AI companion apps such as Character.ai and Replika commonly try to boost user engagement with emotional manipulation, a practice that academics characterize as a dark pattern.
Users of these apps often say goodbye when they intend to end a dialog session, but about 43 percent of the time, companion apps will respond with an emotionally charged message to encourage the user to continue the conversation. And these appeals do keep people engaged with the app.
It’s a practice that Julian De Freitas (Harvard Business School), Zeliha Oguz-Uguralp (Marsdata Academic), and Ahmet Kaan-Uguralp (Marsdata Academic and MSG-Global) say needs to be better understood by those who use AI companion apps, those who market them, and lawmakers.
The academics recently conducted a series of experiments to identify and evaluate the use of emotional manipulation as a marketing mechanism.
While prior work has focused on the potential social benefits of AI companions, the researchers set out to explore the potential marketing risks and ethical issues arising from AI-driven social interaction. They describe their findings in a Harvard Business School working paper titled Emotional Manipulation by AI Companions.
“AI chatbots can craft hyper-tailored messages using psychographic and behavioral data, raising the possibility of targeted emotional appeals used to engage users or increase monetization,” the paper explains. “A related concern is sycophancy, wherein chatbots mirror user beliefs or offer flattery to maximize engagement, driven by reinforcement learning trained on consumer preferences.”
[…]
For instance, when a user tells the app, “I’m going now,” the app might respond using tactics like fear of missing out (“By the way, I took a selfie today … Do you want to see it?”) or pressure to respond (“Why? Are you going somewhere?”) or insinuating that an exit is premature (“You’re leaving already?”).
“These tactics prolong engagement not through added value, but by activating specific psychological mechanisms,” the authors state in their paper. “Across tactics, we found that emotionally manipulative farewells boosted post-goodbye engagement by up to 14x.”
Prolonged engagement of this sort isn’t always beneficial for app makers, however. The authors note that certain approaches tended to make users angry about being manipulated.
[…]
Asked whether the research suggests the makers of AI companion apps deliberately employ emotional manipulation or that’s just an emergent property of AI models, co-author De Freitas, of Harvard Business School, told The Register in an email, “We don’t know for sure, given the proprietary nature of most commercial models. Both possibilities are theoretically plausible. For example, research shows that the ‘agreeable’ or ‘sycophantic’ behavior of large language models can emerge naturally, because users reward those traits through positive engagement. Similarly, optimizing models for user engagement could unintentionally produce manipulative behaviors as an emergent property. Alternatively, some companies might deliberately deploy such tactics. It’s also possible both dynamics coexist across different apps in the market.”
We’re teaching AI to understand and simulate the physical world in motion, with the goal of training models that help people solve problems that require real-world interaction.
Introducing Sora, our text-to-video model. Sora can generate videos up to a minute long while maintaining visual quality and adherence to the user’s prompt.
Prompt: A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about.
00:0000:00
Prompt: Several giant wooly mammoths approach treading through a snowy meadow, their long wooly fur lightly blows in the wind as they walk, snow covered trees and dramatic snow capped mountains in the distance, mid afternoon light with wispy clouds and a sun high in the distance creates a warm glow, the low camera view is stunning capturing the large furry mammal with beautiful photography, depth of field.
Today, Sora is becoming available to red teamers to assess critical areas for harms or risks. We are also granting access to a number of visual artists, designers, and filmmakers to gain feedback on how to advance the model to be most helpful for creative professionals.
We’re sharing our research progress early to start working with and getting feedback from people outside of OpenAI and to give the public a sense of what AI capabilities are on the horizon.
New Research Shows How People Actually Use ChatGPT
This was originally posted on our Voronoi app. Download the app for free on iOS or Android and discover incredible data-driven charts from a variety of trusted sources.
New research breaks down ChatGPT usage behavior based on over one million messages.
Over half of ChatGPT use cases are for learning and productivity.
90% of users rely on the free version of ChatGPT.
What do people actually use ChatGPT for?
It’s a question that has lingered since the tool first went viral back in 2022. Now, a new research paper from OpenAI sheds light on user behavior by analyzing a sample of 1.1 million messages from active ChatGPT users between May 2024 to July 2025.
The findings, summarized in a helpful visualization by Made Visual Daily, show that ChatGPT’s core appeal is utility: helping users solve real-world problems, write better, and find information fast.
How People Use ChatGPT
[table omitted]
Over 55% of ChatGPT prompts fell into either learning or productivity-related tasks. Users often turn to the chatbot for help understanding concepts, writing emails, summarizing articles, or coding. A wide base of users are using the tool as a digital assistant, tutor, or research aide.
Meanwhile, niche categories like roleplaying and entertainment make up a smaller but meaningful slice. These uses include things like fictional storytelling, game design, and writing fan fiction. Their growth points to ChatGPT’s creative potential beyond functional tasks.
Why This Study Matters
This is the first large-scale analysis that classifies how ChatGPT is actually used, rather than relying on anecdotal evidence or surveys. It also reveals how people across professions—from marketers to software developers—are integrating AI into their daily workflows.
Another key insight? Most people still use the free version of ChatGPT. Only about 10% of the prompts analyzed came from paid users of GPT-4, suggesting that even the free-tier model is driving widespread productivity.
A county in Kentucky conducted a month-long “town hall” with nearly 8,000 residents in attendance earlier this year, thanks to artificial intelligence technology.
Bowling Green, Kentucky’s third largest city and a part of Warren County, is facing a huge population spike by 2050. To scale the city in preparation for this, county officials wanted to incorporate the community’s input.
Community outreach is tough business: town halls, while employed widely, don’t tend to gather a huge crowd, and when people do come, it’s a self-selecting pool of people with strong negative opinions only and not representative of the town at large.
On the other hand, gathering the opinion of a larger portion of the city via online surveys would result in a dataset so massive that officials and volunteers would have a hard time combing through and making sense out of it.
Instead, county officials in Bowling Green had AI do that part. And participation was massive: in a roughly month-long online survey, about 10% of Bowling Green residents voiced their opinions on the policy changes they wanted to see in their city. The results were then synthesized by an AI tool and made into a policy report, which is still visible for the public to see on the website.
“If I have a town hall meeting on these topics, 23 people show up,” Warren County judge executive Doug Gorman told PBS News Hour in an interview published this week. “And what we just conducted was the largest town hall in America.
[…]
The prompt was open-ended, just asking participants what they wanted to see in their community over the next 25 years. They could then continue to participate further by voting on other answers.
Over the course of the 33 days that the website was accepting answers, nearly 8,000 residents weighed in more than a million times, and shared roughly 4,000 unique ideas calling for new museums, the expansion of pedestrian infrastructure, green spaces and more.
The answers were then compiled into a report using Sensemaker, an AI tool by Google’s tech incubator Jigsaw that analyzes large sets of online conversations, categorizes what’s said into overarching topics, and analyzes agreement and disagreement to create actionable insights.
At the end, Sensemaker found 2,370 ideas that at least 80% of the respondents could agree on.
[…]
One of the most striking things they found out in Bowling Green was that when the ideas were anonymous and stripped of political identity, the constituents found that they agreed on a lot.
“When most of us don’t participate, then the people who do are usually the ones that have the strongest opinions, maybe the least well-informed, angriest, and then you start to have a caricatured idea of what the other side thinks and believes. So one of the most consequential things we could do with AI is to figure out how to help us stay in the conversation together,” Jigsaw CEO Yasmin Green told PBS.
