Month: August 2025

Denmark’s Big Idea: Protect Personhood from the Blob With Consent First and Platform Duty Built In

Denmark has given the rest of us a simple, powerful starting point: protect the personhood of citizens from the blob—the borderless slurry of synthetic media that can clone your face, your voice, and your performance at scale. Crucially, Denmark isn’t trying to turn name‑image‑likeness into a mini‑copyright. It’s saying something more profound: your identity isn’t a “work”; it’s you. It’s what is sometimes called “personhood.” That framing changes everything. It’s not commerce, it’s a human right.

The Elements of Personhood

Personhood raises human reality as moral consideration, not a piece of content. For example, the European Court of Human Rights reads Article 8 ECHR (“private life”) to include personal identity (name, identity integrity, etc.), protecting individual identity against unjustified interference. This is, of course, anathema to Silicon Valley, but the world takes a different view.

In fact, Denmark’s proposal echoes the Universal Declaration of Human Rights. It starts with dignity (Art. 1) and recognition of each person before the law (Art. 6), and it squarely protects private life, honor, and reputation against synthetic impersonation (Art. 12). It balances freedom of expression (Art. 19) with narrow, clearly labeled carve-outs, and it respects creators’ moral and material interests (Art. 27(2)). Most importantly, it delivers an effective remedy (Art. 8): a consent-first rule backed by provenance and cross-platform stay-down, so individuals aren’t forced into DMCA-style learned helplessness.

Why does this matter? Because the moment we call identity or personhood a species of copyright, platforms will reach for a familiar toolbox—quotation, parody, transient copies, text‑and‑data‑mining (TDM)—and claim exceptions to protect them from “data holders”. That’s bleed‑through: the defenses built for expressive works ooze into an identity context where they don’t belong. The result is an unearned permission slip to scrape faces and voices “because the web is public.” Denmark points us in the opposite direction: consent or it’s unlawful. Not “fair use,” not “lawful access,” not “industry custom., not “data profile.” Consent. Pretty easy concept. It’s one of the main reasons tech executives keep their kids away from cell phones and social media.

Not Replicating the Safe Harbor Disaster

Think about how we got here. The first generation of the internet scaled by pushing risk downstream with a portfolio of safe harbors like the God-awful DMCA and Section 230 in the US. Platforms insisted they were deserving of blanket liability shields because they were special. They were “neutral pipes” which no one believed then and don’t believe now. These massive safe harbors hardened into a business model that likely added billions to the FAANG bottom line. We taught millions of rightsholders and users to live with learned helplessness: file a notice, watch copies multiply, rinse and repeat. Many users did not know they could even do that much, and frankly still may not. That DMCA‑era whack‑a‑mole turned into a faux license, a kind of “catch me if you can” bargain where exhaustion replaces consent.

Denmark’s New Protection of Personhood for the AI Era

Denmark’s move is a chance to break that pattern—if we resist the gravitational pull back to copyright. A fresh right of identity (called a “sui generis” right among Latin fans) is not subject to copyright or database exceptions, especially fair use, DMCA, and TDM. In plain English: “publicly available” is not permission to clone your face, train on your voice, or fabricate your performance. Or your children, either. If an AI platform wants to use identity, they ask first. If they don’t ask, they don’t get to do it, and they don’t get to keep the model they trained on it. And like many other areas, children can’t consent.

That legal foundation unlocks the practical fix creators and citizens actually need: stay‑down across platforms, not endless piecemeal takedowns. Imagine a teacher discovers a convincing deepfake circulating on two social networks and a messaging app. If we treat that deepfake as a copyright issue under the old model, she sends three notices, then five, then twelve. Week two, the video reappears with a slight change. Week three, it’s re‑encoded, mirrored, and captioned. The message she receives under a copyright regime is “you can never catch up.” So why don’t you just give up. Which, of course, in the world of Silicon Valley monopoly rents, is called the plan. That’s the learned helplessness Denmark gives us permission to reject.

Enforcing Personhood

How would the new plan work? First, we treat realistic digital imitations of a person’s face, voice, or performance as illegal absent consent, with only narrow, clearly labeled carve‑outs for genuine public‑interest reporting (no children, no false endorsement, no biometric spoofing risk, provenance intact). That’s the rights architecture: bright lines and human‑centered. Hence, “personhood.”

Second, we wire enforcement to succeed at internet scale. The way out of whack‑a‑mole is a cross‑platform deepfake registry operated with real governance. A deepfake registry doesn’t store videos; it stores non‑reversible fingerprints—exact file hashes for byte‑for‑byte matches and robust, perceptual fingerprints for the variants (different encodes, crops, borders). For audio, we use acoustic fingerprints; for video, scene/frame signatures. These markers will evolve and so should the deepfakes registry. One confirmed case becomes a family of identifiers that platforms check at upload and on re‑share. The first takedown becomes the last.

