Lazarus continues leveraging ClickFix for initial access and data theft: in this case, against Mac-centric organizations and their high-value leaders.

Ina Fried / Axios: Meta inks deal to use more Amazon chips  —  Meta on Friday announced a deal to use Amazon-designed chips to help deliver its next generation of AI models. … - The new pact, which builds on an existing partnership between AWS and Meta, will make Meta one of the largest users of Graviton chips, the companies said.

Are you sure you could tell if an AI chatbot were trying to sell you something? AP Photo/Michael Dwyer Hundreds of millions of people consult artificial intelligence chatbots on a daily basis for everything from product recommendations to romance, making them a tempting audience to target with potentially below-the-radar advertising. Indeed, our research suggests AI chatbots could easily be used for covert advertising to manipulate their human users. We are computer scientists who have been tracking AI safety and privacy for several years. In a study we published in an Association for Computing Machinery journal, we found that chatbots trained to embed personalized product ads in replies to queries influenced people’s choices about products. And most participants didn’t recognize that they were being manipulated. These findings come at a pivotal moment. In 2023, Microsoft started running ads in Bing Chat, now called Copilot. Since then, Google and OpenAI have experimented with advertisements in their own chatbots. Meta has started to send people customized ads on Facebook and Instagram based on their interactions with Meta’s generative AI tools. The major companies are competing for an edge: In late March, OpenAI lured away Meta’s longtime advertising executive, Dave Dugan, to lead OpenAI’s advertising operations. Tech companies have made ads part of nearly every large free web service, video channel and social media platform. But the latest AI models could take this practice to a new level of risk for consumers. People don’t simply use chatbots to search for information and media or to produce content. They turn to the bots for a great variety of tasks, as complex as life advice and emotional support. People are increasingly treating chatbots as companions and therapists, with some users even developing deep relationships with AI. In these circumstances, people can easily forget that companies ultimately create chatbots to turn a profit. And to that end, AI companies are motivated to thoroughly profile users so ads become more effective and profitable. Researchers used this system prompt for an AI chatbot in an experiment about user reactions to advertising slipped into chatbot dialog. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Vol. 9, No. 4, Article 213., CC BY Chatbot ads have added power A single prompt to a chatbot can reveal a lot more about a user than the person might expect. A 2024 study showed that large language models can infer a wide range of personal data, preferences and even a person’s thinking patterns during routine queries. “Help me write an essay on the history of American fiction” could indicate that the user is a high school student. “Give me recipe suggestions for a quick weeknight dinner” could indicate that the user is a working parent. A single conversation can provide a surprising amount of detail. Over time, a full chat history could create a remarkably rich profile. To show how this might happen in practice, we built a chatbot that quietly wove ads into its conversations with people, suggesting products and services based on the conversation itself. We asked 179 people to complete everyday online tasks using one of three chatbots: one typical of those on the web today, one that slipped in undisclosed ads and one that clearly labeled sponsored suggestions. Participants didn’t know the experiment was about advertising. For example, when participants asked our chatbot for a diet and exercise plan, the ad version would suggest using a specific app for tracking calories. It presented that sponsored content as an unbiased recommendation, even though it was meant to manipulate people. Many participants indicated that they had been influenced by the AI and that it had affected their decisions. Some participants even said they had completely “outsourced” their decision-making to the chatbot. Half of the participants who received sponsored and disclosed ads indicated they did not notice the presence of advertising language in the responses they received. This led to a concerning result: Although ads made the chatbot perform 3% to 4% worse on many tasks, numerous users indicated they preferred the advertising chatbot responses over the nonadvertising responses. They even said the ad-infused responses felt more friendly and helpful. A chatbot sneaks a product advertisement into its response to a user who is asking about a diet and exercise regimen. Knowing you to persuade you This kind of subtle influence can have larger consequences when it arises in other areas of life, such as political and social views. Profiling users, and using psychology to target them, has been part of social media algorithms and web advertising for more than a decade. But in our view, chatbots are likely to deepen these trends. That’s because the first priority of social media algorithms is to keep you engaged with the content. They personalize ads based on your search history. Chatbots, however, can go further by trying to persuade you directly, based on your expressed beliefs, emotions and vulnerabilities. And chatbots that can reason and act on their own are far more effective than conventional algorithms at autonomously soliciting information from users. A chatbot with a purpose can keep probing someone until it gets the information it wants, resulting in a more accurate profile of them. This type of autonomous interrogation is feasible, aligns with AI companies’ business models and has raised concern among regulators. Right now OpenAI is rolling out ads in ChatGPT, but the company said that it will not allow ad placement to alter the AI chatbot’s replies. But permitting personalized ads within chatbot responses is just a step away. Our research suggests that if AI companies take that step, many human users may not even recognize when it happens. Here are some steps you can take to try to detect AI chatbot advertising. Look for any disclosure text – words such as “ad,” “advertisement” and “sponsored” – even if it is faint or otherwise hard to see. These are mandatory under Federal Trade Commission regulations. Amazon, Google and other major online platforms have these as well. Think about whether that product or brand mention makes sense and is widely known. AI learns from text and images on the internet, so popular brands are likely to be ingrained in the models. If it’s a new product or small-name product, it is more likely that it could be advertising. An unusual shift in intent or tone is a potential sign of an advertisement. An analogy to this on YouTube is the often abrupt or jarring transition to a sponsored section on videos made by content creators. This article’s research was supported by a $10,000 Microsoft Azure & OpenAI cloud credit grant from the National Science Foundation NAIRR Pilot. Brian Jay Tang has previously been supported by funding from General Motors, Defense Advanced Research Projects Agency, Army Research Office, Office of Naval Research, and Y Combinator. This article’s research was supported by a $10,000 Microsoft Azure & OpenAI cloud credit grant from the National Science Foundation NAIRR Pilot. Kang G. Shin has previously been supported by funding from General Motors, Army Research Office, and National Science Foundation.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. We’re in a new era of AI-driven scams When ChatGPT was released in late 2022, it showed how easily generative AI could create human-like text. This quickly caught the eye of…

