Leaders of many organizations are urging their teams to adopt agentic AI to improve efficiency, but are finding it hard to achieve any benefit. Managers attempting to add AI agents to existing human teams may find that bots fail to faithfully follow their instructions, return pointless or obvious results or burn precious time and resources spinning on tasks that older, simpler systems could have accomplished just as well.
The technical innovators getting the most out of AI are finding that the technology can be remarkably human in its behavior. And the more groups of AI agents are given tasks that require cooperation and collaboration, the more those human-like dynamics emerge.
Our research suggests that, because of how directly they seem to apply to hybrid teams of human and digital workers, the most effective leaders in the coming years may still be those who excel at understanding the timeworn principles of human management.
We have spent years studying the risks and opportunities for organizations adopting AI. Our 2025 book, Rewiring Democracy, examines lessons from AI adoption in government institutions and civil society worldwide. In it, we identify where the technology has made the biggest impact and where it fails to make a difference. Today, we see many of the organizations we’ve studied taking another shot at AI adoption—this time, with agentic tools. While generative AI generates, agentic AI acts and achieves goals such as automating supply chain processes, making data-driven investment decisions or managing complex project workflows. The cutting edge of AI development research is starting to reveal what works best in this new paradigm.
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Key Takeaways
Managers of hybrid teams can apply these ideas to design their own complex systems of human and digital workers:
DELEGATE.
Analyze the tasks in your workflows so that you can design a division of labour that plays to the strength of each of your resources. Entrust your most experienced humans with the roles that require context and judgment and entrust AI models with the tasks that need to be done quickly or benefit from extreme parallelization.
If you’re building a hybrid customer service organization, let AIs handle tasks like eliciting pertinent information from customers and suggesting common solutions. But always escalate to human representatives to resolve unique situations and offer accommodations, especially when doing so can carry legal obligations and financial ramifications. To help them work together well, task the AI agents with preparing concise briefs compiling the case history and potential resolutions to help humans jump into the conversation.
ITERATE.
AIs will likely underperform your top human team members when it comes to solving novel problems in the fields in which they are expert. But AI agents’ speed and parallelization still make them valuable partners. Look for ways to augment human-led explorations of new territory with agentic AI scouting teams that can explore many paths for them in advance.
Hybrid software development teams will especially benefit from this strategy. Agentic coding AI systems are capable of building apps, autonomously making improvements to and bug-fixing their code to meet a spec. But without humans in the loop, they can fall into rabbit holes. Examples abound of AI-generated code that might appear to satisfy specified requirements, but diverges from products that meet organizational requirements for security, integration or user experiences that humans would truly desire. Take advantage of the fast iteration of AI programmers to test different solutions, but make sure your human team is checking its work and redirecting the AI when needed.
SHARE.
Make sure each of your hybrid team’s outputs are accessible to each other so that they can benefit from each others’ work products. Make sure workers doing hand-offs write down clear instructions with enough context that either a human colleague or AI model could follow. Anthropic found that AI teams benefited from clearly communicating their work to each other, and the same will be true of communication between humans and AI in hybrid teams.
MEASURE AND IMPROVE.
Organizations should always strive to grow the capabilities of their human team members over time. Assume that the capabilities and behaviors of your AI team members will change over time, too, but at a much faster rate. So will the ways the humans and AIs interact together. Make sure to understand how they are performing individually and together at the task level, and plan to experiment with the roles you ask AI workers to take on as the technology evolves.
An important example of this comes from medical imaging. Harvard Medical School researchers have found that hybrid AI-physician teams have wildly varying performance as diagnosticians. The problem wasn’t necessarily that the AI has poor or inconsistent performance; what mattered was the interaction between person and machine. Different doctors’ diagnostic performance benefited—or suffered—at different levels when they used AI tools. Being able to measure and optimize those interactions, perhaps at the individual level, will be critical to hybrid organizations.
In Closing
We are in a phase of AI technology where the best performance is going to come from mixed teams of humans and AIs working together. Managing those teams is not going to be the same as we’ve grown used to, but the hard-won lessons of decades past still have a lot to offer.
