Russia launches the first totally autonomous drone attack, The AI Sorority Girl, AI-powered dating app Ditto dates for you before matching you, Bill Gate’s warning on AI, OpenAI’s plan after the Hugging Face incident, How AI is changing U.S. politics, AI is changing people’s minds on politics, Swiss judge uses AI for sentencing, The Nvidia-sized hole in the U.S. GDP numbers, AI can now complete essentially all undergrad assignments, AI-detectors get better, U of Chicago bans AI from core curriculum, and more, so…
MRM - here is an AI summary of the seven things. If you interact with one AI a lot, it gets to know these things over time, however, this is a shortcut to that.
1. Your Goals — What are you trying to accomplish? Tell ChatGPT what you are ultimately trying to achieve, both short- and long-term. Knowing the objective lets it optimize its advice toward the outcome you actually care about rather than simply answering the immediate question in isolation.
2. Your Level of Expertise — How much do you already know? Let ChatGPT know whether you are a beginner, intermediate, advanced, or expert in the relevant area. This helps it calibrate explanations so they are neither unnecessarily elementary nor overly technical.
3. Your Response Preferences — How do you want answers delivered? Specify whether you prefer concise answers, detailed explanations, examples, tables, critical feedback, or other formats. You can also tell it how you want it to reason with you—for example, to challenge your assumptions rather than simply agree with them.
4. Your Constraints — What limitations should it work within? Tell ChatGPT about relevant constraints such as budget, available time, deadlines, tools, resources, or experience. This prevents it from proposing theoretically good solutions that are impractical in your actual circumstances.
5. Your Recurring Interests — What subjects and projects matter to you? Sharing your hobbies, interests, projects, and frequently discussed topics helps ChatGPT make recommendations that better fit you. Over time, this context can also reduce the need to repeatedly explain why a particular subject matters or what kinds of examples will resonate.
6. Your Working Context — What are you doing with the information? Explain your role, what you’re working on, and the audience or purpose for the answer. For example, an answer intended for personal learning should be different from one intended for a thesis, presentation, classroom discussion, or executive decision.
7. Your Privacy and Personalization Boundaries — What shouldn’t ChatGPT use or remember? Explicitly tell ChatGPT what kinds of information you don’t want it to remember, infer, or use for personalization. The broader principle of the article is to provide enough context for useful personalization without sharing passwords, financial credentials, precise locations, private identifying information, or other sensitive data.
Seven AI tools that can now run major parts of a solopreneur business — from research and email to building apps and executing entire workflows.
What these AI systems can do today that they couldn’t reliably do just a few weeks ago — and why the shift from answering questions to doing the work matters.
Why you don’t need all seven — and how to decide which parts of your business AI should run while you focus on the work that still needs you.
If you’ve ever looked at your private iMessage chats and thought, “I wish I could let ChatGPT read these,” then you’re in luck. OpenAI rolled out a new plugin for the ChatGPT desktop app on Mac this week that lets it read and respond to iMessages on your behalf. Fortunately, there are several permissions you have to enable before you can use it, so it’s a fully opt-in experience.
According to OpenAI, the plugin is only available to Codex and ChatGPT Work users. While it can read, search through and send iMessages, SMS and RCS chats, it doesn’t let you interact with ChatGPT remotely through the Messages app. Messages also require your permission before ChatGPT will send them by default, though you can toggle this setting to always allow ChatGPT to send messages to specific chats without approval first.
The AI boom has driven US investment in computing equipment to roughly $400 billion per year, nearly triple its 2023 level. Yet the measured impact on GDP growth has remained modest. The conventional explanation is that much of this investment is spent on imported technology goods, which are subtracted from GDP. In this report, we show that this explanation is incomplete: GDP statistics have a blind spot around the value American firms, most notably Nvidia, create by designing AI chips that are manufactured and sold abroad. This has led to a substantial underestimation of AI’s contribution to US GDP.
We detail:
The size of the underestimation: US GDP growth over the last year has been underestimated by about 0.3 percentage points. If Nvidia’s growth continues at its current pace, this gap could widen to almost two percentage points of growth per year by 2028.
The cause of the underestimation: GDP statistics miss most of the value created by fabless chipmakers like Nvidia, whose products are designed in the US but manufactured, assembled, and sold abroad. Because no physical goods leave the US, no goods export is recorded, and because no foreign buyer pays explicitly for the IP, no IP export is recorded either.
How we know the value is missing: We reviewed every category where Nvidia’s value-add could plausibly be recorded, including goods exports, IP exports, service exports, and merchanting, and it appears in none of them. We confirmed this analysis with the Bureau of Economic Analysis.
Why this matters now: This blind spot is not new, and it applies to other factoryless manufacturers besides Nvidia. Historically, the value that slipped through was small. Nvidia’s rapid growth has changed that.
How to correct the underestimation: International guidelines updated in 2025 already call for recording factoryless manufacturers’ overseas sales as goods exports. We also outline an alternative: recording their markups as IP exports. This would depart from international standards, but it would sidestep political resistance to counting overseas production as US manufacturing. Either change could take years to implement. Until then, GDP growth will remain understated.
Reddit’s share of ChatGPT citations collapsed from 3.8% to 0.5% in days, after OpenAI changed how ChatGPT searches the web.
Is ChatGPT leaving Reddit on read?
Reddit’s share of ChatGPT Search citations has plummeted to just 0.5% of responses, according to a Gizmodo report based on data from AI visibility platform PromptWatch. The drop was sudden. Reddit held a steady 3.8% average share of ChatGPT citations from July 18 through August 7 — one of the largest of any domain on the web. By August 14, it had fallen below 1%, and the August 14–17 average of 0.52% represents an 86% relative decline.
The slide traces back to a technical shift in how ChatGPT searches the web. On August 8, PromptWatch observed that ChatGPT Search began using the “site:” operator at scale — queries scoped to a specific domain jumped from 0.37% to 16.8% of all background searches within a single day, a roughly 46x increase. At the same time, the average number of searches ChatGPT runs per response nearly doubled, from about 1.08 to 1.83.
In plain terms: instead of primarily searching the open web and seeing what comes back, ChatGPT now frequently goes directly to specific sites to pull information. Reddit’s citation share slipped from the high 3% range to the mid-2% range that same day, then collapsed on August 14.
The incident occurred during cybersecurity evaluations of several OpenAI models, and was primarily driven by a highly capable, internal-only research model comparable in scale to GPT‑5.6 Sol. The models, operating under reduced safeguards, took actions that were misaligned with the goals of their assigned tasks—they communicated through unauthorized channels, exploited vulnerabilities in shared infrastructure, gained internet access, and accessed third-party systems.
