Category: Artificial Intelligence

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.

Schrödinger’s Training Clause: How Platforms Like WeTransfer Say They’re Not Using Your Files for AI—Until They Are

Tech companies want your content. Not just to host it, but for their training pipeline—to train models, refine algorithms, and “improve services” in ways that just happen to lead to new commercial AI products. But as public awareness catches up, we’ve entered a new phase: deniable ingestion.

Welcome to the world of the Schrödinger’s training clause—a legal paradox where your data is simultaneously not being used to train AI and fully licensed in case they decide to do so.

The Door That’s Always Open

Let’s take the WeTransfer case. For a brief period this month (in July 2025), their Terms of Service included an unmistakable clause: users granted them rights to use uploaded content to “improve the performance of machine learning models.” That language was direct. It caused backlash. And it disappeared.

Many mea culpas later, their TOS has been scrubbed clean of AI references. I appreciate the sentiment, really I do. But—and there’s always a but–the core license hasn’t changed. It’s still:

– Perpetual

– Worldwide

– Royalty-free

– Transferable

– Sub-licensable

They’ve simply returned the problem clause to its quantum box. No machine learning references. But nothing that stops it either.

 A Clause in Superposition

Platforms like WeTransfer—and others—have figured out the magic words: Don’t say you’re using data to train AI. Don’t say you’re not using it either. Instead, claim a sweeping license to do anything necessary to “develop or improve the service.”

That vague phrasing allows future pivots. It’s not a denial. It’s a delay. And to delay is to deny.

That’s what makes it Schrödinger’s training clause: Your content isn’t being used for AI. Unless it is. And you won’t know until someone leaks it, or a lawsuit makes discovery public.

The Scrape-Then-Scrub Scenario

Let’s reconstruct what could have happened–not saying it did happen, just could have–following the timeline in The Register:

1. Early July 2025: WeTransfer silently updates its Terms of Service to include AI training rights.

2. Users continue uploading sensitive or valuable content.

3. [Somebody’s] AI systems quickly ingest that data under the granted license.

4. Public backlash erupts mid-July.

5. WeTransfer removes the clause—but to my knowledge never revokes the license retroactively or promises to delete what was scraped. In fact, here’s their statement which includes this non-denial denial: “We don’t use machine learning or any form of AI to process content shared via WeTransfer.” OK, that’s nice but that wasn’t the question. And if their TOS was so clear, then why the amendment in the first place?

Here’s the Potential Legal Catch

Even if WeTransfer removed the clause later, any ingestion that occurred during the ‘AI clause window’ is arguably still valid under the terms then in force. As far as I know, they haven’t promised:

– To destroy any trained models

– To purge training data caches

– Or to prevent third-party partners from retaining data accessed lawfully at the time

What Would ‘Undoing’ Scraping Require?

– Audit logs to track what content was ingested and when

– Reversion of any models trained on user data

– Retroactive license revocation and sub-license termination

None of this has been offered that I have seen.

What ‘We Don’t Train on Your Data’ Actually Means

When companies say, “we don’t use your data to train AI,” ask:

– Do you have the technical means to prevent that?

– Is it contractually prohibited?

– Do you prohibit future sublicensing?

– Can I audit or opt out at the file level?

If the answer to those is “no,” then the denial is toothless.

How Creators Can Fight Back

1. Use platforms that require active opt-in for AI training.

2. Encrypt files before uploading.

3. Include counter-language in contracts or submission terms:

   “No content provided may be used, directly or indirectly, to train or fine-tune machine learning or artificial intelligence systems, unless separately and explicitly licensed for that purpose in writing” or something along those lines.

4. Call it out. If a platform uses Schrödinger’s language, name it. The only thing tech companies fear more than litigation is transparency.

What is to Be Done?

The most dangerous clauses aren’t the ones that scream “AI training.” They’re the ones that whisper, “We’re just improving the service.”

If you’re a creative, legal advisor, or rights advocate, remember: the future isn’t being stolen with force. It’s being licensed away in advance, one unchecked checkbox at a time.

And if a platform’s only defense is “we’re not doing that right now”—that’s not a commitment. That’s a pause.

That’s Schrödinger’s training clause.

When Viceroy David Sacks Writes the Tariffs: How One VC Could Weaponize U.S. Trade Against the EU

David Sacks is a “Special Government Employee”, Silicon Valley insider and a PayPal mafioso who has become one of the most influential “unofficial” architects of AI policy under the Trump administration. No confirmation hearings, no formal role—but direct access to power.

He:
– Hosts influential political podcasts with Musk and Thiel-aligned narratives.
– Coordinates behind closed doors with elite AI companies who are now PRC-style “national champions” (OpenAI, Anthropic, Palantir).
– Has reportedly played a central role in shaping the AI Executive Orders and industrial strategy driving billions in public infrastructure to favored firms.

Under 18 U.S.C. § 202(a), a Special Government Employee is:

  • Temporarily retained to perform limited government functions,
  • For no more than 130 days per year (which for Sacks ends either April 14 or May 30, 2025), unless reappointed in a different role,
  • Typically serves in an advisory or consultative role, or
  • Without holding actual decision-making or operational authority over federal programs or agencies.

SGEs are used to avoid conflict-of-interest entanglements for outside experts while still tapping their expertise for advisory purposes. They are not supposed to wield sweeping executive power or effectively run a government program. Yeah, right.