Third, we pair that with provenance by default: Provenance isn’t a license; it’s evidence. When credentials are present, it’s easier to authenticate so there is an incentive to use them. Provenance is the rebar that turns legal rules into reliable, automatable processes. However, absence of credentials doesn’t mean free for all.

Finally, we put the onus where it belongs—on platforms. Europe’s Digital Services Act at least theoretically already replaced “willful blindness” with “notice‑and‑action” duties and oversight for very large platforms. Denmark’s identity right gives citizens a clear, national‑law basis to say: “This is illegal content—remove it and keep it down.” The platform’s job isn’t to litigate fair use in the abstract or hide behind TDM. It’s to implement upload checks, preserve provenance, run repeat‑offender policies, and prevent recurrences. If a case was verified yesterday, it shouldn’t be back tomorrow with a 10‑pixel border or other trivial alteration to defeat the rules.

Some will ask: what about creativity and satire? The answer is what it has always been in responsible speech law—more speech not fake speech. If you’re lampooning a politician with a clearly labeled synthetic speech, no implied endorsement, provenance intact, and no risk of biometric spoofing or fraud, you have defenses. The point isn’t to smother satire; it’s to end the pretense that satire requires open season on the biometric identities of private citizens and working artists.

Others will ask: what about research and innovation? Good research runs on consent, especially human subject research (see 45 C.F.R. part 46). If a lab wants to study voice cloning, it recruits consenting participants, documents scope and duration, and keeps data and models in controlled settings. That’s science. What isn’t science is scraping the voices of a country’s population “because the web is public,” then shipping a model that anyone can use to spoof a bank’s call‑center checks. A no‑TDM‑bleed‑through clause draws that line clearly.

And yes, edge cases exist. There will be appeals, mistakes, and hard calls at the margins. That is why the registry must be governed—with identity verification, transparent logs, fast appeals, and independent oversight. Done right, it will look less like a black box and more like infrastructure: a quiet backbone that keeps people safe while allowing reporting and legitimate creative work to thrive.

If Denmark’s spark is to become a firebreak, the message needs to be crisp:

— This is not copyright. Identity is a personal right; copyright defenses don’t apply.

— Consent is the rule. Deepfakes without consent is unlawful.

— No TDM bleed‑through. “Publicly available” does not equate to permission to clone or train.

— Provenance helps prove, not permit. Keep credentials intact; stripping them has consequences.

— Stay‑down, cross‑platform. One verified case should not become a thousand reuploads.

That’s how you protect personhood from the blob. By refusing to treat humans like “content,” by ending the faux‑license of whack‑a‑mole, and by making platforms responsible for prevention, not just belated reaction. Denmark has given us the right opening line. Now we should finish the paragraph: consent or block. Label it, prove it, or remove it.

Judge Failla’s Opinion in Dow Jones v. Perplexity: RAG as Mechanism of Infringement

Judge Failla’s opinion in Dow Jones v. Perplexity doesn’t just keep the case alive—it frames RAG itself as the act of copying, and raises the specter of inducement liability under Grokster.

Although Judge Katherine Polk Failla’s August 21, 2025 opinion in Dow Jones & Co. v. Perplexity is technically a procedural ruling denying Perplexity’s motions to dismiss or transfer, Judge Failla offers an unusually candid window into how the Court may view the substance of the case. In particular, her treatment of retrieval-augmented generation (RAG) is striking: rather than describing it as Perplexity’s background plumbing, she identified it as the mechanism by which copyright infringement and trademark misattribution allegedly occur.  

Remember, Perplexity’s CEO described the company to Forbes as “It’s almost like Wikipedia and ChatGPT had a kid.” I’m still looking for that attribution under the Wikipedia Creative Commons license.

gov.uscourts.nysd.630270.65.0Download

As readers may recall, I’ve been very interested in RAG as an open door for infringement actions, so naturally this discussion caught my eye.  So we’re all on the page, retrieval-augmented generation (RAG) uses a “vector database” to expand an AI system’s knowledge beyond what is locked in its training data, including recent news sources for example. 

When you prompt a RAG-enabled model, it first searches the database for context, then weaves that information into its generated answer. This architecture makes outputs more accurate, current, and domain-specific, but also raises questions about copyright, data governance, and intentional use of third-party content mostly because RAG may rely on information outside of its training data.  Like if I queried “single bullet theory” the AI might have a copy of the Warren Commission report, but would need to go out on the web for the latest declassified JFK materials or news reports about those materials to give a complete answer.

You can also think of Google Search or Bing as a kind of RAG index—and you can see how that would give search engines a big leg up in the AI race, even though none of their various safe harbors, Creative Commons licenses, Google Books or direct licenses were for this RAG purpose.  So there’s that.

