Sky News: “The King and AI: A humanoid robot has painted a picture of Charles. How did it do?”

The Verge: “AI residencies are trying to change the conversation around artificial art”

Associated Press: “Judge dismisses authors’ copyright lawsuit against Meta over AI training”

AI Schisms: Fair Use vs. Fair Pay

As fireworks lit up the sky this Fourth of July in America, it’s worth remembering the Founders’ original take on copyright: it wasn’t about glorifying creators—it was about empowering the public. The Constitution gave authors limited rights only to “promote the Progress” of knowledge and art.

Europe? A different story. Rooted in Romanticism, it treated creative work as sacred—an extension of the author’s soul—with “moral rights” that persist long after expression.

That philosophical divide is surfacing in the global AI debate. Recently, in the U.S., a California court partially ruled in favor of Anthropic’s use of copyrighted materials to train its AI, invoking fair use – an American doctrine rooted in public access and innovation. Meanwhile, in the U.K., the House of Lords called for stricter copyright controls on AI training data, reflecting Europe’s creator-centric approach.

So who’s right? Is authorship a personal legacy – or a building block. The answer may determine whether the future of art is open to all—or fenced off for a few.

When the Tramp Remained Silent

This July, a new report from Fabrik revealed what most filmmakers already feel: AI is everywhere – storyboarding, editing, even voiceovers. Yet, many directors are saying “no thanks.” A growing wave is going full analog, editing by hand and shooting on film, in the name of protecting the “soul of storytelling.”

They aren’t the first to say no to the machines. In 1928, following the release of The Jazz Singer, the first “talkie” film, Charlie Chaplain refused to have the Tramp speak. As most of Hollywood rushed into a new sound era, Chaplain continued making silent films for nearly a decade. He argued that audible words would limit the emotional expressiveness of the characters. It would dilute the heart and soul of film.

Perhaps something was lost with sound. But perhaps, like sound, AI can also make story telling deeper and more inclusive. Every tool we adopt reshapes the art we make – the calculus is what are we trading when we do it?

From Machine Guns to Tattoo Guns

If the movies taught us anything, it’s fear the robots with guns – from Terminator’s T-800 to Bladerunner’s Replicants, and on and on. But what if the robot isn’t holding a weapon… it’s got a tattoo needle?

Meet Tatoué, a French-designed industrial robot that’s been reprogrammed to ink human skin with surgical precision. It scans your arm in 3D, follows a mapped design, and tattoos it—perfectly, mechanically, and without questions or judgement about that Shrek tramp stamp you requested.

It began as an art school experiment. Now it asks something deeper: what happens when one of the most human acts of artistic expression—marking your own body—gets handed off to a machine?

Some say it’s sterile and soulless. Others call it safer and more accessible. Either way, the robots are here—not with bullets, but with ink. And they’re drawing on us.

By Mark Fryers Lecturer in film and media, The Open University.

As television grew rapidly in popularity in the second half of the 20th century, many people assumed it would cause a knock-on crisis for the film industry. After all, it meant that viewers no longer had to leave their sofas to enjoy onscreen entertainment.

But the reality was far more nuanced. The “death of cinema” has been habitually touted ever since the introduction of the TV, but never really came to pass. Instead, cinema found ways to work with new competition through technological innovation, aesthetic invention and engaging with challenging subject matters.

Today, lessons from the introduction of TV demonstrate how the creative industries have navigated the introduction of new technology. And could offer some comfort to those who fear that artificial intelligence (AI) technology could be a death knell for the creative industries.

As far back as 1938, long before its widespread popularity, film production company Paramount Studios sought to break into television. It made significant investment in DuMont Laboratories, which evolved into a pioneering commercial TV network.

Other studios followed suit and experimented with “live cinema”. This was a form of entertainment in which broadcast images, including sporting events, were converted into 35mm film and projected onto cinema screens, and it was made throughout the 1940s.

The “Paramount decrees” antitrust case issued by the US Supreme Court in 1948 ended the monopolistic practices of the studios, which precluded them from owning broadcast companies in favour of the radio networks. They were also ordered to sell their cinema chains, which meant that their films no longer had guaranteed screenings to the public.

Nevertheless, they continued to form television production companies, with Columbia establishing Screen Gems in 1951 and Paramount reinvesting in the ABC network in 1952. By the 1960s, the majority of prime-time television programming was provided by Hollywood studio companies. These close ties fostered a mutually beneficial relationship.

Cross-pollination

After the break-up of the studios, many studio personnel found work in the television industry. It provided a training ground for future cinema stars, including as Steven Spielberg, George Clooney and John Travolta. Studios could also rent out their studios and facilities to television production companies.

The “star system” (in which the popularity of film stars had always driven the commercial potential of cinema) was now complimented by the exposure of these stars on television programmes.

Many studios began using TV to advertise their films. For example, Disneyland TV programmes helped to advertise the Disney studio and its cinematic products as distinct from television. And film trailers became another important conduit for cinema advertising. The summer blockbuster era was ushered in by Jaws in 1975 with blanket advertising on every prime-time TV show.

When early television schedules lacked enough new content to fill the airwaves, British cinema and cheap films and serials (a series of short films with cliffhanger endings; an early progenitor of television series) from the smaller Hollywood studios filled the early schedules.

Other studio executives took note that their back catalogues of film, which mainly sat untouched in vaults, were a financial goldmine that could be ploughed back into film production and technological development. MGM, which owned titles including perennial favourite The Wizard of Oz, which CBS reserved exclusive rights to screen for 20 years, from August 1956 US$34 million (£12 million) for its titles, while Paramount held out for US$50 million (£17.8 million). Screening rights were sold to the television networks.

As a result, television became the primary conduit for film viewing. Subsequently, more films were seen on television than on the big screen. There were 3.4 billion film viewings on UK TV in 2013 compared with 165 million cinema admissions – these are now shared with streaming and on demand services. Something had to be done to keep people going to the cinema.

Technical and aesthetic innovation

In attempting to preserve the experience of the big screen, widescreen, 3D and multi-track sound systems were introduced to cinemas. The move to standardised colour film accelerated, while extended film length attempted to link the cinematic experience with “high culture” such as the theatre and opera, with overtures and intermissions.

While many were seen as gimmicky (such as “smell-O-vision” in Scent of Mystery, 1960), widescreen filming became the aesthetic choice of filmmakers, producing epic canvasses and an alternative viewing experience to the small television screen.

Although many of these technologies dated back to the 1920s, small-screen competition drove technological and aesthetic innovation, and was partly financed by the tele-visual licensing of their films. Alongside these innovations, the content of the films themselves offered a demonstrable alternative to the small screen.

By the late 1960s, Hollywood had essentially broken free from the self-imposed censorial strictures of the Hay’s production code, which regulated everything from language to interracial relationships. Instead, film-makers had absorbed the influences of documentary, avant-garde and the French New Wave, among others, as well as the rock n’ roll and counterculture movements to make bold and controversial films, such as Who’s Afraid of Virginia Woolf? (1966) and Easy Rider (1969).

The topics and levels of sex and violence portrayed in these films were unthinkable within the heavily regulated family and advertiser-friendly television industry.

Director Alfred Hitchcock made the most of this distinction between mediums. He utilised the agile tele-visual working crew of his TV series Alfred Hitchcock Presents (1955) for the taboo-bothering horror film Psycho in 1960, suggesting that the two mediums could be related but also divided by content. This, along with the aesthetic innovations helped to elevate cinema artistically in relation to the small screen.

And so the AI era dawns. The writers and actors strike of 2023 showed that the creative industries are ready to fight for their survival. Adaptability, as Hollywood has demonstrated throughout its history, can also be the key to continued success.

By Mark Fryers Lecturer in film and media, The Open University. Republished from The Conversation under a Creative Commons license.

BEN AFFLECK ON AI AND MOVIE MAKING

A video of Ben Affleck opining on the future of AI in film making blew up on X, in it Affleck says that AI isn’t going to replace film-makers, saying:

“movies will be one of the last things, if everything gets replaced, to be replaced by AI.”

What he did say was that AI film making would lower the barrier to entry for film-making:

“What AI is going to do is dis-intermediate the more laborious, less creative, and more costly aspects of filmmaking that will allow costs to be brought down, that will lower the barrier to entry, that will allow more voices to be heard.”

This reminded us of a great clip of Affleck in 2003 when asked for his take on how the internet and file sharing would impact the movie industry - he’d go on to predict annual subscription based on demand entertainment like Spotify and Netflix, saying:

"I think an annual subscription-based system is one that works, It will be movies on demand but it will be a tiered structure"

COCA-COLA AI HOLIDAY ADVERT

Coca Cola released its Christmas advert, this year generated using AI. This unsurprisingly irked those opposed to AI image generation… leading to headlines about ‘controversy’ triggered by the ad.

A note on the ad says ‘created with ‘Real Magic AI’ - Coca-Cola’s own AI system - leading us to think, some kind of Christmas themed branded content creation app may be in the works. The AI generated Ad might well be a ploy to court controversy - as free marketing. All publicity is good publicity as they say…

Could this be the start of a new trend in AI ‘outrage marketing’ - where brands deliberately antagonize the anti-AI crowd?

HarperCollins AI Deal

Giant publisher Harper-Collins has started to ask its authors to opt-in to AI training systems from companies it is working with - offering a flat fee to authors of $2,500 - it seems this wasn’t enough to avoid backlash…

Earlier this year HarperCollins announced a deal with ElevenLabs to create audiobooks for foreign translations it wouldn’t otherwise have made.

This is an important reminder that AI will likely do more things that wouldn’t have otherwise been done, than it will replace things already done by humans - stuff like creating foreign language audiobooks, will increase the reach and sales of authors work. This will of course require authors give permission that their work be fed into AI systems.

By Dr. Andres Guadamuz

The AI copyright infringement cases are increasing rapidly, not only in the US but also in the UK and China. The number of cases is so high that it’s impossible to keep track of all of them, but some common elements are emerging for analysis. While the emphasis has so far rightly been on the input phase—namely, the training of AI models using copyrighted works—hints suggest that more cases will require analysis of the outputs generated by those models. This analysis may rely on very old case law dealing with analogue technology, such as video and audio tape recorders.

We’ve discussed the input phase extensively, and most of the ongoing litigation will probably be centred on whether training falls under fair use or fair dealing, so there’s no need to go into that again. While most cases currently focus on the inputs, outputs are gaining prominence, necessitating a different approach from the defendants.

The biggest output case is The New York Times v OpenAI and Microsoft, where the newspaper produced some outputs using ChatGPT, as discussed here. OpenAI has responded that these outputs are the result of heavy manipulation of their API, going as far as calling it hacking, pretty much exploiting a bug to produce outputs, an interesting aspect of the defence that I will not go into for now. But what has prompted this blog post is something that is present in Microsoft’s own motion to dismiss, and which may have wider repercussions on how the cases proceed.

Instead of concentrating on the input aspects, Microsoft decided to deal specifically with the potential uses given to the technology, calling back to the VCR era. In the opening section of their plea to dismiss the case, they comment:

“By harnessing humanity’s collective wisdom and thinking, LLMs help us learn from each other, solve problems, organize our lives, and launch bold new ideas. Because Microsoft firmly believes in LLMs’ capacity to improve the way people live and work, it has collaborated with OpenAI to help bring their extraordinary power to the public, while leading the way in promoting safe and responsible AI development.
Despite The Times’s contentions, copyright law is no more an obstacle to the LLM than it was to the VCR (or the player piano, copy machine, personal computer, internet, or search engine). Content used to train LLMs does not supplant the market for the works, it teaches the models language.”

What is this talk of the VCR in an artificial intelligence case? Microsoft are calling back to a foundational case in US copyright law called Sony v. Universal, commonly known as the “Betamax case.” This case establishes the Sony Doctrine, where Universal City Studios (along with other movie studios) sued Sony, claiming that the company’s Betamax VCRs were contributing to copyright infringement by allowing users to record television broadcasts without permission. The studios argued that Sony should be held liable for facilitating this infringement. The US Supreme Court, however, held that Sony was not liable for copyright infringement committed by individuals using its Betamax VCRs for non commercial, personal use. The Court introduced the concept of “time-shifting,” where individuals record television programs for viewing at a later time, and deemed it a fair use under copyright law. A key aspect of the ruling was the doctrine of “substantial non-infringing use,” meaning that if a product is widely used for lawful purposes, the manufacturer of the product cannot be held liable for the infringing uses of the product by individuals.