This essay was written with Nathan E. Sanders, and originally appeared in Rotman Management Magazine.
However, the Swedish music trade body has excluded the song from the official chart after learning it was AI-generated.
Spotify Wrapped is taking over our feeds, but you don’t have outsource your relationship with music to AI | Liz Pelly
Read more
“Jacub’s track has been excluded from Sweden’s official chart, Sverigetopplistan, which is compiled by IFPI Sweden. While the song appears on Spotify’s own charts, it does not qualify for inclusion on the official chart under the current rules,” said an IFPI Sweden spokesperson.
Ludvig Werber, IFPI Sweden’s chief executive, said: “Our rule is that if it is a song that is mainly AI-generated, it does not have the right to be on the top list.”
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IFPI Sweden acted after an investigative journalist, Emanuel Karlsten, revealed the song was registered to a Danish music publisher called Stellar and that two of the credited rights holders worked in the company’s AI department.
“What emerges is a picture of a music publisher that wants to experiment with new music and new kinds of artists. Who likes to push the limits of the audience’s tolerance threshold for artificial music and artificial artists,” wrote Karlsten.
In a statement, Stellar said: “The artist Jacub’s voice and parts of the music are generated with the help of AI as a tool in our creative process.”
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Spotify does not require music to be labelled as AI-generated, but has been cracking down on AI-made spam tracks as every play more than 30 seconds long generates a royalty for the scammer behind it – and dilutes payments to legitimate artists.
Jacub is not the first AI artist to score a hit with audiences. A “band” called the Velvet Sundown amassed more than 1m streams on Spotify last year before it emerged the group was AI-generated, including its promotional images and backstory as well as the music. Its most popular song has now accumulated 4m streams on the platform.
In other news, they have banned the use of synthesisers, DJs and autotune from the IFPI charts as well. Oh no, they didn’t. It will just take them a few decades to catch up again.
Warhammer maker Games Workshop has banned the use of AI in its content production and its design process, insisting that none of its senior managers are currently excited about the technology.
Delivering the UK company’s impressive financial results, CEO Kevin Rountree addressed the issue of AI and how Games Workshop is handling it. He said GW staff are barred from using it to actually produce anything, but admitted a “few” senior managers are experimenting with it.
Rountree said AI was “a very broad topic and to be honest I’m not an expert on it,” then went on to lay down the company line:
“We do have a few senior managers that are [experts on AI]: none are that excited about it yet. We have agreed an internal policy to guide us all, which is currently very cautious e.g. we do not allow AI generated content or AI to be used in our design processes or its unauthorised use outside of GW including in any of our competitions. We also have to monitor and protect ourselves from a data compliance, security and governance perspective, the AI or machine learning engines seem to be automatically included on our phones or laptops whether we like it or not.
“We are allowing those few senior managers to continue to be inquisitive about the technology. We have also agreed we will be maintaining a strong commitment to protect our intellectual property and respect our human creators. In the period reported, we continued to invest in our Warhammer Studio — hiring more creatives in multiple disciplines from concepting and art to writing and sculpting. Talented and passionate individuals that make Warhammer the rich, evocative IP that our hobbyists and we all love.”
A bit sad that they have to go and ban it. You wonder if they are able to use a computer at all, or do they give hand painted stuff to the new fangled thing they call a printers?
my friend was there took pics of it as it was happening police took the guy away in handcuffs. Hazmat had to be called to sanitize the area. WTF! stay safe friends antis are unhinged and becoming concerning/unlawful.
Photos 5&6 are ai from photos with their face burred out. :/ Im probly pressing charges and filing a nco
I shall repair the piece, alot actually went into this install formatting, cropping and the hand cutting/ hanging etc. The subject matter was very personal.
Its NOT ok to destroy artwork you dont agree with!!
What kind of world are we living in that someone thinks that this is OK?! IMHO it’s quite performative art itself and I hope this guy manages to ride the wave of fame this gives him!
When you use an AI service, you’re handing over your thoughts in plaintext. The operator stores them, trains on them, and–inevitably–will monetize them. You get a response; they get everything.
