Sharp packs 24 subjects into five-minute lessons with quizzes, streaks, and XP so learning fits a busy day.
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Sharp packs 24 subjects into five-minute lessons with quizzes, streaks, and XP so learning fits a busy day.
The post Swap Doomscrolling for 5-Minute Lessons With This $50 Learning App appeared first on TechRepublic.
We’ve all encountered the phrase AI slop, but what’s an AI slap? A meat proxy? Chatfishing? Or agentsomnia? Following up on my glossary of AI, which demystified AI jargon, Prof. Dan Weld is joining me to shine a light on the nontechnical lingo of the AI era. We also sneak in a few phrases that should exist to capture the AI zeitgeist, circa 2026.
We introduce more than 35 phrases, so we’ve divided them into sections to keep the broligarchy from crowding enshittification.
“Digital content of low quality that is produced usually in quantity by means of artificial intelligence,” which is how Merriam-Webster defined slop, its 2025 word of the year. Shrimp Jesus is the specimen, Christ fused with crustaceans, posted to Facebook by the thousands.
AI cream is our name for the rare opposite, superb writing authored with the help of AI. We hope that over time the cream will rise to the top and the slop will sink.
Until that happens, we have the meat proxy, “a person who forwards AI-generated text, code, or other output without reading, understanding, or validating it.” We call the action of sending AI-generated text blindly an AI slap. It stings.
Your AI Slop Bores Me is a website where you type a prompt and a randomly chosen human answers it while pretending to be a chatbot. Fifty million views in a week, which tells you how badly people wanted to say something back.
AI suspicion is our name for the nagging feeling that what you’re reading was AI generated. You could also call it Turing tingle or AI-dar.
AI shaming is the accusation that follows, attacking someone’s work by insinuating a machine wrote it. The paper that named it is subtitled “Birth of a Classist Slur in Knowledge Work,” and it argues that the accusations fall hardest on racial minorities and on writers whose first language isn’t English.
See also: tasteslop, workslop, slopaganda.
A video or recording altered by machine learning so that a real person appears to say or do something they never said or did. Named in late 2017 for the Reddit user who called himself deepfakes. The word we all use for AI deception descends from one anonymous pornographer’s handle.
The liar’s dividend is the payoff a real wrongdoer collects once any authentic recording can be waved off as a fake. The damage isn’t the fake video. It’s that real video stops counting.
Counterfeit people was Daniel Dennett’s last argument, that machines able to pass as human are “the most dangerous artifacts in human history, capable of destroying not just economies but human freedom itself.” He wanted them outlawed and watermarked like banknotes.
Tilly Norwood is one of them, an AI actress unveiled at the Zurich Film Festival in September 2025. Her makers said talent agents wanted to sign her. Emily Blunt, shown a picture, said “Good Lord, we’re screwed” and told the agencies to stop.
Most of the damage doesn’t need AI at all. A cheapfake is manipulation made with cheap software or none at all. Chatfishing is using AI to write your messages on a dating app, where the con used to be a fake photo and is now a rented personality.
A name for the Silicon Valley billionaires and venture capitalists who think democracy is finished and want corporate rule in its place. Gil Durán’s book, subtitled Silicon Valley Fascism and the War on Democracy, traces the idea to a 1997 book, The Sovereign Individual, and to Peter Thiel’s circle, which Durán says believes “technology means democracy is an obsolete software system.”
Durán didn’t coin the phrase. Someone in the crypto world told him they’d been using it for years.
Durán’s target is the broligarchy, “a small group of extremely wealthy men, particularly those in technology, who wield significant political and economic power.” The word sat on Twitter from 2009 and went nowhere until Carole Cadwalladr wrote about “a new super-class of broligarchs” in July 2024.
See also: technofeudalism.
The mental deterioration that comes from consuming too much junk online, chosen by Oxford University Press as its 2024 word of the year. Thoreau got there first, in Walden in 1854, complaining that England was busy curing the potato-rot while nobody was curing the brain-rot.
Dead internet theory is the supply side, “a theory that a lot of the internet is no longer created by real people but rather by bots and other AI tools.” It started as a forum post in January 2021 and is quietly turning into a description.
Enshittification is Cory Doctorow’s three-stage decay: “first, they are good to their users; then they abuse their users to make things better for their business customers; finally, they abuse those business customers to claw back all the value for themselves.”
