5 Factors That Decide Whether AI Recommends Your Business

Open ChatGPT or Claude and ask either one to recommend the best company in your category. Then read the answer. If you’re not in it, you already know the feeling. You have happy customers, a good website, and maybe even some great media features. But the tool your buyers now turn to for recommendations doesn’t seem to know you exist. Here’s what changed. AI search tools are becoming the new front door of the internet. People don’t “Google” things the way they used to, sifting through pages of links to find the one that’s relevant. They ask, get an answer with recommendations, and decide. AI tools pull those answers from third-party, trusted, credible sources. Not your ads or your social posts, but the stories a journalist chose to write because it was interesting and credible. The umbrella term for all of this is generative engine optimization, or GEO. Think “the new SEO.” If it helps, picture it like a recipe: press coverage is the protein, but you still need the rest of the ingredients. Here are the five layers that help AI models decide which brands to recommend: 1. Earned media AI tools treat editorial coverage in high-authority outlets the way we used to treat word of mouth: as credible information. A journalist chose to write about you, and that carries weight no ad or social post can replicate. Your coverage should hit three things: recent, relevant, and reputable. Ask yourself: How much coverage have you landed in the last 12 months? Is it tiny blogs, or real mid-tier and top-tier outlets? And when you show up, are you quoted as an expert or just named in passing? Those aren’t the same value to an AI answer engine. 2. Digital footprint consistency This one trips people up more than you’d expect. Your website calls you a wellness entrepreneur. Your LinkedIn says certified nutritionist and speaker. Your press bio says nutrition expert and author. None of those are wrong, but to an AI model trying to build a clear picture of you, it’s confusing. Think of it like everyone reading from the same one-page bio. When your story is told the same way across your website, LinkedIn, press, and social, AI can describe you confidently. When it’s inconsistent, AI gets tentative, and a tentative model doesn’t recommend you. Your tone can shift from platform to platform, but your positioning can’t. Keep your bio and byline consistent everywhere you show up. 3. Reviews and community presence AI reads Reddit, forum threads, Google reviews, and it’s checking whether what the media says about you matches what regular people say in their own words. Land a great feature but let your Google reviews go stale, and that’s fighting your messaging. The fix isn’t glamorous: Ask new customers for reviews so the old ones get diluted and pushed down. 4. Topical authority Ask ChatGPT to provide a list of the top gut health experts. It surfaces the people cited repeatedly, across multiple sources, on that exact topic. This is what we’ve always called thought leadership. What’s new is that it’s now measurable. So, pick your thing. One brand I work with makes breast pumps, and their entire promise is more milk in less time. It’s the tagline, the blog positioning, and most of their press coverage. AI has started recommending them that way when breastfeeding moms ask for the most powerful pump. The test: If AI read everything published about you, would it land on a clear sense of what you’re known for, or a muddy picture of someone featured 10 different ways? 5. Freshness and structure AI favors recent information. If your last press hit was two years ago and your blog’s been quiet for three, AI isn’t seeing you. Structure gets technical, but you don’t need to become a developer. Just know AI reads your website differently than people do. It wants clear, direct language that answers real questions. A beautiful site full of vague brand-speak is harder to parse than a plain one. “We empower visionaries to unlock their potential” tells AI nothing. Say who you help, how, and why you’re qualified. Why you can’t wait for the analytics Someone asks ChatGPT for the best moisturizer for dry skin, gets a recommendation, makes up their mind. Then they close the window, Google the brand name, and buy. Your analytics logs that as “direct traffic” or “branded search.” Nobody sees the AI in the middle of it. The industry calls this dark search, dark AI, and the attribution gap. Pew Research Center found that once a Google AI summary appears, people click a search result only eight percent of the time (down from the standard 15 percent) and click a link inside the summary just one percent of the time. The answer is now the search term. So set a baseline this week. Block half an hour and run an AI visibility audit on your brand with these five steps: 1. Open an AI tool you want to appear in and ask the questions your customers would. 2. Ask the models what it says about you. 3. Screenshot what comes up. 4. Note anything inaccurate, missing, or surprising. 5. Work the five layers. The effort you put in now builds groundwork you can’t see yet. Document your baseline now, so you can prove the movement later. BY JENNIFER BERSON, FOUNDER @JENERATIONPR