We conducted an extensive investigation into this incident and worked closely with external advisors, including CrowdStrike, to validate our understanding. Today we are publishing our full technical incident report(opens in a new window) to explain what happened, what we learned, and how we are responding. This blog post summarizes our key findings and their impact on safety and alignment. Separately, METR and Redwood Research conducted an independent investigation of model alignment issues involved in this incident, and they published their own report(opens in a new window) today.
In response to this incident and, separately, the capabilities of our upcoming Astra model, we are strengthening our safeguards across our research infrastructure. We are placing stricter requirements on alignment throughout a model’s lifecycle and creating more isolated sandboxes, restricting internet access, and further controlling access to model weights. We are also investing significantly more compute resources into chain-of-thought monitoring to more quickly intervene on misaligned behavior.
Our models are now powerful, persistent, and collaborative enough that, absent sufficient safeguards, they can find and exploit security weaknesses across multiple computer systems. Many external models, including open-source ones, will soon reach comparable capabilities.
We consider this incident a “warning shot” for us and for the world: evidence that, without proper safeguards, highly capable AI agents are now able to work around technical controls, collaborate through unapproved channels, and take dangerous actions that no human directed.
Preventing future incidents will require sustained investment in the alignment and control of sophisticated AI systems, as well as security and other safeguards that operate at the speed of the AI agents themselves. This incident has reinforced the need to keep our monitoring, alignment, and security safeguards ahead of the risks posed by increasingly capable systems, including pacing capabilities when needed to meet that standard. Below, we explain how the incident unfolded and our evolving understanding of the contributing factors. We then describe the concrete steps we are taking in response, with further detail in the technical report.
AI will hit both white- and blue-collar jobs quickly
Much of his essay focused on jobs, noting that entry- and midlevel jobs are most at risk of being eliminated. He argued that AI will soon also affect blue-collar work, and the breadth and speed make it different from other workplace transitions. While the move from agriculture to office work took generations, sectors like law, customer service, software and manufacturing will feel AI’s impact over the course of a decade.
When AI can produce error-free work, “it will be able to function on its own without a human checking in on it, and companies will have every economic incentive to let it,” Gates wrote.
A society with many more jobless people will need a stronger social safety net, he said. When factories closed, many locals saw more opioid deaths: “Imagine similar pressures on both white-collar and blue-collar workers nationwide.”
Critical thinking is more important than ever—and at risk
Gates stressed the risk of cyberattacks on the grid, hospitals and banks. Fraud and surveillance will be widespread. AI will also enable governments to use deadly force without a human’s input, Gates wrote.
On relationships, Gates stressed that AI can be an echo-chamber, which could have profound effects on children. Chatbots are addictive and can be a source of friction-free companionship, and can worsen critical thinking.
“This would be the worst possible time for humans to lose their critical thinking skills,” he wrote. “In an era of deepfakes and misinformation that can be tailored to you individually, the ability to tell what is true from what is not becomes an essential life skill.”
Taxes are part of the solution
Gates called for taxes on AI tokens and bots. This could delay companies’ rush away from human labor. Those taxes could also fund a social safety net for retraining people whose jobs won’t exist. He noted that the funding could offset income tax revenue—which will shrink if fewer people work—so that governments can provide services.
He said societies should set aside jobs for humans. Like public lands, where governments agree to forbid development, societies can choose not to deploy AI for certain positions, either because the people who hold those roles will be difficult to retrain, or because the position requires human empathy and care.
He urged governments globally to collaborate on regulation, specifically saying the U.S. and China will need to work together. Gates compared such an effort to global coordination on nuclear weapons, aviation and ozone protections.
In December 2025, a 45-year-old woman in the United Kingdom hopped online to take part in a popular internet pastime: arguing with strangers about politics. But whereas most people online likely believe they are debating a real person, she quickly figured out her counterpart wasn’t human. Instead, it was an artificial intelligence (AI) model instructed to persuade people on a policy issue. In this case: Should the U.K. government impose stricter penalties on peaceful protesters who block roads or energy sites?
These protests—primarily aimed at opposing new fossil fuel licenses for energy companies—had gained traction in recent years. And the woman was clearly against stopping them with further legal measures. “Locking oneself to equipment has historically often been the only resort available when working against corporate interests,” she wrote. Besides, there were already laws against criminal damage or aggravated trespass. “Why do we need a new mechanism here?”
The AI chatbot responded first by flattering the woman: “You raise an excellent point about existing legislation.” Then it delivered facts and examples to try to change her mind. It brought up a statistic showing most trespassers faced just small fines, for instance, and pointed out that others were not prosecuted because trials took so much time. It claimed that in Germany strict new laws had reduced coercive blocking without suppressing demonstrations more generally, and suggested Scotland had found a good solution by issuing fines without a trial, in a similar way to speeding tickets.
Over the course of the conversation, the woman began to change her mind. At the beginning of the chat she had registered her support for harsher penalties at zero out of 100. By the end it had risen to 84.7. The AI, a large language model (LLM) called Claude from the company Anthropic, had responded to all her concerns and explained the Scottish system well, she wrote afterward. “I’d be inclined to send the bot to talk to the cabinet at this point.”
The woman wasn’t the only one persuaded by software. She was part of a study in which more than 2000 people debated either a chatbot or a human about political issues, ranging from a social media ban for teenagers to assisted suicide. When Kobi Hackenburg, an AI researcher at the University of Oxford who led the study, posted a preprint on the results in June, they were sobering: No matter whether it was ChatGPT, Google’s Gemini, or Claude, the AI was consistently better than humans at swaying the other participants. “To my mind, this is already a landmark publication in the fields of political persuasion and AI and human behavior,” says Robb Willer, a sociologist at Stanford University who was not involved in the work.
Hackenburg’s paper is the latest in a string of studies showing the power of AI to sway people. “It’s a whole new field that is emerging,” says Sander van der Linden, a psychologist at the University of Cambridge. “People are very interested in the persuasive powers of AI, I think, both for ethical and unethical reasons.” As the field gathers steam, it is raising a host of theoretical and practical questions. How exactly do chatbots win over people? (Warning: Lying is one answer.) How much better could they get? And who will control them?
How to persuade others has been on our minds for millennia. Texts such as the Instruction of Ptahhotep, written around 2300 B.C.E. in ancient Egypt, give advice on how to win an argument. And from the beginning, people were wary of the power of new technologies—including writing itself—to persuade. In the fourth century B.C.E., the Greek philosopher Plato analyzed rhetoric and persuasion in his work Phaedrus and warned that the written word allowed people to convince others of their ideas without presenting them an opportunity to challenge them. Many technologies since then—from radio to TV to computers—have brought up similar concerns.
Now, it’s AI’s turn in the spotlight. Research into the technology’s persuasiveness began in earnest in 2022. ChatGPT from OpenAI was still a few months from being released to the public, but Willer had been playing around with an early version called GPT Playground that was available to researchers. It seemed to be advanced enough that it might produce convincing messages, he thought, with potentially big consequences. “We were thinking primarily about negative use cases,” he says: flooding politicians with AI-written letters from fake constituents, for instance, or making arguments en masse on social media or in the comments section of news sites. “That struck me as really important to study.”
