In my 25 years as an entrepreneur and advisor to owner-led companies, Ive seen too many business owners be consumed by the thing thats supposed to give them independence. They work longer hours and solve complex problems. Their company grows, yet theyre no closer to the life they wanted to create.
To be clear, growth in business is generally a good thing. But it does start to become an issue when you want to be bigger for the sake of being bigger.
Its a problem when theres a voice inside your head saying that if you just get to $100 million in revenue, then youll finally be a big shot: that youll get respect from your parents, your peers, and even the people you do not care about at all.
The power of ego
The subconscious reason youre drawn to bigger is that its exactly what your ego wants. Ego is your worst enemy. Its weak, frail, and emotional. It clouds your judgment by convincing you that bigger is better, pulling you toward it like a moth to a flame. It doesnt know why youre going to the flame, or what will really happen once you get there.
Your ego isnt concerned about risk. It lets you become complacent, distracted, or arrogant. It disconnects you from reality, providing you with bad feedback at the very moment you need clarity most. Ego tells you that your success to date was because of talent, rather than the years of grinding and hard work. Its the voice that discounts the important role that discipline and trial and error play in your success.
Whether your net worth is $10,000 or $100 million, the endless pursuit of more can drag you into a doom loop, a cycle of poor choices, and mounting pressure. Thats why growth for growths sake is always a trap.
The discipline of better
Better companies will grow because theyre better. They compound improvement across the four areas that matter mostbetter teams, better customers, better offerings, and better financials. Growth becomes the outcome of doing the right things, rather than the goal in itself.
Better is a choice. It means shaping your business so it enables you to live in the most rewarding way possible for you and the people you love. Better is about building a better life by design. Not just financially, but across the domains that matter: your health, your wealth, your wisdom, your happiness, and your family.
Better is about building an asset that compounds wealth, where that asset works for you. It is about having a business that excites you, that challenges you, and that you enjoy being part of. It is about being intentional with your effort.
When your focus is solely on growth at all costs, theres no guarantee that it will result in the life you want. In fact, many times, the chase for bigger ends up creating problemswhether thats toxic teams, indifferent customers, mediocre products, or ultimately unprofitability.
The irony, of course, is that when you focus on building better, you often end up with a bigger business anyway. But along the way, you also build freedom, wealth, and happiness.
What a better business looks like
If an owner-led company seeks to become better, what does that mean in practice? A better business needs to inspire its people. It should energize owners, leaders, employees, customers, suppliers, and even those watching from the outside.
A better business needs to earn loyalty. That means creating a real connection with customers so they trust you, stay longer, and become advocates. A better business needs to focus on what it can be the best at. That means striving to offer products that are not only good, but great.
Better businesses also need to be financially strong, and that requires a structure and operating system that can withstand tough times. Most importantly, a better business shouldnt drain the energy of its owner. The greatest energy drains come from poor planning, poor strategy, and the absence of a system to run the business.
In the end, a better business is one that creates value, sustains itself, and gives its owner the freedom to live life on their own terms, not the one that values growth above everything else.
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.
I was reading funding news last week, and I came to a big realization: Andreessen Horowitz is not a venture capital fund.
A lot of people are thinking it. So there, I said it.
And its not just Andreessen. Its all the big funds.
They started out as VC. They operate funds that invest in private early-stage companies. But they havent been VC funds for a very long time.
Thats not a knock on their success or influence, both of which are massive.
But to continue calling them venture capital is both disingenuous and damaging to how we understand our industry.
I vote we stop.
VC is not bifurcated. Its two totally different strategies.
First off, its worth noting that all of these firms are legally not venture capital firmsthey are registered investment advisers (RIAs). This means they can, and do, invest beyond early-stage private companies, in things like public companies, crypto tokens, nontraditional assets, and more. Andreessen, Sequoia, Insight, General Catalyst, Thrive Capital, SoftBank Vision Fund, Lightspeed . . . all RIAs. All massive. No longer just VC funds.
They’re big finance with a Sand Hill Road address.
And if you take an honest look at their actual venture strategy, youll also find it isnt really venture anymore.