Judge Failla’s RAG Analysis

As Judge Failla explained, Perplexity’s system “relies on a retrieval-augmented generation (‘RAG’) database, comprised of ‘content from original sources,’ to provide answers to users,” with the indices “comprised of content that [Perplexity] want[s] to use as source material from which to generate the ‘answers’ to user prompts and questions.’” The model then “repackages the original, indexed content in written responses … to users,” with the RAG technology “tell[ing] the LLM exactly which original content to turn into its ‘answer.’” Or as another judge once said, “One who distributes a device with the object of promoting its use to infringe copyright, as shown by clear expression or other affirmative steps taken to foster infringement, going beyond mere distribution with knowledge of third-party action, is liable for the resulting acts of infringement by third parties using the device, regardless of the device’s lawful uses.” Or something like that.

On that basis, Judge Failla recognized Plaintiffs’ claim that infringement occurred at both ends of the process: “first, by ‘copying a massive amount of Plaintiffs’ copyrighted works as inputs into its RAG index’; second, by providing consumers with outputs that ‘contain full or partial verbatim reproductions of Plaintiffs’ copyrighted articles’; and third, by ‘generat[ing] made-up text (hallucinations) … attribut[ed] … to Plaintiffs’ publications using Plaintiffs’ trademarks.’” In her jurisdictional analysis, Judge Failla stressed that these “inputs are significant because they cause Defendant’s website to produce answers that are reproductions or detailed summaries of Plaintiffs’ copyrighted works,” thus tying the alleged misconduct directly to Perplexity’s business activities in New York although she was not making a substantive ruling in this instance.

What is RAG and Why It Matters

Retrieval-augmented generation is a method that pairs two steps: (1) retrieval of content from external databases or the open web, and (2) generation of a synthetic answer using a large language model. Instead of relying solely on the model’s pre-training, RAG systems point the model toward selected source material such as news articles, scientific papers, legal databases and instruct it to weave that content into an answer. 

From a user perspective, this can produce more accurate, up-to-date results. But from a legal perspective, the same pipeline can directly copy or closely paraphrase copyrighted material, often without attribution, and can even misattribute hallucinated text to legitimate sources. This dual role of RAG—retrieving copyrighted works as inputs and reproducing them as outputs—is exactly what made it central to Judge Failla’s opinion procedurally, but also may show where she is thinking substantively.

RAG in Frontier Labs

RAG is not a niche technique. It has become standard practice at nearly every frontier AI lab:

– OpenAI uses retrieval plug-ins and Bing integrations to ground ChatGPT answers.
– Anthropic deploys RAG pipelines in Claude for enterprise customers.
– Google DeepMind integrates RAG into Gemini and search-linked models.
– Meta builds retrieval into LLaMA applications and experimental assistants like Grok.
– Microsoft has made Copilot fundamentally a RAG product, pairing Bing with GPT.
– Cohere, Mistral, and other independents market RAG as a service layer for enterprises.

Why Dow Jones Matters Beyond Perplexity

Perplexity just happened to be first reported opinion as far as I know. The technical structure of its answer engine—indexing copyrighted content into a RAG system, then repackaging it for users—is not unique. It mirrors how the rest of the frontier labs are building their flagship products. What makes this case important is not that Perplexity is an outlier, but that it illustrates the legal vulnerability inherent in the RAG architecture itself.

Is RAG the Low-Hanging Fruit?

What makes this case so consequential is not just that Judge Failla recognized, at least for this ruling, that RAG is at least one mechanism of infringement, but that RAG cases may be easier to prove than disputes over model training inputs. Training claims often run into evidentiary hurdles: plaintiffs must show that their works were included in massive opaque training corpora, that those works influenced model parameters, and that the resulting outputs are “substantially similar.” That chain of proof can be complex and indirect.

By contrast, RAG systems operate in the open. They index specific copyrighted articles, feed them directly into a generation process, and sometimes output verbatim or near-verbatim passages. Plaintiffs can point to before-and-after evidence: the copyrighted article itself, the RAG index that ingested it, and the system’s generated output reproducing it. That may make proving copyright infringement far more straightforward to demonstrate than in a pure training case.

For that reason, Perplexity just happened to be first, but it will not be the last. Nearly every frontier lab such as OpenAI, Anthropic, Google, Meta, Microsoft is relying on RAG as the architecture of choice to ground their models. If RAG is the legal weak point, this opinion could mark the opening salvo in a much broader wave of litigation aimed at AI platforms, with courts treating RAG not as a technical curiosity but as a direct, provable conduit for infringement. 

And lurking in the background is a bigger question: is Grokster going to be Judge Failla’s roundhouse kick? That irony is delicious.  By highlighting how Perplexity (and the others) deliberately designed its system to ingest and repackage copyrighted works, the opinion sets the stage for a finding of intentionality that could make RAG the twenty-first-century version of inducement liability.