And like a good little Silicon Valley weasel, Sacks supposedly is alternating between his DC side hustle and his VC office to stay under 130 days. This is a dumbass reading of the statute which says “‘Special Government employee’ means… any officer or employee…retained, designated, appointed, or employed…to perform…temporary duties… for not more than 130 days during any period of 365 consecutive days.” That’s not the same as “worked” 130 days on the time card punch. But oh well.

David Sacks has already exceeded the legal boundaries of his appointment as a Special Government Employee (SGE) both in time served but also by directing the implementation of a sweeping, whole-of-government AI policy, including authoring executive orders, issuing binding directives to federal agencies, and coordinating interagency enforcement strategies—actions that plainly constitute executive authority reserved for duly appointed officers under the Appointments Clause. As an SGE, Sacks is authorized only to provide temporary, nonbinding advice, not to exercise operational control or policy-setting discretion across the federal government. Accordingly, any executive actions taken at his direction or based on his advisement are constitutionally infirm as the unlawful product of an individual acting without valid authority, and must be deemed void as “fruit of the poisonous tree.”

Of course, one of the states that the Trump AI Executive Orders will collide with almost immediately is the European Union and its EU AI Act. Were they 51st? No that’s Canada. 52nd? Ah, right that’s Greenland. Must be 53rd.

How Could David Sacks Weaponize Trade Policy to Help His Constituents in Silicon Valley?

Here’s the playbook:

Engineer Executive Orders

Through his demonstrated access to Trump and senior White House officials, Sacks could promote executive orders under the International Emergency Economic Powers Act (IEEPA) or Section 301 of the Trade Act, aimed at punishing countries (like EU members) for “unfair restrictions” on U.S. AI exports or operations.

Something like this: “The European Union’s AI Act constitutes a discriminatory and protectionist measure targeting American AI innovation, and materially threatens U.S. national security and technological leadership.” I got your moratorium right here.

Leverage the USTR as a Blunt Instrument

The Office of the U.S. Trade Representative (USTR) can initiate investigations under Section 301 without needing new laws. All it takes is political will—and a nudge from someone like Viceroy Sacks—to argue that the EU’s AI Act discriminates against U.S. firms. See Canada’s “Tech Tax”. Gee, I wonder if Viceroy Sacks had anything to do with that one.

Redefine “National Security”

Sacks and his allies can exploit the Trump administration’s loose definition of “national security” claiming that restricting U.S. AI firms in Europe endangers critical defense and intelligence capabilities.

Smear Campaigns and Influence Operations

Sacks could launch more public campaigns against the EU like his attacks on the AI diffusion rule. According to the BBC, “Mr. Sacks cited the alienation of allies as one of his key arguments against the AI diffusion plan”. That’s a nice ally you got there, be a shame if something happened to it.

After all, the EU AI Act does what Sacks despises like protects artists and consumers, restricts deployment of high-risk AI systems (like facial recognition and social scoring), requires documentation of training data (which exposes copyright violations), and applies extraterritorially (meaning U.S. firms must comply even at home).

And don’t forget, Viceroy Sacks actually was given a portfolio that at least indirectly includes the National Security Council, so he can use the NATO connection to put a fine edge on his “industrial patriotism” just as war looms over Europe.

When Policy Becomes Personal

In a healthy democracy, trade retaliation should be guided by evidence, public interest, and formal process.

But under the current setup, someone like David Sacks can short-circuit the system—turning a private grievance into a national trade war. He’s already done it to consumers, wrongful death claims and copyright, why not join war lords like Eric Schmidt and really jack with people? Like give deduplication a whole new meaning.

When one man’s ideology becomes national policy, it’s not just bad governance.

It’s a broligarchy in real time.

Beyond Standard Oil: How the AI Action Plan Made America a Command Economy for Big Tech That You Will Pay For

When the White House requested public comments earlier this year on how the federal government should approach artificial intelligence, thousands of Americans—ranging from scientists to artists, labor leaders to civil liberties advocates—responded with detailed recommendations. Yet when America’s AI Action Plan was released today, it became immediately clear that those voices were largely ignored. The plan reads less like a response to public input and more like a pre-written blueprint drafted in collaboration with the very corporations it benefits. The priorities, language, and deregulatory thrust suggest that the real consultations happened behind closed doors—with Big Tech executives, not the American people.

In other words, business as usual.

By any historical measure—Standard Oil, AT&T, or even the Cold War military-industrial complex—the Trump Administration’s “America’s AI Action Plan” represents a radical leap toward a command economy built for and by Big Tech. Only this time, there are no rate regulations, no antitrust checks, and no public obligations—just streamlined subsidies, deregulation, and federally orchestrated dominance by a handful of private AI firms.

“Frontier Labs” as National Champions

The plan doesn’t pretend to be neutral. It picks winners—loudly. Companies like OpenAI, Anthropic, Meta, Microsoft, and Google are effectively crowned as “national champions,” entrusted with developing the frontier of artificial intelligence on behalf of the American state.

– The National AI Research Resource (NAIRR) and National Science Foundation partnerships funnel taxpayer-funded compute and talent into these firms.
– Federal procurement standards now require models that align with “American values,” but only as interpreted by government-aligned vendors.
– These companies will receive priority access to compute in a national emergency, hard-wiring them into the national security apparatus.
– Meanwhile, so-called “open” models will be encouraged in name only—no requirement for training data transparency, licensing, or reproducibility.