Since its earliest iterations in the 1940s, venture capital has always meant investing in early-stage companies with the potential to generate alphahigh risk, high reward, uncorrelated with efficient (public) markets.
Its never been about investing in the obvious. Quite the opposite, in fact.
Thats not how big funds invest anymore. Andreessen partner Martin Casados viral tweet last week acknowledged this: Large funds are not picking contrarian bets. Theyre picking consensus ones.
All of them are chasing the same founders, outbidding each other in giant rounds, competing away alpha for themselves and each other.
Theyre okay with this. Their limited partners are okay with this. Theyll make money, presumably, off the beta. They have power, access, and cultural cache.
They are #winning.
But theyre not VC investing.
Make data meaningful again
Meanwhile, all the VC commentators (myself included) are tripping over ourselves about what this all means. My B-school classmate Rob Go wrote about VCs existential crisis (Viva la F!). Sapphires Beezer Clarkson says venture is broken. Carta data guru Peter Walker talks about the bifurcation of venture capital. Eric Newcomer describes it as a break between the haves and the have-nots.
Every VC report thats come out in the last few yearsCarta, PitchBook, Crunchbase, AngelList, all of themshow a few rounds and funds so large that they completely distort the data.
I say its time to split the data in two and analyze both strategies independently.
Remove the mega-funds and youll see a clear, consistent picture of the actual venture ecosystem. Venture capital as its always beensmall, early, messy, contrarian, alpha-seeking.
Analyze the mega rounds/mega funds on their own. Theyre not outliers; theyre their own investment category. I call it consensus capital.
What is Consensus Capital?
I might come back to refine this point in the future. From what I see today, there are four defining factors for consensus capital:
The focus on giant outcomes only: Forget unicorns, their hunt is for trillion-dollar outcomes.
The belief that only one type of founder can achieve such an enormous outcome: the consensus founder, if you will.
Complete price-insensitivity, or a willingness to pay up at the entry point for that one type of founder.
Funds so large that they can deploy huge amounts (tens or hundreds of millions) in a single early-stage round.
Again, none of this is a knock. There are great consensus bets to be made, capital itself becomes the moat for some of these companies, exits may be (are presumed to be) sooner than true venture exits, and youll make money off the beta, highly correlated with the growth of the entire category.
Crucially, you can deploy a lot more capital in one go following this strategy than via true venture capital. And large LPs want to move large amounts of capital. To them, the juice must be worth the squeeze.
In other words, none of the above means you wont make money off consensus capital. It just means youre not seeking alpha.
What this means for founders
You might ask: If money is still flowing into startups, who cares what we call it?
Thats fair enough. The problem is, when we talk about these giant funds as if they represent venture capital, we flatten the story of our ecosystem, and mislead non-consensus founders in the process.
Consensus capital goes to founders who have a very particular, very predictable pedigree. They went to a handful of schools, worked at a handful of startups, or built at a handful of AI labs. They are highly discoverable; you can literally set up an AI agent to find them before they raise. Many consensus investors do.
If youre one of those founders, it should be very easy to raise consensus capital. The different funds will compete aggressively against each other, marking up the price of your company, effectively erasing the alpha from their own portfolio.
If youre not one of those founders, its not the end of the world. Sure, it will be harder. But there is an entire generation of alpha-seeking early-stage investorstrue venture capitalists (what Marc and Ben were 25 years ago)who are looking for outstanding non-consensus founders like you.
This also doesnt mean that the two types of capital cant coinvest. You can become a consensus founder even if you lack the pedigree. The easiest (albeit not easy) way to do it is to go through a top-tier accelerator like Y Combinator. Or you can do it through traction and velocity alone.
When you make your business undeniable, consensus capital will follow.
A call for honest labels
Everyone predicting the death of venture capital is wrong. Plain and simple. As long as there are non-consensus founders with a real shot at the Amercan Dream, investors willing to partner with them, and exit opportunities awaiting on the other side, VC will be alive and well.
But only for those who actually follow the strategy.
Let’s call the different funds what they actually are. Let’s create honest taxonomies that help founders find the right capital for their stage and ambition. Lets split the analysis so the benchmarks for traction, valuations, and round sizes are not distorted by the beta-rich consensus crew. Let’s give LPs clarity about what they’re actually buying when they write checks to different types of funds.
