Tag: technology

You Can’t Prosecute Smuggling NVIDIA chips to CCP and Authorize Sales to CCP at the Same Time

The Trump administration is attempting an impossible contradiction: selling advanced NVIDIA AI chips to China while the Department of Justice prosecutes criminal cases for smuggling the exact same chips into China.

According to the DOJ:

“Operation Gatekeeper has exposed a sophisticated smuggling network that threatens our Nation’s security by funneling cutting-edge AI technology to those who would use it against American interests,” said Ganjei. “These chips are the building blocks of AI superiority and are integral to modern military applications. The country that controls these chips will control AI technology; the country that controls AI technology will control the future. The Southern District of Texas will aggressively prosecute anyone who attempts to compromise America’s technological edge.”

That divergence from the prosecutors is not industrial policy. That is incoherence. But mostly it’s just bad advice, likely coming from White House AI Czar David Sacks, Mr. Trump’s South African AI policy advisor who may have a hard time getting a security clearance in the first place..

On one hand, DOJ is rightly bringing cases over the illegal diversion of restricted AI chips—recognizing that these processors are strategic technologies with direct national-security implications. On the other hand, the White House is signaling that access to those same chips is negotiable, subject to licensing workarounds, regulatory carve-outs, or political discretion.

You cannot treat a technology as contraband in federal court and as a commercial export in the West Wing.

Pick one.

AI Chips Are Not Consumer Electronics

The United States does not sell China F-35 fighter jets. We do not sell Patriot missile systems. We do not sell advanced avionics platforms and then act surprised when they show up embedded in military infrastructure. High-end AI accelerators are in the same category.

NVIDIA’s most advanced chips are not merely commercial products. They are general-purpose intelligence infrastructure or what China calls military-civil fusion. They train surveillance systems, military logistics platforms, cyber-offensive tools, and models capable of operating autonomous weapons and battlefield decision-making pipelines with no human in the loop.

If DOJ treats the smuggling of these chips into China as a serious federal crime—and it should—there is no coherent justification for authorizing their sale through executive discretion. Except, of course, money, or in Mr. Sacks case, more money.

Fully Autonomous Weapons—and Selling the Rope

China does not need U.S. chips to build consumer AI. It wants them for military acceleration.Advanced NVIDIA AI chips are not just about chatbots or recommendation engines. They are the backbone of fully autonomous weapons systems—autonomous targeting, swarm coordination, battlefield logistics, and decision-support models that compress the kill chain beyond meaningful human control.

There is an old warning attributed to Vladimir Lenin—that capitalists would sell the rope by which they would later be hanged. Apocryphal or not, it captures this moment with uncomfortable precision.

If NVIDIA chips are powerful enough to underpin autonomous weapons systems for allied militaries, they are powerful enough to underpin autonomous weapons systems for adversaries like China. Trump’s own National Security Strategy statement clearly says previous U.S. elites made “mistaken” assumptions about China such as the famous one that letting China into the WTO would integrate Beijing into the famous rules-based international order. Trump tells us that instead China “got rich and powerful” and used this against us, and goes on to describe the CCP’s well known predatory subsidies, unfair trade, IP theft, industrial espionage, supply-chain leverage, and fentanyl precursor exports as threats the U.S. must “end.” By selling them the most advanced AI chips?

Western governments and investors simultaneously back domestic autonomous-weapons firms—such as Europe-based Helsing, supported by Spotify CEO Daniel Ek—explicitly building AI-enabled munitions for allied defense. That makes exporting equivalent enabling infrastructure to a strategic competitor indefensible.

The AI Moratorium Makes This Worse, Not Better

This contradiction unfolds alongside a proposed federal AI moratorium executive order originating with Mr. Sacks and Adam Thierer of Google’s R Street Institute that would preempt state-level AI protections.
States are told AI is too consequential for local regulation, yet the federal government is prepared to license exports of AI’s core infrastructure abroad.

If AI is too dangerous for states to regulate, it is too dangerous to export. Preemption at home combined with permissiveness abroad is not leadership. It is capture.

This Is What Policy Capture Looks Like

The common thread is not national security. It is Silicon Valley access. David Sacks and others in the AI–VC orbit argue that AI regulation threatens U.S. competitiveness while remaining silent on where the chips go and how they are used.

When DOJ prosecutes smugglers while the White House authorizes exports, the public is entitled to ask whose interests are actually being served. Advisory roles that blur public power and private investment cannot coexist with credible national-security policymaking particularly when the advisor may not even be able to get a US national security clearance unless the President blesses it.

