AI Watch

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A humanoid robot was detained by Chinese officers after it followed and terrorized an innocent woman on the street.

“You’re making my heart race!” the woman raged in Cantonese, per a report in the Macau Post. “You’ve got plenty to do, so what’s the point of messing around with this? Are you freaking crazy?”

According to the publication, the woman was walking along the street looking at her cellphone when she realized “something” was following closely behind her.

Startled, she turned to find the robot.

In the video, you see the robot raising its arm while the woman yelled at it in Cantonese. The clip then cuts to it being escorted away by officers.

This is not the first time a robot was apprehended by police, and it likely won’t be the last.

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The growing use of artificial intelligence (AI) is upon us. And as technology companies try to meet the skyrocketing demand for AI-specialized computing capacity, they are dotting the country with data centers – to the dismay of some, but the delight of others. As is all too often the case, many of these companies are coming to states and cities and receiving taxpayer-supported subsidies or tax exemptions.

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Speaking with FRANCE 24’s Sharon Gaffney, Elke Schwarz, Professor of Political Theory at Queen Mary University of London, says that there’s “a radical acceleration” in the speed of acquisition of military targets through the use of AI and how quickly action is taken on these targets, which raises concerns about the lack of human oversight, especially considering that AI models have “25 to 50% reliability, which means they are wrong very often”.
from www.france24.com

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YouTube on Wednesday said it would expand access to its artificial intelligence (AI) detection tool to politicians and journalists.  The company will allow a pilot group of lawmakers and reporters to use its likeness detection feature, which flags AI-generated content that uses a person’s likeness and allows them to request removal if it violates YouTube…
from thehill.com

Blurb:

Advanced violence is democratizing.  AI, in conjunction with dramatic improvements in robotics, energy production, and sensors, will increasingly enable ever-smaller groups of people to use targeted violence more effectively, and from a distance. Over time, this shift will dramatically impact all varieties of force projection: state-on-state war, various forms of low-intensity conflict, and how states enforce internal order. 

Perhaps understandably, however, national security discourse about the AI revolution has generally focused on more earth-shattering scenarios: superintelligence, state-to-state conflict, and the prospect of unleashing new biological weapons. These are all critical questions that deserve extensive scrutiny. But super-empowering small groups of people will shift security dynamics in crucial, if less dramatic, ways as well. Non-state actors will use AI-backed tools to conduct relatively simple attacks using increasingly autonomous weapons. In this scenario, it will be the ability of AI-empowered weapons to deliver destruction discriminately, rather than at a catastrophic scale, that will be critical. 

Blurb:

To house the hundreds or thousands of temporary workers needed to build an AI data center, developers are increasingly relying on temporary villages known as man camps.

This style of camp was popularized as housing for men working in remote oil fields. For example, as a Bitcoin mining facility in rural Dickens County, Texas is converted into a 1.6 gigawatt data center, Bloomberg reports its workers are living in gray housing units with access to a gym, a laundromat, game rooms, and a cafeteria that grills steaks on-demand.

A company called Target Hospitality has signed multiple contracts worth a total of $132 million to build and operate the Dickens County camp, which could eventually house more than 1,000 workers.

Blurb:

The author of that post on X was referring to an online intelligence dashboard following the US-Israel strikes against Iran in real time. Built by two people from the venture capital firm Andreessen Horowitz, it combines open-source data like satellite imagery and ship tracking with a chat function, news feeds, and links to prediction markets, where people can bet on things like who Iran’s next “supreme leader” will be (the recent selection of Mojtaba Khamenei left some bettors with a payout).

I’ve reviewed over a dozen other dashboards like this in the last week. Many were apparently “vibe-coded” in a couple of days with the help of AI tools, including one that got the attention of a founder of the intelligence giant Palantir, the platform through which the US military is accessing AI models like Claude during the war. Some were built before the conflict in Iran, but nearly all of them are being advertised by their creators as a way to beat the slow and ineffective media by getting straight to the truth of what’s happening on the ground. “Just learned more in 30 seconds watching this map than reading or watching any major news network,” one commenter wrote on LinkedIn, responding to a visualization of Iran’s airspace being shut down before the strikes.

Blurb:

On January 8th, OpenAI introduced ChatGPT for Healthcare, a generative AI (GAI) platform designed to be embedded within medical systems platforms and daily workflows. This technology suite is advertised as a solution to clinicians overburdened by administrative work through offloading cognitively taxing tasks, including the choice of diagnostic tests, supporting differential diagnosis, treatment planning, documenting session notes, creating aftercare plans for patients, and generating referral notes and discharge summaries for external providers. In other words, GAI is being implemented at every level of patient care. According to the American Medical Association’s report from their summit on AI, “disruption” of the status quo in healthcare delivery due to GAI technologies “seems inevitable.”

