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As I type, Microsoft Copilot suggests ways to continue, restructure, or even rewrite this very sentence. On the surface, it feels like a small thing, no more remarkable than Gmail finishing an email or Google predicting a searchbut small things can have outsize influence. Just as the steady drip of water on rock can carve out new channel over time, so predictive text has already reshaped how we write. Research from Harvard has shown that predictive text systems do not just make texting easierthey change the content of those texts, reducing lexical diversity and making our writing more predictable. This flattening effect is beginning to extend beyond language. Filmmakers have been worried for some time now about the rise of algorithm moviesmovies whose form and content are dictated by what recommendation algorithms tell companies about viewer preferences, instead of by the creative imagination of writers and directors. And if executives arent careful, we can soon expect the emergence of algorithm businessstrategy, operations, and culture flattened out by the rise of LLMs and the race to adopt AI. AI Models as Consensus Machines Large language models have become the invisible architects of business strategy. For an increasing number of executives, these AI systems have become default first advisers, strategists, and thought partners. And, as we have already seen with language and movies, this kind of progression can measurably narrow the range of ideas available to us. Social media is the canary in the coal mine here. Anyone with a LinkedIn account knows that posts from different individuals often sound very similar and that the same ideas are often recirculated again and again. Taken in isolation, this could be seen as a feature of the homogenizing effect of social media algorithms. But the phenomenon is not localized to posts that might be driven by the demands of a recommendation algorithm. Pitches are beginning to sound identical, marketing copy is becoming strangely generic, and if the process continues unchecked, we can expect that the internal documents, analyses, and company strategies will begin to mirror those found in other businesses. In the longer term, we could even see company cultures lose their distinctiveness as differentiating factors begin to blur together. Smarter Alone, Narrower Together Generative AI can massively boost performance and productivity. A recent meta-study found, for example, that humans working with AI were significantly more creative than humans working alone. However, as both that study and a paper in Nature show, while using LLMs improves the average creativity of an individual, it reduces the collective creative diversity of groups. Individuals find their access to new ideas boosted but collectively we end up tapping in to a narrower range of ideas. The result is that AIs promise of supercharged innovation may actually narrow the frontiers of possibility. Competitive Convergence Almost 20 years ago, Michael Porter introduced the idea of competitive convergence. Briefly put, this is a phenomenon that sees companies beginning to resemble their competitors. They chase the same customers in the same ways, their strategies and pricing models become indistinguishable, their operational processes and supply chains end up looking identical. This process traps companies into a race toward the middle, where distinctiveness disappears and profits are squeezed. With AI, businesses risk falling victim to an accelerated and intensified version of this process: a Great AI Convergence in which operational playbooks, strategic vision, and culture become increasingly generic as organizations increasingly drink from the same conceptual fountain. AI can optimize efficiency, but it cant capture the human fingerprints that make a company truly distinctive. Your organizations war stories, hard-won lessons, contrarian beliefs, and cultural quirks dont live in any training set. They live in memory, practice, and identity. And when strategy, messaging, or culture is outsourced to AI, there is a real danger that those differentiating elements will vanish. The risk is that companies will end up losing the authentic, uncommon, and sometimes counterintuitive features that are the vehicle for their uniquenessthe things that makes them them. The Three Pillars of Business Homogenization Business homogenization can be broken down into three pillars.1. Strategic Convergence: When Every Plan Looks the Same Your competitor asks Claude to analyze market opportunities. You ask ChatGPT. Whats the result? Well, the effect is subtle rather than dramatic. Because the same models are shaping the same executives, the outputs dont collapse into outright uniformity so much as drift toward a narrow band of acceptable options. What looks like independent strategic judgment is often just a remix of the same patterns and playbooks. And so, over time, the strategic choices companies make lose their texture and edge. 2. Operational Convergence: The Automation of Averageness Companies are already acting on the huge potential that AI has in the realm of operations. For example, Shopify and Duolingo now require employees to use AI as the default starting point for all tasks, and one of the major reasons for this is the prospect of the efficiency gains that AI can deliver. It is absolutely right that companies use AI to transform operations. But when every company uses similar AI tools for operations, we can expect a drift toward similar processes. Customer service chatbots might converge on the optimal patterns for customer interactions, for exampleand in this convergence lies both danger and opportunity. The opportunity is optimized efficiency. The danger is that companies lose what differentiates them and drives their unique value proposition. It is essential that leaders recognize this danger so they can begin to think intentionally about authenticity as a potential edge in operations. For instance, it might be worth sacrificing a small level of customer handling speed for a chatbot that delivers quirky and engaging responses that reflect the companys authentic culture and character. 