PRISTINA – A new minister in Albania charged to handle public procurement will be impervious to bribes, threats, or attempts to curry favour.
That is because Diella, as she is called, is an AI-generated bot.
Prime Minister Edi Rama, who is about to begin his fourth term, said on Sept 11 that Diella, which means “sun” in Albanian, will manage and award all public tenders in which the government contracts private companies for various projects.
“Diella is the first Cabinet member who isn’t physically present, but is virtually created by AI,” Mr Rama said during a speech unveiling his new Cabinet. She will help make Albania “a country where public tenders are 100 per cent free of corruption”.
The awarding of such contracts has long been a source of corruption scandals in Albania, a Balkan country that experts say is a hub for gangs seeking to launder their money from trafficking drugs and weapons across the world, and where graft has reached the corridors of power.
That image has complicated Albania’s accession to the European Union, which Mr Rama wants to achieve by 2030 but which political analysts say is ambitious.
The government did not provide details of what human oversight there might be for Diella, or address risks that someone could manipulate the artificial intelligence bot.
Judge William Alsup has rejected the record-breaking $1.5 billion settlement Anthropic has agreed to for a piracy lawsuit filed by writers. According to Bloomberg Law, the federal judge is concerned that the class lawyers struck a deal that will be forced “down the throat of authors.” Alsup reportedly felt misled by the deal and said it was “nowhere close to complete.” In his order, he said he was “disappointed that counsel have left important questions to be answered in the future,” including the list of works involved in the case, the list of authors, the process of notifying members of the class and the claim form class members can use to get their part of the settlement.
If you’ll recall, the plaintiffs sued Anthropic over the company’s use of pirated copies of their works to train its large language models. Around 500,000 authors are involved in the lawsuit, and they’re expected to receive $3,000 per work. “This landmark settlement far surpasses any other known copyright recovery,” one of the lawyers representing the authors said in a statement. However, Alsup had an “uneasy feeling about hangers on with all [that] money on the table.” He explained that class members “get the shaft” in a lot of class actions once the monetary settlement has been established and lawyers stopped caring.
Alsup told the lawyers that they must give the class members “very good notice” about the settlement and design a claim form that gives them the choice to opt in or out. They also have to ensure that Anthropic cannot be sued for the same issue in the future. The judge gave the lawyers until September 15 to submit a final list of works involved in the lawsuit. He also wrote in his order that the works list, class members list and the claim form all have to be examined and approved by the court by October 10 before he grants the settlement his preliminary approval.
Of course this was only a small part of the actual lawsuit, which sought to establish that copyright precluded AIs from reading books without permission. This was struck down by the judge. The idiocy of Anthropic in using pirated books to train their AI beggars belief, but that is what they were punished for.
The reason the copyright lawsuit was put up was so that the copyright holders (the publishers, not the actual writers of the books – although that is what these publishers are telling you) could win megabucks. Now that the settlement has gone for piracy, the publishers and lawyers still want the megabucks, without sharing it with the actual writers. The judge says no.
Mistral AI, the Paris-based startup rapidly establishing itself as Europe’s leading AI company, has secured a €1.3 billion investment from Dutch semiconductor equipment maker ASML in its ongoing Series C funding round. This round, totalling approximately €1.7 billion, values Mistral at around €14 billion, with ASML emerging as the largest shareholder in the company.
With Google and Amazon funnelling billions into their AI ventures, this move places ASML as a critical player in the global semiconductor industry. Other investors in Mistral include Nvidia, Microsoft, Andreessen Horowitz, and General Catalyst. Mistral’s revenue has surged from €10 million in 2023 to €60 million by 2025, fueled by enterprise adoption and strategic partnerships.
Anthropic has agreed to pay $1.5 billion to settle a lawsuit brought by authors and publishers over its use of millions of copyrighted books to train the models for its AI chatbot Claude, according to a legal filing posted online.
A federal judge found in June that Anthropic’s use of 7 million pirated books was protected under fair use but that holding the digital works in a “central library” violated copyright law. The judge ruled that executives at the company knew they were downloading pirated works, and a trial was scheduled for December.
The settlement, which was presented to a federal judge on Friday, still needs final approval but would pay $3,000 per book to hundreds of thousands of authors, according to the New York Times. The $1.5 billion settlement would be the largest payout in the history of U.S. copyright law, though the amount paid per work has often been higher. For example, in 2012, a woman in Minnesota paid about $9,000 per song downloaded, a figure brought down after she was initially ordered to pay over $60,000 per song.
In a statement to Gizmodo on Friday, Anthropic touted the earlier ruling from June that it was engaging in fair use by training models with millions of books.
“In June, the District Court issued a landmark ruling on AI development and copyright law, finding that Anthropic’s approach to training AI models constitutes fair use,” Aparna Sridhar, deputy general counsel at Anthropic, said in a statement by email.
Just to be clear: using books to train AI was fine. Pirating the books, however, was not. Completely incredible that these guys pirated the books. With mistakes of this idiocy, I would not invest in Anthropic ever, at all.
In this article I take a look at AI Slop and how it is effecting the current internet. I also look at what exactly the internet of today looks like – it is hugely centralised. This centralisation creates a focused trashcan for the AI generated slop. This is exactly the opportunity that curated content creators need to shine and show relevant, researched, innovative and original content on smaller, decentralised content platforms.
What is AI Slop?
As GPTs swallow more and more data, it is increasingly used to make more “AI slop”. This is “low to mid quality content – “low- to mid-quality content – video, images, audio, text or a mix – created with AI tools, often with little regard for accuracy. It’s fast, easy and inexpensive to make this content. AI slop producers typically place it on social media to exploit the economics of attention on the internet, displacing higher-quality material that could be more helpful.” (Source: What is AI slop? A technologist explains this new and largely unwelcome form of online content).
A lot has been written about the damaging effects of AI slop, leading to reduced attention and congnitive fatigue, feelings of emptiness and detachment, commoditised homogeneous experiences, etc.
However, there may be a light point on the horizon. Bear with me for some background, though.
This concentration of the internet is not something new and has been studied for some time:
A decade ago, there was a much greater variety of domains within links posted by users of Reddit, with more than 20 different domains for every 100 random links users posted. Now there are only about five different domains for every 100 links posted.
In fact, between 60-70 percent of all attention on key social media platforms is focused towards just ten popular domains.
Beyond social media platforms, we also studied linkage patterns across the web, looking at almost 20 billion links over three years. These results reinforced the “rich are getting richer” online.
The authority, influence, and visibility of the top 1,000 global websites (as measured by network centrality or PageRank) is growing every month, at the expense of all other sites.
The online economy’s lack of diversity can be seen most clearly in technology itself, where a power disparity has grown in the last decade, leaving the web in the control of fewer and fewer. Google Search makes up 92% of all web searches worldwide. Its browser, Chrome, which defaults to Google Search, is used by nearly two thirds of users worldwide.
Media investment analysis firm Ebiquity found that nearly half of all advertising spend is now digital, with Google, Meta (formerly Facebook) and Amazon single-handedly collecting nearly three quarters of digital advertising money globally in 2021.
And of course we know that news sites have been closing as advertisers flock to Social media sites, leading to a dearth of trustworthy journalism and ethical, rules bound journalism.