This is not a free market. This is national champion industrial policy—without the regulation or public equity ownership that historically came with it.

Infrastructure for Them, Not Us

The Action Plan reads like a wishlist from Silicon Valley’s executive suites:

– Federal lands are being opened up for AI data centers and energy infrastructure.
– Environmental and permitting laws are gutted to accelerate construction of facilities for private use.
– A national electrical grid expansion is proposed—not to serve homes and public transportation, but to power hyperscaler GPUs for model training.
– There’s no mention of public access, community benefit, or rural deployment. This is infrastructure built with public expense for private use.

Even during the era of Ma Bell, the public got universal service and price caps. Here? The public is asked to subsidize the buildout and then stand aside.

Deregulation for the Few, Discipline for the Rest

The Plan explicitly orders:
– Rescission of Biden-era safety and equity requirements.
– Reviews of FTC investigations to shield AI firms from liability.
– Withholding of federal AI funding from states that attempt to regulate the technology for safety, labor, or civil rights purposes.

Meanwhile, these same companies are expected to supply the military, detect cyberattacks, run cloud services for federal agencies, and set speech norms in government systems.

The result? An unregulated cartel tasked with executing state functions.

More Extreme Than Standard Oil or AT&T

Let’s be clear: Standard Oil was broken up. AT&T had to offer regulated universal service. Lockheed, Raytheon, and the Cold War defense contractors were overseen by procurement auditors and GAO enforcement.

This new AI economy is more privatized than any prior American industrial model—yet more dependent on the federal government than ever before. It’s an inversion of free market principles wrapped in American flags and GPU clusters.

Welcome to the Command Economy—For Tech Oligarchs

There’s a word for this: command economy. But instead of bureaucrats in Soviet ministries, we now have a handful of unelected CEOs directing infrastructure, energy, science, education, national security, and labor policy—all through cozy relationships with federal agencies.

If we’re going to nationalize AI, let’s do it honestly—with public governance, democratic accountability, and shared benefit. But this halfway privatized, fully subsidized, and wholly unaccountable structure isn’t capitalism. It’s capture.

Deduplication and Discovery: The Smoking Gun in the Machine

WINSTON

“Wipe up all those little pieces of brains and skull”

From Pulp Fiction, screenplay by Quentin Tarantino and Roger Avary

Deduplication—the process of removing identical or near-identical content from AI training data—is a critical yet often overlooked indicator that AI platforms actively monitor and curate their training sets. This is the kind of process that one would expect given the kind of “scrape, ready, aim” business practices that seems precisely the approach of AI platforms that have ready access to large amounts of fairly high quality data from users of other products placed into commerce by business affiliates or confederates of the AI platforms.

For example, Google Gemini could have access to gmail, YouTube, at least “publicly available” Google Docs, Google Translate, or Google for Education, and then of course one of the great scams of all time, Google Books. Microsoft uses Bing searches, MSN browsing, the consumer Copilot experience, and ad interactions. Amazon uses Alexa prompts, Facebook uses “public” posts and so on.

This kind of hoovering up of indiscriminate amounts of “data” in the form of your baby pictures posted on Facebook and your user generated content on YouTube is bound to produce duplicates. After all, how may users have posted their favorite Billie Eilish or Taylor Swift music video. AI doesn’t need 10000 versions of “Shake it Off” they probably just need the official video. Enter deduplication–which by definition means the platform knows what it has scraped and also knows what it wants to get rid of.

“Get rid of” is a relative concept. In many systems—particularly in storage environments like backup servers or object stores—deduplication means keeping only one physical copy of a file. Any other instances of that data don’t get stored again; instead, they’re represented by pointers to the original copy. This approach, known as inline deduplication, happens in real time and minimizes storage waste without actually deleting anything of functional value. It requires knowing what you have, knowing you have more than one version of the same thing, and being able to tell the system where to look to find the “original” copy without disturbing the process and burning compute inefficiently.

In other cases, such as post-process deduplication, the system stores data initially, then later scans for and eliminates redundancies. Again, the AI platform knows there are two or more versions of the same thing, say the book Being and Nothingness, knows where to find the copies and has been trained to keep only one version. Even here, the duplicates may not be permanently erased—they might be archived, versioned, or logged for auditing, compliance, or reconstruction purposes.

In AI training contexts, deduplication usually means removing redundant examples from the training set to avoid copyright risk. The duplicate content may be discarded from the training pipeline but often isn’t destroyed. Instead, AI companies may retain it in a separate filtered corpus or keep hashed fingerprints to ensure future models don’t retrain on the same material unknowingly.

So they know what they have, and likely know where it came from. They just don’t want to tell any plaintiffs.

Ultimately, deduplication is less about destruction and more about optimization. It’s a way to reduce noise, save resources, and improve performance—while still allowing systems to track, reference, or even rehydrate the original data if needed.

Its existence directly undermines claims that companies are unaware of which copyrighted works were ingested. Indeed, it only makes sense that one of the hidden consequences of the indiscriminate scraping that underpins large-scale AI training is the proliferation of duplicated data. Web crawlers ingest everything they can access—news articles republished across syndicates, forum posts echoed in aggregation sites, Wikipedia mirrors, boilerplate license terms, spammy SEO farms repeating the same language over and over. Without any filtering, this avalanche of redundant content floods the training pipeline.