Even LinkedIn is getting tired of your AI-written posts

Bland, AI-generated posts have made LinkedIn the butt of many an internet joke this year. The platform has heard its users’ complaints about lengthy, generic missives never touched by human hands loud and clear. As AI has made it easier than ever to create huge volumes of content cheaply and easily, that “slop” has flooded the internet. YouTube, Pinterest, Spotify and other platforms have all sought to limit the amount of spammy, AI-generated content their users see. But LinkedIn has been hit especially hard. Scrolling through the professional networking site earlier this year could mean being bombarded by bullet-pointed lists accented by emojis, references to “B2B sales,” and asks for a follow or comment for more. A report published in July by Pangram, a research firm that tracks AI content online, found that more than 40% of long-form posts on the platform were AI-generated, compared to around 30% on X and Medium and a smaller percentage on Reddit and Substack. So now, LinkedIn is upping its fight against slop. “I know you think you’re doing the right thing with your perfectly bulleted list and your hook at the top, and you followed all the advice from LinkedIn advisors across the internet,” LinkedIn Editor-in-Chief Dan Roth, who leads the platform’s content strategy, told CNN in an interview. “But what works is being human.” There is some irony in this, given that LinkedIn is owned by Microsoft. The tech giant has pushed for people to adopt its AI tools for everything from writing to coding, and it is up against steep competition from the likes of OpenAI and Anthropic. LinkedIn previously offered AI tools to edit or rewrite posts, although it recently scaled the offerings back. But low-quality AI posts are a business problem for LinkedIn. They all start to sound the same, Roth said. If users see too many generic AI posts, instead of content that really teaches them something or gives them a deeper understanding of a professional connection, LinkedIn risks losing them to other platforms. Fighting AI slop In August, LinkedIn rolled out a button letting users flag when a post “seems like AI slop,” which was clicked more than 1 million times within its first two weeks. Now, the company is trying to use its own AI systems to help weed out AI slop on the platform and to remind LinkedIn users how to post like humans. The company built a team largely made up of former journalists and social media experts who have an eye for recognizing generic, sloppy content. These experts help train LinkedIn’s AI model on what authentic, useful posts look like. If the model flags a post as low-quality, AI-generated content, it’ll be less likely to be distributed beyond that user’s direct connections. Also key to the strategy is a creator management team that acts as “content therapists” for executives, subject matter experts and other LinkedIn users, many of whom have gotten accustomed to the convenience of using AI to write what, on the surface, appear to be polished posts, Roth said. “A lot of times now today, they’ll run it through Claude or Gemini or wherever, they’ll post it on LinkedIn and it will perform poorly. And then they come to the team and they’re like, ‘Why did this do badly?’” Roth said. “And the best thing you can do is give it back to them and be like, ‘Did you enjoy reading this? Do you see yourself in this?” Instead, the team encourages users to embrace imperfection in their posts — to make videos by leaning a phone up against a coffee mug, rather than shooting in an expensive studio, or to dash off a post to start a conversation, even if it isn’t a complete thought. The efforts are showing early signs of success. Improvements to LinkedIn’s detection systems have resulted in a 40% decline in views of content classified as AI slop, the platform said last month. That’s not to say LinkedIn is entirely opposed to AI, Roth said, adding that “not everyone is a born communicator.” But failing to edit or personalize content means it’s less likely to reach a wide audience on the platform. “We just try to push people to be more human,” he said. “That is what is required to be able to cut through the noise.” By Clare Duffy

