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The X of Y frameworkWere the Uber of healthcare or the Airbnb of financehas become a kind of startup reflex. Its useful, even comforting, to anchor a new idea to something people already understand. But what feels like clarity can become constraint. When you define your business through another companys success, you risk adopting their playbook instead of rewriting the rules. The best disruptors learn to move past comparison. They articulate what makes their idea not just different, but inevitable. Thats how you build conviction from your team, your investors, and your customers. Why comparison shrinks your story From a branding perspective, letting investors, consumers, or even your own team see your business through the lens of another company is risky. It narrows imagination and compresses potential before the company ever takes off. In Teddy Roosevelts words, comparison is the thief of joy. In the entrepreneurial world, comparison is the thief of innovation. The moment you define yourself through someone elses success, youre not building a new world; youre borrowing a corner of an old one. True disruptors dont emulate, they innovate. And not just in the product, but in how they communicate that product to the world. The biggest tech companies by market capFacebook, Apple, Amazon, Netflix, Google, Nvidiaarent the X of Y. They just are. They didnt build by reference; they built by invention. Yesterdays playbook wont win tomorrows game From a business model standpoint, the X of Y approach doesnt simplify, it hamstrings. What worked in one context often fails in another because conditions change faster than most disruptors realize. YouTubes monetization strategy, for example, only succeeded after the platform reached massive scale. Trying to apply that same model to a niche content business at launch would likely fail. OpenAI trained on freely available web data thats now largely cut off. Imitators entering the space today cant replicate those conditions, nor their success. Timing and first-mover advantage matter. Once a model exists, the data access, regulation, even consumer behavior conditions that allowed it to thrive are already evolving. The world moves on. What worked before doesnt necessarily work now. For disruptors, the takeaway is simple: Learn from others, but dont lean on them. The best leaders translate insight into original structure, a model built for todays conditions, not yesterdays advantages. Create a category: Lessons from Figures IPO I saw this dynamic play out firsthand during Figures IPO. With no natural comparison, we didnt fit into a familiar box. Yet, investors tried; they labeled us a blockchain company, a fintech lender, a financial marketplace. And each comparison carried its own limitations: valuation ceilings, volatility, market constraints. Bringing something truly new to market requires more than a great product. It demands changing perception. You have to teach the market how to think differently and convince them theyre ready for it. At Figure, we had to educate investors that what we were buildingblockchain-based capital marketswasnt a futuristic concept; it was a present-tense opportunity. We emphasized not just what we built, but why it mattered: faster, more transparent capital flows that could unlock a massive market. Once that clicked, investors stopped searching for a comparison and started seeing the scale of the opportunity. That shift made all the difference in a successful offering. Comparisons fall flat faster in todays world Were in an evolutionary moment. Like mobile did before, AI and blockchain are changing the rules of the game. Business models built around past infrastructure will quickly feel dated. Anchoring yourself to yesterdays success stories is like hitching your wagon to Craiglists star in 2008. It looked brilliant, until mobile changed everything. The X of Y mindset is its own kind of entrepreneurial Waiting for Godot. Leaders get stuck in a comparison loop, waiting for validation, for precedent, for permission to move. But the future never arrives for those who wait on it. Pioneering beyond precedent, especially when precedent itself is shifting, is hard. But thats where the opportunity lies. Leaders who thrive in this environment wont ask, Who are we like? Theyll ask, What are we building that no one else has imagined yet? Because real disruptors dont wait for Godot. They build the world everyone else is still waiting for. Michael Tannenbaum is the CEO of Figure.
