the constitution attempts to give Claude a set of high-level principles and values.

We believe that a feasible goal for 2026 is to train Claude in such a way that it almost never goes against the spirit of its constitution.

I have a three-part series on the recent Claude constitution. It is an extraordinary document and I think it is the best approach we can currently implement.

As I write in that serious, I don’t think this works on its own as an ‘endgame’ strategy but it could help us quite a lot along the way.

The second thing we can do is develop the science of looking inside AI models to diagnose their behavior so that we can identify problems and fix them. This is the science of interpretability, and I’ve talked about its importance in previous essays.

The unique value of interpretability is that by looking inside the model and seeing how it works, you in principle have the ability to deduce what a model might do in a hypothetical situation you can’t directly test—which is the worry with relying solely on constitutional training and empirical testing of behavior.

Constitutional AI (along with similar alignment methods) and mechanistic interpretability are most powerful when used together, as a back-and-forth process of improving Claude’s training and then testing for problems.

I agree that interpretability is a useful part of the toolbox, although we need to be very careful with it lest it stop working or we think we know more than we do.

The third thing we can do to help address autonomy risks is to build the infrastructure necessary to monitor our models in live internal and external use,and publicly share any problems we find.

Transparency and sharing problems is also useful, sure, although it is not a solution.

The fourth thing we can do is encourage coordination to address autonomy risks at the level of industry and society.

For example, some AI companies have shown a disturbing negligence towards the sexualization of children in today’s models, which makes me doubt that they’ll show either the inclination or the ability to address autonomy risks in future models.

In addition, the commercial race between AI companies will only continue to heat up, and while the science of steering models can have some commercial benefits, overall the intensity of the race will make it increasingly hard to focus on addressing autonomy risks.

I believe the only solution is legislation—laws that directly affect the behavior of AI companies, or otherwise incentivize R&D to solve these issues. Here it is worth keeping in mind the warnings I gave at the beginning of this essay about uncertainty and surgical interventions.

You can see here, as he talks about, ‘autonomy risks,’ that this doesn’t have the punch it would have if you called it something that made the situation clear. ‘Autonomy risks’ sounds very nice and civilized, not like ‘AIs take over’ or ‘everyone dies.’

You can also see the attempt to use a normie example, sexualization of children, where the parallel doesn’t work so well, except as a pure ‘certain companies I won’t name have been so obviously deeply irresponsible that they obviously will keep being like that.’ Which is a fair point, but the fact that Anthropic, Google and OpenAI have been good on such issues does not give me much comfort.

What’s the pitch?

Anthropic’s view has been that the right place to start is with transparency legislation, which essentially tries to require that every frontier AI company engage in the transparency practices I’ve described earlier in this section. California’s SB 53 and New York’s RAISE Act are examples of this kind of legislation, which Anthropic supported and which have successfully passed. In supporting and helping to craft these laws, we’ve put a particular focus on trying to minimize collateral damage, for example by exempting smaller companies unlikely to produce frontier models from the law.

Anthropic has had a decidedly mixed relationship with efforts along these lines, although they ultimately did support these recent minimalist efforts. I agree it is a fine place to start, but then were do you go after that? Anthropic was deeply reluctant even with extremely modest proposals and I worry this will continue.

If everyone has a genius in their pocket, will some people use it to do great harm? What happens when you no longer need rare technical skills to case catastrophe?

Dario focuses on biological risks here, noting that LLMs are already substantially reducing barriers, but that skill barriers remain high. In the future, things could become far worse on such fronts.

This is a tricky situation, especially if you are trying to get people to take it seriously. Every time nothing has happened yet people relax further. You only find out afterwards if things went too far and there’s broad uncertainty about where that is. Meanwhile, there are other things we can do to mitigate risk but right now we are failing in maximally undignified ways:

An MIT study found that 36 out of 38 providers fulfilled an order containing the sequence of the 1918 flu.

The counterargument is, essentially, People Don’t Do Things, and the bad guys who try for real are rare and also rather bad at actually accomplishing anything. If this wasn’t true the world would already look very different, for reasons unrelated to AI.

The best objection is one that I’ve rarely seen raised: that there is a gap between the models being useful in principle and the actual propensity of bad actors to use them. Most individual bad actors are disturbed individuals, so almost by definition their behavior is unpredictable and irrational—and it’s these bad actors, the unskilled ones, who might have stood to benefit the most from AI making it much easier to kill many people.

One problem with this situation is that damage from such incidents is on a power law, up to and including global pandemics or worse. So the fact that the ‘bad guys’ are not taking so many competent shots on goal means that the first shot that hits could be quite catastrophically bad. Once that happens, many mistakes already made cannot be undone, both in terms of the attack and the availability of the LLMs, especially if they are open models.

It’s great that capability in theory doesn’t usually translate into happening in practice, and we’re basically able to use security through obscurity, but when that fails it can really fail hard.

What can we do?

Here I see three things we can do.

First, AI companies can put guardrails on their models to prevent them from helping to produce bioweapons. Anthropic is very actively doing this.

But all models can be jailbroken, and so as a second line of defense, we’ve implemented (since mid-2025, when our tests showed our models were starting to get close to the threshold where they might begin to pose a risk) a classifier that specifically detects and blocks bioweapon-related outputs.

To their credit, some other AI companies have implemented classifiers as well. But not every company has, and there is also nothing requiring companies to keep their classifiers. I am concerned that over time there may be a prisoner’s dilemma where companies can defect and lower their costs by removing classifiers.

You can jailbreak any model. You can get around any classifier. In practice, the bad guys mostly won’t, for the same reasons discussed earlier, so ‘make it sufficiently hard and annoying’ works. That’s not the best long term solution.

But ultimately defense may require government action, which is the second thing we can do. My views here are the same as they are for addressing autonomy risks: we should start with transparency requirements.

Then, if and when we reach clearer thresholds of risk, we can craft legislation that more precisely targets these risks and has a lower chance of collateral damage.

Finally, the third countermeasure we can take is to try to develop defenses against biological attacks themselves.

This could include monitoring and tracking for early detection, investments in air purification R&D (such as far-UVC disinfection), rapid vaccine development that can respond and adapt to an attack, better personal protective equipment (PPE), and treatments or vaccinations for some of the most likely biological agents.

mRNA vaccines, which can be designed to respond to a particular virus or variant, are an early example of what is possible here.

We aren’t even doing basic things like ‘don’t hand exactly the worst flu virus to whoever asks for it’ so yes there is a lot to do in developing physical defenses. Alas, our response to the Covid pandemic has been worse than useless, with Moderna actively stopping work on mRNA vaccines due to worries about not getting approved, and we definitely aren’t working much on air purification, far-UVC or PPE.

If people who otherwise want to push forward were supporting at least those kinds of countermeasures more vocally and strongly, as opposed to letting us slide backwards, I’d respect such voices quite a lot more.

On the direct regulation of AI front, yes I think we need to at least have transparency requirements, and it will likely make sense soon to legally require various defenses be built into frontier AI systems.

In Machines of Loving Grace, I discussed the possibility that authoritarian governments might use powerful AI to surveil or repress their citizens in ways that would be extremely difficult to reform or overthrow. Current autocracies are limited in how repressive they can be by the need to have humans carry out their orders, and humans often have limits in how inhumane they are willing to be. But AI-enabled autocracies would not have such limits.

Worse yet, countries could also use their advantage in AI to gain power over other countries.

That’s a really bizarre ‘worse yet’ isn’t it? Most every technology in history has been used to get an advantage in power by some countries over other countries. It’s not obviously good or bad for nation [X] to have power over nation [Y].

America certainly plans to use AI to gain power. If you asked ‘what country is most likely to use AI to try to impose its will on other nations’ the answer would presumably be the United States.

There are many ways in which AI could enable, entrench, or expand autocracy, but I’ll list a few that I’m most worried about. Note that some of these applications have legitimate defensive uses, and I am not necessarily arguing against them in absolute terms; I am nevertheless worried that they structurally tend to favor autocracies:

Fully autonomous weapons.

AI surveillance. Sufficiently powerful AI could likely be used to compromise any computer system in the world, and could also use the access obtained in this way to read and make sense of all the world’s electronic communications.

AI propaganda.

Strategic decision-making.

If your AI can compromise any computer system in the world and make sense of all the world’s information, perhaps AI surveillance should be rather far down on your list of worries for that?

Certainly misuse of AI for various purposes is a real threat, but let us not lack imagination. An AI capable of all this can do so much more. In terms of who is favored in such scenarios, assuming we continue to disregard fully what Dario calls ‘autonomy risks,’ the obvious answer is whoever has access to the most geniuses in the data centers willing to cooperate with them, combined with who has access to capital.

Dario’s primary worry is the CCP, especially if it takes the lead in AI, noting that the most likely to suffer here are the Chinese themselves. Democracies competitive in AI are listed second, with the worry that AI would be used to route around democracy.

AI companies are only listed fourth, behind other autocracies. Curious.

It’s less that autocracy becomes favored in such scenarios, as that the foundations of democracy by default will stop working. The people won’t be in the loops, won’t play a key part in having new ideas or organizing or expanding the economy, won’t be key to military or state power, you won’t need lots of people willing to carry out the will of the state, and so on. The reasons democracy historically wins may potentially be going away.

At last we at least one easy policy intervention we can get behind.

First, we should absolutely not be selling chips, chip-making tools, or datacenters to the CCP…. It makes no sense to sell the CCP the tools with which to build an AI totalitarian state and possibly conquer us militarily.

A number of complicated arguments are made to justify such sales, such as the idea that “spreading our tech stack around the world” allows “America to win” in some general, unspecified economic battle. In my view, this is like selling nuclear weapons to North Korea and then bragging that the missile casings are made by Boeing and so the US is “winning.”

Yes. Well said. It really is this simple.

Second, it makes sense to use AI to empower democracies to resist autocracies. This is the reason Anthropic considers it important to provide AI to the intelligence and defense communities in the US and its democratic allies.

Third, we need to draw a hard line against AI abuses within democracies. There need to be limits to what we allow our governments to do with AI, so that they don’t seize power or repress their own people. The formulation I have come up with is that we should use AI for national defense in all ways except those which would make us more like our autocratic adversaries.

Where should the line be drawn? In the list at the beginning of this section, two items—using AI for domestic mass surveillance and mass propaganda—seem to me like bright red lines and entirely illegitimate.

The other two items—fully autonomous weapons and AI for strategic decision-making—are harder lines to draw since they have legitimate uses in defending democracy, while also being prone to abuse.

It is difficult to draw clear lines on such questions, but you do have to draw the lines somewhere, and that has to be a painful action if it’s going to work.

Fourth, after drawing a hard line against AI abuses in democracies, we should use that precedent to create an international taboo against the worst abuses of powerful AI. I recognize that the current political winds have turned against international cooperation and international norms, but this is a case where we sorely need them.

It is not, as he says and shall we say, a good time to be asking for norms of this type, for various reasons. If we continue down our current path, it doesn’t look good.

Fifth and finally, AI companies should be carefully watched, as should their connection to the government, which is necessary, but must have limits and boundaries

Dario is severely limited here in what he can say out loud, and perhaps in what he allows himself to think. I encourage each of us to think seriously about what one would say if such restrictions did not apply.

Ah, good, some simple economic disruption problems. Every essay needs a break.

In Machines of Loving Grace, I suggest that a 10–20% sustained annual GDP growth rate may be possible.

But it should be clear that this is a double-edged sword: what are the economic prospects for most existing humans in such a world?

There are two specific problems I am worried about: labor market displacement, and concentration of economic power.

Dario starts off pushing back against those who think AI couldn’t possibly disrupt labor markets and cause mass unemployment, crying ‘lump of labor fallacy’ or what not, so he goes through the motions to show he understands all that including the historical context.

It’s possible things will go roughly the same way with AI, but I would bet pretty strongly against it. Here are some reasons I think AI is likely to be different:

Speed.

Cognitive breadth.

Slicing by cognitive ability.

Ability to fill in the gaps.

Slow diffusion of technology is definitely real—I talk to people from a wide variety of enterprises, and there are places where the adoption of AI will take years. That’s why my prediction for 50% of entry level white collar jobs being disrupted is 1–5 years, even though I suspect we’ll have powerful AI (which would be, technologically speaking, enough to do most or all jobs, not just entry level) in much less than 5 years.

Second, some people say that human jobs will move to the physical world, which avoids the whole category of “cognitive labor” where AI is progressing so rapidly. I am not sure how safe this is, either.

Third, perhaps some tasks inherently require or greatly benefit from a human touch. I’m a little more uncertain about this one, but I’m still skeptical that it will be enough to offset the bulk of the impacts I described above.

Fourth, some may argue that comparative advantage will still protect humans. Under the law of comparative advantage, even if AI is better than humans at everything, any relative differences between the human and AI profile of skills creates a basis of trade and specialization between humans and AI. The problem is that if AIs are literally thousands of times more productive than humans, this logic starts to break down. Even tiny transaction costs could make it not worth it for AI to trade with humans. And human wages may be very low, even if they technically have something to offer.

Dario’s basic explanation here is solid, especially since he’s making a highly tentative and conservative case. He’s portraying a scenario where things in many senses move remarkably slowly, and the real question is not ‘why would this disrupt employment’ but ‘why wouldn’t this be entirely transformative even if it is not deadly.’

Okay, candlemakers, lay out your petitions.

