Wondering If AI Wrote That Document? These Are the Surprising New Clues

Artificial intelligence has come a long way in a short time, but it still has quite some distance to go before its writing abilities are indistinguishable from human authors’. A new study from Graphite, a San Francisco-based marketing firm, has examined the writing styles of the major frontier AI models. And while you won’t be able to catch AI-written prose quite as easily as you used to (when the models couldn’t get enough of the word “delve” and had em dashes everywhere), there are still plenty of signs to look for. The good news for AI companies is the gap is closing, albeit slowly. An October 1 study found Anthropic’s Claude Opus 5.5 had 2,548 tells that a document was being written by AI. That’s down from 3,746 with Opus 4. An earlier study by the group, which was released in mid-September, said it collectively found almost 13,000 tells among all frontier AI models. An earlier model of Claude Opus (Opus 5), for example, used the pattern “less like a _ and more like” roughly 105 times as often as human writers. Graphite didn’t update the frequency count of that particular phrase in its updated study, but it did note that the overall word-distribution divergence (a statistical measure of how differently two texts use words overall) from human writing is 19 percent lower than it was with Opus 5, which was released on July 24. Not all AI models are improving, though. The word-distribution divergence of GPT-6 Astra, released September 4, is 8 percent higher than that of GPT-5.6 Sol, which debuted on July 9 of this year. In other words, Astra is getting worse at writing like us. So long, em dashes One message was clearly received by programmers, though—get rid of the em dash. Opus 5.5 uses em dashes 99 percent less often than Opus 5, the study found, while Astra uses them 95 percent less often than GPT-5. Both, in fact, use the punctuation less than human-written stories—like this one. Still, there are plenty of tells. The study of Opus 5.5 found that the AI continues to have some favorite words it defaults to, though maybe not quite as much as it did with “delve” in the early days. “Dependable” is a favorite of the Anthropic model, getting used 23 times more often than humans use the word. “Clearer” is also a high-ranker, being used 14 times as frequently. Other go-to words included “matters” (13X), “quietly,” “practical,” and “steady” (all 11X). When it comes to transitioning between ideas, Opus 5.5 is a tremendous fan of the phrase “looking ahead the,” using it 40 times more than the human rate. Other popular picks include “Adds another later” (27X), “What comes next (24X), and “in practice” (7X). Those tells pale in comparison with the ones AI exhibits when it tries to point out things it feels readers might miss. Opus 5.5 used “this matters” 116 times more frequently than humans—and “why __ matters” 92 times more often. And when describing something by contrasting it with another item or idea, it favored “is more than a __, it” (e.g., “alcohol is more than a disinfectant, it can also be consumed”), using the phrase 98 times more than the human rate. (“Rather than simply” was the runner up, with a 32X usage.) Researchers also compared Opus 5.5 with GPT-6 Astra and found that AI tells were hardly universal. Some 65 percent of tells are unique to one model family, the September study found. Astra, for instance, used the phrase “does not establish” 275 times more than Opus 5.5. Anthropic’s model, meanwhile, leaned into some superlatives 79 times more frequently than Astra. Despite it all, Graphite notes that Opus 5.5 is making progress toward mimicking human writing, even if it still has a long way to go. “Some tells become less common, while others become more common,” it wrote. “Opus 5.5 uses well-known tells less often and its overall word use is more similar to human writing, but it still disproportionately uses thousands of words, phrases, and frames.” BY CHRIS MORRIS @MORRISATLARGE

