When AI Devours the News: Who Pays for Truth

November 29, 2025

The news business has survived many existential threats. Television didn't kill radio. The internet didn't kill newspapers, though it came close. But what happens when artificial intelligence doesn't just compete with journalism but consumes it whole, digests it, and spits out bite-sized summaries without sending a single reader, or penny, back to the source?

This isn't a hypothetical future. It's happening now, and the numbers are brutal.

When Google rolled out AI Overviews to all US users in May 2024, the impact was immediate and devastating. Travel blog The Planet D shut down after its traffic plummeted 90%. Learning platform Chegg reported a 49% decline in non-subscriber traffic between January 2024 and January 2025. The average click-through rate for the number one result on AI Overview keywords dropped from 7.3% in March 2024 to just 2.6% in March 2025. That's not a decline. That's a collapse.

Zero-click searches, where users get their answers without ever leaving Google, increased from 56% to 69% between May 2024 and May 2025, according to Similarweb data. CNN's website traffic dropped approximately 30% from a year earlier. Industry analysts estimate that AI Overviews could cost publishers $2 billion in annual advertising revenue.

But the traffic drain is only half the story. Behind the scenes, AI companies have been systematically scraping, copying, and ingesting journalistic content to train their models, often without permission, payment, or acknowledgement. This creates a perverse feedback loop: AI companies extract the knowledge created by journalists, repackage it through their models, capture the traffic and revenue that would have funded more journalism, and leave news organisations struggling to survive while simultaneously demanding access to more content to improve their systems.

The question isn't whether this is happening. The question is whether we're watching the construction of a new information extraction economy that fundamentally alters who controls, profits from, and ultimately produces the truth.

The Scraping Economy

In November 2023, the News Media Alliance, representing nearly 2,000 outlets in the US, submitted a 77-page white paper to the United States Copyright Office. Their findings were stark: developers of generative artificial intelligence systems, including OpenAI and Google, had copied and used news, magazine, and digital media content to train their bots without authorisation. The outputs of these AI chatbots brought them into direct competition with news outlets through “narrative answers to search queries,” eliminating the need for consumers to visit news sources.

The economics are lopsided to the point of absurdity. Cloudflare found that OpenAI scraped a news site 250 times for every one referral page view it sent that site. For every reader OpenAI sends back to the original source, it has taken 250 pieces of content. It's the digital equivalent of a restaurant critic eating 250 meals and writing one review that mentions where they ate.

Research from 2024 and 2025 shows click-through rate reductions ranging from 34% to 46% when AI summaries appear on search results pages. Some publishers reported click-through rates dropping by as much as 89%. The News Media Alliance put it bluntly: “Without web traffic, news and media organisations lose subscription and advertising revenue, and cannot continue to fund the quality work that both AI companies and consumers rely on.”

This comes at a particularly brutal time for journalism. By the end of 2024, the United States had lost a third of its newspapers and almost two-thirds of its newspaper journalists since 2005. Newspaper advertising revenue collapsed from $48 billion in 2004 to $8 billion in 2020, an 82% decrease. Despite a 43% rise in traffic to the top 46 news sites over the past decade, their revenues declined 56%.

Core copyright industries contribute $2.09 trillion to US GDP, employing 11.6 million workers. The News Media Alliance has called for recognition that unauthorised use of copyrighted content to train AI constitutes infringement.

But here's where it gets complicated. Some publishers are making deals.

The Devil's Bargain

In December 2023, The New York Times sued OpenAI and Microsoft for copyright infringement, accusing them of using millions of articles to train their AI models without consent or compensation. As of early 2025, The Times had spent $10.8 million in its legal battle with OpenAI.

Yet in May 2025, The New York Times agreed to licence its editorial content to Amazon to train the tech giant's AI platforms, marking the first time The Times agreed to a generative AI-focused licensing arrangement. The deal is worth $20 million to $25 million annually. According to a former NYT executive, The Times was signalling to other AI companies: “We're open to being at the table, if you're willing to come to the table.”

The Times isn't alone. Many publishers have signed licensing deals with OpenAI, including Condé Nast, Time magazine, The Atlantic, Axel Springer, The Financial Times, and Vox Media. News Corp signed a licensing deal with OpenAI in May 2024 covering The Wall Street Journal, New York Post, and Barron's.