A Line Has to Be Drawn

If a technology is so sensitive that its unauthorized transfer justifies prosecution, its authorized transfer should be prohibited absent extraordinary national interest. AI accelerators meet that test.

Until the administration can articulate a coherent justification for exporting these capabilities to China, the answer should be no. Not licensed. Not delayed. Not cosmetically restricted.

And if that position conflicts with Silicon Valley advisers who view this as a growth opportunity, they should return to where they belong. The fact that the US is getting 25% of the deal (which i bet never finds its way into America’s general account), means nothing except confirming Lenin’s joke about selling the rope to hang ourselves, you know, kind of like TikTok.

David Sacks should go back to Silicon Valley.

This is not venture capital. This is our national security and he’s selling it like rope.

Less Than Zero: The Significance of the Per Stream Rate and Why It Matters

Spotify’s insistence that it’s “misleading” to compare services based on a derived per-stream rate reveals exactly how out of touch the company has become with the very artists whose labor fuels its stock price. Artists experience streaming one play at a time, not as an abstract revenue pool or a complex pro-rata formula. Each stream represents a listener’s decision, a moment of engagement, and a microtransaction of trust. Dismissing the per-stream metric as irrelevant is a rhetorical dodge that shields Spotify from accountability for its own value proposition. (The same applies to all streamers, but Spotify is the only one that denies the reality of the per-stream rate.)

Spotify further claims that users don’t pay per stream but for access as if that negates the artist’s per stream rate payments. It is fallacious to claim that because Spotify users pay a subscription fee for “access,” there is no connection between that payment and any one artist they stream. This argument treats music like a public utility rather than a marketplace of individual works. In reality, users subscribe because of the artists and songs they want to hear; the value of “access” is wholly derived from those choices and the fans that artists drive to the platform. Each stream represents a conscious act of consumption and engagement that justifies compensation.

Economically, the subscription fee is not paid into a vacuum — it forms a revenue pool that Spotify divides among rights holders according to streams. Thus, the distribution of user payments is directly tied to which artists are streamed, even if the payment mechanism is indirect. To say otherwise erases the causal relationship between fan behavior and artist earnings.

The “access” framing serves only to obscure accountability. It allows Spotify to argue that artists are incidental to its product when, in truth, they are the product. Without individual songs, there is nothing to access. The subscription model may bundle listening into a single fee, but it does not sever the fundamental link between listener choice and the artist’s right to be paid fairly for that choice.

Less Than Zero Effect: AI, Infinite Supply and Erasing Artist

In fact, this “access” argument may undermine Spotify’s point entirely. If subscribers pay for access, not individual plays, then there’s an even greater obligation to ensure that subscription revenue is distributed fairly across the artists who generate the listening engagement that keeps fans paying each month. The opacity of this system—where listeners have no idea how their money is allocated—protects Spotify, not artists. If fans understood how little of their monthly fee reached the musicians they actually listen to, they might demand a user-centric payout model or direct licensing alternatives. Or they might be more inclined to use a site like Bandcamp. And Spotify really doesn’t want that.

And to anticipate Spotify’s typical deflection—that low payments are the label’s fault—that’s not correct either. Spotify sets the revenue pool, defines the accounting model, and negotiates the rates. Labels may divide the scraps, but it’s Spotify that decides how small the pie is in the first place either through its distribution deals or exercising pricing power.

Three Proofs of Intention

Daniel Ek, the Spotify CEO and arms dealer, made a Dickensian statement that tells you everything you need to know about how Spotify perceives their role as the Streaming Scrooge—“Today, with the cost of creating content being close to zero, people can share an incredible amount of content”.

That statement perfectly illustrates how detached he has become from the lived reality of the people who actually make the music that powers his platform’s market capitalization (which allows him to invest in autonomous weapons). First, music is not generic “content.” It is art, labor, and identity. Reducing it to “content” flattens the creative act into background noise for an algorithmic feed. That’s not rhetoric; it’s a statement of his values. Of course in his defense, “near zero cost” to a billionaire like Ek is not the same as “near zero cost” to any artist. This disharmonious statement shows that Daniel Ek mistakes the harmony of the people for the noise of the marketplace—arming algorithms instead of artists.