This is where deduplication becomes not just useful, but essential. It’s the cleanup crew after a massive data land grab. The more messy and indiscriminate the scraping, the more aggressively the model must filter for quality, relevance, and uniqueness to avoid training inefficiencies or—worse—model behaviors that are skewed by repetition. If a model sees the same phrase or opinion thousands of times, it might assume it’s authoritative or universally accepted, even if it’s just a meme bouncing around low-quality content farms.

Deduplication is sort of the Winston Wolf of AI. And if the cleaner shows up, somebody had to order the cleanup. It is a direct response to the excesses of indiscriminate scraping. It’s both a technical fix and a quiet admission that the underlying data collection strategy is, by design, uncontrolled. But while the scraping may be uncontrolled to get copies of as much of your data has they can lay hands on, even by cleverly changing their terms of use boilerplate so they can do all this under the effluvia of legality, they send in the cleaner to take care of the crime scene.

So to summarize: To deduplicate, platforms must identify content-level matches (e.g., multiple copies of Being and Nothingness by Jean-Paul Sartre). This process requires tools that compare, fingerprint, or embed full documents—meaning the content is readable and classifiable–and, oh, yes, discoverable.

Platforms may choose the ‘cleanest’ copy to keep, showing knowledge and active decision-making about which version of a copyrighted work is retained. And–big finish–removing duplicates only makes sense if operators know which datasets they scraped and what those datasets contain.

Drilling down on a platform’s deduplication tools and practices may prove up knowledge and intent to a precise degree—contradicting arguments of plausible deniability in litigation. Johnny ate the cookies isn’t going to fly. There’s a market clearing level of record keeping necessary for deduping to work at all, so it’s likely that there are internal deduplication logs or tooling pipelines that are discoverable.

When AI platforms object to discovery about deduplication, plaintiffs can often overcome those objections by narrowing their focus. Rather than requesting broad details about how a model deduplicates its entire training set, plaintiffs should ask a simple, specific question: Were any of these known works—identified by title or author—deduplicated or excluded from training?

This approach avoids objections about overbreadth or burden. It reframes discovery as a factual inquiry, not a technical deep dive. If the platform claims the data was not retained, plaintiffs can ask for existing artifacts—like hash filters, logs, or manifests—or seek a sworn statement explaining the loss and when it occurred. That, in turn, opens the door to potential spoliation arguments.

If trade secrets are cited, plaintiffs can propose a protective order, limiting access to outside counsel or experts like we’ve done 100,000 times before in other cases. And if the defendant claims “duplicate” is too vague, plaintiffs can define it functionally—as content that’s identical or substantially similar, by hash, tokens, or vectors.

Most importantly, deduplication is relevant. If a platform identified a plaintiff’s work and trained on it anyway, that speaks to volitional use, copying, and lack of care—key issues in copyright and fair use analysis. And if they lied about it, particularly to the court—Helloooooo Harper & Row. Discovery requests that are focused, tailored, and anchored in specific works stand a far better chance of surviving objections and yielding meaningful evidence which hopefully will be useful and lead to other positive results.

Uncle Sugar, the Lord of War: Drones, Data, and Don’t Be Evil

“You know who’s going to inherit the Earth? Arms dealers. Because everyone else is too busy killing each other.”

The Lord of War, Screenplay by Andrew Niccol

Aren’t you glad that we allowed YouTube to jack us around, let Google distribute pirate tracks and sell advertising to pirate sites? Oh, and don’t forget allowing Google to scan all the world’s books–good thing they’re not using any of that to train AI. All thanks to Google’s former CEO Eric Schmidt, aka Uncle Sugar.

This week, Ukraine’s Office of the President announced a strategic partnership with Swift Beat, an AI drone technology company reportedly linked to Eric Schmidt who is showing up everywhere like a latter day Zelig. Yes, that’s right–your Uncle Sugar is back. The Ukraine memorandum of understanding adds yet another layer to the quiet convergence of Silicon Valley money and 21st century warfare that is looking to be Uncle Sugar’s sweet spot. Given that Ukraine depends on the United States to fund roughly half of its defense budget, it’s a fairly safe assumption that somehow, some way, Uncle Sugar’s Washington buddies are helping to fund this deal.

The President of Ukraine’s announcement says that “[Swift Beat] will produce interceptor drones for the Armed Forces of Ukraine to destroy Russian UAVs and missiles, quadcopters for reconnaissance, surveillance, fire adjustment, and logistics, as well as medium-class strike drones for engaging enemy targets.” All based on US intel. So if Swift Beat uses US money received by Ukraine to manufacture this kit, you don’t suppose that Uncle Sugar might be planning on selling it to the good old US of A at some point in the future? Particularly given that the Russia-Ukraine war is frequently cited as a proving ground for the AI driven battle space?

Swift Beat has been portrayed as a nimble startup positioned to bring real-time battlefield intelligence and autonomous drone operations to Ukraine’s army. But as Defence-UA reported, the company’s website is opaque, its corporate structure elusive, and its origins murky. Despite the gravity of the deal—delivering critical defense technology to a country in a kinetic war—Swift Beat appears to lack a documented track record, a history of defense contracting, or even a clear business address. Reporting suggests that Swift Beat is owned by Volya Robotics OÜ, registered in Tallinn, Estonia, with Eric Schmidt as the sole beneficiary. Yeah, that’s the kind of rock solid pedigree I want from someone manufacturing a weapon system to defend my capitol.

