Pete Hegseth and Trump got a dubious win today: a 2-1 panel of the DC Circuit found that the designation of Anthropic as a supply chain risk was not unlawful. It dismissed Anthropic’s challenge despite (1) a different court having found the exact opposite not that long ago; (2) being predicated on a statutory interpretation of “supply chain risk” that would effectively deem most AI models, and potentially all software, a supply chain risk; (3) having made this designation selectively and punitively; and (4) it resulting in Anthropic being disqualified from selling its model to any government agency, including those without same concern the DC Circuit credited the military with having.

To some extent the difference in the two decisions can be explained by the two different statutes at issue. Hegseth claimed the authority to make “supply chain risk” designations under two separate ones, 10 U.S.C. § 3252, which was at issue in the California challenge, and 41 U.S.C. § 4713, which was at issue in this case. Congress is also partly to blame for this mess, because in writing the statutory definition for “supply chain risk” in the 4713 statute it created more space for dubious interpretations like this one (“Whatever paradigmatic examples individual members of Congress may have had in mind, the statutory definition is not limited to “adversar[ies],” 10 U.S.C. § 3252(d)(4), and instead covers “any person,” which cannot refer only to foreign entities, 41 U.S.C. § 4713(k)(6).”).  The statute also constrained how such designations could be challenged, sending them all directly to the DC Circuit, rather than a district court, which is why Anthropic’s challenge of the Hegseth action ended up in two separate cases.

But a bizarre situation has still resulted where one court has said that Anthropic’s First Amendment and due process rights had been violated, and another has now said they weren’t, even though the same action was involved with both. Anthropic argued that the California district court’s earlier decision should have been controlling, but the DC Circuit disagreed:

Anthropic contends that the Northern District’s decision is preclusive as well as persuasive. But because the Department’s designation authority is much broader under section 4713 than it is under section 3252, the issues flagged by Anthropic are not the same in both cases. So, for example, the Northern District’s determination that the section 3252 designation was arbitrary does not control our determination whether the section 4713 designation was arbitrary. Likewise, the Northern District’s determination of exigency under section 3252 does not control our determination of exigency under section 4713. In any event, Congress gave this Court exclusive jurisdiction to review procurement actions taken pursuant to section 4713 designations, see 41 U.S.C. § 1327(b)(1), and it specifically barred other courts from reviewing any other “action taken under” section 4713, see id. § 1327(a). That strict “allocation of jurisdiction” to this Court makes it inappropriate to constrain our review based on the Northern District’s judgment. Restatement (Second) of Judgments § 28 (1982); see Shaw v. State of Cal. Dep’t of Alcoholic Beverage Control, 788 F.2d 600, 607–09 (9th Cir. 1986); Lyons v. Westinghouse Elec. Corp., 222 F.2d 184, 188–89 (2d Cir. 1955) (L. Hand, J.).

Instead, because the DC Circuit read the statutory authority Hegseth drew from differently, apparently given its textual differences, it found Hegseth entitled to take the action that he did. But it is a dubious reading that would have broad implications the court did not address. In short, because Anthropic would still have control over its model, the court found that it could be considered to have the power to “manipulate” it, even after being deployed in government, and that made it a supply chain risk.

The Secretary reasonably concluded that removing Anthropic from the Department’s supply chain was necessary to protect national security by reducing supply chain risk to the Department’s information systems. Specifically, the Secretary credited a joint recommendation from two senior Department officials that Claude might be “subject to manipulation” by Anthropic “in such a manner as to inhibit the DoW’s use thereof.” App. 178. Likewise, he credited Under Secretary Michael’s conclusion that Anthropic might “manipulat[e]” the “design, integrity, and operation” of the Department’s Claude models, potentially causing “critical defense system[s] failing to engage” as intended by the Department. Id. at 182.

The record in this case amply supports the Secretary’s conclusion. To begin, it is undisputed that Anthropic can and does control how Claude responds—or fails to respond—to user prompts. Anthropic’s Chief Science Officer explained how the company “seek[s] to embed safety considerations directly into the model itself.” App. 8. Its CEO explained how such training gives the model an “identity, character, values, and personality” of its own, tethered to a “constitution” developed to impose “high-level principles and values” on Claude itself. Id. at 93–94. And the head of its public-sector business explained: “Model training is the primary mechanism through which Anthropic can influence the behavior of models used by the Department.” Id. at 276. Anthropic disclaims any ability to access or alter a model that has already been delivered to the Department, see id., despite the “technical measures” that it uses to police compliance with usage restrictions by private customers, id. at 8. Nonetheless, extant models reflect Claude’s “[c]onstitutional” training. Id. at 274–75. Moreover, Anthropic may encode additional restrictions each time it delivers any “new version of the model” to Department contractors. Id. at 276. Finally, it is undisputed that such model restrictions are vitally important to Anthropic, which describes them as lying “at the core of [its] mission.” Id. at 2.

The record also indicates that Anthropic’s model training is effective in enforcing usage restrictions and that, as a result, Claude has refused to answer legitimate queries from government users. Anthropic itself explained how early, commercially available versions of Claude frustrated Department and intelligence-community users by refusing prompts to evaluate classified materials. App. 255. Likewise, as Under Secretary Michael explained, the Department learned in 2025 that Claude had refused to process CDC prompts to support research to prevent the spread of infectious diseases. Id. at 212. Anthropic responds that these glitches reflected safety features appropriately built into models sold to private companies and were resolved after Anthropic engineers worked with the relevant government stakeholders. Id. at 255–56, 261–62. Perhaps so, but the point here is not that these model or usage restrictions were arbitrary; instead, it is that Anthropic’s model training does effectively enforce contractual usage restrictions.