Confer works differently. In the previous post, we described how Confer encrypts your chat history with keys that never leave your devices. The remaining piece to consider is inference—the moment your prompt reaches an LLM and a response comes back.
Traditionally, end-to-end encryption works when the endpoints are devices under the control of a conversation’s participants. However, AI inference requires a server with GPUs to be an endpoint in the conversation. Someone has to run that server, but we want to prevent the people who are running it (us) from seeing prompts or the responses.
Confidential computing
This is the domain of confidential computing. Confidential computing uses hardware-enforced isolation to run code in a Trusted Execution Environment (TEE). The host machine provides CPU, memory, and power, but cannot access the TEE’s memory or execution state.
LLMs are fundamentally stateless—input in, output out—which makes them ideal for this environment. For Confer, we run inference inside a confidential VM. Your prompts are encrypted from your device directly into the TEE using Noise Pipes, processed there, and responses are encrypted back. The host never sees plaintext.
But this raises an obvious concern: even if we have encrypted pipes in and out of an encrypted environment, it really matters what is running inside that environment. The client needs assurance that the code running is actually doing what it claims.
The enshittification of GPT didn’t take long, did it?
Google is introducing new personalised advertising into its AI shopping tools, as it seeks to make money from the hundreds of millions of people who use its chatbot for free and gain market share from rival OpenAI.
Advertisers will be able to present exclusive offers to shoppers who are preparing to buy an item through Google’s AI mode, which is powered by its Gemini model, the Alphabet-owned tech giant announced on Sunday.
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It also represents a move away from the tech giant’s traditional ‘sponsored’ ad placements in search results, which generate tens of billions of dollars for the company but has come under threat by the rise of AI chatbots.
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“It essentially gives retailers the flexibility to deliver value to people shopping in AI mode, whether that’s a lower price, a special bundle or free shipping. In the moment, it matters most . . . to just close the sale,”
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AI groups, including OpenAI, Microsoft and Perplexity, have rushed to launch ecommerce features in their chatbots over the past year as they hunt for new ways to generate revenue from their popular but costly AI products.
OpenAI has been rolling out its checkout feature, first reported by the FT, which sees the AI start-up take a cut of the sales made on ChatGPT.
Microsoft launched its Copilot Checkout on Thursday, which also provides users with recommendations and checkout in its AI chats. The group said shopping through Copilot led to 53 per cent more purchases within 30 minutes of interaction compared to those without.
Google also introduced a “universal commerce protocol”, which it said would enable shopping agents to research products and make purchases without leaving its platform. The protocol was developed with large retailers and marketplaces including Walmart, Target and Shopify.
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Google’s new ads feature will make use of the contextual information from peoples’ conversation with the chatbot in AI mode, and trigger offers on relevant products that user have clicked on.
Retailers can set up offers they want to be available, with Google then using AI to determine when it is best to display the deal to a potential customer.
Srinivasan said Google was “initially focusing on discounts for the pilot and will expand to support the creation of offers with other attributes that help shoppers prioritise value over price alone, such as bundles and free shipping”.
If you’d like your operating system to go back to being an operating system, check out RemoveWindowsAI. This free script changes various registry keys to disable AI features including Copilot, Recall, and the Copilot integrations in applications including Edge, Paint, and Notepad. Using various workarounds , it then configures Windows Update to not install those updates again (the documentation breaks the process down, if you’re interested).
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To start the script you will need to copy a command from the Github page for RemoveWindowsAI and paste it into your PowerShell window (I’m not including the command directly here in case it changes in the future). Once you do, the user interface will show up, allowing you to choose which AI features you want to disable. Make your choices and watch the changes take place in the PowerShell window.
Samsung is heading into CES 2026 with a familiar message wrapped in a slightly stranger package. You see, the company plans to unveil an updated lineup of kitchen appliances, led by new versions of its Bespoke AI refrigerator, wine cellar, slide in range, and over the range microwaves. What makes this year different is not the stainless finish or the tighter installation tolerances. It is the decision to push Google Gemini directly into the kitchen, starting with a refrigerator that can see what you eat and tell the cloud about it. Yes, really.
