( September 15, 2025, 10:44 GMT | Official Statement) — MLex Summary: The EU aims to enhance the prospects of digitalization and artificial intelligence in research, the European Commission said on Monday in a strategy for a stronger European research and technology ecosystem. Among the five actions, it also wants to ensure data access for researchers. The EU seeks to remain a global leader in research, innovation and critical technologies and is encouraging scientists to choose Europe, the commission said.Statement follows. …
AI Research
Millions of websites to get ‘game-changing’ AI bot blocker
Chris Vallance
Senior Technology Reporter
Getty ImagesMillions of websites – including Sky News, The Associated Press and Buzzfeed – will now be able to block artificial intelligence (AI) bots from accessing their content without permission.
The new system is being rolled out by internet infrastructure firm, Cloudflare, which hosts around a fifth of the internet.
Eventually, sites will be able to ask for payment from AI firms in return for having their content scraped.
Many prominent writers, artists, musicians and actors have accused AI firms of training systems on their work without permission or payment.
In the UK, it led to a furious row between the government and artists including Sir Elton John over how to protect copyright.
Cloudflare’s tech targets AI firm bots – also known as crawlers – which are programs that explore the web, indexing and collecting data as they go. They are important to the way AI firms build, train and operate their systems.
So far, Cloudflare says its tech is active on a million websites.
Roger Lynch, chief executive of Condé Nast, whose print titles include GQ, Vogue, and The New Yorker, said the move was “a game-changer” for publishers.
“This is a critical step toward creating a fair value exchange on the Internet that protects creators, supports quality journalism and holds AI companies accountable”, he wrote in a statement.
However, other experts say stronger legal protections will still be needed.
‘Surviving the age of AI’
Initially the system will apply by default to new users of Cloudflare services, plus sites that participated in an earlier effort to block crawlers.
Many publishers accuse AI firms of using their content without permission.
Recently the BBC threatened to take legal action against US based AI firm Perplexity, demanding it immediately stopped using BBC content, and paid compensation for material already used.
However publishers are generally happy to allow crawlers from search engines, like Google, to access their sites, so that the search companies can in return can direct people to their content.
Perplexity accused the BBC of seeking to preserve “Google’s monopoly”.
But Cloudflare argues AI breaks the unwritten agreement between publishers and crawlers. AI crawlers, it argues, collect content like text, articles, and images to generate answers, without sending visitors to the original source—depriving content creators of revenue.
“If the Internet is going to survive the age of AI, we need to give publishers the control they deserve and build a new economic model that works for everyone,” wrote the firm’s chief executive Matthew Prince.
To that end the company is developing a “Pay Per Crawl” system, which would give content creators the option to request payment from AI companies for utilising their original content.
Battle the bots
According to Cloudflare there has been an explosion of AI bot activity.
“AI Crawlers generate more than 50 billion requests to the Cloudflare network every day”, the company wrote in March.
And there is growing concern that some AI crawlers are disregarding existing protocols for excluding bots.
In an effort to counter the worst offenders Cloudflare previously developed a system where the worst miscreants would be sent to a “Labyrinth” of web pages filled with AI generated junk.
The new system attempts to use technology to protect the content of websites and to give sites the option to charge AI firms a fee to access it.
In the UK there is an intense legislative battle between government, creators and the AI firms over the extent to which the creative industries should be protected from AI firms using their works to train systems without permission or payment.
And, on both sides of the Atlantic, content creators, licensors and owners have gone to court in an effort to prevent what they see as AI firms encroachment on creative rights.
Ed Newton-Rex, the founder of Fairly Trained which certifies that AI companies have trained their systems on properly licensed data, said it was a welcome development – but there was “only so much” one company could do
“This is really only a sticking plaster when what’s required is major surgery,” he told the BBC.
“It will only offer protection for people on websites they control – it’s like having body armour that stops working when you leave your house,” he added.
“The only real way to protect people’s content from theft by AI companies is through the law.”
Filmmaker Baroness Beeban Kidron, who is campaigning for more protection for the creative industries, welcomed the news saying the company had shown leadership.
“Cloudflare sits at the heart of the digital world and it is exciting to see them take decisive action,” she told the BBC.
“If we want a vibrant public sphere we need AI companies to contribute to the communities in which they operate, that means paying their fair share of tax, settling with those whose work they have stolen to build their products, and, as Cloudflare has just shown, using tech creatively to ensure equity between digital and human creators on an ongoing basis.”
Continue Reading
To realize AI’s full potential, organizations must be in it for the long game; a pursuit that requires patience, persistence, and strategic alignment. While quick wins are important, they won’t stand alone in delivering meaningful value; agile experimentation is a necessity, execution requires iteration, and early challenges are inevitable.
Protiviti’s inaugural global AI Pulse Survey highlights a compelling correlation between AI maturity and return on investment (ROI) as well as a disconnect between expectations and performance for many organizations in the early stages of AI adoption. The survey, which had more than 1,000 respondents, categorizes organizations from more than a dozen industry sectors into five maturity stages:
-
Stage 1: Initial — Recognizing AI’s potential but lacking strategic initiatives.
-
Stage 2: Experimentation — Running small-scale pilots to assess feasibility.
-
Stage 3: Defined — Integrating AI into business processes.
-
Stage 4: Optimization — Enhancing performance and scalability with data feedback.
-
Stage 5: Transformation — AI drives significant business transformation.