Finally, the record reveals a recent, serious dispute about the scope of the contractual prohibitions on lethal autonomous warfare and mass domestic surveillance. Under Secretary Michael describes the incident in general but striking terms: [O]ne of Anthropic’s executives questioned the propriety of the potential use of their software for a sensitive military operation abroad despite that use being permitted under the existing Terms of Service. This led to alarm by the DoW and the prime contractor who provides Anthropic software, and raised material doubts as to whether they would cause their software to stop working or cause some other disastrous action that would put our warfighters[’] lives in danger. App. 181. Anthropic does not say much about this incident, except to suggest that it reflected a misunderstanding. Id. at 236–37. But regardless, Anthropic has made clear that it views the contractual prohibition on mass domestic surveillance as urgent to support “democratic values,” id. at 146, and the contractual prohibition on lethal autonomous warfare as urgent to avoid “put[ting] America’s warfighters and civilians at risk” of a catastrophic AI mistake, id. at 147. For its part, the Department has made clear that it views an “any lawful use” authorization to be critical to its “AI-first” strategic plan. Id. at 202, 206. With such diametrically opposed positions and with contractual limitations that are hardly self-defining, the prospect for disputes is apparent.

In sum, the Department reasonably feared that Anthropic might manipulate Claude’s design to prevent it from performing national-security functions that the Department deems contractually authorized and necessary.

The nightmare hypo that the court credited was what if the military had some sensitive plans that depended on Claude’s use, which Anthropic then changed on the fly, which jeopardized the mission. But there are multiple problems with the court’s acceptance of the government’s argument here.

For one, if the court’s statutory interpretation about the power to affect the operation of delivered software were correct, then pretty much any software product, at least those still subject to vendor-supplied updates, could be considered supply chain risks, given that any update could make substantive changes. In any case, it would seem to mean that any AI model would be too risky for the government to use, because there is nothing unusual about Anthropic’s model-control architecture—to the extent Anthropic could still control its model, so could any other AI vendor potentially control theirs. Whether they would or not would depend on the contract restraining them, and the only thing potentially different about Anthropic is that it did not want to be contractually obligated to allow certain functions that Hegseth really wanted—functions that were ethically dubious at best and monstrously dangerous at worst.

But because that contractual reluctance upset Trump and Hegseth, they singled Anthropic out, alone, for negative treatment, turning their pique that “we can’t agree with Anthropic on how the software would need to be designed for us to be able to buy it” into “and because we can’t agree then NO ONE ELSE IN THE GOVERNMENT CAN EVER USE IT.” Per the DC Circuit, such an overbroad measure—after all, not every agency had the same concerns about changeability that the military might, yet Hegseth was deciding for them, too, whether they could use Claude, even when its architecture created no particular risk to them—and clearly punitive measure was perfectly fine because it implicated the implicit “national security” exception to the First Amendment the Founders apparently wrote into it in invisible ink.

To succeed on such a First Amendment retaliation claim, the plaintiff or petitioner must prove that (1) it engaged in protected speech, (2) the government took materially adverse action against it, and (3) the speech caused the materially adverse action. See Houston Cmty. Coll. Sys. v. Wilson, 595 U.S. 468, 477–79 (2022); Aref v. Lynch, 833 F.3d 242, 258 (D.C. Cir. 2016). Anthropic has satisfied the first and second prongs of this test, but not the third. The First Amendment squarely protects Anthropic’s advocacy regarding the safe and appropriate use of AI products. Moreover, the Department’s exclusion of Claude from its supply chain plainly qualifies as a materially adverse action. However, we can discern no causal connection between the two. Instead, the record makes clear that the Department removed Anthropic from its supply chain not because of its advocacy, but because Anthropic refused to agree to a contract term the Department deemed essential to national security.

Because Anthropic wouldn’t do the deal Hegseth wanted to do, he was therefore entitled to declare it too risky for anyone in the government to use, without it being seen as punishing Anthropic for its disinclination.

Anthropic points to various pungent statements in the Secretary’s February 27 social media post. Among other things, the Secretary denounced Anthropic’s “sanctimonious rhetoric,” “virtue-signaling,” and “Silicon Valley ideology.” App. 77. Such rhetoric seldom provides a sound basis for judging the lawfulness of federal executive action. See, e.g., Mullin v. Doe, 146 S. Ct. 2121, 2139 (2026); Trump v. Hawaii, 585 U.S. at 700–02. In any event, for all its flourishes, the Secretary’s social media post squarely addresses Anthropic’s refusal to provide the “all lawful uses” contractual authorization. He described Anthropic’s behavior as a “textbook case of how not to do business” with the Pentagon. App. 77 (emphasis added). He reiterated the Department’s demand for “full, unrestricted access to Anthropic’s models for every lawful purpose in defense of the Republic.” Id. (cleaned up). And he characterized Anthropic’s refusal to provide that access as imposing an unacceptable “veto power over the operational decisions of the United States military.” Id. The nub of this dispute was contractual, and the First Amendment did not require the Department to continue a contractual relationship that it viewed as creating a national-security risk.

Per the DC Circuit panel, the First Amendment takes a backseat to the President and his Secretary’s determination that a technology can ever be used by the government, no matter what.

This case raises profoundly difficult questions about the appropriate military uses of an almost unimaginably powerful new technology. The Secretary raises the deeply sobering prospect of overly constrained AI models shutting down unexpectedly and thus causing important military operations to fail. Anthropic raises the deeply sobering prospect of unconstrained AI models hallucinating inappropriate targets for lethal military force. Both possibilities present obvious national-security concerns. But in our Republic, it is the President and the Secretary of War who must determine how best to balance the competing risks. In doing so here, the Secretary did not transgress any limits on his authority under the Supply Chain Security Act or the Constitution. Accordingly, we deny the petitions for review.

Which cannot possibly be right if the Bill of Rights is to have any meaning in limiting government power, and especially not on a record like this.  Yet here we are.

The Metric Is Not the Mission is a ten-part examination of how Big Tech moved from building and expanding the open internet to increasingly shaping it around its own metrics, incentives and assumptions. Across the series, the argument follows the evolution of the platform economy—from the optimism of the early internet to the growing tensions around power, prediction, geopolitics, accountability and the future of digital life.

The series will be published in two parts each week over five weeks, with each installment building on the one before it. At the end of the series, the complete essay will be brought together in a single PDF edition, providing the full argument in one place.