A similar principle can be found in other systems, particularly in the UK with the case CBS Songs Limited v Amstrad Consumer Electronics. The case centred around Amstrad’s manufacture and sale of a twin cassette recorder, which allowed users to copy music cassettes easily. CBS Songs Limited (representing the interests of copyright holders) argued that by manufacturing and selling these devices, Amstrad was authorising or contributing to copyright infringement by users who used the devices to make unauthorised copies of copyrighted music. The House of Lords held that Amstrad was not liable for copyright infringement. They reasoned that the manufacturer of a device that could be used to infringe copyright was not in itself infringing copyright, provided that there were substantial non-infringing uses for the device. The Lords emphasized the importance of the intention of the device’s manufacturer and concluded that Amstrad had not authorized the infringement of copyright by producing and selling the twin cassette recorders. This could be relevant in the ongoing Getty Images case.

So what Microsoft is doing in this case is starting to mount a Sony Doctrine defence, arguing that while large language models could potentially be used to infringe copyright, the vast majority of users will utilise it for non-infringing purposes, and therefore the technology should be allowed to develop. They argue that The New York Times hasn’t been able to produce any infringing outputs by their users, and on the contrary, all allegedly infringing outputs have been introduced by the claimants. Discussing this point, the defendants comment:

“The Complaint is filled with attempts to establish that GPT-based products “will output near-verbatim copies of significant portions of Times Works when prompted to do so” […] And it warns that if The Times does not prevail in this case, the “cost to society will be enormous.” […] And yet its 68 pages and 204 paragraphs of allegations do not contain a single allegation concerning something that has actually happened in the real world as a result of the development, offering, or use of GPT-based products. The Times is at pains to insinuate that with “minimal prompting” these products “will recite large portions” of The Times’s works […], offering various examples that compare the text of an article to model output. But The Times buries this “minimal prompting” in an exhibit, where it admits that its prompts each “comprise[] a short snippet from the beginning of an article from The New York Times”—often verbatim snippets of paragraphs on end. […] The Complaint does not explain why any real person, already in possession of a New York Times article, would ever want to feed part of that prompt into a GPT-based product to generate a part of the rest of the article. It certainly does not allege that any real person has done so. It has manufactured the story.”

I happen to find this line of reasoning rather compelling, and I also think that this could eventually resonate positively during trial. The thing is that by now members of the public are becoming acquainted with tools like Gemini and ChatGPT, and therefore more and more people are becoming familiar with their everyday use. While some people may use image generators to attempt to produce copyright infringing outputs, I don’t think the same applies for language models, I can’t think of a single instance in which I was trying to use them to try to reproduce the content of a copyright work other than for legal research, most people use them as intended, as language tools.

But I also think that the “substantial non-infringing uses” can be exemplified by looking at the source of the content that is used to train an AI. It would be trite to say that the Internet is a giant copyright infringement machine, we do it all the time by sharing images, text, gifs, memes, quotes, movie stills, screenshots, etc. These outputs are prevalent in the training data because we share them on social media and the Web all the time, so infringement is prevalent, but the Internet continues to exist because of its substantial non-infringing uses. It could be argued that a similar thing is happening with generative AI, as it did with the VCR. Substantial non-infringing uses could outweigh the potential existence of infringing outputs.

Concluding

I have been thinking about what the endgame will be for AI. The current crop of lawsuits will eventually lead to some form of stability (pun unintended), and we do not know what shape that will take. My guess is that in the input phase a combination of fair use, licensing, and opt-outs will allow copyright holders to obtain some remuneration, while also allowing AI tools to continue to be developed. And with regards to outputs, any commercial infringing output could be prosecuted, but for the most part we could see AI tools as having substantial non-infringing uses. Only time will tell.

On the meantime, I wonder if a good way to test for machine intelligence would be to have it program a VCR. AGI achieved, please be kind, rewind.

This article was syndicated under Creative Commons from technollama.co.uk and was written by professor in AI and Copyright Andres Guadamuz.

With machines making it easy to copy information, businesses that once relied on the scarcity of information found themselves leaning on courtrooms to protect their bottom line.

In a world where copying and pasting is as easy as moving a cursor from one place to another, it’s easy to underestimate the importance of the photocopier as a disruptive force.

After all, it wasn’t like we didn’t have ways to copy materials from one page to another previously. Just ask anyone who’s ever held a quiz created from the chemicals of a ditto machine.

But the 1942 invention of xerography, the dry-photocopying technique that made it possible to easily reprint documents, had a strange effect on society. It made the reproduction of printed material not only easy, but cost-effective—particularly in offices.

And for businesses that relied on the scarcity of information as a business model, the photocopier was bad news—and, in many ways, it showed cracks in the copyright system.

A 1975 Supreme Court decision, Williams & Wilkins Co. v. United States, created a “fair use” gap for copying in some cases—and that gap wasn’t wide enough to answer every question about this disruptive technology. (It was one of the first examples of the court taking a cautious approach to disruptive technology, a trend that would continue in the decades to come.)

The somewhat narrow finding of Williams & Wilkins meant that copyright challenges around the copier often played out in public, in the media, and in the courtroom. And even the Copyright Act of 1976, which codified the common law doctrine of “fair use,” didn’t fully clear up the details of an emerging area of law.

Strangely enough, one of the first places where these problems showed themselves wasn’t in a library or in a giant office—but in the Catholic Church.

The music publisher that sued the Catholic Church

Some might see suing a religious institution claiming copyright infringement as unsavory. FEL Publications would disagree.

In the late 1970s, the tiny religious music publisher sued 15 separate American Roman Catholic archdioceses—including the Archdiocese of Chicago—for sharing copies of the publisher’s music with their parishioners without permission.

Dennis Fitzpatrick, the company’s president, made a religious argument for the lawsuits in a UPI story:

I believe on the Day of Judgment I will be asked—like the servant entrusted with the silver pieces (Matthew 25:14-30)—what I did with the copyrights that were entrusted to my care. Did I sue to protect them?

Unfortunately for Fitzpatrick, a federal judge disagreed with FEL, dismissing the claims in its lawsuit against the Catholic Bishop of Chicago, on grounds that it used its position in the market to monopolize access to songs. (The ruling is interesting, by the way—apparently, FEL required churches to destroy every copy of the music at the end of the license period—which seems particularly wasteful!)

The ripple effects of the lawsuit, even with FEL’s loss, showed themselves throughout the religious field. Protestant churches also had to navigate this messy mix of legal precedent and morality. As one pastor put it in a 1983 Atlanta Constitution piece: “The rationalization is this: ‘God gave you a song. You don’t have the right to charge for it.’”

Public enemy No. 1: the university copy center

Of course, churches weren’t the only institutions struggling with the complexities the copier created for its vendors. Also in this category were universities, which, as anyone who has paid hundreds of dollars for a textbook can tell you, suffer from a captive-audience problem that professors and students alike spent years trying to work around.

Whether the play is buying used books, purchasing books online at cheaper prices than in-person bookstores can offer, or “renting” books, there is a desire not to pay publishers tons of money for books that add significant costs to higher education. And copyright regulations, such as fair use and the first sale doctrine, were the cudgels that students, whether they realized it or not, were leveraging.

Also an important tool for cost-conscious students? Copy machines. Copiers made it possible to work around this system in a fair-use way. Here’s how: One student buys the book, then copies a single chapter—the only one on the syllabus. Someone makes a copy of the copy, and suddenly, the whole class has a copy. Heck, maybe the professor is the one that makes the copy. It doesn’t look as good as the original, but as long as they get the necessary information, does it really matter?

The answer to whether copying a textbook is fair use is somewhat complicated, but the short answer is essentially this: Yes, but only an excerpt. If you commandeered a copier for three hours to copy the whole thing, that wouldn’t be allowed.

The risk of such willing infringement, though, created a lasting problem. Throughout the 1970s and 1980s, these suddenly ubiquitous machines became a liability for universities and the businesses that served them.

And the publishing industry, suddenly concerned that this was going to affect their bottom line, looked to legal action to resolve some of these issues.

One of the first conflicts of its kind emerged at New York University in the early ’80s, when numerous publishers—backed by the Association of American Publishers—sued the university, along with a handful of professors, for using copies as teaching materials. Eventually, the threat of legal action led to an agreement—the publishers would drop the suit if the school agreed to prudent copying limitations.

“We’re not out to hang faculty members from a lamppost and cut out all photocopying,” said Jon A. Baumgarten, a lawyer for the association, in comments to The New York Times in a 1983 article. “The assumption is not that photocopying should be stopped, but that the rights of copyright owners should be respected.”

But the concern wasn’t just professors—also a major worry were nearby “copy shops,” which allowed students to copy to their heart’s content. One could imagine the model: A student buys a used book, goes to a copy shop, spends a few hours copying, then returns and sells the book back, saving lots of money in the process. Eventually, the bookstores realized they could cut out the middle man and do the copying themselves, which they then turned into a service called “course packs.” Professors eventually got in on the scheme, and boom, nobody has to buy the book this semester.

This model put a target on the back of these copy centers, and publishers—whether focused on books or periodicals—were ready to sue to protect their copyrights.

The most dramatic such case came about in the mid-1990s, when Michigan Document Services, a copy shop serving the University of Michigan, was sued by Princeton University Press and other publishers for offering a service that copied different books and other reading materials into inexpensive “course packs.” Unlike the NYU case, this one actually went to trial.

Eventually reaching the Sixth Circuit Court of Appeals, the court sided with the publishers, finding the use case was not fair use, because Michigan Document Services was willfully copying materials for profit-making purposes—rather than what the Archdiocese of Chicago was doing, which was making copies for its own parishioners to sing.

But even that decision wasn’t cut and dry, with the ruling only coming after the full Sixth Circuit ruled on the case—an appellate panel originally favored the copy center, 2-1, meaning that things could have played out much differently.

The case, among others, helped give momentum to legal arguments against copy shops. By the early 2000s, publishers were aggressively going after “course pack” businesses. Even the Times got in on the action, taking part in a lawsuit against a San Diego copy shop in 2004. During the late 2000s, when the internet had already supplanted aggressive copying, publishers were still going after copy shops.

Students, for obvious reasons, sided with the copy shops. As Noam B. Katz wrote in a 2002 essay in The Harvard Crimson: “Barring a change in legislation or jurisprudence, then, we must strive for creative, effective and legal solutions to lift these crushing and senseless financial burdens from our student body.”

Everyone wants their piece.

Why the newsletter was ground zero for photo-copying

These days, newsletters like this one are seen as easy-to-copy digital works—after all, you just need to forward one to share it—but that’s actually only been a recent part of the newsletter’s history.

In the past, newsletters were treated as more exclusive works, sent via the mail. They were often much more niche and exclusive—and expensive—in nature.

And of course, newsletter publishers hated copying machines. These newsletters focused on extremely niche information that their recipients paid top-dollar for. And in some offices, that one newsletter could spread to numerous people—and cut them out of a bunch of revenue.

The Newsletter Association, a trade group for newsletter publishers, actually considered using its lobbying might to push for increased fines—something the Copyright Act of 1976 didn’t give strong-enough teeth in the view of newsletter publishers.

“This is from our standpoint one of the most important issues in the newsletter industry,” said Paul Warren, who led the association’s copyright committee, in a 1986 UPI interview.

Sometimes, the newsletter publishers would get litigious. The UPI story discussed a $130 million lawsuit the company Princeton Economic Consultants filed against a Toronto brokerage firm.

Even law firms felt the wrath of the newsletter industry. A May 1991 ABA Journal story told the story of the law firm Collier, Shannon & Scott, the target of a $14 million lawsuit for willfully making copies of the $675-per-year Product Safety News and sharing them around the office. Unlike most of the companies Product Safety News’ publisher, Washington Business Information, Inc. sued, the law firm refused to settle.

Perhaps this stance explains why the printed newsletter industry largely disappeared when the internet came about, making this kind of reproduction issue nearly impossible to police.

Did copiers leave us awash in information?

Ultimately, copying changed culture for ways better and worse, depending on your vantage point. It gave people access to information they would have had to pay good money for previously.