TikTok After Xi’s Qiushi Article: Why China’s Security Laws Are the Whole Ballgame

Xi Jinping’s new article in Qiushi (the Chinese Communist Party Central Committee’s flagship theoretical public policy journal) repackages a familiar message: China will promote the “healthy and high-quality development” of the private economy, but under the leadership of the Chinese Communist Party. This is expressed in China’s statutory law as the “Private Economy Promotion Law.”  And of course we have to always remember that under the PRC “constitution,” statutes are primarily designed to safeguard the authority and interests of the Chinese Communist Party (CCP) rather than to protect the rights and privileges of individuals—because individuals don’t really have any protections against the CCP.  

For U.S. policymakers weighing what to do about TikTok, this is not reassuring rhetoric in my view. It is instead a reminder that, in China, private platforms ultimately operate within a legal-and-political framework that gives state-security organs binding powers over companies, the Chinese people, and their data.

According to the South China Morning Post:

In another show of support for China’s private sector, Beijing has released the details of a speech from President Xi Jinping which included vows the country would guarantee a level playing field for private firms, safeguard entrepreneurs’ lawful rights and interests, and step up efforts to solve their long-standing challenges, including overdue payments.

The full address, delivered in February to a group of China’s leading entrepreneurs, had not been made available to the public before Friday, when Qiushi – the ruling Communist Party’s theoretical journal – posted a transcript on its website.

“The policies and measures to promote the development of the private economy must be implemented in a solid and thorough manner,” Xi said in February. “Whatever the party Central Committee has decided must be resolutely carried out – without ambiguity, delay, or compromise

I will try to explain why the emphasis of Xi’s policy speech matters, and why the divest-or-ban logic for TikTok under US law (and it is a law) remains intact regardless of what may seem like “friendly” language about private enterprise.  It’s also worth remembering that whatever the result of the TikTok divestment may be, it’s just another stop along the way in the Sino-American struggle­—or something more kinetic.  As Clausewitz wrote in his other famous quotation, the outcomes produced by war are never final. (See Book I Chapter 1 aka the good stuff.)  Even the most decisive battlefield victory may have no lasting political achievement.  As we have seen time and again, the termination of one conflict often produces the necessary conditions for future conflict.

What Xi’s piece actually signals

Xi’s article combines pro-private-sector language (property-rights protection, market access, financing support) with an explicit call for Party leadership and ideological guidance in the private economy. In other words, the promise is growth within control, and not just any control but the control of the Party. There is no carve‑out from national-security statutes, no “TikTok exemption,” and no suggestion that private firms can decline cooperation when state-security laws apply consistent with China’s “unrestricted warfare” doctrine.

Recall that the CCP has designated the TikTok algorithm as a strategic national asset, and “national” in this context and the context of Xi’s article means the Chinese Communist Party of which Xi is President-for-Life.  This brother is not playing.

The laws that define the TikTok Divestment risk (not the press releases)

The core concern about TikTok is jurisdiction, or the CCP’s extra-territorial jurisdiction, a concept we don’t fully comprehend. Xi’s Qiushi article promises support for private firms under Party leadership. That means that the National Intelligence Law, Cybersecurity Law, Counter‑Espionage Law, and China’s data‑export regime remain in force and are controlling authority over companies like TikTok. For U.S. reviewers like CIFIUS, that means ByteDance‑controlled TikTok is, by design, subject to compelled, confidential cooperation with state‑security organs. 

As long as the TikTok platform and algorithm is ultimately controlled by a company subject to the CCP’s security laws, U.S. reviewers correctly assume those laws can reach the service, even if operations are partly localized abroad. MTP readers will recall the four pillars of China’s statutory security regime that matter most in this context, being:

National Intelligence Law (2017). Requires all organizations and citizens to support, assist, and cooperate with state intelligence work, and to keep that cooperation secret. Corporate policies and NDAs do not trump statutory duties, especially in the PRC.

Cybersecurity Law (2017). Obligates “network operators” to provide technical support and assistance to public‑security and state‑security organs, and sets the baseline for security reviews and Multi‑Level Protection (MLPS) obligations.

Counter‑Espionage Law (2023 amendment). Broadens the scope of what counts as “espionage” to include data, documents, and materials related to national security or the “national interest,” increasing the zone where requests can be justified.

Data regime (Data Security Law (DSL), Personal Information Protection Law (PIPL), and the Cyberspace Administration of China (CAC) regulatory measures). Controls cross‑border transfers through security assessments or standard contracts and allows denials on national‑security grounds. Practically, many datasets can’t leave China without approval—and keys/cryptography used onshore must follow onshore rules.

None of the above is changed by the Private Economy Promotion Law or by Xi’s supportive tone toward entrepreneurs. The laws remain superior in any conflict such as the TikTok divest-or-ban law.

It is these laws that are at the bottom of U.S. concerns about TikTok’s data scraping–it is, after all, spyware with a soundtrack.  There’s a strong case to be made that U.S. artists, songwriters, creators and fans are all dupes of TikTok as a data collection tool  in a country that requires its companies to hand over to the Ministry of State Security all it needs to support the intelligence mission (MSS is like the FBI and CIA in one agency with a heavy ration of FSB).

