OpenAI Chairman: We are indeed in an "AI bubble"; there will inevitably be huge winners, and many people will suffer heavy losses.

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The fervor in the AI field is triggering a fierce debate about a "bubble," and OpenAI board chairman Bret Taylor has given a clear but complex answer: we are indeed in a bubble, but that does not prevent AI from ultimately creating enormous economic value.

Recently, in an interview with media outlet The Verge, Bret Taylor agreed with OpenAI CEO Sam Altman's previous view, admitting "we are in an AI bubble and some people will lose a lot of money."

Taylor warned that as with any disruptive tech wave, this process will inevitably produce huge winners, but it will also cause heavy losses for many people. At the same time, he believes that AI will change the economic landscape and create immense value, and that the existence of a market bubble and this potential are two facts that can coexist.

Taylor made a direct comparison between today’s AI boom and the internet bubble of the late 1990s. He pointed out that although countless companies collapsed when the bubble burst, in the long run, "those people in 1999 were, in a way, right about the internet’s future."

Today, companies like Amazon and Google, which were born in that era, are now among the highest valued enterprises in the world, proving that the foresight under the bubble eventually became reality:

"In fact, if you look at world GDP, how much the internet actually created or impacted? Some might say that everyone in 1999 was right. It had the same kind of impact on almost all indicators."

The key is to distinguish the 'directionality' of the bubble

Taylor elaborated on his analogy between the AI bubble and the internet bubble. He believes that the key lies in distinguishing between the correctness of direction and the success rate of specific investments.

During the internet bubble, many business models like Webvan (online grocery delivery) ultimately failed, but their core concept was later realized by companies like Instacart and DoorDash once the internet infrastructure matured. This shows that even if initial attempts failed, the trend and needs behind them truly existed.

Similarly, in the early internet days, many companies that invested heavily in fiber-optic networks went bankrupt, but the infrastructure they built was eventually utilized by later players, supporting the prosperity of the entire digital economy.

Taylor said, "The statements 'AI will change the economy' and 'a lot of people will lose money' can both be true at the same time:"

"I think it's a fact that AI will change the economy, I think it will, like the internet, create enormous economic value in the future. At the same time, I believe that we are also in a bubble and many people will lose a lot of money. I believe both are absolutely true, and there is a lot of historical precedent for these two things happening together."

This means that the massive investments being made today, regardless of which company they ultimately go to, are paving the way for the next generation of AI applications—but not all participants will share in the final rewards.

Why AI is so “burning-cash”: the market is still immature

Taylor does not agree with the view that "model iteration has significantly slowed down." He takes coding tasks as an example, pointing out that new models are still making “step-change” improvements in specific areas. But he also believes that as model capabilities mature and become widely adopted, for many tasks, models have already reached a "good enough" level.

He predicts that in the future, building AI applications will be more like "how to use a database," rather than "how to write a database."

Regarding market doubts about the input-output ratio of AI—such as an MIT report noting that much corporate AI spending hasn’t delivered results—Taylor believes that this is mainly because the market is still immature. Many companies are engaging in "AI tourism," trying to build their own solutions, a process that is complex and prone to failure.

He believes the right path is to purchase mature AI solutions focused on specific domains, like Sierra (for customer service) or Harvey (for legal). As more "application-oriented AI companies" emerge, enterprises will be able to more directly buy AI agents that address their pain points and thus truly realize the value of AI.

"I think we are still in the early stages of AI, and there has yet to be an excellent supplier to solve every problem you encounter in your business. Therefore, you either have to wait, or you have to build it yourself."

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