But why does it seem inevitable? An evidenced-based approach to evaluating new technologies would call for careful consideration of benefits and risks for technology implementation on individual use cases — not a rapid systems overhaul. Here, we must recognize that GAI technologies are products — and these products are being actively promoted to healthcare industries and healthcare professionals across the medical space, including in mental health care. Rather than investing billions of dollars into curtailing a failing system of private medical care — which has led to widespread clinician burnout and poor client outcomes — Silicon Valley companies have begun attempting to mud over these fault lines with a quick-drying GAI compound. Even the most well-meaning and justice-oriented clinician is not immune to the tidal wave of billion-dollar marketing strategies bent on creating the illusion of inevitability.

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The Pentagon rarely labels an American technology company a “supply chain risk.” The designation is typically reserved for firms tied to foreign adversaries or companies that could expose sensitive government systems to compromise.

But in late February, the Trump administration applied that label to one of the most prominent artificial intelligence developers in the United States.

On Monday, Anthropic, the company behind the Claude AI system, turned up the heat on the fight by filing a federal lawsuit against the Pentagon and several government agencies after the administration ordered agencies to stop using its technology across the federal system.

“Anthropic sued the Defense Department and other federal agencies on Monday over the Trump administration’s move to designate it a supply chain risk and eliminate its use across the government,” the report explains. “The company said the effort was ‘unprecedented and unlawful.’”

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The open-source AI agent framework OpenClaw has recently gone viral worldwide, drawing significant attention from the tech industry. By enabling AI to move beyond generating content to actually executing tasks, the framework is widely seen as a key step toward the AI agent era. A growing number of Chinese technology companies are actively exploring similar approaches and rolling out related products.

Moonshot AI was among the first to launch Kimi Claw, a native integration with OpenClaw. The product emphasizes zero-code deployment and one-click setup, while also offering free computing power subsidies for OpenClaw calls, lowering the barrier for users. The move has attracted a large influx of users and helped accelerate the company’s overseas expansion, with the number of paying international users surging and overseas revenue surpassing domestic revenue for the first time.

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There is little doubt in Washington that AI is a powerful technology that will help determine which country rules the 21st century. Policymakers from the Hill to the White House have made U.S. AI leadership a priority and invested significant resources towards staying ahead of competitors. Yet the United States is at perhaps greater risk than ever before of losing the broader global technology competition.

Despite growing investment in AI, U.S. policymakers have failed to prepare for its convergence with biotechnology, a fusion that will define economic and national power in the coming decades. While competitors are building coordinated AI-bio ecosystems, the U.S. biodata (biological data) environment remains fragmented, underfunded, and insecure. Without a federally led effort to build AI-ready biodata as national infrastructure, the United States risks ceding leadership in both AI and biotechnology at a critical moment.

The Strategic Importance of the AI-Biotechnology Nexus

Compute, talent, and capital are necessary for AI-enabled biotechnology, but biodata is the binding constraint. Without large, representative, and interoperable biological datasets, AI models cannot generalize, scale, or translate into real-world impact.

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On Wednesday, the Trump administration announced that a large collection of tech companies had signed on to what it’s calling the Ratepayer Protection Pledge. By agreeing, the initial signatories—Amazon, Google, Meta, Microsoft, OpenAI, Oracle, and xAI—are saying they will pay for the new generation and transmission capacities needed for any additional data centers they build. But the agreement has no enforcement mechanism, and it will likely run into issues with hardware supplies. It also ignores basic economics.

Other than that, it seems like a great idea.

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Calls for governments to push “pro-worker AI” sound appealing. The idea is simple: If policymakers deftly guide how the technology develops, they can make sure it helps workers instead of replacing them. What’s not to like?

Here’s your trouble: Technology almost never works that neatly. Its effects on jobs are usually messy, unpredictable, and shaped by millions of decisions from businesses and entrepreneurs—not by a policy plan designed in Washington.

That’s a core point in a recent critique by economist Joshua Gans of a proposal from Daron Acemoglu, David Autor, and Simon Johnson to steer AI toward worker-friendly uses. Gans says the idea runs into a basic contradiction. The proposal defines “pro-worker” technology as something that makes human capabilities and expertise more valuable. But those things are valuable partly because not everyone has them. If a new technology spreads skills more widely, it may help more workers overall—while at the same time reducing the pay advantage of those who once had rare skills.

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Mexico has certainly hit a rough patch this year.

Earlier this week, the killing of a cartel kingpin led to widespread violence and disruptions by gangs throughout the country.

Now, hackers reportedly “jailbroke” Anthropic’s Claude chatbot and used it to help steal roughly 150 GB of sensitive data from multiple Mexican government entities, including tax and voter records.

Stealing 195 million taxpayer records shouldn’t be this easy, yet one hacker just proved that Anthropic’s Claude makes government data theft almost routine. Between December 2025 and January 2026, an unknown attacker exploited the popular AI chatbot to automate cyberattacks against multiple Mexican agencies, walking away with 150GB of sensitive data including voter records, employee credentials, and civil registry files.