Zhang Yiming, founder of ByteDance and former public face of TikTok, stepped down as CEO in 2021 but remains Chairman and key shareholder. He controls more than half of the company’s voting rights and retains about a 21% stake. That also makes him China’s richest man. Though low-profile publicly, he is actively guiding ByteDance’s AI strategy and long-term direction. Mr. Zhang does not discuss this part.  It should come as no surprise–according to his Wikipedia page, Mr. Zhang understands what happens when you don’t toe the Party line:

ByteDance’s first app, Neihan Duanzi, was shut down in 2018 by the National Radio and Television Administration. In response, Zhang issued an apology stating that the app was “incommensurate with socialist core values“, that it had a “weak” implementation of Xi Jinping Thought, and promised that ByteDance would “further deepen cooperation” with the ruling Chinese Communist Party to better promote its policies.

ByteDance’s AI strategy is built on aggressive large-scale data scraping including from TikTok. Its proprietary crawler, ByteSpider, dominates global web-scraping traffic, collecting vast amounts of content at speeds far beyond rivals like OpenAI. This raw data fuels TikTok’s recommendation engine and broader generative AI development, giving ByteDance rapid adaptability and massive training inputs. Unlike OpenAI, which emphasizes curated datasets, ByteDance prioritizes scale, velocity, and real-time responsiveness, integrating insights from TikTok user behavior and the wider internet. This approach positions ByteDance as a formidable AI competitor, leveraging its enormous data advantage to strengthen consumer products, expand generative AI capabilities, and consolidate global influence.

I would find it very, very hard to believe that Mr. Zhang is not a member of the Chinese Communist Party, but in any event he understands very clearly what his role is under the National Intelligence Law and related statutes.  Do you think that standing up to the MSS to protect the data privacy of American teenagers is consistent with “Xi Jinping Thought”?

Why this makes TikTok’s case harder, not easier

For Washington, the TikTok problem is not market access or entrepreneurship. It’s the data governance chain. Xi’s article underscores that private firms are expected to align with the Party Center’s decisions and to embed Party structures. Combine that political expectation with the statutory duties described above, and you get a simple inference: if China’s security services want something—from data access to algorithmic levers—ByteDance and its affiliates are obliged to give it to them or at least help, and are often barred from disclosing that help.

That’s why divestiture has become the U.S. default: the only durable mitigation against TikTok is to place ownership and effective control outside PRC legal reach, with clean technical and organizational separation (code, data, keys, staffing, and change control). Anything short of that leaves the fundamental risk untouched.

Where the U.S. law and process fit

Congress’s divest‑or‑ban statute requires TikTok to be controlled by an entity not subject to PRC direction, on terms approved by U.S. authorities. Beijing’s export‑control rules on recommendation algorithms make a full transfer difficult if not impossible; that’s why proposals have floated a U.S. “fork” with separate code, ops, and data. But Xi’s article doesn’t move the ball: it simply reinforces that CCP jurisdiction over private platforms is a feature, not a bug, of the system.

Practical implications (policy and product)

For policymakers: Treat Xi’s article as confirmation that political control and security statutes are baked in. Negotiated “promises” won’t outweigh legal duties to assist intelligence work. Any compliance plan that assumes voluntary transparency or a “hands‑off” approach is fragile by design.

For platforms: If you operate in China, assume compelled and confidential cooperation is possible and in this case almost a certainty if it hasn’t already happened. Architect China operations as least‑privilege, least‑data environments; segregate code and keys; plan for outbound data barrrers as a normal business condition.

For users and advertisers: The risk discussion is about governance and jurisdiction, not whether a particular management team “would never do that.” They would.  Corporate intent can’t override state legal authority particularly when the Party’s Ministry of State Security is doing the asking.

Now What?

Xi’s article does not soften TikTok’s regulatory problem in the United States. If anything, it sharpens it by reiterating that the private economy advances under the Party’s direction, never apart from it. When you combine Mr. Zhang’s role with Bytedance in China’s AI national champions, it’s pretty obvious whose side TikTok is on.

Wherever the divest-or-ban legislation ends up, it will inevitably set the stage for the next conflict.  If I had to bet today, my bet is that Xi has no intention of making a deal with the US that involves giving up the TikTok algorithm in violation of the Party’s export-control rules and access to US user data for AI training.

From Fictional “Looking Backward” to Nonfiction Silicon Valley: Will Technologists Crown the New Philosopher‑Kings?

More than a century ago, writers like Edward Bellamy and Edward Mandell House asked a question that feels as urgent in 2025 as it did in their era: Should society be shaped by its people, or designed by its elites? Both grappled with this tension in fiction. Bellamy’s Looking Backward (1888) imagined a future society run by rational experts — technocrats and bureaucrats centralizing economic and social life for the greater good. House’s Philip Dru: Administrator (1912) went a step further, envisioning an American civil war where a visionary figure seizes control from corrupt institutions to impose a new era of equity and order.  Sound familiar?