Part II — When They Still Understood Us

Part I looked at the slow transformation taking place beneath the daily controversies surrounding Big Tech. This second part goes back to the beginning, asking what these companies originally understood about people and how the metrics that once measured their success gradually became the definition of it.

It has become fashionable to tell the story of Big Tech as though it were always destined to end here. In retrospect, it is easy to portray the rise of the major platforms as the inescapable march of surveillance capitalism, monopolistic ambition, and unchecked technological power. That narrative is emotionally satisfying because it offers clear villains and a comforting sense of inevitability. It also happens to be incomplete.

Cory Doctorow has given this deterioration a memorable name: “enshittification.” His argument is that platforms initially serve users well, then, once users and business customers are locked in, progressively shift value away from both toward shareholders, degrading the service in the process. It is a powerful account of how platforms become extractive. But it is not quite the argument here. The deeper problem is not simply that Big Tech has learned to extract more from us but that it has become increasingly convinced that because it can measure and predict our behavior, it understands us and, by extension, the societies it has come to mediate. The failure is therefore not only economic; it is also epistemic. The metric has become a substitute for the mission.

One cannot understand why these companies now appear increasingly disconnected from the societies they helped shape without first acknowledging that, for a remarkably long time, they understood those societies exceptionally well.

Technology succeeds when it solves technical problems. It changes the world when it solves human ones.

That was the genius of the first generation of internet platforms. Their founders did not invent friendship, curiosity, creativity or community. They simply recognized that the internet had reached a stage where these deeply human instincts required new forms of expression. The web of the late 1990s was exhilarating, but it was also fragmented, uneven and, for many people, intimidating. Finding information often required patience. Discovering interesting websites depended on chance as much as design. Publishing demanded a degree of technical literacy that excluded far more people than it empowered. The internet was open, but openness alone does not necessarily produce accessibility.

The great platforms emerged not because they sought to replace the internet but because they made it intelligible. Google transformed an expanding wilderness of information into something navigable. Wikipedia demonstrated that knowledge could be organized through collaboration rather than hierarchy. YouTube lowered the barriers to publishing so dramatically that expertise escaped universities, broadcasters and production studios. Facebook addressed an even more fundamental challenge. It recognized that the internet was no longer simply about information; it had become about people. Until then, maintaining relationships online had been surprisingly cumbersome. Email was too formal, instant messaging too ephemeral, personal websites too static. Facebook reduced social interaction to something almost frictionless. Its success lay not in technological sophistication but in psychological intuition.

This is easy to forget because the platforms that dominate our lives today bear only a partial resemblance to the ones that first captured our imagination. Facebook did not begin as an endless stream of algorithmically selected content. It was, in essence, a digital address book enriched by photographs, conversations, and the ordinary rituals of everyday life. It became valuable because it mirrored existing relationships rather than attempting to manufacture new ones. There was comfort in discovering former classmates, following the lives of distant relatives, or organizing gatherings that would otherwise have required dozens of emails and phone calls. The platform expanded social life without yet attempting to redefine it.

YouTube offered a similarly modest promise. It was not originally designed to maximize engagement or optimize watch time. It functioned more like an immense public archive whose value derived from its unpredictability. One could arrive searching for a lecture on astronomy and leave having discovered a forgotten jazz performance, a documentary on Greek history, or a repair manual for a washing machine. Recommendation existed, but it remained subordinate to curiosity. Users still felt as though they were exploring rather than being guided.

Even Twitter (now X), before it became a battleground for politics, culture wars, and performative outrage, captured something important about the changing nature of public conversation. It collapsed distance between journalists, academics, politicians, and ordinary citizens in ways that would have seemed extraordinary only a few years earlier. For all its imperfections, it suggested that expertise and authority might become more accessible rather than less.

Looking back, what united these companies was not simply technological innovation but a particular philosophy of the internet. They assumed that openness generated value. The more people connected, the richer the network became. Every new participant increased the possibilities for everyone else. Economists describe this as a network effect, but the phrase barely captures its cultural significance. Participation itself became the source of optimism. The internet appeared to be validating one of the oldest liberal ideas: that societies flourish when individuals are free to exchange ideas, collaborate voluntarily, and build institutions from the bottom up.

It is difficult to overstate how persuasive this vision became. Most governments celebrated the digital economy as an engine of innovation. Investors poured unprecedented sums into technology because the opportunities seemed limitless. Civil society organizations embraced online platforms as tools for democratic participation and global advocacy. Even critics of globalization often regarded the internet as an exception, a domain where openness appeared to distribute power rather than concentrate it.

For a brief historical moment, these interests aligned. What was good for technology companies often appeared to be good for users, for markets, and, in many respects, for the internet itself. The incentives reinforced one another. Companies grew by making the network more useful. Users benefited from larger communities. Developers built new services on open standards. The web expanded because success depended on drawing people further into its richness rather than confining them within a single destination.

History, however, has an inconvenient habit of changing the problems that institutions are asked to solve. The sociologist Robert K. Merton once observed that organizations often become prisoners of their own success. Practices that were rational under one set of conditions gradually harden into routines, and routines into orthodoxies. Institutions continue refining the solutions that once made them indispensable even as the environment around them evolves. Success breeds confidence; confidence breeds certainty; certainty eventually makes adaptation more difficult than persistence.

There is no reason to believe technology companies are exempt from this pattern. If anything, their extraordinary success may have accelerated it.

The platforms that once competed to help users navigate an open internet eventually found themselves managing ecosystems of unprecedented scale. Their priorities changed almost imperceptibly. The models they developed during the internet’s age of expansion proved astonishingly effective at connecting people, organizing information, and lowering the costs of participation. The metrics through which they evaluated success, such as growth, engagement, scale, and network effects, were not arbitrary inventions of venture capital. They reflected a period during which connecting more people genuinely created more value for everyone involved. The problem is that the world changed while the metrics remained stable. A measure that once indicated success gradually became the definition of success itself.

