Innovative, creative, and productive implementation of 'the world-changing technology,' whether the internet or AI, was and is the key.
The dot-com bubble is alive and well. The usual suspects—the media, the analysts, the prognosticators—have talked incessantly this last couple of months about how the AI bubble is so much like the dot-com bubble , following Sam Altman ’s lead.
In August, the king of AI compared today’s investors getting “overexcited” about anything “labeled AI” to 1990s investors piling into dot-com startups, enticed by the promise of the internet, “a world-changing technology” just like AI.,” suggesting that just like the dot-com boom and bust cycle, after the AI bubble bursts, we will see a period of reassessment followed by the emergence of sustainable businesses and productive business practices. Unlike Altman’s musings, however, mine were not followed by the likes of theThe similarities that these observers highlight include billions spent on infrastructure with no adequate return because capacity growth may vastly outstrip demand; soaring valuations of anything labeled AI; and an increasing concentration of technology companies with stretched valuations within market indices. All of these macroeconomic and general indicators may be on point, but they may miss an important parallel with the dot-com crash. Why did investors, starting on March 10, 2000, sell shares of companies with stretched valuations and continue to sell for a very long time? Maybe they began to realize that the multitude of new internet-based dot-coms, new types of companies in a “new economy,” have failed to deliver on the promised revenues and the expected productivity boom? Webvan, a pioneer of grocery home delivery, was a prime example of the dot-com crash. Venture capitalists invested more than $396 million in it, and it became a public company valued at $4.8 billion in November 1999. Webvan filed for bankruptcy in June 2001. The reasons usually cited for its failure include adhering to the Silicon Valley mantra of “get big fast,” targeting mass market consumers instead of less price-sensitive ones, and paying Bechtel $1 billion to construct warehouses from scratch instead of relying on existing infrastructure.These microeconomic measures and business model-related decisions are no doubt to blame for the Webvan debacle. Still, what is missing—and what is the key lesson for the AI bubble —has more to do with operational decisions, with the actual implementation of “the world-changing technology.” Webvan hired Mick Mountz in 1999 to study the “business process” in their first warehouse in Oakland, California. He wanted to figure out why “the cost of order fulfillment was overwhelming any kind of profit margin that they had on these orders,” Mountz told me when I reached out to him last week. Focusing on the “pick and pack process,” Mountz found out that Webvan’s cost structure was leading to a loss on every order: “It was costing the company about $30 to get 30 items of groceries into these totes. I remember thinking to myself, geez, we're spending a dollar to get this can of soup in the tote, and we're only charging 89 cents for it, so we're clearly losing money on this can of soup.” Beyond pinpointing the key operational vulnerability that led to Webvan’s demise, Mountz’s insight also leads to a broader conclusion: A new business in a “new economy” requires new ways of doing things. Webvan not only created a warehouse infrastructure from scratch but also adhered to existing warehouse practices, which meant adopting traditional, costly processes instead of inventing new ones.in 2002. Instead of having operators walk around the warehouse to get the items required, “what if the stuff just came to them?” was Mountz’s out-of-the-box and in-the-new-warehouse thinking. “We kind of just flipped the warehouse around using mobile robotics and some mobile shelves that we could put the products on,” recalls Mountz. Amazon bought Kiva Systems in 2012 for $775 million. Mountz thinks that while the dot-com startups and their bubble depended primarily on advertising revenues that did not materialize, AI startups are focused on “domain-specific” solutions for paying enterprise customers. Still, “one of the problems AI needs to solve over the next five years is energy consumption and heat dissipation in these data centers. You may have some innovation in those areas that becomes the next Kiva, if you will, because they found a better way to do these transformer models on these Nvidia chips without using so much power and energy,” says Mountz. Given the projected energy consumption of AI data centers, more efficient LLMs will undoubtedly be needed, promising a more sustainable future . I would go even further and argue that the AI startups that will thrive and survive the coming AI bubble crash, if there is one, are those that will develop innovative ways—activities, processes, organization of work—in which people work with AI to be more productive, creative, and efficient. In the 1990s, “re-engineering” guru Michael Hammer advised businesses, “Don’t automate, obliterate.” Today’s business rallying cry should be: “Don’t automate, innovate with AI.”
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