“You need to realize you’ve already rejected tradition by not getting a dark roast coffee. You’ve embraced modernism,” Hedrick says. “And if you’re going to embrace modernism and reject traditionalism, you must always also reject traditional shot parameters.”
But terrific light roast is possible. There are two ways to go.
You can go traditional—changing your dose and ratios a bit but aiming for a cup with intensity and balance. That’s what I’ve been honing for the past year.
But there’s also a wilder, weirder path: The turbo shot, also called a gusher. Hedrick, following the results of new scientific research from University of Oregon biochemistry professor Christopher Hendon and others, has gone all in on throwing out the entire traditional espresso rulebook in his pursuit of light roast espresso that’s neither sour nor bitter.
Here are two ways of making light roast espresso, and the results.
How to Make a “Traditional” Light Espresso Shot
Some of the knee-jerk advice for light roast espresso was just to keep grinding finer and finer and jack up the temperature on your machine in order to get better extraction.
Problem is, the finer you grind, the more likely you’ll choke your machine. And also the more likely that water will clog up in places and find a path of least resistance through your coffee puck. Which is to say, it’ll “channel” through only some of the coffee, extracting too much from some parts of your coffee puck while under-extracting from other parts. The results will be intense, bitter, and sour. It’ll taste like those early light roast espressos that put me off of light roast espresso.
There’s a different path.
Instead of pretending light roast is dark roast and going finer and finer, you can instead adjust the amount of coffee and water. Use more coffee and pull longer, for more time—and grind fine but not ridiculously fine.
This was the approach used on a recent visit to Sterling Coffee Roasters, one of the few Portland, Oregon, roasters I’ve found that regularly (and expertly) pulls light roast espresso shots. The shop offered up an excellent, cranberry-fruity light roast Ethiopia Bensa Bombe using this method. My barista let a two-ounce shot drag out for 37 seconds until its fruity-acidic flavors mixed with a little bit of backbone, not to mention the flavors of ferment resulting from natural-process beans.
Photograph: Matthew Korfhage
This is the classic approach I’ve arrived at through trial and error, a bit of research, and a lot of conversation with smart baristas:
Increase the amount of coffee you use. A darker-roast double shot is often 15 or 18 grams. But going bigger, about 20 grams, can extend the extraction time without having to grind so fine you choke your machine.
Increase the water-to-coffee ratio. Standard espresso is a 1:2 ratio. That means if you use 15 grams of espresso, you’ll aim for 30 grams of espresso in your cup. Longer ratios, often called “lungo,” will also help increase extraction by simply running more water through a certain volume of coffee. I often go as long as 1:3, which is about 60 grams (two ounces) for a 20-gram espresso shot.
Go a little longer. It’s a long shot, and a lot of coffee. Don’t worry about the “25 to 30 seconds” you’ve been told is the only way to go. Drift a little longer, maybe into the mid-30s or so. You may find a more balanced shot by the end of it.
Photograph: Matthew Korfhage
Grind only as finely as you need to, but don’t go crazy. Longer shots, and thicker pucks, will offer resistance to the flow of water, without needing powder-fine espresso dust that ends up creating more unpredictable results.
Spritz your beans. A recent paper by authors including Hendon showed that there’s real science behind the idea that spritzing water on coffee beans can help reduce static electricity and clumping, leading to more even extraction.
Look for natural-process beans, not washed. Most modern beans, until recently, were “washed,” which removes all of the coffee fruit before processing, leading to a more predictable result. But lately, a lot of growers in Latin America and Africa have begun to try out natural process beans, fermenting some of the coffee berry sugars or mucilage. Natural processing, or honey and bourbon processing, can lead to more body, more sweetness, and more complexity. It can also lead to less acidity. The result, in light roast espresso, is coffee that’s not just more balanced but more nuanced, with added earthy notes that can bind the coffee’s flavors into a more organic whole.