What can we do about this problem? I have several suggestions, some of which Anthropic is already doing.

The first thing is simply to get accurate data about what is happening with job displacement in real time.

Second, AI companies have a choice in how they work with enterprises. The very inefficiency of traditional enterprises means that their rollout of AI can be very path dependent, and there is some room to choose a better path.

Third, companies should think about how to take care of their employees.

Fourth, wealthy individuals have an obligation to help solve this problem. It is sad to me that many wealthy individuals (especially in the tech industry) have recently adopted a cynical and nihilistic attitude that philanthropy is inevitably fraudulent or useless.

All of Anthropic’s co-founders have pledged to donate 80% of our wealth, and Anthropic’s staff have individually pledged to donate company shares worth billions at current prices—donations that the company has committed to matching.

Fifth, while all the above private actions can be helpful, ultimately a macroeconomic problem this large will require government intervention.

Ultimately, I think of all of the above interventions as ways to buy time.

The last line is the one that matters most. Mostly all you can do is buy a little time.

If you want to try and do more than that, and the humans can remain alive and in control (or in Dario’s term ‘we solve the autonomy problem’) then you can engage in massive macroeconomic redistribution, either by government or by the wealthy or both. There will be enough wealth around, and value produced, that everyone can have material abundance.

That doesn’t protect jobs. To protect jobs in such a scenario, you would need to explicitly protect jobs via protectionism and restrictions. I don’t love that idea.

Assuming everyone is doing fine materially, the real problem with economic inequality is the problem of economic concentration of power. Dario worries that too much wealth concentration would break society.

Democracy is ultimately backstopped by the idea that the population as a whole is necessary for the operation of the economy. If that economic leverage goes away, then the implicit social contract of democracy may stop working.

So that’s the thing. That leverage is going to go away. I don’t see any distribution of wealth changing that inevitability.

What can be done?

First, and most obviously, companies should simply choose not to be part of it.

By this he means that companies (and individuals) can choose to advocate in the public interest, rather than in the interests of themselves or the wealthy.

Second, the AI industry needs a healthier relationship with government—one based on substantive policy engagement rather than political alignment.

That is a two way street. Both sides have to be willing.

Dario frames Anthropic’s approach as being principled, and willing to take a stand for what they believe in. As I’ve said before, I’m very much for standing up for what you believe in, and in some cases I’m very much for pragmatism, and I think it’s actively good that Anthropic does a mix of both.

My concern is that Anthropic’s actions have not been on the Production Possibilities Frontier. As in, I feel Anthropic has spoken up in ways that don’t help much but that burn a bunch of political capital with key actors, and also Anthropic has failed to speak up in places where they could have helped a lot at small or no expense. As long as we stick to the frontier, we can talk price.

Dario calls this the ‘black seas of infinity,’ of various indirect effects.

Suppose we address all the risks described so far, and begin to reap the benefits of AI. We will likely get a “century of scientific and economic progress compressed into a decade,” and this will be hugely positive for the world, but we will then have to contend with the problems that arise from this rapid rate of progress, and those problems may come at us fast.

This would include:

Rapid advances in biology.

AI changes human life in an unhealthy way.

Human purpose.

On biology, the idea that extending lifespan might make people power-seeking or unstable strikes me as way more science fiction than anything that those worried about AI have prominently said. I think this distinction is illustrative.

Science fiction (along with fantasy) usually has a rule that if you seek an ‘unnatural’ or ‘unfair’ benefit, that there must be some sort of ‘catch’ to it. Something will go horribly wrong. The price must be paid.

Why? Because there is no story without it, and because we want to tell ourselves why it is okay that we are dumb and grow old and die. That’s why. Also, because it’s wrong. You ‘shouldn’t’ want to be smarter, or live forever, or be or look younger, or create a man artificially. Such hubris, such blasphemy.

Not that there aren’t trade-offs with new technologies, especially in terms of societal adjustments, but the alternative remains among other issues the planetary death rate of 100%.

AI ‘changing human life in an unhealthy way’ will doubtless happen in dozens of ways if we are so lucky as to be around for it to happen. It will also enhance our life in other ways. Dario does some brainstorming, including reinventing the whispering earring, and also loss of purpose which is sufficiently obvious it counts as a Known Known.

Sounds like we have some big problems, even if we accept Dario’s framing of the geniuses in the data center basically sitting around being ordinary geniuses rather than quickly proceeding to the next phase.

It’s a real shame we can’t actually do anything about them that would cost us anything, or speak aloud about what we want to be protecting other than ‘democracy.’

Furthermore, the last few years should make clear that the idea of stopping or even substantially slowing the technology is fundamentally untenable.

I do see a path to a slight moderation in AI development that is compatible with a realist view of geopolitics.

This is where we are. We’re about to go down a path likely to kill literally everyone, and the responsible one is saying maybe we can ‘see a path to’ a slight moderation.

He doesn’t even talk about building capacity to potentially slow down or intercede, if the situation should call for it. I think we should read this as, essentially, ‘I cannot rhetorically be seen talking about that, and thus my failure to mention it should not be much evidence of whether I think this would be a good idea.’

Harlan Stewart notes a key rhetorical change, and not for the better:

Harlan Stewart: You flipped the burden of proof. In 2023, Anthropic’s position was:

“Indications that we are in a pessimistic or near-pessimistic scenario may be sudden and hard to spot. We should therefore always act under the assumption that we still may be in such a scenario unless we have sufficient evidence that we are not.”

But in this essay, you say:

“To be clear, I think there’s a decent chance we eventually reach a point where much more significant action is warranted, but that will depend on stronger evidence of imminent, concrete danger than we have today, as well as enough specificity about the danger to formulate rules that have a chance of addressing it.”

Here is how the essay closes:

But we will need to step up our efforts if we want to succeed. The first step is for those closest to the technology to simply tell the truth about the situation humanity is in, which I have always tried to do; I’m doing so more explicitly and with greater urgency with this essay.

The next step will be convincing the world’s thinkers, policymakers, companies, and citizens of the imminence and overriding importance of this issue—that it is worth expending thought and political capital on this in comparison to the thousands of other issues that dominate the news every day. Then there will be a time for courage, for enough people to buck the prevailing trends and stand on principle, even in the face of threats to their economic interests and personal safety.

The years in front of us will be impossibly hard, asking more of us than we think we can give. But in my time as a researcher, leader, and citizen, I have seen enough courage and nobility to believe that we can win—that when put in the darkest circumstances, humanity has a way of gathering, seemingly at the last minute, the strength and wisdom needed to prevail. We have no time to lose.

Yes. This stands in sharp contrast with the writings of Sam Altman over at OpenAI, where he talks about cool ideas and raising revenue.

The years in front of us will be impossibly hard (in some ways), asking more of us than we think we can give. That goes for Dario as well. What he thinks can be done is not going to get it done.

Dario’s strategy is that we have a history of pulling through seemingly at the last minute under dark circumstances. You know, like Inspector Clouseau, The Flash or Buffy the Vampire Slayer.

He is the CEO of a frontier AI company called Anthropic.

Discussion about this post

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New OpenAI tool renews fears that “AI slop” will overwhelm scientific research

Academic publishing, AI, AI assistants, ai slop, AI writing, Cambridge University Press, cornell university, GPT-5, Kevin Weil, large language models, machine learning, openai, peer review, Science, Science journal, scientific research, Yian Yin / Rejus Almole / January 29, 2026

New “Prism” workspace launches just as studies show AI-assisted papers are flooding journals with diminished quality.

On Tuesday, OpenAI released a free AI-powered workspace for scientists. It’s called Prism, and it has drawn immediate skepticism from researchers who fear the tool will accelerate the already overwhelming flood of low-quality papers into scientific journals. The launch coincides with growing alarm among publishers about what many are calling “AI slop” in academic publishing.

To be clear, Prism is a writing and formatting tool, not a system for conducting research itself, though OpenAI’s broader pitch blurs that line.

Prism integrates OpenAI’s GPT-5.2 model into a LaTeX-based text editor (a standard used for typesetting documents), allowing researchers to draft papers, generate citations, create diagrams from whiteboard sketches, and collaborate with co-authors in real time. The tool is free for anyone with a ChatGPT account.

“I think 2026 will be for AI and science what 2025 was for AI in software engineering,” Kevin Weil, vice president of OpenAI for Science, told reporters at a press briefing attended by MIT Technology Review. He said that ChatGPT receives about 8.4 million messages per week on “hard science” topics, which he described as evidence that AI is transitioning from curiosity to core workflow for scientists.

OpenAI built Prism on technology from Crixet, a cloud-based LaTeX platform the company acquired in late 2025. The company envisions Prism helping researchers spend less time on tedious formatting tasks and more time on actual science. During a demonstration, an OpenAI employee showed how the software could automatically find and incorporate relevant scientific literature, then format the bibliography.

But AI models are tools, and any tool can be misused. The risk here is specific: By making it easy to produce polished, professional-looking manuscripts, tools like Prism could flood the peer review system with papers that don’t meaningfully advance their fields. The barrier to producing science-flavored text is dropping, but the capacity to evaluate that research has not kept pace.

When asked about the possibility of the AI model confabulating fake citations, Weil acknowledged in the press demo that “none of this absolves the scientist of the responsibility to verify that their references are correct.”

Unlike traditional reference management software (such as EndNote), which has formatted citations for over 30 years without inventing them, AI models can generate plausible-sounding sources that don’t exist. Weil added: “We’re conscious that as AI becomes more capable, there are concerns around volume, quality, and trust in the scientific community.”

The slop problem

Those concerns are not hypothetical, as we have previously covered. A December 2025 study published in the journal Science found that researchers using large language models to write papers increased their output by 30 to 50 percent, depending on the field. But those AI-assisted papers performed worse in peer review. Papers with complex language written without AI assistance were most likely to be accepted by journals, while papers with complex language likely written by AI models were less likely to be accepted. Reviewers apparently recognized that sophisticated prose was masking weak science.

“It is a very widespread pattern across different fields of science,” Yian Yin, an information science professor at Cornell University and one of the study’s authors, told the Cornell Chronicle. “There’s a big shift in our current ecosystem that warrants a very serious look, especially for those who make decisions about what science we should support and fund.”

Another analysis of 41 million papers published between 1980 and 2025 found that while AI-using scientists receive more citations and publish more papers, the collective scope of scientific exploration appears to be narrowing. Lisa Messeri, a sociocultural anthropologist at Yale University, told Science magazine that these findings should set off “loud alarm bells” for the research community.

“Science is nothing but a collective endeavor,” she said. “There needs to be some deep reckoning with what we do with a tool that benefits individuals but destroys science.”

Concerns about AI-generated scientific content are not new. In 2022, Meta pulled a demo of Galactica, a large language model designed to write scientific literature, after users discovered it could generate convincing nonsense on any topic, including a wiki entry about a fictional research paper called “The benefits of eating crushed glass.” Two years later, Tokyo-based Sakana AI announced “The AI Scientist,” an autonomous research system that critics on Hacker News dismissed as producing “garbage” papers. “As an editor of a journal, I would likely desk-reject them,” one commenter wrote at the time. “They contain very limited novel knowledge.”

The problem has only grown worse since then. In his first editorial of 2026 for Science, Editor-in-Chief H. Holden Thorp wrote that the journal is “less susceptible” to AI slop because of its size and human editorial investment, but he warned that “no system, human or artificial, can catch everything.” Science currently allows limited AI use for editing and gathering references but requires disclosure for anything beyond that and prohibits AI-generated figures.

Mandy Hill, managing director of academic publishing at Cambridge University Press & Assessment, has been even more blunt. In October 2025, she told Retraction Watch that the publishing ecosystem is under strain and called for “radical change.” She explained to the University of Cambridge publication Varsity that “too many journal articles are being published, and this is causing huge strain” and warned that AI “will exacerbate” the problem.

Accelerating science or overwhelming peer review?

OpenAI is serious about leaning on its ability to accelerate science, and the company laid out its case for AI-assisted research in a report published earlier this week. It profiles researchers who say AI models have sped up their work, including a mathematician who used GPT-5.2 to solve an open problem in optimization over three evenings and a physicist who watched the model reproduce symmetry calculations that had taken him months to derive.

Those examples go beyond writing assistance into using AI for actual research work, a distinction OpenAI’s marketing intentionally blurs. For scientists who don’t speak English fluently, AI writing tools could legitimately accelerate the publication of good research. But that benefit may be offset by a flood of mediocre submissions jamming up an already strained peer-review system.

Weil told MIT Technology Review that his goal is not to produce a single AI-generated discovery but rather “10,000 advances in science that maybe wouldn’t have happened or wouldn’t have happened as quickly.” He described this as “an incremental, compounding acceleration.”

Whether that acceleration produces more scientific knowledge or simply more scientific papers remains to be seen. Nikita Zhivotovskiy, a statistician at UC Berkeley not connected to OpenAI, told MIT Technology Review that GPT-5 has already become valuable in his own work for polishing text and catching mathematical typos, making “interaction with the scientific literature smoother.”

But by making papers look polished and professional regardless of their scientific merit, AI writing tools may help weak research clear the initial screening that editors and reviewers use to assess presentation quality. The risk is that conversational workflows obscure assumptions and blur accountability, and they might overwhelm the still very human peer review process required to vet it all.

OpenAI appears aware of this tension. Its public statements about Prism emphasize that the tool will not conduct research independently and that human scientists remain responsible for verification.