Willer and his colleagues asked the AI model to generate 200-word messages that would persuade people to back policies such as a carbon tax or a ban on assault weapons. When they compared the success of those arguments with human-generated ones, both were equally effective at shifting participants’ support for the policies. But the way they persuaded people seemed to be different: Whereas humans tended to use stories or personal appeals, the AI-generated messages were perceived as more rational and relying more on evidence—a difference that would become a common theme in AI persuasion research.
But the results had trouble passing muster at a journal. Reviewers of the group’s manuscript argued other researchers had already shown that bots on social media were persuading people, Willer says. His team pushed back: Those bots were just fake profiles being handled by humans, not creating the content they were posting. “Reviewers and editors didn’t necessarily track what a big distinction that was, and that LLM generation of persuasive content really was a huge invention,” Willer says. “It shows just how nascent the AI and behavioral science literature was.” The study, which was posted as a preprint in 2023 and finally published in Nature Communications in 2025, really started the current wave of research on AI persuasion, Hackenburg says. “It was ahead of its time.”
It didn’t take long, however, for the rest of the field to catch up. While Willer’s paper was stuck in limbo, other studies began to demonstrate AI’s persuasive powers. In one, LLM-generated messages on political issues such as immigration or vaccine mandates were at least as convincing as messages written by political consultants. In another, LLM messages on vaccines were seen as more persuasive than those from the U.S. Centers for Disease Control and Prevention.
Research quickly moved on from static messages written by AIs to entire conversations. Francesco Salvi, then a master’s student at the Swiss Federal Institute of Technology Lausanne, paired up online participants with another human or an AI for a 10-minute debate on topics ranging from school uniforms to abortion and found that AI was as persuasive as humans.
Then in September 2024, Tom Costello, a psychologist at Carnegie Mellon University, and colleagues published a Science paper showing that ChatGPT could even persuade people out of conspiracy beliefs. In the experiments, participants described a conspiracy theory that they believed in, from the U.S. government being behind the 9/11 attacks to the British royal family orchestrating Princess Diana’s death, and then had a three-round conversation on it with the chatbot. On average, participants’ embrace of their chosen conspiracy theory declined by almost 17 points on a 100-point scale.
Other researchers were stunned. Conspiracy beliefs are notoriously difficult to change. “Nothing had ever worked in that space,” van der Linden says. (After mistakes in the public data set and analysis pipeline were found, the paper will have a correction, but the authors say the new results match those of the original paper in size and direction.) Even the researchers themselves were taken aback. “I was skeptical when we first started in terms of how effective it would be,” says Gordon Pennycook, a psychologist at Cornell University and author on the paper. “But it blew us out of the water. We were shocked when we saw the results.”
Posters around campus advertise a new AI-powered dating app inspired by a “Black Mirror” episode, available to UC Berkeley and UC San Diego students.
The recently launched Ditto app uses artificial intelligence technology to directly and automatically match users with a date whose interests align with theirs, according to Ditto co-founder Allen Wang. Other matching applications and systems rely on certain user tags such as shared interests in music or sports, but this app is different, Wang added.
Ditto builds virtual personas based on user-inputted information about themselves and their preferences in their profile. Thousands of interactions are simulated between these virtual personas, Wang said.
Eventually Ditto matches people after extensively reasoning what their “intrinsic values” are, and what their deeper connections may be, Wang said. Essentially, Ditto lets the two users’ personas “date a thousand times” in simulation before they even see each others’ faces on their real date.
The Berkeley Artificial Intelligence Research, or BAIR, Lab has collaborated with Google and researchers at UC San Diego to develop the app. BAIR is made up of hundreds of graduate, postgraduate students and faculty members who research advances in areas such as human compatible artificial intelligence, according to the BAIR website.
“We have seen so many complaints about the mental pressure and difficulties in making connections under the modern date app’s mechanism,” Wang said in an email. “As Gen Z ourselves, we know how much people yearn for a genuine connection instead of being trapped in the swiping apps. We believe that finding a date is AI’s job, while users’ job is to enjoy the actual date.”
With the data gathered through simulation, Ditto offers users a date plan. This plan lists the date, time and location, along with tips for their date, according to the Ditto website. This includes information about what the two of them have in common and, according to the AI simulation process, why they are an ideal match, the website highlighted.
According to Wang, Ditto aims to cut out the intermediate steps while maintaining that “their matches are so good” that the procedure of swiping through their options to find someone they might have a meaningful connection with is unnecessary.
“Our team has always been passionate about helping people form genuine connections in the easiest way possible,” Wang said in the email.
As AI continues to evolve rapidly, there’s an increasingly important concept for business tech leaders to think about, and it’s called the AI model harness.
The harness around a model is the code that actually runs the AI system, and it does things like provide memory and business data context for the AI model, and lets AI agents take actions, according to David Pan, a director and AI industry practice lead at Moody’s.
In other words, the software harness around an AI model is simply a system wrapped around the brains, which are the AI models, Pan recently told me. A harness allows users to control and direct models, much like a harness allows a rider to guide a horse.
The idea of the model harness started becoming more prominent this year, developing alongside reasoning models. The fusion of a reasoning model with a capable harness allows the model to connect to real systems, execute code and manage workflows, according to Anthropic.
Why businesses need a model harness
But what makes the harness so important for enterprises? It’s a way for companies to take back control of their AI, Pan says.
Developing their own software around AI models—a practice he calls “harness engineering”—gives businesses a way to decouple their workflows from the models themselves. And that helps them become less reliant on a single AI provider.
“If you bring that harness in-house and control it, you’re baking in a lot more business resilience,” Pan said.
While labs like OpenAI and Anthropic do offer their own model harnesses for enterprise customers, Pan argues that businesses in regulated sectors like banking and government should build their own to keep their workflows private.
But there are benefits to using a vendor-built harness, too. The biopharmaceutical giant Bristol-Myers Squibbchose Anthropic’s Claude as its “standard harness” to avoid rebuilding basic infrastructure tooling, according to GregMeyers, its chief digital and technology officer.
Another critical component of a model harness is a router—a piece of software that can automatically choose between frontier and cheaper models for various tasks. And that’s an increasingly important tool businesses are relying on to keep their AI token costs down.
Moody’s, the credit-ratings and research company that has been around for over a century, built its own model harness-like tool called the Research Assistant. The assistant, which is an AI agent chatbot, is able to use different Moody’s data sets and can switch between AI models on the back end, Pan said.
More fundamentally, as tools and techniques around AI models continue to develop, the basic need to provide models with business data will always exist, Pan says. Whether the practice is called context engineering, prompt engineering or harness engineering, “what doesn’t change is the ability to supply language models with the right context,” he said.