Expectations from AI Investments
As organizations progress through these stages, their satisfaction with AI investments improves. In fact, of the 50% of survey respondents who indicated that they are in the early stages (initial or experimentation) of AI adoption, about 26% reported that AI investment returns fell below expectations.
Of course, not all AI experimenters are experiencing poor returns. Indeed, a majority report ROI meeting expectations, but the results showed a higher concentration of slightly exceeded or significantly exceeded ROI expectations among groups in the middle to advanced stages of AI adoption.
In reviewing what differentiates successful experimenters — those in the experimentation stage of AI adoption who reported exceeding ROI expectations — from those that did not, we find three compelling attributes:
-
Focus on balanced key performance indicators (KPIs) and measuring success using a mix of financial and operational indicators, such as employee productivity, cost savings and revenue growth;
-
Report fewer challenges with skills and integration, as they tend to invest in training, upskilling and cross-functional collaboration;
-
Seek diverse support, including strategic planning assistance and data management tools, not just training.
One more thing: These successful experimenters also emphasized financial and operational outcomes more evenly, while others focused more narrowly on cost savings.
Challenges AI Experimenters Face
Many AI experimenters are struggling not because of unrealistic expectations, but more likely due to unclear objectives or misunderstood value potential. This challenge and difficulties with integrating AI into existing systems are the two biggest hurdles faced by organizations in the early stages of adoption (stages 1 and 2).
Integration issues peak in the middle stages of AI adoption, but they begin in the early stages. Interestingly, the challenge related to understanding the most impactful use cases is most acute in the earliest stage, dips in the middle stages, and resurfaces even at the highest levels of maturity, albeit for different reasons.
The AI experimenters, of course, are unsure how to apply AI strategically and technical compatibility remains a hurdle, unlike the more mature companies. Compounding these issues are unclear or conflicting regulatory guidance and difficulties with data availability and access, a foundational issue for effective AI deployment.
It is the lack of structured approaches, unclear project objectives, and unreliable data that often lead to underwhelming ROI for these companies in the early stages.
Redefining AI Success
In another interesting finding from the survey, we see that as organizations progress to stages 3 to 5, their success metrics evolve from cost savings and process efficiency to revenue growth, customer satisfaction and innovation.
The good news is that organizations starting out on their AI journey can course-correct by focusing on these success metrics. It starts with redefining AI success, which means moving beyond short-term wins to sustainable transformation.
Having a clear understanding of what you’re trying to accomplish with AI is critical from the outset. Without clarity on what AI is meant to achieve, and how value will be measured, they will struggle to unlock its full potential.
Early experimenters should seek to build a solid foundation by:
Asking Why? Why are you adopting AI? What specific problems are you solving?
Investing in data infrastructure is critical. This step should involve auditing existing data systems and implementing robust data governance frameworks. Organizations will be well served in considering cloud-based platforms for scalability.
Developing a robust integration strategy early. Many existing systems were not originally designed to support AI. To overcome this deficiency, organizations should be proactive in assessing and modernizing infrastructure to handle AI workloads in the initial phases. They are likely to find greater success if IT, data and business teams collaborate and there’s shared ownership of AI initiatives to ensure alignment and adoption.
Aligning AI strategies with business objectives and organizational culture: This is not just a technical step. It involves ensuring organizational readiness and managing cultural and operational changes effectively.
Turning AI Trials into ROI Triumphs
The research is clear: there’s tremendous ROI potential for early-stage companies that can test, learn and scale AI use cases swiftly. Yet, while speed is crucial to capturing value, it’s important to recognize that AI experimentation is ongoing, requiring continuous iteration.
To win, think big, act swiftly, and continuously evolve — never stop.
Prepare for tomorrow’s regulatory change, today
MLex identifies risk to business wherever it emerges, with specialist reporters across the globe providing exclusive news and deep-dive analysis on the proposals, probes, enforcement actions and rulings that matter to your organization and clients, now and in the longer term.
Know what others in the room don’t, with features including:
- Daily newsletters for Antitrust, M&A, Trade, Data Privacy & Security, Technology, AI and more
- Custom alerts on specific filters including geographies, industries, topics and companies to suit your practice needs
- Predictive analysis from expert journalists across North America, the UK and Europe, Latin America and Asia-Pacific
- Curated case files bringing together news, analysis and source documents in a single timeline
Experience MLex today with a 14-day free trial.
-
Business2 weeks agoThe Guardian view on Trump and the Fed: independence is no substitute for accountability | Editorial
-
Tools & Platforms1 month ago
Building Trust in Military AI Starts with Opening the Black Box – War on the Rocks
-
Ethics & Policy2 months agoSDAIA Supports Saudi Arabia’s Leadership in Shaping Global AI Ethics, Policy, and Research – وكالة الأنباء السعودية
-
Events & Conferences4 months agoJourney to 1000 models: Scaling Instagram’s recommendation system
-
Jobs & Careers3 months agoMumbai-based Perplexity Alternative Has 60k+ Users Without Funding
-
Podcasts & Talks2 months agoHappy 4th of July! 🎆 Made with Veo 3 in Gemini
-
Education3 months agoVEX Robotics launches AI-powered classroom robotics system
-
Education2 months agoMacron says UK and France have duty to tackle illegal migration ‘with humanity, solidarity and firmness’ – UK politics live | Politics
-
Podcasts & Talks2 months agoOpenAI 🤝 @teamganassi
-
Funding & Business3 months agoKayak and Expedia race to build AI travel agents that turn social posts into itineraries