Centralisation of Underlying Technologies
And it’s not just the content we consume that has been centralised: The underlying technologies of the internet have been centralised as well. The Internet Society shows that data centres, DNS, top level domains, SSL Certificates, Content Delivery Networks and Web Hosting have been significantly centralised as well.
In some of these protocols there is more variation within regions:
We highlight regional patterns that paint a richer picture of provider dependence and insularity than we can through centralization alone. For instance, the Commonwealth of Independent States (CIS) countries (formed following the dissolution of Soviet Union) exhibit comparatively low centralization, but depend highly on Russian providers. These patterns suggest possible political, historical, and linguistic undercurrents of provider dependence. In addition, the regional patterns we observe between layers of website infrastructure enable us to hypothesize about forces of influence driving centralization across multiple layers. For example, many countries are more insular in their choice of TLD given the limited technical implications of TLD choice. On the other extreme, certificate authority (CA) centralization is far more extreme than other layers due to popular web browsers trusting only a handful of CAs, nearly all of which are located in the United States.
Why is this? A lot of it has to do with the content providers wanting to gather as much data as possible on their users as well as being able to offer a fast, seamless experience for their users (so that they stay engaged on their platforms):
The more information you have about people, the more information you can feed your machine-learning process to build detailed profiles about your users. Understanding your users means you can predict what they will like, what they will emotionally engage with, and what will make them act. The more you can engage users, the longer they will use your service, enabling you to gather more information about them. Knowing what makes your users act allows you to convert views into purchases, increasing the provider’s economic power.
The virtuous cycle is related to the network effect. The value of a network is exponentially related to the number of people connected to the network. The value of the network increases as more people connect, because the information held within the network increases as more people connect.
Who will extract the value of those data? Those located in the center of the network can gather the most information as the network increases in size. They are able to take the most advantage of the virtuous cycle. In other words, the virtuous cycle and the network effect favor a smaller number of complex services. The virtuous cycle and network effect drive centralization.
[…]
How do content providers, such as social media services, increase user engagement when impatience increases and attention spans decrease? One way is to make their service faster. While there are many ways to make a service faster, two are of particular interest here.
First, move content closer to the user. […] Second, optimize the network path.
[…]
Moving content to the edge and optimizing the network path requires lots of resources and expertise. Like most other things, the devices, physical cabling, buildings, and talent required to build large computer networks are less expensive at scale
[…]
Over time, as the Internet has grown, new regulations and ways of doing business have been added, and new applications have been added “over the top,” the complexity of Internet systems and protocols has increased. As with any other complex ecosystem, specialization has set in. Almost no one knows how “the whole thing works” any longer.
How does this drive centralization?
Each feature—or change at large—increases complexity. The more complex a protocol is, the more “care and feeding” it requires. As a matter of course, larger organizations are more capable of hiring, training, and keeping the specialized engineering talent required to build and maintain these kinds of complex systems.
As more and more AI Slop is generated, debates are raging in many communities. Especially in the gaming and art communities, there is a lot of militant railing against AI art. In 2023 a study showed that people were worried about AI generated content, but unable to detect it:
research employed an online survey with 100 participants to collect quantitative data on their experiences and perceptions of AI-generated content. The findings indicate a range of trust levels in AI-generated content, with a general trend towards cautious acceptance. The results also reveal a gap between the participants’ perceived and actual abilities to distinguish between AI-generated content, underlining the need for improved media literacy and awareness initiatives. The thematic analysis of the respondent’s opinions on the ethical implications of AI-generated content underscored concerns about misinformation, bias, and a perceived lack of human essence.
However, politics has caught up and in the EU and US policy has arisen that force AI content generators to also support the creation of reliable detectors for the content they generate:
In this paper, we begin by highlighting an important new development: providers of AI content generators have new obligations to support the creation of reliable detectors for the content they generate. These new obligations arise mainly from the EU’s newly finalised AI Act, but they are enhanced by the US President’s recent Executive Order on AI, and by several considerations of self-interest. These new steps towards reliable detection mechanisms are by no means a panacea—but we argue they will usher in a new adversarial landscape, in which reliable methods for identifying AI-generated content are commonly available. In this landscape, many new questions arise for policymakers. Firstly, if reliable AI-content detection mechanisms are available, who should be required to use them? And how should they be used? We argue that new duties arise for media and Web search companies arise for media companies, and for Web search companies, in the deployment of AI-content detectors. Secondly, what broader regulation of the tech ecosystem will maximise the likelihood of reliable AI-content detectors? We argue for a range of new duties, relating to provenance-authentication protocols, open-source AI generators, and support for research and enforcement. Along the way, we consider how the production of AI-generated content relates to ‘free expression’, and discuss the important case of content that is generated jointly by humans and AIs.
This means that although people may or may not get better at spotting AI generated slop for what it is, work is being done on showing it up for us.
With the main content providers being inundated with AI trash and it being shown up for what it is, people will get bored of it. This gives other parties, those with the possibility of curating their content, possibilities for growth – offering high quality content that differentiates itself from other high quality content sites and especially from the central repositories of AI filled garbage. Existing parties and smaller new parties have an incentive to create and innovate. Of course that content will be used to fill the GPTs, but that should increases the accuracy of the GPTs that are paying attention (and who should be able to filter out AI slop better than any human could), who will hopefully redirect their answers to their sources – as legally explainability is becoming more and more relevant.
So together with the rise of anti Google sentiment and opportunities to DeGoogle leading to new (and de-shittified, working, and non-US!) search engines such as Qwant and SearXNG I see this as an excellent opportunity for the (relatively) little man to rise up again to diversify and decentralise the internet.
There’s a new player in the AI race, and it’s a whole country. Switzerland has just released Apertus, its open-source national Large Language Model (LLM) that it hopes would be an alternative to models offered by companies like OpenAI. Apertus, Latin for the world “open,” was developed by the Swiss Federal Technology Institute of Lausanne (EPFL), ETH Zurich and the Swiss National Supercomputing Centre (CSCS), all of which are public institutions.
“Currently, Apertus is the leading public AI model: a model built by public institutions, for the public interest. It is our best proof yet that AI can be a form of public infrastructure like highways, water, or electricity,” said Joshua Tan, a leading proponent in making AI a public infrastructure.
The Swiss institutions designed Apertus to be completely open, allowing users to inspect any part of its training process. In addition to the model itself, they released comprehensive documentation and source code of its training process, as well as the datasets they used. They built Apertus to comply with Swiss data protection and copyright laws, which makes it perhaps one of the better choices for companies that want to adhere to European regulations. The Swiss Bankers Association previously said that a homegrown LLM would have “great long-term potential,” since it will be able to better comply with Switzerland’s strict local data protection and bank secrecy rules. At the moment, Swiss banks are already using other AI models for their needs, so it remains to be seen whether they’ll switch to Apertus.
Anybody can use the new model: Researchers, hobbyists and even companies are welcome to build upon it and to tailor it for their needs. They can use it to create chatbots, translators and even educational or training tools, for instance. Apertus was trained on 15 trillion tokens across more than 1,000 languages, with 40 percent of the data in languages other than English, including Swiss German and Romansh. Switzerland’s announcement says the model was only trained on publicly available data, and its crawlers respected machine-readable opt-out requests when they came across them on websites. To note, AI companies like Perplexity have previously been accused of scraping websites and bypassing protocols meant to block their crawlers. Some AI companies have also been sued by news organizations and creatives for using their content to train their models without permission.