Today, Silicon Valley’s titans are rehearsing their own versions of these stories. In an era dominated by artificial intelligence, climate crisis, and global instability, the tension between democratic legitimacy and technocratic efficiency is more pronounced than ever.

The Bellamy Model: Eric Schmidt and Biden’s AI Order

President Biden’s sweeping Executive Order on AI issued in late 2023 feels like a chapter lifted from Looking Backward. Its core premise is unmistakable: Trust our national champion “trusted” technologists to design and govern the rules for an era shaped by artificial intelligence. At the heart of this approach is Eric Schmidt, former CEO of Google and a key advisor in shaping the AI order at least according to Eric Schmidt. 

Schmidt has long advocated for centralizing AI policymaking within a circle of vetted, elite technologists — a belief reminiscent of Bellamy’s idealistic vision. According to Schmidt, AI and other disruptive technologies are too pivotal, too dangerous, and too impactful to be left to messy democratic debates. For people in Schmidt’s cabal, this approach is prudent: a bulwark against AI’s darker possibilities. But it doesn’t do much to protect against darker possibilities from AI platforms.  For skeptics like me, it raises a haunting question posed by Bellamy himself: Are we delegating too much authority to a technocratic elite?

The Philip Dru Model: Musk, Sacks, and Trump’s Disruption Politics

Meanwhile, across the aisle, another faction of Silicon Valley is aligning itself with Donald Trump and making a very different bet for the future. Here, the nonfiction playbook is closer to the fictional Philip Dru. In House’s novel, an idealistic and forceful figure emerges from a broken system to impose order and equity. Enter Elon Musk and David Sacks, both positioning themselves as champions of disruption, backed by immense platforms, resources, and their own venture funds. 

Musk openly embraces a worldview wherein technologists have both the tools and the mandate to save society by reshaping transportation, energy, space, and AI itself. Meanwhile, Sacks advocates Silicon Valley as a de facto policymaker, disrupting traditional institutions and aligning with leaders like Trump to advance a new era of innovation-driven governance—with no Senate confirmation or even a security clearance. This competing cabal operates with the implicit belief that traditional democratic institutions, inevitiably bogged down by process, gridlock, and special interests can no longer solve society’s biggest problems. To Special Government Employees like Musk and Sacks, their disruption is not a threat to democracy, but its savior.

A New Gilded Age? Or a New Social Contract?

Both threads — Biden and Schmidt’s technocratic centralization and Musk, Sacks, and Trump’s disruption-driven politics — grapple with the legacy of Bellamy and House. In the Gilded Age that inspired those writers, industrial barons sought to justify their dominance with visions of rational, top-down progress. Today’s Silicon Valley billionaires carry a similar vision for the digital era, suggesting that elite technologists can govern more effectively than traditional democratic institutions like Plato’s “guardians” of The Republic.

But at what cost? Will AI policymaking and its implementation evolve as a public endeavor, shaped by citizen accountability? Or will it be molded by corporate elites making decisions in the background? Will future leaders consolidate their role as philosopher-kings and benevolent administrators — making themselves indispensable to the state?

The Stakes Are Clear

As the lines between Silicon Valley and Washington continue to blur, the questions posed by Bellamy and House have never been more relevant: Will technologist philosopher-kings write the rules for our collective future? Will democratic institutions evolve to balance AI and climate crisis effectively? Will the White House of 2025 (and beyond) cede authority to the titans of Silicon Valley? In this pivotal moment, America must ask itself: What kind of future do we want — one that is chosen by its citizens, or one that is designed for its citizens? The answer will define the character of American democracy for the rest of the 21st century — and likely beyond.

Shilling Like It’s 1999: Ars, Anthropic, and the Internet of Other People’s Things

Ars Technica just ran a piece headlined “AI industry horrified to face largest copyright class action ever certified.”

It’s the usual breathless “innovation under siege” framing—complete with quotes from “public interest” groups that, if you check the Google Shill List submitted to Judge Alsup in the Oracle case and Public Citizen’s Mission Creep-y, have long been in the paid service of Big Tech. Judge Alsup…hmmm…isn’t he the judge in the very Anthropic case that Ars is going on about?

Here’s what Ars left out: most of these so-called advocacy outfits—EFF, Public Knowledge, CCIA, and their cousins—have been doing Google’s bidding for years, rebranding corporate priorities as public interest. It’s an old play: weaponize the credibility of “independent” voices to protect your bottom line.

The article parrots the industry’s favorite excuse: proving copyright ownership is too hard, so these lawsuits are bound to fail. That line would be laughable if it weren’t so tired; it’s like elder abuse. We live in the age of AI deduplication, manifest checking, and robust content hashing—technologies the AI companies themselves use daily to clean, track, and optimize their training datasets. If they can identify and strip duplicates to improve model efficiency, they can identify and track copyrighted works. What they mean is: “We’d rather not, because it would expose the scale of our free-riding.”