Second, the notion that the cost of creating recordings is “close to zero” is absurd. Real artists pay for instruments, studios, producers, engineers, session musicians, mixing, mastering, artwork, promotion, and often the cost of simply surviving long enough to make the next record or write the next song. Even the so-called “bedroom producer” incurs real expenses—gear, software, electricity, distribution, and years of unpaid labor learning the craft. None of that is zero. As I said in the UK Parliament’s Inquiry into the Economics of Streaming, when the day comes that a soloist aspires to having their music included on a Spotify “sleep” playlist, there’s something really wrong here.

Ek’s comment reveals the Silicon Valley mindset that art is a frictionless input for data platforms, not an enterprise of human skill, sacrifice, and emotion. When the CEO of the world’s dominant streaming company trivializes the cost of creation, he’s not describing an economy—he’s erasing one.

While Spotify tries to distract from the “per-stream rate,” it conveniently ignores the reality that whatever it pays “the music industry” or “rights holders” for all the artists signed to one label still must be broken down into actual payments to the individual artists and songwriters who created the work. Labels divide their share among recording artists; publishers do the same for composers and lyricists. If Spotify refuses to engage on per-stream value, what it’s really saying is that it doesn’t want to address the people behind the music—the very creators whose livelihoods depend on those streams. In pretending the per-stream question doesn’t matter, Spotify admits the artist doesn’t matter either.

Less Than Zero or Zeroing Out: Where Do We Go from Here?

The collapse of artist revenue and the rise of AI aren’t coincidences; they’re two gears in the same machine. Streaming’s economics rewards infinite supply at near-zero unit cost which is really the nugget of truth in Daniel Ek’s statements. This is evidenced by Spotify’s dalliances with Epidemic Sound and the like. But—human-created music is finite and costly; AI music is effectively infinite and cheap. For a platform whose margins improve as payout obligations shrink, the logical endgame is obvious: keep the streams, remove the artists.

  • Two-sided market math. Platforms sell audience attention to advertisers and access to subscribers. Their largest variable cost is royalties. Every substitution of human tracks with synthetic “sound-alikes,” noise, functional audio, or AI mashup reduces royalty liability while keeping listening hours—and revenue—intact. You count the AI streams just long enough to reduce the royalty pool, then you remove them from the system, only to be replace by more AI tracks. Spotify’s security is just good enough to miss the AI tracks for at least one royalty accounting period.
  • Perpetual content glut as cover. Executives say creation costs are “near zero,” justifying lower per-stream value. That narrative licenses a race to the bottom, then invites AI to flood the catalog so the floor can fall further.
  • Training to replace, not to pay. Models ingest human catalogs to learn style and voice, then output “good enough” music that competes with the very works that trained them—without the messy line item called “artist compensation.”
  • Playlist gatekeeping. When discovery is centralized in editorial and algorithmic playlists, platforms can steer demand toward low-or-no-royalty inventory (functional audio, public-domain, in-house/commissioned AI), starving human repertoire while claiming neutrality.
  • Investor alignment. The story that scales is not “fair pay”; it’s “gross margin expansion.” AI is the lever that turns culture into a fixed cost and artists into externalities.

Where does that leave us? Both streaming and AI “work” best for Big Tech, financially, when the artist is cheap enough to ignore or easy enough to replace. AI doesn’t disrupt that model; it completes it. It also gives cover through a tortured misreading through the “national security” lens so natural for a Lord of War investor like Mr. Ek who will no doubt give fellow Swede and one of the great Lords of War, Alfred Nobel, a run for his money. (Perhaps Mr. Ek will reimagine the Peace Prize.) If we don’t hard-wire licensing, provenance, and payout floors, the platform’s optimal future is music without musicians.

Plato conceived justice as each part performing its proper function in harmony with the whole—a balance of reason, spirit, and appetite within the individual and of classes within the city. Applied to AI synthetic works like those generated by Sora 2, injustice arises when this order collapses: when technology imitates creation without acknowledging the creators whose intellect and labor made it possible. Such systems allow the “appetitive” side—profit and scale—to dominate reason and virtue. In Plato’s terms, an AI trained on human art yet denying its debt to artists enacts the very disorder that defines injustice.

Too Dynamic to Question, Too Dangerous to Ignore

When Ed Newton-Rex left Stability AI, he didn’t just make a career move — he issued a warning. His message was simple: we’ve built an industry that moves too fast to be honest.