Use a grinder well-attuned to light roast espresso. Some geometries are better attuned to light-roast beans than others, notes coffee expert Hedrick, largely because light roast beans grind less easily. Hexagonal or pentagonal geometries, with more “points” on the conical burr, tend to have better results. Assuming you’re not on a huge budget, Hedrick recommends the Kingrinder K6 manual grinder that’s also recommended by WIRED. I’ve been using it for months, with good results, to make light roast espresso.
Photograph: Matthew Korfhage
Kingrinder
K6 Manual Coffee Grinder
How to Make a Turbo Espresso Shot, or “Gusher”
Here’s the new-school approach laid out by coffee expert Lance Hedrick, following new findings published in 2020 by coffee scientist Christopher Hendon at the University of Oregon, among others. The turbo espresso shot, also called a gusher, involves up-ending pretty much every assumption about how good espresso is made—grinding coarser for light roast espresso and running a whole lot of water through the puck quickly and at lower pressure.
The result is a fully extracted shot, sometimes even better extracted than a classic one. But the flavor is different: It tends to be sweeter, aromatic, and almost devoid of bitterness.
Crazy, right? Not really. There’s a bit of science behind it, which you can read about in the bottom section of the article. But first, here’s how to make a turbo shot, according to advice from coffee expert Hedrick, who says the best shots he’s pulled all come from this method.
Use less beans by volume. Try out a 15-gram double shot to better facilitate flow of water through the puck.
Grind coarser. In my own attempts to replicate Hedrick’s method, I’ve found that you need a coarseness a lot closer to the coarsest espresso.
Use a high ratio. Try out up to a 1:3 ratio, meaning 45 grams of espresso for 15 grams of coffee.
Let it gush. The resulting fast flow will knock out a big shot in 10 to 15 seconds or so, way faster than any traditional espresso.
Don’t worry about crema. You’re not going to get the same stable crema you’ll get from robusta-dark-roast Italian beans on traditional methods. But crema is not the most important part of your espresso, and less important to mouthfeel and body than many assume. “Don’t worship crema,” Hedrick says. “In fact, crema is the most bitter part of your espresso.”
Don’t neglect your water. Good water means good extraction. Filter your water, of course, which will help keep your machine running longer. But also? Throw a little baking soda in the tank, if you’ve got soft water, and it’ll help reduce the acidity of your espresso.
First, adjust yield. Then grind size. Don’t play with your grind first. If your coffee is sour, try running the shot to a higher volume. If bitter, dial it back. You can get more consistent results playing with yield than with grind. (Though, you may also need to adjust your grind.)
OK, the pressure thing. Hendon’s research showed best extraction on a turbo shot with 6 bars of pressure, which helps slow water’s path through the puck. But unless you do some modding or hacks on your espresso machine, you probably have a machine designed to pump 9 bars. Is it all for nought? According to Hedrick, it’s probably kinda fine, even if you don’t have a machine that can program lower pressure. With a coarse grind, a fast shot, and fewer grounds, you likely won’t build up 9 bars anyway. Just roll with what tastes good.
The Theory Behind Turbo Espresso Shots
OK, so how does a turbo shot work?
A gusher is exactly what it sounds like. It’s an espresso shot that practically just pours out of the portafilter so it’s over in about 15 seconds, even at high volume—a heresy among traditional espresso people. Conventional wisdom says this shot should taste terrible, underextracted, sour. But magically, it doesn’t. Extraction is in some ways better and more reliable.
Finer grinds don’t necessarily or always mean better extraction, they showed. And the 25-second espresso shot is a tradition … not a scientific certainty. Often, a lot of the unpleasant flavor compounds start to emerge after a mere 20 seconds. But especially, Hendon tells WIRED, grinding more coarsely, and using lower pressure and lower volumes of beans, leads to much more consistency between shots.
“What we were trying to do is find brew parameters that would allow us to make highly reproducible espresso,” he said. What he and his collaborators learned was that if you grind finer, extraction got better, but not forever. At some “critical point,” grinding finer actually led to worse extraction. Coffee clumped up. It clogged. Water actually got less contact with coffee grounds, not more.
