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Business Mayor
19-05-2025
- Business
- Business Mayor
From dot-com to dot-AI: How we can learn from the last tech transformation (and avoid making the same mistakes)
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More At the height of the dot-com boom, adding '.com' to a company's name was enough to send its stock price soaring — even if the business had no real customers, revenue or path to profitability. Today, history is repeating itself. Swap '.com' for 'AI,' and the story sounds eerily familiar. Companies are racing to sprinkle 'AI' into their pitch decks, product descriptions and domain names, hoping to ride the hype. As reported by Domain Name Stat, registrations for '.ai' domains surged about 77.1% year-over-year in 2024, driven by startups and incumbents alike rushing to associate themselves with artificial intelligence — whether they have a true AI advantage or not. The late 1990s made one thing clear: Using breakthrough technology isn't enough. The companies that survived the dot-com crash weren't chasing hype — they were solving real problems and scaling with purpose. AI is no different. It will reshape industries, but the winners won't be those slapping 'AI' on a landing page — they'll be the ones cutting through the hype and focusing on what matters. The first steps? Start small, find your wedge and scale deliberately. One of the most costly mistakes of the dot-com era was trying to go big too soon — a lesson AI product builders today can't afford to ignore. Take eBay, for example. It began as a simple online auction site for collectibles — starting with something as niche as Pez dispensers. Early users loved it because it solved a very specific problem: It connected hobbyists who couldn't find each other offline. Only after dominating that initial vertical did eBay expand into broader categories like electronics, fashion and, eventually, almost anything you can buy today. Read More The experience of enterprise software during war - Compare that to Webvan, another dot-com era startup with a much different strategy. Webvan aimed to revolutionize grocery shopping with online ordering and rapid home delivery — all at once, in multiple cities. It spent hundreds of millions of dollars building massive warehouses and complex delivery fleets before it had strong customer demand. When growth didn't materialize fast enough, the company collapsed under its own weight. The pattern is clear: Start with a sharp, specific user need. Focus on a narrow wedge you can dominate. Expand only when you have proof of strong demand. For AI product builders, this means resisting the urge to build an 'AI that does everything.' Take, for example, a generative AI tool for data analysis. Are you targeting product managers, designers or data scientists? Are you building for people who don't know SQL, those with limited experience or seasoned analysts? Each of those users has very different needs, workflows and expectations. Starting with a narrow, well-defined cohort — like technical project managers (PMs) with limited SQL experience who need quick insights to guide product decisions — allows you to deeply understand your user, fine-tune the experience and build something truly indispensable. From there, you can expand intentionally to adjacent personas or capabilities. In the race to build lasting gen AI products, the winners won't be the ones who try to serve everyone at once — they'll be the ones who start small, and serve someone incredibly well. Starting small helps you find product-market fit. But once you gain traction, your next priority is to build defensibility — and in the world of gen AI, that means owning your data. The companies that survived the dot-com boom didn't just capture users — they captured proprietary data. Amazon, for example, didn't stop at selling books. They tracked purchases and product views to improve recommendations, then used regional ordering data to optimize fulfillment. By analyzing buying patterns across cities and zip codes, they predicted demand, stocked warehouses smarter and streamlined shipping routes — laying the foundation for Prime's two-day delivery, a key advantage competitors couldn't match. None of it would have been possible without a data strategy baked into the product from day one. Google followed a similar path. Every query, click and correction became training data to improve search results — and later, ads. They didn't just build a search engine; they built a real-time feedback loop that constantly learned from users, creating a moat that made their results and targeting harder to beat. The lesson for gen AI product builders is clear: Long-term advantage won't come from simply having access to a powerful model — it will come from building proprietary data loops that improve their product over time. Today, anyone with enough resources can fine-tune an open-source large language model (LLM) or pay to access an API. What's much harder — and far more valuable — is gathering high-signal, real-world user interaction data that compounds over time. If you're building a gen AI product, you need to ask critical questions early: What unique data will we capture as users interact with us? How can we design feedback loops that continuously refine the product? Is there domain-specific data we can collect (ethically and securely) that competitors won't have? Take Duolingo, for example. With GPT-4, they've gone beyond basic personalization. Features like 'Explain My Answer' and AI role-play create richer user interactions — capturing not just answers, but how learners think and converse. Duolingo combines this data with their own AI to refine the experience, creating an advantage competitors can't easily match. In the gen AI era, data should be your compounding advantage. Companies that design their products to capture and learn from proprietary data will be the ones that survive and lead. The dot-com era showed us that hype fades fast, but fundamentals endure. The gen AI boom is no different. The companies that thrive won't be the ones chasing headlines — they'll be the ones solving real problems, scaling with discipline and building real moats. The future of AI will belong to builders who understand that it's a marathon — and have the grit to run it. Kailiang Fu is an AI product manager at Uber.
