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