If you ground beans more coarsely, and let the water flow longer through lower volumes of beans, you could get more even extraction, they discovered after analysis. This method also offered more repeatability. Using less coffee, and lower pressure, likewise allowed water to spend more time in contact with the coffee grounds—leading to even better extraction.
Photograph: Matthew Korfhage
And so, grind coarser. Use less coffee. Use less pressure. Let it gush. Result: excellent extraction of sweet and aromatic compounds. Almost no bitterness. Hedrick tells WIRED that the best shots he’s pulled in recent memory have come using this method.
Hendon figures few would have paid attention to his findings if Hedrick hadn’t taken up the research and run with it—making video after video about the new technique for making what Hedrick now calls “modern” espresso, highlighting a bean’s bright aromatics without all the bitterness. Traditional shots just don’t get the flavors Hedrick wants, and have too many of the bitter flavors he hates.
Now, in the meantime, there are caveats. Hendon published a more recent paper showing that clumping at finer grinds could be avoided if you just spritzed your beans with a bit of water before grinding. (Coffee nerds had been doing this for a while; it just hadn’t been backed up by science.)
Which is to say, while turbo shots are a new and interesting and fun discovery, classic light roast espresso shots can also get good results.
Which Is Better, Classic Light Roast Espresso or Modern Turbo Shots?
Classic light roast espresso shots and turbo shots are both achievable. But note that turbo shots are a lot easier to pull off: Coarser grinds are quite simply more manageable. You’ll get more consistent shots time after time with gushers, Hedrick and Hendon both note.
So, how does a turbo shot taste? It is, on my attempts over the past couple of weeks, not quite as complex as more traditional, longer, finer-ground shots—at least when I’ve attempted them with more traditional 9-bar machines, like the Breville Oracle Jet and the new Meraki espresso machine I’m currently testing.
The combination of coarse grind and fast flow actually end up reminding me somewhat of results from some newer superautomatic espresso machines like the excellent De’Longhi Rivelia. These machines grind coarser and flow faster, and smooth out the edges of traditional shots. The results on my turbo shots were likewise smooth and flavorful, and a bit more sweet, but maybe also a less exciting and eventful ride.
Photograph: De’Longhi
Photograph: Matthew Korfhage
Photograph: Matthew Korfhage
This said, I’ve also struck intense flavor gold with some turbo shots. And when they were good, the results were shockingly good. I have drunk a 12-second light roast espresso with flavor so round and full it made me question everything I’d previously been told about how good espresso should be made.
The difference between turbo and classic light roast shots is actually, if I’m comparing, a lot like the difference between a new-school hazy IPA and a West Coast IPA. The turbo shot, like a modern hazy IPA, offers more juiciness and less bitterness. Maybe it also offers a little less complexity. But in exchange, it’s an easy, smooth ride across the palate that’s more in line with modern tastes. It’s delicious.
So which do you prefer? Juicy or balanced? Complexity and intensity, or affable aroma and sweetness? A difficult test of espresso mettle, or an easy win? Shoot your shot.
Meet the Experts
Lance Hedrick is one of the most-followed coffee industry voices on YouTube, a two-time World Latte Art champion, two-time US Brewers Cup finalist, and director of EU and West Coast wholesale for Onyx Coffee.
Christopher Hendon is associate professor of computational materials chemistry at the University of Oregon and has authored or coauthored numerous published works on the chemistry of coffee flavor and extraction.
It can be tricky to figure out how to clean your Keurig, but it’s important work. If your household is like mine, your pod coffee maker runs anywhere from three to seven times per day. All of that use can cause buildup and gunk, which can affect the taste of your coffee and the lifespan of your machine. But with proper maintenance and a dedicated routine, cleaning is a breeze. Here’s everything you need to know about light daily cleaning as well as deeper cleans.
To clean the housing of your Keurig coffee maker or other pod machine, just take a damp cloth and wipe down the outside. You can clean the K-Cup holder and needle by brushing or vacuuming away any loose debris like coffee grounds—be careful near the needle part since, obviously, it’s sharp.