AI’s defenders insist that regulation can’t keep up, that oversight will “stifle innovation.” But that speed isn’t a by-product; it’s the business model. The system is engineered for planned obsolescence of accountability — every time the public begins to understand one layer of technology, another version ships, invalidating the debate. The goal isn’t progress; it’s perpetual synthetic novelty, where nothing stays still long enough to be measured or governed, and “nothing says freedom like getting away with it.”

We’ve seen this play before. Car makers built expensive sensors we don’t want that fail on schedule; software platforms built policies that expire the moment they bite. In both cases, complexity became a shield and a racket — “too dynamic to question.” And yet, like those unasked-for, but paid for, features in the cars we don’t want, AI’s design choices are too dangerous to ignore. (Like what if your brakes really are going out, not just the sensor is malfunctioning.)

Ed Newton-Rex’s point — echoed in his tweets and testimony — is that the industry has mistaken velocity for virtue. He’s right. The danger is not that these systems evolve too quickly to regulate; it’s that they’re designed that way designed to fail just like that brake sensor. And until lawmakers recognize that speed itself is a form of governance, we’ll keep mistaking momentum for inevitability.

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.

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.

AI Needs Ever More Electricity—And Google Wants Us to Pay for It

Uncle Sugar’s “National Emergency” Pitch to Congress

At a recent Congressional hearing, former Google CEO Eric “Uncle Sugar” Schmidt delivered a message that was as jingoistic as it was revealing: if America wants to win the AI arms race, it better start building power plants. Fast. But the subtext was even clearer—he expects the taxpayer to foot the bill because, you know, the Chinese Communist Party. Yes, when it comes to fighting the Red Menace, the all-American boys in Silicon Valley will stand ready to fight to the last Ukrainian, or Taiwanese, or even Texan.

Testifying before the House Energy & Commerce Committee on April 9, Schmidt warned that AI’s natural limit isn’t chips—it’s electricity. He projected that the U.S. would need 92 gigawatts of new generation capacity—the equivalent of nearly 100 nuclear reactors—to keep up with AI demand.

Schmidt didn’t propose that Google, OpenAI, Meta, or Microsoft pay for this themselves, just like they didn’t pay for broadband penetration. No, Uncle Sugar pushed for permitting reform, federal subsidies, and government-driven buildouts of new energy infrastructure. In plain English? He wants the public sector to do the hard and expensive work of generating the electricity that Big Tech will profit from.

Will this Improve the Grid?

And let’s not forget: the U.S. electric grid is already dangerously fragile. It’s aging, fragmented, and increasingly vulnerable to cyberattacks, electromagnetic pulse (EMP) weapons, and even extreme weather events. Pouring public money into ultra-centralized AI data infrastructure—without first securing the grid itself—is like building a mansion on a cracked foundation.

If we are going to incur public debt, we should prioritize resilience, distributed energy, grid security, and community-level reliability—not a gold-plated private infrastructure buildout for companies that already have trillion-dollar valuations.

Big Tech’s Growing Appetite—and Private Hoarding

This isn’t just a future problem. The data center buildout is already in full swing and your Uncle Sugar must be getting nervous about where he’s going to get the money from to run his AI and his autonomous drone weapons. In Oregon, where electricity is famously cheap thanks to the Bonneville Power Administration’s hydroelectric dams on the Columbia River, tech companies have quietly snapped up huge portions of the grid’s output. What was once a shared public benefit—affordable, renewable power—is now being monopolized by AI compute farms whose profits leave the region to the bank accounts in Silicon Valley.

Meanwhile, Microsoft is investing in a nuclear-powered data center next to the defunct Three Mile Island reactor—but again, it’s not about public benefit. It’s about keeping Azure’s training workloads running 24/7. And don’t expect them to share any of that power capacity with the public—or even with neighboring hospitals, schools, or communities.

Letting the Public Build Private Fortresses

The real play here isn’t just to use public power—it’s to get the public to build the power infrastructure, and then seal it off for proprietary use. Moats work both ways.

That includes:
– Publicly funded transmission lines across hundreds of miles to deliver power to remote server farms;
– Publicly subsidized generation capacity (nuclear, gas, solar, hydro—you name it);
– And potentially, prioritized access to the grid that lets AI workloads run while the rest of us face rolling blackouts during heatwaves.

All while tech giants don’t share their models, don’t open their training data, and don’t make their outputs public goods. It’s a privatized extractive model, powered by your tax dollars.