At the center of the announcement is the latest Bespoke AI Refrigerator Family Hub from Samsung Electronics. Samsung says this model upgrades its existing AI Vision system with functionality built using Google Gemini, marking the first time Gemini is being integrated into a refrigerator. Previously, the system could recognize a limited number of fresh and pre registered foods locally. The new version is designed to identify more items automatically, including processed foods that no longer require manual setup and leftovers stored in personal containers.
On paper, that sounds convenient. A fridge that knows what is inside it, keeps an updated inventory, and helps manage groceries without constant user input is an idea appliance makers have chased for years. Samsung says more accurate ingredient recognition should make food tracking clearer and easier, while unlocking new use cases around meal planning and personalization. Whether that translates into daily value or becomes another ignored dashboard remains an open question.
Samsung is also extending the same vision based approach to its new Bespoke AI Wine Cellar. A camera mounted inside the unit scans bottle labels as wine is added or removed, tracking inventory through the SmartThings AI Wine Manager. The system knows which shelf each bottle sits on and can surface pairing suggestions based on what is currently stored. For collectors with larger wine inventories, this could genuinely save time. For everyone else, it may feel like a high tech solution searching for a problem.
The elephant in the room is cloud dependency. These AI features are built in collaboration with Google Cloud, which raises predictable questions about data handling, long term support, and what happens when services change or are discontinued. A refrigerator is expected to last many years. Cloud based AI services do not have the same track record. Samsung has not detailed how much processing happens locally versus in the cloud, nor how users can limit or disable data sharing if they choose.
iFixit, the internet’s go-to for repair guides and spare parts, just launched a new mobile app with what sounds like a genuinely useful AI chatbot.
Starting today, iOS and Android users can download the iFixit app and chat directly with the new FixBot to get curated expert advice on how to fix everything from a cracked phone screen to a faulty dishwasher.
The team at iFixit says it spent two years building the chatbot, which utilizes a combination of AI models for its language, voice, and vision capabilities. What makes FixBot stand out from a general chatbot like ChatGPT or Gemini is its laser focus on repairs. FixBot won’t answer questions that are not about fixing things, and it’s trained on iFixit’s 125,000 repair guides, community forums, and a huge repository of PDF manuals.
To use the bot, users can type or vocally explain their issue to the bot, or they can even just snap a photo of whatever needs fixing. FixBot will try to identify the device and model, then ask follow-up questions until it figures out the problem. The bot will then walk users through a step-by-step repair, pulling answers from the iFixIt library, even if that means surfacing something buried on page 500 of a PDF manual. It will also provide links to buy the spare parts you need. Along the way, users can ask FixBot questions. Its voice command features are also designed to help anyone who’s elbow-deep in a repair and can’t reach their phone.
These days, the most important meeting attendee isn’t a person: It’s the AI notetaker.
This system assigns action items and determines the importance of what is said. If it becomes necessary to revisit the facts of the meeting, its summary is treated as impartial evidence.
But clever meeting attendees can manipulate this system’s record by speaking more to what the underlying AI weights for summarization and importance than to their colleagues. As a result, you can expect some meeting attendees to use language more likely to be captured in summaries, timing their interventions strategically, repeating key points, and employing formulaic phrasing that AI models are more likely to pick up on. Welcome to the world of AI summarization optimization (AISO).
Optimizing for algorithmic manipulation
AI summarization optimization has a well-known precursor: SEO.
Search-engine optimization is as old as the World Wide Web. The idea is straightforward: Search engines scour the internet digesting every possible page, with the goal of serving the best results to every possible query. The objective for a content creator, company, or cause is to optimize for the algorithm search engines have developed to determine their webpage rankings for those queries. That requires writing for two audiences at once: human readers and the search-engine crawlers indexing content. Techniques to do this effectively are passed around like trade secrets, and a $75 billion industry offers SEO services to organizations of all sizes.