Some machines come with a needle cleaning tool that you insert into the top and bottom of the needle, and a few people on various forums have used a paper clip instead. Some machines have removable pod holders that can be soaked in hot water. It’s always a good idea to refer to your specific model’s user guide, and you’ll probably want to unplug your machine beforehand.
To clean your drip tray and water reservoir, remove them and wash them by hand with hot, soapy water (though avoid using too much dish soap to prevent buildup). If your machine came with a carafe, wash it by hand or pop it in the dishwasher if it’s dishwasher-safe. Let them air dry or wipe them down with a lint-free towel after rinsing them off. You should be replacing the fresh water in your reservoir often, especially if it’s been sitting for a while. If your machine has a water filter in its reservoir, replace it every two to three months. Most machines with these types of filters have maintenance reminders—heed them!
For cleaning out the internal bits and pieces, you can use something like a Keurig Rinse Pod, which helps to flush out any excess oils or flavors that might be lingering. They are especially handy after brewing with flavored K-Cups like hot cocoa or some coffee varieties. You can also just run a hot water cycle every so often, which is a particularly good idea if you haven’t used your machine for a few days.
Keurig
Rinse Pods
These rinse pods help keep your Keurig clean and free from unwanted flavors.
Keurig
Water Filter Refill Cartridges
Keep your compatible Keurig water reservoir fresh with these filters, which should be replaced every two months or 60 water cycles.
Deeper Cleaning and Descaling
Some manufacturers recommend using filtered water or distilled water instead of tap water in your reservoirs, but I’ve always used tap water with the knowledge that I might have to clean my machine more frequently. You should deep-clean or descale your pod coffee maker every three to six months, or possibly more often if you notice hard water stains, calcium deposits, or mineral buildup, or your machine prompts you to deep-clean it.
You can do this a few ways. For the DIY method, fill your water tank with white vinegar and water (about half and half) and run large-capacity brew cycles until the reservoir is empty; Halfway through, consider letting the vinegar solution soak for a while, around 20 to 30 minutes. Follow up with a few rinsing cycles using clean water until the vinegar smell is gone. Alternatively, you can use a dedicated Keurig descaling solution according to the instructions on the bottle. That solution can be used on non-Keurig machines too. Make sure your machine is fully rinsed out before brewing your next cup of coffee.
It’s important to perform these deeper cleaning cycles on a regular basis to ensure your machine lasts as long as possible. And that your coffee tastes good, of course.
Keurig
Descaling Solution
This descaling solution can be used to remove mineral buildup every few months.
Keurig
Brewer Maintenance Kit
Get every piece you’ll need with this all-in-one maintenance kit.
UK airline EasyJet has announced it is working with leading UK comms provider BT to deliver thousands of mobile connections and keep operational assets linked up.
EasyJet was founded by Stelios Haji-Ioannou to offer low-cost fares in Europe, with the inaugural flights taking off in November 1995, flying from London Luton to Glasgow and Edinburgh. A year later, it introduced its first international route, from London Luton to Amsterdam, with further routes to Nice and Barcelona following that year, when it also operated its first wholly owned aircraft. In 1997, it was recognised as a financially viable airline, receiving its own Air Operating Certificate, and started to grow rapidly, introducing new routes and a second UK base in Liverpool.
By 2024, EasyJet had expanded its network to 30 bases by opening two new bases in Alicante and Birmingham, and now claims to be one of the largest airlines in the world, with 355 aircraft, operating 1,207 routes across 38 countries and 164 airports. The company has an all-Airbus fleet flying 82 A319 aircraft, 180 A320ceo, 75 A320neo and 19 A321neo planes.
In November 2025, the airline celebrated the 25th anniversary of its listing on the London Stock Exchange, a milestone that it said reflected “a quarter-century of growth, innovation and commitment to making travel easy and affordable for millions of customers across Europe”. In January 2026, it announced the retrofit of all its remaining Airbus A320ceo aircraft, with Airbus-manufactured “sharklets”, a key initiative that it said would deliver further fuel, carbon and cost efficiencies across its fleet.