Been Burning for Decades

Don’t forget: Google and YouTube have already been burning massive amounts of electricity for 20 years. It didn’t start with ChatGPT or Gemini. Serving billions of search queries, video streams, and cloud storage events every day requires a permanent baseload—yet somehow this sudden “AI emergency” is being treated like a surprise, as if nobody saw it coming.

If they knew this was coming (and they did), why didn’t they build the power? Why didn’t they plan for sustainability? Why is the public now being told it’s our job to fix their bottleneck?

The Cold War Analogy—Flipped on Its Head

Some industry advocates argue that breaking up Big Tech or slowing AI infrastructure would be like disarming during a new Cold War with China. But Gail Slater, the Assistant Attorney General leading the DOJ’s Antitrust Division, pushed back forcefully—not at a hearing, but on the War Room podcast.

In that interview, Slater recalled how AT&T tried to frame its 1980s breakup as a national security threat, arguing it would hurt America’s Cold War posture. But the DOJ did it anyway—and it led to an explosion of innovation in wireless technology.

“AT&T said, ‘You can’t do this. We are a national champion. We are critical to this country’s success. We will lose the Cold War if you break up AT&T,’ in so many words. … Even so, [the DOJ] moved forward … America didn’t lose the Cold War, and … from that breakup came a lot of competition and innovation.”

“I learned that in order to compete against China, we need to be in all these global races the American way. And what I mean by that is we’ll never beat China by becoming more like China. China has national champions, they have a controlled economy, et cetera, et cetera.

We win all these races and history has taught by our free market system, by letting the ball rip, by letting companies compete, by innovating one another. And the reason why antitrust matters to that picture, to the free market system is because we’re the cop on the beat at the end of the day. We step in when competition is not working and we ensure that markets remain competitive.”

Slater’s message was clear: regulation and competition enforcement are not threats to national strength—they’re prerequisites to it. So there’s no way that the richest corporations in commercial history should be subsidized by the American taxpayer.

Bottom Line: It’s Public Risk, Private Reward

Let’s be clear:

– They want the public to bear the cost of new electricity generation.
– They want the public to underwrite transmission lines.
– They want the public to streamline regulatory hurdles.
– And they plan to privatize the upside, lock down the infrastructure, keep their models secret and socialize the investment risk.

This isn’t a public-private partnership. It’s a one-way extraction scheme. America needs a serious conversation about energy—but it shouldn’t begin with asking taxpayers to bail out the richest companies in commercial history.

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.

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.

What Bell Labs and Xerox PARC Can Teach Us About the Future of Music

When we talk about the great innovation engines of the 20th century, two names stand out: Bell Labs and Xerox PARC. These legendary research institutions didn’t just push the boundaries of science and technology—they found solutions that brought us breakthroughs to challenges. The transistor, the laser, the UNIX operating system, the graphical user interface, and Ethernet networking all trace their origins to these hubs of long-range, cross-disciplinary thinking.

These breakthroughs didn’t happen by accident. They were the product of institutions that were intentionally designed to explore what might be possible outside the pressures of quarterly earnings reports–which means monthly which means weekly. Bell Labs and Xerox PARC proved that bold ideas need space, time, and a mandate to explore—even if commercial applications aren’t immediately apparent. You cannot solve big problems with an eye on weekly revenues–and I know that because I worked at A&M Records.

Now imagine if music had something like Bell Labs and Xerox PARC.

What if there were a Bell Labs for Music—an independent research and development hub where songwriters, engineers, logisticians, rights experts, and economists could collaborate to solve deep-rooted industry challenges? Instead of letting dominant tech platforms dictate the future, the music industry could build its own innovation engine, tailored to the needs of creators. Let’s consider how similar institutions could empower the music industry to reclaim its creative and economic future particularly confronted by AI and its institutional takeover.

Big Tech’s Self-Dealing: A $500 Million Taxpayer-Funded Windfall

While creators are being told to “adapt” to the age of AI, Big Tech has quietly written itself a $500 million check—funded by taxpayers—for AI infrastructure. Buried within the sprawling “innovation and competitiveness” sections of legislation being promoted as part of Trump’s “big beautiful bill,” this provision would hand over half a billion dollars in public funding—more accurately, public debt—to cloud providers, chipmakers, and AI monopolists with little transparency and even fewer obligations to the public.

Don’t bother looking–it will come as no surprise that there are no offsetting provisions for musicians, authors, educators, or even news publishers whose work is routinely scraped to train these AI models. There are no earmarks for building fair licensing infrastructure or consent-based AI training databases. There is no “AI Bell Labs” for the creative economy.