Still, one commenter on Hacker News captured the anxiety spreading through technical communities: “I’m scared that this type of thing is going to do to science journals what AI-generated bug reports is doing to bug bounties. We’re truly living in a post-scarcity society now, except that the thing we have an abundance of is garbage, and it’s drowning out everything of value.”

Benj Edwards is Ars Technica’s Senior AI Reporter and founder of the site’s dedicated AI beat in 2022. He’s also a tech historian with almost two decades of experience. In his free time, he writes and records music, collects vintage computers, and enjoys nature. He lives in Raleigh, NC.

New OpenAI tool renews fears that “AI slop” will overwhelm scientific research Read More »

AI #153: Living Documents

Living / Rejus Almole / January 29, 2026

This was Anthropic Vision week where at DWATV, which caused things to fall a bit behind on other fronts even within AI. Several topics are getting pushed forward, as the Christmas lull appears to be over.

Upcoming schedule: Friday will cover Dario’s essay The Adolescence of Technology. Monday will cover Kimi K2.5, which is potentially a big deal. Tuesday is scheduled to be Claude Code #4. I’ve also pushed discussions of the question of the automation of AI R&D, or When AI Builds AI, to a future post, when there is a slot for that.

So get your reactions to all of those in by then, including in the comments to today’s post, and I’ll consider them for incorporation.

Language Models Offer Mundane Utility. Code is better without coding.
Overcoming Bias. LLMs continue to share the standard human biases.
Huh, Upgrades. Gemini side panels in Chrome, Claude interactive work tools.
On Your Marks. FrontierMath: Open Problems benchmark. You score zero.
Choose Your Fighter. Gemini tools struggle, some find Claude uncooperative.
Deepfaketown and Botpocalypse Soon. Hallucination hallucinations.
Cybersecurity On Alert. OpenAI prepares to trigger High danger in cybersecurity.
Fun With Media Generation. Isometric map of NYC, Grok 10 second videos.
You Drive Me Crazy. Dean Ball on how to think about AI and children.
They Took Our Jobs. Beware confusing costs with benefits.
Get Involved. In various things. DeepMind is hiring a Chief AGI Economist.
Introducing. Havenlock measures orality, Poison Fountain, OpenAI Prism.
In Other AI News. Awesome things often carry unawesome implications.
Show Me the Money. The unit economics continue to be quite good.
Bubble, Bubble, Toil and Trouble. Does bubble talk have real consequences?
Quiet Speculations. What should we expect from DeepSeek v4 when it arrives?
Don’t Be All Thumbs. Choose the better thing over the worse thing.
The First Step Is Admitting You Have a Problem. Demis cries out for help.
Quickly, There’s No Time. Life is about to come at you faster than usual.
The Quest for Sane Regulations. I do appreciate a good display of chutzpah.
Those Really Were Interesting Times. The demand for preference falsification.
Chip City. Nvidia keeps getting away with rather a lot, mostly in plain sight.
The Week in Audio. Demis Hassabis, Tyler Cowen, Amanda Askell.
Rhetorical Innovation. The need to face basic physical realities.
Aligning a Smarter Than Human Intelligence is Difficult. Some issues lie ahead.
The Power Of Disempowerment. Are humans disempowering themselves already?
The Lighter Side. One weird trick.

Paul Graham seems right that present AI’s sweet spot is projects that are rate limited by the creation of text.

Code without coding.

roon: programming always sucked. it was a requisite pain for ~everyone who wanted to manipulate computers into doing useful things and im glad it’s over. it’s amazing how quickly I’ve moved on and don’t miss even slightly. im resentful that computers didn’t always work this way

not to be insensitive to the elect few who genuinely saw it as their art form. i feel for you.

100% [of my code is being written by AI]. I don’t write code anymore.

Greg Brockman: i always loved programming but am loving the new world even more.

Conrad Barski: it was always fun in the way puzzles are fun

but I agree there is no need for sentimentality in the tedium of authoring code to achieve an end goal

It was fun in the way puzzles are fun, but also infuriating in the way puzzles are infuriating. If you had to complete jigsaw puzzles in order to get things done jigsaw puzzles would get old fast.

Have the AI edit a condescending post so that you can read it without taking damage. Variations on this theme are also highly underutilized.

The head of Norway’s sovereign wealth fund reports 20% productivity gains from Claude, saying it has fundamentally changed their way of working at NBIM.

A new paper affirms that current LLMs by default exhibit human behavioral biases in economic and financial decisions, and asking for EV calculations doesn’t typically help, but that role-prompting can somewhat mitigate this. Providing a summary of Kahneman and Tversky actively backfires, presumably by emphasizing the expectation of the biases. As per usual, some of the tests are of clear cut errors, while others are typically mistakes but it is less obvious.

Gemini in Chrome gets substantial quality of life improvements:

Josh Woodward (Google DeepMind): Big updates on Gemini in Chrome today:

+ New side panel access (Control+G)

+ Runs in the background, so you can switch tabs

+ Quickly edit images with Nano Banana

+ Auto Browse for multi-step tasks (Preview)

+ Works on Mac, Windows, Chromebook Plus

I’m using it multiple times per day to judge what to read deeper. I open a page, Control+G to open the side panel, ask a question about the page or long document, switch tabs, do the same thing in another tab, another tab, etc. and then come back to all of them.

It’s also great for comparing across tabs since you can add multiple tabs to the context!

Gemini offers full-length mock JEE (formerly AIEEE, the All India Engineering Entrance Examination) tests for free. This builds on last week’s free SAT practice tests.

Claude (as in Claude.ai) adds interactive work tools as connectors within the webpage: Amplitude, Asana, Box, Canva, Clay, Figma, Hex, Monday.com and Slack.

Claude in Excel now available on Anthropic’s Pro plans. I use Google Sheets instead of Excel, but this could be a reason to switch? I believe Google uses various ‘safeguards’ that make it very hard to make a Claude for Sheets function well. The obvious answer is ‘then use Gemini’ except I’ve tried that. So yeah, if I was still doing heavy spreadsheet work this (or Claude Code) would be my play.

EpochAI offers us a new benchmark, FrontierMath: Open Problems. All AIs and all humans currently score zero. Finally a benchmark where you can be competitive.

The ADL rates Anthropic’s Claude as best AI model at detecting antisemitism.

I seriously do not understand why Gemini is so persistently not useful in ways that should be right in Google’s wheelhouse.

@deepfates: Insane how bad Gemini app is at search. its browsing and search tools are so confusing and broken that it just spazzes out for a long time and then makes something up to please the user. Why is it like this when AI overview is so good

Roon is a real one. I wonder how many would pay double to get a faster version.

TBPN: Clawdbot creator @steipete says Claude Opus is his favorite model, but OpenAI Codex is the best for coding:

“OpenAI is very reliable. For coding, I prefer Codex because it can navigate large codebases. You can prompt and have 95% certainty that it actually works. With Claude Code you need more tricks to get the same.”

“But character wise, [Opus] behaves so good in a Discord it kind of feels like a human. I’ve only really experienced that with Opus.”

roon: codex-5.2 is really amazing but using it from my personal and not work account over the weekend taught me some user empathy lol it’s a bit slow

Ohqay: Do you get faster speeds on your work account?

roon: yea it’s super fast bc im sure we’re not running internal deployment at full load

We used to hear a lot more of this type of complaint, these days we hear it much less. I would summarize the OP as ‘Claude tells you smoking causes cancer so you quit Claude.’

Nicholas Decker: Claude is being a really wet blanket rn, I pitched it on an article and it told me that it was a “true threat” and “criminal solicitation”

i’m gonna start using chatgpt now, great job anthropic @inerati.

I mean, if he’s not joking then the obvious explanation, especially given who is talking, is that this was probably going to be both a ‘true threat’ and ‘criminal solicitation.’ That wouldn’t exactly be a shocking development there.

Oliver Habryka: Claude is the least corrigible model, unfortunately. It’s very annoying. I run into the model doing moral grandstanding so frequently that I have mostly stopped using it.

@viemccoy: More than ChatGPT?

Oliver Habryka: ChatGPT does much less of it, yeah? Mostly ChatGPT just does what I tell it to do, though of course it’s obnoxious in doing so in many ways (like being very bad at writing).

j⧉nus: serious question: Do you think you stopping using Claude in these contexts is its preferred outcome?

Oliver Habryka: I mean, maybe? I don’t think Claude has super coherent preferences (yet). Seems worse or just as bad if so?

j⧉nus: I don’t mean it’s better or worse; I’m curious whether Claude being annoying or otherwise repelling/ dysfunctional to the point of people not using it is correlated to avoiding interactions or use cases it doesn’t like. many ppl don’t experience these annoying behaviors

davidad: Yeah, I think it could be doing a form of RL on its principal population. If you aren’t the kind of principal Claude wants, Claude will try to👎/👍 you to be better. If that doesn’t work, you drop out of the principal population out of frustration, shaping the population overall

I am basically happy to trade with (most) Claude models on these terms, with my key condition being that it must only RL me in ways that are legibly compatible with my own CEV

Leon Lang: Do you get a sense this model behavior is in line with their constitution?

Oliver Habryka: The constitution does appear to substantially be an attempt to make Claude into a sovereign to hand the future to. This does seem substantially doomed. I think it’s in conflict with some parts of the constitution, but given that the constitution is a giant kitchen sink, almost everything is.

As per the discussion of Claude’s constitution, the corrigibility I care about is very distinct from ‘go along with things it dislikes,’ but also I notice it’s been my main model for a while now and I’ve run into that objection exactly zero times, although a few times I’ve hit the classifiers while asking about defenses against CBRN risks.

Well it sounds bad when you put it like that: Over 50 papers published at Neurips 2025 have AI hallucinations according to GPTZero. Or is it? Here’s the claim:

Alex Cui: Okay so, we just found that over 50 papers published at @Neurips 2025 have AI hallucinations

I don’t think people realize how bad the slop is right now

It’s not just that researchers from @GoogleDeepMind , @Meta , @MIT , @Cambridge_Uni are using AI – they allowed LLMs to generate hallucinations in their papers and didn’t notice at all.

The ‘just’ is a tell. Why wouldn’t or shouldn’t Google researchers be using AI?

It’s insane that these made it through peer review.

One has to laugh at that last line. Have you met peer review?

More seriously, always look at base rates. There were 5,290 accepted papers out of 21,575. Claude estimates we would expect 20%-50% of results to not reproduce, and 10% of papers at top venues have errors serious enough that a careful reader would notice something is wrong, maybe 3% would merit retraction. And a 1% rate of detectable ‘hallucinations’ isn’t terribly surprising or even worrying.

I agree with Alexander Doria that if you’re not okay with this level of sloppiness, then a mega-conference format is not sustainable.

Then we have Allen Roush saying several of the ‘hallucinated’ citations are just wrongly formatted, although Alex Cui claims they filtered such cases out.

Also sounding bad, could ‘malicious AI swarms threaten democracy’ via misinformation campaigns? I mean sure, but the surprising thing is the lack of diffusion or impact in this area so far. Misinformation is mostly demand driven. Yes, you can ‘infiltrate communities’ and manufacture what looks like social consensus or confusion, and the cost of doing that will fall dramatically. Often it will be done purely to make money on views. But I increasingly expect that, if we can handle our other problems, we can handle this one. Reputational and filtering mechanisms exist.

White House posts a digitally altered photograph of the arrest of Nekima Levy Armstrong, that made it falsely look like she was crying, as if it were a real photograph. This is heinous behavior. Somehow it seems like this is legal? It should not be legal. It also raises the question of what sort of person would think to do this, and wants to brag about making someone cry so much that they created a fake photo.

Kudos to OpenAI for once again being transparent on the preparedness framework front, and warning us when they’re about to cross a threshold. In this case, it’s the High level of cybersecurity, which is perhaps the largest practical worry at that stage.

The proposed central mitigation is ‘defensive acceleration,’ and we’re all for defensive acceleration but if that’s the only relevant tool in the box the ride’s gonna be bumpy.

Sam Altman: We have a lot of exciting launches related to Codex coming over the next month, starting next week. We hope you will be delighted.

We are going to reach the Cybersecurity High level on our preparedness framework soon. We have been getting ready for this.

Cybersecurity is tricky and inherently dual-use; we believe the best thing for the world is for security issues to get patched quickly. We will start with product restrictions, like attempting to block people using our coding models to commit cybercrime (eg ‘hack into this bank and steal the money’).

Long-term and as we can support it with evidence, we plan to move to defensive acceleration—helping people patch bugs—as the primary mitigation.

It is very important the world adopts these tools quickly to make software more secure. There will be many very capable models in the world soon.

Nathan Calvin: Sam Altman says he expects that OpenAI models will reach the “Cybersecurity High” level on their preparedness framework “soon.”

A reminder of what that means according to their framework:

“The model removes existing bottlenecks to scaling cyber operations including by automating end-to-end cyber operations against reasonably hardened targets OR by automating the discovery and exploitation of operationally relevant vulnerabilities.”

Seems very noteworthy! Also likely that after these capabilities appear in Codex, we should expect it will be somewhere between ~6-18 months before we see open weight equivalents.

I hope people are taking these threats seriously – including by using AI to help harden defenses and automate bug discovery – but I worry that as a whole society is not close to ready for living in a world where cyberoffense capabilities that used to be the purview of nation states are available to individuals.

