Category: Links

  • YouTube’s AI slop purge is punishing the human creators who never showed their faces

    Ana Maria Constantin

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    Concatena says

    Summary: YouTube cracked down on mass-produced AI videos but its fixes are hurting legitimate "faceless" creators who never used AI. The algorithm now favors on-camera humans, causing demonetisation and channel removals based on proxy signals. Creators are scrambling to show faces or change formats while YouTube balances building AI tools and limiting their spread.

    Takeaway: Oh, that age old law of unintended consequences… whenever a platform declares it’s banning, or tagging, AI slop, we should always ask how it’s planning on doing that. Those who perhaps were taking the sensible option of preserving their privacy (as I did when I was a old-time mummy blogger) are penalised because of AI content finding them easier to emulate. We really need to think more steps ahead that just one… Crowdsourced ratings, automatic labels and channel-level enforcement risk wrongful demonetisation or bans, while YouTube simultaneously builds AI tools — a conflicted approach that harms privacy-focused, anonymous creators and invites perverse incentives.

    YouTube cracked down on mass-produced AI videos but its fixes are hurting legitimate "faceless" creators who never used AI. The algorithm now favors on-camera humans, causing demonetisation and channel removals based on proxy signals. Creators are scrambling to show faces or change formats while YouTube balances building AI tools and limiting their spread.

    Highlights

    Crowdsourcing AI detection has obvious limitations. Research consistently shows that people are poor at identifying AI-generated content, and their accuracy is declining as the tools improve. There is also no indication of how YouTube will weight the ratings or whether a threshold of negative viewer feedback will trigger demonetisation or suppression.

    YouTube is now testing a new approach: a mobile pop-up that asks viewers to rate whether a video feels like AI slop on a five-point scale from “*not at all*” to “*extremely.*” The feature appeared in March 2026 and adds a third layer of detection on top of YouTube’s existing automated and human review systems.

    YouTube has a growing AI slop problem, and its efforts to fix it are catching legitimate creators in the crossfire. In January 2026, the platform terminated 16 channels with a combined 35 million subscribers and 4.7 billion lifetime views under its inauthentic content policy, a quiet rename of the old “*repetitious content*” rules. The channels were producing mass-generated, low-effort content at scale, but the algorithm changes that followed are now penalising a much broader group: faceless creators who have never used AI at all.

  • Image AI models now drive app growth, beating chatbot upgrades

    Sarah Perez

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    Concatena says

    Our Take:

    Image and visual models are now the biggest download drivers for AI apps, far outpacing traditional conversational model updates; they create strong curiosity and trial installs even if they don’t reliably convert to paid users. The downloads-to-revenue disconnect matters — a big spike in installs often buys attention, not sustainable monetisation, with only a few players (notably OpenAI) successfully converting that interest into meaningful short-term spending.

    Your Takeaway:

    If you run or advise apps, treat image-model launches as high-impact user-acquisition plays that need a conversion plan baked in — optimize onboarding, trial-to-pay flows, and clear value props before release. Also, watch costs and privacy risks around image data and model access: don’t assume downloads justify long-term spend without a follow-through commercial and data-control strategy.

    New image AI models are driving much more app growth than chatbot updates, with 6.5 times more downloads. Apps like ChatGPT and Google’s Gemini saw millions of new installs after releasing image models. However, more downloads don’t always mean more revenue, except in ChatGPT’s case.

  • Digital Omnibus reality check: 83.5% of access requests not properly answered

    noyb

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    Concatena says

    Our Take: This analysis from noyb (who may have a particular point to make) shows companies – not data subjects – are the real problem: 83.5% of access requests tied to noyb cases were either incomplete or unanswered, including many from big tech, who one would have imagined would have sophisticated enough systems to automate such a process. Noyb suggest this indicates that proposals in the Digital Omnibus to restrict access rights are misdirected.