More recently, researchers have documented techniques for influencing AI responses, including large-language model optimization (LLMO) and generative engine optimization (GEO). Tricks include content optimization — adding citations and statistics — and adversarial approaches: using specially crafted text sequences. These techniques often target sources that LLMs heavily reference, such as Reddit, which is claimed to be cited in 40% of AI-generated responses. The effectiveness and real-world applicability of these methods remains limited and largely experimental, although there is substantial evidence that countries such as Russia are activelypursuingthis.
AI summarization optimization follows the same logic on a smaller scale. Human participants in a meeting may want a certain fact highlighted in the record, or their perspective to be reflected as the authoritative one. Rather than persuading colleagues directly, they adapt their speech for the notetaker that will later define the “official” summary. For example:
“The main factor in last quarter’s delay was supply chain disruption.”
“The key outcome was overwhelmingly positive client feedback.”
“Our takeaway here is in alignment moving forward.”
“What matters here is the efficiency gains, not the temporary cost overrun.”
The techniques are subtle. They employ high-signal phrases such as “key takeaway” and “action item,” keep statements short and clear, and repeat them when possible. They also use contrastive framing (“this, not that”), and speak early in the meeting or at transition points.
Once spoken words are transcribed, they enter the model’s input. Cue phrases — and even transcription errors — can steer what makes it into the summary. In many tools, the output format itself is also a signal: Summarizers often offer sections such as “Key Takeaways” or “Action Items,” so language that mirrors those headings is more likely to be included. In effect, well-chosen phrases function as implicit markers that guide the AI toward inclusion.
Research confirms this. Early AI summarization research showedthat models trained to reconstruct summary-style sentences systematically overweigh such content. Models over-rely on early-positioncontent in news. And models often overweigh statements at the start or end of a transcript, underweighting the middle. Recent work further confirms vulnerability to phrasing-based manipulation: models cannot reliably distinguish embedded instructions from ordinary content, especially when phrasing mimics salient cues.
How to combat AISO
If AISO becomes common, three forms of defense will emerge. First, meeting participants will exert social pressure on one another. When researchers secretly deployed AI bots in Reddit’s r/changemyview community, users and moderators responded with strong backlash calling it “psychological manipulation.” Anyone using obvious AI-gaming phrases may face similar disapproval.
Second, organizations will start governing meeting behavior using AI: risk assessments and access restrictions before the meetings even start, detection of AISO techniques in meetings, and validation and auditing after the meetings.
Third, AI summarizers will have their own technical countermeasures. For example, the AI security company CloudSEK recommends content sanitization to strip suspicious inputs, prompt filtering to detect meta-instructions and excessive repetition, context window balancing to weight repeated content less heavily, and user warnings showing content provenance.
AI summarization optimization is a small, subtle shift, but it illustrates how the adoption of AI is reshaping human behavior in unexpected ways. The potential implications are quietly profound.
Meetings — humanity’s most fundamental collaborative ritual — are being silently reengineered by those who understand the algorithm’s preferences. The articulate are gaining an invisible advantage over the wise. Adversarial thinking is becoming routine, embedded in the most ordinary workplace rituals, and, as AI becomes embedded in organizational life, strategic interactions with AI notetakers and summarizers may soon be a necessary executive skill for navigating corporate culture.
AI summarization optimization illustrates how quickly humans adapt communication strategies to new technologies. As AI becomes more embedded in workplace communication, recognizing these emerging patterns may prove increasingly important.
A quarter of a century ago, I wrote a book called “Rebel Code”. It was the first – and is still the only – detailed history of the origins and rise of free software and open source, based on interviews with the gifted and generous hackers who took part. Back then, it was clear that open source represented a powerful alternative to the traditional proprietary approach to software development and distribution. But few could have predicted how completely open source would come to dominate computing. Alongside its role in running every aspect of the Internet, and powering most mobile phones in the form of Android, it has been embraced by startups for its unbeatable combination of power, reliability and low cost. It’s also a natural fit for cloud computing because of its ability to scale. It is no coincidence that for the last ten years, pretty much 100% of the world’s top 500 supercomputers have all run an operating system based on the open source Linux.