AI Has a New Name: Super Intelligence. Why You Should Start Using SI Right Now

An executive order directing federal agencies to use “Super Intelligence” and “SI” in place of “Artificial Intelligence” and “AI” has big implications. A new Super Intelligence Force was also announced last week to coordinate the federal response. This all makes SI more than a slogan. As I’ve written about before, language is a critical leadership lever that can shift mindsets and mobilize action. With SI now the official language of the U.S. executive branch, business leaders need to understand what changed and why, including the implications for leadership, innovation, and organizational transformation. The Definition of Super Intelligence SI is currently defined using the same basic definition as AI, while allowing 60 days for the Assistant to the President for Science and Technology to propose a formal federal definition. The concept of SI has been around for a while. In 1998, Nick Bostrom from Oxford University introduced the idea that superintelligence is intelligence that is much smarter than the best human minds in every field. Depending on your perspective, we might already have achieved this. For business leaders, SI works best as a strategic term. AI has become a catchall for chatbots, automation, and applications. SI raises the conversation to the level CEOs and boards should address: how a dramatic increase in intelligence could—and should—reshape the way a company makes decisions, operates, and competes. The SI Summit that created a word that will stick The White House summit assembled leaders from chipmakers, data centers, cloud platforms, and frontier model companies. David Sacks, chair of the President’s Council of Advisors on Science and Technology, called it the first gathering of the critical players across the industry’s full stack. Many were competitors. Some had deep disagreements. They still produced an agreement. Six companies signed the White House Accord on Super Intelligence. It calls for internal controls, an internal team to verify them, outside audits, and an independent board committee. Specifically: Implement robust internal controls to monitor the capabilities and alignment of its models during training and deployment around areas like cybersecurity, biosecurity, and chemical threats, and to ensure that its models do not hack or access technical systems in unintended ways. Empower an internal team to ensure all of the controls, monitoring, and detection are operating as intended, and that any issues are remediated. Partner with an independent external auditor or evaluator to carry out independent assessments of whether the controls, monitoring, and detection are operating as intended. Designate an independent committee of the board of directors to oversee and receive reports from the teams operating the controls and the internal and external auditors and evaluators, as well as to ensure any issues identified are remediated. The summit’s most useful lesson came from who was included. Anthropic CEO Dario Amodei attended despite sharp disagreements with many others in the room. All the key players who needed to sign up for voluntary controls on the future of AI (now SI) were present. That’s been a fundamental principle of change management. Engagement is key for buy-in and ultimately implementation. The new federal task force extends that pattern. Its leaders come from intelligence, the Federal Trade Commission, the Pentagon, and the Office of Personnel Management. It also plans outreach to consumers, public interest groups, religious organizations, infrastructure providers, and technology companies. Build Your Own Super Intelligence War Room Most businesses will never train a frontier model. But every business, small and large, will face choices about where these systems are used in strategy, innovation, operations, and more. And also what data they give access to the SI to use, and who carries the risk when things fail. The idea of “superintelligence” can be a forcing function to elevate AI from chatbot discussions to truly taking a look at the implications for business strategy. For example, a small company can bring together the owner, the salesperson closest to customers, the operations lead, and outside technology or legal advisers. A large company needs the leaders responsible for technology, business strategy, workforce decisions, customer experience, security, legal risk, and operations. Include at least one informed skeptic as well. Start with one important use case tied to the business strategy of the company. For example: how you develop new products, identify emerging competitors, monetize your business model, or anything else that’s mission-critical. Describe how a new “super intelligent system” could provide you with a step change in innovation and value, whether saving money or generating new revenue streams. Also explore what a system like this would ideally do on its own and where a person must review its work to help minimize risks. This is basically a strategic brainstorm, but anchored in real value creation and a recognition that SI might be here to stay – and play a critical role in business strategy. Names Matter Names matter. Use specific technical terms like chatbot, data center, open-weight model, or application to “declutter” the concept of artificial intelligence (e.g., making it more descriptive while simplifying what it means). Use “Super Intelligence” in strategy conversations to signal that the company is making choices about a new source of capability with consequences across the business. The first test of any SI strategy is simple: what will you do or create with real super intelligence? EXPERT OPINION BY SOREN KAPLAN, WSJ BESTSELLING AUTHOR, KEYNOTE SPEAKER, AND LEADERSHIP STRATEGY ADVISOR

