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Tesla hasnt enjoyed a lot of good news lately. Just the last few days have brought news of the electric car-makers dimming sales in Europedown 42% in the European Union compared to a year ago, in the face of competition from Chinese rival BYDand a recall in Australia of Model Y SUVs to address a potential software glitch that may cause passenger windows to close with excessive force. In its most recent quarter, the company reported revenue of $22.5 billion, a 16% decrease. So it makes sense that this week Tesla made a fresh effort to change the story about the brand and its trajectory. It did so with the release of its latest Master Plan, a public document intended to declare its future goals and plans. Its safe to say, not everyone was convinced. Critics complained that, at best, its vague, and at worst sounded like AI-generated buzzword soufflé. (Our desire to push beyond what is considered achievable will foster the growth needed for truly sustainable abundance.”) While this did not appear to be the conversation-changer Tesla sought, the company is right to try to give the narrative around its business a boost. CEO Elon Musk seemed to acknowledge this when he posted on X that 80% of Teslas value will eventually come from its Optimus robotsa prediction that, while lacking details, was quickly picked up as a sign that he is thinking beyond EVs. The problem is that Musk has projected similar narratives before: namely, that Teslas future is really about self-driving taxis, humanoid robots, artificial intelligence, or a combination of all the above. The narrative hasnt stuck, partly because Tesla isnt really a leader in any of those sectors. Even his latest Optimus prediction was tossed in among what has now become a familiar manic barrage of X posts about a variety of cultural and political issues that have nothing to do with making cars or running a business. The Tesla Master Plan seemed to be worth only a sliver of the CEOs attention. (And thats despite the company recently granting Musk $29 billion in new Tesla shares in what seemed like an attempt to get him to focus). A master plan to rule all other master plans This is actually Teslas fourth Master Plan, and in a way they have all been marketing documents. The first two were written by Musk himself, basically blog posts as impassioned manifestos; the third was a nerdier, benchmark-filled white paper. Each offered a narrative around Teslas mission, its context, its supposedly world-changing implications. But lately, the Tesla narrative has come from without: the apparent flop of the Cybertruck, the loss of market share for even its more successful vehicles, the endless controversies stemming from Musks DOGE adventures, culminating in his de facto dismissal from President Trumps inner circle. The upshot is an ongoing case study of a once-mighty but now-tarnished brand. And to be clear, the brands historic strength is significant, and retains considerable loyalty from consumer and investor fans alike. The company is still worth more than $1 trillion, and its shares are trading at around $340, which is right in between a recent range of about $220 in March to $488 last December. It might even get a short-term sales bump from EV shoppers looking to buy before the Trump administration phases out consumer incentives. The whole sector is in for a challenge once that happens. While Tesla spins its wheels on putting forward a story about that brands next chapter, rivals are racing to define the brand from the outside. Musk, his attention still seemingly divided as he posts constantly on X, flirts with politics, and scraps for a lead role in the AI free-for-all, used to be the lead author of the Tesla narrative. Its a brand that has been defined far less by traditional marketing than by its CEOs endless zeal. With that zeal directed elsewhere, a different Tesla story is taking hold: an EV maker run by a guy who doesnt really care about cars anymore.
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E-Commerce
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.
Category:
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.
Category:
E-Commerce
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