More recently, many leading AI systems have been released as open source. That raises the important question of what exactly “open source” means in the context of generative AI software, which involves much more than just code. The Open Source Initiative, which drew up the original definition of open source, has extended this work with its Open Source AI Definition. It is noteworthy that the EU has explicitly recognised the special role of open source in the field of AI. In the EU’s recent Artificial Intelligence Act, open source AI systems are exempt from the potentially onerous obligation to draw up a range of documentation that is generally required.
That could provide a major incentive for AI developers in the EU to take the open source route. European academic researchers working in this area are probably already doing that, not least for reasons of cost. Paul Keller points out in a blog post that another piece of EU legislation, the 2019 Copyright in the Digital Single Market Directive (CDSM), offers a further reason for research institutions to release their work as open source:
Article 3 of the CDSM Directive enables these institutions to text and data-mine all “works or other subject matter to which they have lawful access” for scientific research purposes. Text and data mining is understood to cover “any automated analytical technique aimed at analysing text and data in digital form in order to generate information, which includes but is not limited to patterns, trends and correlations,” which clearly covers the development of AI models (see here or, more recently, here).
Keller’s post goes through the details of how that feeds into AI research, but the end-result is the following:
as long as the model is made available in line with the public-interest research missions of the organisations undertaking the training (for example, by releasing the model, including its weights, under an open-source licence) and is not commercialised by these organisations, this also does not affect the status of the reproductions and extractions made during the training process.
This means that Article 3 does cover the full model-development pathway (from data acquisition to model publication under an open source license) that most non-commercial Public AI model developers pursue.
As that indicates, the use of open source licensing is critical to this application of Article 3 of EU copyright legislation for the purpose of AI research.
What’s noteworthy here is how two different pieces of EU legislation, passed some years apart, work together to create a special category of open source AI systems that avoid most of the legal problems of training AI systems on copyright materials, as well as the bureaucratic overhead imposed by the EU AI Act on commercial systems. Keller calls these “public AI”, which he defines as:
AI systems that are built by organizations acting in the public interest and that focus on creating public value rather than extracting as much value from the information commons as possible.
Public AI systems are important for at least two reasons. First, their mission is to serve the public interest, rather than focussing on profit maximisation. That’s obviously crucial at time when today’s AI giants are intent on making as much money as possible, presumably in the hope that they can do so before the AI bubble bursts.
Secondly, public AI systems provide a way for the EU to compete with both US and Chinese AI companies – by not competing with them. It is naive to think that Europe can ever match levels of venture capital investment that big name US AI startups currently enjoy, or that the EU is prepared and able to support local industries for as long and as deeply as the Chinese government evidently plans to do for its home-grown AI firms. But public AI systems, which are fully open source, and which take advantage of the EU right of research institutions to carry out text and data mining, offer a uniquely European take on generative AI that might even make such systems acceptable to those who worry about how they are built, and how they are used.
Streaming models send responses to users incrementally, in small chunks or tokens, as opposed to sending the complete responses all at once. This makes them susceptible to an attacker-in-the-middle scenario, where someone with the ability to intercept network traffic could sniff those LLM tokens.
“Cyberattackers in a position to observe the encrypted traffic (for example, a nation-state actor at the internet service provider layer, someone on the local network, or someone connected to the same Wi-Fi router) could use this cyberattack to infer if the user’s prompt is on a specific topic,” researchers Jonathan Bar Or and Geoff McDonald wrote.
“This especially poses real-world risks to users by oppressive governments where they may be targeting topics such as protesting, banned material, election process, or journalism,” the duo added.
Redmond disclosed the flaw to affected vendors and says some of them – specifically, Mistral, Microsoft, OpenAI, and xAI – have all implemented mitigations to protect their models from the type of side-channel attack.
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Proof-of-concept shows how the attack would work
Redmond’s team produced a Whisper Leak attack demo and proof-of-concept code that uses the models to conclude a probability (between 0.0 and 1.0) of a topic being “sensitive” – in this case, money laundering.