    Your Takeaway: it’s true that data subject access requests can be misused, but they are a vital check and balance for data protection. If responding to a subject access request is hard, you may wish to consider whether that shows some weakness in your overall data governance practices. Give us a call – we can help!

    Most companies do not properly answer requests for access to personal data, with 83.5% of such requests ignored or incomplete. Big tech firms often fail to provide full replies, making it hard for people to check their data use. The European Commission wants to limit these access rights, but experts warn this would harm people’s privacy protections.

    Highlights

    **Access Requests not a relevant workload.** At the same time, a recently published *noyb* survey made clear that the majority (over 70%) of Data Protection Officers (DPOs) working in companies think that data subject rights – and the Right of Access in particular – don’t create a significant workload, while being a useful tool for protecting people’s rights.

    **Real-life data: 83.5% of access requests not properly answered.** In practice, however, the primary problem concerning the right of access is not “abusive” complaints, but the huge amount of requests that don’t receive a proper answer. This also explains why a significant number of complaints before authorities concern the lack of a full reply to access requests. To gain more insight into how companies deal with the right of access, *noyb* analysed 121 access requests that have been filed in relation to *noyb* cases since 2018*. The results are clear: only 16.5% of those requests received a satisfying reply, while 53.7% were incomplete – and almost 30% were not answered at all. Overall, 83.5% of requests were not responses in line with the law.

    **The most commonly exercised right under the GDPR is the right of access to one’s personal data that is being processed by companies. After all, it’s often the prerequisite to know if there is inaccurate or unlawful personal data that needs to be corrected or deleted. However, a new** **analysis of** ***noyb*** **cases shows: Only 16.5% of all access requests** ***noyb*** **has sent to companies in the past 8 years received a satisfactory reply, while 53.7% of replies were incomplete – and almost 30% were not answered at all. In other words: while companies are lobbying Brussels to limit people’s right of access because of an alleged “abuse”, the real problem is non-compliance by these exact companies.**

  • Final storage and access technologies guidance published

    ico.org.uk

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    Concatena says

    Our Take: I’ve not had a chance to fully read into this yet, but my initial big takeaway is the uphill battle that the ICO has in trying to convince people that terms like SATs mean the same thing as they understand when they here cookies. I know how they feel, it’s driven me mad for years, but sometimes you need to meet people where they are. I’m slightly concerned about the references to consulting with the online advertising industry to help shape future initiatives – I’d really like to see consultation with third sector or indeed businesses who are reliant on the advertising revenue but also value their customers to pitch in here too. Final thought is to about how it’s intended that “demonstrably low privacy risks” are quantified. In 2004 I remember the then commissioner, Richard Thomas, warning that we were sleepwalking into a surveillance society. Whilst the current commissioner has stepped away for a while, I hope the ICO still remembers that report.

    Your Takeaway: Nothing really to see here, yet – but if online tracking or advertising is important to your business, or to your ethics, it’s worth a closer read – and maybe getting involved in the ongoing discussions.

    The ICO has today published its finalised guidance on Storage and Access Technologies (SATs), alongside an update on its online tracking strategy.

    Highlights

    The guidance, which covers how the Privacy and Electronic Communications Regulations (PECR) (and where relevant, the UK GDPR) apply to cookies, tracking pixels, device fingerprinting and similar technologies (‘storage and access technologies’), incorporates updates following two consultations and changes introduced by the Data (Use and Access) Act. It includes new examples and points of clarification to help organisations comply with the law. It reflects the law as it currently stands, and sits separately from our ongoing work to review regulation 6 of PECR for online advertising purposes, on which further updates will follow in the coming weeks.

    We have today published our finalised guidance on Storage and Access Technologies (SATs), alongside an update on our online tracking strategy.

  • Online tracking strategy update – April 2026

    ico.org.uk

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    Concatena says

    Our Take: We’ve commented on the SATs guidance in a separate post, but this wider summary from the ICO is worth a read too. I still don’t love the focus on consent for “cookies/SATs” (and don’t even get me started on consent-or-pay) – I don’t see how the average user can possibly understand the network that lies behind that little button – but that’s the legal landscape were in.