They Won a Prestigious Writing Prize. Then These Key Giveaways Sparked Allegations of AI

A London-based literary competition is facing major scrutiny after three of five winners have been accused of using AI—partly or wholly—to write their prize-winning stories. The 2026 Commonwealth Short Story Prize selected one winner each from five regions that span Africa, Asia, Canada and Europe, the Caribbean, and the Pacific. Following publication of the winning entries in literary magazine Granta, online sleuths called foul. The Caribbean regional winner, Jamir Nazir, was praised for the “lyrical precision and haunting atmosphere” of his short story “The Serpent in the Grove,” as well as “the confidence and restraint of its voice,” according to a post on social media platform X by Commonwealth Foundation Creatives. But internet denizens allege that the very same voice that won the prize may not be human at all. Nabeel S. Qureshi, an AI marketing entrepreneur and a former visiting scholar at the Mercatus Center at George Mason University, flagged certain signs he said were AI tells such as “‘Not X, not Y, but Z’ sentences,” as well as the use of the word “‘hum,'” in a post on X. He concluded by writing: “A major milestone for AI, at any rate …” Following the allegations, Wired ran the text of “The Serpent in the Grove” through AI detection tool Pangram, which the publication notes has consistently outperformed other similar tools. It determined that the text was 100 percent AI-generated. It’s worth noting that no AI-detection tools are totally accurate. “The Serpent in the Grove” isn’t the only story under scrutiny. Pangram determined that “The Bastion’s Shadow” by John Edward DeMicoli, the winner from Malta, was also fully AI-generated and that “Mehendi Nights” by Sharon Aruparayil, the winner from India, was partially written using AI. Holly Ann Miller’s “Second Skin,” and Lisa-Anne Julien’s “Me and Ma’am,” however, were ruled “fully human-written” by Pangram, according to Wired. The regional winners were chosen from 7,806 entries, which the Commonwealth Foundation noted on its site is the “second highest number in the Prize’s history.” A final winner from among the five will be announced on June 30. Following the allegations, the Commonwealth Foundation released a statement on its website, acknowledging the challenges generative AI poses to literary and creative work. The statement also noted that the foundation’s judging process is robust, but judges do not currently use AI checkers in any stage. “We are aware of allegations and discussion regarding generative AI and our Short Story Prize. We take these claims seriously and are committed to responding to them with care and transparency,” the statement reads. “When they submit stories to the Prize, writers accept our entry rules and guidelines. These include confirming that their submission is their own original work. All shortlisted writers have personally stated that no AI was used and, upon further consultation, the Foundation has confirmed this.” The foundation also noted that until a reliable tool emerges with which the organization can screen unpublished work for AI, the prize competition “must operate on the principle of trust.” As always, Redditers had much to say about the subject, some assuming AI guilt, others questioning the accuracy of the AI checking tools, and many picking at the quality of the stories more broadly. “This…doesn’t surprise me given the state of contemporary literary prose. It honestly just reads like bog-standard ‘MFA voice,’” one Redditor wrote of Nazir’s story. A recent report from digital marketing agency Graphite found that since the debut of ChatGPT in 2022, there has been a meteoric rise of AI-generated content on the internet. The number of articles written by AI now equals that of human-written content, although the overall share seems to have plateaued. Axios reported at the time that the quality of AI-generated writing has meaningfully improved, not to mention that it can be difficult to determine what constitutes AI writing. Whereas some content is mostly or entirely AI-generated, some writers use AI tools throughout the process of drafting and editing. BY CHLOE AIELLO @CHLOBO_ILO