3. Cultural Convergence: When Companies Lose Their Souls Perhaps the most insidious risk is cultural convergence. When AI drafts your company communications, writes your value statements, and shapes your employee handbooks, it imports the average corporate culture encoded in its training data. he quirks, the specific language, the unique ways of thinking that define your organizationall get smoothed into statistical averages. Over time, the effect will not only dilute external brand perception but also diminish the sense of belonging employees feel. When people can no longer recognize their companys voice in its own communications, engagement erodes in ways that spreadsheets wont immediately capture. From Artificial Intelligence to Authentic Intelligence If AI accelerates sameness, then competitive advantage comes from protecting and amplifying what makes you different. Heres how: Audit your uniquenessIdentify the knowledge, stories, and perspectives your company holds that no AI model can access. What do you know that others dont? Create proprietary datasetsFeed AI your unique datacustomer insights, field notes, experiments, failuresinstead of relying on the generic pool of information available to everyone. Establish AI-free zonesDeliberately protect areas where human judgment and lived experience matter moststrategy off-sites, cultural rituals, moments of customer intimacy. Adversarial promptingDont just ask AI for answers. Ask it for the contrarian view, the blind spot, the uncomfortable perspective. Authentic Intelligence In a world in which every company has access to the same artificial intelligence, the real competitive advantage isnt having AIits having something AI cant replicate. And that can only come from authentic intelligence: the messy, contradictory, beautifully human insights that no model can generate. AI is the price of admission. Authenticity is how you win.
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E-Commerce
When you apply sunscreen at the beach, it doesnt necessarily stay on your skin. Some of that sunscreen can wash off when you swim, and the chemicals that shield you from ultraviolet rays end up damaging marine life such as coral reefs, sea urchins, and green algae. Each year, an estimated 6,000 to 14,000 metric tons of commercial sunscreen gets into the ocean. Places like Hawaii and Aruba have already banned certain sunscreens. A new sunscreen created by material scientists at Nanyang Technological University in Singapore, however, doesnt harm corals. And its not a mineral sunscreen either, which are often thick and can leave a white cast on your skin. Instead of using chemical or mineral filters, it blocks UV waves, thanks to the pollen in camellia flowersand it also keeps your skin cool in the sunlight. The research team was specifically looking at bio-inspired materials as a way to make a more sustainable, safer sunscreen. We were inspired by the natural resilience of pollen grains, which have evolved over millions of years to protect plant genetic material from harsh UV radiation and environmental stress, Nam-Joon Cho, a professor at NTU and the President’s Chair in Materials Science and Engineering, says over email. Though pollen has been studied in cosmetic science before for its antioxidant or nutrient properties, it hasnt, to Cho’s knowledge, been used directly as a UV shield before. Pollen is unique because its structure makes it capable of filtering out harmful UV rays while being visually transparent; its also biodegradable. To turn pollen into sunscreen, the researchers processed the inner parts of the pollen shell into a microgel formula, which applies as an ultra-thin layer on the skin. The pollen-based sunscreen also creates a cooling effect because those microgels block UV light while letting most of the visible and near-infrared light pass through, without absorbing them. Since those wavelengths carry most of the suns heat, less energy gets trapped and converted into warmth on the skin, Cho says. As a result, the skin stays cooler compared to when commercial sunscreens, which absorb more of that heat-carrying light, are used. (Researchers also made a sunscreen from sunflower pollenwhich blocked UV rays but didnt have that cooling effectthough it wasnt as effective in tests.) In lab tests, the camellia pollen-based sunscreen blocked UV radiation at levels comparable with conventional mineral sunscreens, with an SPF of about 30. In lab tests with corals, commercial sunscreen spurred coral bleaching in just two days, with coral death happening in around six days. But the pollen-based sunscreen didnt harm the corals, even up to 60 days. That was crucial for the researchers. Using pollen, which is already a natural component of ecological cycles, allowed us to design with environmental safety in mind, Cho says. The pollen sunscreen may be safer for humans, too. It doesnt include nanoparticles such as titanium dioxide or zinc oxide, which sometimes raise inhalation and safety concerns, Cho says. And even if you suffer from spring allergies, the pollen sunscreen shouldn’t bother you. Camellia pollen is generally considered nonallergenic, and when the pollen is processed, any allergenic proteins are removed. Next, the researchers want to optimize the sunscreen for longer wear and water resistance. They’re also looking at ways to use pollen in all other applications, like drug delivery or food protection. The larger vision, Cho says, is to build a portfolio of bio-inspired, eco-friendly materials that can replace petrochemical-based products in everyday life.