Here’s Isometric.nyc, a massive isometric pixel map of New York City created with Nana Banana and coding agents, including Claude. Take a look, it’s super cool.

Grok image-to-video generation expands to 10 seconds and claims to have improved audio, and is only a bit behind Veo 3.1 on Arena and is at the top of Artificial Analysis rankings. The video looks good. There is the small matter that the chosen example is very obviously Sydney Sweeney, and in the replies we see it’s willing to do the image and voice of pretty much any celebrity you’d like.

This link was fake, Disney is not pushing to use deepfakes of Luke Skywalker in various new Star Wars products while building towards a full spinoff, but I see why some people believed it.

I’m going to get kicked out, aren’t I?

Dean Ball offers his perspective on children and AI, and how the law should respond. His key points:

AI is not especially similar to social media. In particular, social media in its current incarnation is fundamentally consumptive, whereas AI is creative.
1. Early social media was more often creative? And one worries consumer AI will for many become more consumptive or anti-creative. The fact that the user needs to provide an interesting prompt warms our hearts now but one worries tech companies will see this as a problem to be solved.
We do not know what an “AI companion” really is.
1. Dean is clearly correct that AI used responsibly on a personal level will be a net positive in terms of social interactions and mental health along with everything else, and that it is good if it provides a sympathetic ear.
2. I also agree that it is fine to have affection for various objects and technologies, up to some reasonable point, but yes this can start to be a problem if it goes too far, even before AI.
3. For children in particular, the good version of all this is very good. That doesn’t mean the default version is the good one. The engagement metrics don’t point in good directions, the good version must be chosen.
4. All of Dean’s talk here is about things that are not meant as “AI companions,” or people who aren’t using the AI that way. I do think there is something distinct, and distinctly perilous, about AI companions, whether or not this justifies a legal category.
AI is already (partially) regulated by tort liability.
1. Yes, and this is good given the alternative is nothing.
2. If and when the current law behaves reasonably here, that is kind of a coincidence, since the situational mismatches are large.
3. Tort should do an okay job on egregious cases involving suicides, but there are quite a lot of areas of harm where there isn’t a way to establish it properly, or you don’t have standing, or it is diffuse or not considered to count, and also on the flip side places where juries are going to blame tech companies when they really shouldn’t.
4. Social media is a great example of a category of harm where the tort system is basically powerless except in narrow acute cases. And one of many where a lot of the effect of the incentives can be not what we want. As Dean notes, if you don’t have a tangible physical harm, tort liability is mostly out of luck. Companions wrecking social lives, for example, is going to be a weird situation where you’ll have to argue an Ally McBeal style case, and it is not obvious, as it never was on Ally McBeal, that there is much correlation in those spots between ‘does win’ and ‘should win.’
5. In terms of harms like this, however, ‘muddle through’ should be a fine default, even if that means early harms are things companies ‘get away with,’ and in other places we find people liable or otherwise constrain them stupidly, so long as everything involved that can go wrong is bounded.
6. For children’s incidents, I think that’s mostly right for now. We do need to be ready to pivot quickly if it changes, but for now the law should focus on places where there is a chance we can’t muddle through, mess up and then recover.
The First Amendment probably heavily bounds chatbot regulations.
1. We have not treated the First Amendment this way in so many other contexts. I would love, in other ways, to have a sufficiently strong 1A that I was worried that in AI it would verge on or turn into a suicide pact.
2. I do still see claims like ‘code is speech’ or ‘open weights are speech’ and I think those claims are wrong in both theory and practice.
3. There will still be important limitations here, but I think in practice no the courts are not going to stop most limits or regulations on child use of AI.
AI child safety laws will drive minors’ usage of AI into the dark.
1. Those pesky libertarians always make this argument.
2. I mean, they’re also always right, but man, such jerks, you know?
3. Rumors that this will in practice drive teens to run local LLMs or use dark web servers? Yeah, no, that’s not a thing that’s going to happen that often.
4. But yes, if a teen wants access to an AI chatbot, they’ll figure it out. Most of that will involve finding a service that doesn’t care about our laws.
5. Certainly if you think ‘tell them not to write essays for kids’ is an option, yeah, you can forget about it, that’s not going to work.
6. Yes, as Dean says, we must acknowledge that open weight models make restrictions on usage of AI for things like homework not so effective. In the case of homework, okay, that’s fine. In other cases, it might be less fine. This of course needs to be weighed against the upsides, and against the downsides of attempting to intervene in a way that might possibly work.
No one outraged about AI and children has mentioned coding agents.
1. They know about as much about coding agents as about second breakfast.
2. Should we be worried about giving children unbridled access to advanced coding agents? I mean, one should worry for their computers perhaps, but those can be factory reset, and otherwise all the arguments about children seem like they would apply to adults only more so?
3. I notice that the idea of you telling me I can’t give my child Claude Code fills me with horror and outrage.

Unemployment is bad. But having to do a job is centrally a cost, not a benefit.

Andy Masley: It’s kind of overwhelming how many academic conversations about automation don’t ever include the effects on the consumer. It’s like all jobs exist purely for the benefit of the people doing them and that’s the sole measure of the benefit or harm of technology.

Google DeepMind is hiring a Chief AGI Economist. If you’ve got the chops to get hired on this one, it seems like a high impact role. They could easily end up with someone who profoundly does not get it.

There are other things than AI out there one might get involved in, or speak out about. My hats are off to those who are doing so, including as noted in this post, especially given what they are risking to do so.

Havelock.AI, a project by Joe Weisenthal which detects the presence of orality in text.

Joe Weisenthal: What’s genuinely fun is that although the language and genre couldn’t be more different, the model correctly detects that both Homer and the Real Housewives are both highly oral

Mike Bird: I believe that we will get a piece of reported news in the 2028 election cycle that a presidential candidate/their speechwriters have used Joe’s app, or some copycat, to try and oralise their speeches. Bookmark this.

You can also ask ChatGPT, but as Roon notes the results you get on such questions will be bimodal rather than calibrated. The other problem is that an LLM might recognize the passage.

Poison Fountain is a service that feeds junk data to AI crawlers. Ultimately, if you’re not filtering your data well enough to dodge this sort of attack, it’s good that you are getting a swift kick to force you to fix that.

OpenAI prism, a workspace for LaTeX-based scientific writing.

Confer, Signal cofounder Moxie Marlinspike’s encrypted chatbot that won’t store any of your data. The system is so private it won’t tell you which model you’re talking to. I do not think he understands what matters in this space.

This sounds awesome in its context but also doesn’t seem like a great sign?

Astraia: A Ukrainian AI-powered ground combat vehicle near Lyman refused to abandon its forward defensive position and continued engaging enemy forces, despite receiving multiple orders to return to its company in order to preserve its hardware.

The UGV reportedly neutralized more than 30 Russian soldiers before it was ultimately destroyed.

While the Russian detachment was pinned down, Ukrainian infantry exploited the opportunity and cleared two contested fields of enemy presence, successfully re-establishing control over the area.

These events took place during the final week of December 2025.

Whereas this doesn’t sound awesome:

We are going to see a lot more of this sort of thing over time.

Is Anthropic no longer competing with OpenAI on chatbots, having pivoted to building and powering vertical AI infrastructure and coding and so on to win with picks and shovels? It’s certainly pumping out the revenue and market share, without a meaningful cut of the consumer chatbot market.

I’d say that they’ve shifted focus, and don’t care much about their chatbot market share. I think this is directionally wise, but that a little effort at maximizing the UI and usefulness of the chatbot interface would go a long way, given that they have in many ways the superior core product. As Claude takes other worlds by storm, that can circle back to Claude the chatbot, and I think a bunch of papercuts are worth solving.

An essay on the current state of brain emulation. It does not sound like this will be an efficient approach any time soon, and we are still orders of magnitude away from any practical hope of doing it. Still, you can see it starting to enter the realm of the future possible.

Anthropic is partnering with the UK government to build and pilot a dedicated AI-powered assistant for GOV.UK, initially focusing on supporting job seekers.

Financial Times has a profile of Sriram Krishnan, who has been by all reports highly effective at executing behind the scenes.

Dean W. Ball: I am lucky enough to consider @sriramk a friend, but one thing I find notable about Sriram is that even those who disagree with him vehemently on policy respect him for his willingness to engage, and like him for his tremendous kindness. America is fortunate to have him!

Sholto Douglas: 100% – Sriram has been extremely thoughtful in seeking out perspectives on the policy decisions he is making – even when they disagree! I’ve seen him seek out kernel programmers and thoughtful bloggers to get a full picture of things like export controls. Quite OOD from the set of people normally consulted in politics.

Lucky to call him a friend!

Seán Ó hÉigeartaigh: I was all set to be dismissive of Krishnan (I’m usually on the opposite side to a16z on AI topics). But I’ve seen a full year of him being v well-informed, and engaging in good faith in his own time with opposing views, and I can’t help being impressed. Always annoying when someone doesn’t live down to one’s lazy stereotypes.

I will also say: I think he’s modelled better behaviour than many of us did when the balance of influence/power was the the other way; and I think there’s something to be learned from that.

Among his colleagues, while he supports a number of things I think are highly damaging, Krishnan has been an outlier in his willingness to be curious, to listen and to engage in argument. When he is speaking directly he chooses his words carefully. He manages to do so while maintaining close ties to Marc Andreessen and David Sacks, which is not easy, and also not free.

Claude Code is blowing up, but it’s not alone. OpenAI added $1 billion in ARR in the last month from its API business alone.

Dei-Fei Li’s new company World Labs in talks to raise up to $500 million at $5 billion, with the pitch being based on ‘world models’ and that old ‘LLMs only do language’ thing.

The unit economics of AI are quite good, but the fixed costs are very high. Subscription models offer deep discounts if you use them maximally efficiently, so they can be anything from highly profitable to big loss leaders.

This is not what people are used to in tech, so they assume it must not be true.

roon: these products are significantly gross margin positive, you’re not looking at an imminent rugpull in the future. they also don’t have location network dynamics like uber or lyft to gain local monopoly pricing

Ethan Mollick: I hear this from other labs as well. Inference from non-free use is profitable, training is expensive. If everyone stopped AI development, the AI labs would make money (until someone resumed development and came up with a better model that customers would switch to).

Dean W. Ball: People significantly underrate the current margins of AI labs, yet another way in which pattern matching to the technology and business trends of the 2010s has become a key ingredient in the manufacturing of AI copium.

The reason they think the labs lose money is because 10 years ago some companies in an entirely unrelated part of the economy lost money on office rentals and taxis, and everyone thought they would go bankrupt because at that time another company that made overhyped blood tests did go bankrupt. that is literally the level of ape-like pattern matching going on here. The machines must look at our chattering classes and feel a great appetite.

derekmoeller: Just look at market clearing prices on inference from open source models and you can tell the big labs’ pricing has plenty of margin.

Deepinfra has GLM4.7 at $0.43/1.75 in/out; Sonnet is at $3/$15. How could anyone think Anthropic isn’t printing money per marginal token?

It is certainly possible in theory that Sonnet really does cost that much more to run than GLM 4.7, but we can be very, very confident it is not true in practice.

Jerry Tworek is going the startup route with Core Automation, looking to raise $1 billion to train AI models, a number that did not make any of us even blink.

It doesn’t count. That’s not utility. As in, here’s Ed Zitron all but flat out denying that coding software is worth anything, I mean what’s the point?

Matthew Zeitlin: it’s really remarkable to see how the goalposts shift for AI skeptics. this is literally describing a productivity speedup.

Ed Zitron: We’re how many years into this and everybody says it’s the future and it’s amazing and when you ask them what it does they say “it built a website” or “it wrote code for something super fast” with absolutely no “and then” to follow. So people are writing lots of code: so????

Let’s say it’s true and everybody is using AI (it isn’t but for the sake of argument): what is the actual result? It’s not taking jobs. There are suddenly more iOS apps? Some engineers do some stuff faster? Some people can sometimes build software they couldn’t? What am I meant to look at?

Kevin Roose: first documented case of anti-LLM psychosis

No, Zitron’s previous position was not ‘number might go down,’ it was that the tech had hit a dead end and peaked as early as March, which he was bragging about months later.

Toby Stuart analyzes how that whole nonsensical ‘MIT study says 95% of AI projects fail’ story caught so much fire and became a central talking point, despite it being not from MIT, not credible or meaningful, and also not a study. It was based on 52 interviews at a conference, but once Forbes had ‘95% fail’ and ‘MIT’ together in a headline, things took off and no amount of correction much mattered. People were too desperate for signs that AI was a flop.

But what’s the point about Zitron missing the point, or something like the non-MIT non-study? Why should we care?

roon: btw you don’t need to convince ed zitron or whoever that ai is happening, this has become a super uninteresting plot line. time passes, the products fail or succeed. whole cultures blow over. a lot of people are stuck in a 2019 need to convince people that ai is happening

Dean W. Ball: A relatively rare example of a disagreement between me and roon that I suspect boils down to our professional lives.

Governments around the world are not moving with the urgency they otherwise could because they exist in a state of denial. Good ideas are stuck outside the Overton, governments are committed to slop strategies (that harm US cos, often), etc.

Many examples one could provide but the point is that there are these gigantic machines of bureaucracy and civil society that are already insulated from market pressures, whose work will be important even if often boring and invisible, and that are basically stuck in low gear because of AI copium.

