Tag: Anthropic

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

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

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

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

gov.uscourts.nysd.630270.65.0Download

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

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

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

Judge Failla’s RAG Analysis

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

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

What is RAG and Why It Matters

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

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

RAG in Frontier Labs

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

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

Why Dow Jones Matters Beyond Perplexity

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

Is RAG the Low-Hanging Fruit?

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

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

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

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

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

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

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

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

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

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

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

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

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

AI 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.

From Plutonium to Prompt Engineering: Big Tech’s Land Grab at America’s Nuclear Sites–and Who’s Paying for It?

In a twist of post–Cold War irony, the same federal sites that once forged the isotopes of nuclear deterrence are now poised to fuel the arms race of artificial intelligence under the leadership of Special Government Employee and Silicon Valley Viceroy David Sacks. Under a new Department of Energy (DOE) initiative, 16 legacy nuclear and lab sites — including Savannah River, Idaho National Lab, and Oak Ridge Tennessee — are being opened to private companies to host massive AI data centers. That’s right–Tennessee where David Sacks is riding roughshod over the ELVIS Act.

DOE Giveaway 2025-05936Download

But as this techno-industrial alliance gathers steam, one question looms large: Who benefits — and how will the American public be compensated for leasing its nuclear commons to the world’s most powerful corporations? Spoiler alert: We won’t.

A New Model, But Not the Manhattan Project

This program is being billed in headlines as a “new Manhattan Project for AI.” But that comparison falls apart quickly. The original Manhattan Project was:
– Owned by the government
– Staffed by public scientists
– Built for collective defense

Today’s AI infrastructure effort is:
– Privately controlled
– Driven by monopolies and venture capital
– Structured to avoid transparency and public input
– Uses free leases on public land with private nuclear reactors

Call it the Manhattan Project in reverse — not national defense, but national defense capture.

The Art of the Deal: Who gets what?

What Big Tech Is Getting

– Access to federal land already zoned, secured, and wired
– Exemption from state and local permitting
– Bypass of grid congestion via nuclear-ready substations
– DOE’s help fast-tracking nuclear microreactors (SMRs)
– Potential sovereign AI training enclaves, shielded from export controls and oversight

And all of it is being made available to private companies called the “Frontier labs”: Microsoft, Oracle, Amazon, OpenAI, Anthropic, xAI — the very firms at the center of the AI race.

What the Taxpayer Gets (Maybe)

Despite this extraordinary access, almost nothing is disclosed about how the public is compensated. No known revenue-sharing models. No guaranteed public compute access. No equity. No royalties.

Land lease payments? Not disclosed. Probably none.
Local tax revenue? Minimal (federal lands exempt)
Infrastructure benefit sharing? Unclear or limited

It’s all being negotiated quietly, under vague promises of “national competitiveness.”

Why AI Labs Want DOE Sites

Frontier labs like OpenAI and Anthropic — and their cloud sponsors — need:
– Gigawatts of energy
– Secure compute environments
– Freedom from export rules and Freedom of Information Act requests
– Permitting shortcuts and national branding

The DOE sites offer all of that — plus built-in federal credibility. The same labs currently arguing in court that their training practices are “fair use” now claim they are defenders of democracy training AI on taxpayer-built land.

This Isn’t the Manhattan Project — It’s the Extraction Economy in a Lab Coat

The tech industry loves to invoke patriotism when it’s convenient — especially when demanding access to federal land, nuclear infrastructure, or diplomatic cover from the EU’s AI Act. But let’s be clear:

This isn’t the Manhattan Project. Or rather we should hope it isn’t because that one didn’t end well and still hasn’t.
It’s not public service.
It’s Big Tech lying about fair use, wrapped in an American flag — and for all we know, it might be the first time David Sacks ever saw one.

When companies like OpenAI and Microsoft claim they’re defending democracy while building proprietary systems on DOE nuclear land, we’re not just being gaslit — we’re being looted.

If the AI revolution is built on nationalizing risk and privatizing power, it’s time to ask whose country this still is — and who gets to turn off the lights.

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. 