To support the new communications network, BT will provide 23,000 mobile connections across the infrastructure of its EE network, supporting EasyJet’s operations in 35 countries and over 150 airports, from London Gatwick to Gran Canaria.
The airline provider will use the EE network to connect a range of devices, and BT is confident it can enable all pilots and cabin crew to access flight information and real-time training “seamlessly” on the go.
BT will also support EasyJet to deliver smart messaging to keep customers updated on flights; connect iPads that pilots and crew use to provide real-time flight information; connect smartphones, mobile phones and aircraft phones to allow communication between airline colleagues; and provide laptops and other hardware for workers.
All devices will be equipped with embedded subscriber identity modules (eSIMs), which BT says will offer a smarter, more efficient way to manage mobile connectivity. The eSIMs will enable remote setup, enhance security, reduce costs and simplify logistics, as well as support global operations while reducing waste and improving user experience.
Describing the partnership with EasyJet, Chris Sims, chief commercial officer at BT Business, said it was all about delivering the smart, seamless connectivity crucial for businesses operating at scale.
“By equipping thousands of devices with eSIMs on EE’s award-winning network, we’re enabling EasyJet to manage connections remotely, switch networks across borders and reduce the complexity of traditional SIMs,” he said. “It’s a future-ready solution that enhances security, boosts efficiency and keeps teams connected when they need it most.”
Widespread concern about the use of creative works to train artificial intelligence (AI) systems has prompted the UK government to begin exploring how the country’s copyright rules can be changed to satisfy the complex, often conflicting demands of both the creative and tech sectors.
The options being assessed include keeping copyright and related laws remain as they are; strengthening copyright to require licenses in all cases; implementing a broad data mining exemption for AI companies; or creating a more limited data mining exemption that allows copyright holders to reserve their rights, underpinned by measures to promote and support greater transparency from developers.
However, given structural imbalances within existing copyright markets – which favour giant corporations over individual creators – it is unclear to what extent the AI-related reforms to the UK intellectual property rules being considered will help creative workers themselves.
Creators vs AI developers
Questions around the use of creative works to train AI systems have become one of the most intense areas of debate since the advent of generative AI (GenAI) and large language models (LLMs) with the release of OpenAI’s ChatGPT in November 2022.
Aside from a lack of transparency from AI companies about the data included in their training corpuses, creatives have variously complained about the absence of enforceable mechanisms to protect their copyrighted works within the context of scraping at scale, as well as the impacts of AI on creative job markets and competition.
For AI companies, on the other hand, access to vast amounts of high-quality data is of paramount importance, particularly when it comes to the development of LLMs such as Claude, ChatGPT or Gemini.
A submission to the US Copyright Office on 30 October 2023 by Amazon and Google-backed LLM developer Anthropic is indicative of how these firms view their use of copyrighted material, and how integral it is for creating generative AI models.
“To the extent copyrighted works are used in training data, it is for analysis (of statistical relationships between words and concepts) that is unrelated to any expressive purpose of the work,” it said. “This sort of transformative use has been recognised as lawful in the past and should continue to be considered lawful in this case.”
It added that using copyrighted works to train its Claude model would count as “fair use”, because “it does not prevent the sale of the original works, and, even where commercial, is still sufficiently transformative”.
As part of a separate legal case brought against Anthropic by major music publishers in November 2023, the firm took the argument further, claiming “it would not be possible to amass sufficient content to train an LLM like Claude in arm’s-length licensing transactions, at any price”.
It added that Anthropic is not alone in using data “broadly assembled from the publicly available internet”, and that “in practice, there is no other way to amass a training corpus with the scale and diversity necessary to train a complex LLM with a broad understanding of human language and the world in general”.
If licences were required to train LLMs on copyrighted content, today’s general-purpose AI tools simply could not exist Anthropic
“Any inclusion of plaintiffs’ song lyrics – or other content reflected in those datasets – would simply be a byproduct of the only viable approach to solving that technical challenge,” it said.