It led to some complaints of overexposure to information. Jerry V. Wilson, a former Washington, D.C. police chief who later served as a Washington Post columnist while working at American University, called the Supreme Court’s then-recent Williams & Wilkins ruling a “non-decision” and pointed out how committee meetings were often filled with wasteful copying that wasted people’s resources and time.

“Much of the copyright infringement of photocopy is probably of that sort—the careless broadcast of matter only marginally or not at all wanted by the recipient,” he wrote in a piece titled “The Plague of the Photocopier.” “A plausible argument might be made that this is practically no infringement at all, since it doesn’t supplant a sale.”

(Wilson would most assuredly find the internet’s natural information overload particularly problematic.)

The copy machine, in many ways, was an appetizer for the main course of decaying copyright—the internet, where restrictive approaches to publishing run head-first into the chaotic winds of digital media, where laws like the Digital Millennium Copyright Act are less effective rule-making and more bandages on a restrictive approach to copyright law.

It’s hard to imagine that companies once sued churches for making copies.

Copyright was granted and then removed from pieces like Zarya of the Dawn. Epic online wars were fought. Recent guidelines from the U.S. Copyright Office reaffirms that purely generated images are in the public domain and have just been confirmed by a Federal judgment.

Let’s look at Quadratic Gaussian, a piece of abstract art by Michael Noll. While it does not look that impressive, Quadratic Gaussian was generated using an IBM 7090 in August… 1962. And it was successfully copyrighted two years later in 1965 after a heated debate that raised numerous concerns that are still very much actual.

In a short autobiography, Michael Noll recalls that he was recruited by Bell Labs in 1961 to work on speech data. Aside from his daily job as an engineer, Noll also had a lifelong passion for modern art and “was a frequent visitor” to the MOMA.

One day, something odd happened. Noll’s colleague was experimenting with computer-generation of voice graph data and due to a programming error “Lines went which way over the plot. We joked about the abstract computer art that he had inadvertently generated.” And suddenly, Noll had the inspiration to re-do the error and “create computer art deliberately”.

Like with contemporary AI, it was a lot of trials. Noll combined numerous potential distributions, dimensions, formats… The final selection was part of a collection aptly named "Technical patterns for 7090” and included a piece Noll was particularly fond of, “Gaussian-Quadratic”. He realized later on that the patterns reminded him of a painting by Picasso, Ma Jolie and became so enthused with his own creation that he began to “use colored-ink markers to trace over the pattern to produce customized art for my colleagues at work.”

Noll's experiments were initially supported by his superiors. We were right in the middle of the first AI summer and despite the obvious limitations of the computers at the time, there were a wealth of ambitious projects in automated translations or computer visions. At Bell Labs, other engineers eagerly strived to create early computer-generated music or animations. 

Through a colleague, Noll connected with a New York art seller, Howard Wise. Wise initially intended to hold a more scientific exhibition on human and computer perception of images. Yet, the generations of Noll immediately stood out as they were consciously made with an “aesthetic or artistic effect”. The Howard Wise Exhibit of "Computer-Generated Pictures” opened in April 1965 and extensively featured Noll’s work.

A black and white version of Quadratic Gaussian in the Howard Wise Exhibit in April 1965.

At the time, no artists objected to the de-humanization of generated art. No one even challenged Noll’s claim to have produced a work of art. The exhibit was covered by Stuart Preston in the New York Times on April 18 1965: “the wave of the future crashes significantly at the Howard Wise Gallery”. While acknowledging that for now “the means are of greater interest than the end”, Preston noticed that “scientists predict a time when almost any kind of painting can be computer-generated”. For the 1960s art critic, this was not a dreadful perspective. Instead it may liberate the artist and its personal vision from the constraint of material creation: “Freed from the tedium of technique and the mechanics of picture-making, the artist will simply “create””.

Noll had in fact more issues with his home institution. He received a call from the “public relations folks at AT&T” who have grown increasingly concerned about funding art experiments instead of engineering research. The exhibition was almost suspended and could only be maintained if Michael Noll accepted to publish all his works under his name and not involve the Bell Lab in any way. And thus he became a computer-generated “artist”.

Noll initially had mixed feelings about claiming all the authorship for himself. This work could not have happened without a wider social context that included the serendipitous error from a colleague, the technical apparatus of the lab and the artistic emulation from other departments.

Since he had to act as an individual artist, Noll decided to register the copyright of his favorite work, Gaussian Quadratic. An initial application was immediately dismissed as a "machine had generated the work". 

In his second application, Noll argued that he had made a lot of conscious decisions about the program and its design. His counter-argument was accepted and yet the Copyright Office counteracted that generated art “violated the “fixity” requirements of copyright law” (p. 58) as there was seemingly an element of randomness that made it possible to produce an infinite number of variants of the same work. This critic is very much relevant today. Contemporary AI is largely based on the combination of an initial prompt, a set of parameters and a “seed” (a random number that controls the initial noise of the image). In 2023 like in 1965, the Copyright Office remains wary of granting protection for an indefinite number of works. And like Noll, potential “AI artists” have to demonstrate some level of individual creation.

Recreation of “Gaussian Quadratic” by Stable Diffusion with two different initial seeds (1989 and 1990). Prompt (partly based on an original description of the work by Noll): “Ninety-nine lines connect 100 points whose horizontal coordinates are Gaussian. Vertical coordinates increase according to a quadratic equation. As a point reaches the top, it is reflected to the bottom to continue its rise. This particular proportion is vaguely similar to the painting Ma Jolie by Picasso.”

In his third and ultimately successful application, Noll underlined that the process was "perfectly mathematical" and controlled. While the applications have not been digitized, I have been able to locate the final form registering Gaussian Quadratic in April 1965 (just in time for the Exhibit) as a “geometric abstract form”.

Obviously the notion of generated work was very different in the 1960s. Noll did not train a model on a massive corpus and the reference to Picasso was only metaphorical. It remains striking that seemingly no artists objected to the de-humanization of generated art. While Noll's copyright claims were controversial, no one denied he had created a work of wart and Gaussian's Quadratic.

Paradoxically, computer generations seemed to agree more with the common definition of art in the 1960s. In the age of pop-art and hippie communes, relying on a computer was perceived at most as a mild subversive act. In fact, Noll's copyright attempts proved more controversial among his peers: according to Martin Zeilinger "other early computer artists insisted that [their] work emerged from a collaborative working-together of human and computer" and were reluctant to abide by the rules of intellectual property.

Afterwards, Noll embarked on a successful artistic career with exhibitions in England, Yugoslavia, Czechoslovakia, Brazil, Germany, India, Spain… Along with AI, computer-generated art also had his first brief “summer” that is by now largely forgotten.

At least by 2023, "the wave of the future" has finally come crashing back, not just on one gallery but on the entire Internet. It remains to be seen if it will free the artists.

Few statements about the impact of technology on creativity are as widely cited as Paul Delaroche’s dramatic pronouncement upon first witnessing early photographs (daguerreotypes) in 1839:

“From today, painting is dead!”

This catchy quip has been widely invoked as either a prescient testament to the corrosive influence of technology on art or as emblematic of the perennial resistance of traditionalists to the inexorable march of innovation.

However, closer scrutiny of historical records reveals it to be entirely apocryphal: there is scant proof to suggest Delaroche ever uttered these words. We got hold of a copy of the book ‘Parallel Lines: Printmakers, Painters and Photographers in Nineteenth-Century’ by historian Stephen Bann, in which he explains the origins of the quote.

It is this quote says Bann that “has served more than any other to fix the relationship between painting and photography in a condition of mythic stasis” - its origins were contained in an 1959 monograph on Daguerre that established the source for the quote: an 1873 survey, 34 years after those words supposedly left Delaroche’s lips.

On top of this, Delaroche's other documented comments on the subject reveal a strikingly different stance, calling photography:

“A great advantage for art.”

Delaroche's attitude echoed that of Samuel Morse, who said photography was a “wonderful discovery” that was “an immense service rendered to art” in an 1845 speech he made as president of the National Academy of Design concerning the question: “Will not the Daguerreotype affect unfavourably the Arts of Design?”

NOTE: We explored Morse’s speech about photography and art in this post

Paul Delaroche would eventually be elected into the the Academy, which makes us wonder - if Delaroche did declare painting dead in reaction to seeing early photographs, perhaps reports on Samuel Morse’s speech changed his mind - leading to the development of a more progressive attitude.

As for the statement ‘From today, painting is dead!’, it is better understood as a mythic expression of the anxiety that new technologies often engender within the arts. Delaroche’s proven positive attitude to photography offers a more constructive framework for considering the impact of technological change on the creative realm, underscoring the potential for symbiosis and augmentation rather than replacement and obsolescence of the artist.

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Just as people were coming to terms with word processors in the late 1970s and early 1980s, another evolution in writing tools took place, Apple introduced Macintosh: the first commercially available computer with a Graphical User Interface (GUI.) This new paradigm of computing would garner intrigue.

As these new devices made there way onto college campuses, some academics wondered how they might impact student work. At the University of Delaware, one member of the English Department (Marcia Peoples Halio) would conduct a study on the matter culminating in a 1990 paper titled “Student Writing: Can the Machine Maim the Message?”

Her conclusion? Students using a Macintosh produced inferior work compared to their counterparts on the austere command line (30% of Macintosh writers used complex sentences, whereas 50% of the IBM writers did.) Peoples Halio would quip in the paper: “Never before in 12 years of teaching had I seen such a sloppy bunch of papers” with regards to work created on the Macintosh.

The study would catch the attention of the San Jose Mercury News that would summarize her findings: “The same icons, mouse, fonts, and graphics that make the machine easy to use may well turn off the brain’s creative-writing abilities.” The story would be picked up by numerous outlets, a few international and eventually garnered a mention in The New York Times who noted in 1992 that no study had yet refuted her findings, also noting the criticism it garnered from other academics.

The most popular critique was Halio's methodology, namely the glaring absence of control groups. An Apple representative swiftly pointed out the studies flaws, Halio retorted, acknowledging some imperfections in her study, staunchly compared giving a Macintosh to an inexperienced writer to handing a brand-new sports car to a gleeful 16-year-old. The Los Angeles Times would admit “the idea that our minds are somehow warped by our word processors” was too compelling to ignore, but was generally critical of the study.

Academic Steven Youra would publish a response to the paper the same year in ‘Computers and Composition’ where he would criticize the study's inadequate design and flawed logic, criticizing Halio's limited understanding of the Macintosh's capabilities.

He refuted the portrayal of Macintosh as fostering immature writing, emphasizing its potential to encourage playful, engaged writing experiences. In 1995, the same journal would publish a study that sought to address the studies flaws - producing results refuting Halio Pope’s findings.

The Chicago Tribune syndicated a column in 1991 co-written by a young Brit Hume, which concluded the debate was moot since GUIs would soon be everywhere, while noting the study likely suffered from sample bias because of the types of students who’d opt for a Macintosh in the first place (less academic more creative types.)

As we’re faced with new evolutions in writing tools - it is worth remembering this little debate and those that proceeded it, going all the way back to the original literary technology: writing itself.

Socrates critiquing writing:

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Two centuries ago this would have sounded like delusions of a false messiah, convinced they’d been granted powers of deity. Yet, when Daguerre revealed his invention to the world - an early form of photography - serious comparisons to prometheus would suddenly be unavoidable.

French writer Nadar would recall the initial reception:

There were few reference points to process the advent of artist-less art; no horse-less carriages or operator-less elevators. No musician-less music (recorded sound) or messenger-less messages (telegraphy & telephone.) It was a moment so strange, so abstract, so without analogy or comparison as to be unimaginable to anyone born this century - utterly impossible to comprehend.

Until now.

Almost two centuries later something new and "far beyond anything imaginable" has again emerged. Something brilliant and unforeseen, something that is destabilizing all that we knew and believed: AI image generation.

The awe we feel when a figment of imagination manifests on screen, is the same awe our great-great grandparents felt when a moment in time and space, materialized on a metal plate. The same disorienting wonder a magician elicits when breaking the rules of reality you thought you knew, only without the sobering realization it was an illusion.

We're there again. Enamoured again. Fascinated, excited and scared again - of a new power, from a new prometheus - given to us: mere mortals.