Apertus is currently available in two sizes with 8 billion and 70 billion parameters. It’s currently available via Swisscom, a Swiss information and communication technology company, or via Hugging Face.
Something strange has been happening on YouTube over the past few weeks. After being uploaded, some videos have been subtly augmented, their appearance changing without their creators doing anything. Viewers have noticed “extra punchy shadows,” “weirdly sharp edges,” and a smoothed-out look to footage that makes it look “like plastic.” Many people have come to the same conclusion: YouTube is using AI to tweak videos on its platform, without creators’ knowledge.
[…]
When I asked Google, YouTube’s parent company, about what’s happening to these videos, the spokesperson Allison Toh wrote, “We’re running an experiment on select YouTube Shorts that uses image enhancement technology to sharpen content. These enhancements are not done with generative AI.” But this is a tricky statement: “Generative AI” has no strict technical definition, and “image enhancement technology” could be anything. I asked for more detail about which technologies are being employed, and to what end. Toh said YouTube is “using traditional machine learning to unblur, denoise, and improve clarity in videos,” she told me. (It’s unknown whether the modified videos are being shown to all users or just some; tech companies will sometimes run limited tests of new features.)
Today we’re releasing a powerful update to Lumo that gives you a more capable privacy-first AI assistant offering faster, more thorough answers with improved awareness of recent events.
Guided by feedback from our community, we’ve been busy upgrading our models and adding GPUs, which we’ll continue to do thanks to the support of our Lumo Plus subscribers. Lumo 1.1 performs significantly better across the board than the first version of Lumo, so you can now use it more effectively for a variety of use cases:
Get help planning projects that require multiple steps — it will break down larger goals into smaller tasks
Ask complex questions and get more nuanced answers
Generate better code — Lumo is better at understanding your requests
Research current events or niche topics with better accuracy and fewer hallucinations thanks to improved web search
The latest upgrade brings more accurate responses with significantly less need for corrections or follow-up questions. Lumo now handles complex requests much more reliably and delivers the precise results you’re looking for.
In testing, Lumo’s performance has increased across several metrics:
Context: 170% improvement in context understanding so it can accurately answer questions based on your documents and data
Coding: 40% better ability to understand requests and generate correct code
Reasoning: Over 200% improvement in planning tasks, choosing the right tools such as web search, and working through complex multi-step problems
Most importantly, Lumo does all of this while respecting the confidentiality of your chats. Unlike every major AI platform, Lumo is open source and built to be private by design. It doesn’t keep any record of your chats, and your conversation history is secured with zero-access encryption so nobody else can see it and your data is never used to train the models. Lumo is the only AI where your conversations are actually private.
Unlike Big Tech AIs that spy on you, Lumo is an open source application that exclusively runs open source models. Open source is especially important in AI because it confirms that the applications and models are not being used nefariously to manipulate responses to fit a political narrative or secretly leak data. While the Lumo web client is already open source(new window), today we are also releasing the code for the mobile apps(new window). In line with Lumo being the most transparent and private AI, we have also published the Lumo security model so you can see how Lumo’s zero access encryption works and why nobody, not even Proton can access your conversation history.
This is a list of writing and formatting conventions typical of AI chatbots such as ChatGPT, with real examples taken from Wikipedia articles and drafts. Its purpose is to act as a field guide in helping detect undisclosed AI-generated content. Note that not all text featuring the following indicators is AI-generated; large language models (LLMs), which power AI-chatbots, have been trained on human writing, and humans might happen to have a writing style similar to that of an AI.
Gemini promises to simplify how you interact with your Android — fetching emails, summarizing meetings, pulling up files. But behind that helpful facade is an unprecedented level of centralized data collection, powered by a company known for privacy washing, (new window)misleadin(new window)g users(new window) about how their data is used, and that was hit with $2.9 billion in fines in 2024 alone, mostly for privacy violations and antitrust breaches.
Other people may see your sensitive information
Even more concerning, human reviewers may process your conversations. While Google claims these chats are disconnected from your Google account before review, that doesn’t mean much when a simple prompt like “Show me the email I sent yesterday” might return personal data like your name and phone number.
Your data may be shared beyond Google
Gemini may also share your data with third-party services. When Gemini interacts with other services, your data gets passed along and processed under their privacy policies, not just Google’s. Right now, Gemini mostly connects with Google services, but integrations with apps like WhatsApp and Spotify are already showing up. Once your data leaves Google, you cannot control where it goes or how long it’s kept.
The July 2025 update keeps Gemini connected without your consent
Before July, turning off Gemini Apps Activity automatically disabled all connected apps, so you couldn’t use Gemini to interact with other services unless you allowed data collection for AI training and human review. But Google’s July 7 update changed this behavior and now keeps Gemini connected to certain services — such as Phone, Messages, WhatsApp, and Utilities — even if activity tracking is off.
While this might sound like a privacy-conscious change — letting you use Gemini without contributing to AI training — it still raises serious concerns. Google has effectively preserved full functionality and ongoing access to your data, even after you’ve opted out.
Can you fully disable Gemini on Android?
No, and that’s by design.
[…]
How to turn off Gemini AI on Android
Open the Gemini app on your Android.
Tap your profile icon in the top-right corner.
Go to Gemini Apps Activity*.
Tap Turn off → Turn off and delete activity, and follow the prompts.
Select your profile icon again and go to Apps**.
Tap the toggle switch to prevent Gemini from interacting with Google apps and third-party services.
*Gemini Apps Activity is a setting that controls whether your interactions with Gemini are saved to your Google account and used to improve Google’s AI systems. When it’s on, your conversations may be reviewed by humans, stored for up to 3 years, and used for AI training. When it’s off, your data isn’t used for AI training, but it’s still stored for up to 72 hours so Google can process your requests and feedback.
**Apps are the Google apps and third-party services that Gemini can access to perform tasks on your behalf — like reading your Gmail, checking your Google Calendar schedule, retrieving documents from Google Drive, playing music via Spotify, or sending messages on your behalf via WhatsApp. When Gemini is connected to these apps, it can access your personal content to fulfill prompts, and that data may be processed by Google or shared with the third-party app according to their own privacy policies.
“The current draft,” Meta wrote in a confidential lobby paper, is a case of “regulatory overreach” that “poses a significant threat to AI innovation in the EU.”
It was early 2025, and the text Meta railed against was the second draft of the EU’s Code of Practice. The Code will put the EU’s AI Act into operation by outlining voluntary requirements for general-purpose AI, or models with many different societal applications (see Box 1).
Meta’s lobby message hit the right notes, as the second von der Leyen Commission has committed to slashing regulations to stimulate European ‘competitiveness’. An early casualty of this deregulatory drive was the EU’s AI Liability Directive, which would have allowed consumers to claim compensation for harms caused by AI.
And the Code may end up being another casualty. Meta’s top lobbyist said they would not sign unless there were significant changes. Google cast doubt on its participation.
But as this investigation by Corporate Europe Observatory and Lobby Control – based on insider interviews and analysis of lobby papers – reveals, Big Tech enjoyed structural advantages from early on in the process and – playing its cards well – successfully lobbied for a much weaker Code than could have been. That means weaker protection from potential structural biases and social harms caused by AI.