The Delay’s The Thing: Anthropic Leapfrogs Its Own November Valuation Despite Litigation from Authors and Songwriters in the Heart of Darkness

If you’ve read Joseph Conrad’s Heart of Darkness, you’ll be familiar with the Congo Free State, a private colony of Belgian King Leopold II that is today largely the Democratic Republic of the Congo. When I say “private” I mean literally privately owned by his Leopoldness. Why would old King Leo be so interested in owning a private colony in Africa? Why for the money, of course. Leo had to move some pieces around the board and get other countries to allow him to get away with essentially “buying” the place, if “buying” is the right description.

So Leo held an international conference in Berlin to discuss the idea and get international buy-in, kind of like the World Economic Forum with worse food and no skiing. Rather than acknowledging his very for-profit intention to ravage the Congo for ivory (aka slaughtering elephants) and rubber (the grisly extraction of which was accomplished by uncompensated slave labor) with brutal treatment of all concerned, Leo convinced the assembled nations that his intentions were humanitarian and philanthropic. You know, don’t be evil. Just lie.

Of course, however much King Leopold may have foreshadowed our sociopathic overlords from Silicon Valley, it must be said that Leo’s real envy won’t so much be the money as what he could have done with AI himself had he only known. Oh well, he just had to make do with Kurtz.

Which bring us to AI in general and Anthropic in particular. Anthropic’s corporate slogan is equally humanitarian and philanthropic: “Anthropic is an AI research company that focuses on the safety and alignment of AI systems with human values.” Oh yes, all very jolly.

All very innocent and high minded, until you get punched in the face (to coin a phrase). It turns out–quelle horreur–that Anthropic stands accused of massive copyright infringement rather than lauded for its humanitarianism. Even more shocking? The company’s valuation is going through the stratosphere! These innocents surely must be falsely accused! The VC’s are voting with their bucks, so they wouldn’t put their shareholders’ money or limiteds money on the line for a–RACKETEER INFLUENCED CORRUPT ORGANIZATION?!?

Bartz v Anthropic ComplaintDownload

Not only have authors brought this class action against Anthropic which is both Google’s stalking horse and cats paw to mix a metaphor, but the songwriters and music publishers have sued them as well. Led by Concord and Universal, the publishers have sued for largely the same reasons as the authors but for their quite distinct copyrights.

Concord and Universal v Anthropic ComplaintDownload

So let’s understand the game that’s being played here–as the Artist Rights Institute submitted in a comment to the UK Intellectual Property Office in the IPO’s current consultation on AI and copyright, the delay is the thing. And thanks to Anthropic, we can now put a valuation on the delay since the $4,000,000,000 the company raised in November 2024: $3,500,000,000. This one company is valued at $61.5 billion, roughly half of the entire creative industries in the UK and roughly equal to the entire U.S. music industry. No wonder delay is their business model.

However antithetical, copyright and AI must be discussed together for a very specific reason:  Artificial intelligence platforms operated by Google, Microsoft/OpenAI, Meta and the like have scraped and ingested works of authorship from baby pictures to Sir Paul McCartney as fast and as secretly as possible.  And the AI platforms know that the longer they can delay accountability, the more of the world’s culture they will have devoured—or as they might say, the more data they will have ingested.  And Not to mention the billions in venture capital they will have raised, just like Anthropic. For the good of humanity, of course, just like old King Leo.

As the Hon. Alison Hume, MP recently told Parliament, this theft is massive and has already happened, another example of why any “opt out” scheme (as had been suggested by the UK government) has failed before it starts:

This week, I discovered that the subtitles from one of my episodes of New Tricks have been scraped and are being used to create learning materials for artificial intelligence.  Along with thousands of other films and television shows, my original work is being used by generative AI to write scripts which one day may replace versions produced by mere humans like me.

This is theft, and it’s happening on an industrial scale.  As the law stands, artificial intelligence companies don’t have to be transparent about what they are stealing.[1]

Any delay[2] in prosecuting AI platforms simply increases their de facto “text and data mining” safe harbor while they scrape ever more of world culture.  As Ms. Hume states, this massive “training” has transferred value to these data-hungry mechanical beasts to a degree that confounds human understanding of its industrial scale infringement.  This theft dwarfs even the Internet piracy that drove broadband penetration, Internet advertising and search platforms in the 1999-2010 period.  It must be said that for Big Tech, commerce and copyright are once again inherently linked for even greater profit.