It further claimed that the scale of the datasets required to train LLMs is simply too large to for an effective licensing regime to operate: “One could not enter licensing transactions with enough rights owners to cover the billions of texts necessary to yield the trillions of tokens that general-purpose LLMs require for proper training. If licences were required to train LLMs on copyrighted content, today’s general-purpose AI tools simply could not exist.”
While the submission and the court case are specific to the US context, the application of “fair use” exemptions to copyright is not dissimilar in UK. Under current UK copyright laws, original works are automatically protected upon their creation, giving the creators exclusive rights to copy, distribute, perform or adapt their work.
There are, however, limited exemptions that allow the “fair dealing” of copyrighted material for the purposes of, for example, research, criticism, review and reporting. A further exemption was added in 2014, allowing text and data mining for purely non-commercial research purposes.
As it stands, unless one of these exemptions applies, AI companies would therefore need to obtain permission from copyright holders to use these works in their model’s training data.
UK government consultation backlash
According to a previous UK government consultation on the matter, which closed in February 2025, “the application of UK copyright law to the training of AI models is disputed”.
It said that while rights holders are finding it difficult to control the use of their works in training AI models, and are seeking to be remunerated for its use, AI developers are similarly finding it difficult to navigate copyright law in the UK. It noted “this legal uncertainty is undermining investment in and adoption of AI technology”.
In an attempt to solve the dispute, the UK government proposed a new policy in late 2024 that would allow AI companies to train their models on copyrighted works unless rights holders explicitly opted out. This means that, rather than requiring AI companies to seek permission from rights holders for the use of their work, the burden would be placed on the creators themselves to actively object.
The opt-out proposal provoked significant backlash from creatives, who viewed it as too conciliatory to the narrow interests of tech companies. Out of the more than 10,000 people that responded to the government’s consultation on these measures, just 3% backed it’s opt-out proposal, while 95% called for either called for copyright to be strengthened, a requirement for licensing in all cases, or no change to current copyright law.
Others cited issues around the practicality of such proposals, noting that in the context of the current digital landscape – where copyrighted content is scraped at scale and included in training datasets, often without attribution – it may be impossible for someone to know when their work has been used, let alone opt out.
In the wake of this widespread opposition, the UK government has since committed to exploring a licence-first system that would require AI companies to seek explicit permission from creatives and provide them with compensation.
Balancing interests?
A year later, in December 2025, technology secretary Liz Kendall told Parliament there was “no clear consensus” on the AI-copyright issue, saying that the government would “take the time to get this right” while promising to make policy proposals by 18 March 2026.
“Our approach to copyright and AI must support prosperity for all UK citizens, and drive innovation and growth for sectors across the economy, including the creative industries,” she said. “This means keeping the UK at the cutting edge of science and technology so UK citizens can benefit from major breakthroughs, transformative innovation and greater prosperity. It also means continuing to support our creative industries, which make a huge economic contribution, shape our national identity and give us a unique position on the world stage.”
While government rhetoric on AI and copyright has revolved around the need to support both the UK’s creative and tech sectors, there is a sense that – so far at least – it is prioritising the latter in its ambition to make the country a tech superpower.
Beeban Kidron, a crossbench peer and former film director, for example, has previously described the use of copyrighted material by AI companies as “state-sanctioned theft”, claiming ministers would be “knowingly throwing UK designers, artists, authors, musicians, media and nascent AI companies under the bus” if they don’t move to protect their output from being harvested by AI firms.
Owen Meredith, chief executive of the New Media Association, has also previously urged the UK government to rule out any new copyright exception. “This will send a clear message to AI developers that they must enter into licensing agreements with the UK’s media and creative copyright owners, unlocking investment and strengthening the market for the high-quality content that is the most valuable ingredient in producing safe, trustworthy AI models,” he said.