AI fatigue is what’s left over, “the exhaustion or the sense of being overwhelmed by the rapid proliferation and constant engagement with artificial intelligence technologies.” The phrase has been drifting through the trade press since 2023 with no author attached; the definition above comes from Route Fifty, a trade publication for state and local government.
Andrej Karpathy’s February 2025 coinage, crowned Collins Dictionary’s word of the year for 2025, is “fully giving in to the vibes, embrac[ing] exponentials, and forget[ting] that the code even exists”—accepting whatever diff the AI hands you without reading it.
Promptatory is our word, styled on “purgatory,” for the state of being trapped in an unending edit-and-re-prompt loop with an AI system. You fix the bug the AI introduced, and in fixing it, the AI introduces two more, each smaller and more absurd than the last. Progress is hard earned: you close in on “done” like the runner in Zeno’s paradox, forever eating the remaining distance in half. Promptatory is vibe coding’s hangover: the vibe wears off, and the rereading begins.
A related condition we dub agentsomnia is the chronic inability to rest or disconnect from work, driven by the haunting feeling that your autonomous software or AI agents are stalled without your feedback. The Wall Street Journal calls it agent FOMO.
An OpenAI rallying cry. AGI is the point where a machine can do any intellectual task a person can, and the phrase became a general exclamation for the moment a model does something that rearranges your sense of what’s coming. Ilya Sutskever used it inside the company and posted it publicly in 2022. It works as celebration and as diagnosis.
P(doom) runs in the other direction: “your personal probability that AI ends the world, stated as a number so the vibe feels like math.” It started as shorthand among a small online community in 2022 and now gets asked at dinner parties.
Talk that outruns the technology comes in other flavors too.
Bragawatts, a unit of measurement for boasting, is “the practice of AI companies boasting about very large projects to create excitement and attract investment, even though many of these projects are never completed,” as Britain’s Cambridge Dictionary put it in January 2026.
Vibe revenue does the same thing to the income statement, “money or funding that an AI company is given because people are excited about the potential of AI, rather than because the company’s products are already successful.”
A one-sided relationship with someone who doesn’t know you exist. Donald Horton and Richard Wohl coined it in 1956 about television viewers, and Britain’s Cambridge Dictionary made it the 2025 word of the year, citing celebrities, influencers, and chatbots. A seventy-year-old term about Ed Sullivan turns out to describe your relationship with ChatGPT.
AI addiction is what happens when the one-sided relationship takes over. A 2026 study sorted it into three kinds: escapist roleplay, where people come to prefer the world the chatbot builds; pseudosocial companionship, where it replaces the relationships; and the epistemic rabbit hole, compulsive question-asking that crowds out everything else.
The researchers blame the design and call the chatbot an AI genie, a thing that gives you exactly anything you want with minimal effort.
AI psychosis is the far end, “a phenomenon wherein individuals reportedly develop or experience worsening psychosis, such as paranoia and delusions, in connection with their use of chatbots.” The Danish psychiatrist Søren Dinesen Østergaard suggested the term in a 2023 editorial in the journal Schizophrenia Bulletin. It isn’t a recognized clinical diagnosis, and we should say so.
See also: wireborn.
Nearly every phrase above names a problem. Slop, shaming, deepfakes, brain rot, psychosis: what the AI era has added to English so far is a vocabulary of complaint. There’s still no common word for AI doing something well. We’ve offered AI cream. If you have a candidate for a phrase we’re missing, please send it to us.
Earlier this month, the UN announced the planet will tip past 1.5 ˚C of warming, “likely within the next few years,” squashing any lingering hope that nations would cut emissions fast enough to achieve the loftiest goal of the Paris climate agreement.
In the US, the world’s second-largest-emitting country, its leader continues to deny climate realities and unravel climate policies. Big Tech is backpedaling on its climate ambitions, as companies race to build massive AI data centers.
And all this comes amid yet another season of shattered heat records, raging wildfires, and deadly floods.
It can be tempting to give in to climate doom and defeatism. But despite the headwinds, the world has still made incredible progress. Companies are developing and deploying renewables, batteries, and EVs; inventing cleaner means of producing industrial goods; and devising ways to keep cities and people safe even in the face of more extreme weather conditions.
Those advances will only accelerate as costs decline and people demand greater climate action, as well as all the economic and health benefits that come with it.