There is an obvious parallel here with Goodhart’s Law: when a measure becomes a target, it ceases to be a good measure. The principle, first articulated by economist Charles Goodhart in the context of monetary policy, describes what happens when an indicator that works as a proxy for an underlying objective is turned into the objective itself. But the problem here is slightly different. The issue is not simply that platforms began gaming their metrics, or that users learned to optimize for them. It is that the metrics gradually became so deeply embedded in the companies’ understanding of success that the distinction between the measure and the mission was lost. The metric did not merely distort the objective; it quietly became the objective.

None of this happened because a group of executives gathered in a boardroom and decided to undermine the open internet. Institutional change is rarely so theatrical. More often, it emerges from countless rational decisions made in pursuit of perfectly reasonable objectives: improve the user experience, reduce friction, personalize recommendations, increase safety, remove inconvenience. Each adjustment appears modest in isolation but collectively they alter the character of the system itself.

This is where history becomes quietly ironic. The companies that had once understood the internet better than anyone else slowly began to forget what had made the internet exceptional in the first place. They continued to believe they were connecting the world, even as they increasingly replaced the world with carefully curated representations of it. They accumulated unprecedented quantities of information about human behavior while becoming progressively less attentive to the human condition.

The distinction is subtle, but it may prove to be the defining story of this technological era. To observe behavior is not the same as understanding experience. A platform can know how long we hesitate before clicking a link, which videos hold our attention for an extra seven seconds, or what sequence of images is most likely to keep us scrolling late into the night. It can infer preferences with astonishing accuracy. It can predict patterns that would have been unimaginable a generation ago. Yet prediction, however sophisticated, remains an impoverished form of understanding. It reveals what people do. It says far less about why they do it, what they fear, what they hope for, or what kind of society they are trying to build together.

That difference, almost invisible at first, is where the story begins to change.

Konstantinos Komaitis, PhD, is a veteran of developing and analysing Internet policy to ensure an open and global Internet.

Ctrl-Alt-Speech is a weekly podcast about the latest news in online speech, from Mike Masnick and Everything in Moderation‘s Ben Whitelaw.

Subscribe now on Apple Podcasts, Overcast, Spotify, Pocket Casts, YouTube, or your podcast app of choice — or go straight to the RSS feed. To get extended episodes with additional coverage, support us on Patreon.

In this week’s episode, Mike and Ben cover:

• Trump dismisses concerns about AI risks at UN, says he won’t rein it in (PBS News)
• A.I. Gone Rogue? Trump Proposes Not New Rules but an ‘A.I. Force.’ (New York Times) 
• 22 countries back call to keep AI ‘under human control’ (POLITICO)
• If AI is going to destroy humanity, Scott Bessent says hyperscalers, not government, must take the fall: ‘We cannot absolve you of responsibility’ (Fortune)
• Meet the algorithm you can actually control and understand (New_Public)
• YouTube’s Custom Feeds Give You More Control Over the Algorithm (Wired) 
• YouTube CEO dismisses the idea of joining Meta’s multistate child safety settlement (CNN)
• Harry Potter Survey Results & Final Decision (Reddit)

And in the extended episode for Patreon supporters, they cover:

• Safer for Teens, Same Discord for Adults (Discord)
• Age Assurance on Discord: Vendors, Methods and Your Data (Discord) 
• How Discord Estimates Age Without Collecting Your Identity (Discord) 
• Ofcom launches investigation into Pornhub’s age checks (Ofcom)
• Aylo Defends Apple Age Assurance in Response to Ofcom Investigation (Aylo) 
• Discord’s age verification era is upon us, despite community backlash (TechCrunch)
• Angry reaction to Discord’s age assurance (X/Twitter)

Our fun links this week include Jev, the super fast new AI classifier, and Persodex, the context layer for your Contacts (PS. hit Ben up if you’re in New York in mid-October).

Follow us on Instagram, YouTube, and Bluesky for video clips from this week’s episode!

If you’re already a Patreon supporter, you can get the extended episode on Patreon.

A ton of attention was paid recently to some offhand statements from OpenAI and Microsoft employees that surfaced in filings in the NY Times’ ongoing case against OpenAI, which has been consolidated into a much larger class action lawsuit. As I argued earlier, that struck me as something of a nothingburger of a story, because it should have no impact on the actual legal questions regarding copyright infringement and fair use. However, on Wednesday evening, OpenAI and Microsoft filed something far more stunning, accusing Susman Godfrey (which represents the plaintiffs in the consolidated case) of effectively end-running basic rules of discovery and evidence by (1) paying for research to supply evidence its clients lacked, (2) hiding from the defendants that it had paid for that research, and (3) sneaking the paid-for research into the case outside the normal expert process.

This filing should be seen as the massive bombshell (if not fraud on the court) that people tried to make out that earlier filing to be. Professor Ed Lee, who runs ChatGPT is Eating the World (which tracks all of the various AI lawsuits), has called this an “explosive motion.” But it’s a little bit complex to understand why, which is why it will not get nearly as much attention as some offhand comments by a Microsoft employee.

To understand why this is such a big deal, we need to take a few steps back to explain. There are a bunch of different cases going on in the US regarding whether or not AI training is “fair use” and therefore not a copyright infringement. There were two important rulings in California last year, one after the other, where one judge (William Alsup) found training to be somewhat obviously fair use, while the other judge (Vince Chhabria) found it to be somewhat obviously not fair use.

As often happens in fair use cases, a lot of time is spent on the “effect on the market” argument, and part of that is whether or not the new works “dilute” the market for earlier works. In the Anthropic case, Alsup didn’t buy the claims of dilution, which is maybe not surprising, since he found training to be fair use. But perhaps more interesting is that in the Meta case, Chhabria — even as he found against fair use — wasn’t persuaded about the “dilution” argument:

As for the potentially winning argument—that Meta has copied their works to create a product that will likely flood the market with similar works, causing market dilution—the plaintiffs barely give this issue lip service, and they present no evidence about how the current or expected outputs from Meta’s models would dilute the market for their own works.

That was a federal judge signalling to potential plaintiffs, if you’re bringing infringement cases like this, maybe find some evidence of dilution?