And let’s remember what venture capital really is: messy, early, conviction-driven, non-consensus bets on the future.
Austin just got its first official branding in nearly 200 years, and it’s an homage to natural springs, rolling hills, and a city thats emerged as a liberal island in an overwhelmingly red state.
The branding, revealed at a press conference on September 4, includes an official logo for the city, a wordmark, and a set of guidelines for how Austins government will show up online. It was designed through a partnership between the Austin-based agency TKO, which handled an extensive preliminary interview process, and the Austin branch of the design firm Pentagram, which led the actual brand design.
According to Pentagram partner DJ Stout, the process was a balancing act of creating an identity that was both authoritative enough for the city government and representative of the average Austinite. Already the result is generating a firestorm of negative attention on social mediabut Stout says thats completely expected in todays divisive branding reaction ecosystem.
The new logo (left) and the previous city seal [Images: City of Austin]
Input from all over
In the years since its founding in 1839, Austin has developed many fragmented logos and symbols for various departments across the city, but never a cohesive brand system. By 2018, when Austin began considering a unified identity, Stout says there were more than 300 different amalgamations of logos for the city floating around.
Pentagram officially started on the branding effort in October 2024 after winning an initial competition to helm the project. At that point, the city and TKO had already spent several months conducting surveys with citizens, stakeholders, and government employees about how the identity should show up.
One of the main challenges that Stouts team faced in the brainstorming process was a common obstacle when working with bureaucratic organizations: The final look had to pass muster with multiple audiences, often with vastly different opinions and agendas.
[Image: courtesy Pentagram]
Obviously its meant to represent all the Austinites and everybody in the city, Stout says. But [the city] was very clear that our objective was to come up with a solution for the citys needs. Talk about design by committeedesigning anything for city government is the ultimate design by committee.
Initially Stout championed the idea of an Austin star symbol that would nod to Texass iconic lone star while also adding some unique Austin flair to a commonly used shape in place-based identities, similar to Chicagos star logo or even Canadas maple leaf.
However, just from showing these to lots of different groups, audiences felt that anything that smacked of state government did not fit the personality and the attitude of people who live in Austin, Stout says. Austin is a little liberal island, politically.
For a city that lives by the tagline “Keep Austin Weird” and celebrates “hippie” culture, as Stout puts it, the identity needed to tap into a less politically leaning idea.
[Image: courtesy Pentagram]
A logo inspired by nature
Ultimately, instead of referencing Texas at large, Austins new identity is intended as a love letter to the citys natural landscape. Stout was specifically inspired by a movement that started around the time he moved to Austin in 1986 called Save Our Springs, when the local community came together to fight water pollution and advocate for the protection of Barton Springs and the Edwards Aquifer that feeds it.
Barton Springs Pool [Photo: Brandon Bell/Getty Images]
I would say without a doubt that the reason people love Austin so much is because we have all this beautiful water, Stoutsays. We have Barton Spring[s], which is this giant spring-fed pool; we have Deep Eddy, which is a spring-fed public pool; the Colorado River runs right through Austin. The whole modern city is built around Lady Bird Lake.
[Image: Pentagram]
To reflect those water sources, the logoa stylized Aincludes a flowing blue wave. Its sandwiched by two green lines: One represents Austin as the start of Texass Hill Country, and the other references the urban canopy that covers the city. Even the hue of blue that Stouts team chose was inspired by a local phenomenon nicknamed the violet crown, which refers to the purplish color of the sunset.
The wordmark font, a serif called Museo Slab, was chosen both because it feels a little Western, Stout says, and because it offered a more authoritative element for the city government to work with alongside the logo.
The identitys overall aesthetic is predictably simple and corporate for a project with so many voices at the table. (The colorful Los Angeles Tourism logo is one exception.) Still, the Austin logo does manage several clever nods to the features that make the city unique.
[Image: courtesy Pentagram]
Why the internet hates rebrands
Austinites wasted little time voicing their discontent with the identity. A post from the local Instagram account @365thingsAustin has drawn more than 1,000 comments, most of which are resoundingly negative.