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E-Commerce
Bitcoin is having a horrible week. Until yesterday, the cryptocurrency had declined by roughly 2.5% over the preceding five days. But in the last 24 hours alone, the coin has taken a major hitdown more than 10%. Worse, fear and greed indices, which measure the emotional state of investors who buy and sell Bitcoin, are near historic lows. Heres what you need to know. Why is Bitcoin sinking? Bitcoin has dropped precipitously over the past 24 hours. As of the time of this writing, it’s down more than 10% to $82,185 per token. Thats a low the coin has not seen since April. But why has Bitcoin been falling so much over the past 24 hours? There are two major factors at play. The first has to do with what happened in the stock market yesterday. When markets opened, AI-related stocks were flying high due to the previous days news that Nvidia Corporation (Nasdaq: NVDA) had exceeded expectations for its Q3 2026 earnings. This good news, momentarily, gave investors a confidence boost. Nvidias results were a sign, many argued, that the AI bubble people have been talking about for months was perhaps overstated. But as the day continued, those bubble fears resurfaced, and investors sold Nvidia heavily, along with other AI stocks and other tech stocks. This selloff contributed to a steep decline in the markets, which ended down for the day. Unfortunately for cryptocurrencies, many people who invest in volatile AI stocks also invest in crypto. And when one of those assets declines, they tend to sell off the other asset to lock in any accumulated profits and buffer against losses elsewhere in their portfolio. However, you cant blame Nvidia and the tech stock slide yesterday for all of Bitcoins woes. A second factor likely influencing Bitcoins massive 24-hour drop is that, as CNBC notes, America’s job numbers for September were released, and they showed stronger-than-expected job growth data (119,000 new jobs versus the roughly 50,000 analysts expected). Why would good job numbers send Bitcoins price down? Because those better-than-expected jobs numbers sent the probability of a December rate cut by the Federal Reserve down from 50% to about 40%. Rate cuts are generally seen as good news for the prices of assets like Bitcoin because the cuts boost liquidity in the markets. At the beginning of November, many analysts expected there was a 90% chance of Fed rate cuts in December. By mid-November, that chance had been slashed to 50%. Now its down to 40%. This increasing likelihood that the Fed will not cut rates is likely weighing heavily on Bitcoins price today. Crypto fear and greed indices near historic lows A fear and greed index measures the emotional state of investors in a particular asset. Several crypto-focused platforms maintain their own Fear and Greed Indexes, including CoinMarketCap and Binance. As CoinMarketCap notes, its fear and greed index measures the prevailing sentiment in the cryptocurrency market on a scale of 0 (extreme fear) to 100 (extreme greed). This index helps investors understand the emotional state of the market, which can influence buying and selling behaviors. Currently, CoinMarketCaps Crypto Fear and Greed Index is at an 11. Thats the lowest level it’s recorded since June 2023, the farthest back the index goes. At 11, the index is currently lower than the 15 it was at on March 11, 2025, when crypto markets were also tumbling. This suggests that the emotional state of cryptocurrency investors right now is extremely fearful. Similarly, Binance’s Crypto Fear & Greed Index is also at an 11 (it ranges from 0 to 100). Thats four points lower than where it was yesterday, and 50% lower than where it was last week. While seeing the historic lows of the fear range of the index might further alarm Bitcoin investors, it should be noted that these indices can help track periods of over-selling (fear side of the spectrum) or when the token may be over-bought (greed side of the spectrum). However, these indices cant predict whether any token will continue to be sold off or if its price will rebound. Other cryptocurrencies are seeing a large selloff, too As the crypto Fear and Greed indices suggest, its not just Bitcoin that is seeing major selloffs as of late. Other cryptocurrencies are also down significantly across the board. This includes Ethereum (down 12% to $2,650), XRP (down 12.25% to $1.85), BNB (down 11.4% to $797), Solana (down 13.45% to $122.73), and Dogecoin (down 14.7% to $0.134).
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E-Commerce
Hi again, and welcome back to Fast Companys Plugged In. On November 18, Google announced a new product. More precisely, it declared that it was ushering in a new erawhich is what tech companies do when they really want you to pay attention. The product in question is Gemini 3 Pro, the latest version of Googles LLM. Its not just the foundation of Googles ChatGPT-like chatbot, also called Gemini. It underlies vast quantities of features in flagship offerings such as Google Search, Gmail, and Android. It powers Antigravity, a new Google AI coding platform that debuted on the same day. And thanks to Google Cloud, the model is also available to third-party developers as an ingredient for their apps. In short, Gemini 3 Pro could hardly be more essential to Googles aspiration to be AIs most important player. As Google DeepMind CEO Demis Hassabis said in the announcement, the company sees it as a big step on the path toward AGIAI thats at least as capable as humans are at most cognitive tasks. Already, the announcement stated, Gemini 3 Pro demonstrates PhD-level reasoning. Google supported its claims with a table listing 20 AI benchmarks in which Gemini 3 Pro beatand often just plain trouncedGemini 2 Pro, OpenAIs GPT-5.1, and Anthropics Claude Sonnet 4.5. Humanitys Last Exam, for example, is a 2,500-question test covering mathematics, physics, the humanities, and other topics. Its designed to be remarkably difficult (hence the name) and there has been debate over whether its so nebulous that some of the theoretically correct answers are nuanced or wrong. According to Googles table, GPT-5.1 achieved a score of 26.5%, while Claude Sonnet 4.5 managed only 13.7%. By contrast, Gemini 3 Pro scored 37.5%, and did even better when allowed to do searches and run code, with a score of 45.8%. Outside