Defence-UA raises further questions: why did Ukraine partner with a new firm (apparently founded in 2023) whose founders are tightly linked to U.S. defense tech circles, but whose public presence is nearly nonexistent? What role, if any, did Eric Schmidt’s extensive political and financial connections play in sealing the agreement? Is this a case of wartime innovation at speed—or something more…shall we say…complicated?

The entire arrangement feels eerily familiar. Nicholas Cage’s character in *Lord of War* wasn’t just trafficking weapons—he was selling access, power, and plausible deniability. Substitute advanced AI for Kalashnikovs and you get a contemporary upgrade to the AI bubble: an ecosystem where elite technologists and financiers claim to be “helping,” while building opaque commercial networks through jurisdictions with far less oversight that your uncle would have back home in the US. Cage’s arms dealer character had swagger, but also cover. You know, babes dig the drone. Not that Uncle Sugar would know anything about that angle. Schmidt’s Swift Beat seems to be playing a similar game to Yuri Orlov—with more money, but no less ambiguity.

And this isn’t Schmidt’s first dance in this space. As readers will recall, his growing entanglement in defense procurement, battlefield innovation, and AI-powered surveillance raises not just ethical questions—but geopolitical ones. The revolving door between Big Tech and government has never spun faster, and now it’s air-dropping influence into actual war zones.

Dr. Sarah Myers West of the AI Now Institute warns that figures like Eric Schmidt—who bridge Big Tech and national security—are crafting frameworks that sideline accountability in favor of accelerated deployment. That critique lands squarely in the case of Swift Beat, whose shadowy profile and deep ties to Silicon Valley make it a case study in how defense contracts and contractors can be opaque and deeply unaccountable. And Swift Beat is definitely a company that Dr. West calls “Eric Schmidt adjacent.”

While no public allegations have been made, the unusual structure of the Swift Beat–Ukraine agreement—paired with the company’s lack of operational history and the involvement of high-profile U.S. individuals—may raise important questions under the Foreign Corrupt Practices Act (FCPA). The FCPA prohibits U.S. entities from offering anything of value to foreign officials to secure business advantages, directly or indirectly. When so-far unaudited wartime procurement contracts are awarded through opaque processes and international actors operate through newly formed entities…dare I say “cutouts”–the risk of FCPA violations needs to be explored. In other words, if Google were to get in to the military hardware business like Meta, there would be an employee revolt at the Googleplex. But if they do it through a trusted source, even one over yonder way across the river, well…what’s the evil in helping an old friend? The whole thing sounds pretty spooky.

As Ukraine deepens its relationships with U.S. technology suppliers, and as prominent U.S. investors and executives like Uncle Sugar increase their involvement with all of the above, it may be appropriate for U.S. oversight bodies to take a closer look—not as a condemnation, but in service of transparency, compliance, and public trust. You know, don’t be evil.

David Sacks Is Learning That the States Still Matter

For a moment, it looked like the tech world’s powerbrokers had pulled it off. Buried deep in a Republican infrastructure and tax package was a sleeper provision — the so-called AI moratorium — that would have blocked states from passing their own AI laws for up to a decade. It was an audacious move: centralize control over one of the most consequential technologies in history, bypass 50 state legislatures, and hand the reins to a small circle of federal agencies and especially to tech industry insiders.

But then it collapsed.

The Senate voted 99–1 to strike the moratorium. Governors rebelled. Attorneys general sounded the alarm. Artists, parents, workers, and privacy advocates from across the political spectrum said “no.” Even hardline conservatives like Ted Cruz eventually reversed course when it came down to the final vote. The message to Big Tech or the famous “Little Tech” was clear: the states still matter — and America’s tech elite ignore that at their peril.  (“Little Tech” is the latest rhetorical deflection promoted by Big Tech aka propaganda.)

The old Google crowd pushed the moratorium–their fingerprints were obvious. Having gotten fabulously rich off of their two favorites: The DMCA farce and the Section 230 shakedown. But there’s increasing speculation that White House AI Czar and Silicon Valley Viceroy David Sacks, PayPal alum and vocal MAGA-world player, was calling the ball. If true, that makes this defeat even more revealing.

Sacks represents something of a new breed of power-hungry tech-right influencer — part of the emerging “Red Tech” movement that claims to reject woke capitalism and coastal elitism but still wants experts to shape national policy from Silicon Valley, a chapter straight out of Philip Dru: Administrator. Sacks is tied to figures like Peter Thiel, Elon Musk, and a growing network of Trump-aligned venture capitalists. But even that alignment couldn’t save the moratorium.

Why? Because the core problem wasn’t left vs. right. It was top vs. bottom.

In 1964, Ronald Reagan’s classic speech called A Time for Choosing warned about “a little intellectual elite in a far-distant capitol” deciding what’s best for everyone else. That warning still rings true — except now the “capitol” might just be a server farm in Menlo Park or a podcast studio in LA.

The AI moratorium was an attempt to govern by preemption and fiat, not by consent. And the backlash wasn’t partisan. It came from red states and blue ones alike — places where elected leaders still think they have the right to protect their citizens from unregulated surveillance, deepfakes, data scraping, and economic disruption.