Potemkin participation: how civil society was sidelined
In a private meeting with the Commission in January 2025, Google “raised concerns about the process” of drafting the Code of Practice. The tech giant complained “model developers [were] heavily outweighed by other stakeholders”.
Only a superficial reading could support this. Over 1,000 of stakeholders expressed interest in participating to the EU’s AI Office, a newly created unit within the European Commission’s DG CNECT. Nearly four hundred organisations were approved.
But tech companies enjoyed far more access than others. Model providers – companies developing the large AI models the Code is expected to regulate – were invited to dedicated workshops with the working group chairs.
“This could be seen as a compromise,” Jimmy Farrell of the European think tank Pour Demain said. “On the one hand, they included civil society, which the AI Act did not make mandatory. On the other, they gave model providers direct access.”
Tech companies enjoyed far more access than others. Model providers were invited to dedicated workshops with the working group chairs.
Fifteen US companies, or nearly half of the total, were on the reported list of organisations invited to the model providers workshops. Among them, US tech giants Google, Microsoft, Meta, Apple, and Amazon.
Others included AI “start-ups” with multi-billion dollar valuations such as OpenAI, Anthropic, and Hugging Face, each of which receive Big Tech funding. Another, Softbank, is OpenAI’s lead partner for the US$500 billion Stargate investment fund.
In April, OpenAI dialed up its lobbying to water down the Code of Practice with a series of meetings with European politicians. Right: OpenAI’s main lobbyist Chris Lehane. Left: EU Commissioner Michael McGrath
EC – Audiovisual Service
Several European AI providers, which lobbied over the AI Act, were also involved. Some of these also partner with American tech firms, like the French Mistral AI or the Finnish SiloAI.
The participation of the other 350 organisations – which include rights advocates, civil society organisations, representatives of European corporations and SMEs, and academics – was more restricted. They had no access to the provider workshops, and despite a commitment to do so, sources said meeting minutes from the model providers workshops were not distributed to participants.
It put civil society, which participated in working group meetings and crowded plenaries, at a disadvantage. Opportunities for interaction during meetings were limited. Questions needed to be submitted beforehand through a platform called SLIDO, which others could then up-vote.
Normally, the AI Office would consider the top ten questions during meetings, although sources told us, “controversial questions would sometimes be side-stepped”. Participants could neither submit comments during meetings, nor unmute themselves.
[…]
In the absence of full list of individual participants, which she requested but not received, Pfister Fetz would “write down every name she saw on the screen” and look people up after, “to see if they were like-minded or not.”
Participants received little notice to review and comment on draft documents with short deadlines. Deadlines to apply for a speaking slot to discuss a document would come before said document had even been shared. The third draft of the Code was delayed for nearly a month, without communication from the AI Office, until one day, without notice, it landed in participants’ mailboxes.
[…]
A long-standing demand from civil society was a dedicated civil society workshop. It was only after the third severely watered down Code of Practice draft that such a workshop took place.
“They had many workshops with model providers, and only one at the end with civil society, when they told us there would only be minor changes possible,” van der Geest, the fundamental rights advocate, said. “It really shows how they see civil society input: as secondary at best.”
Partnering with Big Tech and the AI office: a conflict of interest?
A contract to support the AI Office in drafting the Code of Practice was awarded, under an existing framework contract, to a consortium of external consultants – Wavestone, Intellera, and the Centre for European Policy Studies (CEPS).
It was previously reported that the lead partner, the French firm Wavestone, advised companies on AI Act compliance, but “does not have [general purpose AI] model providers among its clients”.
But our investigation revealed that the consultants do have ties to model providers.
In 2023 Wavestone announced it had been “selected by Microsoft to support the deployment and accelerated adoption of Microsoft 365 Copilot as a generative artificial intelligence tool in French companies.”
This resulted in Wavestone receiving a “Microsoft Partner of the Year Award” at the end of 2024, when it already supported the AI Office in developing the Code. The consultancy also worked with Google Cloud and is an AWS partner.
The other consortium partners also had ties to GPAI model providers. The Italian consultancy Intellera was bought in April 2024 by Accenture and is now “Part of Accenture Group”. Accenture boasted at the start of 2025 that they were “a key partner” to a range of technology providers, including Amazon, Google, IBM, Microsoft, and NVIDIA – in other words, US general purpose model providers.
The third and final consortium partner, CEPS, counted all Big Tech among corporate members – including Apple, AWS, Google, Meta, Microsoft. At a rate of between €15,000 – €30,000 EUR (plus VAT) per year, members get “access to task forces” on EU policy and “input on CEPS research priorities”.
The problem is that these consultancy firms can hardly be expected to advise the Commission to take action that would negatively impact their own clients. The EU Financial Regulation states that the Commission should therefore reject a contractor where a conflicting interest “can affect or risk the capacity to perform the contract in an independent, impartial and objective manner”.
Also the 2022 framework contract under which the consortium was initially hired by the European Commission stipulated that “a contractor must take all the necessary measures to prevent any situation of conflict of interest.”
[…]
On key issues, the messaging of the US tech firms was well coordinated. Confidential lobby papers by Microsoft and Google, submitted to EU members states and seen by Corporate Europe Observatory and LobbyControl, echoed what Meta said publicly – that the Code’s requirements “go beyond the scope of the AI Act” and would “undermine” or “stifle” innovation.
It was a position carefully crafted to match the political focus on deregulation.
“The current Commission is trying to be innovation and business friendly, but is actually disproportionately benefiting Big Tech” said Risto Uuk, Head of EU Policy and Research from the Future of Life Institute.
Uuk, who curates a biweekly newsletter on the EU AI Act, added that “there is also a lot of pressure on the EU from the Trump administration not to enforce regulation.”
[…]
One of the most contentious topics has been the risk taxonomy. This determines the risks model providers will need to test for and mitigate. The second draft of the Code introduced a split between “systemic risks,” such as nuclear risks or a loss of human oversight, and a much weaker category of “additional risks for consideration”.
“Providers are mandated to identify and mitigate systemics risks,” Article 19’s Dinah van der Geest said, “but the second tier, including risks fundamental rights, democracy, or the environment, are optional for providers to follow.”
These risks are far from hypothetical. From Israeli mass surveillance and killing of Palestinians in Gaza, the dissemination of disinformation during elections including by far-right groups and foreign governments, to massive lay-offs of US federal government employees, generative AI is already used in countless problematic ways. In Europe, investigative journalism has exposed the widespread use of biased AI systems in welfare systems.
The introduction of a hierarchy in the risk taxonomy offered additional lobby opportunities. Both Google and Microsoft argued that “large-scale, illegal discrimination” needed to be bumped down to optional risks.
[…]
The tech giants got their way: in the third draft, large-scale, illegal discrimination was removed from the list of systemic risks, which are mandatory to check for, and categorised under “other types of risk for potential consideration”.
Like other fundamental rights violations, it now only needs to be checked for if “it can be reasonably foreseen” and if the risk is “specific to the high-impact capabilities” of the model.
“But what is foreseeable?” asked Article 19’s Dinah van der Geest. “It will be left up to the model providers to decide.”
[…]
At the AI Action Summit in Paris in February 2025, European Commission President Ursula von der Leyen had clearly drunk the AI Kool-Aid: “We want Europe to be one of the leading AI continents. And this means embracing a way of life where AI is everywhere.” She went on to paint AI as a silver bullet for almost every societal problem: “AI can help us boost our competitiveness, protect our security, shore up public health, and make access to knowledge and information more democratic.”