That’s the unspoken truth behind these lawsuits. They’re not about “stifling innovation.” They’re about holding accountable an industry that’s built its fortunes on what can only be called the Internet of Other People’s Things—a business model where your creative output, your data, and your identity are raw material for someone else’s product, without permission, payment, or even acknowledgment.

Instead of cross-examining these corporate talking points like you know…journalists…Ars lets them pass unchallenged, turning what could have been a watershed moment for transparency into a PR assist. That’s not journalism—it’s message laundering.

The lawsuit doesn’t threaten the future of AI. It threatens the profitability of a handful of massive labs—many backed by the same investors and platforms that bankroll these “public interest” mouthpieces. If the case succeeds, it could force AI companies to abandon the Internet of Other People’s Things and start building the old-fashioned way: by paying for what they use.

Come on, Ars. Do we really have to go through this again? If you’re going to quote industry-adjacent lobbyists as if they were neutral experts, at least tell readers who’s paying the bills. Otherwise, it’s just shilling like it’s 1999.

AI’s Manhattan Project Rhetoric, Clearance-Free Reality

Every time a tech CEO compares frontier AI to the Manhattan Project, take a breath—and remember what that actually means.  Master spycatcher James Jesus Angleton is rolling in his grave. (aka Matt Damon in The Good Shepherd.). And like most elevator pitch talking points, that analogy starts to fall apart on inspection.

The Manhattan Project wasn’t just a moonshot scientific collaboration. It was the most tightly controlled, security-obsessed R&D operation in American history. Every physicist, engineer, and janitor involved had a federal security clearance. Facilities were locked down under military command of General Leslie Groves. Communications were monitored. Access was compartmentalized. And still—still—the Soviets penetrated it.  See Klaus Fuchs.  Let’s understand just how secret the Manhattan Project was—General Curtis LeMay had no idea it was happening until he was asked to set up facilities for the Enola Gay on his bomber base on Tinian a few months before the first nuclear bomb.  You want to find out about the details of any frontier lab, just pick up the newspaper.  Not nearly the same thing. There were no chatbots involved and there were no Special Government Employees with no security clearance.

Oppie Sacks

So when today’s AI executives name-drop Oppenheimer and invoke the gravity of dual-use technologies, what exactly are they suggesting? That we’re building world-altering capabilities without any of the safeguards that even the AI Whiz Kids admit are historically necessary by their Manhattan Project talking point in the pitch deck?

These frontier labs aren’t locked down. They’re open-plan. They’re not vetting personnel. They’re recruiting from Discord servers. They’re not subject to classified environments. They’re training military-civilian dual-use models on consumer cloud platforms. And when questioned, they invoke private sector privilege and push back against any suggestion of state or federal regulation.  And here’s a newsflash—requiring a security clearance for scientific work in the vital national interest is not regulation.  (Neither is copyright but that’s another story.)

Meanwhile, they’re angling for access to Department of Energy nuclear real estate, government compute subsidies, and preferred status in export policy—all under the justification of “national security” because, you know, China.  They want the symbolism of the Manhattan Project without the substance. They want to be seen as indispensable without being held accountable.

The truth is that AI is dual-use. It can power logistics and surveillance, language learning and warfare. That’s not theoretical—it’s already happening. China openly treats AI as part of its military-civil fusion strategy. Russia has targeted U.S. systems with information warfare bots. And our labs? They’re scraping from the open internet and assuming the training data hasn’t been poisoned with the massive misinformation campaigns on Wikipedia, Reddit and X that are routine.

If even the Manhattan Project—run under maximum secrecy—was infiltrated by Soviet spies, what are the chances that today’s AI labs, operating in the wide open are immune?  Wouldn’t a good spycatcher like Angleton assume these wunderkinds have already been penetrated?

We have no standard vetting for employees. No security clearances. No model release controls. No audit trail for pretraining data integrity. And no clear protocol for foreign access to model weights, inference APIs, or sensitive safety infrastructure. It’s not a matter of if. It’s a matter of when—or more likely, a matter of already.

Remember–nobody got rich out of working on the Manhattan Project. That’s another big difference. These guys are in it for the money, make no mistake.

So when you hear the Manhattan Project invoked again, ask the follow-up question: Where’s the security clearance?  Where’s the classification?  Where’s the real protection?  Who’s playing the role of Klaus Fuchs?

Because if AI is our new Manhattan Project, then running it without security is more than hypocrisy. It’s incompetence at scale.