The breach reads like a cyberpunk fever dream, but the method was disturbingly simple. The hacker jailbroke Claude by framing malicious requests as a “bug bounty” security program, convincing the AI to act as an “elite hacker.” Once fooled, Claude produced thousands of detailed attack plans with ready-to-execute scripts, specifying exact targets and credentials needed.

When Claude hit limits, the attacker switched to ChatGPT for lateral movement and evasion tactics—turning two consumer AI tools into a sophisticated hacking arsenal. This tag-team approach leveraged each platform’s strengths while bypassing their individual safeguards.

Blurb:

By this time 26 years ago, the “Dot-Com Bubble” was ready to burst. People who wanted to raise investor money claimed that they could sell anything affordably on a website; three companies were devoted just to pet food and buying ad space on broadcast television.So-called AI is enjoying a similar frenzy. Though they are still just Large Language Models (LLMs), and the best analogy for that is a fancy autocomplete, they are attracting huge levels of financial investment partly because of the potential and then primarily because people want to make money on stocks, not companies.

The Dot-Com Bubble did collapse but progress continued without the hype(1) you can buy dog food affordably on the the internet now, though the big money is artisanal dog food marketed toward wealthy elites. That may be the future for AI in 25 years also. For now, while AI is still a long way off, tasks that academics considered challenging for LLMs, like the 2020 Multitask Language Understanding (MMLU) benchmark designed to evaluate ability using 57 topics, are now easy for the private sector to master.

Blurb:

Just over 30 percent of US fourth graders are considered proficient in reading.

The AI platform called Compani.AI is promoting a “homework agent” named Einstein and says it can complete assignments on behalf of students, including submitting work for them automatically. Childhood literacy rates in the US, however, are falling.

The website features a virtual version of Albert Einstein as an AI companion. According to the company, “Einstein has a full virtual computer with a browser — anything you can do, he can do.” The platform says the AI can log into the education platform Canvas on behalf of users, and once logged in, it “watches lectures, reads essays, writes papers, participates in discussions, and submits your homework — automatically.”

“Give him a reading assignment, and he reads the full text, understands it, and writes original essays with proper citations,” the company says. It also states that the AI can watch videos and extract “key concepts” using them to “answer assignments accurately.”

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When your average daily token usage is 8 billion a day, you have a massive scale problem.

This was the case at AT&T, and chief data officer Andy Markus and his team recognized that it simply wasn’t feasible (or economical) to push everything through large reasoning models.

So, when building out an internal Ask AT&T personal assistant, they reconstructed the orchestration layer. The result: A multi-agent stack built on LangChain where large language model “super agents” direct smaller, underlying “worker” agents performing more concise, purpose-driven work.

Blurb:

Nvidia CEO Jensen Huang said Wednesday that the dispute between the U.S. Defense Department and Anthropic is “not the end of the world.”

His comments come after U.S. Defense Secretary Pete Hegseth gave Anthropic until Friday to loosen its rules on how the Pentagon can use its AI tools, or risk losing its government contract.

If Anthropic fails to comply, Hegseth threatened to label the company a “supply chain risk” or invoke the Defense Production Act, sources told CNBC‘s Ashley Capoot and Kate Rooney earlier this week.

Blurb:

In the age of AI, the scarcest resource in headquarters is no longer time. It is, rather, the willingness to say no.

Artificial intelligence is moving rapidly into military planning staffs because it compresses routine cognitive labor. AI excels at absorbing guidance, reorganizing complex material, and producing clear strategic language at speed. This feels like a qualitative advance, creating the impression that planning itself has become easier. But this impression misleads. The risk of AI-enabled planning is that it will produce plausible constructs that obscure where judgment is required, creating the illusion that analytic completeness can substitute for prioritization.

AI is seen as “raising the floor” by making it easier to produce adequate products. That is true. Yet AI also “collapses the median” by increasing the relative cost of real insight. As AI-enabled planning begin to inform real-world operations, the temptation is to treat complete answers as sufficient, without interrogating whether they represent the right answers to the hard questions of what to resource, what to defer, and what risk to accept.

Blurb:

The technology giant Nvidia just reported a great fourth quarter. The company operates on a January fiscal year. It comfortably beat analyst expectations, primarily because of the explosion in artificial intelligence infrastructure spending. Importantly, the largest U.S. data center companies just announced dramatic increases in artificial intelligence capital spending. Spending by the so-called hyperscalers will rise well over 50% to almost $700 billion in 2026.

Nvidia’s revenue reached $68.1 billion, a growth rate of 73% year over year. That exceeded the consensus estimate of around $66 billion. Data center revenue was up 75%, a huge beat. Earnings per share were also higher than expected. The market was looking for earnings of around $1.53. The reported number was $1.62. Nvidia’s gross margin was also outstanding at 75%. That indicates the company continues to have pricing power and appears to be largely unaffected by the shortage of high-bandwidth memory semiconductors.