In an attempt to address the complexity people often experience in dealing with government services, the UK government has unveiled CustomerFirst.
This is a new unit within the Department for Science, Innovation and Technology (DSIT), led by Tristan Thomas, formerly of Monzo, and Greg Jackson, CEO of Octopus Energy. It aims to bring together the best civil service operators alongside leading private sector disruptors and transformation specialists. The plan is to use CustomerFirst expertise to rewire government services, making use of AI and best practices from the private sector.
The Blueprint for modern digital government report, published last year, set out six steps to achieve a digital state. At the time, the government also recognised major challenges that were preventing digital government from progressing.
Although the government spends over £26bn annually on digital technology and employs a workforce of nearly 100,000 digital and data professionals, institutionalised fragmentation is holding back digital government services. Problem areas include persistent legacy, cyber and resilience risk; siloed data; under-digitisation; inconsistent leadership; a skills shortfall; diffused buying power; and outdated funding models.
A year later, the government has published its Roadmap for a modern digital government. The roadmap states that public services are being redesigned to be quicker, more accessible and easier to use, while also being cheaper to run and costing less to the taxpayer. The roadmap includes digitisation of the planning system to accelerate house building and a goal to simplify how people manage their benefits and taxes online.
CustomerFirst is being positioned as one of the initiatives the government will use to deliver savings for taxpayers through end‑to‑end reform and smarter use of technology by departments. There is a potential £4bn saving from moving service processing online, rather than by phone, post or in-person.
Discussing the need to streamline how people interact with government departments, the minister for digital government, Ian Murray, said: “Too often, people are put off from interacting with the services they need by the frustration that comes with waiting on hold, filling in endless forms, and jumping through hoop after hoop.”
He said the government would redesign services so they meet the demands of modern life.
Technology such as artificial intelligence (AI) will be deployed to achieve this objective. Greg Jackson, founder and CEO of Octopus Energy, said: “With modern technology, including AI, and even more importantly empowered teams whose job it is to help citizens, we can improve service without increasing costs.”
The Driver and Vehicle Licensing Agency (DVLA) is the first government department to work with CustomerFirst. It aims to improve how the DVLA handles millions of customer interactions each year related to driving licences, vehicle registration and other motoring services.
DSIT said the DVLA will become a blueprint for improving services across government departments. DVLA CEO Tim Moss said: “DVLA has a track record of delivering great digital services, and we are keen to build on this and further develop the next generation of high-quality services that citizens should expect.”
As part of its roadmap for modern digital government, DSIT said it was looking for senior and experienced talent with expertise in service design, solutions architecture and product management.
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.
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.
The mass roll-out of fibre networks is seen as the key to the expansion of digital services across major economies, but research from Kearney is warning that a €174bn funding shortfall facing Europe’s telecoms sector is putting 2030 gigabit and 5G connectivity targets at risk, meaning around 45 million Europeans could remain without adequate high-speed connectivity by the end of the decade.
In the European telecom health index, Kearney surveyed 20,000 consumers across 21 European countries using consumer research and commercial performance data.
Topline findings showed customer behaviour was a major barrier to monetisation, with weaker markets seeing higher switching, lower satisfaction and weaker bundling. Looking at successful territories, the survey showed that the leading countries are achieving take-up of up to 84% while fibre investments struggle in slow-adopter markets.
Kearney’s research shows Europe’s healthiest telecom markets are concentrated in the north, with Norway (82), Sweden (81) and Switzerland (76) leading the rankings. These countries typically combined strong fibre adoption, higher customer satisfaction and stronger commercial outcomes.
Specifically, in slow-adopter markets – Italy, the Netherlands, Poland, Ireland and Denmark – returns on capital employed have dropped to 6%, with fibre take-up closer to 45%. By contrast, high-performing markets such as Sweden, Norway, France, Spain and Portugal are achieving fibre take-up of up to 84%, supporting significantly stronger returns of 11%.
Assessing the reasons for lack of uptake in the laggard countries, Kearney’s report found that demand-side behaviour was the main barrier to fibre monetisation. In the bottom-five markets, including the UK, customers were 7% more likely to switch providers, 10% more likely to demand faster speeds, and 6% more likely to demand better customer service compared with those in stronger markets.
By contrast, the top-five countries – Sweden, Norway, France, Spain and Portugal – record significantly higher sentiment. Customers are 11% more satisfied with their mobile provider, 13% more satisfied with fixed broadband, and 13% more likely to hold multiple mobile subscriptions with the same provider. Stronger customer relationships also deliver better financial outcomes. In top-performing markets, operators typically saw average revenue per user rise by up to 15%, while customer turnover drops by 10-15%.
The UK ranked 18th out of 20 European markets, despite fibre reaching nearly 80% of homes across the nation. The report stressed that the UK still struggles with slow adoption and weaker customer sentiment, reflected in lower bundling levels of just 28%. The UK joins Belgium and Italy in the lowest-performing group – markets where fibre availability was growing, but commercial performance is failing to keep pace.
Kearney partner Christophe Firth noted that while there was no shortage of fibre in the ground, the returns for providers weren’t adding up, and that the challenge now would be to convert homes passed into paying customers, improving service experiences and rethinking how operators go to market.
“In some countries, operators have passed 90% of homes but connected fewer than 40% – that’s a massive commercial gap that needs to be addressed,” he said.
“Instead of chasing roll-out targets, operators need to focus on actually getting more consumers to sign up to the service. That means improving how they cross-sell fixed and mobile, creating bundles that genuinely appeal to households, making digital sign-up simpler, and targeting the right customers with the right offers. The infrastructure is already there – now, it’s about turning it into a consistent revenue stream.”
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