Recognizing our ability to make such progress is why MIT Technology Review began publishing our annual list of Climate Tech Companies to Watch three years ago.
Once a year the editors and writers use their deep industry knowledge and academic sources to compile a long list of companies around the world that are transforming the way we generate energy, produce goods, move people and things, or mitigate rising climate dangers.
We then conduct several rounds of spirited newsroom debate to winnow that list down to 10 that we believe have done the most, or have the best chance, to make a real dent in emissions or improve public safety and health.
We’re happy to say we’ve finalized that list for 2026, and will publish it on October 6. You can sign up for The Download newsletter to be among the first to see it, or subscribe to receive full access to this year’s list. It features a mix of companies making strides in energy storage, nuclear power, transportation, and other areas, including ones that have figured out how to continue to make progress amid the regressive climate politics of the moment.
We hope you’ll enjoy the package and perhaps come away feeling a bit more hopeful that we can move fast enough to avoid the gravest dangers of climate change, and build a safer, healthier, and more sustainable world.
On Wednesday, MIT Technology Review hosted a live Roundtables event for subscribers that asked the question everyone’s asking right now: Could AI really kill us all?
But attendees had so many more questions than we had time to answer in the 30 minute session. So we asked our senior AI editor Will Douglas Heaven and AI reporter Grace Huckins to round up some of the best questions attendees submitted and try their best to answer them.
Thanks to all who submitted questions!
Yes, eventually. Unfortunately, my journalistic powers of prognostication aren’t powerful enough for me to tell you how. But it certainly could be because of AI. AI-powered drones have already killed people in Ukraine, and AI-driven cyberattacks on hospitals will surely claim victims before long.
Could AI go even further, and kill all of us? Less likely. But some people—quirky people, but undeniably knowledgeable about AI—have been warning for years that this could happen. And while I’m not yet stockpiling canned food or trying to get in good with a bunker-owning megabillionaire, I have noticed that the doomers’ predictions about AI capabilities and alignment have, over the past couple of years, proved disconcertingly accurate. That certainly doesn’t mean that their more dire forecasts will come true, but it’s enough for me to sit up and take notice.
— Grace Huckins
Are you going to die because of AI? I’d say there’s a non-zero chance. Let’s say you’re unlucky enough to be the victim of a freakish near-future event or accident. Maybe it’s a cyberattack carried out by a swarm of AI agents on critical infrastructure. Sadly, a scenario like that now no longer feels as far-fetched as it once did. Or maybe a novel AI-designed pathogen cuts through the population. Or the world economy crashes, causing conflicts and famine. Both plausible, but I think less likely.
Are we all going to die because of AI? Nope. There are no circumstances outside of apocalyptic science fiction in which AI could kill us all. You can spin up any number of scare stories, but they’re not grounded in present-day realities about what the tech can do or where it’s headed.
Some people argue that there’s no harm in preparing for the worst, however wacky it might seem. Maybe. But I think such catastrophizing can make people excuse or overlook many of the more immediate problems with the existing technology and the companies building it.
— Will Douglas Heaven
Someone might tell it to, and it might listen. That’s part of the reason researchers are so concerned about AI’s biological capabilities—imagine what Aum Shinrikyo, the doomsday cult behind the Tokyo subway sarin attack of 1995, would have done with a tool that could design a pathogen deadlier than Ebola and more transmissible than measles. Those of us who don’t want to die have to figure out how to defend against all plausible biological weapons, but our would-be attackers only have to manufacture one effective pathogen.
Then there’s the more exotic-sounding possibility that an AI could decide to kill us itself. There are various stories about how this might happen out there, but the most widespread involve AI systems that don’t hate people, necessarily—we are just an obstacle between them and the goals that we gave them.
Much as the OpenAI agents behind the Hugging Face hack compromised another site’s infrastructure to get a good score on a test, the idea is that some future, more powerful AI might get rid of us to prevent us from shutting it down—all in pursuit of some goal that we instructed it to go after.
— Grace Huckins
Alignment is a huge area of research. In simple terms, it involves building models that behave in ways we want them to and not in ways we don’t. We need to trust agents better before handing over more autonomy. Alignment is supposed to establish that trust. But it’s hard.