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E-Commerce
Despite billions of dollars of AI investment, Googles Gemini has always struggled with image generation. The companys Flash 2.5 model has long felt like a sidenote in comparison to far better generators from the likes of OpenAI, Midjourney, and Ideogram. That all changed last week with the release of Googles new Nano Banana image AI. The wonkily named new system is live for most Gemini users, and its capabilities are insane. To be clear, Nano Banana still sucks at generating new AI images. But it excels at something far more powerful, and potentially sinisterediting existing images to add elements that were never there, in a way thats so seamless and convincing that even experts like myself cant detect the changes. That makes Nano Banana (and its inevitable copycats) both invaluable creative tools and an existential threat to the trustworthiness of photosboth new and historical. In short, with tools like this in the world, you can never trust a photo you see online again. Come fly with me As soon as Google released Nano Banana, I started putting it through its paces. Lots of examples onlinemine includedfocus on cutesy and fun uses of Nano Bananas powerful image-editing capabilities. In my early testing, I placed my dog, Lance, into a Parisian street scene filled with piles of bananas and showed how I would look wearing a Tilley Airflo hat. (Answer: very good.) [Image: Thomas Smith] Immediately, though, I saw the systems potential for generating misinformation. To demonstrate this on a basic level, I tried editing my standard professional headshot to place myself into a variety of scenes around the world. [Image: Thomas Smith] Heres Nano Bananas rendering of me on a beach in Maui. [Image: Thomas Smith] If youve visited Wailea Beach, youll recognize the highly realistic form of the West Maui Mountains in soft focus in the background. I also placed myself atop Mount Everest. My parka looks convincingthe fact that Im still wearing my Travis Matthew polo, less so. [Image: Thomas Smith] 200s a crowd These personal examples are fun. Im sure I could post the Maui beach photo on social media and immediately expect a flurry of comments from friends asking how I enjoyed my trip. But I was after something bigger. I wanted to see how Nano Banana would do at producing misinformation with potential for real-life impact. During last years Presidential elections here in America, accusations of AI fakery flew between both candidates. In an especially infamous example, now-President Donald Trump accused Kamala Harriss campaign of using AI to fake the size of a crowd during a campaign rally. All reputable accounts of the event support the fact that photos of the Harris rally were real. But I wondered if Nano Banana could create a fake visual of a much smaller crowd, using the real rally photo as input. Heres the result: [Image: Thomas Smith] The edited version looks extremely realistic, in part because it keeps specific details from the actual photo, like the people in the foreground holding Harris-Walz signs and phones. But the fake image gives the appearance that only around 200 people attended the event and were densely concentrated in a small space far from the plane, just as Trumps campaign claimed. If Nano Banana had existed at the time of the controversy, I could easily see an AI-doctored photo like this circulating on social media, as proof that the original crowd was smaller than Harris claimed. Before, creating a carefully altered version of a real image with tools like Photoshop would have taken a skilled editor daystoo long for the result to have much chance of making it into the news cycle and altering narratives. Now, with powerful AI editors, a bad actor wishing to spread misinformation could convincingly alter photos in seconds, with no budget or editing skills needed. Fly me to the moon Having tested an example from the present day, I decided to turn my attention to a historical event that has yielded countless conspiracy theories: the 1969 moon landing. Conspiracists often claim that the moon landing was staged in a studio. Again, theres no actual evidence to support this. But I wondered if tools like Nano Banana could fake some. To find out, I handed Nano Banana a real NASA photo of astronaut Buzz Aldrin on the moon. [Image: NASA] I then asked it to pretend the photo had been faked, and to show it being created in a period-appropriate photo studio. [Image: NASA/Thomas Smith] The resulting image is impressive in its imagined detail. A group of men (it was NASA in the 1960sof course theyre all men!) in period-accurate clothing stand around a soundstage