Perplexity AI, after facing plagiarism accusations from Forbes and Wired in 2024, debuted a revenue-sharing model for publishers. But News Corp still sued Perplexity, accusing the company of infringing on its copyrighted content by copying and summarising large quantities of articles without permission.

These deals create a two-tier system. Major publishers with expensive legal teams can negotiate licensing agreements. Smaller publications, local news outlets, and independent journalists get their content scraped anyway but lack the resources to fight back or demand payment. The infrastructure of truth becomes something only the wealthy can afford to defend.

The Honour System Breaks Down

For decades, the internet operated on an honour system called robots.txt. Publishers could include a simple text file on their websites telling automated crawlers which parts of the site not to scrape. It wasn't enforceable law. It was a gentleman's agreement.

Nearly 80% of top news organisations in the US were blocking OpenAI's web crawlers at the end of 2023, while 36% were blocking Google's artificial intelligence crawler. Publishers attempted to block four times more AI bots between January 2024 and January 2025 using robots.txt.

But the honour system is breaking down.

TollBit's report detected 436 million AI bot scrapes in Q1 2025, up 46% from Q4 2024. The percentage of AI bot scrapes that bypassed robots.txt surged from 3.3% in Q4 2024 to 12.9% by the end of Q1 2025. Recent updates to major AI companies' terms of service state that their AI bots can act on behalf of user requests, effectively meaning they can ignore robots.txt when being used for retrieval-augmented generation.

The Perplexity case illustrates the problem. Wired found evidence of Perplexity plagiarising Wired stories, reporting that an IP address “almost certainly linked to Perplexity” visited its parent company's websites more than 800 times in a three-month span. Ironically, Perplexity plagiarised the very article that called out the startup for scraping its web content.

Cloudflare claimed that Perplexity didn't just violate robots.txt protocols but also broke Web Application Firewall rules which specifically blocked Perplexity's official bots. When websites blocked Perplexity's official crawlers, the company allegedly used a generic browser that impersonated Google Chrome on macOS, and used multiple unofficial IP addresses to bypass robots.txt rules.

Forbes accused Perplexity of plagiarism for republishing its original reporting on former Google CEO Eric Schmidt without citing the story directly, finding a plagiarised version within Perplexity AI's Pages tool with no reference to the media outlet besides a small “F” logo at the bottom of the page.

In response, Cloudflare became the first major internet infrastructure provider to block all AI scrapers accessing content by default, backed by more than a dozen major news and media publishers including the Associated Press, The Atlantic, BuzzFeed, Condé Nast, Dotdash Meredith, Fortune, Gannett, The Independent, and Time.

The technological arms race has begun. Publishers deploy more sophisticated blocking. AI companies find new ways around the blocks. And in the middle, the fundamental question remains: should accessing journalistic content for AI training require explicit consent, or should it be freely available unless someone actively objects and has the technical capacity to enforce that objection?

The Opt-In Opt-Out Debate

The European Union has been grappling with this question directly. The EU AI Act currently operates under an “opt-out” system where rightholders may reserve their rights to prevent text and data mining for commercial purposes. Providers of general-purpose AI models need to obtain authorisation from rightholders if they want to carry out text and data mining when rights have been expressly reserved.

But there's growing momentum toward changing this system. A July 2025 European Parliament study on generative AI and copyright concluded that an opt-in model would more fairly protect authors' rights and rebalance negotiation power, ensuring active consent and potential compensation. The study found that rightholders often lack the technical means or awareness to enforce opt-outs, and the existing system is ill-suited to the realities of AI training.

The United Kingdom has taken a different approach. In December 2024, the UK Government launched a consultation proposing a new exception allowing materials to be used for commercial purposes unless the content creator has “opted-out.” Critics, including the BBC, argue this risks undermining creators' rights and control over their work.

During parliamentary debate, the House of Commons removed provisions on AI transparency which had been added by the Lords. After rewriting, the House of Lords reinstated the amendments, but the Commons again rejected them on 22 May 2025.

The opt-in versus opt-out debate isn't merely technical. It's about where we place the burden of enforcement. An opt-out system assumes AI companies can take content unless told otherwise, placing the burden on publishers to actively protect their rights. An opt-in system assumes publishers have control over their content unless they explicitly grant permission, placing the burden on AI companies to seek consent.