Ed Newton-Rex, a prominent commentator on AI and intellectual property, has also criticised the balance of UK government’s approach, noting that while the government described its consultation proposals at the time as a “win-win … this is very far from the truth. It would be a huge coup for AI companies, and the most damaging legislation for the creative industries in decades”.
He added that a broad copyright exception that allows unlicensed training on copyrighted “would hand the life’s work of the UK’s creators to AI companies, letting them use it to build highly scalable competitors to those creators with impunity”.
[A broad copyright exception] would hand the life’s work of the UK’s creators to AI companies, letting them use it to build highly scalable competitors to those creators with impunity Ed Newton-Rex, AI and intellectual property commentator
AI companies, of course, disagree. In its October 2023 submission to the US Copyright Office, Anthropic argued that requiring licences would be inappropriate, as it would lock up access to the vast majority of works and benefit “only the most highly resourced entities” that are able to pay their way into compliance
“Requiring a licence for non-expressive use of copyrighted works to train LLMs effectively means impeding use of ideas, facts and other non-copyrightable material,” it said. “Even assuming that aspects of the dataset may provide greater ‘weight’ to a particular output than others, the model is more than the sum of its parts. Thus, it will be difficult to set a royalty rate that is meaningful to individual creators without making it uneconomical to develop generative AI models in the first place.”
Others from the tech sector have also argued that diverging from other jurisdictions too greatly – for example, by implementing a UK-specific licensing arrangement preferred by the creative sector, or requiring firms to disclose detailed data inputs – would simply mean AI companies avoid deploying in the UK.
Trade association TechUK, for example, argued that in the context of AI-copyright related amendments to the government Data Use and Access Bill – which would have forced developers to publish their training corpuses but which were ultimately not included in the final Act of Parliament – departing too much from existing UK and international frameworks would risks companies being “discouraged from operating, training and deploying AI products and models in the UK”.
This was also recognised by the government in its consultation, which noted requiring licenses in all cases “is highly likely to make the UK significantly less competitive compared to other jurisdictions – such as the EU and US – which do not have such restrictive laws. This would make the UK a less attractive location for AI development, reducing investment in the sector. In doing so, it may not actually increase the level of licensing undertaken by AI firms.”
It added that models trained in other jurisdictions which do not meet any new UK standards may be difficult to restrict from the UK market, and risks some of the most capable AI models not being made available in the UK: “This would significantly limit innovation, consumer choice and wider benefits of AI adoptions across the UK economy.”
The technical caveats of copyright law
Under UK copyright law, it should be noted that creating “transient copies” of works is allowed if certain conditions are met. This includes if it’s not a permanent copy and serves a brief, ancillary purpose; if it’s a necessary step in a technological process; if its only goal is enabling lawful use or network transmission; and the copy itself doesn’t hold separate commercial value.
When looking at AI model training processes – which often, but not always, retain only a very small portion of each training item – this indicates it would be technically wrong to assert a copyright infringement has taken place, as Anthropic has argued in the context of the US.
However, this doesn’t mean that a model would never infringe copyright, as it is also technically possible for most models to “memorise” copyrighted works, turning a transitory copy into a permanent, infringing one.
Although the specificities of whether a particular model or AI-generated output infringes this current copyright regime will be hashed out in individual court cases, some have argued that looking for copyright to solve the tension between creatives and AI companies is a non-starter.
In their book Chokepoint capitalism: How big tech and big content captured creative labor markets and how we’ll win them back, for example, authors Cory Doctorow and Rebecca Giblin argue that while the past 40 years have been spent elaborating international copyright rules, the financial benefits of this have largely accrued to big business rather than creators themselves, whose share of growing entertainment industry profits have declined in that time.
In essence, their argument is that expanding copyright is very unlikely to protect the jobs or incomes of already underpaid creatives, who have themselves been exploited by entertainment behemoths wielding copyright laws against them for decades.
In their May 2024 book, Who owns this sentence? A history of copyright and wrongs, authors Alexandre Montagu and David Bellos similarly argue that copyright protections – which were originally intended to protect the livelihoods of individual creators – have since been transferred to giant corporations instead, which use them to extract a form of “rent” from consumers globally, while also locking the employees who helped contribute to the creation of the IP out from ownership and the consequent benefits.