Did they make it on Canva?! one commenter asked. [I]t is so bad i want to give it a zero but thats not possible so i give it a 1, another said.
Several locals seem particularly concerned with the cost of the project. Jessica King, Austins chief communications director, said in the press conference that the total cost to the city was $1.1 million. Stout adds that Pentagram and TKO split a total payout of $200,000.
That cost $1.1M?! I could think of so many better ways to spend that money . . . there had to have been some prepping for winter weather that couldve taken place instead of being surprised every year! one commenter said.
Stout is unfazed by the backlash, which he says has become a side effect of working in the industry over the past several years. He points to Pentagrams work on Hillary Clintons presidential campaign and his own rebrand for Loyola Marymount University as two examples of design efforts that received intense pushback when they first debuted but have since been recognized as solid identities.
Its because of social media, Stout says. Back when I first started about 40 years ago, nobody even knew what an identity system was.
In an era when branding has become a hot topic in the internet reaction economy (see: Cracker Barrel retracting its latest rebrand over backlash) its almost impossible to design an identity that the internet will actually celebrate. As for the Austin branding, Stout believes Austinites will eventually come around to the look after the unfamiliarity wears off.
I’ve been a partner for 25 yearsthis is not one of the higher-paying jobs, Stout says. I did it because I love this city, and because I’m from the city, and it really means a lot to me.
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.
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.
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]
Youve probably encountered images in your social media feeds that look like a cross between photographs and computer-generated graphics. Some are fantasticalthink Shrimp Jesusand some are believable at a quick glanceremember the little girl clutching a puppy in a boat during a flood?
These are examples of AI slop, or low- to mid-quality contentvideo, images, audio, text or a mixcreated with AI tools, often with little regard for accuracy. Its fast, easy, and inexpensive to make this content. AI slop producers typically place it on social media to exploit the economics of attention on the internet, displacing higher-quality material that could be more helpful.
AI slop has been increasing over the past few years. As the term slop indicates, thats generally not good for people using the internet.
AI slops many forms
The Guardian published an analysis in July 2025 examining how AI slop is taking over YouTubes fastest-growing channels. The journalists found that 9 out of the top 100 fastest-growing channels feature AI-generated content like zombie football and cat soap operas.
The song “Let it Burn,” allegedly recorded by a band called The Velvet Sundown, was AI-generated.
Listening to Spotify? Be skeptical of that new band, The Velvet Sundown, that appeared on the streaming service with a creative backstory and derivative tracks. Its AI-generated.
In many cases, people submit AI slop thats just good enough to attract and keep users attention, allowing the submitter to profit from platforms that monetize streaming and view-based content.
The ease of generating content with AI enables people to submit low-quality articles to publications. Clarkesworld, an online science fiction magazine that accepts user submissions and pays contributors, stopped taking new submissions in 2024 because of the flood of AI-generated writing it was getting.
These arent the only places where this happenseven Wikipedia is dealing with AI-generated low-quality content that strains its entire community moderation system. If the organization is not successful in removing it, a key information resource people depend on is at risk.
Last Week Tonight with John Oliver delves into AI slop.
Harms of AI slop
AI-driven slop is making its way upstream into peoples media diets as well. During Hurricane Helene, opponents of President Joe Biden cited AI-generated images of a displaced child clutching a puppy as evidence of the administrations purported mishandling of the disaster response. Even when its apparent that content is AI-generated, it can still be used to spread misinformation by fooling some people who briefly glance at it.
AI slop also harms artists by causing job and financial losses and crowding out content made by real creators. The placement of this lower-quality AI-generated content is often not distinguished by the algorithms that drive social media consumption, and it displaces entire classes of creators who previously made their livelihood from online content.
Wherever its enabled, you can flag content thats harmful or problematic. On some platforms, you can add community notes to the content to provide context. For harmful content, you can try to report it.
Along with forcing us to be on guard for deepfakes and inauthentic social media accounts, AI is now leading to piles of dreck degrading our media environment. At least theres a catchy name for it.