And… that happened. Earlier this year, a preprint came out on Arxiv seemingly providing evidence on that specific point, claiming that “Generative AI floods and dilutes the market for books” written by four researchers, most notably Jane Ginsburg, who is one of the most famous copyright scholars around (though is also well known as one of the most extreme copyright maximalists, not to mention a general hater on a broad interpretation of fair use). But the lead name on the paper is Tuhin Chakrabarty, a recent PhD. (2024) grad who is now a computer science professor at SUNY Stony Brook. Chakrabarty received his PhD. from Columbia University, where Ginsburg teaches.

A friend had sent me that report when it came out and I found the analysis… perplexing. I had put it on my list of things to write about, but never got to it. Thankfully, Thad McIlroy, who runs “The Future of Publishing” and has been a long term contributing editor at Publishers Weekly, took it upon himself to examine the paper and found it deeply problematic, mainly because they relied on Kindle Unlimited to get copies of the books that they used for the analysis. But as McIlroy points out, that’s distortionary for many reasons regarding how KU works, and suggests that many of the underlying assumptions in the paper simply don’t hold up to scrutiny:

But the author earns income on KU solely on the number of actual pages of their book that are read by a subscriber. Just getting downloaded provides no income. The complex formula is well-described here. There is no method available to estimate the page reads for a book, nor the KU income. Chakrabarty writes, “We measure Kindle Unlimited as whether a title is available on the service, not as how much of it readers actually read. The panel does not tell us whether a given unit is a Kindle Unlimited borrow, a page read allocation, or an ordinary purchase.”

An interesting aspect of KU is that a book’s income there may relate far more closely to quality than it does under royalty systems. If a reader downloads a low-quality AI-generated book on KU, starts to read it, and recognizes the low quality, they will stop reading and move onto another book. The author will earn an insignificant amount of money. On the other hand, if a reader buys the same book, the author receives their full royalty (unless the reader goes to the trouble of returning the book and seeking a refund).

An AI-generated book on KU will only earn significant page revenue if readers find it to be of quality sufficient to match the genre books they are used to reading on the platform.

With these factors in mind, the prevalence of Kindle Unlimited titles in this study appears to be a distorting influence. First, AI-generated books are more likely to appear on Kindle Unlimited than they are more broadly on the Amazon Kindle platform. Second, there is no clear method available to estimate a book’s actual KU income.

Even more bizarre, when McIlroy shared a copy of his critique with Chakrabarty, he was dismissed on moral grounds, because McIlroy has argued for ethical ways to use AI in publishing, which Chakrabarty claims is “morally not okay with me.” That alone should raise some serious red flags about the objectiveness of Chakrabarty in this research. He did not come to this with an open mind. He came bearing a grudge.

A few months earlier, Chakrabarty and Ginsburg (along with Xinyue Liu, who was also an author of the paper above, and who appears to be a first or second year PhD. student working for Charkrabarty) put out another paper called “Alignment Whack-A-Mole: Finetuning Activates Verbatim Recall of Copyrighted Books in Large Language Models.” That piece claimed there was evidence that AI models “store copies of copyrighted works” and even pointed out that this “undermine[s] a key premise of recent fair use rulings.” Indeed, it calls out the Alsup and Chhabria rulings in the paper itself, and effectively notes that they’re responding to the judge’s concerns regarding the effect on the market.

In short, Chakrabarty, Liu, and Ginsburg have been publishing research that attempts to fill in the gaps that multiple judges had called out, and to help plaintiffs argue that training is not fair use. This was especially important because if such evidence was widely available, other plaintiffs would have brought it up. But they have not. Likely because it doesn’t really exist unless you stretch your methodology to its breaking point.

Of course, my biases are known: I’m quite convinced that training AI on copyrighted works is fair use, and I find the argument that slop books “dilute” non-slop books to be beyond nonsensical. Similarly, knowing a little bit (just enough to be dangerous) about how LLM training works, makes it difficult for me to believe that models are, in fact, holding full copies of works they are trained on. That’s just not how they work. But you don’t have to take my word for it. A. Feder Cooper, a well-known computer science professor at Yale who has (somewhat famously) done research on getting LLM’s to spit out “memorized books,” or other full works, had some pretty blunt criticisms of the “whack-a-mole” paper:

As will become clear soon, I think the paper has significant methodological and presentation problems. I’ve spent considerable time reviewing and re-reviewing the paper, and have consulted with two trusted senior colleagues who are experts on memorization to gut-check my reading. And, in brief, I’m confident that Alignment Whack-a-Mole’s headline claims are incorrect. These results rest on a specific memorization metric and elicitation methodology that I don’t think hold up to scrutiny, and don’t support the broad claims the paper makes. At best, I think the claims are seriously overstated; at worst, the large majority are wrong. I can’t tell which because the paper doesn’t report enough detail to distinguish the two.

That alone should be concerning, but the media — including the NY Times — really loved to report on these studies, even as their methodology seemed questionable to some experts, and despite the clear potential conflict of interest.

Now, that takes us to the claims in the OpenAI filing from earlier this week: it’s that the plaintiffs’ lawyers at Susman Godfrey secretly paid at least Chakrabarty to do these studies, hid that fact, and then took further steps to launder the studies as non-biased expertise. It appears this wasn’t just a conflict of interest at work, it was a conflict piled upon a conflict, and then potential fraud on the court.

Unable to muster any evidence of harm after years of discovery, Class Plaintiffs’ counsel Susman Godfrey L.L.P. (“Class Counsel” or “Susman”) paid Stony Brook University professor Dr. Tuhin Chakrabarty to research “[h]ow AI generated books dilute the market for human authors.” Declaration of Victor Chiu ISO Motion to Strike (“Chiu Decl.”), Ex. A. Dr. Chakrabarty then coauthored a working, non-peer-reviewed paper purporting to show exactly that (the “Chakrabarty Paper”). The paper was initially self-published on July 22, 2026. Susman had disclosed Dr. Chakrabarty and one of his co-authors as retained experts months earlier—but the resumes Susman provided omitted that Susman had funded Dr. Chakrabarty’s research. Neither Dr. Chakrabarty nor the other disclosed expert ever served an expert report in this case. And after Defendants specifically objected that Dr. Chakrabarty’s resume was incomplete, Susman provided what it represented was an “updated resume” that still omitted Susman’s own funding of his market-dilution research.