It follows, then, that there is little reason to believe these same companies will now treat their creative workers more fairly if they receive compensation as a copyright holder from AI companies.
To alter this dynamic, Doctorow and others argue it would require changing the very structure of creatives markets so that the benefits accrue to creatives, rather than large corporations that essentially run “tollbooths” to facilitate and control access to creative’s work, which in turn allows them to extract disproportionally high profits for themselves.
Writing for the Electronic Frontier Foundation (EFF) in February 2025, Tori Noble argued that “expanding copyright will not mitigate” the harm to creative workers, and that “what neither Big Tech nor Big Media will say is that stronger antitrust rules and enforcement would be a much better solution”.
She added that looking beyond copyright can future-proof protections, including stronger environmental protections, comprehensive privacy laws, worker protections and media literacy, adding: “[This will] create an ecosystem where we will have defences against any new technology that might cause harm in those areas, not just generative AI. Expanding copyright, on the other hand, threatens socially beneficial uses of AI – for example, to conduct scientific research and generate new creative expression – without meaningfully addressing the harms.”
Collective copyright and labour law
As it currently stands, UK government looks to be on course to introduce a new licensing regime for AI companies’ use of copyrighted materials. Observers have said this would need to include mechanisms that allow creators to identify when and how their works are used, as well as to object or seek compensation as they see fit.
However, given the clear tensions that already exist between individual and corporate copyright holders, even a licensing regime could still disproportionally benefit the latter. It could also disproportionally benefit large AI developers, as the pool of actors with the ability to pay for enough copyright licenses to effectively train a model is vanishingly small.
The use of AI in creative endeavours throws up further issues around labour and competition: even if creators received compensation for the use of their copyrighted material, AI’s entire development is underpinned by a neoliberal logic of austerity. This means that, in the current political-economic context, those with the decision-making power to deploy AI largely do so because it allows them to cut labour costs – the biggest overhead for any capitalist enterprise.
In the current political-economic context, those with the decision-making power to deploy AI largely do so because it allows them to cut labour costs – the biggest overhead for any capitalist enterprise
In November 2024, data from the Harvard Business Review showed the impact that generative AI models were already having on labour markets, which highlights how creatives will essentially end up competing with the very models that ingest their data. Specifically, it highlighted how the introduction of ChatGPT decreased writing and coding jobs by 30% and 20% respectively, while AI image generators similarly decreased image creation jobs by 17%.
Given the sheer scale at which models ingest data, it is not hard to see how creatives – even with a licensing regime in place – could be undermined by bosses who would rather pay for a relatively cheap corporate licence to an AI model, rather than the comparatively expensive labour of human beings.
While many argue that the advent of AI is inevitable, its impacts are certainly not. In November 2023, for example, the Autonomy think tank in the UK argued that while automating jobs with LLMs could lead to significant reductions in working time without a loss of pay or productivity, realising the benefits of AI-driven productivity gains in this way will require concerted political action.
The think tank added this was because it is clear that productivity gains are not always shared evenly between employers and employees, and depend on “geographic, demographics, economic cycle and other intrinsic job market factors” such as workers’ access to collective bargaining.
To deliver positive AI-led changes for workers and not just employers, Autonomy recommended setting up “automation hubs”, underpinned by trade union and industry agreements, to boost the adoption of LLMs in ways that are equitable.
In the context of the creative industries and copyright, a similar situation has already taken place with the 2023 Hollywood writers’ strike, whose collective sector-wide action ended with an agreement from studios that AI cannot be used to write or rewrite scripts, and which gave them the ability to prohibit the use of their writing in model training.
Instead of replying purely on copyright law – which historically has been wielded against individual creatives by entertainment and media companies – the answer may be found in attempting to build up collective copyright mechanisms and improving the underlying labour protections for creative workers to stop them being ripped off by companies, with or without the help of AI.
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