    Your Takeaway: As with the SAT guidance, there’s nothing requiring action here yet (unless you didn’t check your cookie banner compliance last year… in which case, I’d recommend a look now). Still, some ongoing discussions here it’s worth keeping on top of – and contributing to as well.

    At the start of 2025, we published our online tracking strategy setting out our plans to give people meaningful choice and control over how they are tracked online, and provide businesses with certainty to innovate responsibly.

    Highlights

    After careful consideration and review of our concerns, we concluded that further action would not be appropriate after observing positive improvements from the platforms as compared to their historical processing practices. This was communicated to the platforms in January of this year.

    We assessed key areas of concern, including: the validity of consent for the data processing carried out by these platforms and their lawful basis relied upon for processing.

    We have driven improvements in the standard products offered to website owners by working directly with key cookie banner vendors responsible for the largest market shares across the UK’s most popular websites. For example, OneTrust and Usercentrics have developed UK-specific templates aligned with our guidance. This is in addition to a range of other improvements made by these platforms and changes implemented by Sourcepoint and Inmobi to enhance their existing templates and guidance. This engagement has raised the bar across a significant portion of the market and made it easier for online businesses to offer fair, compliant choices to users.

    We committed to reviewing cookie banners on the top 1,000 websites in the UK. As we updated in December, our action has seen significant changes. It has lowered the prevalence of cookies being placed before a user has expressed their choice and has driven an increase of clear reject options on consent banners, making it easier for users to control how they are tracked.

    Next month, we will be publishing our advice to government on where PECR requirements to obtain consent for the use of storage and access technologies for online advertising purposes could be removed. We understand that the government is exploring whether to create an exception or exceptions for some online advertising purposes, using secondary regulation-making powers under regulation 6A of PECR. This work will help inform government policy–making.

    Last year, we opened a call for views on our review of regulation 6 PECR where the use of storage and access technologies for advertising may pose demonstrably low privacy risks.

  • Adobe’s legal chief calls for creator protection as policymakers and tech companies reframe copyright in the era of AI

    Craig Hale

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    Concatena says

    Our Take: Adobe’s legal chief urges a pragmatic path for AI regulation – don’t tear up copyright law but clarify it and protect creators whose work fuels AI. I hate to say it, but I agree – let’s focus on the fundamentals, but importantly let’s also think about whether the means for enforcing individual contributors rights is accessible in this new world, and if not, whether there ought to be a supportive regime which regulates bad actors.

    Your Takeaway: IP is always something to keep an eye on. The article talks about creator protections and provenance tools, and they are worth looking at and understanding; but it’s unclear how much control they truly give. Make sure you’re not cutting corners in your own IP compliance with third party materials at the same time as protecting your output.

    While the world establishes copyright for AI-generated assets, Adobe’s legal chief calls for greater creator protection and asset verification.

    Highlights

    The difficulty at the moment is that regions like the US, EU and UK are pushing their own goals. "It’s a fallacy to think there would be a universal standard that would apply globally," Pentland said. "but we can dream."

    When asked about watermarking, Pentland rejected visible marks as the default solution, favoring options like metadata or QR-style verification to preserve the integrity of an artist’s work.

    To date, the ‘Big Five’ camera makers (Fujifilm, Sony, Canon, Nikon and Leica) and some Android manufacturers (Google Pixel and Samsung Galaxy) have implemented Content Credentials, as have a number of popular platforms like LinkedIn, YouTube, Meta and TikTok.

    Adobe sees this type of verification protecting consumers against threats like deepfakes, enabling users to verify authenticity.

    For Adobe, this means pushing Content Credentials, which the company describes separately as "a durable, industry-standard metadata type that acts like a digital nutrition label for content," in a bid to create verifiable content trails.