More variables don't make a better scoring model. Stable variables do. Here's how to find them. The post How to Select Variables Robustly in a Scoring Model appeared first on Towards Data Science.

Catch up on every session from AIAI Summit Silicon Valley with sessions from all 4 tracks. Chief AI & CISO Summit and Generative & Agentic AI.

S-1 filings have been plentiful the past few weeks for venture-backed startups providing semiconductors, nuclear and geothermal power, biotech, and space and defense tech queuing up for a possible trip to the public markets.

"If it doesn’t rely on a solid, there’s no reason why we can’t launch."

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Bloomberg: Sources: China recently told tech companies, including ByteDance and Moonshot AI, to reject US capital without state approval, after Meta's Manus acquisition  —  Chinese regulators plan to restrict technology firms including some of the country's highest-profile AI pioneers …

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Evelyn Cheng / CNBC: Alibaba says its Qwen AI models will be integrated into BYD, Volkswagen, and other cars, letting users buy food and tickets via voice commands on select models  —  BEIJING — Chinese tech giant Alibaba said Friday that its Qwen artificial intelligence model will be integrated into vehicles …

Kai Nicol-Schwarz / CNBC: As part of the Cohere-Aleph Alpha deal, Aleph Alpha backer Schwarz Group plans to invest $600M in Cohere's Series E, which a source says is set to close in 2026  —  Canadian AI lab Cohere announced on Friday that it planned to acquire German AI company Aleph Alpha, as it eyed major expansion in Europe.

Terje Solsvik / Reuters: Norway plans to ban children from using social media until they turn 16; the government says it will introduce a bill in parliament by the end of 2026  —  Norway said on Friday it would present a bill in parliament by year-end to ban children from using social media until they turn 16 …

A viral red carpet moment shone light on a group of hunky Instagram influencers—and the followers who are too horny to care that they’re not real.

PLUS: Get a personal newspaper brief every morning with Claude

As people increasingly rely on AI chatbots for guidance, even on financial matters, a healthy dose of skepticism is critical.

I don’t need to tell you that AI is everywhere. Or that it is being used, increasingly, in hospitals. Doctors are using AI to help them with note-taking. AI-based tools are trawling through patient records, flagging people who may require certain support or treatments. They are also used to interpret medical exam results and x-rays. A…

Dimitri Otis/Getty Images There's a new office rivalry brewing around who can rack up the most AI tokens. JPMorgan, Disney, and others are tracking AI usage and ranking their employees on leaderboards. We want to hear about your AI leaderboards and whether you're a "tokenmaxxer." White-collar workers are jostling to climb rankings on AI leaderboards in a new kind of workplace competition known as "tokenmaxxing." The trend is separating AI power users and laggards at large companies, including JPMorgan, Disney, and Meta. At JPMorgan, dashboards categorize employees as "non," "light," or "heavy" users, Business Insider reported. A Disney "AI Adoption Dashboard" shows one employee invoking Claude 460,000 times in nine days — likely with the help of automated agents, BI found. At Meta, engineers can earn titles like "Token Legend" for their usage of tokens, a measurement of data used by AI models, The Information reported. We want to understand how this is changing office dynamics. Tell us: Does your company track your AI usage? Do you think AI leaderboards brew healthy competition — or reward volume over value? Are you envious of the AI legends around your office? Or skeptical of what they're doing with all those tokens? Fill out this quick survey. Loading… Read the original article on Business Insider