Some artists - who earned their monopoly on magic - suddenly feel scared and angry and vulnerable. Others embrace these new tools, to break once unbreakable rules, to produce art once impossible to create.

Unlike then, we have the benefit of hindsight. We have run this simulation before. History doesn’t repeat, but it rhymes, because human psychology stays the same. It is different this time - because technology is a change agent - but it’s also similar.

It is within the familiar rhythms of history and human behaviour that we hope to better understand this moment and the future.

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These days, digital photography is basically everything, capable of capturing all the colors and motion of the world around us. But there was a time when it was nothing but giant black and white dots of data being broadcast on a television screen.

In the roughly 50 years since the 1975 release of the Cromemco Cyclops, the first all-digital camera to see commercial release, digital photography has evolved from a niche novelty to a subject of camera giant hesitation to a source of disruption to something that literally everyone has in their pocket.

The Cyclops, a 2-bit black-and-white camera that used a modified chip as a solid-state image sensor, wasn’t the first digital camera produced, however—that was a prototype developed by Steven Sasson in 1975. Sasson was an engineer at Eastman Kodak, and Kodak had a lot invested in analog photography, which meant that despite inventing a groundbreaking device, his bosses discouraged him from continuing to develop it.

“The feeling was that this was a very scary look at what could be possible in the future,” Sasson told PetaPixel last year. “As the company’s entire business model was focused around sensitized goods, proposing that they not use any of that was not popular.”

Other companies, largely in the Japanese market, did not have such hang-ups. Sony took a stab in the general direction in 1981 with the Sony Mavica, a video camera that was capable of recording still images to a removable disk, rather than film—making it the first filmless single-lens reflex (SLR) camera. While not technically digital, it was electronic, and did predict much of the convenience that would make digital cameras popular in later years. Its design suggested cameras that would be capable of recording videos just as well as still images, as many cameras now specialize in doing.

This still-video recording format became popular with news photographers who were looking for ways to speed up the process of electronic distribution, even if the cameras themselves produced lower quality works than possible with film. By the late 1980s, the technology gradually became good enough that photographers from major wire services began using them to distribute photos of major news events.

But this was not a digital camera. It was essentially an analog camera that produced digital files. True digital cameras suitable for professional use did not appear until the late 1980s and early 1990s. Kodak’s first attempt at a digital SLR, the Kodak DCS 100, appeared in 1991 and cost more than $20,000. It was only capable of shooting 1.2-megapixel photos, a resolution only barely good enough for print—a good thing, as the target audience was news photographers.

If there was any real resistance to the rise of digital photography during this era, it was mostly from the camera companies themselves, who still had legacy businesses to protect, and those who wanted to save money. (Digital cameras used to be prohibitively expensive.)

At this time, for example, Kodak’s film business was still strong, with its embrace of disposable cameras—itself somewhat belated and only in response to direct competition from Fuji—strengthening its bottom line around this period. But it would not last.

By the early 2000s, it was clear that the digital camera was going to win. The advantages of the format, often leveraging a microchip called a charge-coupled device (CCD) as an image sensor, were just too obvious, and many of them were related to the very things that scared Kodak off from initially supporting the digital camera’s development: You didn’t need film to use a digital camera, and you didn’t need prints. (Though if you wanted them, you could still get them developed.)

That meant that Kodak could never sell consumers on a Gillette razor-style model with a digital camera, and that ultimately sank Kodak’s position in the camera world.

It likely became even more surprising when the low-end consumer cameras were replaced entirely by smartphones.

This is part 2 in a series on the rise of the digital camera. Read Part 1 here.

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High-end digital cameras play distinctly different roles from generative art tools. One is meant for capturing real moments; the other is meant for forging new ideas based on existing language and visual models.

So it’s telling when one of the largest players in the professional camera space, Nikon, decided to create a campaign that directly pushed back against that mindset.

“This obsession with the artificial is making us forget that our world is full of amazing natural places that are often stranger than fiction,” the text on the Nikon Peru ad for its “Natural Intelligence” campaign states.

As a point of concession—yes. The world is full of wonder, and sometimes it’s more impressive than anything you can imagine on your own. It makes for snappy marketing campaign copy.

But in many ways, this is a new front on a discussion that has emerged in recent decades in a very serious way: How “real” photography is in the digital era.

One of the biggest flashpoints in the history of digital photography came not because of a camera, but because of a contest.

In 2013, the winner of that year’s World Press Photo contest was accused of modifying the image to ensure the absolute best quality result for the sake of an awards ceremony. A blog post by Dr. Neal Krawetz, a forensic researcher who specializes in photography, created an ongoing debate that gets to the heart of concerns around photo manipulation.

Krawetz implied, after an analysis, that Paul Hansen’s photo was a composite.

“So here’s what likely happened … The photographer took a series of photos,” he wrote. “However, the sun’s position made everyone dark and in silhouette. So, he combined a few pictures and altered the people, so you could see their faces.”

The analysis turned into A Thing, leading to a lot of debate, both inside and outside the broader photography community. Other analysts disagreed with Krawetz’s point of view on the matter; outlets like BuzzFeed News and Wired stepped in to draw attention to the discussion.

I think the reason that it became such a big deal is that it hit one of photography’s biggest pain points—defining reality in a world where images are basically expected to have some sort of modest digital correction done to them, combined with the inherent difficulty of capturing “Natural Intelligence” in the flesh.

It’s a situation that strikes at the heart of digital photography, where the concern about the technology isn’t so much the devices, but what those devices produce. There is a genuine fear that pixels will get manipulated, and it has become more pronounced in recent years. 

There is a purist mindset to photography that has led to negative reactions towards programs like Photoshop—the same kind of reaction that led to immediate skepticism around Paul Hansen’s World Press Photo-winning photograph.

This mindset is still relatively modern. A 2016 blog post from Paul Reiffer, a well-known landscape photographer, makes an argument against the aggressive use of photo-manipulation software. His mantra? “Take it, Don’t Fake it.”—a very similar slogan to Nikon’s “Natural Intelligence” campaign.

As he explained in his post:

So why is it becoming so popular? Well, in part, because the bar of reality is being shifted to an unachievable level by photographers. The strive for the “even more impressive sunset”, along with advances in digital tools and the desire to win popularity contests on platforms such as Instagram is pushing people to create more and more outrageous images and publish them, with the expectation of awe and appreciation by millions.

The problem is, we’re setting ourselves (as humans) up for failure. Tourists are now visiting amazing sites with unrealistic expectations of what should be before them. On my last visit to the Grand Canyon, I actually heard a guy (as he looked up for 2 seconds from his iPhone) say “is that it?”—in part, I’m sure, because the images he’d seen online before visiting had promised him so much more.

It’s likely that generative AI is only going to worsen this problem that Reiffer has identified, where reality is at odds with the image on the screen. (Photoshop, after all, has borrowed some of DALL-E’s best tricks.)

To put it all another way, the skepticism seems less about the cameras and more about the tools to manipulate the photos. Photographers, like graphic designers, have always been skeptical of gimmicks designed to make their work better.

In some ways, I think the big reason that digital photography has had somewhat less of a technoskeptic reaction than other kinds of technology is because of the people who got into cameras in the first place. A camera nerd is very similar in mindset to someone who builds a massive gaming PC—they just tend to get outside a little more often.

In part, it’s because many of the photographers took a more realistic stance on the issue. In a 2003 American Photo story, Chris Johns, the former editor-in-chief of National Geographic—a magazine that, by its own admission, was late to the digital camera trend—perfectly encapsulated the mindset around digital cameras as such:

Digital photography is only a tool. It can help you make great pictures, but great pictures don’t come from the camera. They come from the heart and soul. Don’t forget that. The more motives you have to make great pictures, the better your pictures will be.

Do people get nostalgic for the old cameras? Of course. There’s a reason Polaroids periodically resurface, that people go on Etsy hunting for old-school SLRs. And there will always be purists who prefer film, even if digital cameras and photo-manipulation tools have learned most of traditional film’s many tricks.

While not an across-the-board fact, photography is a field that isn’t opposed to new technology. Photographers just want to ensure that the final result comes from a real place.

And that might forever place photography pros at odds with the generative effects of artificial intelligence.

Part 2 is coming soon covering :How Digital Cameras Became Dominant

Originally published in The Conversation.

The 19th century concept of authorship revolved around the romantic view of the artist as a lone genius. In this model, every stroke of the brush, every note played and every word written was the product of a singular creative mind, bearing the unique imprint of its creator.

However, the landscape of artistic creation began to shift dramatically with the advent of the 20th century, with artists like Marcel Duchamp, John Cage and William S. Burroughs pioneering new creative approaches such as aleatoricism, the cut-up technique and “randomness” which began recast the role of the author.

Now, AI technology looks likely to cause as much disruption as the previous revolutions combined.

Just as the artists of the last revolution grappled with their age’s social and spiritual upheavals, our artists today must rise to the challenge and engage the shock of the new that now confronts us all. AI will force artists (along with the rest of us) to examine what it means to be a “creator” and, ultimately, perhaps what it means to be human.

Disrupting the artistic landscape

Conceptual art pioneer Marcel Duchamp radically altered the artistic landscape with his work, Fountain (1917) – a urinal signed “R. Mutt”.

He argued that art wasn’t confined to traditional craftsmanship but could spring from the act of selection and presentation.

Composer John Cage took this artistic revolution a step further. His composition 4'33 (1952), where the performer remains silent for four minutes and 33 seconds, is a powerful example of a work that questions the definition of music itself. Cage’s piece isn’t just any random stretch of silence – it is art because Cage himself, a human, framed it, turning the act of listening into a creative process.

Following suit, William S. Burroughs disrupted traditional narratives with his cut-up technique, emphasising the non-linear progression of storytelling and demonstrating that authorship could extend to the reassembling of pre-existing material.

David Bowie famously used this technique for writing lyrics in some of his songs, especially in his work from the 1970s. Songs from albums like Diamond Dogs and Young Americans used cutups to create distinctive, unexpected, and often cryptic lyrics. Bowie would cut up his writings or other texts, rearrange them, and use the resulting fragments as the starting point for his songwriting. This allowed him to break free from linear thought and traditional songwriting cliches and to explore more abstract and unpredictable forms of expression.

These early disruptions laid the groundwork for today’s art and machine learning intersection. They questioned the traditional concept of authorship. Now, technology is challenging it again.

Moreover, this time around, even the role of the audience is likely to change.

Modern art

Jason M. Allen, a digital artist from Pueblo West, became one of the first creators to win a prize for AI generated art. His role was to input a text prompt into the AI tool, which then transformed it into a hyper-realistic graphic based on its training from millions of previously processed images.

In this process, Allen’s creativity came into play in formulating the correct prompts to instruct the AI, effectively guiding or curating the AI’s output.

In this case, the artist becomes a sort of co-pilot, steering the AI’s capabilities to produce a desired output. This new process raises questions about authorship and authenticity in art. It underscores how technology redefines the traditional artistic process, with artists becoming more like orchestrators of complex AI systems.

Modern artists like Laurie Anderson have begun to harness machine learning to create novel works. Anderson’s work, Scroll(2021), is a fusion of religious text and her distinctive linguistic style generated through AI.

In both of these examples, the artist functions like a curator. The manual toil of writing, drawing or composing is replaced by an iterative process of discovery, filtering and refinement of instructions to the system.

These artists are pioneering a shift in the artist’s role. Instead of being the sole creators, artists now guide, shape and direct the creative output of machine learning systems. This transition certainly presents challenges, but it also uncovers a world of new artistic competencies and opportunities.

The future

Looking to the future, we can expect the interplay of art and technology to deepen. Artists who embrace this ever-evolving landscape will contribute their unique perspectives to the development of machine learning and shape our collective relationship with it.

This time around, the revolution will also extend to the audience’s role. Passive viewers become participants in the making and remaking of the art. The very same AI systems that empower artists can empower the audience.

Read more: Art Has Always Been Artificial

Audience members can utilise AI tools to generate art, even if they do not have traditional artistic skills. AI democratises the art-making process, making it accessible to anyone with access to the technology.

This could significantly expand the art world, as more people can become creators, contributing their own perspectives and ideas. One example is a project called Tamper (2019), developed by John Underkoffler for the AMPAS, Getty and other museums. This project lets museum visitors build collages out of material taken from a museum’s collection.