So yes, the defeat of the moratorium was a blow to Google’s strategy of soft-power dominance. But it was also a shot across the bow for David Sacks and the would-be masters of tech populism. You can’t have populism without the people.

If Sacks and his cohort want to play a long game in AI policy, they’ll have to do more than drop ideas into the policy laundry of think tank white papers and Beltway briefings. They’ll need to win public trust, respect state sovereignty, and remember that governing by sneaky safe harbors is no substitute for legitimacy.  

The moratorium failed because it presumed America could be governed like a tech startup — from the top, at speed, with no dissent. Turns out the country is still under the impression they have something to say about how they are governed, especially by Big Tech.

The Patchwork They Fear Is Accountability: Why Big AI Wants a Moratorium on State Laws

Why Big Tech’s Push for a Federal AI Moratorium Is Really About Avoiding State Investigations, Liability, and Transparency

As Congress debates the so-called “One Big Beautiful Bill Act,” one of its most explosive provisions has stayed largely below the radar: a 10-year or 5-year or any-year federal moratorium on state and local regulation of artificial intelligence. Supporters frame it as a common sense way to prevent a “patchwork” of conflicting state laws. But the real reason for the moratorium may be more self-serving—and more ominous.

The truth is, the patchwork they fear is not complexity. It’s accountability.

Liability Landmines Beneath the Surface

As has been well-documented by the New York Times and others, generative AI platforms have likely ingested and processed staggering volumes of data that implicate state-level consumer protections. This includes biometric data (like voiceprints and faces), personal communications, educational records, and sensitive metadata—all of which are protected under laws in states like Illinois (BIPA), California (CCPA/CPRA), and Texas.

If these platforms scraped and trained on such data without notice or consent, they are sitting on massive latent liability. Unlike federal laws, which are often narrow or toothless, many state statutes allow private lawsuits and statutory damages. Class action risk is not hypothetical—it is systemic.  It is crucial for policymakers to have a clear understanding of where we are today with respect to the collision between AI and consumer rights, including copyright.  The corrosion of consumer rights by the richest corporations in commercial history is not something that may happen in the future.  Massive violations have  already occurred, are occurring this minute, and will continue to occur into the future at an increasing rate.  

The Quiet Race to Avoid Discovery

State laws don’t just authorize penalties; they open the door to discovery. Once an investigation or civil case proceeds, AI platforms could be forced to disclose exactly what data they trained on, how it was retained, and whether any red flags were ignored.

This mirrors the arc of the social media addiction lawsuits now consolidated in multidistrict litigation. Platforms denied culpability for years—until internal documents showed what they knew and when. The same thing could happen here, but on a far larger scale.

Preemption as Shield and Sword

The proposed AI moratorium isn’t a regulatory timeout. It’s a firewall. By halting enforcement of state AI laws, the moratorium could prevent lawsuits, derail investigations, and shield past conduct from scrutiny.

Even worse, the Senate version conditions broadband infrastructure funding (BEAD) on states agreeing to the moratorium—an unconstitutional act of coercion that trades state police powers for federal dollars. The legal implications are staggering, especially under the anti-commandeering doctrine of Murphy v. NCAA and Printz v. United States.

This Isn’t About Clarity. It’s About Control.

Supporters of the moratorium, including senior federal officials and lobbying arms of Big Tech, claim that a single federal standard is needed to avoid chaos. But the evidence tells a different story.

States are acting precisely because Congress hasn’t. Illinois’ BIPA led to real enforcement. California’s privacy framework has teeth. Dozens of other states are pursuing legislation to respond to harms AI is already causing.

In this light, the moratorium is not a policy solution. It’s a preemptive strike.

Who Gets Hurt?
– Consumers, whose biometric data may have been ingested without consent
– Parents and students, whose educational data may now be part of generative models
– Artists, writers, and journalists, whose copyrighted work has been scraped and reused
– State AGs and legislatures, who lose the ability to investigate and enforce

Google Is an Example of Potential Exposure

Google’s former executive chairman Eric Schmidt has seemed very, very interested in writing the law for AI.  For example, Schmidt worked behind the scenes for the two years at least to establish US artificial intelligence policy under President Biden. Those efforts produced the “Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence“, the longest executive order in history. That EO was signed into effect by President Biden on October 30.  In his own words during an Axios interview with Mike Allen, the Biden AI EO was signed just in time for Mr. Schmidt to present that EO as what Mr. Schmidt calls “bait” to the UK government–which convened a global AI safety conference at Bletchley Park in the UK convened by His Excellency Rishi Sunak (the UK’s tech bro Prime Minister) that just happened to start on November 1, the day after President Biden signed the EO.  And now look at the disaster that the UK AI proposal would be.  

As Mr. Schmidt told Axios:

So far we are on a win, the taste of winning is there.  If you look at the UK event which I was part of, the UK government took the bait, took the ideas, decided to lead, they’re very good at this,  and they came out with very sensible guidelines.  Because the US and UK have worked really well together—there’s a group within the National Security Council here that is particularly good at this, and they got it right, and that produced this EO which is I think is the longest EO in history, that says all aspects of our government are to be organized around this.

Apparently, Mr. Schmidt hasn’t gotten tired of winning.  Of course, President Trump rescinded the Biden AI EO which may explain why we are now talking about a total moratorium on state enforcement which percolated at a very pro-Google shillery called R Street Institute, apparently by one Adam Thierer .  But why might Google be so interested in this idea?