Adam Nemeroff is an assistant provost for innovations in learning, teaching, and technology at Quinnipiac University.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
A few years ago, we had a bottleneck within our organization at Super.com, the membership program focused on saving, earning, and credit building. Every new idea depended on our engineers, and our internal requests were piling up faster than we could clear them.
We were adding new people to our company every week, but our engineering team was underwater. Every new feature, every minor internal tool, every process tweak depended on our developers. We were hiring as fast as we could, but it felt like shoveling sand against the tide.
So we tried something different. We started teaching nontechnical employees (designers, product managers, operations leads, corporate support teams) how to build their own tools and automations. At first it felt radical. Then it felt obvious.
Fast forward: we grew to over $200M in annual revenue with only about 200 people. We stopped the hiring frenzy, but the business was growing faster than ever. Along the way, we documented everything in an internal playbook for our team. Soon our LinkedIn inboxes and emails were full of messages from other leaders trying to do the same thing. Turns out the problems we had been solving werent unique.
Here are the five lessons that have resonated most with other leaders about empowering nontechnical teams to build their own solutions, and why they matter to any leader.
1. Create Spaces Where Technical and NonTechnical Minds Meet
Inside our company we set up two AI guilds: one for technical implementation (e.g., building tools) and one focused on adoption and use cases (e.g., using tools). They met monthly, included people from every department, and shared concrete experiments. A product manager might present how she used an AI tool to understand the codebase without tapping on the shoulder of an engineer. An operations lead might show how he used a simple script to automate dispute management.
The takeaway: dont keep AI or automation knowledge locked in engineering. Build crossfunctional forums that normalize sharing wins, questions, and learnings. Those conversations surface use cases youd never see from the top down.
2. Invest in Easy-to-Use Tools
You cant empower nonengineers if the only tools they have require a CS degree. We invested in lowcode environments like Superblocks, Zapier, Amplitude, and Glean Agents, and we made tools that are typically only used by developers, such as Cursor (AI IDE) and Coder (Remote Environments), accessible.
Our developer operations team took on the challenge of making onboarding as simple as possible. They stripped out every unnecessary step and automated the rest, until getting set up took less than 10 minutes. We learned quickly that if a tool required more than 10 minutes of training, adoption would stall. Most non-technical teammates could follow the instructions on their own, but for anyone who preferred extra help, our IT team sat down with them one-on-one.
3. Set Guardrails That Empower
We published a clear internal AI policy that spelled out approved use cases (like automation scripts, bugfix prototypes, and research tools), quality standards (human oversight required for anything customerfacing or that becomes part of routine process), and security guidelines (no sensitive data in prompts without review).
Engineers didnt police these policiesthey coached. Any piece of code went through review, whether it came from an engineer or not. That consistency was the point: non-technical team members could submit pull requests, and instead of dismissing them, engineers gave feedback the same way they would with peers. Coaching meant guiding contributors through fixes and best practices, not shutting the door. Guardrails, not gatekeepers, is what makes experimentation sustainable.
4. Celebrate Small Wins Publicly
When someone outside engineering built something that moved the needle (like an operations process automation or AI triage of customer reported issues), we made sure everyone heard about it. These wins were shared in weekly business reviews and companywide meetings. That visibility did more than motivate others to try; it changed our culture. During objectives and key results planning wed prompt each team to consider, Could AI help me hit my goals faster? Could I build this myself instead of waiting? Sharing wins turns isolated hacks created by individuals into company-wide capabilities.
5. Rethink the Role of Your Engineers
When non-engineers have access to the right tools, software engineers become even more valuable. At our company, 93% of developers use AI tools daily. Engineers still own the hard, highimpact work: major features, architecture, deep debugging. But now they spend less time answering basic questions or making tiny fixes for other teams. The result: your best technical talent gets to focus on ambitious projects, while everyone else can handle the smaller, more routine tasks themselves.
In a world where AI and lowcode tools are everywhere, the companies that win wont just have great engineers. Theyll have a culture that empowers everyone to build.
While executives spend billions on meditation apps, yoga retreats, and wellness programs, American stress levels continue to skyrocket. A recent study of 90 workplace wellness interventions found most (with one exception detailed below*) had no positive effectand sometimes even made things worse. Our research from last year found that the majority of us tend to stress out more trying to get rid of stress. Talk about a negative spiral!