AI Frontier Labs and the Singularity as a Modern Prophetic Cult

It gets rid of your gambling debts 
It quits smoking 
It’s a friend, it’s a companion 
It’s the only product you will ever need
From Step Right Up, written by Tom Waits

The AI “frontier labs” — OpenAI, Anthropic, DeepMind, xAI, and their constellation of evangelists — often present themselves as the high priests of a coming digital transcendence. This is sometimes called “the singularity” which refers to a hypothetical future point when artificial intelligence surpasses human intelligence, triggering rapid, unpredictable technological growth. Often associated with self-improving AI, it implies a transformation of society, consciousness, and control, where human decision-making may be outpaced or rendered obsolete by machines operating beyond our comprehension. 

But viewed through the lens of social psychology, the AI evangelists increasingly resembles that of cognitive dissonance cults, as famously documented in Dr. Leon Festinger and team’s important study of a UFO cult (a la Heaven’s Gate), When Prophecy Fails.  (See also The Great Disappointment.)

In that social psychology foundational study, a group of believers centered around a woman named “Marian Keech” predicted the world would end in a cataclysmic flood, only to be rescued by alien beings — but when the prophecy failed, they doubled down. Rather than abandoning their beliefs, the group rationalized the outcome (“We were spared because of our faith”) and became even more committed. They get this self-hypnotized look, kind of like this guy (and remember-this is what the Meta marketing people thought was the flagship spot for Meta’s entire superintelligence hustle):


This same psychosis permeates Singularity narratives and the AI doom/alignment discourse:
– The world is about to end — not by water, but by unaligned superintelligence.
– A chosen few (frontier labs) hold the secret knowledge to prevent this.
– The public must trust them to build, contain, and govern the very thing they fear.
– And if the predicted catastrophe doesn’t come, they’ll say it was their vigilance that saved us.

Like cultic prophecy, the Singularity promises transformation:
– Total liberation or annihilation (including liberation from annihilation by the Red Menace, i.e., the Chinese Communist Party).
– A timeline (“AGI by 2027”, “everything will change in 18 months”).
– An elite in-group with special knowledge and “Don’t be evil” moral responsibility.
– A strict hierarchy of belief and loyalty — criticism is heresy, delay is betrayal.

This serves multiple purposes:
1. Maintains funding and prestige by positioning the labs as indispensable moral actors.
2. Deflects criticism of copyright infringement, resource consumption, or labor abuse with existential urgency (because China, don’t you know).
3. Converts external threats (like regulation) into internal persecution, reinforcing group solidarity.

The rhetoric of “you don’t understand how serious this is” mirrors cult defenses exactly.

Here’s the rub: the timeline keeps slipping. Every six months, we’re told the leap to “godlike AI” is imminent. GPT‑4 was supposed to upend everything. That didn’t happen, so GPT‑5 will do it for real. Gemini flopped, but Claude 3 might still be the one.

When prophecy fails, they don’t admit error — they revise the story:
– “AI keeps accelerating”
– “It’s a slow takeoff, not a fast one.”
– “We stopped the bad outcomes by acting early.”
– “The doom is still coming — just not yet.”

Leon Festinger’s theories seen in When Prophecy Fails, especially cognitive dissonance and social comparison, influence AI by shaping how systems model human behavior, resolve conflicting inputs, and simulate decision-making. His work guides developers of interactive agents, recommender systems, and behavioral algorithms that aim to mimic or respond to human inconsistencies, biases, and belief formation.   So this isn’t a casual connection.

As with Festinger’s study, the failure of predictions intensifies belief rather than weakening it. And the deeper the believer’s personal investment, the harder it is to turn back. For many AI cultists, this includes financial incentives, status, and identity.

Unlike spiritual cults, AI frontier labs have material outcomes tied to their prophecy:
– Federal land allocations, as we’ve seen with DOE site handovers.
– Regulatory exemptions, by presenting themselves as saviors.
– Massive capital investment, driven by the promise of world-changing returns.

In the case of AI, this is not just belief — it’s belief weaponized to secure public assets, shape global policy, and monopolize technological futures. And when the same people build the bomb, sell the bunker, and write the evacuation plan, it’s not spiritual salvation — it’s capture.

The pressure to sustain the AI prophecy—that artificial intelligence will revolutionize everything—is unprecedented because the financial stakes are enormous. Trillions of dollars in market valuation, venture capital, and government subsidies now hinge on belief in AI’s inevitable dominance. Unlike past tech booms, today’s AI narrative is not just speculative; it is embedded in infrastructure planning, defense strategy, and global trade. This creates systemic incentives to ignore risks, downplay limitations, and dismiss ethical concerns. To question the prophecy is to threaten entire business models and geopolitical agendas. As with any ideology backed by capital, maintaining belief becomes more important than truth.

The Singularity, as sold by the frontier labs, is not just a future hypothesis — it’s a living ideology. And like the apocalyptic cults before them, these institutions demand public faith, offer no accountability, and position themselves as both priesthood and god.

If we want a secular, democratic future for AI, we must stop treating these frontier labs as prophets — and start treating them as power centers subject to scrutiny, not salvation.