    In 2025, the US Copyright Office granted protection to an image that was created with AI assistance, making this the first time anyone has ever been granted copyright protection for AI-generated work.

    "We don’t want it to stifle innovation," she said, "but at the same time, we can’t leave it completely unchecked."

    At the same time, Pentland also advocated for tech companies to get involved – not to redefine copyright law, but to maintain authenticity and protect creators in this era of AI assistance.

    Speaking with *TechRadar Pro* in an exclusive interview at Adobe Summit 2026, the company’s Chief Legal Officer, Louise Pentland, urged policymakers to resist radical changes, and for courts and companies instead to focus on a more pragmatic approach.

  • Will human minds still be special in an age of AI?

    Tom Griffiths

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    Concatena says

    Our Take: This article argues that AI isn’t a single linear upgrade on human minds – it’s a different kind of intelligence shaped by different limits and experiences, so claims that machines will simply “overtake” us are misleading. I think there’s another point here too – we remove an important experience and learning opportunity from humans when we automate everything.

    Your Takeaway: When evaluating or deploying AI, focus on the problem you’re trying to solve, and whether it’s one which can be helped by automation and customisation from a LLM, and what the extent of that help should be. Design your processes to make sure that you’re putting humans at the right point of the journey – not just as a box tick exercise at the end, but actually contributing to the process, supported, where appropriate, by these tools.

    Human intelligence is shaped by our limits, like short lives and simple communication, which makes us special. AI can do many tasks but works differently and faces other challenges. Instead of rivals, humans and AI should be seen as different minds with unique strengths.

    Highlights

    This isn’t the only place where AI runs into difficulties. Imagine you are assisting a pharmacist. They need a drug with a concentration of 785 parts per million (ppm). Two test tubes are available: one containing 685 ppm and the other 791 ppm. Your task is to determine which test tube provides the most similar concentration to your required dosage. Hopefully you would pick 791 ppm. However, some of the time even leading AI systems pick 685 ppm. Why? Because the artificial neural networks used to build AI systems tend to blur things together. When there are two possible answers, they choose something in between. The number 785 can be represented as either a string of digits (“7”, “8”, and “5”) or as a quantity (seven-hundred-and-eighty-five). If it is a string, 785 is more similar to 685 – they are just one digit apart. But if it is a quantity, then it is more similar to 791. Mixing up these two answers can have significant consequences.

    Here’s a simple example. How many letters are in this sequence: aaaaaaaaaaaaaaaaaaaaaaaaaaaaa? For a human, it’s not particularly difficult to answer – you can just count them up. For an AI system, it’s trickier. They are constrained by how they represent language and how they are trained. They like to break up words into parts (called “tokens”), which can make it hard for them to answer questions about spelling. And they tend to favour sequences of tokens that appear more often in their training data as answers. We found that OpenAI’s GPT-4 model, which was hailed as showing “sparks of artificial general intelligence”, was more likely to correctly answer this question when given 30 letters rather than 29. Why? Because the number 30 is written down more often than the number 29.

    Human intelligence is a response to our limitations. To make the most of our lives, we have an amazing ability to learn from limited experience. Yes, AlphaGo can beat the best human go players, but it was trained on many human lifetimes of games. Yes, ChatGPT can hold a reasonable conversation, but it’s drawing on thousands of years of language. No AI system can produce sentences with the creativity of a human five-year-old when exposed to the same amount of data.

    AI systems face none of these constraints. They can process more data than any human might see in a lifetime. They can expand their capacity by using more computers. And they can easily share what they see and learn with other machines.

    Humans are no different. Our minds have been shaped by our biology. We only live for a few decades and have to learn everything we are going to learn and do everything we are going to do in that short time. All that learning and doing will be carried out at the direction of a kilogram or so of neurons trapped inside our bony skulls. We can only share our thoughts with others by making noises with our mouths or tapping and wiggling our fingers.