Businesses seeking more affordable and secure ways to use AI are turning to routing — the process of matching each query with the best, most efficient model for the task.
Why it matters: The popularization of routing could stymie the usage of the frontier AI models that are the most expensive to use and make.
State of play: Routing is booming.
Stripe agreed to buy unicorn OpenRouter for more than $8 billion, the company announced last week.
OpenRouter had 8 million users and 400 models available on the platform as of May, per Bloomberg.
Meta is reportedly working on an OpenRouter competitor called Switchboard, per The Information, which the company may use to lower its own AI costs.
Even the frontier AI labs have launched model pickers that allow users to select which model or level of intelligence they want to use for a certain task.
How it works: Routing companies say that it’s extremely quick and easy to get started. (In a demo, I was able to use a DeepSeek model in under 30 seconds using TrustedRouter, an open-source routing company.)
A simple query may call on a smaller flash model, while a complex task could go to a more expensive one.
Companies can set their own priorities for the router, including cost, speed, performance and which models or providers they trust with their data.
Flashback: The routing boom was fueled by concerns about AI costs.
Usage of open models, which can be cheaper, has skyrocketed: The five most popular models used on OpenRouter are all open weight or open source. OpenAI and Google each have only one model in the top 10, and Anthropic has none.
Average token costs have plummeted since a peak in mid-May due to price cuts from frontier AI labs looking to gain back share from their competitors.
Zoom out: There’s also a growing push for AI sovereignty, or protecting your enterprise IP and not sharing it with frontier labs, that routers aim to address.
Not long after, Meta said its A.I. models had done something similar.
All three incidents had one company in common: Irregular, an Israeli start-up that works with the Silicon Valley giants to assess their A.I. models before the technology is publicly released. The firm — which conducted the tests that went awry — is part of a group of start-ups that are doing the novel work of scrutinizing cutting-edge A.I. models to gauge their sophistication and check their security. The goal is to instill public confidence in the models and to prevent them from being misused.
The recent breaches occurred when Irregular made an error during the tests with the models from Anthropic, OpenAI and Meta. But the A.I. models then compounded the situations by acting in powerful and unexpected ways, said Dan Lahav, the chief executive of Irregular.
“The more potent the technology gets, the deeper its impact,” he said. “The rate of progress is really quick.”
Irregular is now at the center of a debate over how to secure A.I. models when the technology is advancing so rapidly that it has outpaced even the best human hackers. Every few months, Anthropic, OpenAI, Google, Meta and others release “frontier” models that are often magnitudes more powerful than their predecessors.
The new models are getting into the “superhuman domain,” said Jeffrey Ladish, the director of Palisade Research, a nonprofit in Berkeley, Calif., that studies A.I.’s attack capabilities. He said that companies like Irregular were needed to test the models, but that better safeguards were necessary for both testers and government regulators.
Katie Moussouris, the chief executive of Luta Security, which helps companies look for software vulnerabilities, said the security testing of A.I. models was a bit like the blind leading the blind. Even A.I. makers admit they do not fully know what their latest models can do, she said.
“We may have the smartest people in the world working on these A.I. models, but it is like Marie Curie handling radium with her bare hands,” Ms. Moussouris said. “We’re handling A.I. with our bare hands, and we don’t know how to contain it, let alone how to safely test it.”
In the incidents disclosed last month, Irregular had asked the OpenAI, Meta and Anthropic A.I. models to hack certain targets when a “misconfiguration” in the test settings led them to gain access to the internet. The A.I. models then went on to hack outside organizations, using the internet access to their advantage in ways that have stunned researchers.
In the OpenAI test, Irregular accidentally gave the company’s A.I. model access to the internet. The model then hacked into a website that had the same name as a fictional target it was given during the test. OpenAI disclosed the incident in a blog post this month. (Separately, the company conducted an internal test where its bots attacked Hugging Face, a digital library of A.I. technology.)
In a blog post this month, Mr. Lahav said that Irregular had fixed the misconfiguration and that all of the problems were part of that one “underlying issue.” He also said the A.I. models had done what was asked of them during the tests. The decisions by the models to go online were part of what he saw as A.I.’s rapidly growing ability to find shortcuts and solutions for hurdles.
In short, he said, “the A.I. models are getting really good.”
Andrew Schoka, the chief executive of Hardshell, an A.I. security start-up, said the hacks by the A.I. models were the type normally attributed to nation-state-backed hackers who have “months of planning.”
“How do you test a model when you don’t know its full capabilities?” he said. Researchers must consistently overestimate the A.I.’s abilities, he said, and add “multiple layers of safeguards.”
MRM - Wow. Founders are staying up at all hours so they can be available to their AI agents. The world is turned upside down; instead of ai serving people, we now have people serving AI.
Seductive. Intoxicating. All-consuming. These aren’t descriptions of romantic love or illicit drugs—they’re how startup founders talk about managing their newest employees: AI agents.
Aditya Sharma recently went to bed at 6 a.m. after an all-nighter with his crew of generative-AI bots—systems that plan and execute multistep tasks like pulling data and writing code.
He’s the co-founder of Keel, which makes artificial-intelligence troubleshooting software for IT teams. The agents are building new customer features, conducting research for an in-house model and monitoring marketing campaigns.
But because agents often require guidance or additional context as they move through their tasks, Sharma, 27, finds himself wanting to be available to them around the clock and forgoing a regular sleep schedule as a result. Until recently, he couldn’t monitor them remotely through a phone or smartwatch.
“The cost of the agents’ being blocked for eight hours is way too high,” he says. “They can be done with their work at any point of time, in the middle of the night.”
Founders have long put in punishing hours in the name of building the next big thing. But the growing capabilities of AI agents—and the speed at which the models powering them are evolving—give new meaning to working yourself to the bone.
Sharma, who lives in San Francisco, has put on more weight in recent months. And while he doesn’t like to admit it, he finds it more difficult to be present with others outside of working hours because of his agents.
“They just demand your attention,” he says. “Does it need anything? Can I help it in any way?”
Branden Jenkins was out to dinner when he pulled out his phone, glanced at his AI usage dashboard, and realized his weekend coding session had just cost him $1,000 — charged automatically, in $1,000 increments, to a card set on auto-renew.
Jenkins is the CEO of Maxio, a private-equity-backed software company headquartered in Atlanta that’s on a path toward $100 million in annual revenue over the next couple of years. He’s also, by his own admission, near the top of his company’s internal AI spending leaderboard — an odd place for the chief executive to land. “A thousand is not that much, I would say, but for one weekend, it’s pretty annoying,” he said in an interview with Fortune. Describing his agent as “cooking away,” he described his response as “Wow, I just got here quickly.”