Once again, we see that innovation policy is being written by and for the same old monopolists who already control the platforms and the Internet itself, while the people whose work fills those platforms are left unprotected, uncompensated, and uninformed. If we are willing to borrow hundreds of millions to accelerate private AI growth, we should be at least as willing to invest in creator-centered infrastructure that ensures innovation is equitable—not extractive.

Innovation Needs a Home—and a Conscience

Bell Labs and Xerox PARC were designed not just to build technology, but to think ahead. They solved many future challenges often before the world even knew they existed.

The music industry can—and must—do the same. Instead of waiting for another monopolist to exercise its political clout to grant itself new safe harbors to upend the rules–like AI platforms are doing right now–we can build a space where songwriters, developers, and rights holders collaborate to define a better future. That means metadata that respects rights and tracks payments to creators. That means fair discovery systems. That means artist-first economic models.

It’s time for a Bell Labs for music. And it’s time to fund it not through government dependency—but through creator-led coalitions, industry responsibility, and platform accountability.

Because the future of music shouldn’t be written in Silicon Valley boardrooms. It should be composed, engineered, and protected by the people who make it matter.

Does “Publicly Available” AI Scraping Mean They Take Everything or Just Anything That’s Not Nailed Down?

Let’s be clear: It is not artificial intelligence as a technology that’s the existential threat. It’s the people who make the decisions about how to train and use artificial intelligence that are the existential threat. Just like nuclear power is not an existential threat, it’s the Czar Bomba that measured 50 megatons on the bangometer that’s the existential threat.

If you think that the tech bros can be trusted not to use your data scraped from their various consumer products for their own training purposes, please point to the five things they’ve done in the last 20 years that give you that confidence? Or point to even one thing.

Here’s an example. Back in the day when we were trying to build a library of audio fingerprints, we first had to rip millions of tracks in order to create the fingerprints. One employee who came to us from a company with a free email service said that there were millions of emails with audio file attachments just sitting there in users’ sent mail folders. Maybe we could just grab those audio files? Obviously that would be off limits for a host of reasons, but he didn’t see it. It’s not that he is an immoral person–immoral people recognize that there are some rules and they just want to break them. He was amoral–he didn’t see the rules and he didn’t think anything was wrong with his suggestion.

But the moral of the story–so to speak–is that I fully believe every consumer product is being scraped. That means that there’s a fairly good chance that Google, Microsoft, Meta/Facebook and probably other Big Tech players are using all of their consumer products to train AI. I would not bet against it.

If you think that’s crazy, I would suggest you think again. While these companies keep that kind of thing fairly quiet, it’s not the first time that the issue has come up–Big Tech telling you one thing, but using you to gain a benefit for something entirely different that you probably would never have agreed to had you known.

Take the Google Books saga. The whole point of Google’s effort at digitizing all the world’s books wasn’t because of some do-gooder desire to create the digital library of Alexandria or even the snippets that were the heart of the case. No–it was the “nondisplay uses” like training Google’s translation engine using “corpus machine translation”. The “corpus” of all the digitized books was the real value and of course was the main thing that Google wouldn’t share with the authors and didn’t want to discuss in the case.

Another random example would be “GOOG-411”. We can thank Marissa Meyer for spilling the beans on that one.

According to PC World back in 2010:

Google will close down 1-800-GOOG-411 next month, saying the free directory assistance service has served its purpose in helping the company develop other, more sophisticated voice-powered technologies.

GOOG-411, which will be unplugged on Nov. 12, was the search company’s first speech recognition service and led to the development of mobile services like Voice Search, Voice Input and Voice Actions.

Google, which recorded calls made to GOOG-411, has been candid all along about the motivations behind running the service, which provides phone numbers for businesses in the U.S. and Canada.

In 2007, Google Vice President of Search Products & User Experience Marissa Mayer said she was skeptical that free directory assistance could be viable business, but that she had no doubt that GOOG-411 was key to the company’s efforts to build speech-to-text services.

GOOG 411 is a prime example of how Big Tech plays the thimblerig, especially the “has been candid all along about the motivations behind running the service.” Doesn’t that phrase just ooze corporate flak? That, as we say in the trade, is a freaking lie.

None of the GOOG-411 collateral ever said, “Hey idiot, come help us get even richer by using our dumbass “free” directory assistance “service”.” Just like they’re not saying, “Hey idiot, use our “free” products so we can train our AI to take your job.” That’s the thimblerig, but played at our expense.