Anthropic Just Banned Being Cruel to Claude. The Reason Should Concern Us All

There are lots of reasons to have feelings about artificial intelligence. It seems like everyone thinks AI will replace human workers. There are real questions about the ethics and legality of scooping up basically the entire internet to train models. There are even questions about how they work and whether they could someday become dangerous. If you understand nothing else about AI chatbots, understand this: They do not have feelings. They are not people, and they don’t experience pain or frustration. They’re token-generation machines. You give them a prompt, they break it down into tokens, and then they generate new tokens one at a time using a predictive model trained on enormous amounts of human writing. At its basic level, it’s 1s and 0s running through transistors in a GPU in some data center in Boise, Idaho, or wherever. Sometimes those 1s and 0s come back out as tokens that sound a lot like feeling words. But, and this is important, using words to describe feelings isn’t the same thing as having feelings. Claude produces those words only because it was trained to produce a response the way humans would respond in a similar situation with similar language input. That doesn’t make it human or capable of human experiences. Which is why it’s worth noting that Anthropic issued an update to its usage policy that bans being cruel to its model. You cannot be cruel to something incapable of experiencing pain or suffering, and an LLM is capable of neither. Look, thinking that it’s absurd that Anthropic is making this a policy isn’t the same as condoning bad behavior. I don’t condone cruelty in any situation. No one should. But my reason is just that it’s bad to practice cruelty, not because you might hurt a chatbot’s feelings. In its updated usage policy, which takes effect November 12, Anthropic says it now prohibits “sustained and needless abusive or cruel behavior toward our models.” The company says the new policy applies only to what it calls extreme cases of repeated abuse, not ordinary frustration, criticism, or legitimate research. Apparently, you’ll still be able to tell Claude that it’s doing a bad job; you just can’t do it repeatedly and for no reason. On the one hand, that might seem like a relatively harmless policy. We can even all agree that antisocial or cruel behavior shouldn’t be encouraged. But I also think that you can believe more than one thing at a time. For example, you can believe it’s not useful for you or the experience of using the model to be overly aggressive, taunting, or insulting toward a piece of computer software. Likewise, if you find yourself drawn toward trying to repeatedly engage an AI chatbot in disturbing conversations, you might want to do some self-reflection and seek help. But I don’t think that is inconsistent with also believing that Anthropic’s policy raises a much bigger question about how AI companies want us to think about their products. Specifically, why would a company that builds software believe it needs to protect that software from cruelty? Anthropic’s stance on Claude’s feelings This isn’t the first time the company has said that its AI models might need some measure of moral consideration. Last year, the company announced that Claude could end conversations with users who are repeatedly abusive. At the time, Anthropic explained that the decision was motivated primarily by research into what it calls “model welfare.” Anthropic has contributed to this problem with its stance on whether its models are conscious or capable of subjective experiences. It has repeatedly suggested the possibility that they might be, and now it’s using that as a reason to establish rules about how people interact with its software. There is a meaningful debate about consciousness and why physical and chemical interactions result in a sense of self-being and subjective experience. It’s a big enough challenge that it’s even called the hard problem of consciousness. But it’s a leap to go from the idea that scientists and philosophers don’t fully understand consciousness to suggesting that a chatbot might be capable of experiencing cruelty. To be fair, there’s nothing wrong with Anthropic deciding that it doesn’t want people using Claude to practice abusive behavior. There are reasonable arguments for discouraging that sort of thing, especially if it might reinforce harmful behavior toward actual people. But that’s not the same thing as saying people shouldn’t be cruel to Claude. The people problem The bigger problem is that Anthropic is suggesting something that should concern us all: It’s conditioning us to think of chatbots as though they were people. We already know that users can develop emotional attachments to AI companions. AI chatbots are designed to communicate naturally, similar to the way a human would. For people who are lonely or emotionally vulnerable, the distinction between a simulated relationship and actual human connection can become increasingly difficult to discern. That’s why the language these AI companies use is especially important. When a chatbot tells someone it cares about them, the company behind it should be making clear that those words are just tokens generated by software, not evidence of some kind of personal connection or affection. But Anthropic is, instead, suggesting the possibility that the relationship might work both ways. If Claude can be treated cruelly, wouldn’t that suggest it can also experience kindness? If it can suffer, couldn’t it also care about the person it’s talking to? To be fair, Anthropic doesn’t explicitly make those claims. But suggesting that its models need to be protected from pain and suffering introduces a dynamic that points to only one thing—it sure seems like it wants you to think of it as a person. I would argue that’s exactly the sort of confusion AI companies should be working to prevent. Right now, AI companies are pushing their technology at a relentless pace. The result of that is that it is becoming far more useful. At the same time, it is becoming incredibly complex, and it’s getting harder to understand how it works. Those companies should be trying to help users understand the difference between language that sounds like human feelings and emotion, and actual human experience. Obviously, Anthropic has every right to set rules for how customers use its products. But that doesn’t mean we shouldn’t ask questions about those rules, especially when they reinforce potentially misleading ideas about what artificial intelligence is. In fact, the most concerning thing about Anthropic’s new policy isn’t whether Claude might someday develop feelings. It’s that one of the companies building the world’s most powerful AI systems is already asking us to behave as though it has. EXPERT OPINION BY JASON ATEN, TECH COLUMNIST @JASONATEN