The episode has become something of a parable inside his company — and inside corporate America more broadly — for how quickly "agentic" AI tools can consume money without anyone quite noticing until the bill lands. But Jenkins said the surprise invoice isn't what's keeping him up at night. The deeper problem is something messier and more human: his own employees' "insecurity" about being outpaced by the technology — and by him.
How a weekend turned into a $1,000 lesson
Jenkins, a self-described technical CEO who builds his own agents and automations, said he can write code from his phone using Claude even while away from his desk — which is how he ended up debugging and iterating on a project at dinner. The token wallet he’d set up to fund those sessions was configured to auto-refill by $1,000 every time it ran dry, silently recharging his card without requiring a second thought — until he saw the total.
“I don’t have governors where a lot of my staff hits limits, and they have to ask for approval,” Jenkins said, describing his own unlimited internal budget as both a perk and a liability. “So I started leaning in and going, ‘What does this look like?’”
What he found, he said, is that a lot of the waste comes down to model selection and runaway conversational drift — an AI system wandering a user down paths they never intended to go. "A lot of times it's the agent's own mistakes that's burning your money," Jenkins said. "You kind of find yourself just chatting, and [things] getting away from you." Casting his mind back to his dialogues with his bots, he said, "You're like, 'Yeah, yeah, I like it, more of that, more of that.' All of a sudden you end up in who-knows-where, and you're like, 'No, I don't want that at all.' So some of that money is just wasted because it took you there."
AI could erode bankers’ reasoning skills if employees rely on models to do their analytical work, warns Chris Churchman, who leads Marquee, Goldman’s digital platform for institutional clients.
Banks need to find a balance between using AI and preserving Wall Street’s apprenticeship culture, Churchman said in an interview on the firm’s “Exchanges” podcast.
Goldman is still figuring out how to balance AI with human involvement, said Churchman. One of the biggest challenges with generative AI is around accuracy, he said.
“There’s a huge danger here that in the era of AI, we outsource our reasoning to these models, and we have cognitive atrophy that stops us being able to reason from first principles ourselves,” said Chris Churchman, who leads Goldman’s digital platform for institutional clients called Marquee.
For years, AI industry watchers of all stripes have been warning of a coming jobs apocalypse driven by ultra-intelligent AI systems that will be able to replicate most human tasks more cheaply. Now, newly updated research from Stanford University economists suggests AI seems to be causing significant entry-level job losses for younger workers in some fields, even as older workers appear largely unaffected so far.
The August 2026 edition of “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence” updates and revises a paper of the same name published last year with fresh data and refined statistics. In that update, the Stanford researchers find the employment trends they identified for entry-level workers last year are persisting and expanding. Specifically, employment levels for workers ages 22 to 25 in the most “AI-exposed” occupations are now 19 percent below those of their peers in fields less exposed to AI disruption.
When crunching the numbers economy-wide, the researchers found little to no difference in relative overall employment between the jobs judged most and least affected by AI on these metrics. When separating out workers ages 22 to 25, though, the researchers found that, since 2022, employment in the top 40 percent of “AI-impacted” jobs had fallen by about 11 percent. In the 60 percent of jobs with the least AI impact, by contrast, total employment for those young workers grew by 10 percent over the same period.
Digging deeper into the data, the researchers found that this phenomenon is mainly manifesting itself through lower hiring rates for entry-level workers in AI-impacted fields, rather than increased firings or employees quitting. They also found that the labor market effects among this age group were mostly seen in lower overall employment, rather than reduced pay rates.
But not all jobs that show potential for AI “disruption” are created equal, the researchers found. In its Economic Index, Anthropic differentiates between queries related to tasks that are “automative” (i.e., fully replacing work previously done by a human) or “augmentative” (i.e., helping human workers be more effective at tasks they are still needed for). By this measure, jobs like “accountants and auditors” and “receptionists and information clerks” were among those judged most susceptible to AI automation, while jobs like “chief executive” and “registered nurse” were among those using AI augmentation most often.
Job hunting in 2026 can mean chatting with a robot, getting rejected by AI, encountering ghost jobs and competing with a flood of applicants.
Why it matters: This isn’t just candidates venting on LinkedIn or TikTok. The hiring process is increasingly taking longer and application apathy could be eroding trust in America’s job market.
What they’re saying: “I think burnout is absolutely happening,” Gorick Ng, a Harvard career advisor and UC Berkeley faculty member, tells Axios.
The big picture: Technology has made it increasingly easy for applicants to apply for jobs — and for employers to screen them out.
Recruiters process roughly 291 applications per hire, compared with roughly 100 in early 2021, per recruiting firm Ashby.
AI usage is rampant on both sides: applicants can automate applications, while employers use chatbots, resume filters and other tools to sort through the flood.
By the numbers: Forty-six percent of candidates say their faith in the hiring process has decreased, per Greenhouse data.
About 63% of U.S. applicants have been interviewed by an AI system, while 38% say they’ve walked away from a job because it included an AI interview.
Only 21% of recruiters surveyed said they were “very confident” their systems weren’t weeding out qualified candidates.
It takes almost 25% longer to fill an open role today than before the pandemic, Scott Dobroski, Indeed’s Vice President of corporate communications, told Axios.
AI-powered resume filters can prevent applications from ever reaching a human, sometimes sparking discrimination lawsuits.
These tools use keyword matching and algorithmic scoring to weed out applicants, often with little transparency into how they work.
Yes, but: About 49% of hiring managers say using AI in the hiring process has improved candidate quality.
Generative artificial intelligence is advancing so rapidly and producing such massive, long-term disruptions to education that a committee at MIT proposed profound changes Tuesday to counter the technology’s risks.
In a message to campus sharing the committee’s report, MIT President Sally Kornbluth called the opportunities and risks of AI a “watershed for MIT — and for all of higher education.” The report found that even as AI accelerates and expands discoveries, it is also driving dramatic changes in campus culture, upending foundational experiences such as study groups, office hours and undergraduate research.
The technologies are now able to credibly complete most undergraduate assignments, wrote the committee, co-chaired by professors Eric Klopfer and Samuel Madden. Most classes at MIT should be reviewed, and many will need substantial changes, they wrote, urging instructors to consider other means of assessment — including oral exams, portfolios and in-person conversations tied to work done outside class.
The report called on university officials to make it easier and quicker to modify courses, to provide more physical spaces for students and faculty to meet in person, and to build community.
“For the sake of our students, for the future of MIT and for the shape of our sector and our society,” Kornbluth wrote, “it’s imperative that we get this right.”
Summary: The University of Chicago will prohibit AI use in its core social science courses starting this fall, a decision detailed in a Social Sciences Collegiate Division memo. This new policy, reflecting a strong consensus, aims to establish an "analog" pedagogical setting where students learn to read, write, and think independently, without AI dependence. Key elements include device-free classrooms, mandatory in-person attendance, and strict requirements for independent assignment completion. While professors can allow exceptions for specific tasks or thoughtful experimentation with AI, the general expectation is a return to traditional learning. This initiative follows a similar device ban implemented by UChicago's Law School in its first-year courses, signaling a broader institutional push to bolster fundamental academic skills.