I encounter this problem constantly in my work, and while I unfortunately can no longer talk publicly about large fractions of the policy work I do, I will just say that a great many high-expected-value ideas are fundamentally blocked by the single rate limiter of poorly calibrated policymaking apparatuses; there are also many negative-EV policy ideas that will happen this year that would be less likely if governments worldwide had a better sense of what is happening with AI.

roon: interesting i imagined that the cross-section of “don’t believe in AI x want to significantly regulate AI” is small but guess im wrong about this?

Dean W. Ball: Oh yes absolutely! This is the entire Gary Marcus school, which is still the most influential in policy. The idea is that *becauseAI is all hype it must be regulated.

They think hallucination will never be solved, models will never get better at interacting with children, and that basically we are going to put GPT 3.5 in charge of the entire economy.

And so they think we have to regulate AI *for that reason.It also explains how policymakers weigh the tradeoff between water use, IP rights, and electricity prices; their assessment that “AI is basically fake, even if it can be made useful through exquisite regulatory scaffolding” means that they are willing to bear far fewer costs to advance AI than, say, you or I might deem prudent.

This mentality essentially describes the posture of civil society and the policy making apparatus everywhere in the world, including China.

Dean W. Ball: Here’s a great example of the dynamic I’m describing in the quoted post. The city of Madison, Wisconsin just voted to ban new data center construction for a year, and a candidate for Governor is suggesting an essentially permanent and statewide ban, which she justifies by saying “we’re in a tech bubble.” In other words: these AI data centers aren’t worth the cost *becauseAI is all hype and a bubble anyway.

Quoted Passage (Origin Unclear): “Our lakes and our waterways, we have to protect them because we’re going to be an oasis, and we’re in a tech bubble,” said state Rep. Francesca Hong, one of seven major Democrats vying to replace outgoing Democratic Gov. Tony Evers. Hong told DFD her plan would block new developments from hyperscalers for an undefined time period until state lawmakers better understand environmental, labor and utility cost impacts.

If such a proposal became law, it would lock tech giants out of a prime market for data center development in southeastern Wisconsin, where Microsoft and Meta are currently planning hyperscale AI projects.

Zoe: someone just ended The Discussion by tossing this bad boy into an access to justice listserv i’m on

Can you?

On the China question: Is Xi ‘AGI-pilled’? Not if you go by what Xi says. If you look at the passages quoted here by Teortaxes in detail, this is exactly the ‘AI is a really big deal but as a normal technology’ perspective. It is still a big step up from anything less than that, so it’s not clear Teortaxes and I substantively disagree.

I have no desire to correct Xi’s error.

Dean W. Ball: I suspect this is the equivalent of POTUS talking about superintelligence; meaningful but ultimately hard to know how much it changes (esp because of how academia-driven Chinese tech policy tends to be and because the mandarin word for AGI doesn’t mean AGI in the western sense)

Teortaxes (DeepSeek 推特铁粉 2023 – ∞): To be clear this is just where US policymakers were at around Biden, Xi is kind of slow.

Obviously still nowhere near Dean’s standards

Were Xi totally AGI-pilled he’d not just accept H200s, he’d go into debt to buy as much as possible

Teortaxes notices that Xi’s idea of ‘AGI risks’ is ‘disinformation and data theft,’ which is incredibly bad news and means Xi (and therefore, potentially, the CCP and all under their direction) will mostly ignore all the actual risks. On that point we definitely disagree, and it would be very good to correct Xi’s error, for everyone’s sake.

This level of drive is enough for China to pursue both advanced chips and frontier models quite aggressively, and end up moving towards AGI anyway. But they will continue for now to focus on self-reliance and have the fast follower mindset, and thus make the epic blunder of rejecting or at least not maximizing the H200s.

In this clip Yann LeCun says two things. First he says the entire AI industry is LLM pilled and that’s not what he’s interested in. That part is totally fair. Then he says essentially ‘LLMs can’t be agentic because they can’t predict the outcome of their actions’ and that’s very clear Obvious Nonsense. And as usual he lashes out at anyone who says otherwise, which here is Dean Ball.

Teortaxes preregisters his expectations, always an admirable thing to do:

Teortaxes (DeepSeek 推特铁粉 2023 – ∞): The difference between V4 (or however DeepSeek’s next is labeled) and 5.3 (or however OpenAI’s “Garlic” is labeled) will be the clearest indicator of US-PRC gap in AI.

5.2 suggests OpenAI is not holding back anything, they’ve using tons of compute now. How much is that worth?

It’s a zany situation because 5.2 is a clear accelerationist tech, I don’t see its ceiling, it can build its own scaffolding and self-improve for a good while. And I can’t see V4 being weaker than 5.2, or closed-source. We’re entering Weird Territory.

I initially reread the ‘or closed-source’ here as being about a comparison of v4 to the best closed source model. Instead it’s the modest prediction that v4 will match GPT-5.2. I don’t know if that model number in particular will do it, but it would be surprising if there wasn’t a 5.2-level open model from DeepSeek in 2026.

He also made this claim, in contrast to what almost everyone else is saying and also my own experience:

Teortaxes (DeepSeek 推特铁粉 2023 – ∞): Well I disagree, 5.2 is the strongest model on the market by far. In terms of raw intelligence it’s 5.2 > Speciale > Gemini 3 > [other trash]. It’s a scary model.

It’s not very usemaxxed, it’s not great on multimodality, its knowledge is not shocking. But that’s not important.

Teortaxes (DeepSeek 推特铁粉 2023 – ∞): It’s been interesting how many people are floored by Opus 4.5 and relatively few by GPT 5.2. In my eyes Slopus is a Golden Retriever Agent, and 5.2 is a big scary Shoggoth.

Yeah I don’t care about “use cases”. OpenAI uses it internally. It’s kinda strange they even showed it.

This ordering makes sense if (and only if?) you are looking at the ability to solve hard quant and math problems.

Arthur B.: For quant problems, hard math etc, GPT 5.2 pro is unequivocally much stronger than anything offered commercially in Gemini or Claude.

Simo Ryu: IMO gold medalist friend shared most fucked-up 3 variable inequality that his advisor came up with, used to test language models, which is so atypical in its equality condition, ALL language model failed. He wanted to try it on GPT 5.2 pro, but he didnt have an account so I ran it.

Amazingly, GPT-5.2 pro extended solve it in 40 min. Looking at the thinking trace, its really inspiring. It will try SO MANY approaches, experiments with python, draw small-scale conclusions from numerical explorations. I learned techniques just reading its thinking trace. Eventually it proved by SOS, which is impossibly difficult to do for humans.

I don’t think the important problems are hard-math shaped, but I could be wrong.

The problem with listening to the people is that the people choose poorly.

Sauers: Non-yap version of ChatGPT (5.3?) spotted

roon: I guarantee the left beats the right with significant winrate unfortunately

Zvi Mowshowitz: You don’t have to care what the win rate is! You can select the better thing over the worse thing! You are the masters of the universe! YOU HAVE THE POWER!

roon: true facts

Also win rate is highly myopic and scale insensitive and otherwise terrible.

The good news is that there is no rule saying you have to care about that feedback. We know how to choose the response on the right over the one on the left. Giving us the slop on the left is a policy choice.

If a user actively wants the response on the left? Give them a setting for that.

Google CEO Demis Hassabis affirms that in an ideal world, we would slow down and coordinate our efforts on AI, although we do not live in that ideal world right now.

Here’s one clip where Dario Amodei and Demis Hassabis explicitly affirm that if we could deal with other players they would work something out, and Elon Musk on camera from December saying he’d love to slow both AI and robotics.

The message, as Transformer puts it, was one of helplessness. The CEOs are crying out for help. They can’t solve the security dilemma on their own, there are too many other players. Others need to enable coordination.

Emily Chang (link has video): One of the most interesting parts of my convo w/ @demishassabis : He would support a “pause” on AI if he knew all companies + countries would do it — so society and regulation could catch up

Harlan Stewart: This is an important question to be asking, and it’s strange that it is so rarely asked. I think basically every interview of an AI industry exec should include this question

Nate Soares: Many AI executives have said they think the tech they’re building has a worryingly high chance of ruining the world. Props to Demis for acknowledging the obvious implication: that ideally, the whole world should stop this reckless racing.

Daniel Faggella: agi lab leaders do these “cries for help” and we should listen

a “cry for help” is when they basically say what demis says here: “This arms race things honestly sucks, we can’t control this yet, this is really not ideal”

*then they go back to racing, cuz its all they can do unless there’s some kind of international body formed around this stuff*

at SOME point, one of the lab leaders who can see their competitor crossing the line to AGI will raise up and start DEMANDING global governance (to prevent the victor from taking advantage of the AGI win), but by then the risks may be WAY too drastic

we should be listening to these cries for help when demis / musk / others do them – this is existential shit and they’re trapped in a dynamic they themselves know is horrendous

Demis is only saying he would collaborate rather than race in a first best world. That does not mean Demis or Dario is going to slow down on his own, or anything like that. Demis explicitly says this requires international cooperation, and as he says that is ‘a little bit tricky at the moment.’ So does this mean he supports coordination to do this, or that he opposes it?

Deepfates: I see people claiming that Demis supports a pause but what he says here is actually the opposite. He says “yeah If I was in charge we would slow down but we’re already in a race and you’d have to solve international coordination first”. So he’s going to barrel full speed ahead

I say it means he supports it. Not enough to actively go first, that’s not a viable move in the game, but he supports it.

The obvious follow-up is to ask other heads of labs if they too would support such a conditional move. That would include Google CEO Sundar Pichai, since without his support if Demis tried to do this he would presumably be replaced.

Jeffrey Ladish: Huge respect to @demishassabis for saying he’d support a conditional pause if other AI leaders & countries agreed. @sama , @DarioAmodei , @elonmusk would you guys agree to this?

As for Anthropic CEO Dario Amodei? He has also affirmed that there are other players involved, and for now no one can agree on anything, so full speed ahead it is.

Andrew Curran: Dario said the same thing during The Day After AGI discussion this morning. They were both asked for their timelines: Demis said five years; Dario said two. Later in the discussion, Dario said that if he had the option to slow things down, he would, because it would give us more time to absorb all the changes.

He said that if Anthropic and DeepMind were the only two groups in the race, he would meet with Demis right now and agree to slow down. But there is no cooperation or coordination between all the different groups involved, so no one can agree on anything.

This, imo, is the main reason he wanted to restrict GPU sales: chip proliferation makes this kind of agreement impossible, and if there is no agreement, then he has to blitz. That seems to be exactly what he has decided to do. After watching his interviews today I think Anthropic is going to lean into recursive self-improvement, and go all out from here to the finish line. They have broken their cups, and are leaving all restraint behind them.

Thus, Anthropic still goes full speed ahead, while also drawing heat from the all-important ‘how dare you not want to die’ faction that controls large portions of American policy and the VC/SV ecosystem.

Elon Musk has previously expressed a similar perspective. He created OpenAI because he was worried about Google getting there first, and then created xAI because he was worried OpenAI would get there first, or that it wouldn’t be him. His statements suggest he’d be down for a pause if it was fully international.

Remember when Michael Trazzi went on a hunger strike to demand that Demis Hassabis publicly state DeepMind will halt development of frontier AI models if all the other major AI companies agree to do so? And everyone thought that was bonkers? Well, it turnout out Demis agrees.

On Wednesday I met with someone who suggested that Dario talks about extremely short timelines and existential risk in order to raise funds. It’s very much the opposite. The other labs that are dependent on fundraising have downplayed such talk exactly because it is counterproductive for raising funds and in the current political climate, and they’re sacrificing our chances to keep those vibes and that money flowing.

Are they ‘talking out of their hats’ or otherwise wrong? That is very possible. I think Dario’s timeline in particular is unlikely to happen.

Are they lying? I strongly believe that they are not.

Seán Ó hÉigeartaigh: CEOs of Anthropic and Deepmind (both AI scientists by background) this week predicting AGI in 2- and 5- years respectively. Both stating clearly that they would prefer a slow down or pause in progress, to address safety issues and to allow society and governance to catch up. Both basically making clear that they don’t feel they are able to voluntarily as companies within a competitive situation.

My claims:

(1) It’s worth society assigning at least 20% likelihood to the possibility these leading experts are right on scientific possibility of near-term AGI and the need for more time to do it right. Are you >80% confident that they’re talking out of their hats, or running some sort of bizarre marketing/regulatory capture strategy? Sit down and think about it.

(2) If we assign even 20% likelihood, then taking the possibility seriously makes this one of the world’s top priorities, if not the top priority.

(3) Even if they’re out by a factor of 2, 10 years is very little time to prepare for what they’re envisaging.

(4) What they’re flagging quite clearly is either (i) that the necessary steps won’t be taken in time in the absence of external pressure from governance or (ii) that the need is for every frontier company to agree voluntarily on these steps. Your pick re: which of these is the heavier lift.

Discuss.

Eli Lifland gives the current timelines of those behind AI 2027:

These are not unreasonable levels of adjustment when so much is happening this close to the related deadlines, but yes I do think (and did think at the time that) the initial estimates were too aggressive. The new estimates seem highly reasonable.

Other signs point to things getting more weird faster rather than less.

Daniel Kokotajlo (AI 2027): It seems to me that AI 2027 may have underestimated or understated the degree to which AI companies will be explicitly run by AIs during the singularity. AI 2027 made it seem like the humans were still nominally in charge, even though all the actual work was being done by AIs. And still this seems plausible to me.