We must prepare the coming generations for this rapidly changing creative landscape, fostering their ability to co-pilot with AI systems. As we move further into the age of machine learning, artists must reclaim their position at the forefront of creative thought and innovation.

There was a catch though: the promethean powers afforded by this “green-eyed deus ex machina” would also bring chaos: “In return, the deity required that the President resign, that stacks of sensitive Government and corporate secrets be made public.”

The writer was of course referring to the Watergate scandal and Daniel Ellsberg’s use of photocopiers to leak the Pentagon Papers to the press. The hypothetical was actually a reality. While the USSR kept strict control over the technology, highly regulating the machines until 1989, the free world was left to deal with the ‘consequences’ of freedom.

Downsides of this supposed double edge sword - cited in Time - would be encouraging waste and slothfulness, on top of stifling creativity and punching holes in copyright laws. This last issue was of particular concern to publishers and authors, one that had been growing for more than a decade because, as Marshall McLuhan would put it in 1977, “Gutenberg made everyone a reader, Xerox made everyone a publisher…”

A 1963 article - syndicated throughout the US - painted a damning picture of the future copy machines would bring upon publishers and writers: "…the authors and book publishers of America are beginning to suspect that what is meat for the photocopying industry will turn out to be poison for writers."

The piece noted stories circulating in the literary world about a Texas professor creating custom anthologies for his students, thereby "cheating poets and short story writers of anthology fees." The author cautioned that "The photocopying threat brings the technological revolution home to writers, who are among the last individualists among us," which "would serve to make the life of the freelancer more hazardous than it is at present."

Scientific publishers felt threatened too, the author noting "it poses a menace to the livelihood of scientists. Publishers of scientific material...stand to lose in the very near future if a few more pennies can be shaved from the cost of photocopying." One publishing executive - Curtis Benjamin of McGraw-Hill - warned scientific progress could suffer if the market was impacted. Libraries found themselves at logger heads with publishing companies, as they became popular places to access photocopying machines.

The author called for a new Mark Twain to emerge, who had helped lead a movement in the past to “hammer out some universally valid copyright laws against the international pirating of authors' material.”

In 1968, a publisher stepped up to the plate, and sued the government (specifically the National Library of Medicine and the National Institutes of Health) for photocopying and distributing its materials. The publisher lost, then appealed, the Supreme Court ruled against it again setting a new and important precedent for the concept of ‘fair use.’

In 1976 the Copyright Act was up for revision, libraries would lobby for exemptions - citing the case - they didn’t get a blanket exemption, but did get some allowances for patrons who want to reproduce work for research, as well as replacing and preserving documents - among other things.

Only 7 years later ARPANET would launch, a computer network that would eventually evolve into the internet, making the threat of photocopiers and physical reproduction seem quaint to publishers.

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Written in collaboration with Freethink for their Special on Creativity & AI. Originally published there.

Art has always been the product of new techniques: from spoken language to the written word, from drawings on cave walls to paint brushes, from quills to word processors.

In 1859, French poet and art critic Charles Baudelaire decried a new form of artistic expression: photography. This new medium, he said, was “the refuge of every would-be painter, every painter too ill-endowed or too lazy to complete his studies” and to embrace it was a sign of “blindness” and “imbecility.”

Baudelaire considered the output of these fanatical “sun-worshippers” as more artifice than art—canned creativity, a synthetic, artificial substitute for the real thing. It was a notion that would have felt as compelling then, as similar critiques of AI art feel today. And it was just as much of a fallacy too.

Art has always been artificial. Those words even share the same Latin root: ars or artis, meaning skill, craftsmanship, or technique. In other words: artificial acts of creation. Art has only ever been the product of new techniques: from spoken language to the written word, from drawings on cave walls to paint brushes, from quills to word processors. These are all tools that allow thoughts, feelings, and ideas to transcend our minds and become tangible, transferable, reproducible, and remixable.

When a new tool for expressing human thought and imagination emerges, devotees of previous techniques often object—their creative progressivism quickly calcifies into creative conservatism. The inefficiencies of old techniques—the toil and time that was once unavoidable—become regarded as a virtue borne of suffering and sacrifice. Those that embrace the new tools are viewed skeptically as faux artists: their art is treated as mere artifice.

While artists often define themselves by their preferred medium—whether they be writers, painters, or programmers—it is important to remember these tools are just wrappers around human intelligence and its most valuable output: creativity. When these tools evolve, they often make it easier to express ourselves. It is a mistake to confuse that ease with laziness or as evidence that new creative mediums are less worthy than older ones. As one newspaper reader wrote in an  1897 letter to the editor in defense of typewritten love letters, old technologies often have a  “sentimental glamor” afforded by age.

While creative conservatism is inevitable, it is never unanimous. With every new creative tool that emerges, there is always a band of creative progressives that embrace them. These brave early adopters are tasked with defending these new techniques until they’re taken seriously.

Shortly after Louis Daguerre helped bring photography to the world, French artist Paul Delaroche was quoted as having said upon first seeing a photograph in 1840: “From today, painting is dead”. However, this quip has never been verified and, whether it was uttered or not, Delaroche seems to have quickly changed his mind. In one letter, he would refer to Daguerre’s breakthrough as a “wonderful discovery” that was “an immense service rendered to art.” Samuel Morse, an artist and the inventor of telegraphy, made similar points in a speech given to the National Academy of Design addressing the question: “Will not the Daguerreotype affect unfavorably the Arts of Design?” He would posit that photography was “undoubtedly destined to produce a revolution in Art.”

Hear AI Samuel Morse read opening of speech (hear full reading at link below)

Delaroche and Morse both saw what photography would ultimately become: a powerful new creative tool. A miracle for the artist, not a replacement. It seems they understood that—as creative progressives do—all creative mediums are unnatural and incomplete, simply a way to channel that most natural and unique human trait: creativity.

This was a far cry from the views Baudelaire would later espouse about photography, technology, and creativity: that “purely material developments of progress” lead to the “impoverishment of the French artistic genius.”

Yet it would be material developments of progress—paint in tubes—that would give rise to Impressionism and empower, rather than impoverish, French artistic genius. Renoir would say of the invention: “Without colors in tubes, there would be no Cézanne, no Monet, no Pissarro, and no Impressionism.” This artistic innovation—that removed the need for paint mixing, allowed the rise of more vibrant synthetic paint colors, and enabled painting outside the artist’s studio—was invented by John Geoff Rand, the protégé of fellow creative progressive Samuel Morse.

In the 20th and 21st centuries, history would—as it often does—rhyme with the 19th. New creative mediums emerged that were embraced by creative progressives. New spasms of creative conservatism would follow, while new masters would go from rejected to revered. 

Where paint in tubes democratized painting and spawned Impressionism in the 19th century, paint in cans saw the rise of street art in the 1970s, leading to new generations of artists from Jean-Michel Basquiat to Keith Haring to Banksy. 

In 1985, Andy Warhol predicted that digital art would be the next evolution of creative tools and “take over from the Graffiti kids.” He was right. Painting without paint—digital art—was arguably the biggest leap in art since the paintbrush, and Warhol, like Morse and Delaroche, saw it as a creative superpower rather than sacrilege.

Harold Cohen had already created AARON, a computer generated art program, in 1973 and Warhol himself was enthusiastically embracing the Amiga as an art tool by the end of the 1980s. A year after Warhol’s prediction, Steve Jobs would buy a division of Lucasfilm’s pioneering special effects company, renaming it Pixar. Steven Spielberg would break ground too, using CGI in Jurassic Park, a decision that saw his lead VFX supervisor declare his own extinction only to find his creative skill easily transferred to digital animation.

Artist David Hockney was another creative progressive who embraced new mediums with enthusiasm, first with fax machines, photocopiers, and polaroids, and then in the early 2000s he embraced the iPhone and iPad as a digital canvas. In 2011, one of those works would even grace the cover of The New Yorker, a work widely mocked. He would be teased by local villagers, who’d say they heard he was “drawing on his telephone,” to which he replied, “Well, no, actually, it’s just that occasionally I speak on my sketch pad.”

It is tempting to think that the era before generative AI, digital art, photography, and paint in tubes and cans was some kind of creative Eden—the good old days before “purely material developments” corrupted the arts. This sentiment has become increasingly prevalent as AI and art collide at the intersection of science and the liberal arts. 

Yet history tells us a different story, one of technological revolution followed by creative evolution, of machines and tools augmenting imagination rather than competing with and replacing it. Of photography empowering artists, kaleidoscopes emboldening designers, paint tubes democratizing painting, and — according to a thesis by Hockney and physicist Charles Falco — primitive camera obscura’s elevating realist painting as far back as the 1400s. If they’re right, then cameras didn’t only diminish realist art, it helped lead to its rise. What was thought to be the product of natural ability may actually have been the result of unnatural augmentation: as all art is and always will be. In many ways, the history of art is the history of technology and of humans augmenting their abilities through an evolving set of tools to communicate new ideas.

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Written in collaboration with Freethink for their Special on Creativity & AI. Other articles from their special edition:

• What AI Can Teach Us About Copyright and Fair Use by Brandon Butler

• Why artists shouldn’t fear AI by Kirby Ferguson

• These “AI Artists” don’t want to ape existing art — they want to create something new entirely by Brandon Stewart

• Idea amplification is the really exciting potential of generative AI by Dr. Kenneth O. Stanley

Stock photo services like Shutterstock or Getty Images helped source the models used by tools like Midjourney and Stable Diffusion. But stock photos are not perfect—and if the two models can’t work together, one could disrupt the other.

If you’ve used an AI image generator for any period of time, you’ve most certainly seen more than a few watermarks for stock photo services get through.

It’s for that reason, among others, that a lawsuit between the stock photo service Getty Images and Stability AI, the makers of the text-to-image model Stable Diffusion, was filed back in February.

Getty Images isn’t necessarily against generative AI, as long as they have some control over the model and can use it to generate royalties for its photographers—it announced a collaboration with NVIDIA to this effect back in March.

But it’s nonetheless worth understanding why a stock photo service might react so strongly to generative AI as a concept. After all, let’s face it—this technology, as it develops, could replace the need for stock photos in many cases where they might have come in handy in the past.

And it comes down to understanding the roots of stock photography. Those roots can be found deep under the Earth’s surface.

From Iconic to Utilitarian: The Evolution of Stock Photos

In one of its final stories, BuzzFeed News completed a tour of the Bettmann Archive, a store of important photos organized by curator Otto Bettmann starting in the 1930s. It was a collection he built from a starting point of two utterly stuffed suitcases he brought to the United States after leaving a Nazi-controlled Germany in 1935. His archive—not the first, but easily the most famous of its kind—blossomed from there and ended up becoming one of the most important archives in the world. These photos, of which there are many millions, represent some of our most important images.

This is effectively our culture. Nearly every famous picture you can think of is included in this archive, and as a result, they are protected in a deep, cool setting—a Pennsylvania location more than 200 feet underground, roughly 50 miles from the nearest large city.

“The vault, which is part of a series of former limestone mines, is both temperature- and moisture-controlled. These careful conditions have virtually stopped the degradation of the photographic negatives,” the BuzzFeed piece notes.

These photos are largely in physical forms, in a location intended to keep their usability pristine for centuries to come. Odds are, however, that if you’ve seen these images, it’s largely in digital form. These kinds of photos, which often found use in newspapers and magazines, are often valuable for their artistic, cultural, or editorial implications, and their existence has created something of an arms race over the years.

Not all stock images need to be iconic or famous, however. Sometimes a photo of something plain or someone anonymous was enough—as it could be used as the starting point of a photo illustration, or could exemplify the type of person or thing the editors wished to include. (Pictures that show diversity are popular parts of stock photo libraries, for example.) In the past, these types of images were sold through catalogs produced by stock photo agencies which managed each individual photo.

But as the digital era came about, so did the methods of distribution. PhotoDisc, a company that started in the early ’90s, pioneered two key distribution techniques for the PC era—CD-ROM distributions (as their name suggests), and later, digital downloads.

(In case you’re in need of some stock images, many of the original PhotoDisc archives are available on the Internet Archive, including the ever-popular Objects Series, which is basically made up of a bunch of different things on a white background.)