  • English councils to trial Google AI tool to speed up planning decisions

    Chris Smyth

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    Concatena says

    Our Take: Using AI to generate efficiencies could really support public services to get more done, and to be more consistent. Human in the loop is vital – but you need to ensure that those humans are empowered to really BE in that loop and to contradict the machine. “Computer says no” can be very difficult to pass over…

    Your Takeaway: Make sure that any humans in the loop using LLM powered systems have appropriate training and understanding of their outputs, so that system can support *their* critical thinking, not outsource it.

    English councils will start using a new AI tool from Google to help speed up building project decisions. The AI will give recommendations, but humans will make the final call. The government hopes this will make planning faster and support building more homes.

    Highlights

    Under the programme, humans will make the final decisions with AI providing a recommendation. For more complex applications, the AI tool will probably give officials a framework for decisions rather than a definitive answer.

    “There is a risk that in the push to harness efficiencies and insights, planning’s decision-making systems are redesigned to work well with AI, and not for optimal outcomes. There’s no value in processing applications more quickly if the developments that follow are low quality.”

    Recommendations on whether to grant or refuse building projects will be generated using a custom AI system — the Augmented Planning Decision Tool — before being signed off by council officers.

    Planning decisions in England will for the first time be made with the help of Google-built AI starting this month, in a pilot ministers say will speed up approvals.

  • Mathematicians Claim Significant Discovery Using ChatGPT

    Frank Landymore

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    Concatena says

    Our Take: Sounds amazing. But then I also remember this: https://www.psychologytoday.com/gb/blog/understanding-suicide/202511/chatgpt-made-him-delusional

    Your Takeaway: LLMs can do amazing things. They can also do dumb things. And even the amazing things need your help.

    A young man named Liam Price used ChatGPT to solve a difficult math problem that had puzzled experts for over 60 years. Experts say the AI found a new way to approach the problem, but humans had to fix its mistakes. This breakthrough shows AI might help solve tough math questions, but caution is still needed.

    Highlights

    “The raw output of ChatGPT’s proof was actually quite poor. So it required an expert to kind of sift through and actually understand what it was trying to say,” Jared Lichtman, a mathematician at Stanford University whose doctoral thesis centered on one Erdős’s conjectures, told *SciAm*.

    Still, it required humans to apply the finishing touches.

    Earlier this month, 23-year-old Liam Price shared a solution to one of the so-called Erdős problems, a series of famously abstruse math conjectures left behind by the Hungarian mathematician Paul Erdős. While some of these conjectures have gotten the better of savants in the field, Price, who has no advanced math degree, seemingly stumbled on a solution for one of them by simply prompting GPT-5.4 for an answer.

    Did ChatGPT just solve an arcane math problem that’s foiled mathematicians for over sixty years? Some leading experts say yes, *Scientific American* reports.

  • Usage-based pricing killing your vibe – here’s how to roll your own local AI coding agents

    Tobias Mann and Thomas Claburn

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    Concatena says

    Our Take: I’m not necessarily encouraging you to rolll your own here, but it is worth being aware of this business model change – and the fact that from the get-go the definition of a token as a metric has been less than clear and open.

    Your Takeaway: If you’re reliant on third party LLMs, remember to account for the risk of them changing their measurement metrics and charging – it’s been on the cards for a while.

    Usage-based pricing for AI coding tools is becoming expensive and restrictive. This article shows how to run local AI coding agents like Claude Code, Pi Coding Agent, and Cline to avoid those costs. Local models work well for small projects but may need human approval to avoid mistakes.

    Highlights

    Over the past few weeks, we’ve seen Anthropic toy with dropping Claude Code from its most affordable plans while Microsoft has skipped testing the waters and moved GitHub Copilot to a purely usage-based model. The whole debacle got us thinking. Do we even need Anthropic or OpenAI’s top models, or can we get away with a smaller local model? Sure, it might be slower, less capable, and a little more frustrating to work with, but you can’t beat the price of free… Well, assuming you’ve already got the hardware that is.