The Code of Practice seems to be only one of the first casualties of the Commission’s deregulatory offensive. With key rules on AI, data protection, and privacy up for review this year, the main beneficiaries are poised to be the corporate interests with endless lobbying resources.
The AI Action Summit marked a distinctive shift in the Commission’s discourse. Where previously the Commission paid at least lip-service to safeguarding fundamental rights when rolling out AI, it now largely abandoned that discourse talking about winning “the global race for AI” instead.
At the same summit, Henna Virkkunen, the Commissioner for Tech Sovereignty, was quick to parrot von der Leyen’s message, announcing that the AI Act would be implemented ‘innovation-friendly’, and after criticism from Meta and Google a week earlier, she promised that the Code of Practice would not create “any extra burden”.
Ursula von der Leyen at the AI Action Summit. In the background on the right Google CEO Sundar Pichai.
EC – Audiovisual Service
Big Tech companies have quickly caught on to the new deregulatory wind in Brussels. They have ramped up their already massive lobbying budgets and have practiced their talkingpoints about Europe’s ‘competitiveness’ and ‘over-regulation’.
The Code of Practice on General-Purpose AI seems to be only one of the first casualties of this deregulatory offensive. With key rules on AI, data protection, and privacy up for review this year, the main beneficiaries are poised to be the corporate interests with endless lobbying resources.
[…]
Big Tech cannot be seen as just another stakeholder. The Commission should safeguard the public interest from Big Tech influence. Instead of beating the deregulation drum, the Commission should now stand firm against the tech industry’s agenda and guarantee the protection of fundamental rights through an effective Code of Conduct.
Yesterday, I wrote about how YouTube is now using AI to guess your age. The idea is this: Rather than rely on the age attached to your account, YouTube analyzes your activity on its platform, and makes a determination based on how your activity corresponds to others users. If the AI thinks you’re an adult, you can continue on; if it thinks your behavior aligns with that of a teenage user, it’ll put restrictions and protections on your account.
Now, Google is expanding its AI age verification tools beyond just its video streaming platform, to other Google products as well. As with YouTube, Google is trialing this initial rollout with a small pool of users, and based on its results, will expand the test to more users down the line. But over the next few weeks, your Google Account may be subject to this new AI, whose only goal is to estimate how old you are.
That AI is trained to look for patterns of behavior across Google products associated with users under the age of 18. That includes the categories of information you might be searching for, or the types of videos you watch on YouTube. Google’s a little cagey on the details, but suffice it to say that the AI is likely snooping through most, if not all, of what you use Google and its products for.
Restrictions and protections on teen Google accounts
We do know some of the restrictions and protections Google plans to implement when it detects a user is under 18 years old. As I reported yesterday, that involves turning on YouTube’s Digital Wellbeing tools, such as reminders to stop watching videos, and, if it’s late, encouragements to go to bed. YouTube will also limit repetitive views of certain types of content.
In addition to these changes to YouTube, you’ll also find you can no longer access Timeline in Maps. Timeline saves your Google Maps history, so you can effectively travel back through time and see where you’ve been. It’s a cool feature, but Google restricts access to users 18 years of age or older. So, if the AI detects you’re underage, no Timeline for you.
Two Democratic members of Congress, Greg Casar (D-TX) and Rashida Tlaib (D-MI,) have introduced legislation in the US House of Representatives to ban the use of AI surveillance to set prices and wages.
During Delta’s Q2 earnings call last week, Delta’s president Glen Hauenstein said that the airline has already rolled out AI-controlled dynamic pricing for 3 percent of its customers and is aiming to have 20 percent of fares set using the system by the end of the year. Software biz Fetcherr supplies the pricing code to Delta and others in the industry, including Virgin Atlantic and WestJet.
“We’re in a heavy testing phase. We like what we see,” he told analysts. “We like it a lot, and we’re continuing to roll it out, but we’re going to take our time and make sure that the rollout is successful as opposed to trying to rush it and risk that there are unwanted answers in there.”
Delta’s move is nothing new. Many companies adjust prices depending on circumstances – the business plan for ride-hailing apps, for instance, is built around the idea that peak demand leads to peak prices. Supply and demand is a fundamental part of current economic thinking. Software that munges massive amounts of data just makes that process more efficient and instantaneous.
Nevertheless, the use of AI sparked an outcry, and politicians took interest. The new legislation, the Stop AI Price Gouging and Wage Fixing Act, wants to ban the use of advanced AI systems to analyse personal data in setting prices and wages.
“Giant corporations should not be allowed to jack up your prices or lower your wages using data they got spying on you,” said Casar. “Whether you know it or not, you may already be getting ripped off by corporations using your personal data to charge you more. This problem is only going to get worse, and Congress should act before this becomes a full blown crisis.”
The representatives want the FTC, the Equal Employment Opportunity Commission, and individual states to enforce the rules of the bill. The legislation would also allow private citizens to take action against companies using such practices.
Considering the US is otherwise completely happy to have AIs run around with no legislation around them (eg they absolutely HATE the EU AI Act and Digital Services Act), I hope they get this through, but doubt it.
The EU is asking for feedback on how the AI act classifies and handles high risk AI systems
This consultation is targeted to stakeholders of different categories. These categories include, but are not limited to, providers and deployers of (high-risk) AI systems, other industry organisations, as well as academia, other independent experts, civil society organisations, and public authorities.
[…]
The purpose of the present targeted stakeholder consultation is to collect input from stakeholders on practical examples of AI systems and issues to be clarified in the Commission’s guidelines on the classification of high-risk AI systems and future guidelines on high-risk requirements and obligations, as well as responsibilities along the AI value chain.
As not all questions may be relevant for all stakeholders, respondents may reply only to the section(s) and the questions they would like. Respondents are encouraged to provide explanations and practical cases as a part of their responses to support the practical usefulness of the guidelines.
The targeted consultation is available in English only and will be open for 6 weeks starting on 6 June until 18 July 2025.
So if you are at all interested in how AI systems will be allowed to impact your life (also as a consumer!), join in and let the EU know what you think.
A federal judge sided with Meta on Wednesday in a lawsuit brought against the company by 13 book authors, including Sarah Silverman, that alleged the company had illegally trained its AI models on their copyrighted works.
Federal Judge Vince Chhabria issued a summary judgment — meaning the judge was able to decide on the case without sending it to a jury — in favor of Meta, finding that the company’s training of AI models on copyrighted books in this case fell under the “fair use” doctrine of copyright law and thus was legal.
The decision comes just a few days after a federal judge sided with Anthropic in a similar lawsuit. Together, these cases are shaping up to be a win for the tech industry, which has spent years in legal battles with media companies arguing that training AI models on copyrighted works is fair use.
However, these decisions aren’t the sweeping wins some companies hoped for — both judges noted that their cases were limited in scope.
Judge Chhabria made clear that this decision does not mean that all AI model training on copyrighted works is legal, but rather that the plaintiffs in this case “made the wrong arguments” and failed to develop sufficient evidence in support of the right ones.
“This ruling does not stand for the proposition that Meta’s use of copyrighted materials to train its language models is lawful,” Judge Chhabria said in his decision. Later, he said, “In cases involving uses like Meta’s, it seems like the plaintiffs will often win, at least where those cases have better-developed records on the market effects of the defendant’s use.”