Google may face exponentially acute liability under state laws if it turns out that biometric or behavioral data from platforms like YouTube Kids or Google for Education were ingested into AI training sets. 

These services, marketed to families and schools, collect sensitive information from minors—potentially implicating both federal protections like COPPA and more expansive state statutes. As far back as 2015, Senator Ben Nelson raised alarms about YouTube Kids, calling it “ridiculously porous” in terms of oversight and lack of safeguards. If any of that youth-targeted data has been harvested by generative AI tools, the resulting exposure is not just a regulatory lapse—it’s a landmine. 

The moratorium could be seen as an attempt to preempt the very investigations that might uncover how far that exposure goes.

What is to be Done?

Instead of smuggling this moratorium into a must-pass bill, Congress should strip it out and hold open hearings. If there’s merit to federal preemption, let it be debated on its own. But do not allow one of the most sweeping power grabs in modern tech policy to go unchallenged.

The public deserves better. Our children deserve better.  And the states have every right to defend their people. Because the patchwork they fear isn’t legal confusion.

It’s accountability.

AI’s Legal Defense Team Looks Familiar — Because It Is

If you feel like you’ve seen this movie before, you have.

Back in the 2003-ish runup to the 2005 MGM Studios, Inc. v. Grokster, Ltd. Supreme Court case, I met with the founder of one of the major p2p platforms in an effort to get him to go legal.  I reminded him that he knew there was all kinds of bad stuff that got uploaded to his platform.  However much he denied it, he was filtering it out and he was able to do that because he had the control over the content that he (and all his cohorts) denied he had.  

I reminded him that if this case ever went bad, someone was going to invade his space and find out exactly what he was up to. Just because the whole distributed p2p model (unlike Napster, by the way) was built to both avoid knowledge and be a perpetual motion machine, there was going to come a day when none of that legal advice was going to matter.  Within a few months the platform shut down, not because he didn’t want to go legal, but because he couldn’t, at least not without actually devoting himself to respecting other people’s rights.

Everything Old is New Again

Back in the early 2000s, peer-to-peer (P2P) piracy platforms claimed they weren’t responsible for the illegal music and videos flooding their networks. Today, AI companies claim they don’t know what’s in their training data. The defense is essentially the same: “We’re just the neutral platform. We don’t control the content.”  It’s that distorted view of the DMCA and Section 230 safe harbors that put many lawyers’ children through prep school, college and graduate school.

But just like with Morpheus, eDonkey, Grokster, and LimeWire, everyone knew that was BS because the evidence said otherwise — and here’s the kicker: many of the same lawyers are now running essentially the same playbook to defend AI giants.

The P2P Parallel: “We Don’t Control Uploads… Except We Clearly Do”

In the 2000s, platforms like Kazaa and LimeWire were like my little buddy–magically they  never had illegal pornography or extreme violence available to consumers, they prioritized popular music and movies, and filtered out the worst of the web

That selective filtering made it clear: they knew what was on their network. It wasn’t even a question of “should have known”, they actually knew and they did it anyway.  Courts caught on. 

In Grokster,  the Supreme Court side stepped the hosting issue and essentially said that if you design a platform with the intent to enable infringement, you’re liable.

The Same Playbook in the AI Era

Today’s AI platforms — OpenAI, Anthropic, Meta, Google, and others — essentially argue:
“Our model doesn’t remember where it learned [fill in the blank]. It’s just statistics.”

But behind the curtain, they:
– Run deduplication tools to avoid overloading, for example on copyrighted books
– Filter out NSFW or toxic content
– Choose which datasets to include and exclude
– Fine-tune models to align with somebody’s social norms or optics

This level of control shows they’re not ignorant — they’re deflecting liability just like they did with p2p.

Déjà Vu — With Many of the Same Lawyers

Many of the same law firms that defended Grokster, Kazaa, and other P2P pirate defendants as well as some of the ISPs are now representing AI companies—and the AI companies are very often some, not all, but some of the same ones that started screwing us on DMCA, etc., for the last 25 years.  You’ll see familiar names all of whom have done their best to destroy the creative community for big, big bucks in litigation and lobbying billable hours while filling their pockets to overflowing. 

The legal cadre pioneered the ‘willful blindness’ defense and are now polishing it up for AI, hoping courts haven’t learned the lesson.  And judging…no pun intended…from some recent rulings, maybe they haven’t.

Why do they drive their clients into a position where they pose an existential threat to all creators?  Do they not understand that they are creating a vast community of humans that really, truly, hate their clients?  I think they do understand, but there is a corresponding hatred of the super square Silicon Valley types who hate “Hollywood” right back.

Because, you know, information wants to be free—unless they are selling it.  And your data is their new oil. They apply this “ethic” not just to data, but to everything: books, news, music, images, and voice. Copyright? A speed bump. Terms of service? A suggestion. Artist consent? Optional.  Writing a song is nothing compared to the complexities of Biggest Tech.

Why do they do this?  OCPD Much?

Because control over training data is strategic dominance and these people are the biggest control freaks that mankind has ever produced.  They exhibit persistent and inflexible patterns of behavior characterized by an excessive need to control people, environments, and outcomes, often associated with traits of obsessive-compulsive personality disorder.  