This subterfuge has big consequences for people like lawyers. As I wrote in my 2014 piece in Texas Lawyer:

“A lawyer’s duty to maintain the confidentiality of privileged communications is axiomatic. Given Google’s scanning and data mining capabilities, can lawyers using Gmail comply with that duty without their clients’ informed consent? In addition to scanning the text, senders and recipients, Google’s patents for its Gmail applications claim very broad functionality to scan file attachments. (The main patent is available on Google’s site. A good discussion of these patents is in Jeff Gould’s article, “The Natural History of Gmail Data Mining”, available on Medium.)”

Google has made a science of enticing users into giving up free data for Google to evolve even more products that may or may not be useful beyond the “free” part. Does the world really need another free email program? Maybe not, but Google does need a way to snarf down data for its artificial intelligence platforms–deceptively.

Fast forward ten years or so and here we are with the same problem–except it’s entirely possible that all of the Big Tech AI platforms are using their consumer products to train AI. Nothing has changed for lawyers, and some version of these rules would be prudent to follow for anyone with a duty of confidentiality like a doctor, accountant, stock broker or any of the many licensed professions. Not to mention social workers, priests, and the list goes on. If you call Big Tech on the deception and they will all say that they operate within their privacy policies, “de-identify” user data, only use “public” information, or other excuses.

I think the point of all this is that the platforms have far too many opportunities to cross-collateralize our data for the law to permit any confusion about what data they scrape.

What We Think We Know

Microsoft’s AI Training Practices

Microsoft has publicly stated that it does not use data from its Microsoft 365 products (e.g., Word, Excel, Outlook) to train its AI models. The company wants us to believe they rely on “de-identified” data from sources such as Bing searches, Copilot interactions, and “publicly available” information, whatever that means. Microsoft emphasizes its commitment to responsible AI practices, including removing metadata and anonymizing data to protect user privacy. See what I mean? Given Microsoft takes these precautions, that makes it all fine.

However, professionals using Microsoft’s tools must remain vigilant. While Microsoft claims not to use customer data from enterprise accounts for AI training, any inadvertent sharing of sensitive information through other Microsoft services (e.g., Bing or Copilot) could pose risks for users, particularly people with a duty of confidentiality like lawyers and doctors. And we haven’t even discussed child users yet.

Google’s AI Training Practices

For decades, Google has faced scrutiny for its data practices, particularly with products like Gmail, Google Docs, and Google Drive. Google’s updated privacy policy explicitly allows the use of “publicly available” information and user data for training its AI models, including Bard and Gemini. While Google claims to anonymize and de-identify data, concerns remain about the potential for sensitive information to be inadvertently included in training datasets.

For licensed professionals, these practices raise significant red flags. Google advises users not to input confidential or sensitive information into its AI-powered tools–typically Googlely. The risk of human reviewers accessing “de-identified” data can happen to anyone, but why in the world would you ever trust Google?

Does “Publicly Available” Mean Everything or Does it Mean Anything That’s Not Nailed Down?

These companies speak of “publicly available” data as if data that is publicly available is free to scrape and use for training. So what does that mean?

Based on the context and some poking around, it appears that there is no legally recognizable definition of what “publicly available” actually means. If you were going to draw a line between “publicly available” and the opposite, where would you draw it? You won’t be surprised to know that Big Tech will probably draw the line in an entirely different place than a normal person.

As far as I can tell, “publicly available” data would include data or content that is accessible by a data scraping crawler or by the general public without a subscription, payment, or special access permissions. This likely includes web pages, posts on social media like baby pictures on Facebook or Instagram, or other platforms that do not restrict access to their content through paywalls, registration requirements, or other barriers like terms of service prohibiting data scraping, API or a robots.txt file (which like a lot of other people including Ed Newton-Rex, I’m skeptical of even working).

While discussions of terms of service, notices prohibiting scraping and automated directions to crawlers sound good, in reality there’s no way to stop a determined crawler. The vulpine lust for data and cold hard cash by Big Tech is not realistically possible to stop at this point. Stopping the existential onslaught explains why the world needs to escalate punishment for these violations to a new level that may seem extreme at this point or at least unusually harsh.

Yet the massive and intentional copyright infringement, privacy violations, and who knows what else are so vast they are beyond civil penalties particularly for a defendant that seemingly prints money.