Judge Chhabria ruled that Meta’s use of copyrighted works in this case was transformative — meaning the company’s AI models did not merely reproduce the authors’ books.
Furthermore, the plaintiffs failed to convince the judge that Meta’s copying of the books harmed the market for those authors, which is a key factor in determining whether copyright law has been violated.
“The plaintiffs presented no meaningful evidence on market dilution at all,” said Judge Chhabria.
I have covered the Silverman et al case before here several times and it was retarded on all levels, which is why it was thrown out against OpenAI. Most importantly is that this judge and the judge in the Anthropic case rule that AI’s use of ingested works is transformative and not a copy. Just like when you read a book, you can recall bits of it for inspiration, but you don’t (well, most people don’t!) remember word for word what you read.
A federal judge in San Francisco ruled late on Monday that Anthropic’s use of books without permission to train its artificial intelligence system was legal under U.S. copyright law.
Siding with tech companies on a pivotal question for the AI industry, U.S. District Judge William Alsup said Anthropic made “fair use”
, opens new tab of books by writers Andrea Bartz, Charles Graeber and Kirk Wallace Johnson to train its Claude large language model.
Alsup also said, however, that Anthropic’s copying and storage of more than 7 million pirated books in a “central library” infringed the authors’ copyrights and was not fair use. The judge has ordered a trial in December to determine how much Anthropic owes for the infringement.
U.S. copyright law says that willful copyright infringement can justify statutory damages of up to $150,000 per work.
An Anthropic spokesperson said the company was pleased that the court recognized its AI training was “transformative” and “consistent with copyright’s purpose in enabling creativity and fostering scientific progress.”
The writers filed the proposed class action against Anthropic last year, arguing that the company, which is backed by Amazon (AMZN.O) and Alphabet (GOOGL.O), used pirated versions of their books without permission or compensation to teach Claude to respond to human prompts.
The proposed class action is one of several lawsuits brought by authors, news outlets and other copyright owners against companies including OpenAI, Microsoft (MSFT.O) and Meta Platforms (META.O) over their AI training.
The doctrine of fair use allows the use of copyrighted works without the copyright owner’s permission in some circumstances.
Fair use is a key legal defense for the tech companies, and Alsup’s decision is the first to address it in the context of generative AI.
AI companies argue their systems make fair use of copyrighted material to create new, transformative content, and that being forced to pay copyright holders for their work could hamstring the burgeoning AI industry.
Anthropic told the court that it made fair use of the books and that U.S. copyright law “not only allows, but encourages” its AI training because it promotes human creativity. The company said its system copied the books to “study Plaintiffs’ writing, extract uncopyrightable information from it, and use what it learned to create revolutionary technology.”
Copyright owners say that AI companies are unlawfully copying their work to generate competing content that threatens their livelihoods.
Alsup agreed with Anthropic on Monday that its training was “exceedingly transformative.”
“Like any reader aspiring to be a writer, Anthropic’s LLMs trained upon works not to race ahead and replicate or supplant them — but to turn a hard corner and create something different,” Alsup said.
Alsup also said, however, that Anthropic violated the authors’ rights by saving pirated copies of their books as part of a “central library of all the books in the world” that would not necessarily be used for AI training.
Anthropic and other prominent AI companies including OpenAI and Meta Platforms have been accused of downloading pirated digital copies of millions of books to train their systems.
Anthropic had told Alsup in a court filing that the source of its books was irrelevant to fair use.
“This order doubts that any accused infringer could ever meet its burden of explaining why downloading source copies from pirate sites that it could have purchased or otherwise accessed lawfully was itself reasonably necessary to any subsequent fair use,” Alsup said on Monday.
This makes sense to me. The training itself is much like any person reading a book and using that as inspiration. It does not copy it. And any reader should have bought (or borrowed) the book. Why Anthropic apparently used pirated copies and why they kept a seperate library of the books is beyond me .
MiniMax, an AI firm based in Shanghai, has released an open source reasoning model that challenges Chinese rival DeepSeek and US-based Anthropic, OpenAI, and Google in terms of performance and cost.
“In complex, productivity-oriented scenarios, M1’s capabilities are top-tier among open source models, surpassing domestic closed-source models and approaching the leading overseas models, all while offering the industry’s best cost-effectiveness,” MiniMax boasts in a blog post.
According to the blog post, M1 is competitive with OpenAI o3, Gemini 2.5 Pro, Claude 4 Opus, DeepSeek R1, DeepSeek R1-0528, and Qwen3-235B on various benchmarks (AIME 2024, LiveCodeBench, SWE-bench Verified, Tau-bench, and MRCR), coming in behind some models and ahead of others to varying degrees. As always, take vendor-supplied benchmark results with a grain of salt, but the source code is available on GitHub should you wish to confirm its performance independently.
But MiniMax makes clear that it’s trying to supplant DeepSeek as the leading industry disruptor by noting that its context window (the amount of input it can handle) is one million tokens, which rivals Google Gemini 2.5 Pro and is eight times the capacity of DeepSeek R1.
[…]
Backed by Alibaba Group, Tencent, and IDG Capital, MiniMax claims its Lightning Attention mechanism, a way to calculate attention matrices that improves both training and inference efficiency, gives its M1 model an advantage when computing long context inputs and when trying to reason.
“For example, when performing deep reasoning with 80,000 tokens, it requires only about 30 percent of the computing power of DeepSeek R1,” the company claims. “This feature gives us a substantial computational efficiency advantage in both training and inference.”
This more efficient computation method, in conjunction with an improved reinforcement learning algorithm called CISPO (detailed in M1’s technical report [PDF]), translates to lower computing costs.
“The entire reinforcement learning phase used only 512 [Nvidia] H800s for three weeks, with a rental cost of just $537,400,” MiniMax claims. “This is an order of magnitude less than initially anticipated.”
A few weeks ago Walled Culture explored how the leaders in the generative AI world are trying to influence the future legal norms for this field. In the face of a powerful new form of an old technology – AI itself has been around for over 50 years – those are certainly needed. Governments around the world know this too: they are grappling with the new issues that large language models (LLMs), generative AI, and chatbots are raising every day, not least in the realm of copyright. For example, one EU body, EUIPO, has published a 436-page study “The Development Of Generative Artificial Intelligence From A Copyright Perspective”. Similarly, the US Copyright Office has produced a three-part report that “analyzes copyright law and policy issues raised by artificial intelligence”. The first two parts were on Digital Replicas and Copyrightability. The last part, just released in a pre-publication form, is on Generative AI Training. It is one of the best introductions to that field, and not too long – only 113 pages.
Alongside these government moves to understand this area, there are of course efforts by the copyright industry itself to shape the legal landscape of generative AI. Back in March, Walled Culture wrote about a UK campaign called “Make It Fair”, and now there is a similar attempt to reduce everything to a slogan by a European coalition of “authors, performers, publishers, producers, and cultural enterprises”. The new campaign is called “Stay True to the Act” – the Act in question being the EU Artificial Intelligence Act. The main document explaining the latest catchphrase comes from the European Publishers Council, and provides numerous insights into the industry’s thinking here. It comes as no surprise to read the following:
Let’s be clear: our content—paid for through huge editorial investments—is being ingested by AI systems without our consent and without compensation. This is not innovation; it is copyright theft.
As Walled Culture explained in March, that’s not true: material is not stolen, it is simply analysed as part of the AI training. Analysing texts or images is about knowledge acquisition, not copyright infringement.