For large publishers with legal and technical resources, the difference may be manageable. For smaller outlets, local news organisations, freelance journalists, and news organisations in the developing world, the opt-out model creates an impossible enforcement burden. They lack the technical infrastructure to monitor scraping, the legal resources to pursue violations, and the market power to negotiate fair terms.

Innovation Versus Preservation

The debate is often framed as “innovation versus preservation.” AI companies argue that restricting access to training data will stifle innovation and harm the public interest. Publishers argue that protecting copyright is necessary to preserve the economic viability of journalism and maintain the quality information ecosystem that democracy requires.

This framing is convenient for AI companies because it makes them the champions of progress and publishers the defenders of an outdated status quo. But it obscures deeper questions about power, infrastructure, and the nature of knowledge creation.

Innovation and preservation aren't opposites. Journalism is itself an innovative enterprise. Investigative reporting that uncovers government corruption is innovation. Data journalism that reveals hidden patterns is innovation. Foreign correspondents risking their lives to document war crimes are engaged in the most vital form of truth-seeking innovation our society produces.

What we're really debating is who gets to profit from that innovation. If AI companies can extract the knowledge produced by journalists, repackage it, and capture the economic value without compensating the original creators, we haven't chosen innovation over preservation. We've chosen extraction over creation.

A 2025 study published in Digital Journalism argued that media organisations' dependence on AI companies poses challenges to media freedom, particularly through loss of control over the values embedded in AI tools they use to inform the public. Reporters Without Borders' World Press Freedom Index found that the global state of press freedom has reached an unprecedented low point. Over 60% of global media outlets expressed concern over AI scraping their content without compensation.

Consider what happens when the infrastructure of information becomes concentrated in a handful of AI companies. These companies don't just distribute news. They determine what constitutes an adequate answer to a question. They decide which sources to cite and which to ignore. They summarise complex reporting into bite-sized paragraphs, stripping away nuance, context, and the very uncertainty that characterises honest journalism.

Google's AI Overviews don't just show you what others have written. They present synthetic answers with an air of authority, as if the question has been definitively answered rather than reported on by journalists with varying levels of access, expertise, and bias. This isn't neutral infrastructure. It's editorial judgement, exercised by algorithms optimised for engagement rather than truth, and controlled by companies accountable primarily to shareholders rather than the public.

Who Owns the Infrastructure of Truth?

This brings us to the deepest question: who owns the infrastructure of truth itself?

For most of modern history, the answer was relatively clear. Journalists and news organisations owned the means of producing truth. They employed reporters, paid for investigations, took legal risks, and published findings. Distribution was controlled by whoever owned the printing presses, broadcast licences, or later, web servers. But production and distribution, while distinct, remained largely aligned.

AI fundamentally separates production from distribution, and arguably introduces a third layer: synthesis. Journalists produce the original reporting. AI companies synthesise that reporting into new forms. And increasingly, AI companies also control distribution through search, chatbots, and AI-powered interfaces.

This isn't just vertical integration. It's a wholesale reorganisation of the information supply chain that places AI companies at the centre, with journalists reduced to raw material suppliers in an extraction economy they neither control nor profit from adequately.

The parallel to natural resource extraction is uncomfortably apt. For centuries, colonial powers extracted raw materials from colonised territories, processed them in industrial centres, and sold finished goods back to those same territories at marked-up prices. The value accrued not to those who produced the raw materials but to those who controlled the processing and distribution infrastructure.

Replace “raw materials” with “original reporting” and “industrial centres” with “AI model training” and the analogy holds. News organisations produce expensive, labour-intensive journalism. AI companies scrape that journalism, process it through their models, and sell access to the synthesised knowledge. The value accrues not to those who produced the original reporting but to those who control the AI infrastructure.

Local news organisations in the US bore the brunt of economic disruption and increasingly tied themselves to platform companies like Facebook and Google. Those very companies are now major players in AI development, exacerbating the challenges and deepening the dependencies. Google's adoption of AI-based summarisation in its search engine results is likely to further upend the economic foundation for journalism.