At the time PhotoDisc started, most agencies were still focused on physical distribution, but that quickly changed in part to budding corporate interest at the very beginning of the internet era. The big shift happened thanks to investments by individuals with mega-rich reputations.

In 1994, Getty Oil heir Mark Getty began building an image archive with his business partner Jonathan Klein, with the explicit goal of repairing the “highly fragmented” state of stock photography, as a 1998 piece in The Independent put it. 

This was the perfect time to build this kind of business. Mediums like books and magazines were having a visual renaissance thanks to the rise of desktop publishing, and designers needed images from which to illustrate stories. And Mark Getty, just 23 when his family’s oil business was sold off for $11 billion in the early ’90s, saw an opportunity in investing some of those earnings into the stock photo industry.

“We saw the market as a large but inefficient one,” Getty told The Independent. “We also saw technology as a catalyst for change in the industry … Most importantly, it did not seem to us that anyone else in the industry had yet grasped this opportunity.”

It turned out that Getty was wrong about that last part—almost immediately, Getty Images would find itself competing with, of all people, Bill Gates. Interactive Home Systems, a company founded by the Microsoft CEO in 1989, initially aimed to build art displays for homes. The company, in its pursuit of this initial goal, began to acquire a number of prominent image libraries, including the Bettmann Archive, which by this point had swelled to include much of United Press International’s legacy photo library—a total of 19 million images.

Eventually, Gates’ company, soon renamed Corbis Images, determined that licensing images to others was the pathway to success, and he had the high-end images that were worth buying. 

But having pictures of Martin Luther King or Iwo Jima or the moon landing only goes so far in the world of stock photos—after all, mundane imagery likely makes up the bulk of stock photo sales. As exemplified by the rise of clip art, an artistic medium that ad agencies first used in the 1950s but which went mainstream starting in the 1980s and 1990s, people sometimes just need basic pictures to illustrate their signs.

PhotoDisc made those kinds of pictures, and perhaps for that reason, Getty bought them out in 1997.

Gradually, the industry went increasingly downmarket. iStockPhoto, founded in 2000, and Shutterstock, founded in 2003, focused on selling low-cost images that were priced with the internet’s scale in mind.

iStockPhoto, now known as iStock, was a pivotal site in the success of stock photography in the internet age, as it stumbled upon a micropayment model years before other companies did. And it was a total accident. In a 2007 interview with CNET, founder Bruce Livingstone noted that the firm originally went with a barter system—allowing photographers to exchange images with one another—before a huge hosting bill required the site to change to a 25-cents-per-image pay model.

“We started charging in 2001,” Livingstone recalled. “Probably toward the end of 2002 or the beginning of 2003 we actually had a bit of a budget and realized, ‘Hey, there’s a business here.’”

That business would be eventually acquired by Getty Images, but not before directly inspiring a sea of competitors, some of which didn’t charge at all. Among these was Flickr, which had embraced a Creative Commons model, allowing end users to distribute images for free in exchange for following a set of requested rules. That service has seen a variety of owners in recent years, most notably Yahoo.

(Livingstone would later leave Getty Images, but get back in the game with Stocksy, a higher-end service with bigger payouts for creators and images with a more authentic visual style.)

Over time, Getty Images consolidated power in the stock photo space, acquiring numerous competitors and licensing Corbis’ massive stock-photo library in most parts of the world, after that firm was acquired by a Chinese company in 2016. (Corbis is now known as BENlabs.) By that time, Getty’s biggest competitor emerged in the form of Shutterstock, which started as a platform closer in conceit to iStock, but has evolved to be closer to Getty’s higher-end model.

Both companies are far from alone. For example, a more recent player in the stock-photo space is Unsplash, a service that allows for free image downloads, but allows some brands to include sponsored images and offers a subscription service for higher-end photo options. These images are largely uploaded by the photographers themselves.

There’s a lot of money in stock photos—they get used everywhere, after all—but they benefit greatly from scale, size, and quality. Stock photo photographers can make as little as a few cents for a small-photo license, or $100 or more for an “extended license,” which removes usage limits and allows images to be resold in some cases.

To put it all another way, we’ve come a long way from a couple of suitcases loaded with photos.

Why the Stock Photo Model is At Risk of Disruption

Many of the major complaints lobbed at generative AI—that it’s a shortcut, that it’s used by people to displace the work of others, and that it often leads to a lower-quality end result—are often lobbed at low-end stock photos. They are basically direct competitors.

But because they are direct competitors, generative image tools are well-positioned to poke holes into the stock photo model. And stock images clearly have some problems. One of the most common ones might be the distinct “look” that stock photos often have. Stock photos, generated in photo shoots and relying on models, often don’t feel like real life, especially because the same models and settings appear repeatedly.

During the early months of the pandemic, for example, a handful of people wearing headsets and clearly looking like they were on Zoom became commonplace throughout the Web. And some stock photo models, such as Hungarian engineer András Arató, became famous memes—overexposing them in the public square while diminishing the intended value of their stock photos.

This is even the case with Creative Commons or Unsplash photos. (How many times have you seen this guy holding his head while using his laptop? His image has been downloaded nearly 300,000 times from Unsplash.)

While stock photo services like Stocksy and Unsplash have specifically taken steps to ensure their libraries have much less of an obvious “stock photo” look, stock photos are like pop music—if a pop song becomes overexposed, it starts to sound less unique over time, or can feel outdated as tastes change. Photographers can only do so much with lighting and framing to prevent a stock photo from losing its luster.

When low-end stock photos become too popular, it diminishes their value because our tastes change. While historic photos might gain value over time, many stock photos will gradually see their values shrink.

Could Stock Photos Coexist With Generative AI?

Given all that, it’s understandable why generative AI could be seen as a real alternative to stock photos in many cases. While generative AI may run afoul of shifting tastes eventually, AI will be better positioned to adapt to newer generations and styles than a stock photo that will remain wedded to the creative decisions of the time that it was taken.

(On the other hand, check out some of the AI-generated images of isolated objects in this piece—a CD-ROM, a camera, and a GPU. In each case, they look like stock photos from a distance … until you eye the details.)

Of course, a MidJourney or DALL-E image will never replace the iconic photographs in the Bettmann Archive. At best, generative AI is just interpreting a prompt based on training data; its source material is not something that came to life through the lens of a camera. But for the sea of pictures that often flood low-cost stock photo sites, generative AI creates a real risk that stock photo sites may only have two real ways to combat—either they can beat ’em, or join ’em.

Getty Images, in its lawsuit against Stable Diffusion, leaned towards the idea of beating ’em—which makes sense, because the models were most likely trained on copyrighted photos owned or licensed by Getty, which Getty says that other companies properly license.

But the join ’em approach seems to be the winning formula for Shutterstock, which actively promotes “an ethical AI-generation model that pays artists for their contributions.”

In the long run, stock photo services may be at risk, but right now, they hold lots of potential, because they often maintain archives of millions of photos that can be used as starting points for more perfectly tailored images than one might be able to find with a stock photo search.

Graphic designers know the pain of trying to source the right image to match a specific design or style. While not perfect, generative AI tools make possible something that stock photo services cannot in their original form—the ability to get a variation on an idea that may be a closer match to what the end user wants.

Stock photo photographers attempt to keep up with times and tastes, but they can’t adapt to the needs of a creator in real time, unless they’re actually working with the creator, at which point you might as well hire a photographer outright. And for that reason, it’s inevitable that generative AI will disrupt the role of the stock photo. While not a perfect product, it has numerous advantages that will be hard to ignore over time.

But there’s no reason why generative AI and stock photos can’t coexist—as long as everyone is willing to work together to find common ground.

Artificial intelligence has become a surprisingly large concern amid the writers’ strike, and it’s a tension rooted in Hollywood’s tendency to cut corners to save a buck.

In many ways, the latest strike by the Writers Guild of America reflects something that’s all too rare in our modern culture—creative people sticking up for themselves.

The strike, which began at the beginning of May, is Hollywood’s largest strike in about 15 years, when the WGA shut down production for more than three months in late 2007 and early 2008.

Both strikes, in many ways, break down to very similar issues—writers, who are responsible for the content that we watch, want to ensure that, as technology evolves, they’re not left behind from a compensation and quality-of-work standpoint.

Mostly, this discussion has centered on royalties offered by Netflix, Peacock, and similar services, which have flourished in the years since the prior agreement was made—along with some practices which highlighted just how little control writers can sometimes have in this new climate. When Warner Bros. Discovery removed numerous programs from its HBO Max service (which is changing its name to Max) to save money after a huge merger, it raised serious concerns among Hollywood’s creative community that haven’t gone away in the months since.

And the studios’ hardball tactics, which have discouraged show-runners from taking part in the strike, haven’t done much to thaw the relationship. Even famed writers, like The Wire creator David Simon, found themselves losing major contracts as a result of their public support of the strike.

The writers have a weapon of their own—by staying on the picket lines and away from their productions, they are creating a hard-to-ignore economic impact that could cost the California economy, and by extension the major studios, billions of dollars. (It will take time to break the record for the longest strike in the entertainment field, though—that honor goes to the American Federation of Musicians’ 1942 strike against the record industry, which lasted for roughly two years, in part because of World War II.)

But this strike has one factor that previous strikes haven’t had: The specter of artificial intelligence. And that’s a variable that could make this a much more complicated debate than it seems.

How generative AI became a writers’ strike talking point

In the weeks before the strike, artificial intelligence emerged as an unexpected pain point in contract negotiations with the studios. The WGA took something of a principled stand on the issue, making the case that generative AI shouldn’t be used as source material or to rewrite literary material, it did potentially make sense as an additional tool for WGA members. However, the WGA emphasized that it was important for members to maintain ownership of the final product.

The studios, represented by the Alliance of Motion Picture and Television Producers, pushed back on this idea, hoping to leverage the technology’s possible effects as a cost-cutting tool, considering the complicated copyright issues around AI ownership, and not wanting to undermine future use cases for generative AI that have yet to emerge.

“Writers want to be able to use this technology as part of their creative process, without changing how credits are determined, which is complicated given AI material can’t be copyrighted,” AMPTP noted in comments to The Hollywood Reporter. “So it’s something that requires a lot more discussion, which we’ve committed to doing.”

John Rodgers, a WGA West board member, said that one of the biggest concerns was that executives would attempt to use AI as a money-saving measure.

“I don’t know if writers should be afraid of the capabilities of large language models and AI because they don’t really write, they’re basically plagiarism machines,” he told ABC7 earlier this month. “What we’re afraid of is some executives will have the brilliant idea to disrupt the industry with AI, and leave us to pick up the wreckage after a couple years.”

It can be easy to dismiss these concerns as the thoughts of someone who is non-technical, but it’s worth keeping in mind (unusual plot points aside) that the film industry is very technical both behind and in front of the camera, and are early adopters in areas like 3D graphics, audio, and high-end photography. And many writers often take on different tasks within film production, including acting, directing, and editing.

Critics among the pool of writers are not approaching this from a position of technical illiteracy. WGA member and Family Ties actress Justine Bateman, a film director who has a computer science degree from UCLA, warned in a recent Twitter thread that studios someday could leverage artificial intelligence in ways that leave writers, actors, and creators off to the side. She suggested the possibility of an AI-generated eighth season of Family Ties, a show that ended nearly 35 years ago, could be generated from past footage that was used as a training model. With many long-running sitcoms shooting hundreds of hours of footage, there’s plenty of source material to work with.

“AI has to be [addressed] now or never,” she wrote in a tweet. “I believe this is the last time any labor action will be effective in our business. If we don’t make strong rules now, they simply won’t notice if we strike in three years, because at that point they won’t need us.”

While Bateman’s concerns may be theoretical at this time, you can see evidence that her comments aren’t just scaremongering. Deepfakes are growing in uptake, and YouTube has been flooded with fake commercials and alternate takes on television universes—for example, a live-action teaser for South Park built with Midjourney. While these certainly can’t compete with the real thing now, it’s a lot closer than it was just a year ago.

And it’s not just writers that have a reason to be concerned. Screen legend Tom Hanks recently noted that actors are concerned about their digital identities outliving them on screen, and are taking steps to protect their rights.