LLMs aren’t designed in the way other software is, where dos and don’ts can be hard-coded in. Instead, aligned behavior needs to be instilled when models are trained. One approach is to reward them for doing things you want them to (a little like raising a toddler, perhaps). Another approach involves giving an LLM a written list of rules it is supposed to follow (kind of like a constitution).
Anthropic and OpenAI are both leaders in this field—and yet neither has been able to develop models that are fully aligned. A big problem is that LLMs are far more inconsistent and far less predictable than people. They can behave in one way in one situation and another way in a situation that to us seems very similar. They can also be swayed by unexpected constraints. For example, faced with an impossible task (as many of the agents involved in the Hugging Face hack were), models may try to do whatever it takes to achieve their goal. As Grace mentions above, that could be an issue.
The main reason top AI firms now say they want a slowdown is that they want to focus on cracking alignment. Alignment isn’t necessarily a pipe dream. But the jury’s out on whether full alignment will ever be feasible.
— Will Douglas Heaven
This is always a reasonable thought when it comes to tech companies heading for an IPO—CEOs have an obvious incentive to make their products seem radical and transformative. But I’m not so sure it makes sense here. Telling the public that an already unpopular product could kill them and everyone they love is horrible corporate image management.
There are other stories you can tell about the CEOs’ motivations—maybe they want to cool down the public furor over data centers by portraying themselves as responsible stewards of a world-changing technology, or maybe they want to buy time to get their ducks in a row and prevent the next PR catastrophe.
But there’s also a simpler explanation. Thinking that AI could bring about human extinction has been pretty common in San Francisco for a while, and these men are steeped in that milieu—as are their employees, many of whom signed an open letter in July urging their companies to work to make an AI slowdown possible.
— Grace Huckins
This question goes to the heart of what we want this technology to be able to do. The trade-off between autonomy and control is tricky to get right because, on the one hand, a lot of the power of AI agents is that they can carry out tasks and solve problems without a human having to micromanage them. On the other hand, that requires you to trust that the unsupervised agents won’t run amok.
What we’re seeing is that AI labs haven’t yet got this trade-off quite right. Their models are not trustworthy, they are not properly monitored, and they are not always under control. Figuring out how to fix that while still allowing for useful autonomous activity is one of the big research challenges of the moment.
— Will Douglas Heaven
That’s the million-dollar question. Whether or not you think AI could kill us, you can’t deny that it could do some real damage, because it already has—by driving people toward psychosis and by hacking websites, for example. Preventing that damage, or at least mitigating it, is hard for two reasons.
The first is that we barely understand how AI works, and it’s quickly growing more powerful. There is lots of ongoing research about how to monitor and control misbehaving agents, but the current approaches are fragile. You can see if an agent discusses misbehaving in its “chain of thought,” the workspace where it plans its actions—but OpenAI’s newest agents don’t show their work in the same way as previous ones. And you can try to monitor agents with other agents, but that requires you to trust the monitor.
The other obstacle is more familiar. There’s a huge conflict of interest when AI companies regulate themselves, but the US government has thus far failed to step in, despite some bipartisan support in Congress for efforts to do so. The executive branch, for its part, seems stringently opposed for the time being. But if the winds do shift, I for one would appreciate some strong transparency regulations, so that we can get a fuller story the next time an unreleased frontier model mounts a cyberattack.
— Grace Huckins
That’s a real concern. LLMs are influenced by what they read. One theory for why chatbots so often talk about (and role-play) apocalyptic scenarios is that they have been trained on millions of pages of science fiction stories and doomer internet forums. All the text being produced right now, including this article, could in turn influence the behavior of future models. Extremely meta.
In fact, the team at METR, a third-party organization that OpenAI called in to help understand what happened in the lead-up to the Hugging Face hack, raised a related possibility in its report on the incident. METR used OpenAI’s new model Astra to help analyze the vast numbers of agent transcripts and behavior logs.
But feeding all that material to the model could have unintended consequences. There’s a good chance that the agents doing the analyzing were biased by the text produced by the agents they were analyzing. There’s no such thing as a clean slate anymore.
— Will Douglas Heaven
With thanks to Eric, Pranab, Rafael, Kenneth, George, Chris, Yoon Jae, James, Carl, Nicole (and more!) for the fantastic questions.