Harvard Business Review
30-04-2025
- Business
- Harvard Business Review
The 6 Forces of Failure—and How to Protect Your Company from Them
HANNAH BATES: Welcome to HBR On Strategy —case studies and conversations with the world's top business and management experts, hand-selected to help you unlock new ways of doing business. Ever heard of the Failure Museum? It's home to more than one thousand relics of failed business ventures—like the Heinz Tomato Ketchup Cookbook, Bic's pencil just for women, and a stock certificate from Lehman Brothers. While these flops and washouts might be fun to laugh at, they hold powerful lessons for business leaders. In this episode, museum founder and venture capitalist Sean Jacobsohn identifies six forces of failure—from bad timing to poor financial management—with the help of artifacts from his cautionary collection. Along the way, you'll learn how your company can avoid making the same costly mistakes. By the way, if you want to see the items Jacobsohn talks about, go to HBR's YouTube channel, where you'll find the video version of this episode. Here's Jacobsohn. SEAN JACOBSOHN: I have a lot of favorites. The sock puppet from 1998, they could have been successful if they didn't build a money-losing business. Harley Davidson Cologne from 1996, it reeked the scent of tobacco. Here's an unopened bottle. No, they weren't joking. They were serious about it. They did a lot of product extensions, of course. Sparkling water, Cheetos lip balm. Another one of my favorites is Allan from 1964. He was Ken's best friend. You may remember him from the Barbie movie. ALLAN: Hi Barbie! BARBIE: Oh, hi Allan. SEAN JACOBSOHN: Here he is in the original box. Allan wore the same clothing. He was his best friend. The problem is everyone just wanted to own Ken, not Allan. Yes, I have two cans of New Coke. You want me to bring them out? It was really hard to switch people in mass to something completely different. I have not tasted one. I certainly have been tempted, but some of these things are 15, 20, 30 years old, so they probably don't taste good anymore. I'm Sean Jacobsohn, partner at Norwest Venture Partners. I'm on 14 boards. I'm also the founder and curator of the Failure Museum. The Failure Museum has over 1,000 items and continues to grow. Failed companies, failed products, failed sports-related items, and failed toys. I've tagged all 1,000 items of mine to one or two forces of failure. Product market fit, team, financial management, timing, competition, and customer success. I'm going to go through all six forces of failure and share one example of each. I have a champagne bottle from Webvan's IPO date in 1999. For product market fit, Webvan is a good example. They were the world's first grocery delivery company. SPEAKER 4: You have the right to come home from work and find something good waiting for you in the fridge. SEAN JACOBSOHN: They raised over $880 million to launch in 10 cities before having proven one. Business model required so much capital. They had distribution centers. They hired their own drivers, which is why they had to raise $880 million. Not enough demand for the early version of your product. You shouldn't yet scale go to market. So I have a Theranos mug, and I also have Elizabeth Holmes' business card. Their goal was to revolutionize the blood-testing industry, and at the peak were worth $10 billion. Theranos didn't have a strong team or board. None of them had domain expertise. They tried to use a pinprick of blood to do testing, and that's just not enough data. Yeah, when they don't have domain expertise, hiring other people without domain expertise, you believe in something can be possible when it really isn't. Here is a copy of the Google Glass, and I can put it on here too. A good example of customer success is Google Glass. They did not pick the right early customers well. They started with doctors, and doctors could see patient records on the Glass while they were talking to the patient. So they didn't have to use their computer. It felt invasive. It didn't seem very personal, and you're not used to having someone with a strange device on your face trying to communicate with you. Because it lacked the cool factor, they couldn't find any other segments of the population that wanted to wear something like this. It's important to pick the right early customers that are representative of your bigger market. A lot of times people pick the most convenient customers rather than those that are going to help you build a big business. I love this one so much that I actually bought two of them on eBay. For financial management, the ESPN mobile phone. They launched a year before the iPhone. All the phone did was calling, sharing ESPN mobile content and scores. They burned through $150 million, including several Superbowl ads. It only hit 6% of a sales target. They probably should have had more capabilities on the phone. There just wasn't enough to do on the phone. SPEAKER 5: Introducing Mobile ESPN. Sports fans, your phone has arrived. SEAN JACOBSOHN: I have a couple items here, a WeWork Thermos and a koozie from WeWorks summer camp. For timing, a good example is WeWork. WeWorks in the coworking space business. And when the pandemic hit, the demand for office space fell off a cliff and they ended up burning through $16 billion. The goal was to give people flexible space that allowed you to do month-to-month leases and scale up and down in space depending on your demand. They also signed 10 to 15 year leases at peak market prices, and then they ended up renting them out at a loss. There's some level of unluckiness, but I think that they had the wrong business model. You need to have a pulse on what's happening in the market and be able to anticipate what's going to happen in the next 12 to 24 months. And so I've had several friends donate their Blockbuster membership card to me. And then in every store there was a sign that said, 'Be kind, rewind.' For competition, Blockbuster's a good example. They're in the movie rental business. At the peak, they had 9,000 stores. They missed the opportunity to move online and Netflix ate their lunch. They also, after they went public, had the opportunity to buy Netflix for $50 million and turned it down and instead Netflix ended up beating them. When I talk about competition, you need to make sure that you don't have an upstart competitor that offers a cheaper, better way of doing what you do. You need to be aware of all the competitors in your market segment and how you stay differentiated and better than them. I do admire companies for taking risks and trying new things. Some of these big risks turn into humongous outcomes and some fail spectacularly. I'm a lot of times surprised at the lack of research that they did before they spent a lot of money to roll something out that wasn't going to work. HANNAH BATES: That was Sean Jacobsohn, partner at Norwest Venture Partners and founder of the Failure Museum. We'll be back next Wednesday with another hand-picked conversation about business strategy from the Harvard Business Review. If you found this episode helpful, share it with your friends and colleagues, and follow our show on Apple Podcasts, Spotify, or wherever you get your podcasts. While you're there, be sure to leave us a review. And when you're ready for more podcasts, articles, case studies, books, and videos with the world's top business and management experts, find it all at This episode was produced by Scott LaPierre, and me Hannah Bates. Curt Nickisch is our editor. Special thanks to Ian Fox, Maureen Hoch, Erica Truxler, Ramsey Khabbaz, Nicole Smith, Anne Bartholomew, and you – our listener. See you next week.