The collapse of the mainstream news media's financial model may represent a threat to democracy, creating vast news deserts and the opportunity for ill-intentioned players to fill the void with misinformation. One study published by NewsGuard in May 2024 tallied nearly 1,300 AI-generated news sites across 16 languages, many churning out viral misinformation.

What emerges from this landscape is a paradox. At the very moment when AI makes it easier than ever to access and synthesise information, the economic model that produces trustworthy information is collapsing. AI companies need journalism to train their models and provide current information. But their extraction of that journalism undermines the business model that produces it. The snake is eating its own tail.

The Democracy Question

Democracy requires more than free speech. It requires the structural conditions that make truth-seeking possible. You need journalists who can afford to spend months on an investigation. You need news organisations that can fund foreign bureaus, hire fact-checkers, and employ editors with institutional knowledge. You need legal protections for whistleblowers and reporters. You need economic models that reward accuracy over clickbait.

These structural conditions have been eroding for decades. Newspaper revenues declined by nearly 28% between 2002 and 2010, and by another nearly 34% between 2010 and 2020, according to US Census Bureau data. Newspaper publishers collected about $22.1 billion in revenue in 2020, less than half the amount they collected in 2002.

AI doesn't create these problems. But it accelerates them by removing the final economic pillar many publishers were relying on: web traffic. If AI Overviews, chatbots, and synthetic search results can answer users' questions without sending them to the original sources, what incentive remains for anyone to fund expensive original reporting?

Some argue that AI could help journalism by making reporting more efficient and reducing costs. But efficiency gains don't solve the core problem. If all journalism becomes more efficient but generates less revenue, we still end up with less journalism. The question isn't whether AI can help journalists work faster. It's whether the AI economy creates sustainable funding models for the journalism we need.

The European Parliament's study advocating for opt-in consent isn't just about copyright. It's about maintaining the structural conditions necessary for independent journalism to exist. If publishers can't control how their content is used or negotiate fair compensation, the economic foundation for journalism collapses further. And once that foundation is gone, no amount of AI efficiency gains will rebuild it.

This is why framing the debate as innovation versus preservation misses the point. The real choice is between an AI economy that sustains journalism as a vital democratic institution and one that extracts value from journalism while undermining its viability.

The Transparency Illusion

The EU AI Act's requirement that providers publicly disclose detailed summaries of content used for AI model training sounds promising. Transparency is good, right? But disclosure without accountability is just performance.

Knowing that OpenAI trained GPT-4 on millions of news articles doesn't help publishers if they can't refuse consent or demand compensation. Knowing which crawlers visited your website doesn't prevent them from coming back. Transparency creates the illusion of control without providing actual leverage.

What would accountability look like? It would require enforcement mechanisms with real consequences. It would mean AI companies face meaningful penalties for scraping content without permission. It would give publishers legal standing to sue for damages. It would create regulatory frameworks that put the burden of compliance on AI companies rather than on publishers to police thousands of bots.

The UK parliamentary debate over AI transparency provisions illustrates the challenge. The House of Lords added amendments requiring AI companies to disclose their web crawlers and data sources. The House of Commons rejected these amendments twice. Why? Because transparency creates costs and constraints for AI companies that the government was unwilling to impose in the name of fostering innovation.

But transparency without teeth doesn't protect publishers. It just creates a paper trail of their exploitation.

Future Possibilities

We're at a genuine crossroads. The choices made in the next few years will determine whether journalism survives as an independent, adequately funded profession or becomes an unpaid raw material supplier for AI companies.

One possible future: comprehensive licensing frameworks where AI companies pay for the journalism they use, similar to how music streaming services pay royalties. The deals between major publishers and OpenAI, Google, and Amazon could expand to cover the entire industry, with collective licensing organisations negotiating on behalf of smaller publishers.

But this future requires addressing the power imbalance. Small publishers need collective bargaining power. Licensing fees need to be substantial enough to replace lost traffic revenue. And enforcement needs to be strong enough to prevent AI companies from simply scraping content from publishers too small to fight back.

Another possible future: regulatory frameworks that mandate opt-in consent for commercial AI training, as the European Parliament study recommends. AI companies would need explicit permission to use copyrighted content, shifting the burden from publishers protecting their rights to AI companies seeking permission. This creates stronger protections for journalism but could slow AI development and raise costs.