with a fake sky backdrop, fake lunar regolith on the floor, and a prop moon lander. In the center of the regolith stands an actor in a space suit, his stance perfectly matching Aldrins slight forward lean in the actual photo. Various flats and other theatrical equipment are unceremoniously stacked to the sides of the room. As a real-life professional photographer, I can vouch for the fact that the technical details in the Nano Bananas image are spot-on. A giant key light above the astronaut actor stands in for the bright, atmosphere-free lighting of the lunar surface, while various lighting instruments provide shadows perfectly matching the lunar lander shadow in the real image. A photographer crouches on the floor, capturing the imagined astronaut actor from an angle that would indeed match the angle in the real-life photograph. Even the unique lighting on the slightly crumpled American flagwith a small circular shadow in the middle of the flagmatches the real image. In short, if you were going to fake the moon landing, Nano Bananas imagined soundstage would be a pretty reasonable photographic setup to use. If you posted this AI photo on social media with a caption like REVEALED! Deep in NASAs archive, we found a photo that PROVES the moon landing was staged. The Federal Government doesnt want you to see this COVER UP, Im certain that a critical mass of people would believe it. But why stop there? After using Nano Banana to fake the moon landing, I figured Id go even further back in history. I gave the system the Wright Brothers iconic 1903 photo of their first flight at Kitty Hawk, and asked the system to imagine that it, too, had been staged. [Image: John T. Daniels] Sure enough, Nano Banana added a period-accurate wheeled stand to the plane. [Image: John T. Daniels/Thomas Smith] Presumably, the plane could have been photographed on this wheeled stand, which could then be masked out in the darkroom to yield the iconic image weve all seen reprinted in textbooks for the last century. Believe nothing In many ways, Nano Banana is nothing new. People have been doctoring photos for almost as long as theyve been taking them. An iconic photo of Abraham Lincoln from 1860 is actually a composite of Lincolns head and the politician John Calhouns much more swole body, and other examples of historical photographic manipulation abound. Still, the ease and speed with which Nano Banana can alter photos is new. Before, creating a convincing fake took skill and time. Now, it takes a cleverly written prompt and a few seconds. To their credit, Google is well aware of these risks, and is taking important steps to defend against them. Each image created by Nano Banana comes with an (easy to remove) physical watermark in the lower right corner, as well as a (harder to remove) SynthID digital watermark invisibly embedded directly into the images pixels. This digital watermark travels with the image, and can be read with special software. If a fake Nano Banana image started making the rounds online, Google could presumably scan for its embedded SynthID and quickly confirm that it was a fake. They could likely even trace its provenance to the Gemini user that created it. Google scientists have told me that the SynthID can survive common tactics that people use to obscure the origin of an image. Cropping a photo, or even taking a screenshot of it, wont remove the embedded SynthID. Google also has a robust and nuanced set of policies governing the use of Nano Banana. Creating fake images with the intent to deceive people would likely get a user banned, while creating them for artistic or research purposes, as Ive done for this article, is generally allowed. Still, once a groundbreaking new AI technology rolls out from one provider, others quickly copy it. Not all image generation companies will be as careful about provenance and security as Google. The (rhinestone-studded, occasionally surfing) cat is out of the bag; now that tools like Nano Banana exist, we need to assume that every image we see online could have been created with one. Nano Banana and its ilk are so good that even photographic experts like myself wont be able to reliably spot its fakes. As users, we therefore need to be consistently skeptical of visuals. Instead of trusting our eyes as we browse the Internet, our only recourse is to turn to reputation, provenance, and good old-fashioned media literacy to protect ourselves from fakes. Now, if youll excuse me, Burning Man is just ending, and I should really get back to the festivities. [Image: Thomas Smith]
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E-Commerce
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