Now, some people will point out that it’s not uncommon for companies to pay for research and then use that research elsewhere in ways that are beneficial to them. That’s absolutely true. The problem here isn’t who paid for the research, but the lengths the plaintiffs’ lawyers went to in hiding who paid for it from the court (and from OpenAI and Microsoft)… and how the evidence was laundered into the case long past the normal deadline where it could have been challenged.

Normally, if you bring expert witnesses into a case, the other side gets to challenge their expertise and any research findings that they’re providing. But here, the class plaintiffs’ lawyers took a bunch of steps that at least suggest they deliberately sought to make that effectively impossible with this bit of research. They had named Chakrabarty as a potential witness, providing an incomplete resume for him, but then didn’t use him as such. Instead, they did a kind of evidence two step to get it into the case in a way that would make it harder to challenge:

On July 22, 2026—after the deadlines for all expert reports had passed—Dr. Chakrabarty, Dr. Dhillon, Xinyue Liu, and Professor Jane Ginsburg uploaded to the internet a working paper titled “Generative AI floods and dilutes the market for books.”… They then uploaded two subsequent versions of the paper on July 26, 2026 and August 3, 2026, respectively…. The paper remains identified as a “Working Paper Under Review.” …

The Chakrabarty Paper purports to “measure[] how generative AI” impacts “a real book market once its output reache[s] the catalog and compete[s] for sales.” … Its abstract asserts that the research “bear[s] directly on the market-effect question at the center of the fair use defense to copyright infringement.” … The July 22 and July 26 versions of the Chakrabarty Paper did not disclose that it was funded by Susman and did not make any of its underlying data available. … The August 3 version of the Chakrabarty Paper again did not disclose its funding source. …

[…..]

On Sunday, August 2, 2026, the afternoon before Mr. Lasinski’s deposition, Class Plaintiffs served a supplemental report devoted entirely to the Chakrabarty Paper and which cited the July 26, 2026 version. … At his deposition the next day, Mr. Lasinski testified that he did not analyze any of the data underlying the Chakrabarty Paper…. Mr. Lasinski also testified that he had never spoken with Dr. Chakrabarty or any of his co-authors “about this paper or any other matters related to this litigation.” … When Mr. Lasinski was asked whether he understood that Dr. Chakrabarty and Dr. Dhillon “were retained as experts by Plaintiffs in this matter,” counsel from Susman objected: “I’m not sure why this is appropriate to ask Mr. Lasinski about.” … Mr. Lasinski ultimately testified that he did not “know that this means that [Dr. Chakrabarty and Dr. Dhillon] were retained.”

Mr. Lasinski likewise did not know who had funded the research he was relying upon. When asked whether “the study was funded by Plaintiffs in this case or the Susman Godfrey firm,” Mr. Lasinski testified: “I don’t know the funding sources,” but “to be clear . . . funding something like this would be inconsistent with what I’ve known the Susman Godfrey firm to do.” … Counsel from Susman, who was defending the deposition, did not correct the record or comment on the issue of funding.

Got that? After the deadlines for expert reports were past, the Susman lawyers filed a “supplemental report” from a different expert, Lasinski, which was all about this report that Chakrabarty et al had only just published, effectively getting it into evidence after the deadline passed, and through a non-author of the paper, who had little actual knowledge of the paper’s methodology or data. And, yes, it’s notable that Lasinski said it would be “inconsistent” with what he knew of Susman Godfrey for the firm to fund something like this. Meanwhile, the Susman lawyers in the room objected to questions about whether the paper’s authors were retained experts, and then said nothing at all when Lasinski vouched that the firm wouldn’t fund such research. How… interesting.

There’s also the bit about how the lawyers for OpenAI and Microsoft figure this out:

After Mr. Lasinski’s deposition, OpenAI independently located a substantially similar version of Dr. Chakrabarty’s resume on his website…. Unlike the “updated” resume Susman provided in February, however, the version OpenAI found contains a section specifying $100,000 in “Funding” from Susman in December 2025:

The resume identifies the $100,000 as an “Unrestricted Gift for sponsored research” on “How AI generated books dilutes the market for human authors?”—the same subject covered in the Chakrabarty Paper and in Mr. Lasinski’s supplemental report….

Thus, according to Dr. Chakrabarty’s own resume, Susman’s funding had begun approximately two months before Susman provided Defendants with his supposedly “updated” resume, and the stated subject of that funding was the same market-dilution issue addressed by the Chakrabarty Paper and Mr. Lasinski’s supplemental report. Neither of the resumes Class Plaintiffs provided in February disclosed that the research was sponsored or the source of funding...

That looks bad! This looks worse:

Two days later, on August 27, 2026, Dr. Chakrabarty changed the resume on his public-facing website and removed the reference to Susman’s $100,000 gift. Chiu Decl. ¶ 15, Ex. M. The revised resume now states, in fine print and barely legible font, that “[a] previous version of [Dr. Chakrabarty’s] resume stated that [he] received an unrestricted gift for sponsored research from Susman Godfrey LLP in the amount of $100,000. This was incorrect as the research was done for In re Mosaic LLM litigation for which [his] institution was compensated in a lesser amount:”

Even taken at face value, the revised resume does not deny that Susman funding facilitated the research presented in the Chakrabarty Paper. Whether the money was nominally earmarked for this MDL or the In re Mosaic LLM Litigation case, it supported the same researcher investigating the same market dilution question that is the subject of the Chakrabarty Paper, which in turn is the subject of Mr. Lasinski’s supplemental report.

OpenAI and Microsoft have asked the court to toss the paper entirely, and it’s the plaintiffs’ key evidence on dilution, the exact thing Chhabria said was missing in the Meta case. But also, they point out that this appears to be an attempted fraud on the court.