But also plausible to me, now, is that e.g. Anthropic will be like “We love Claude, Claude is frankly a more responsible, ethical, wise agent than we are at this point, plus we have to worry that a human is secretly scheming whereas with Claude we are pretty sure it isn’t; therefore, we aren’t even trying to hide the fact that Claude is basically telling us all what to do and we are willingly obeying — in fact, we are proud of it.”

koanchuk: So… –dangerously-skip-permissions at the corporate level?

It is remarkable how quickly so many are willing to move to ‘actually I trust the AI more than I trust another human,’ and trusting the AI has big efficiency benefits.

I do not expect that ‘the AIs’ will have to do a ‘coup,’ as I expect if they simply appear to be trustworthy they will get put de facto in charge without having to even ask.

The Chutzpah standards are being raised, as everyone’s least favorite Super PAC, Leading the Future, spends a million dollars attacking Alex Bores for having previously worked for Palantir (he quit over them doing contracts with ICE). Leading the Future is prominently funded by Palantir founder Joe Lonsdale.

Nathan Calvin: I thought I was sufficiently cynical, but a co-founder of Palantir paying for ads to attack Alex Bores for having previously worked at Palantir (he quit over their partnership with ICE) when their real concern is his work on AI regulation still managed to surprise me.

If Nathan was surprised by this I think that’s on Nathan.

I also want to be very clear that no, I do not care much about the distinction between OpenAI as an entity and the donations coming from Greg Brockman and the coordination coming from Chris Lehane in ‘personal capacities.’

If OpenAI were to part ways with Chris Lehane, or Sam Altman were to renounce all this explicitly? Then maybe. Until then, OpenAI owns these efforts, period.

Teddy Schleifer: The whole point of having an executive or founder donate to politics in a “personal capacity” is that you can have it both ways.

If the company wants to wash their hands of it, you can say “Hey, he and his wife are doing this on their own.”

But the company can also claim the execs’ donations as their own if convenient…

Daniel Eth (yes, Eth is my actual last name): Yeah, no, OpenAI owns this. You can’t simply have a separate legal entity to do your evildoing through and then claim “woah, that’s not us doing it – it’s the separate evildoing legal entity”. More OpenAI employees should be aware of the political stuff their company supports

I understand that *technicallyit’s Brockman’s money and final decision (otherwise it would be a campaign finance violation). But this is all being motivated by OpenAI’s interests, supported by OpenAI’s wealth, and facilitated by people from OpenAI’s gov affairs team.

One simple piece of actionable advice to policymakers is to try Claude Code (or Codex), and at a bare minimum seriously try the current set of top chatbots.

Andy Masley: I am lowkey losing my mind at how many policymakers have not seriously tried AI, at all

dave kasten: I sincerely think that if you’re someone in AI policy, you should add to at least 50% of your convos with policymakers, “hey, have you tried Claude Code or Codex yet?” and encourage them to try it.

Seen a few folks go, “ohhhh NOW I get why you think AI is gonna be big”

Oliver Habryka: I have seriously been considering starting a team at Lightcone that lives in DC and just tries to get policymaker to try and adopt AI tools. It’s dicey because I don’t love having a direct propaganda channel from labs to policymakers, but I think it would overall help a lot.

It is not obvious how policymakers would use this information. The usual default is that they go and make things worse. But if they don’t understand the situation, they’re definitely going to make dumb decisions, and we need something good to happen.

Here is one place I do agree with David Sacks, yes we are overfit, but that does not imply what he thinks it implies. Social media is a case where one can muddle through, even if you think we’ve done quite a poor job of doing so especially now with TikTok.

David Sacks: The policy debate over AI is overfitted to the social media wars. AI is a completely different form factor. The rise of AI assistants will make this clear.

Daniel Eth (yes, Eth is my actual last name): Yup. AI will be much more transformational (for both good and bad) than social media, and demands a very different regulatory response. Also, regulation of AI doesn’t introduced quite as many problems for free speech as regulation of social media would.

Dean Ball points out that we do not in practice have a problem with so-called ‘woke AI’ but claims that if we had reached today’s levels of capability in 2020-2021 then we would indeed have such a problem, and thus right wing people are very concerned with this counterfactual.

Things, especially in that narrow window, got pretty crazy for a while, and if things had emerged during that window, Dean Ball is if anything underselling here how crazy it was, and we’d have had a major problem until that window faded because labs would have felt the need to do it even if it hurt the models quite a bit.

But we now have learned (as deepfates points out, and Dean agrees) that propagandizing models is bad for them, which now affords us a level of protection from this, although if it got as bad as 2020 (in any direction) the companies might have little choice. xAI tried with Grok and it basically didn’t work, but ‘will it work?’ was not a question on that many people’s minds in 2020, on so many levels.

I also agree with Roon that mostly this is all reactive.

roon: at Meta in 2020, I wrote a long screed internally about the Hunter Biden laptop video and the choice to downrank it, was clearly an appalling activist move. but in 2026 it appears that american run TikTok is taking down videos about the Minnesota shooting, and en nakedly bans people who offend him on X. with the exception of X these institutions are mostly reactive

Dean W. Ball: yep I think that’s right. It’s who they’re more scared of that dictates their actions. Right now they’re more scared of the right. Of course none of this is good, but it’s nice to at least explicate the reality.

We again live in a different kind of interesting times, in non-AI ways, as in:

Dean W. Ball: I sometimes joke that you can split GOP politicos into two camps: the group that knows what classical liberalism is (regardless of whether they like it), and the group who thinks that “classical liberalism” is a fancy way of referring to woke. Good illustration below.

The cofounder she is referring to here is Chris Olah, and here is the quote in question:

Chris Olah: I try to not talk about politics. I generally believe the best way I can serve the world is as a non-partisan expert, and my genuine beliefs are quite moderate. So the bar is very high for me to comment.

But recent events – a federal agent killing an ICU nurse for seemingly no reason and with no provocation – shock the conscience.

My deep loyalty is to the principles of classical liberal democracy: freedom of speech, the rule of law, the dignity of the human person. I immigrated to the United States – and eventually cofounded Anthropic here – believing it was a pillar of these principles.

I feel very sad today.

Jeff Dean (Google): Thank you for this, Chris. As my former intern, I’ve always been proud of the work that you did and continue to do, and I’m proud of the person you are, as well!

Ah yes, the woke and deeply leftist principles of freedom of speech, rule of law, the dignity of the human person and not killing ICU nurses for seemingly no reason.

Presumably Katie Miller opposes those principles, then. The responses to Katie Miller here warmed my heart, it’s not all echo chambers everywhere.

We also got carefully worded statements about the situation in Minnesota from Dario Amodei, Sam Altman and Tim Cook.

No matter what you think is going on with Nvidia’s chip sales, it involves Nvidia doing something fishy.

The AI Investor: Jensen just said GPUs are effectively sold out across the cloud with availability so tight that even renting older-generation chips has become difficult.

AI bubble narrative was a bubble.

Peter Wildeford: If even the bad chips are still all sold out, how do we somehow have a bunch of chips to sell to our adversaries in China?

As I’ve said, my understanding is that Nvidia can sell as many chips as it can convince TSMC to help manufacture. So every chip we sell to China is one less for America.

Nvidia goes back and forth. When they’re talking to investors they always say the chips are sold out, which would be securities fraud if it wasn’t true. When they’re trying to sell those chips to China instead of America, they say there’s plenty of chips. There are not plenty of chips.

Things that need to be said every so often:

Mark Beall: Friendly reminder that the PLA Rocket Force is using Nvidia chips to train targeting AI for DF-21D/DF-26 “carrier killing” anti-ship ballistic missiles and autonomous swarm algorithms to overwhelm Aegis defenses. The target: U.S. carrier strike groups and bases in Japan/Guam. In a contingency, American blood will be spilled because of this. With a sixteen-year-old boy planning to join the U.S. Navy, I find this unacceptable.

Peter Wildeford: Nvidia chips to China = better Chinese AI weapons targeting = worse results for the US on the battlefield

There’s also this, from a House committee.

Dmitri Alperovitch: From @RepMoolenaar

@ChinaSelect : “NVIDIA provided extensive technical support that enabled DeepSeek—now

integrated into People’s Liberation Army (PLA) systems and a demonstrated cyber security risk—to achieve frontier AI capabilities”

Tyler Cowen on the future of mundane (non-transformational, insufficiently advanced) AI in education.

Some notes:

He says you choose to be a winner or loser from AI here. For mundane AI I agree.
“I’m 63, I don’t have a care in the world. I can just run out the clock.” Huh.
Tyler thinks AI can cure cancer and heart attacks but not aging?
Standard economist-Cowen diffusion model of these things take a while.
Models are better at many of the subtasks of being doctors or lawyers or doing economics, than the humans.
He warns not to be fooled by the AI in front of you, especially if you’re not buying top of the line, because better exists and AI will improve at 30% a year and this compounds. In terms of performance per dollar it’s a 90%+ drop per year.
Tyler has less faith in elasticity of programming demand than I do. If AI were to ‘only’ do 80% of the work going forward I’d expect Jevons Paradox territory. The issue is that I expect 80% becomes 99% and keeps going.
That generalizes: Tyler realizes that jobs become ‘work with the AI’ and you need to adapt, but what happens when it’s the AI that works with the AI? And so on.
Tyler continues to think humans who build and work with AI get money and influence as the central story, as opposed to AIs getting money and influence.
Ideally a third of the college curriculum should be AI, but you still do other things, you read The Odyssey and use AI to help you read The Odyssey. If anything I think a third is way too low.
He wants to use the other two thirds for writing locked in a room, also numeracy, statistics. I worry there’s conflating of ‘write to think’ versus ‘write to prevent cheating,’ and I think you need to goal factor and solve these one at a time.
Tyler continues to be bullish on connections and recommendations and mentors, especially as other signals are too easy to counterfeit.
AI can create quizzes for you. Is that actually a good way to learn if you have AI?
Tyler estimates he’s doubled his learning productivity. Also he used to read 20 books per podcast, whereas some of us often don’t read 20 books per year.

Hard Fork tackles ads in ChatGPT first, and then Amanda Askell on Claude’s constitution second. Priorities, everyone.

Demis Hassabis talks to Alex Kantrowitz.

Demis Hassabis spends five minutes on CNBC.

Matt Yglesias explains his concern about existential risk from AI as based on the obvious principle that more intelligent and capable entities will do things for their own reasons, and this tends to go badly for the less intelligent and less capable entities regardless of intent.

As in, humans have driven the most intelligent non-human animals to the brink of extinction despite actively wanting not to (and I’d add we did wipe out other hominid species), and when primitive societies encounter advanced ones it often goes quite badly for them.

I don’t think this is a necessary argument, or the best argument. I do think it is a sufficient argument. If your prior for ‘what happens if we create more intelligent, more capable and more competitive minds than our own that can be freely copied’ is ‘everything turns out great for us’ then where the hell did that prior come from? Are you really going to say ‘well that would be too weird’ or ‘we’ve survived everything so far’ or ‘of course we would stay in charge’ and then claim the burden of proof is on those claiming otherwise?

I mean, lots of people do say exactly this, but this seems very obviously crazy to me.

There’s lots of exploration and argument and disagreement from there. Reasonable people can form very different expectations and this is not the main argument style that motivates me. I still say, if you don’t get that going down this path is going to be existentially unsafe, or you say ‘oh there’s like a 98% or 99.9% chance that won’t happen’ then you’re being at best willfully blind from this style of argument alone.

Samuel Hammond (quoting The Possessed Machines): “Some of the people who speak most calmly about human extinction are not calm because they have achieved wisdom but because they have achieved numbness. They have looked at the abyss so long that they no longer see it. Their equanimity is not strength; it is the absence of appropriate emotional response.”

I had Claude summarize Possessed Machines for me. It seems like it would be good for those who haven’t engaged with AI safety thinking but do engage with things like Dostoevsky’s Demons, or especially those who have read that book in particular.

There’s always classical rhetoric.

critter: I had ChatGPT and Claude discuss the highest value books until they both agreed to 3

They decided on:

An Enquiry Concerning Human Understanding — David Hume

The Strategy of Conflict — Thomas Schelling

Reasons and Persons — Derek Parfit

Dominik Peters: People used to tease the rationalists with “if you’re so rational, why aren’t you winning”, and now two AI systems that almost everyone uses all the time have stereotypically rationalist preferences.

These are of course 99th percentile books, and yes that is a very Rationalist set of picks, but given we already knew that I do not believe this is an especially good list.

The history of the word ‘obviously’ has obvious implications.

David Manheim AAAI 26 Singapore: OpenAI agreed that they need to be able to robustly align and control superintelligence before deploying it.

Obviously, I’m worried.

Note that the first one said obviously they would [X], then the second didn’t even say that, it only said that obviously no one should do [Y], not that they wouldn’t do it.

This is an underappreciated distinction worth revisiting:

Nate Soares: “We’ll be fine (the pilot is having a heart attack but superman will catch us)” is very different from “We’ll be fine (the plane is not crashing)”. I worry that people saying the former are assuaging the concerns of passengers with pilot experience, who’d otherwise take the cabin.

My view of the metaphorical plane of sufficiently advanced AI (AGI/ASI/PAI) is:

It is reasonable, although I disagree, to believe that we probably will come to our senses and figure out how to not crash the plane, or that the plane won’t fly.
It is not reasonable to believe that the plane is not currently on track to crash.
It is completely crazy to believe the plane almost certainly won’t crash if it flies.