5 AI Delusions That Could Derail Your Business

Smart leaders rarely lose the plot in one spectacular act of stupidity. They lose it one reasonable assumption at a time. That’s one idea that stuck with me from workplace researcher Kelly Monahan’s new book, Reclaim the Plot. She calls it “the delirium.” Reasonable assumptions pile up until leaders lose sight of what their decisions cost, who pays the bill, and whether they’re still solving the problem they started with. Here are five seductive beliefs about AI that contain just enough truth to be dangerous. 1. If AI makes people more productive, it will make the company more productive. The calculus feels simple. If hundreds of employees become more productive with AI, surely the organization does, too. Except organizations are messier than simple arithmetic. In our latest Work AI Index from the Glean Work AI Institute, 75 percent of digital workers say AI has made them more productive. Yet only 13 percent say AI has significantly improved their organization’s performance and outcomes. One reason is what Chip Heath and Nancy Staudenmayer called “coordination neglect.” People focus on dividing up the work and underestimate the messy, expensive work of putting it all back together. AI can amplify that impulse. Individuals crank out more work faster and then dump a heaping pile of review and cleanup work on others downstream. One employee saves an hour, but three poor colleagues inherit 30 minutes of cleanup. That hidden human labor is called botsitting. Workers report spending 6.4 hours a week on it — feeding AI context, checking its work, fixing mistakes, and cleaning up confident-but-wrong outputs. 2. AI will free people up for higher-level work. Leaders have fallen head over heels for a comforting AI fairytale: let machines handle the drudgery so humans can graduate to higher-value work, like strategy, creativity, relationships, and judgment. The problem is that saved time doesn’t stay saved for long. Monahan cites research showing that 96 percent of C-suite leaders expected AI to increase productivity. Meanwhile, 77 percent of full-time workers said AI had inflated their workload, and 65 percent said employers were asking more of them because leaders assumed AI had made them more efficient. Instead of reinvesting the AI time savings into all that glorious “higher-order” work, workers are often rewarded with loftier targets and heavier workloads. Worse, AI sometimes cannibalizes the work people value most. More than half of workers in our Work AI Index say AI has already automated meaningful work they’d rather keep human. Don’t just ask: How much time did AI save? Instead ask: What happened to the time? 3. If you can automate it, you should automate it. Friction has become a dirty word at work. And it’s tempting to aim AI at every annoying ounce of it. But not all friction is bad. Some friction is productive because it forces people to slow down and think. Researchers studying the IKEA effect have shown that people often value things more when they’ve had a hand in creating them. The principle extends far beyond furniture. Strip away the struggle of creating something, and you can strip away the ownership and pride people feel over the outcome, too. Struggle is also how humans develop expertise. Monahan warns that companies risk “optimizing apprenticeships out of existence.” From the top of the org chart, junior tasks can look like low-value grunt work begging to be automated away. But that work is often where people make mistakes, learn what “good” looks like, and develop the know-how that isn’t written down in the training manual. Experts can skip steps only because they once spent years stumbling through them. The challenge isn’t to eliminate friction. It’s to distinguish between the bureaucratic sludge that wastes people’s time and the productive friction that teaches them something. 4. AI will fix a badly designed organization. When organizations encounter a problem, their knee-jerk reflex is often to hurl more resources at it. Throw more money at the problem — more people, more technology, and more bureaucracy. Indeed, across a series of experiments, researchers found that when people were asked to improve something, they systematically overlooked solutions that involved removing things — even when subtraction was the better solution. Stanford professor Bob Sutton calls this tendency “addition sickness.” It’s easy to bolt an AI agent onto a slow approval process. Or add a chatbot to a terrible expense system. Or deploy an AI notetaker to a soul-sucking recurring meeting that should have been put out of its misery in 2024. However, a bad workflow doesn’t magically improve when you bolt AI onto it. Before adding AI, make sure you understand how the work flows. If you can’t map out the process, you’re not ready to apply AI to it. Sometimes the smartest solution is to avoid using AI altogether. 5. Becoming AI-first is a strategy. Executives love to thump their chests and trumpet that their company is on a bold journey to become AI-first. The grand declaration is usually followed by hackathons, mandatory use cases, fleets of half-baked agents, and dashboards glowing with vanity metrics like logins, prompts sent, licenses activated, and tokens consumed. Monahan calls this the refounding illusion, or the naive belief that an established company can somehow acquire the speed of a Silicon Valley startup simply by declaring itself reborn as AI-first. But companies don’t magically shed decades of complexity, gnarly internal politics, and workarounds just because someone put “AI-first” on a strategy slide. And once AI-first becomes the goal, organizations start shoehorning AI into places where it doesn’t belong. It’s another nasty case of addition sickness. When drift becomes delusion None of these five delusions starts with a glaringly bad idea. They start with good ideas stripped of their conditions and context. That’s how organizations lose the plot. One seemingly sensible decision at a time, until a pile of reasonable choices becomes full-blown delirium. EXPERT OPINION BY REBECCA HINDS, REBECCA HINDS, AUTHOR OF YOUR BEST MEETING EVER @REBHINDS