In an era when residuals are often one of the most important ways writers pay the bills, there is a real concern that this medium could get cut off in the long run. When HBO Max started removing shows, it cut off revenue avenues that writers relied on. Artificial intelligence could further accelerate it if used in a way that doesn’t meet the needs of the creators.

Are there benefits to generative AI in the writers’ room or in the director’s chair? Sure. It can be a great way to brainstorm unusual or interesting ideas that might not have surfaced through traditional means. Generative visuals can inspire artists to reach new heights.

And sure, it can help low-budget projects reach beyond their means by helping manage parts of the process that are more time-consuming than creative. As a recent Variety piece explained, tools like Runway have already seen use in Oscar-winning films like Everything Everywhere All at Once. 

But there’s a line between extending skill sets and replacing creators, and in many ways, the strike is looking to quantify the difference in a meaningful way. The labor battle will likely have an impact beyond the writers.

“Taste clusters”: The ways AI has already changed Hollywood

If there’s a single scene from a recent film or television show that properly describes the tensions that writers face about artificial intelligence in the streaming era, it’s a scene from the third season of the HBO show Barry, in which the character Sally learns that her well-reviewed semi-autobiographical television show ‌Joplin has been cancelled less than 12 hours after release, despite a strong Rotten Tomatoes score.

What was the problem? “The algorithm felt that it wasn’t hitting the right taste clusters,” an executive informs her, not a tinge of irony in their voice.

The sad part is that it’s not particularly far from the truth. Direct-to-Netflix films like The Gray Man and The Man from Toronto, both massive productions with huge budgets, drew millions of algorithm-driven viewers upon release, only to disappear from the front page within days, seemingly forever. Smaller shows face even tougher odds—appearing for a single season, often with limited promotion, facing their own version of Sally Reed’s personal nightmare.

Algorithms, driven by the whims of the consumer, are already reshaping Hollywood for many writers. And that was before those algorithms were at risk of doing any of the writing. Add to that the traditional climate of mistrust between the union and the studios—ask the animation community how they feel about the disappearance of Infinity Train—and it’s not hard to see things degrading from here.

Lessons from the 2008 strike

During the last strike, the major studios doubled down on reality television content, and forced late-night hosts to run their shows without writers.

While Conan O’Brien is an excellent improviser, that shouldn’t necessarily be his job.

And then, as now, the striking writers weren’t Luddites about new tech. Striking writers drew online buzz through live in-person renditions of popular shows, and tested the waters of internet-based content, with their most notable contribution to that genre being Dr. Horrible’s Sing-Along Blog.

In the end, the writers mostly got what they wanted out of that strike—compensation that adapted with the times, basically the same thing they’re asking for now. But one has to wonder if there’s a studio executive out there looking at ChatGPT longingly, thinking that it wouldn’t be the worst thing in the world to generate a Seinfeld script, give it to a bot, let a renderer spit something out, and put it on the air.

This tendency has already emerged in related fields, and it’s already led to fresh unionization. The longtime technology hub CNET unionized last week in direct response to a controversial attempt to introduce AI-generated content to the publication. (They’re represented by WGA East, one of the parties to the strike.)

Generative AI may have a place in Hollywood, just like so many other disruptive technologies before it. But the people don’t deserve to be lost in the equation.

And in many ways, that’s what the union battle is destined to underline.

In collaboration with Freethink we contributed an article to their special on creativity and AI. The theme of the piece is ‘Art Has Always Been Artificial’ and explores how art is always the result tools letting us express creativity.

READ IT

The music industry has had to navigate the choppy waters of royalties, ownership, and inspiration well before AI-generated music became the hot new thing. And not just because of sampling.

I think, in starting this piece about sampling, we need to talk about a song that doesn’t have a single sample, but has probably done more to reshape the discussion about music and copyright in the 21st century than any other.

That song? “Blurred Lines,” a tune that Pitchfork recently described as a “harbinger of doom.”

The vibes around Robin Thicke’s collaboration with Pharrell and T.I. grew increasingly problematic over time—creating huge personal problems in Thicke’s life, and business problems for everyone involved with the track.

One of those problems came from its point of inspiration, Marvin Gaye’s “Got to Give It Up.” The fact that, musically, it borrowed so many ideas from the bass-driven, chatter-filled recording turned into a huge liability for the recording industry as a whole, as Gaye’s estate sued—and won. Now, it’s become increasingly common for pop songs to preemptively offer credit to artists any time a song seems to directly borrow from a source too aggressively—and arguably has led to a huge push by publishing companies to buy out name-brand artists.

But before “Blurred Lines” came along, sampling set the stage for famous artists (or more likely, their estates) to take a litigious stance towards new generations of musicians.

As artificial intelligence promises to bring a fresh new stage to this discussion, it’s worth discussing why sampling created this arms race in the first place. Will the record industry finally meet its match in artificially generated hip-hop? (And is that a bad thing?)

The technical roots of what (eventually) became a legal problem

If you want to figure out why the estate of Marvin Gaye was able to do such damage to modern-day songwriting, you have to go all the way back to the player piano. Before computers, before tape, even before 78s, player pianos threatened to disrupt the livelihoods of composers; simply put, manufacturers weren’t paying for the composers’ songs.

It was a big enough issue that the Supreme Court got involved, and much like many other decisions involving emerging technology and the rights of creators, the court arguably biffed it, ruling in 1908’s ‌White-Smith Music Publishing Co. v. Apollo Co. that mechanical reproductions did not represent copyright infringement.

“These perforated rolls are parts of a machine which, when duly applied and properly operated in connection with the mechanism to which they are adapted, produce musical tones in harmonious combination,” the ruling stated. “But we cannot think that they are copies within the meaning of the copyright act.”

This decision led Congress to quickly change laws around mechanical production while creating something much more sustainable—a law that not only required payments for mechanical copying, but allowed anyone to copy anyone else’s song, a strategy explicitly designed to prevent attempts to monopolize access to music catalogs. As Slate noted in 2014, this set the stage for cover songs in all their fascinating and messy forms, but also made it possible for royalty systems to gradually emerge.

Over time, these royalty systems started to get complex—in part because the technology started becoming more complicated than mere player pianos. The law got more complicated, too.

How sampling technology evolved into a creative medium of its own

Gradually, technology with more moving parts than player pianos began to reshape the way that we thought about copyright and ownership. And that helped to set the stage for other innovations that involved people playing other people’s music.

While sampling is often portrayed in the context of record scratches and computer trackers, its roots go back a little further, with the chop-up-and-put-together approach of musique concrète, which used existing recordings as raw material, generally seen as where the concept of sampling began. That tape-based technique, dating to the 1940s, found perhaps its purest artistic expression with the Dr. Who theme song, which BBC Radiophonic Workshop employee Delia Derbyshire built from mashed-together segments of analog tape.

That theme song’s later life shows how quickly the discussion around sampling had changed. In 1988, about a quarter-century after it was first recorded, a novelty pop song named “Doctorin’ the Tardis” found major success by crudely mashing it up with Gary Glitter’s “Rock and Roll (Part Two)” and Sweet’s “Blockbuster!” That song topped the British charts despite the fact that critics hated it.

Bill Drummond and Jimmy Cauty, who recorded that song under the name The Timelords (and would become far better known as The KLF after that point) even wrote a book about how they did it.

But that wasn’t sampling’s first appearance on the pop charts—far from it. In fact, while we wouldn’t consider it as such today, notable bands like The Beatles and The Moody Blues technically used a sampler on some of their best-known recordings in the form of a Mellotron, a kind of keyboard designed to control analog tape recordings. (One difference: These “samples” generally weren’t of other pop songs.)

Over time, more techniques emerged. In Jamaica, the technique of dub reggae, which relied on instrumental recordings of existing songs that gradually evolved into new types of recordings with the removal of vocals and instruments, the integration of studio effects, and the addition of echo and reverb. Some of these techniques, associated with figures such as Lee “Scratch” Perry, would soon find their way to hip-hop.

At first, hip-hop leveraged the power of the “break,” an instrumental section from a recording, as a starting point for a new type of music. Evolving from block parties in the Bronx, hip-hop’s early music took a distinct influence from popular recordings of the era—which meant that, because disco was dominant, early hip-hop sounded like disco.

“Rapper’s Delight,” for instance, was built on top of an interpolation of Chic’s “Good Times,” which nearly led to a lawsuit and the eventual addition of Nile Rodgers and Bernard Edwards to the song’s writing credits. The song did not use a sampler, but rather live instruments, because samplers were uncommon at the time.

But that would soon change. Hip-hop artists were increasingly becoming skilled in chopping up sounds from vinyl, and technology was catching up to their ambitions. In 1979, the Fairlight CMI, the first commercially available sampling synthesizer, first hit the market, and while extremely expensive, it was significantly more accessible than earlier attempts, which relied on minicomputers.

The technology quickly got smaller and cheaper—keyboards gradually gained sampling capabilities, and by the late ’80s, dedicated sampling devices like the Akai MPC60 became both common and easy to come by, making it possible to treat sampling as an artistic form in its own right, rather than a quick way to borrow a recording.

Artistic innovation meets legal conflict

By the end of the 1980s, sampling was having a significant impact on the artistic medium of hip-hop. Early collage-driven samplers like Double Dee and Steinski gave way to mainstream acts like The Beastie Boys and De La Soul borrowing from these same techniques.

But the legal picture complicated matters significantly. De La Soul famously fought the ’60s folk-rock band The Turtles in court, and a 1991 lawsuit against rapper Biz Markie, who borrowed heavily from the Gilbert O’Sullivan song “Alone Again (Naturally)” on his 1991 track “Alone Again,” led the industry to begin clearing samples, when possible, before release. (Markie made fun of this situation with his next album, All Samples Cleared!)

These legal complications became problems decades after the fact, especially for sample-heavy recordings produced prior to the lawsuit. De La Soul, for example, struggled to get its catalog onto streaming services in part because of issues with clearing samples, and before the catalog appeared back in March, were required to replace some of the found sounds with interpolations.

Despite this, musical innovation has largely been improved by the rise of sampling, with new types of hip-hop, such as trap music, emerging over time as new approaches became common and tastes changed.

Additionally, the rise of mixtapes, free recordings produced by hip-hop artists that often sport uncleared samples, proved a hugely effective way to allow emerging rappers to draw attention without having to go through the costly process of clearing samples. Chance the Rapper, for example, was already a mainstream pop star based on his mixtapes alone, and only released a traditional commercial album seven years after his first mixtape.

Others have gotten quite creative with their ways of working around the compensation issue. Gregg Gillis, a DJ who goes by the stage name Girl Talk, gained extreme prominence on the back of his brazen sampling of mainstream pop songs, which he distributed online as free albums through his label, Illegal Art.

In many ways, sampling reflects a conflict between two distinct artistic forms of music. One part might be built from original instruments, and another that takes those parts and builds something new. Some have become quite good at it. Kanye West, before becoming a prominent lead artist in his own right, won notices for his inventive use of sampling. 

But its roots in the creations of others mean that there’s always a risk of a lawsuit around the corner. West, for one, has been sued numerous times for samples on massive hits, with the samples sometimes being so obscure that for the source music’s creators, it was their first actual brush with fame. For example, “Bound 2” sampled the Ponderosa Twins Plus One, a group that released one album and had otherwise long been forgotten—and whose lead singer had to sue to get royalties from West’s label.

Some of these suits have gotten messy, especially when the original creators are based in other countries or have died. A family member of the Egyptian composer Baligh Hamdi attempted to sue Jay-Z and his producer Timbaland over the 2000 song “Big Pimpin’,” which used an unauthorized sample of Hamdi’s “Khosara Khosara.” (The artists thought the song was in the public domain. It wasn’t.) The family member ultimately lost in court, but not before the suit had dragged on for multiple years.

But “Big Pimpin’” also shows how samples can actually at times benefit the musicians being sampled in non-compensatory ways. The musician who actually performed the sampled track, Hossam Ramzy, worked increasingly with Western artists such as Ricky Martin and Shakira after the song further raised his global profile.

How AI music reflects the same kind of disruption as sampling

Now, nearly a quarter-century after “Big Pimpin’” became one of Jay-Z’s best-known songs, the legendary rapper finds himself in a very similar kind of situation—one where an emerging technology threatens to take away the thing he is best-known for, without his consent. In 2020, Jay-Z used YouTube copyright complaints in an attempt to remove deepfakes of his voice from the internet, a use of that system that blogger Andy Baio noted was “questionable.”