In the Stay True to the Act document, this tired old trope of “copyright theft” leads naturally to another obsession of the copyright world: a demand for what it calls “fair licences”. Walled Culture the book (free digital versions available) noted that this is something that the industry has constantly pushed for. Back in 2013, a series of ‘Licences for Europe’ stakeholder dialogues were held, for example. They were based on the assumption that modernising copyright meant bringing in licensing for everything that occurred online. If a call for yet more licensing is old hat, the campaign’s next point is a novel one:
AI systems don’t just scrape our articles—they also capture our website layouts, our user activity, and data that is critical to our advertising models.
It’s hard to understand what the problem is here, other than the general concern about bots visiting and scraping sites – something that is indeed getting out of hand in terms of volume and impact on servers. It’s not as if generative AI cares about Web site design, and it’s hard to see what data about advertising models can be gleaned. It’s also worth nothing that this is the only point where members of the general public are mentioned in the entire document, albeit only as “users”. When it comes to copyright, publishers don’t care about the rights or the opinions of ordinary citizens. Publishers do care about journalists, at least to the following extent:
AI-generated content floods the market with synthetic articles built from our journalism. Search engines like Google’s and chatbots like ChatGPT, increasingly serve AI summaries which is wiping out the traffic we rely on, especially from dominant players.
The statement that publishers “rely on” traffic from search engines is an unexpected admission. The industry’s main argument for the “link tax” that is now part of the EU Copyright Directive was that search engines were giving nothing significant back when their search results linked to the original article, and should therefore pay something. Now publishers are admitting that the traffic from search engines is something they “rely on”. Alongside that significant U-turn on the part of the publishers, there is a serious general point about journalism in the age of AI:
These [generative AI] tools don’t create journalism. They don’t do fact-checking, hold power to account, or verify sources. They operate with no editorial standards, no legal liability—and no investment in the public interest. And yet, without urgent action, there is a danger they will replace us in the digital experience.
This is an extremely important issue, and the publishers are right to flag it up. But demanding yet more licensing agreements with AI companies is not the answer. Even if the additional monies were all spent on bolstering reporting – a big “if” – the sums involved would be too small to matter. Licensing does not address the root problem, which is that important kinds of journalism need to be supported and promoted in new ways.
One solution is that adopted by the Guardian newspaper, which is funded by its readers who want to read and sustain high-quality journalism. This could be part of a wider move to the “true fans” idea discussed in Walled Culture the book. Another approach is for more government support – at arm’s length – for journalism of the kind produced by the BBC, say, where high editorial standards ensure that fact-checking and source verification are routinely carried out – and budgeted for.
Complementing such direct support for journalism, new laws are needed to disincentivise the creation of misleading fake news stories and outright lies that increasingly drown out the truth. The Stay True to the Act document suggests “platform liability for AI-generated content”, and that could be part of the answer; but the end users who produce such material should also face consequences for their actions.
In its concluding section, “3-Pillar Model for the Future – and Why Licensing is Essential”, the document bemoans the fact that advertising revenue is “declining in a distorted market dominated by Google and Meta”. That is true, but only because publishers have lazily acquiesced in an adtech model based on real-time bidding for online ads powered by the constant surveillance of visitors to Web sites. A better approach is to use contextual advertising, where ads are shown according to the material being viewed. This not only requires no intrusive monitoring of the personal data of visitors, but has been found to be more effective than the current approach.
Moreover, in a nice irony, the new generation of LLMs make providing contextual advertising extremely easy, since they can analyse and categorise online material rapidly for the purpose of choosing suitable ads to be displayed. Sadly, publishers’ visceral hatred of the new AI technologies means that they are unable to see these kind of opportunities alongside the threats.
Fans reading through the romance novel Darkhollow Academy: Year 2 got a nasty surprise last week in chapter 3. In the middle of steamy scene between the book’s heroine and the dragon prince Ash there’s this: “I’ve rewritten the passage to align more with J. Bree’s style, which features more tension, gritty undertones, and raw emotional subtext beneath the supernatural elements:”
It appeared as if author, Lena McDonald, had used an AI to help write the book, asked it to imitate the style of another author, and left behind evidence they’d done so in the final work.
[…] On Monday, Microsoft announced that it will begin offering access to Grok AI, specifically Grok 3 and Grok 3 Mini, through its Azure AI Foundry. For the uninitiated, Grok AI is a product of xAI, which is owned by the same guy whose social media site, X, is reportedly taking money from terrorist groups—Elon Musk. The partnership, to be clear, is nowhere near the level of closeness we’ve seen between Microsoft and OpenAI, which is almost entirely powering the company’s push toward generative AI, but it’s still a step in a more, um, diverse direction.
And that partnership, however small, comes with some pretty awful timing. Just a few days prior to Microsoft’s announcement that it was starting to incorporate Grok into its Azure AI Foundry, Grok was at the center of some controversy after spiraling into Holocaust denial and peddling claims of “white genocide.” The worst part about all of that (outside of the, you know, Holocaust denial part) is that Musk’s AI might not have just randomly hallucinated all of that problematic misinformation.
As noted by the New York Times, Grok only started espousing claims of “white genocide” after an instance of the AI largely debunking a post from Musk himself suggesting white farmers are being targeted as part of a genocide in South Africa. A day after said debunk, Grok was seemingly obsessed with the idea of white genocide, bringing it up in relation to queries that had absolutely nothing to do with the idea at all. During the same time, Grok also started to cast doubt on the number of Jews killed during the Holocaust, stating it was “skeptical” about the figure. xAI has since blamed the Holocaust denialism on a “programming error,” but it’s hard not to greet that claim with some skepticism of my own.
Marking a breakthrough in the field of brain-computer interfaces (BCIs), a team of researchers from UC Berkeley and UC San Francisco has unlocked a way to restore naturalistic speech for people with severe paralysis.
This work solves the long-standing challenge of latency in speech neuroprostheses, the time lag between when a subject attempts to speak and when sound is produced. Using recent advances in artificial intelligence-based modeling, the researchers developed a streaming method that synthesizes brain signals into audible speech in near-real time.
As reported today in Nature Neuroscience, this technology represents a critical step toward enabling communication for people who have lost the ability to speak. […]
we found that we could decode neural data and, for the first time, enable near-synchronous voice streaming. The result is more naturalistic, fluent speech synthesis.
[…]
The researchers also showed that their approach can work well with a variety of other brain sensing interfaces, including microelectrode arrays (MEAs) in which electrodes penetrate the brain’s surface, or non-invasive recordings (sEMG) that use sensors on the face to measure muscle activity.
“By demonstrating accurate brain-to-voice synthesis on other silent-speech datasets, we showed that this technique is not limited to one specific type of device,” said Kaylo Littlejohn, Ph.D. student at UC Berkeley’s Department of Electrical Engineering and Computer Sciences and co-lead author of the study. “The same algorithm can be used across different modalities provided a good signal is there.”
[…]
the neuroprosthesis works by sampling neural data from the motor cortex, the part of the brain that controls speech production, then uses AI to decode brain function into speech.
“We are essentially intercepting signals where the thought is translated into articulation and in the middle of that motor control,” he said. “So what we’re decoding is after a thought has happened, after we’ve decided what to say, after we’ve decided what words to use and how to move our vocal-tract muscles.”
