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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.
Category:
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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