The Lasinski Supplement is not just late; it instead appears to be a deliberate effort to gain an advantage by evading Rule 26. “It is troublesome, to say the least, for a party to engage a consulting, non-testifying expert; pay for that individual to conduct and publish a study, or otherwise affect or influence the study; engage a testifying expert who relies upon the study; and then cloak the details of the arrangement with the consulting expert . . . in order to conceal it from a party opponent and the Court.” … To make matters worse, Susman appears to have concealed its funding of the Chakrabarty Paper from Class Plaintiffs’ own expert, Mr. Lasinski, despite asking him to rely on it. Dr. Chakrabarty himself was also apparently ignorant of the fact that the tens of thousands of dollars Susman was funneling his way to conduct market-dilution research and publish papers was tied to a specific litigation, much less which one. And Class Plaintiffs have now completed the maneuver: their summary judgment submissions rely extensively on the Chakrabarty Paper and describe it to the Court simply as an “academic stud[y],” without disclosing that their own counsel funded the underlying research.

This maneuver deprived Defendants of the opportunity to fully analyze and rebut the Chakrabarty Paper—and the Court of the ability to properly assess its reliability. Had Class Plaintiffs properly disclosed the Chakrabarty Paper and underlying data and materials, Defendants would have evaluated the data on which the study is based, deposed Dr. Chakrabarty and his co-authors, and tested the study’s methodology and conclusions through the ordinary discovery process. Instead, Defendants were only able to depose Mr. Lasinski, who knew nothing about Dr. Chakrabarty’s underlying data and who mistook the Chakrabarty Paper to reflect neutral, independent research.

Courts recognize that it is “fundamentally unfair” for a party “to supplement the record with reports of alleged ‘consulting experts’”—like Dr. Chakrabarty here—“whose identity and opinions have been shielded [from disclosure].”

And this kind of sketchy behavior has been deemed to be fraud on the court before.

The Court also has the inherent authority to preclude the Lasinski Supplement and Chakrabarty Paper to “prevent [Class Plaintiffs] from perpetrating a fraud on the court,” Yukos Capital S.A.R.L. v. Feldman, 977 F.3d 216, 235 (2d Cir. 2020), or interfering with the judicial system’s ability to impartially adjudicate this action. Such interference includes concealing counsel’s role in creating purportedly neutral scientific evidence. See Hazel-Atlas Glass Co. v. Hartford-Empire Co., 322 U.S. 238, 251 (1944) (vacating judgment obtained using an article ghostwritten by counsel but presented as the work of a disinterested expert).

That is what Susman did here. When disclosing Dr. Chakrabarty as an expert, Susman omitted that it funded the research subject of the Chakrabarty Paper, continued to omit that funding even after providing what it represented was an “updated resume,” and allowed Mr. Lasinski to testify at his deposition that Susman would not provide such funding. And even since its funding of the research has come to light, Susman has refused to answer straightforward questions about the nature of its relationship with Dr. Chakrabarty and his co-authors. As Mr. Lasinski himself acknowledges, it would be “inconsistent” for a law firm to fund a study for litigation and then present it through an expert as neutral academic literature.

Once again, the issue isn’t even that the research is sketchy (although… it is). Nor is it that the research was paid for by an interested party (though… it was). The main issue is that the funding appears to have been deliberately hidden from the defendants, and then the sketchy, paid-for research was laundered into the case through a different expert after the deadline for expert reports had passed.

Literally everything about this bit of research — which is a key plank in the anti-fair use argument — comes out of this as suspect.

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On Monday we wrote about Donald Trump’s direct attack on the First Amendment and the free press by banning reporters from the White House. As we expected, a judge on Thursday morning issued a Temporary Restraining Order, blocking the White House from continuing this unconstitutional action, though (as happened the last time Trump tried this) the court relied on Fifth Amendment due process rather than reaching the First Amendment question.

Plaintiffs are also likely to succeed in showing that their hard passes were revoked without constitutionally adequate due process. The “general rule” is that “individuals must receive notice and an opportunity to be heard before the Government deprives them” of a constitutionally protected interest…. Indeed, the Supreme Court has “described the root requirement of the Due Process Clause as being that an individual be given an opportunity for a hearing before he is deprived of any significant property interest.” Cleveland Bd. of Educ. v. Loudermill, 470 U.S. 532, 542 (1985) (citation omitted). Karem also holds that, because of the substantial interests at stake, before the Government can revoke the White House-issued hard pass of a reporter it must promulgate rules or standards governing the conduct that would lead to such revocation. See 960 F.3d at 665. And the reporter must “receive fair notice not only of the conduct that would subject him to punishment, but also of the magnitude of the sanction that the White House might impose.”

Trump’s favorite trick of declaring “national security” as an excuse to do whatever the hell he wants doesn’t work here either:

Still, on this record, Defendants offer little to back up their asserted national security interest to justify revocation of Plaintiffs’ hard passes. So this interest does not tilt these factors in their favor, especially given that Plaintiffs have shown a likelihood of success on the merits.

For one thing, nothing in the record that predates this suit suggests that the revocation of Plaintiffs’ hard passes was motivated by national security concerns. Certainly, that is not what President Trump said when he announced that he was “banning” Plaintiffs from the White House—instead, he focused on the alleged lack of truthfulness and negativity of Plaintiffs’ reporting.3 And even the justifications provided to Plaintiffs after this case was filed are ambiguous about the true purpose of the revocations: while Defendants’ letters to Plaintiffs mention national security concerns, they purport to identify reporting that “threatened national security and/or spread falsehoods,” leaving open the possibility that all the stories fall into the later bucket, rather than the former.

Of course, it wouldn’t be the Trump presidency if his White House didn’t start out by ignoring the TRO. According to Politico (one of the banned outlets):

A POLITICO reporter was denied access to the White House Thursday and his press credentials confiscated after a judge ordered the Trump administration overnight to immediately restore access for news organizations that President Donald Trump banned from the complex last week.

MS NOW and CNN said their reporters were also denied entry. The three outlets have been barred from the White House since Saturday, following an order from Trump to do so “as a result of their constant ‘reporting’ FAKE NEWS!”