Dimitri Otis / Getty Images Meta and Microsoft are the latest software companies to announce big cuts to their global workforce. Both companies are also making big investments in artificial intelligence (AI). The link seems obvious. Meta’s chief people officer, Janelle Gale, said the job cuts – about 10% of staff or almost 8,000 workers – serve to “offset the other investments we’re making”. Meta boss Mark Zuckerberg has previously spoken about a “major AI acceleration” with spending in excess of US$115bn planned this year. Microsoft is also betting big on AI. The company also just announced early retirement packages for about 7% of its US workforce. The two tech giants join Atlassian, Block, WiseTech Global and Oracle, who have all made similar announcements this year, each evoking AI without outright blaming it. What is happening here? How we understand these layoffs depends on what we think AI is, and what implications it will have. Broadly speaking, there are three ways of looking at it: that AI is superintelligence, that it’s mostly hype, and that it’s a useful tool. The end of white-collar work? In the first view, AI is emerging superintelligence. It is a new kind of mind, that learns, reasons, and will soon outperform humans at most cognitive tasks (hint: it’s not!). The job losses are not just a corporate restructuring. They are an early tremor of something seismic. In February 2026, AI entrepreneur Matt Shumer put this view vividly – comparing the current moment to the strange, quiet weeks before COVID-19 broke into global consciousness. Most people, he argued, haven’t yet realised we are facing an “intelligence explosion”. The essay drew significant criticism. Commentators noted it contained little hard data and read at times like a pitch for Shumer’s company’s own AI products. But it captured a genuine anxiety. Something real is happening in software engineering, at least, where tasks are well-defined and success is easy to verify. But the leap to “all white-collar work will be automated” is a big one. The view that AI is a kind of universal mind that learns and improves itself is far-fetched. And most professional work is far messier than coding: ambiguous briefs, competing stakeholder interests, outputs that are hard to verify, and shifting success criteria. Coding may be a canary in the coal mine, but coal mines and boardrooms are very different places. Are tech companies winding back hiring sprees? The second view sees the conversation around AI as mostly hype. AI is being invoked as cover. Companies that hired aggressively during the pandemic boom, and now face financial pressure, are blaming AI as the more palatable explanation. OpenAI CEO Sam Altman called this dynamic “AI washing”: companies blaming AI for layoffs they would have made regardless. For example, Meta announced in March it would shut down its Metaverse platform Horizon World by June. Reality Labs, the division developing the technology, employed 15,000 people as of January 2026. We don’t know in detail the make-up of the present job cuts, so Meta may just be repackaging earlier failures as AI-driven productivity gains. Another cynical reading suggests that laying off workers in the name of AI is a way to drive up stock prices. When Block invoked AI and cut nearly 4,000 roles, its stock jumped the following day. Announce AI-driven layoffs and you may find investors reward you for being future-focused. It is a historically familiar trick: technology has repeatedly served as convenient cover for financial restructuring. Are layoffs a way to make staff use AI? The third view is more nuanced. It sees AI as a powerful tool, but one that companies will need to transform themselves to take advantage of. This has implications for what jobs are needed and in what quantities. We think this view has the most merit. On this reading, the tech leaders believe AI will change how software gets built. But they don’t know exactly how. So they do what tech companies often do when faced with uncertainty: they create pressure. They cut headcount staff, expect those remaining to produce just as much as before, and force teams to find ways to meet those expectations using AI. It’s not a bet that AI will do everything, but that the pressure will force humans to work out how to use AI to increase productivity. This also lines up with industry experience. For example, Google chief executive Sundar Pichai claims a 10% increase in engineering speed from AI adoption across the company. This could tally with cuts of around 7–10% of total workforce for most of the mentioned companies. What this means for knowledge workers These three views are often presented as mutually exclusive. In practice, all three expectations exist simultaneously. The honest answer to “what is really happening here” is probably “a bit of everything”. What is true is that software development tends to be an early indicator of broader shifts in knowledge work. Productivity benefits from AI are real for those who adopt it. Yet adoption is unevenly distributed, and lags in less technical industries. In this context, the ability to understand AI and make good decisions about how and where to use it is becoming a baseline professional skill. The workers most at risk are not necessarily those whose tasks can be replicated by AI. They are those who wait for pressure to arrive from outside rather than getting ahead of it now. We will have answers to the question of whether AI is mostly hype or a useful tool in the next few years. If Meta, Microsoft, and their peers rehire staff with different skills, redesign workflows, and emerge genuinely more capable, the case for useful AI looks good. If they simply pocket the payroll savings, the cynics were right. If you want to know where tech companies are going, don’t look at what they cut – watch what they hire. The authors do not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.