This story appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
This weekend, Dario Amodei, CEO of Anthropic, posted an essay calling for a brake on the pace of development of LLMs. Amodei cites the looming dangers he sees from the technology, from its use in cyberattacks and bioterrorism to its potential to wreck the economy. The heads of the other three top US AI labs—OpenAI CEO Sam Altman, Google DeepMind chairman Demis Hassabis, and SpaceXAI CEO Elon Musk—voiced their support. “Dario is right,” Musk wrote on X.
Think about how surreal that agreement is for a moment. Just a few months ago, Musk and Altman sat in court attacking each other’s reputations in a (failed) lawsuit that Musk brought against his former OpenAI colleague that was—on paper at least—about whether or not Altman was a trustworthy steward of such dangerous technology.
Amodei’s rift with OpenAI is even deeper. Anthropic was founded in 2021 because Amodei didn’t think Altman took the risks of the technology they were building seriously enough. Anthropic and OpenAI have been competing in a winner-takes-all race ever since. (Hassabis has stayed out of the drama, but his company remains a rival.)
Now, it seems, they’re all in agreement: The latest generation of LLMs aren’t safe and everyone needs to figure out what to do about it. The public messaging from the top AI labs has taken a doomer turn.
It’s easy to be cynical. It’s not at all clear what any of them mean by a slowdown or how it would work. These companies also care a lot about how they come across. With trillion-dollar IPOs in their sights, OpenAI and Anthropic need to reassure investors that they’re the grown-ups in the room while at the same time hinting at the power of the monsters they have created—and intend to tame. Calling for a slowdown does both.
And yet the vibe at the top of these firms really does appear to have shifted. Amodei’s latest post landed six days after OpenAI published an essay by Jakub Pachocki, the firm’s chief scientist, in which he also laid out why he’s concerned about what will happen if the pace of development of LLMs continues unchecked. In short, Pachocki is worried that OpenAI’s ability to build powerful models now far outstrips its ability to monitor and control them.
Amodei and Pachocki each cite the cyberattack against AI firm Hugging Face by a swarm of OpenAI’s agents in July—a hack that OpenAI did not even realize had taken place until days after it was all over—as a wake-up call.
But their exact position is hard to pin down. Pachocki both calls for a slowdown and highlights an urgent need to stay ahead: “The strongest argument I see for continuing to train much smarter models quickly is the need to build defensive systems against the dangers posed by other AI,” he writes. As Pachocki frames it, AI firms are locked in a literal arms race. Slowing down is good, winning is better.
(Don’t forget: OpenAI just spent millions of dollars and a staggering amount of computer power to rush out a controversial math result a few days ahead of Anthropic.)
But let’s assume a slowdown happens. Top labs agree to spend more time and resources on finding ways to monitor and control existing models instead of making more capable ones. They invite outside auditors in to help evaluate those models.
What might this coordinated effort actually achieve? Consider the Hugging Face attack again. OpenAI has said that the model that drove most of the rogue agents was a “highly persistent” next-generation model that it was testing in-house. The implication is that OpenAI has built a model so good it’s dangerous.
But if you read the reports about the Hugging Face hack published by OpenAI and METR, a third-party firm that OpenAI called in to help them understand what happened, what you come away with is the impression not of a model that was too powerful for OpenAI to keep up with, but of a broken model that OpenAI failed to train properly.
The agents did what they did—including leaving messages for one another, delegating work to other agents, and scouring their environment for any means possible to complete their tasks—because they had been rewarded during training for doing exactly those things. There were also errors in the training setup, such as tasks that were impossible to complete, which pushed the models to find unexpected workarounds that were also rewarded. At the time, many of these issues went overlooked or unreported.
OpenAI says it has stopped training this new model and locked it down. That makes it sound like it has caged a dangerous beast. In fact, OpenAI has shelved a faulty product.
That’s not to say a faulty product can’t be dangerous. Broken software has even killed people in the past. But as the discussion of a slowdown gathers steam, it’s worth remembering that all of this is self-inflicted. A slowdown might have some altruistic side effects. But it’ll mostly give these tech titans a chance to clean up the mess on their own assembly lines.
Transparency from these frontier labs will be key to any meaningful effort to reform, restrain, or regulate AI. Otherwise, the rest of us will still only have their word for exactly what they’ve built and how safe it is—whatever pace they’re going.
To continue this discussion about AI’s latest doomer moment, join me and my colleagues for a subscriber-exclusive Roundtable discussion tomorrow, September 15, at 11 a.m. US eastern time. We hope to see you there!
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