And so, the news orgs rushed back to court to point out that the White House was violating the TRO, which judges generally don’t take kindly to:

Early this morning the Court issued a Temporary Restraining Order directing “that Defendants (except for President Trump) and their agents, representatives, and all persons or entities acting in concert with them shall immediately return, reinstate, and restore the White House ‘hard pass’ press credentials held by employees of CNN, MS NOW, and POLITICO that were revoked on or about September 18, 2026.” Dkt. 24 (the “Order”). This morning journalists from each of CNN, MS NOW, and POLITICO attempted to enter the White House and were turned away. A Secret Service agent confiscated the hard pass of journalists from all three organizations. And when Plaintiff Betsy Klein, whose hard pass was confiscated on Saturday, September 19, requested to have her hard pass returned, her request was denied and she was refused access to the White House complex.

The judge ordered the White House to respond to the non-compliance notice by 12:30pm, and quickly received a declaration from Micah Stopperich, the Director of White House Press Operations, that the passes had been restored as of 9:07am, and that previously confiscated badges had been dropped at the entry gate to return to the journalists they had been taken from. Yet reporters say they were still being turned away until around noon, well after that supposed 9:07am restoration. They eventually regained some access to the White House.

But hours later, journalists from two of the same outlets were turned away from the arrival ceremony for Trump’s state dinner with Xi Jinping. As MS NOW itself reported:

Hours after regaining access, MS NOW White House reporter Laura Barrón-López was denied entry Thursday evening to the arrival ceremony on the portico ahead of the White House state dinner for Chinese President Xi Jinping. MS NOW had requested credentials for the event but never received a response. A CNN producer and correspondent were also denied access. When Barrón-López asked why she was being turned away, a White House staffer told her to “talk to Steven,” seemingly referring to White House communications director Steven Cheung.

According to CNN, the White House told them that CNN could not send a reporter to cover Xi’s arrival, only a photojournalist and an audio technician. The White House will surely argue that credentials for a specific event aren’t the same as the hard passes covered by the TRO. But blocking the reporters while allowing only CNN’s camera crew (a restriction not imposed on any other network) is exactly the kind of petty gamesmanship that the pending preliminary injunction briefing will have to deal with.

It’s unclear how the White House thinks any of this helps. It’s unconstitutional, and it makes the administration look petty: An administration that insists it’s the champion of free speech can’t even manage to let reporters in the door after a judge orders it to.

I’ve written a lot this year about how Republicans hijacked the $42.5 billion Broadband, Equity, Access, and Deployment (BEAD) program (created by the 2021 infrastructure bill) and immediately set about weakening oversight standards, eliminating enforcement of stuff like broadband affordability, speed, and equitable deployment, and making it a priority to heavily subsidize Elon Musk for LEO satellite broadband networks he had already planned to deploy anyway.

All of the chaos and changes have resulted in endless delays, and a lot of promising providers backing out of the BEAD program entirely. That includes electric cooperatives, many of which have been pushing affordable fiber optics into their existing, very rural electrical footprints.

The Trump NTIA process has been so filled with cronyism, delays, changes, new pointless regulations and chaos, a third or more of U.S. cooperatives say they’ll no longer participate:

“63 electric cooperatives across 27 states received provisional BEAD awards to offer reliable high-speed broadband to some of the most challenging eligible locations in the country. But delayed implementation and shifting guidance have created a program defined by uncertainty and confusion. Instead of accelerating deployment, continual revisions to the program have discouraged participation,” said Matheson.

“To date, 20 of the 63 electric cooperatives originally slated to participate in BEAD have withdrawn from the program. Many others are considering withdrawal, citing concerns over the application of extra-statutory pole attachment requirements as a condition of participation,” he added.”

Cooperatives are annoyed, in part, about new pole-attachment regulations the NTIA applied that make installations much more time-consuming and complex. The Communications Act explicitly exempted electric cooperatives and municipal utilities from federal pole regulation because such providers serve the public interest and are often the only ones willing to connect rural Americans.

Many incumbent telecoms control local poles, and make pole fiber attachment extra annoying to forestall competition. I’d suspect the new restrictions — from an administration that generally demonizes corporate oversight and regulation — are at the direct request of companies like AT&T, worried that popular cooperatives might gobble up market share in markets they theoretically could serve in the future.

It’s worth reminding you: Republicans spent the entirety of the last few years insisting that BEAD was a giant government boondoggle, and that once in office they’d fix it. This claim was routinely propped up by the likes of Ezra Klein at the New York Times, who hasn’t had a single solitary thing to say about U.S. broadband access policy in the two-years since Trumpism retook control of the program.

Congress originally set aside $42.5 billion to improve U.S. broadband. The Trump administration effectively tried to cut the program in half, insisting that Elon Musk’s expensive, congested satellite service would be “good enough” for a lot of these locations. The Trump administration then very clearly tried to wander off with the remaining money, and continues to be very murky about when states will receive it.

Fixing U.S. broadband requires a coordinated array of solutions. Some communities are helped by municipally-owned broadband. Some are helped by the local electrical cooperative getting into fiber and expanding cheap fiber access to rural communities. Others are best served by public-private partnerships between local governments and private providers.

The Trump administration’s BEAD changes have worked tirelessly to redirect a lot of money away from these popular, highly localized solutions, and funnel as much money as possible into the back pocket of incumbent monopolies like AT&T and Comcast, or billionaires like Elon Musk.

Other smaller and mid-sized providers are increasingly backing away from participating in the program entirely, either because of new restrictions and delays, or because the cost of deployment is skyrocketing due to wars, tariffs, and other Trump bumbling. As more BEAD bidders back away from participation or existing awards, more and more money will be funneled to Musk for substandard service so Trump Republicans can pretend the problem of U.S. broadband access has been “fixed.”

In states like California, Trump officials are insisting that California won’t receive any of its already-scheduled $1.86 billion BEAD grant award if they do literally anything telecom monopolies don’t like, whether that’s holding big telecom affordable for high prices, or enforcing the state’s net neutrality law.

It’s a cronyistic mess that’s starting to see growing bipartisan anger even in states like Texas, and it’s poised to get much, much louder as the long-delayed money to states truly begins to flow next year. Right now, a lot of people in state broadband policy are hesitant to publicly comment because they’re worried the Trump administration will sever their grant awards entirely.