the lab, Gemini 3 Pro has been received as enthusiastically as any new AI model I can remember. Ethan Mollick, one of my favorite providers of AI analysis based on hands-on usage, pronounced it very good. Others said it delivered on the great expectations that OpenAIs GPT-5 stoked but failed to satisfy. As I write, Ive been playing with the Gemini chatbot for just a few days. Much of that experience has been positive. Two writing assignments I gave it came out exceptionally well: an article on the future of the penny, and a detailed report on pricing for Digital Equipment Corp.s 1960s minicomputers. Its first pass at a simple vibe coding projectbuilding a search engine for Fast Companys Next Big Things in Techwas a bit of a mess, but when I explicitly put it into Build mode, it nailed the assignment in a few minutes. It also excelled at figuring out what was going on in an assortment of photos I uploaded. Yet for all thats gone right so far, I also encountered significant glitches with Gemini 3 Pro from almost the moment I tried it. They left me particularly wary of Googles blanket claims about the LLM being ready to help users learn anything and delivering responses that are smart, concise and direct, trading cliché and flattery for genuine insight. My interactions gone wrong were mostly about animation and comics, topics I turn to when fooling around with new AI because I know them well enough to spot mistakes. Asked about these subjects, Gemini repeatedly spewed hallucinations. For instance, when I asked if Walt Disney himself had ever worked on the Mickey Mouse comic strip, the LLM gave a correct answer (yes, though only briefly) but then volunteered a bunch of facts I hadnt asked for that werent actually factual. For example, it said that when the strips longtime artist retired, his final panel showed Mickey and Minnie gazing into a sunset, a subtle way of marking his departure. (No such strip appeared.) In a different chat, it manufactured an elaborate, entirely fictional backstory involving a different cartoonist also being a noted animation historian, which it told me was well-documented and recognized. It wasnt just that Gemini hallucinated. ChatGPT and Claude still do that, too. But more than other models, Gemini tended to compound its failures by gaslighting me. Helpfully pointing out its gaffes led to some of the strangest exchanges Ive had with AI since February 2023, when Microsofts Bing said it didnt want to talk to me anymore. (Full disclosure: I understand that AI is just stringing together a sequence of words it doesnt understand. All of its human-seeming qualities, be they impressive or annoying, are simulated. But its hard to write about them without slipping into a certain degree of anthropomorphizing!) Repeatedly, Gemini acknowledged its inaccuracies but insisted they were lore, common misconceptions, or examples of my own confusion. In one case, it eventually confessed: I have failed you in this conversation by fabricating details to cover up previous errors. In another instance, it continued to insist that it was right, providing citations that didnt even mention the topic at hand. Im not arguing that the fate of AI hangs on how much the technology knows about old cartoons. However, if any company is burdened with the responsibility of ensuring that its LLM is a trustworthy source of general information, its Google. That I tumbled into an abyss of AI-generated misinformation so quickly isnt an encouraging sign. Part of the problem lies in the fact that Gemini 3 Pro offers two modes, Fast and Thinking. The first is the default and was responsible for the prevarications I encountered, at least one of which involved it conflating two separate topics Id brought up. So far, Thinking mode has worked better in my experiments. But even the speediest of AI models should meet a baseline of accuracy and good behavior, at leastif theyre being presented as a way to learn anything. (Like many AI tools, the Gemini chatbot does carry a mistakes-are-possible disclaimer.) To repeat myself, Gemini 3 Pro is impressive in many ways. Still, its launch is yet another example of the AI industry presenting an overly rosy portrait of what it has achieved. It also underlines that benchmarks tell us only so much about a models real-world performance. When OpenAI introduced ChatGPT three years ago this month, it did so in a brief blog post that took pains to detail the bots limitations and avoid grand pronouncements about its future. Letting its breakthrough new product speak for itself turned out to be a pretty effective marketing strategy. Even as AIs giants jostle for bragging rights in what may be the most hypercompetitive tech category of all time, they should remember that lesson. Youve been reading Plugged In, Fast Companys weekly tech newsletter from me, global technology editor Harry McCracken. If a friend or colleague forwarded this edition to youor if you’re reading it on fastcompany.comyou can check out previous issues and sign up to get it yourself every Friday morning. I love hearing from you: Ping me at hmccracken@fastcompany.com with your feedback and ideas for future newsletters. I’m also on Bluesky, Mastodon, and Threads, and you can follow Plugged In on Flipboard. More top tech stories from Fast Company A battle against the AI oligarchy is brewing in this wealthy New York districtTwo congressional candidates have made AI a major issue in the campaign. Read More Crypto’s path to legitimacy depends on the industry itself, not just politiciansOnly an internal cultural shift and rigorous self-policing can deliver mainstream approval. Read More AI chatbots won’t save the media. But what powers them mightPublisher-built agents grounded in trusted archives may turn years of reporting into real products instead of just another chat widget. Read More This massive new data center is powered by used EV batteriesA new project from battery recycling startup Redwood Materials and data center builder Crusoe shows that it’s possible to build data centers cheaper and faster while also slashing emissions.Read More Why Trump’s AI diplomacy is doomed to failThis week, chips were on the menu in the White House Read More Even (especially) in the age of AI, here’s why I hire for character over skillBecause that’s what reveals true talent. Read More
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