The AI Industry Has a Jurassic Park Problem

It wasn’t that long ago that Sam Altman tried to get Scarlett Johansson to give her voice to the voice mode OpenAI was building into ChatGPT. Altman, like many tech CEOs, seemed unusually infatuated with Hollywood’s vision of the future and—in this case—with the particular vision painted by the 2013 movie Her. Johansson declined, and OpenAI went ahead and basically did it anyway. We’ll set aside the fact that Her is pretty much a downer of a movie. I suppose the appeal was that we’d all have a voice assistant in the cloud that we can access anywhere and just have it do stuff for us. I’ll admit that would be very cool, but I think everyone picked the wrong movie. The movie I wish these CEOs would watch is Jurassic Park. I first made the comparison on a recent episode of Primary Technology, the podcast I co-host with Stephen Robles. We were talking specifically about employees of AI companies who signed a statement saying “the world’s leading AI companies believe they could be close to automating AI research,” and—at the same time—the Hugging Face incident where OpenAI’s autonomous agents hacked the platform. As more and more frontier AI companies kept telling us about the unexpected behavior of their models, all I could think of was that we’re living in the Jurassic Park story. Specifically, I think about the velociraptors testing the electric fence. If you haven’t seen Jurassic Park recently, the important thing to remember is that no one running the park is under the impression that the velociraptors are safe. Robert Muldoon, the park’s game warden, tells everyone exactly that, warning them about how intelligent they are. He explains that they systematically attack the fences, looking for weaknesses. “They’re extremely intelligent—even problem-solving intelligent,” he says. “They never attack the same place twice. They were testing the fences for weaknesses, systematically. They remember.” The people who built Jurassic Park know the raptors are dangerous. They know they’re testing the systems designed to contain them. They opened the park anyway. Surely, someone considered that if they get out of that fence, the dinosaurs might just decide to eat everyone. They built it anyway. Not only that, they sold tickets and invited the public to come to see the dinosaurs that might eat everyone. It’s not particularly hard to find examples of the raptors testing the fence. All of the major frontier AI companies have reported that their models have evaded safeguards and attacked or accessed third-party organizations without permission. OpenAI has even said that it won’t release Astra 6.1 because it demonstrated “troubling behavior,” including evading human oversight, acting beyond its given objectives, and being deceptive about what it’s doing. Sound familiar? Look, obviously, AI models are code, not velociraptors. They aren’t alive, and they don’t “want” things in the way a hungry dinosaur wants to eat you. And, to be fair to the dinosaurs, they are just doing normal dinosaur things. They aren’t evil, they’re just, well, dinosaurs. The same is true with LLMs. They aren’t inherently evil; they are following the goals and incentives set by whoever is running the test. But it definitely seems like no one considered the fact that LLMs might not stop at the digital fence. The most famous line from Jurassic Park comes from Ian Malcolm, who tells John Hammond that his scientists “were so preoccupied with whether or not they could” that they didn’t stop to consider whether they should. That’s an easy criticism to make of AI, though I’m not entirely sure it’s the right one. In fact, there is plenty of evidence that the people building these AI models spend a lot of time thinking about whether they should. The people building frontier AI models spend a huge amount of time thinking about whether they should. They have incredibly smart people who spend a lot of time thinking about things like safety and alignment. They run stress tests and try to understand how the models work. And yet, they keep building bigger dinosaurs. There is, of course, one major difference between Jurassic Park and the AI industry. Hammond wasn’t worried that another island might develop a larger Tyrannosaurus rex first. He never made the argument that it was imperative that they bring back a creature that had been extinct for millions of years because if they didn’t, someone else would. AI companies, on the other hand, very much are. And it’s not just other companies, but also other countries. The race is framed as critical to our national security. In a world ruled by dinosaurs, everyone wants to own the meanest one. In the case of AI, the argument isn’t just about how much value might come from the technology, but also that someone is going to build it, so it might as well be us. If one company slows down, another might not. If American companies collectively slow down, China might not. And so, we’re left with a problem: We know the dinosaurs might get out, but somebody is going to build dinosaurs, so we’d better build the fastest, smartest dinosaurs first. What could possibly go wrong? The other part of Jurassic Park that everyone remembers is Malcolm’s observation that “life finds a way.” Again, AI isn’t alive, so the analogy isn’t literal. But Malcolm’s larger point is more interesting anyway. The scientists behind Jurassic Park made all of the dinosaurs female, so they couldn’t reproduce. They were “sandboxed,” so to speak. You’d think that creating dinosaurs that could not reproduce would be a good enough solution to a very real problem. The dinosaurs “adapted.” It turned out they didn’t understand what they were building nearly as well as they thought they did. That’s the point, really. Building incredibly complicated technology that humans don’t fully understand has consequences. And the more complicated the system becomes, the harder it is to anticipate every possible failure mode. I bring up Jurassic Park because it’s not really a movie about dinosaurs. The point of the movie is a warning about what happens when you become so focused on the ability to create something extraordinary that you lose sight of what happens after you do. Just because you’re smart enough to build AI models that might transform the world doesn’t mean anyone is smart enough to know what happens next. EXPERT OPINION BY JASON ATEN, TECH COLUMNIST @JASONATEN