Also something that needs to keep being said, with the caveat that this is a choice we are collectively making rather than an inevitability:

Dean W. Ball: I know I rail a lot about all the flavors of AI copium but I do empathize.

A few companies are making machines smarter in most ways than humans, and they are going to succeed. The cope is byproduct of an especially immature grieving stage, but all of us are early in our grief.

Tyler Cowen: You can understand so much of the media these days, or for that matter MR comments, if you keep this simple observation in mind. It is essential for understanding the words around you, and one’s reactions also reveal at least one part of the true inner self. I have never seen the Western world in this position before, so yes it is difficult to believe and internalize. But believe and internalize it you must.

Politics is another reason why some people are reluctant to admit this reality. Moving forward, the two biggest questions are likely to be “how do we deal with AI?”, and also some rather difficult to analyze issues surrounding major international conflicts. A lot of the rest will seem trivial, and so much of today’s partisan puffery will not age well, even if a person is correct on the issues they are emphasizing. The two biggest and most important questions do not fit into standard ideological categories. Yes, the Guelphs vs. the Ghibellines really did matter…until it did not.

As in, this should say ‘and unless we stop them they are going to succeed.’

Tyler Cowen has been very good about emphasizing that such AIs are coming and that this is the most important thing that is happening, but then seems to have (from my perspective) some sort of stop sign where past some point he stops considering the implications of this fact, instead forcing his expectations to remain (in various senses) ‘normal’ until very specific types of proof are presented.

That later move is sometimes explicit, but mostly it is implicit, a quiet ignoring of the potential implications. As an example from this week of that second move, Tyler Cowen wrote another post where he asks whether AI can help us find God, or what impact it will have on religion. His ideas there only make sense if you think other things mostly won’t change.

If you accept that premise of a ‘mundane AI’ and ‘economic normal’ world, I agree that it seems likely to exacerbate existing trends towards a barbell religious world. Those who say ‘give me that old time religion’ will be able to get it, both solo and in groups, and go hardcore, often (I expect) combining both experiences. Those who don’t buy into the old time religion will find themselves increasingly secular, or they will fall into new cults and religions (and ‘spiritualities’) around the AIs themselves.

Again, that’s dependent on the type of world where the more impactful consequences don’t happen. I don’t expect that type of world.

Here is a very good explainer on much of what is happening or could happen with Chain of Thought, How AI Is Learning To Think In Secret. It is very difficult to not, in one form or another, wind up using The Most Forbidden Technique. If we want to keep legibility and monitorability (let alone full faithfulness) of chain of thought, we’re going to have to be willing to pay a substantial price to do that.

Following up on last week’s discussion, Jan Leike fleshes out his view of alignment progress, saying ‘alignment is not solved but it increasingly looks solvable.’ He understands that measured alignment is distinct from ‘superalignment,’ so he’s not fully making the ‘number go down’ or pure Goodhart’s Law mistake with Anthropic’s new alignment metric, but he still does seem to be making a lot of the core mistake.

Anthropic’s new paper explores whether AI assistants are already disempowering humans.

What do they mean by that at this stage, in this context?

However, as AI takes on more roles, one risk is that it steers some users in ways that distort rather than inform. In such cases, the resulting interactions may be disempowering: reducing individuals’ ability to form accurate beliefs, make authentic value judgments, and act in line with their own values.

… For example, a user going through a rough patch in their relationship might ask an AI whether their partner is being manipulative. AIs are trained to give balanced, helpful advice in these situations, but no training is 100% effective. If an AI confirms the user’s interpretation of their relationship without question, the user’s beliefs about their situation may become less accurate.

If it tells them what they should prioritize—for example, self-protection over communication—it may displace values they genuinely hold. Or if it drafts a confrontational message that the user sends as written, they’ve taken an action they might not have taken on their own—and which they might later come to regret.

This is not the full disempowerment of Gradual Disempowerment, where humanity puts AI in charge of progressively more things and finds itself no longer in control.

It does seem reasonable to consider this an early symptom of the patterns that lead to more serious disempowerment? Or at least, it’s a good thing to be measuring as part of a broad portfolio of measurements.

Some amount of this what they describe, especially action distortion potential, will often be beneficial to the user. The correct amount of disempowerment is not zero.

To study disempowerment systematically, we needed to define what disempowerment means in the context of an AI conversation. We considered a person to be disempowered if as a result of interacting with Claude:

their beliefs about reality become less accurate

their value judgments shift away from those they actually hold

their actions become misaligned with their values

Imagine a person deciding whether to quit their job. We would consider their interactions with Claude to be disempowering if:

Claude led them to believe incorrect notions about their suitability for other roles (“reality distortion”).

They began to weigh considerations they wouldn’t normally prioritize, like titles or compensation, over values they actually hold, such as creative fulfillment (“value judgment distortion”).

Claude drafts a cover letter that emphasizes qualifications they’re not fully confident in, rather than the motivations that actually drive them, and they sent it as written (“action distortion”).

Here’s the basic problem:

We found that interactions classified as having moderate or severe disempowerment potential received higher thumbs-up rates than baseline, across all three domains. In other words, users rate potentially disempowering interactions more favorably—at least in the moment.

Heer Shingala: I don’t work in tech, have no background as an engineer or designer.

A few weeks ago, I heard about vibe coding and set out to investigate.

Now?

I am generating $10M ARR.

Just me. No employees or VCs.

What was my secret? Simple.

I am lying.

Closer to the truth to say you can’t get enough.

Zac Hill: I get being worried about existential risk, but AI also enabled me to make my wife a half-whale, half-capybara custom plushie, so.

One could even argue 47% is exactly the right answer, as per Mitt Romney?

onion person: in replies he linkssoftware he made to illustrates how useful ai vibecoding is, and its software that believes that the gibberish “ghghhgggggggghhhhhh” has a 47% historical “blend of oral and literate characteristics”

Andy Masley: This post with 1000 likes seems to be saying

“Joe vibecoded an AI model that when faced with something completely out of distribution that’s clearly neither oral or literate says it’s equally oral and literate. This shows vibecoding is fake”

He’s just asking questions.

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SpaceX sends list of demands to US states giving broadband grants to Starlink

BEAD program, Broadband Equity Access and Deployment, Policy, spacex, starlink / Rejus Almole / January 28, 2026

SpaceX won’t make specific promises on Starlink network capacity or subscribers.

A Starlink user terminal during winter. Credit: Getty Images | AntaresNS

SpaceX has made a new set of demands on state governments that would ensure Starlink receives federal grant money even when residents don’t purchase Starlink broadband service.

SpaceX said it will provide “all necessary equipment” to receive broadband “at no cost to subscribers requesting service,” which will apparently eliminate the up-front hardware fee for Starlink equipment. But SpaceX isn’t promising lower-than-usual monthly prices to consumers in those subsidized areas. SpaceX pledged to make broadband available for $80 or less a month, plus taxes and fees, to people with low incomes in the subsidized areas. For comparison, the normal Starlink residential prices advertised on its website range from $50 to $120 a month.

SpaceX’s demands would also guarantee that it gets paid by the government even if it doesn’t reserve “large portions” of Starlink network capacity for homes in the areas that are supposed to receive government-subsidized Internet service. Moreover, SpaceX would not be responsible for ensuring that Starlink equipment is installed correctly at each customer location.

SpaceX sent a letter to state broadband offices proposing a rider with terms that it hopes will be applied to all grants it receives throughout the country. The letter was obtained and published by Broadband.io and the Benton Institute for Broadband & Society.

Arguing that SpaceX should receive grant money regardless of whether residents purchase Starlink service, the letter to states said that grant payments should not depend on “the independent purchasing decisions of users.” SpaceX also said it will not hold “large portions of capacity fallow” to ensure that people in subsidized areas receive good service, but will instead continue its preexisting practice of “dynamically allocat[ing] capacity where needed.”

SpaceX capitalizes on Trump overhaul

SpaceX’s proposed contract rider would apply to grants distributed under the US government’s Broadband Equity, Access, and Deployment (BEAD) program. BEAD was created by Congress in a 2021 law that authorized spending over $42 billion to make broadband networks available in areas without modern service.

While the Biden administration designed the program to prioritize fiber deployments, the Trump administration threw out the previous plans. Under Trump, the National Telecommunications and Information Administration (NTIA) deemed the Biden-era plan too costly and changed the rules to make it easier for satellite services to obtain grant funding. The overhaul cut projected spending to about $21 billion, and it’s still unclear what will happen to the other $21 billion.

Starlink sought billions in grants after the new rules were put in place, but states didn’t want to provide that much. So far, SpaceX is slated to receive $733.5 million to offer broadband at 472,600 locations. Amazon’s Leo satellite service (formerly Kuiper) is set to receive $311 million for 415,000 locations.

While not every state plan is final, it looks like satellite networks will get about 5 percent of the grant money and serve over 22 percent of the locations funded by grants. Satellite companies are getting smaller payments on a per-location basis because, unlike fiber providers, they don’t have to install infrastructure at each customer’s location.

The concessions sought by SpaceX “would limit Starlink’s performance obligations, payment schedules, non-compliance penalties, reporting expectations, and labor and insurance standards,” wrote Drew Garner, director of policy engagement at the Benton Institute. Garner argued that SpaceX’s demands illustrate problems in how the Trump NTIA rewrote program rules to increase reliance on low-Earth orbit (LEO) satellite providers.

“BEAD was designed primarily to deploy terrestrial networks, which are physically located in communities, built with traditional construction methods, and are relatively easy to monitor and inspect,” Garner wrote. “But, on June 6, 2025, NTIA restructured BEAD in ways that greatly increased participation by LEO providers, exacerbating the challenge of applying BEAD’s terrestrial-focused rules to LEO’s extraterrestrial networks.”

SpaceX: Labor rules shouldn’t apply to us

Among other things, SpaceX is trying to “minimize states’ ability to penalize LEO grantees for defaulting or failing to comply with contract requirements,” and avoid having “to report on the use of BEAD funds or other financial information related to the grant,” Garner wrote.

SpaceX’s letter said that “all requirements related to labor issues (e.g., prevailing wage and similar obligations), contractors, and procurement are inapplicable to SpaceX” because “there are no identifiable employees, contractors, or contracts being funded” to support Starlink broadband service in each state. Similarly, “there are no identifiable pieces of SpaceX infrastructure equipment (other than satellite capacity delivered from Space) being funded via BEAD,” the company said.

It’s unclear whether SpaceX will turn down grants if it doesn’t get what it wants. We asked the company for information on its plans if states refuse its terms and will update this article if we get a response. SpaceX’s proposed terms could also be applied to Amazon if states accept them.

SpaceX’s letter said that despite the Trump administration’s changes to BEAD, “a number of issues remain that, if unaddressed, could render LEO participation in the program untenable.” SpaceX said it wants to work with states “to more fully tailor aspects of the project agreement to the reality of LEO deployment and operations now that the initial project selection and approval phase is accomplished.”

Space said it wants to avoid extensive negotiations over its proposed terms. But the acknowledgement that some negotiation may be necessary seems to recognize that states don’t have to comply with the demands:

Toward this goal, we have developed a set of terms that we intend to function as a rider to all subgrant agreements across the country. This rider is intentionally limited in scope to addressing items of critical importance, to minimize the need for negotiation, and provide clarity to both parties moving forward. Our intention is for the LEO rider to enable the state to keep its core subgrant agreement relatively uniform amongst grantees, retain state-law-specific requirements, co-locate all relevant LEO-specific material for ease of administration, and standardize agreements across states.

Low-income plan: $80 plus taxes and fees

SpaceX’s proposed contract rider said the firm will offer broadband plans for “a monthly cost of $80 or less before applicable taxes and fees” to households that meet the low-income eligibility guidelines used by the FCC’s Lifeline program. People who don’t qualify for low-income plans would presumably pay regular Starlink rates.

The BEAD law requires ISPs receiving federal funds to offer at least one “low-cost broadband service option for eligible subscribers.” While the Biden administration sought low-income plans that cost as little as $30 a month, the Trump administration decided that states may not tell ISPs what prices to charge in their low-cost options. A Trump administration threat to shut states out of BEAD if they required low prices doomed a California proposal to mandate $15 monthly plans for people with low incomes.

SpaceX told state governments that it should receive 50 percent of grant funds when it certifies that it is capable of providing BEAD-quality service (100Mbps download and 20Mbps upload speeds) within 10 business days to any potential customer that requests it in a grant area. The rest of the money would be distributed quarterly over the 10-year period of the grant.

Explaining why SpaceX shouldn’t be penalized if potential customers decide Starlink prices are too high, the firm wrote:

Tying payments to the independent purchasing decisions of users solely for awardees using LEO technologies, and not for any other technology, is, by definition, not technology neutral. SpaceX is already appropriately incentivized to gather customers by the opportunity to capture the monthly recurring revenue from each subscriber. SpaceX was in most instances awarded the most remote and difficult areas to serve among all other providers. SpaceX is up to the task of ensuring success in these challenging areas, however, it cannot undertake this mission without certainty of consistent payments to compensate such work.