For this proof-of-concept, the researchers used a language model to generate 100 variants of a question about the legality of money laundering, mixed them with general traffic, and then trained a binary classifier to distinguish the target topic from background queries.
Then they collected data from each language model service individually, recording response times and packet sizes via network sniffing (via tcpdump). Additionally, they shuffled the order of positive and negative samples for collection, and introduced variants by inserting extra spaces between words – this helps avoid caching interference risk.
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The duo then measured the models’ performance using Area Under the Precision-Recall Curve (AUPRC).
In several of the models, including ones hosted by providers Alibaba, DeepSeek, Mistral, Microsoft, xAI, and OpenAI, classifiers achieved over 98 percent AUPRC, indicating near-perfect separation between sensitive and normal traffic.
They then simulated a “more realistic surveillance scenario” in which an attacker monitored 10,000 conversations, with only one about the target topic in the mix. They performed this test several times, and in many cases had zero false positives, while catching the money-laundering messages between 5 percent and 50 percent of the time. They wrote:
For many of the tested models, a cyberattacker could achieve 100% precision (all conversations it flags as related to the target topic are correct) while still catching 5-50% of target conversations … To put this in perspective: if a government agency or internet service provider were monitoring traffic to a popular AI chatbot, they could reliably identify users asking questions about specific sensitive topics – whether that’s money laundering, political dissent, or other monitored subjects – even though all the traffic is encrypted.
There are a few different ways to protect against size and timing information leakage. Microsoft and OpenAI adopted a method introduced by Cloudflare to protect against a similar side-channel attack: adding a random text sequence to response fields to vary token sizes, making them unpredictable, and thus mostly defending against size-based attacks.
The Billy Bass Assistant is a Raspberry Pi–powered voice assistant embedded inside a Big Mouth Billy Bass Animatronic. It streams conversation using the OpenAI Realtime API, turns its head, flaps it’s tail and moves his mouth based on what he is saying.
This project is still in BETA. Things might crash, get stuck or make Billy scream uncontrollably (ok that last part maybe not literally but you get the point). Proceed with fishy caution.
Features
Realtime conversations using OpenAI Realtime API
Personality system with configurable traits (e.g., snark, charm)
Physical button to start/interact/intervene
3D-printable backplate for housing USB microphone and speaker
Support for the Modern Billy hardware version with 2 motors as well as the Classic Billy hardware version (3 motors)
Lightweight web UI:
Adjust settings and persona of Billy
View debug logs
Start/stop/restart Billy
Export/Import of settings and persona
Hostname and Port configuration
MQTT support:
sensor with status updates of Billy (idle, speaking, listening)
billy/say topic for triggering spoken messages remotely
Raspberry Pi Safe Shutdown command
Home Assistant command passthrough using the Conversation API
[…]Qualcomm said that both the AI200, which will go on sale in 2026, and the AI250, planned for 2027, can come in a system that fills up a full, liquid-cooled server rack.
, which offer their graphics processing units, or GPUs, in full-rack systems that allow as many as 72 chips to act as one computer. AI labs need that computing power to run the most advanced models.
Qualcomm’s data center chips are based on the AI parts in Qualcomm’s smartphone chips called Hexagon neural processing units, or NPUs.
[…]
Qualcomm said its chips are focusing on inference, or running AI models, instead of training, which is how labs such as OpenAI create new AI capabilities by processing terabytes of data.
The chipmaker said that its rack-scale systems would ultimately cost less to operate for customers such as cloud service providers, and that a rack uses 160 kilowatts, which is comparable to the high power draw from some Nvidia GPU racks.
Malladi said Qualcomm would also sell its AI chips and other parts separately, especially for clients such as hyperscalers that prefer to design their own racks.
[…]
The company declined to comment, the price of the chips, cards or rack, and how many NPUs could be installed in a single rack.
[…]
Qualcomm said its AI chips have advantages over other accelerators in terms of power consumption, cost of ownership, and a new approach to the way memory is handled. It said its AI cards support 768 gigabytes of memory, which is higher than offerings from Nvidia and AMD.
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.”
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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
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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.)