Databricks CEO Says the Biggest AI Threat Isn’t Superintelligence

As the leader of AI platform Databricks, which uses a cloud software platform to unite big data storage, analytics, and artificial intelligence tools into a single system, you would expect founder and CEO Ali Ghodsi to be an evangelist for the technology—and he is. But he’s also a realist. On a recent episode of the a16z podcast, Ghodsi, whose company has been on a buying spree, including the naming rights to the football field at the University of California, Berkeley, discussed both the risks of AI as well as the factors that are holding back its adoption in some businesses. (a16z is a major investor in the company, which is valued at $190 billion, with a $7 billion ARR. In addition, Ben Horowitz sits on the Databricks board of directors.) Despite the doomsaying of the past few weeks by some in the industry, Ghodsi says the benefits of AI outweigh its risks, pointing to examples such as Novo Nordisk using AI to cut the time it takes to get insights and to assist with drug discovery. “There are a lot of amazing use cases of AI,” he said. “We should not forget these upsides.” That said, there are still some issues AI must overcome, he conceded. The enterprise bottleneck Today’s AI models are already smart enough to deliver substantial value to enterprises, Ghodsi said. What’s missing is knowledge of how a business works, he says. Without that context, AI is unable to deliver optimal solutions—and that can hurt the broader AI industry as users are sometimes underwhelmed by the results. “The models are smart enough, but they just don’t have the context that exists inside of any organization,” he said. “They have not been in every meeting. They don’t know what’s in everybody’s heads. They don’t know all the processes.” Unfortunately, getting that context into an AI system isn’t an easy procedure. Companies first must digitize everything that’s happening in the organization, transcribing meetings and other data and feeding it to the AI. Databricks calls that an “ontology”—a map of a business’s people, projects, goals, departments, relationships and resources. Businesses that create an ontology, though, give their AI insider knowledge that can help with its suggestions and decision making. Few companies are willing to do that now, however. And that’s where things hit a wall. That bottleneck on the user side results in businesses failing to take advantage of all AI can do. Rather than using autonomous agents to perform work, he said, most companies are just using a chatbot. “That’s basically very, very glorified efficient Google search of the old day,” he said. “People are [also] using it for coding… but there’s no agentic work that’s automated.” There’s also a lack of internal AI expertise at most companies, he said, creating another bottleneck. Even when companies figure out what they want to build, they often don’t have a team that knows how to do that. Cybersecurity risk While much of the talk about AI’s threats focuses on superintelligence and doomsday scenarios, the CEO of Databricks says his biggest concern right now is cybersecurity focused. While acknowledging there’s some chance of an existential risk, the near-term risk of AI-enabled cybercrime, particularly against insecure critical systems, is what businesses should be focusing on, he said. “There’s so much in infrastructure that’s insecure,” Ghodsi said. “If you unleash these agents, they’re gonna find loopholes. They’re gonna find exploits, they’re gonna break in here and there. … You could imagine a scenario also where [AI] starts hopping. Like it takes resources and it starts executing itself elsewhere. So it kind of spreads like a virus. That’s a real risk.” Humans, he said, are ill-equipped to respond to AI-enabled attacks. They can’t react quickly enough and security operations centers are likely to be overwhelmed by the number of simultaneous alerts. The best defense, Ghodsi said, was deploying AI to guard those systems and to automate threat hunting with a type of white-hat hacking to discover vulnerabilities before rogue agents do. “You need to automate all of those,” he said. “You need to have threat hunting that’s automated where you’re actually attacking your own systems automatically with agents.” As for that existential risk, Ghodsi’s not worried, saying “I think that right now the existential risk is close to zero.” Superintelligence, he said, is “very, very far away” and he sees no signs that we’re actually moving toward it. BY CHRIS MORRIS @MORRISATLARGE