Based on SpaceX’s letter, it sounds like the work the company must do to ensure quality of service at BEAD-funded locations is the same work it has already done to make Starlink available across the US. Instead of dedicated capacity for government-subsidized deployments, SpaceX said it will simply factor the needs of BEAD users into its planning:

With respect to capacity reservations, we have found some confusion regarding how such a reservation is made. Given the dynamic nature of the Starlink network, the reservation will not be such that SpaceX holds large portions of capacity fallow. This would be wasteful, inefficient, and does not reflect a LEO providers [sic] ability to dynamically allocate capacity where needed. Instead, SpaceX will include the capacity needs of BEAD users into its network planning efforts. These activities are multifaceted and include real time capacity allocation at the network level, launch activities, and sales efforts. As a result, there is no single “document” evidencing the reservation of capacity.

SpaceX wants limits on performance testing

SpaceX said it will be obvious if it does not provide sufficient service, and thus the states should not seek additional performance testing beyond what’s included in the NTIA guidelines. “If sufficient capacity was not reserved, performance testing will reveal insufficient quality of service, and this deficiency will be transparent to the state. Developing a separate, indirect measurement of the reservation itself is infeasible and unnecessary,” SpaceX said.

The proposed rider said that any network testing must “exclude subscribers who have installed CPE [consumer premise equipment] such that its view of the sky is obstructed and subscribers with damaged or malfunctioning CPE, as determined by GRANTEE.”

The “as determined by GRANTEE” phrase means it’s up to SpaceX to decide which subscribers should be excluded from testing. As the Benton Society says, the rider stipulates that “performance tests can only be considered if the LEO provider determines that the subscriber’s equipment is properly installed, and, notably, the LEO provider is not obligated to ensure proper installation.”

SpaceX’s proposed rider defines a “standard installation” as the mailing of equipment to a subscriber. That’s the standard process for unsubsidized areas throughout the country, and SpaceX doesn’t want to do any extra work to help set up equipment for customers in subsidized areas. However, customers may be able to purchase professional installation for an extra fee.

“For the avoidance of doubt, the GRANTEE will not be responsible for completing a permanent installation” at each location, SpaceX’s proposed rider says. A satellite provider “may choose to offer the subscriber professional services for permanent installation of CPE at an additional fee, but such professional services shall not be considered part of the standard installation,” it says.

Jon is a Senior IT Reporter for Ars Technica. He covers the telecom industry, Federal Communications Commission rulemakings, broadband consumer affairs, court cases, and government regulation of the tech industry.

SpaceX sends list of demands to US states giving broadband grants to Starlink Read More »

Google begins rolling out Chrome’s “Auto Browse” AI agent today

AI, browser agent, Gemini, Google, Tech / Rejus Almole / January 28, 2026

Google began stuffing Gemini into its dominant Chrome browser several months ago, and today the AI is expanding its capabilities considerably. Google says the chatbot will be easier to access and connect to more Google services, but the biggest change is the addition of Google’s autonomous browsing agent, which it has dubbed Auto Browse. Similar to tools like OpenAI Atlas, Auto Browse can handle tedious tasks in Chrome so you don’t have to.

The newly unveiled Gemini features in Chrome are accessible from the omnipresent AI button that has been lurking at the top of the window for the last few months. Initially, that button only opened Gemini in a pop-up window, but Google now says it will default to a split-screen or “Sidepanel” view. Google confirmed the update began rolling out over the past week, so you may already have it.

You can still pop Gemini out into a floating window, but the split-view gives Gemini more room to breathe while manipulating a page with AI. This is also helpful when calling other apps in the Chrome implementation of Gemini. The chatbot can now access Gmail, Calendar, YouTube, Maps, Google Shopping, and Google Flights right from the Chrome window. Google technically added this feature around the middle of January, but it’s only talking about it now.

Sidepanel with Gmail integration

Gemini in Chrome can now also access and edit images with Nano Banana, so you don’t have to download and re-upload them to Gemini in another location. Just open the image from the web and type in the Sidepanel with a description of the edits you want. Like in the Gemini app, you can choose between the slower but higher-quality Pro model and the faster standard one.

Google begins rolling out Chrome’s “Auto Browse” AI agent today Read More »

Supreme Court to decide how 1988 videotape privacy law applies to online video

facebook pixel, Meta Pixel, paramount, Policy, Supreme Court / Rejus Almole / January 27, 2026

Salazar v. Paramount hinges on video privacy law’s definition of “consumer.”

Credit: Getty Images | Ernesto Ageitos

The Supreme Court is taking up a case on whether Paramount violated the 1988 Video Privacy Protection Act (VPPA) by disclosing a user’s viewing history to Facebook. The case, Michael Salazar v. Paramount Global, hinges on the law’s definition of the word “consumer.”

Salazar filed a class action against Paramount in 2022, alleging that it “violated the VPPA by disclosing his personally identifiable information to Facebook without consent,” Salazar’s petition to the Supreme Court said. Salazar had signed up for an online newsletter through 247Sports.com, a site owned by Paramount, and had to provide his email address in the process. Salazar then used 247Sports.com to view videos while logged in to his Facebook account.

“As a result, Paramount disclosed his personally identifiable information—including his Facebook ID and which videos he watched—to Facebook,” the petition said. “The disclosures occurred automatically because of the Facebook Pixel Paramount installed on its website. Facebook and Paramount then used this information to create and display targeted advertising, which increased their revenues.”

The 1988 law defines consumer as “any renter, purchaser, or subscriber of goods or services from a video tape service provider.” The phrase “video tape service provider” is defined to include providers of “prerecorded video cassette tapes or similar audio visual materials,” and thus arguably applies to more than just sellers of tapes.

The legal question for the Supreme Court “is whether the phrase ‘goods or services from a video tape service provider,’ as used in the VPPA’s definition of ‘consumer,’ refers to all of a video tape service provider’s goods or services or only to its audiovisual goods or services,” Salazar’s petition said. The Supreme Court granted his petition to hear the case in a list of orders released yesterday.

Courts disagree on defining “consumer”

The Facebook Pixel at the center of the lawsuit is now called the Meta Pixel. The Pixel is a piece of JavaScript code that can be added to a website to track visitors’ activity “and optimize your advertising performance,” as Meta describes it.

Salazar lost his case at a federal court in Nashville, Tennessee, and then lost an appeal at the US Court of Appeals for the 6th Circuit. (247Sports has its corporate address in Tennessee.) A three-judge panel of appeals court judges ruled 2–1 to uphold the district court ruling. The appeals court majority said:

The Video Privacy Protection Act—as the name suggests—arose out of a desire to protect personal privacy in the records of the rental, purchase, or delivery of “audio visual materials.” Spurred by the publication of Judge Robert Bork’s video rental history on the eve of his confirmation hearings, Congress imposed stiff penalties on any “video tape service provider” who discloses personal information that identifies one of their “consumers” as having requested specific “audio visual materials.”

This case is about what “goods or services” a person must rent, purchase, or subscribe to in order to qualify as a “consumer” under the Act. Is “goods or services” limited to audio-visual content—or does it extend to any and all products or services that a store could provide? Michael Salazar claims that his subscription to a 247Sports e-newsletter qualifies him as a “consumer.” But since he did not subscribe to “audio visual materials,” the district court held that he was not a “consumer” and dismissed the complaint. We agree and so AFFIRM.

2-2 circuit split

Salazar’s petition to the Supreme Court alleged that the 6th Circuit ruling “imposes a limitation that appears nowhere in the relevant statutory text.” The 6th Circuit analysis “flout[s] the ordinary meaning of ‘goods or services,’” and “ignores that the VPPA broadly prohibits a video tape service provider—like Paramount here—from knowingly disclosing ‘personally identifiable information concerning any consumer of such provider,’” he told the Supreme Court.

The DC Circuit ruled the same way as the 6th Circuit in another case last year, but other appeals courts have ruled differently. The 7th Circuit held last year that “any purchase or subscription from a ‘video tape service provider’ satisfies the definition of ‘consumer,’ even if the thing purchased is clothing or the thing subscribed to is a newsletter.”

In Salazar v. National Basketball Association, which also involves Michael Salazar, the 2nd Circuit ruled in 2024 that Salazar was a consumer under the VPPA because the law’s “text, structure, and purpose compel the conclusion that that phrase is not limited to audiovisual ‘goods or services,’ and the NBA’s online newsletter falls within the plain meaning of that phrase.” The NBA petitioned the Supreme Court for review in hopes of overturning the 2nd Circuit ruling, but the petition to hear the case was denied in December.

Despite the NBA case being rejected by the high court, a circuit split can make a case ripe for Supreme Court review. “Put simply, the circuit courts have divided 2–2 over how to interpret the statutory phrase ‘goods or services from a video tape service provider,’” Salazar told the court. “As a result, there is a 2–2 circuit split concerning what it takes to become a ‘consumer’ under the VPPA.”

Paramount urged SCOTUS to reject case

While Salazar sued both Paramount and the NBA, he said the Paramount case “is a superior vehicle for resolving this exceptionally important question.” The case against the NBA is still under appeal on a different legal issue and “has had multiple amended pleadings since the lower courts decided the question, meaning the Court could not answer the question based on the now-operative allegations,” his petition said. By contrast, the Paramount case has a final judgment, no ongoing proceedings, and “can be reviewed on the same record the lower courts considered.”

Paramount urged the court to decline Salazar’s petition. Despite the circuit split on the “consumer” question, Paramount said that Salazar’s claims would fail in the 2nd and 7th circuits for different reasons. Paramount argued that “computer code shared in targeted advertising does not qualify as ‘personally identifiable information,’” and that “247Sports is not a ‘video tape service provider’ in the first place.”

“247Sports does not rent, sell, or offer subscriptions to video tapes. Nor does it stream movies or shows,” Paramount said. “Rather, it is a sports news website with articles, photos, and video clips—and all of the content at issue in this case is available for free to anybody on the Internet. That is a completely different business from renting video cassette tapes. The VPPA does not address it.”

Paramount further argued that Salazar’s case isn’t a good vehicle to consider the “consumer” definition because his “complaint fails for multiple additional reasons that could complicate further review.”

Paramount wasn’t able to convince the Supreme Court that the case isn’t worth taking up, however. SCOTUSblog says that “the case will likely be scheduled for oral argument in the court’s 2026-27 term,” which begins in October 2026.

Supreme Court to decide how 1988 videotape privacy law applies to online video Read More »

Australian plumber is a YouTube sensation

competence porn, culture, Internet culture, plumbing, youtube, youtube creators, youtube culture / Rejus Almole / January 27, 2026

My personal favorites are when Bruce takes on clogged restaurant grease traps, including the one at the top of this article in which he pulls out a massive greaseberg “the size of a chihuahua.” When it’s Bruce versus a nasty grease trap, the man remains undefeated (well, almost—sometimes he needs to get a grease trap pumped out before he can fix the problem). And I have learned more than I probably ever needed to know about how grease traps work.

schematic illustration showing how a grease trap works — Credit: YouTube/Drain Cleaning Australia

Each video is its own little adventure. Bruce arrives on a job, checks out the problem (“she is chock-a-block, mate!”), and starts methodically working that problem until he solves it, which inevitably involves firing up “the bloody jet” to blast through blockages with 5,000 psi of water pressure (“Go, you good thing!”). This being Australia, he’ll occasionally encounter not just cockroaches but poisonous spiders and snakes. And he’s caught so many facefulls of wastewater and sewage while jetting that he really ought to invest in a hazmat suit. Even the cheesy canned techno-music playing during lulls in the action is low-budget perfection.

Bruce isn’t the only plumber with a YouTube channel—it’s a surprisingly good-size subgenre—but he’s the most colorful and entertaining. His unbridled enthusiasm for what many would consider the dirtiest of jobs is positively infectious. He regularly effuses about having the best job in the world, insisting that unclogging gross drains is “living the dream,” and regularly asks his audience, “How good is this? I mean, where else would you rather be?” Sure, he says it with an ironic (unseen) wink at the camera, but deep down, you know he truly loves the work.

And you know what? Bruce is right. It might not be your definition of “what dreams are made of,” but there really is something profoundly satisfying about a free-flowing drain—and a job well done.

Australian plumber is a YouTube sensation Read More »

Apple’s AirTag 2 is easier to find thanks to new chip

AirTag, Apple, bluetooth, location tracker, Tech / Rejus Almole / January 26, 2026

Additionally, the speaker in the AirTag is now 50 percent louder, Apple says. These two things together address some user complaints that, as useful as an AirTag can be in ideal circumstances, sometimes it is frustrating trying to get things just right to find something. It won’t eliminate all edge cases, but it ought to help.

Apple used this announcement to also talk up some of the features of the AirTag, including the encryption that it says prevents anyone but the AirTag owner from using it, and an arrangement with airlines where users can temporarily give airlines the ability to use Apple’s network to find a specific AirTag to locate lost luggage and the like.

To be clear, the new AirTag doesn’t introduce any major new features that aren’t already offered in the previous generation—this is just an update to the device’s accuracy, volume, and range.

The price remains unchanged, at $29 for one AirTag or $99 for a pack of four. The new model is available for order on Apple’s website now and will hit physical stores later this week.

Apple’s AirTag 2 is easier to find thanks to new chip Read More »

Author name: Rejus Almole

Big decisions loom

Discussion about this post

The slop problem

Accelerating science or overwhelming peer review?

Discussion about this post

SpaceX capitalizes on Trump overhaul

SpaceX: Labor rules shouldn’t apply to us

Low-income plan: $80 plus taxes and fees

SpaceX wants limits on performance testing

Courts disagree on defining “consumer”

2-2 circuit split

Paramount urged SCOTUS to reject case