We’re trying harder than ever to eliminate stress, yet workplace anxiety has reached crisis levels, just in time for AI disruption to take people over the edge.
Here’s the uncomfortable truth: You’ll never eliminate stress from your career (or your life). But as a stress physiologist, Im here to tell you that’s actually a good thing! My research reveals that our obsession with stress reduction is fundamentally flawed. Instead of fighting stress, the most successful professionals learn to harness it.
The only people with zero stress are dead people. Our aim should not be death.
Here are five evidence-based strategies to rewire how you can work with stressnot against it.
1. Reframe Your Biology as Your Competitive Edge
When your heart pounds before a big presentation, your brain screams “danger.” But that physiological responseincreased heart rate, heightened alertness, elevated energyis identical to excitement. The difference lies in interpretation.
A Harvard study found that participants who stated “I am excited” before delivering speeches were rated as significantly more persuasive and confident than those who tried to “stay calm.” The nervous energy remained the same, but performance dramatically improved.
Stop telling yourself to calm down. Your stress response is a feature, not a flaw. Start declaring: “This energy is preparing me to excel.” Your stress response isn’t sabotaging youit’s upgrading your operating system.
2. Ask “Is This Actually a Tiger?”
Your brain evolved to treat missed emails like charging predators. This served our ancestors well but creates havoc in modern workplaces. When stress hits, pause and ask: “Will this kill me in the next three minutes?”
If not, you’re experiencing what I call a “paper tiger”a stressor that feels life-threatening but isn’t. Once you recognize the false alarm, you can redirect that energy productively instead of spiralling into fight-or-flight paralysis.
3. Convert Anxiety into AngerStrategically
When facing seemingly insurmountable challenges, excitement might feel impossible. That’s where anger becomes your ally. Studies reveal that anger increases effort toward goals and sparks greater creativity than neutral emotional states.
The key is directing anger at the problem, not people. Instead of fuming at difficult colleagues, channel that energy toward solving systemic issues. Transform “This situation is impossible” into “This problem needs fixing, and I’m going to figure out how.”
Anger mobilizes action. Point it in the right direction.
4. Think Micro-Goals, Not Mega-Outcomes
Stress often stems from feeling overwhelmed by massive objectives. Break intimidating projects into actions so small they’re nearly impossible to fail. When you complete micro-goals, your brain releases dopamine, creating an addictive cycle of progress.
When we think we have to leap Everest in a single jump, or relearn our entire job because of AI, our brain naturally defaults to helplessness. But by taking action, even incredibly small moves, we begin to regain agency and feel more in control. This actionable hope ultimately moves us beyond our state of learned helplessness.
Ask yourself: “What’s the smallest possible step forward?” Then take it. Winning becomes neurologically addictive (even if the perfect outcome isnt guaranteed).
5. Make It Bigger Than You
The most transformative reframe involves expanding your perspective beyond personal gain. When you anchor goals in serving something largeryour team, customers, communitythe fear centers in your brain quiet down.
Back to those workplace wellness studies. In the 90 workplace stress interventions, the only thing that consistently improved employee well-being was service to others. When stress serves a purpose beyond yourself, it transforms from burden to fuel.
Before your next high-stakes meeting, shift from “How do I not mess this up?” to “How can I serve my audience?” The stress remains, but now it’s powering something meaningful and reminding you that stress is often simply a barometer for how much you care.
The Paradox of Peak Performance
Olympic athletes don’t break world records during practice. They achieve greatness when pressure peaks. Your biggest professional breakthroughs likely occurred during your most stressful periods, not your calmest.
This isn’t about glorifying burnout or toxic work cultures. It’s about recognizing that stress, properly channeled, is the raw material of achievement. The goal isn’t eliminationit’s transformation.
Your stress isn’t going anywhere. But your relationship with it can change everything. Stop trying to manage it away. Start using it as the high-octane fuel it was designed to be.
The question isn’t whether you’ll face stress today; It’s whether you’ll let it defeat you or springboard you forward.