Google’s Newest Gemini Model Can Handle Your Invoices, Contracts, and Taxes. There’s Just 1 Catch

Buried in administrative tasks? Google’s newest Gemini model could help do them for you. On Wednesday, the search giant unveiled Gemini 4 Argon, its new frontier AI model. Google said Argon excels at complex tasks in areas like legal research, taxes, and finance. It ranks first on a key leadership board for knowledge work tasks, beating the likes of Anthropic’s Claude and OpenAI’s GPT-6 models. To support Argon’s complex workflows, Google is expanding the model’s output limit from 64,000 tokens to a whopping 1 million tokens. (Tokens are the chunks of data AI models process and generate.) Google said the higher limit gives Gemini more room to reason through tough problems and generate longer responses in one go. For founders, that could mean tackling complex workflows without breaking them into smaller requests. Google claims Argon can process invoices, draft contracts, and plan taxes exceptionally well, leaving more time for humans to build brands, land clients, and iterate on products. It could also mean higher-quality outputs so the tokens you’re burning through are well spent. Google announced introductory pricing of $2 per million input tokens and $10 per million output tokens, meaning longer workflows can rack up your AI bills. Google said Argon is accelerating its internal workflows, including optimizing quantum computing algorithms, reducing memory use in data centers, and migrating large codebases to new programming languages, helping its teams build faster. The company also touts Argon’s enhanced cybersecurity capabilities, saying it can find, validate, and fix software vulnerabilities. Founders will have to wait to get their hands on Argon. Google began its initial rollout through the Fairwind program, which gives vetted partners, including governments, healthcare providers, and telecommunications operators access to its AI cybersecurity tools. Participants can use Argon to find and address security flaws, with access restricted to internal cybersecurity teams. Google says it will gather feedback and strengthen safeguards before expanding availability. The release comes four weeks after Google launched Gemini 3.8 Flash, described as offering stronger reasoning and coding capabilities at the same speed and low cost as its predecessor. Meanwhile, Gemini’s audience keeps growing. In August, Google said the Gemini app surpassed 1 billion monthly users, making it the fastest-growing product in the company’s history. Google has its eyes on small businesses, too. Josh Woodward, who oversees Gemini and Google Labs, said on X that businesses are using Gemini to create marketing materials. For startups, Google offers a vertically integrated approach, hosting AI models, cloud infrastructure, its own chips, and technical assistance under one roof. Google Cloud’s startup chief Darren Mowry previously told Inc. that this approach can shorten the time it takes founders to build. BY AARON MOK

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