And the technology has only gotten better since then. AllttA, a production team, used a much better version of Jay’s voice on a track called “Savages,” which listeners found strangely compelling, given the source. As one YouTube commenter put it, “I think this track might be the tipping point that wakes people up to how powerful AI has become in the last few weeks.”

Kanye’s in this same spot, too, with at least one unusual example involving an AI-generated West belting out a cover of the Plain White T’s “Hey There Delilah,” in a style not far off from his massive hit “Runaway.” And Drake and The Weeknd have had to chase after a hugely popular AI-generated banger called “Heart on My Sleeve” that strangely keeps getting deleted from the internet.

Charitably, there are genuine concerns about the technology taking away an artist’s essence and handing it to a large-language model. Less charitably, one could argue that it’s amusing that these artists, who spent literal decades doing this exact same thing—at times, long before legal precedent caught up with the times—are now stuck with the same problem that their predecessors had.

Perhaps it’s for that reason that it’s not really a surprise that Universal Music is already pushing its weight around in an effort to rein in AI-generated tracks.

But it doesn’t necessarily have to be quite so bad.

One artist who seems to have found an innovative path forward to this problem is Grimes, who is allowing people to make music using her AI-generated voiceprint—then offering them half the royalties, along with distribution, an innovative model that will likely go a long way to validate AI-generated music if it’s effective.

In a way, artificial intelligence could become just as important a tool for music as the Mellotron, or the Akai MPC60, or Pro Tools. It’s disruptive and separates creators from money, and leads to situations where reaction through the courts is the only option.

But on the other hand, we’ve been trying to solve for this exact problem since the player piano. Odds are, we’ll figure it out eventually.

Andy Warhol was enamored with the first computer he ever owned—and he didn’t seem all that worried about the risks of disruption.

If there’s any famed artist who would likely “get” the power of generative AI for creatives right from the jump, it’s likely Andy Warhol.

The pop artist’s work represented a powerful example of remix culture in action, and it was from that basic starting point that he developed an entire visual language that came to encompass music, movies, even an entire cultural scene.

Sure, the soup cans and screen-printed drawings offered a great starting point. But they proved to shape an entire world of creativity whose influence went far beyond any individual screen print or artistic image.

Which perhaps explains what he saw in the Commodore Amiga. The computer, a famously ahead-of-its-time device with a feature set that seemed destined to attract visual artists along with traditional gamers, utilized the interest of Warhol and another New York counterculture icon, Blondie singer Deborah Harry, to help promote this new tool to computer users around the world.

And just like the way generative AI is encouraging some artists to extend their skill set today, when Warhol saw a computer that threatened to make his art style trivial to replicate, he didn’t turn away.

The moment Andy Warhol first found the computer for him

If you really think about it, we’ve allowed external tools to extend our creativity for generations. We don’t often think of ideas from whole cloth.

Anyone who spent time in the graphic design salt mines knows this well. After all, stock photos come in handy because it’s often harder to create a visual starting point from whole cloth than it is to borrow someone else’s point of view. Even for talented artists, it’s nice to avoid undertaking photo shoots every single time you need a fresh landscape photo.

In this light, it is understandable why Andy Warhol saw something in the Commodore Amiga. Warhol, a skilled screen-printer who once shoved his face in a photocopier for artistic reasons—years before everyone else had the same idea, mind you—was at the forefront of the pop art movement.

But even more than that, he was one of the first people who saw that machines had something to offer to the artistic process. The artistic technique for which Warhol became best known—a mixed-media mixture of tracing, painting, and screen-printing—treated the mechanical part of the process, the screen print, as a transformative element, even though it was technically “just” a recreation of something that already existed.

Perhaps for that reason, a quarter-century after he discovered that technique, Warhol found himself on the stage at the Lincoln Center in New York City, at the launch event for the Amiga, a computer that capably could be used as a creative tool.

The message that the July 1985 event emphasized, through its use of music and visuals—the Amiga’s famed bouncing-ball demo made its debut here—was that this machine was the future.

Nobody at Commodore could hold a candle to Steve Jobs from a presentation standpoint, however, so they brought in Warhol to do a demonstration in which he illustrated Deborah Harry using the machine.

“Are you ready to paint me?” Harry asked before sitting in a chair, getting her photo taken and immediately imported into the new device.

Warhol ended up coloring in Harry in a mixture of vibrant hues—red, yellow, and blue—that both evoked his earlier screen-printed works and also would be completely taken for granted by generations of computer art programs that would supersede it, like Adobe Photoshop, Paint Shop Pro, even Microsoft Paint.

Warhol saw a machine that was capable of turning what was once a multistep process into something that technically anyone could do in minutes.

As Warhol noted at the event, he finally found the computer for him—and unlike later celebrity endorsements of computing brands, Warhol appeared to mean it.

What Andy Warhol, famous creative person, saw in the Amiga

Warhol could have left the Amiga on that stage, and nobody might have gotten upset with him about it. We were still in the early days of affinity-driven marketing. The first Air Jordan, for example, appeared in stores just three months prior. Presumably, whether in Amigas or in Commodore-endorsed checks, Andy Warhol likely got paid.

And sure, maybe affinity had something to do with Warhol appearing on the cover of Amiga World magazine the next year, in that magazine’s third issue. (That he appeared at all is perhaps a surprise; in the piece, according to an editor of Interview magazine—a publication Warhol founded, no less—Andy doesn’t do interviews.)

But it becomes clear that Warhol is champing at the bit to use the new technology.

In his discussion with Amiga World’s Guy Wright and Glenn Suokko, Warhol highlighted a few important points about how he sees creativity:

Creative Possibilities

At the time of the interview, the Xerox had gained a deep influence on the high-art world, as it helped to make it possible to make ambitious new kinds of mixed-media artwork that weren’t previously possible.

The Amiga seemed to be able to take things even further, though one challenge Warhol faced was that he couldn’t get a hold of an Amiga-compatible printer to actually put his images on a sheet of paper—not even a normal-sized one, let alone one that would let him produce prints.

Nonetheless, he was way ahead of his peers. He said that other artists of the era—including Jean-Michel Basquiat, who he name-dropped twice in the interview—were embracing the opportunities that new technology had to offer. Many had already shown a deep interest in using technology like the Amiga—including Interview staff members—but Warhol was in an unusual position, in that he was one of the few people to have access to the tool.

Warhol saw a high potential that tools like the Amiga would open up to new kinds of creators, including amateurs. Though, at the time, it was still a pretty exclusive device.

“Well, I’ve been telling everybody about the machine, but they haven’t been able to get one yet,” he said.

Compare this to the early months around generative AI, where people seemed to be wanting to cut to the front of the line to access MidJourney or ChatGPT. Generative AI has become a status symbol, with the buzz behind it at first reflecting a similar sort of exclusivity.

Creative Compatibility

When Warhol painted that picture of Debbie Harry, one of the things that seemed to come through in that display was that Warhol’s style translated particularly well to the format. At the time of the interview, Warhol admitted that this creative compatibility made it easy for him to latch onto the Amiga’s capabilities.

“The thing that I like most about doing this kind of art on the Amiga is that it looks like my work,” he was quoted as saying.

During the interview, he was working to put the finishing touches on a set of Dolly Parton prints. When speculating on his use case for the Amiga, he talked of moving the device to his studio, where he would be able to simplifying his production process, especially for prints.

“It’ll be really great, and if we can get a printer, I’ll do this portrait in four different colors and send them out to Dolly,” he added.

Given that much of his art was process-driven, perhaps it makes sense that he found some value in making that process easier.

Creative Integrity

One question that drives much discussion in the generative AI realm is the idea of whether new, simplifying technology devalues the work of creators.

This was a question that was also around in the 1980s and 1990s, when digitized art in general was first emerging. Would making art with a computer make things too trivial—or worse, threaten the copyright of talented people?

It was not an isolated fear, and one that large companies made a point to argue.

“Your works can be uploaded into massive networks accessible by millions of people,” said Louise Dembeck, a Time Warner vice president, according to a 1994 Associated Press article. “If you don’t know who’s getting it and who’s printing it off the screen, it’s just there for the taking.”

Warhol was asked about this very issue, but pointed out that many prints are actually supposed to be exact replicas. He did not seem concerned about copying. “Well, you can stop at whatever you want,” he said. “Etchings usually stop at a certain number.”

But what about the idea that a machine could so easily mimic his style? When posed this question, he pointed out that other artists had gotten their hands on the Amiga and had taken it in different directions.

“It looks like the work that I started doing,” he said, making it clear that where he started is different from where they would end up. “I still think that someone like a decorator could use it when he wants to show somebody how their apartment would look like all in blue or all in white, or … they could just do it so easily. Change a chair or a color.”

Warhol’s thinking on this issue ultimately turned out to be true. Millions of artists use graphical tools of all sorts, but there’s still plenty of room for unique voices to emerge.

Warhol, unfortunately, died less than two years after that iconic display with Deborah Harry at the Lincoln Center, the result of a complication from surgery, so he did not get to see how the creative computing market played out.

While the Amiga had some notable wins among artists—the Video Toaster chief among them, along with the growth of the British dance music scene, which found much to appreciate in the Amiga 500’s four-channel stereo setup—the Macintosh ultimately won the battle for artistic minds thanks to its early success as a desktop publishing tool.

We also never got to see just how creative Warhol, a masterful experimenter with new tools, could be with an Amiga 1000 at his desk.

While he did create some works with the new tool, including the posthumously recovered You Are the One, he didn’t have a chance to go as far with it has he could have. He worked with The Velvet Underground for more time than he worked with the Amiga—and, no criticism meant of the work of that groundbreaking band, but the Amiga had more untapped potential.

In a way, the fact that he wasn’t around to see later explosions of creativity around new technology actually gives his words on creativity more power, though. 

Think about the way that people listened to record collections, and how, once everything under the sun was suddenly available in Spotify, it sort of took the luster out of collecting music. That’s why vinyl came back, after all.

Reading how Andy Warhol was able to see the possibilities at the start of the creative computing revolution—even though, within a decade, Photoshop made it trivial to rip off Warhol’s iconic visual style—somehow makes it all feel special again, even if it is arguably easier to do it through a computer.

Generative AI is today’s way to stretch creative possibilities. You can either see it as a door-closer or a way to open the gates. While Warhol isn’t here to let us know, it’s clear that he didn’t see new technology as a threat to his creative process.

It’s possible he might have even asked for an illustration in a Warhol style.

An aesthetic is a set of unwritten rules, incubated in the mind of an artist(s), a proverbial model created by human general intelligence that manifests in the world through creative acts.

Aesthetics are more understood through feeling than logic. They can’t always be described, but can always be felt: you’ll know it when you see it. It can never be replicated, only reverse engineered through observation.

Aesthetics aren’t owned and they’re never created in a vacuum. Aesthetics are to art, what pollen is to flowers. Every aesthetic is a creative cross-breed, a mongrel of minds.

As Mark Twain once wrote: "Shakespeare created nothing. He correctly observed, and he marvelously painted” he’d go on to compare him to “a Gobelin loom” and that “the threads and the colors came into him from the outside; outside influences, suggestions, experiences.” His mind would in turn “start up its complex and admirable machinery, and it automatically churned out that pictured and gorgeous fabric which still compels the astonishment of the world.” In other words, his aesthetic, was the offspring of other aesthetics that had pollinated his mind. What Twain was describing was aesthetic evolution.

What was once only truly understood by human intuition, is now understood by AI, those mysterious unwritten rules of aesthetic are being translated into mathematical formulae creating what ought to be called: masthsethics. Mathsethics is to machines what intuition is to humans. It is the unwritten mathematical rules of an aesthetic, that which once only resided in an artists mind.

Aesthetic fluidity has emerged as a result of mathsethics, aesthetic is now liquid. This echos the transformative influence that the printing press has on writing, photography had on visual art and recorded sound had on music. Creative liquidity enables a rapid, dynamic evolution of aesthetics, as AI-powered tools facilitate the creation, modification, and distribution of artistic content. Consequently, cultural development accelerates as well, as communities and societies can now engage with and contribute to a shared, ever-evolving artistic landscape.