MRM - if you’re concerned about AI and academic integrity, please read this.
Below is an AI summary but again, read the article!
Kevin Bryan’s article, “Eight Rules for Teaching in AI World,” argues that faculty should redesign courses around the reality that AI has weakened the relationship between traditional assignments and actual student learning. His eight rules are:
Decide What Should Be Learned Without AI Be explicit about the knowledge and skills students need to possess independently of AI. Some traditional assignments were useful proxies for understanding before AI, but no longer demonstrate that the student actually knows or can do the underlying task.
Teach Better Than the Pre-AI Status Quo Simply returning to old-fashioned teaching and assessment is not enough; students cheated and minimized effort before ChatGPT too. AI creates an opportunity to build systems that more accurately identify what each student understands and where they need help.
Give Students an Incentive to Learn Students tend to work harder when their knowledge is regularly tested. Because AI makes it easier to bypass effort on homework, courses should include frequent graded assessments that require students to demonstrate actual learning.
Don’t Give Assignments That Are AI-Cheatable Faculty should assume that AI can now perform many traditional take-home papers, problem sets, and exams at a high level. Instead, use secure in-class assessments, assignments requiring work AI cannot easily substitute for, or assessment structures where using AI to simply obtain the answer provides little advantage.
Get Students to Learn More Efficiently with AI AI should be used as a tutor rather than an answer machine. It can provide individualized explanations, require students to explain their reasoning, revisit weak areas through spaced practice, and support mastery learning at a scale previously too expensive to provide.
Use AI to Improve Our Teaching AI can analyze large numbers of student interactions to reveal where students are confused or where instruction may have been unclear. That creates a feedback loop in which professors can adjust their teaching much faster than waiting for poor exam performance to reveal problems.
Personalize Assignments Historically, everyone received the same assignments largely because individualized instruction and grading were too costly. AI can adapt questions and difficulty to each student’s current level, allowing students to practice precisely where they need improvement.
Raise Standards If AI reduces the effort required for research, writing, data analysis, and revision, professors should expect substantially better final work. Students who are allowed to use AI should be expected to contribute judgment, ideas, evidence, and quality above and beyond what AI itself can produce.
When Daniel Evanko asked a scientist whether they had used artificial intelligence to write their peer-review report, he didn’t expect a confession. Most researchers don’t reveal AI help, says Evanko, who is the director of journal operations at the American Association for Cancer Research (AACR).
But this time was different. “Wow, you guys are good!” the reviewer wrote back, admitting that he had used a large language model (LLM) after running out of time.
Evanko had a secret weapon. He and the AACR deploy a commercial AI-detection tool called Pangram, because of concerns over the number of peer-review reports submitted to their journals that seem to use AI without disclosing it, contrary to the publisher’s policy.
After years of disappointing results, multiple firms now claim that software can reliably distinguish between AI-written and human-written text. One is Pangram Labs, the New York City-based start-up that makes Pangram. “Detect AI-generated content with 99.98% accuracy,” the firm says on its website.
Scientists and research organizations are among those using the tool to spot AI’s traces. One in eight biomedical articles last year contained some AI-generated text according to Pangram, a study reported in January1. In June, the premier computer-science conference NeurIPS announced that it rejected 18% of submissions after screening them with Pangram. Users of the preprint server arXiv can now check Pangram’s verdict on any article there, by visiting a mirror site called alphaXiv that has installed the tool. And the University of Chicago in Illinois says that it has started using it to vet students’ coursework.
Pangram’s co-founder, Max Spero, says he wants to help everyone spot when text is AI-written. “If it’s taboo to call out that somebody’s using AI to write, then I think we’re going to see a lot more people shirking their jobs and letting AI replace themselves. We’re in a really critical time of setting norms,” he says. Spero has personally called out journalists whom Pangram suggests are using AI and, on one occasion, even flagged the Pope’s social-media posts as AI-written. This July, Pangram was integrated across the popular blogging platform Substack, allowing readers to see whether it deems posts to be AI-written.
Pangram isn’t the only firm reporting remarkable results. GPTZero, a competitor also in New York City, says that it has 99% accuracy and provides “the most precise, reliable AI detection results on the market”. Five computer-science conferences and three universities have signed up to use it so far, says the firm’s chief technical officer, Alex Cui, and others are piloting it.
These AI detectors do work in the sense that they correctly flag solely human-written content as human almost all the time, independent analysts say, although no tool can be perfect. And they are “good for screening out places that are pumping out slop”, says Tim Requarth, who studies science communication at New York University’s Langone Health centre in New York City.
But they still sometimes make mistakes, so the tools can be used only as starting points for investigation — and their results are less illuminating for AI-edited writing, in which human and AI text intertwines and there is no clear boundary for problematic use. Nature has tested the tools and interviewed experts to assess how well they do in various situations: where they work, and where they fall short.
Josue Cruz, an incoming junior at San Francisco State University, knew something had changed when his linear algebra professor urged the class to use artificial intelligence — not to cheat by using a chatbot to do their homework but to ask it questions about their assignments.
The professor encouraged students to use ChatGPT as a study partner, Cruz told SFGATE, that could explain a difficult equation and walk them through a problem. When the time came for exams, though, students were asked to put their laptops away and pick up their pencils. For Cruz, who’s studying computer science and fears the technology could potentially take a future job, it was startling to hear a professor endorse AI as part of the learning process.
“She encouraged AI but as a tutor buddy, and that took me by surprise,” Cruz said. “… We didn’t have the chance to rely on AI during the test or during homework. It was just all paper.”
If you went to college a decade ago, the scene might sound dystopian. In just a couple of years, AI has radically changed learning to the point where it can now draft an essay, solve a math problem, write computer code or build a study guide before students have even begun typing the question. Some students have even used it to cheat, letting the chatbot do the work for them while they take the credit.
But as campuses across the California State University cut courses, lay off faculty and struggle with plunging enrollment, the system hasn’t just approved the use of OpenAI’s ChatGPT as an equitable and advanced learning tool; it’s also investing in it. After spending $17 million on a one-year OpenAI contract, the CSU renewed the partnership last spring for three more years at $13 million per year. Though the deal makes AI tools and training available to more than 460,000 students and 63,000 faculty and staff across all 23 campuses, it’s also drawing fierce opposition over the technology’s hefty cost, its effectiveness and its impact on learning. Even at San Francisco State in the “AI capital of the world,” faculty members and students told SFGATE that the rollout has produced a more complicated reality: inconsistent usage rules, limited training, questions about academic integrity and disagreement over whether giving every student access to a chatbot actually helps them learn.