As the Right Honourable Baroness Kidron said in her successful opposition to the UK Government’s AI legislation in the House of Lords:

The Government are doing this not because the current law does not protect intellectual property rights, nor because they do not understand the devastation it will cause, but because they are hooked on the delusion that the UK’s best interests and economic future align with those of Silicon Valley.[3]  

Baroness Kidron identifies a question of central importance that mankind is forced to consider by the sheer political brute force of the AI lobbying steamroller:  What if AI is another bubble like the Dot Com bubble?  AI is, to a large extent, a black box utterly lacking in transparency much less recordkeeping or performance metrics.  As Baroness Kidron suggests, governments and the people who elect them are making a very big bet that AI is not pursuing an ephemeral bubble like the last time.

Indeed, the AI hype has the earmarks of a bubble, just as the Dot Com bubble did.  Baroness Kidron also reminds us of these fallacious economic arguments surrounding AI:

The Prime Minister cited an IMF report that claimed that, if fully realised, the gains from AI could be worth up to an average of £47 billion to the UK each year over a decade. He did not say that the very same report suggested that unemployment would increase by 5.5% over the same period. This is a big number—a lot of jobs and a very significant cost to the taxpayer. Nor does that £47 billion account for the transfer of funds from one sector to another. The creative industries contribute £126 billion per year to the economy. I do not understand the excitement about £47 billion when you are giving up £126 billion.[4]  

As Hon. Chris Kane, MP said in Parliament,  the Government runs the risk of enabling a wealth transfer that itself is not producing new value but would make old King Leo feel right at home: 

Copyright protections are not a barrier to AI innovation and competition, but they are a safeguard for the work of an industry worth £125 billion per year, employing over two million people.  We can enable a world where much of this value  is transferred to a handful of big tech firms or we can enable a win-win situation for the creative industries and AI developers, one where they work together based on licensed relationships with remuneration and transparency at its heart.

[1] Paul Revoir, AI companies are committing ‘theft’ on an ‘industrial scale’, claims Labour MP – who has written for TV series including New Tricks, Daily Mail (Feb. 12, 2025) available at https://www.dailymail.co.uk/news/article-14391519/AI-companies-committing-theft-industrial-scale-claims-Labour-MP-wrote-TV-shows-including-New-Tricks.html

[2] See, e.g., Kerry Muzzey, [YouTube Delay Tactics with DMCA Notices], Twitter (Feb. 13, 2020) available at https://twitter.com/kerrymuzzey/status/1228128311181578240  (Film composer with Content ID account notes “I have a takedown pending against a heavily-monetized YouTube channel w/a music asset that’s been fine & in use for 7 yrs & 6 days. Suddenly today, in making this takedown, YT decides “there’s a problem w/my metadata on this piece.” There’s no problem w/my metadata tho. This is the exact same delay tactic they threw in my way every single time I applied takedowns against broadcast networks w/monetized YT channels….And I attached a copy of my copyright registration as proof that it’s just fine.”); Zoë Keating, [Content ID secret rules], Twitter (Feb. 6. 2020) available at https://twitter.com/zoecello/status/1225497449269284864  (Independent artist with Content ID account states “[YouTube’s Content ID] doesn’t find every video, or maybe it does but then it has selective, secret rules about what it ultimately claims for me.”).

[3] The Rt. Hon. Baroness Kidron, Speech regarding Data (Use and Access) Bill [HL] Amendment 44A, House of Lords (Jan. 28, 2025) available at https://hansard.parliament.uk/Lords%E2%80%8F/2025-01-28/debates/9BEB4E59-CAB1-4AD3-BF66-FE32173F971D/Data(UseAndAccess)Bill(HL)#contribution-9A4614F3-3860-4E8E-BA1E-53E932589CBF 

[4] Id.