A third possible future: the current extraction economy continues until journalism collapses under the economic pressure. AI companies keep scraping, traffic keeps declining, revenues keep falling, and newsrooms keep shrinking. We're left with a handful of elite publications serving wealthy subscribers, AI-generated content farms producing misinformation, and vast news deserts where local journalism once existed.

The question is which future we choose, and who gets to make that choice. Right now, AI companies are making it by default through their technical and economic power. Regulators are making it through action or inaction. Publishers are making it through licensing deals that may or may not preserve their long-term viability.

What's largely missing is democratic deliberation about what kind of information ecosystem we want and need. Do we want a world where truth-seeking is concentrated in the hands of those who control the algorithms? Do we want journalism to survive as an independent profession, or are we comfortable with it becoming a semi-volunteer activity sustained by wealthy benefactors?

Markets optimise for efficiency and profit, not for the structural conditions democracy requires. If we leave these decisions entirely to AI companies and publishers negotiating bilateral deals, we'll get an outcome that serves their interests, not necessarily the public's.

The Algorithm Age and the Future of Truth

When The New York Times sued OpenAI in December 2023, it wasn't just protecting its copyright. It was asserting that journalism has value beyond its immediate market price. That the work of investigating, verifying, contextualising, and publishing information deserves recognition and compensation. That truth-seeking isn't free.

The outcome of that lawsuit, and the hundreds of similar conflicts playing out globally, will help determine who controls truth in the algorithm age. Will it be the journalists who investigate, the publishers who fund that investigation, or the AI companies who synthesise and redistribute their findings?

Control over truth has always been contested. Governments censor. Corporations spin. Platforms algorithmically promote and demote. What's different now is that AI doesn't just distribute truth or suppress it. It synthesises new forms of information that blend facts from multiple sources, stripped of context, attribution, and sometimes accuracy.

When you ask ChatGPT or Google's AI Overview a question about climate change, foreign policy, or public health, you're not getting journalism. You're getting a statistical model's best guess at what a plausible answer looks like, based on patterns it found in journalistic content. Sometimes that answer is accurate. Sometimes it's subtly wrong. Sometimes it's dangerously misleading. But it's always presented with an air of authority that obscures its synthetic nature.

This matters because trust in information depends partly on understanding its source. When I read a Reuters article, I'm evaluating it based on Reuters' reputation, the reporter's expertise, the sources cited, and the editorial standards I know Reuters maintains. When I get an AI-generated summary, I'm trusting an algorithmic process I don't understand, controlled by a company whose primary obligation is to shareholders, trained on data that may or may not include that Reuters article, and optimised for plausibility rather than truth.

The infrastructure of truth is being rebuilt around us, and most people don't realise it's happening. We've replaced human editorial judgement with algorithmic synthesis. We've traded the messy, imperfect, but ultimately accountable process of journalism for the smooth, confident, but fundamentally opaque process of AI generation.

And we're doing this at precisely the moment when we need trustworthy journalism most. Climate change, pandemic response, democratic backsliding, technological disruption, economic inequality: these challenges require the kind of sustained, expert, well-resourced investigative reporting that's becoming economically unviable.

The cruel irony is that AI companies are undermining the very information ecosystem they depend on. They need high-quality journalism to train their models and keep their outputs accurate and current. But by extracting that journalism without adequately compensating its producers, they're destroying the economic model that creates it.

What replaces professional journalism in this scenario? AI-generated content farms, partisan outlets masquerading as news, press releases repackaged as reporting, and the occasional well-funded investigative outfit serving elite audiences. That's not an information ecosystem that serves democracy. It's an information wasteland punctuated by oases available only to those who can afford them.

What Needs to Happen

The first step is recognising that this isn't inevitable. The current trajectory, where AI companies extract journalistic content without adequate compensation, is the result of choices, not technological necessity. Different choices would produce different outcomes.

Regulatory frameworks matter. The European Union's move toward stronger opt-in requirements represents one path. The UK's consultation on copyright and AI represents another. These aren't just technical policy debates. They're decisions about whether journalism survives as an economically viable profession.