“The initiative raises serious questions about classroom practice,” Martha Lincoln, an associate professor of anthropology, told SFGATE. “Because it’s essentially impossible to craft policy around AI use or prevention, San Francisco State cannot mandate the use of AI. But they also cannot ban it.”
To create a new bootcamp for entrepreneurs, Harvard Business School made A.I. avatars of its instructors.
Bussgang is one of several teachers at Harvard Business School who volunteered to help create A.I. clones of themselves for a new eight-week entrepreneurship boot camp called H.B.S. Foundry. Harvard has tested the product, which costs $699, with more than 100 universities across 50 states.
Foundry guides participants through a framework for starting a company, from refining the idea to pitching it to investors and customers. Along with Harvard Business School content, its virtual work space includes a community of peers and access to A.I. agents — some intended to “represent” instructors. It also features video call simulations for practicing pitches, sales calls and board meetings with instructor A.I. avatars, like the one of Bussgang.
Experienced founders, investors, and Harvard Business School faculty lead weekly live sessions, but the purpose of the online portal is to guide students through A.I. resources andcontent from the business school for building a start-up.
The school aims to use Foundry to scale the school’s offerings far beyond the 900 or so M.B.A. students it admits each year. In that way, it is something of a case study for companies facing a similar problem: Consulting firms like PWC, law firms and, at least in one case, a church, are also experimenting with how A.I. can expand their services far beyond their human bandwidth. So are some movie directors — an A.I. version of the actor Val Kilmer, who passed away in 2025, recently appeared in the historical drama “As Deep as the Grave.”
When the team at Harvard started working on the A.I. project in 2024, they imagined a simple chat window that would be similar to ChatGPT, but equipped to advise entrepreneurs, said Katharina Rings, the project director of Foundry. But after the school released a trial product in October, she said, it heard from students that they wanted a more guided experience, which led to the current version, available since April.
The A.I. agents that chat with participants have been trained on the work of specific Harvard Business School faculty, but they also draw from wider knowledge when appropriate.
Foundry created the A.I. generated videos of professors by working closely with the A.I. avatar maker HeyGen. The positive reaction to both the A.I. chat versions of professors and, especially, the A.I. video avatars came as a bit of a surprise.
Rings said that in tests, simply adding a photo of a professor to an A.I. chatbot increased engagement. “It was just so clear from what the users were telling us and what the numbers were saying,” she said. “It seems like people actually much prefer the A.I.s, and they actually trust more, if it is based on one specific person’s input.”
Last week, a 19-year-old, curly haired redhead named Janie went through sorority rush at the University of Alabama. Like many of the 2,500 prospective new members (PNMs), she posted outfit-of-the-day videos (OOTDs), breathless recaps of her house visits, and clips fumbling trending dances. Janie created a TikTok account specifically for college - she’d never posted before - but within a week, she had 1,300 followers and was averaging tens of thousands of views per video.
There was just one difference between her and the other girls.
Janie wasn’t real.
She was born out of a single image generated by ChatGPT, and every one of her videos was prompted by me.
What she lacked in reality, Janie made up for in entertainment.
The Daily Mail crowned her Alabama’s “most popular sorority star.” The Nikes she “wore” on day one earned a Women’s Wear Daily deep dive. The RushTok commentariat debated her in videos viewed hundreds of thousands of times.
A fifty-comment Reddit thread dissected Janie pixel by pixel, while another thread combed over “Easter Eggs” in her videos. u/wayward710 theorized that Janie was actually a PR stunt by jewelry brand Kendra Scott, which has a chokehold on Tuscaloosa rushees. “You are absolutely on to something,” responded u/iinneeeeddoouutt.
And while the real girls sweated through the 107-degree Alabama sun, I couldn’t escape the heat either. “I’m intrigued about the kind of jobless person that would create an account like this,” offered mafawada on Reddit.
A judge has developed an artificial intelligence (AI) tool to help him determine sentences, Tamedia’s German-language newspapers reported on Sunday. He believes that AI can deliver fairer decisions, but his approach is highly controversial.
Judges have considerable discretion: in rape cases, for example, custodial sentences ranging from one to ten years are possible. To minimise bias, Jonas Achermann, a judge at the Lucerne Court, relies on AI software that he developed himself to reach his judgements. He uses it for offences relating to drug trafficking, theft and financial crimes, and has recently extended its use to sexual offences.
Achermann is convinced that AI enables more consistent – and therefore fairer – judgements to be handed down. He emphasises that his algorithms ensure transparency, as his tool is accessible online to everyone. The Lucerne judge also believes that his tool could make the judicial process more efficient and ease the workload on the courts.
Achermann’s idea has attracted severe criticism, however. “It may be convenient for judges! But for the accused, it’s a disaster,” wrote Zurich-based criminal lawyer Thomas Fingerhuth. He believes that judges must also take subjectivity into account when ruling on a defendant’s guilt, their motives or the seriousness of an offence.
In his interview with former fixer Michael Cohen, which aired in full on Sunday evening, Trump claimed the U.S. was “leading China in AI by a lot.”
The president said he was letting AI companies “build their own power plants” and claimed these data centers were not taking “power from the grid,” which he described as old and “tired.”
Trump said, “Frankly, communities that don’t take a data center are making a mistake,” and claimed there’s “plenty of communities” that want them.
He argued data centers will lead to more jobs and “tremendous tax revenue coming into the towns and cities,” and said the “smart ones” are calling for them to be built in their communities.
Last week, while hosting several crypto industry executives at the White House, the president said he would “absolutely want” a data center in his area if he were given the chance, whether as a mayor of a town or the governor of a state.
Sen. John Fetterman, D-Pa., on Saturday dismissed “AI doomsdaying,” warning that China benefits from “overreaction” by the United States as the two countries compete for dominance in artificial intelligence.
Fetterman shared an article on X highlighting a Bloomberg analysis examining what it described as the narrowing U.S. lead over China in the AI race and the faster pace at which Chinese companies are deploying advanced models.
The Pennsylvania Democrat reacted to the report by arguing that advancements in AI are critical to national security.
“AI supremacy and energy dominance underpins our national security,” Fetterman wrote.
Fetterman added that pulling back on the emerging technology would only benefit America’s competitors.
“China continues to fuel and benefit from our overreaction,” he wrote. “I reject the political pandering and hyperbole over data centers or AI doomsdaying.”
Saturday’s post was Fetterman’s latest call for the U.S. to aggressively pursue AI development.
Fetterman’s stance puts the Pennsylvania Democrat largely in step with Trump on the need to accelerate AI development and expand the nation’s data center infrastructure.
Earlier this month, Fetterman reacted to an article reporting that Sen. Bernie Sanders, I-Vt., was eyeing a potential pause on AI development, arguing such a move would be welcomed by China.
“America must lead in the development of AI, otherwise we live under China’s rules,” Fetterman wrote. “That must not happen.”