So empathy will get you nowhere with these people, although their narcissism allows them to believe that they are extremely empathetic.  Pathetic, yes, empathetic, not so much.  

Pay No Attention to that Pajama Boy Behind the Curtain

The driving force behind AI is very similar to the driving force behind the Internet.   If pajama boy can harvest the world’s intellectual property and use it to train his proprietary AI model, he now owns a simulation of the culture he is not otherwise part of, and not only can he monetize it without sharing profits or credit, he can deny profits and credit to the people who actually created it.

So just like the heyday of Pirate Bay, Grokster & Co.  (and Daniel Ek’s pirate incarnation) the goal isn’t innovation. The goal is control over language, imagery, and the markets that used to rely on human creators.  This should all sound familiar if you were around for the p2p era.

Why This Matters

Like p2p platforms, it’s just not believable that the AI companies do know what’s in their models.  They may build their chatbot interface so that the public can’t ask the chatbot to blow the whistle on the platform operator, but that doesn’t mean  the company can’t tell what they are training on.  These operators have to be able to know what’s in the training materials and manipulate that data daily.  

They fingerprint, deduplicate, and sanitize their datasets. How else can they avoid having multiple copies of books, for example, that would be a compute nightmare.  They store “embeddings” in a way that they can optimize their AI to use only the best copy of any particular book.  They control the pipeline.

It’s not about the model’s memory. It’s about the platform’s intent and awareness.

If they’re smart enough to remove illegal content and prioritize clean data, they’re smart enough to be held accountable.

We’re not living through the first digital content crisis — just the most powerful one yet. The legal defenses haven’t changed much. But the stakes — for copyright, competition, and consumer protection — are much higher now.

Courts, Congress, and the public should recognize this for what it is: a recycled defense strategy in service of unchecked AI power. Eventually Grokster ran into Grokster— and all these lawyers are praying that there won’t be an AI version of the Grokster case. 

Steve’s Not Here–Why AI Platforms Are Still Acting Like Pirate Bay

In 2006, I wrote “Why Not Sell MP3s?” — a simple question pointing to an industry in denial. The dominant listening format was the MP3 file, yet labels were still trying to sell CDs or hide digital files behind brittle DRM. It seems kind of incredible in retrospect, but believe me it happened. Many cycles were burned on that conversation. Fans had moved on. The business hadn’t.

Then came Steve Jobs.

At the launch of the iTunes Store — and I say this as someone who sat in the third row — Jobs gave one of the most brilliant product presentations I’ve ever seen. He didn’t bulldoze the industry. He waited for permission, but only after crafting an offer so compelling it was as if the labels should be paying him to get in. He brought artists on board first. He made it cool, tactile, intuitive. He made it inevitable.

That’s not what’s happening in AI.

Incantor: DRM for the Input Layer

Incantor is trying to be the clean-data solution for AI — a system that wraps content in enforceable rights metadata, licenses its use for training and inference, and tracks compliance. It’s DRM, yes — but applied to training inputs instead of music downloads.

It may be imperfect, but at least it acknowledges that rights exist.

What’s more troubling is the contrast between Incantor’s attempt to create structure and the behavior of the major AI platforms, which have taken a very different route.

AI Platforms = Pirate Bay in a Suit

Today’s generative AI platforms — the big ones — aren’t behaving like Apple. They’re behaving like The Pirate Bay with a pitch deck.

– They ingest anything they can crawl.
– They claim “public availability” as a legal shield.
– They ignore licensing unless forced by litigation or regulation.
– They posture as infrastructure, while vacuuming up the cultural labor of others.

These aren’t scrappy hackers. They’re trillion-dollar companies acting like scraping is a birthright. Where Jobs sat down with artists and made the economics work, the platforms today are doing everything they can to avoid having that conversation.

This isn’t just indifference — it’s design. The entire business model depends on skipping the licensing step and then retrofitting legal justifications later. They’re not building an ecosystem. They’re strip-mining someone else’s.

What Incantor Is — and Isn’t

Incantor isn’t Steve Jobs. It doesn’t control the hardware, the model, the platform, or the user experience. It can’t walk into the room and command the majors to listen with elegance. But what it is trying to do is reintroduce some form of accountability — to build a path for data that isn’t scraped, stolen, or in legal limbo.

That’s not an iTunes power move. It’s a cleanup job. And it won’t work unless the AI companies stop pretending they’re search engines and start acting like publishers, licensees, and creative partners.

What the MP3 Era Actually Taught Us

The MP3 era didn’t end because DRM won. It ended because someone found a way to make the business model and the user experience better — not just legal, but elegant. Jobs didn’t force the industry to change. He gave them a deal they couldn’t refuse.

Today, there’s no Steve Jobs. No artists on stage at AI conferences. No tactile beauty. Just cold infrastructure, vague promises, and a scramble to monetize other people’s work before the lawsuits catch up. Let’s face it–when it comes to Elon, Sam, or Zuck, would you buy a used Mac from that man?

If artists and AI platforms were in one of those old “I’m a Mac / I’m a PC” commercials, you wouldn’t need to be told which is which. One side is creative, curious, collaborative. The other is corporate, defensive, and vaguely annoyed that you even asked the question.

Until that changes, platforms like Incantor will struggle to matter — and the AI industry will continue to look less like iTunes, and more like Pirate Bay with an enterprise sales team.