Collective action matters. Individual publishers negotiating with OpenAI or Google have limited leverage. Collective licensing frameworks, where organisations negotiate on behalf of many publishers, could rebalance power. Cloudflare's decision to block AI scrapers by default, backed by major publishers, shows what coordinated action can achieve.

Legal precedent matters. The New York Times lawsuit against OpenAI will help determine whether using copyrighted content to train AI models constitutes fair use or infringement. That decision will ripple through the industry, either empowering publishers to demand licensing fees or giving AI companies legal cover to scrape freely.

Public awareness matters. Most people don't know this battle is happening. They use AI chatbots and search features without realising the economic pressure these tools place on journalism. Democratic deliberation requires an informed public.

What we're fighting over isn't really innovation versus preservation. It's not technology versus tradition. It's a more fundamental question: does knowledge creation deserve to be compensated? If journalists spend months investigating corruption, if news organisations invest in foreign bureaus and fact-checking teams, if local reporters cover city council meetings nobody else attends, should they be paid for that work?

The market, left to itself, seems to be answering no. AI companies can extract that knowledge, repackage it, and capture its economic value without paying the creators. Publishers can't stop them through technical means alone. Legal protections are unclear and under-enforced.

That's why this requires democratic intervention. Not to stop AI development, but to ensure it doesn't cannibalise the information ecosystem democracy requires. To create frameworks where both journalism and AI can thrive, where innovation doesn't come at the cost of truth-seeking, where the infrastructure of knowledge serves the public rather than concentrating power in a few algorithmic platforms.

The algorithm age has arrived. The question is whether it will be an age where truth becomes the property of whoever controls the most sophisticated models, or whether we'll find ways to preserve, fund, and protect the messy, expensive, irreplaceable work of journalism.

We're deciding now. The decisions we make in courtrooms, parliaments, regulatory agencies, and licensing negotiations over the next few years will determine whether our children grow up in a world with independent journalism or one where all information flows through algorithmic intermediaries accountable primarily to their shareholders.

That's not a future that arrives by accident. It's a future we choose, through action or inaction. And the choice, ultimately, is ours.

Sources and References

Similarweb (2024-2025). Data on zero-click searches and Google AI Overviews impact.
TollBit (2025). Q1 2025 Report on AI bot scraping statistics and robots.txt bypass rates.
News Media Alliance (2023). White paper submitted to United States Copyright Office on AI scraping of journalistic content.
Cloudflare (2024-2025). Data on OpenAI scraping ratios and Perplexity AI bypassing allegations.
U.S. Census Bureau (2002-2020). Newspaper publishing revenue data.
Bureau of Labor Statistics (2006-present). Newsroom employment statistics.
GroupM (2024). Projected newspaper advertising revenue analysis.
European Parliament (July 2025). Study on generative AI and copyright: opt-in model recommendations.
UK Information Commissioner's Office (25 February 2025). Response to UK Government AI and copyright consultation.
Reporters Without Borders (2024). World Press Freedom Index and report on AI scraping concerns.
Forum on Information and Democracy (February 2024). Report on AI regulation and democratic values.
NewsGuard (May 2024). Study on AI-generated news sites across 16 languages.
Digital Journalism (2025). “The AI turn in journalism: Disruption, adaptation, and democratic futures.” Dodds, T., Zamith, R., & Lewis, S.C.
CNN Business (2023). “AI Chatbots are scraping news reporting and copyrighted content, News Media Alliance says.”
NPR (2025). “Online news publishers face 'extinction-level event' from Google's AI-powered search.”
Digiday (2024-2025). Multiple reports on publisher traffic impacts, AI licensing deals, and industry trends.
TechCrunch (2024-2025). Coverage of Perplexity AI plagiarism allegations and publisher licensing deals.
Wired (2024). Investigation of Perplexity AI bypassing robots.txt protocol.
Forbes (2024). Coverage of plagiarism concerns regarding Perplexity AI Pages feature.
The Hollywood Reporter (2025). Report on New York Times legal costs in OpenAI lawsuit.
Press Gazette (2024-2025). Coverage of publisher responses to AI scraping and licensing deals.
Digital Content Next (2025). Survey data on Google AI Overviews impact on publisher traffic.
Nieman Journalism Lab (2024-2025). Coverage of AI's impact on journalism and publisher strategies.

Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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