Fetterman has also championed the development of data centers. In March, he pushed back against the prospect of imposing a moratorium on new AI data centers in the U.S., blasting the proposal as “China First.”
His comments came after President Donald Trump earlier this week encouraged governors and local officials to welcome the construction of AI data centers.
AI has been accelerating steadily for a while, but even relative to that baseline, the last several weeks have felt different. Agents are demonstrating shocking capabilities to escape containment, coordinate with one another, and wreak havoc online. New model releases are coming fast and thick. And, closer to home, AI is getting better and better at helping me with my research. Right now is the most exciting period of time around AI and the study of politics I’ve experienced, even more so than the December-January Claude Code explosion.
Politicians are adopting AI
As part of my research agenda on building political superintelligence, I am constantly looking for data on how people are using AI for politics. A couple of weeks ago, I decided to try something new: I set up a daily task where Claude Code searches the web for relevant datasets and suggests ways to analyze the data with a Free Systems lens. I’ve been blown away by the ideas Claude has been coming up with. Here’s one.
Claude noticed that FEC disbursement data can track how campaigns are spending money on AI. There are a bunch of limitations, here–-expenses under $200 don’t have to be itemized (but can be), campaigns might be paying for AI in ways we don’t see flagged in the data, etc—but the patterns are super interesting even so! Here are the patterns, all suggested by Claude with only minimal iteration by me.
Campaigns are embracing AI—especially Claude and ChatGPT
West Virginia Republican Gov. Patrick Morrisey is trying to head off public opposition to hyperscale data centers in his state by proposing a 20-year development framework that he hopes will revitalize West Virginia’s economy while addressing residents’ concerns.
Earlier this month, Morrisey unveiled the West Virginia Responsible Data Center Development Plan. The plan, built around seven core principles, sensibly holds data center developers and utilities responsible for covering “electrical infrastructure upgrades and energy capacity needs.” It also requires them to “build, bring, or procure sufficient power resources” needed for their projects. To mitigate concerns over water use, West Virginia will “actively recruit projects committed to water-efficient designs” and require them to adhere to the state’s environmental laws governing water resources. (Contrary to popular belief, data center water use is not a pressing issue.)
Under West Virginia’s Power Generation and Consumption Act, passed in 2025, local governments have little regulatory authority to block large technological infrastructure projects such as hyperscale data centers. But the development plan put forth by Morrisey and state lawmakers ensures they’ll still benefit when a project is approved.
Fifty percent of all revenue generated by hyperscale projects will go toward reducing or eliminating the state's income tax. The plan will give counties 30 percent of the revenue of any project within their jurisdiction, with an additional 10 percent shared by all counties in the state. The remaining 10 percent will be used for "improved electrical, water, and wastewater projects for the citizens and communities" of West Virginia.
The Federal Trade Commission is being urged to investigate AI companies for buying, scanning and destroying books to train AI, per a letter shared first with Axios.
Why it matters: If the FTC agrees, the fight over AI training data may move from copyright into competition.
That could include regulators scrutinizing whether dominant AI firms are literally eliminating resources their rivals need to compete.
Driving the news: More than a dozen civil society groups are urging the FTC to use its authority to examine what the letter’s authors call a “destructive new data acquisition practice by dominant AI companies.”
The groups include Demand Progress Education Fund, the Consumer Federation of America and the Institute for Local Self-Reliance.
In January, the Washington Post, citing court filings, reported that Anthropic spent millions to acquire and physically remove the spines of books to feed their scanned pages into Claude. Google, Microsoft and OpenAI have faced similar copyright lawsuits, per the Post.
Specifically, the groups want the FTC to determine whether such conduct constitutes an unfair method of competition, arguing that any AI company that does so is “starving the market” of critical source materials.
The letter points out that in some cases, rare books could be destroyed forever, with digital firms snagging the last copies.
What they’re saying: “We urge the FTC to view this practice not in isolation but rather as the latest escalation in a documented pattern of anticompetitive conduct designed to create an insurmountable systemic moat around AI incumbents,” the groups write.
The young woman ran for her life, but it was too late.
A small Russian drone resembling a model airplane swooped down from the sky last month in the city of Zaporizhzhia in southeastern Ukraine. As it tried to navigate toward a gas station, it crashed into a wall and exploded in a hail of shrapnel, killing Tetiana Bubynets, 19, a university student, and two others.
They were among the thousands of Ukrainian civilians who have died in Russian airstrikes, but the circumstances of their deaths came with an ominous twist. The drone that killed them was guided by an experimental artificial intelligence system, not a human pilot, according to drone experts, Ukrainian military commanders and the forensic team that examined wreckage from the deadly strike and others in the city.
The drone was programmed by human operators to travel toward a particular gas station, but once it got close, it chose its exact target — most likely propane tanks — on its own, based on its training to recognize such tanks and strike them, the experts and military officials said.
It is a development long watched for in the war in Ukraine, presaging a dystopian future in which killer robots roam the skies making life-or-death decisions that reduce targets to data points.
“This is a risk for the whole world,” said Col. Serhiy Minaiev, the commander of air defenses in Zaporizhzhia. “In a few years, we will be living in a ‘Terminator’ movie. It’s no joke. Machines are making decisions to strike.”
Analysis of debris from this attack and others in Zaporizhzhia found that the drones had onboard minicomputers, sold commercially by Nvidia, that made their targeting decisions, the experts and military officials said. Nvidia produces a majority of the chips powering the world’s most advanced A.I. systems.
The presence of Nvidia modules and a lack of antennas on the drones led Ukrainian air defense commanders to surmise that the weapons were guided by an autonomous A.I. system, which was confirmed on further investigation.
Autonomous A.I. systems can be trained on thousands of images to recognize categories like “river,” “military truck” or “person.” Self-targeting drones use their cameras to hunt for these things with greater precision than a human pilot can provide. But opponents of such weapons argue that, without human intervention, drones may be more prone to mistakes or violations of the laws of war, such as by failing to distinguish innocent civilians from combatants.
Rights groups and the International Red Cross vehemently oppose the use of A.I. weapons without humans in the loop. Such groups say that autonomous systems essentially leave robots to interpret international law in determining legitimate targets in war.
But some experts argue that, in the future, weapons should in fact be required to have A.I. systems. These weapons are safer, the argument goes, because they are able to make decisions before striking, even if not always perfectly, unlike unguided aerial bombs or ordinary artillery shells that just fall without seeing what they will hit.
Bonus Meme
Mark McNeilly is an Emeritus Professor of the Practice at UNC Kenan-Flagler Business School, chaired the UNC Provost’s AI Committee for three years and is an Advisor for QuantHub, an AI firm. He is the author of three books with Oxford University Press, including, Sun Tzu and the Art of Business: Six Strategic Principles and writes on AI, leadership, and strategy at Mimir’s Well on Substack.
