OpenAI董事长Bret Taylor在播客中指出，当前试图训练自家前沿AI模型是“烧钱”行为，其成本高达数百万美元。他认为，只有OpenAI、Anthropic、Google和Meta等少数巨头才有能力承担如此高昂的训练费用。Taylor建议创业者应专注于构建AI服务和用例，利用现有的大型模型平台，而非自行开发基础模型。他将此类行为比作淘金热中的“挖矿工具”，并提到中国公司DeepSeek通过优化芯片使用降低成本，成功推出高排名AI应用，引发了关于AI模型开发成本的讨论。Taylor还建议创业者探索AI工具市场和应用型AI公司，而非投入巨资研发“快速贬值”的自研模型。

💰 训练前沿AI模型成本极高：OpenAI董事长Bret Taylor表示，自行训练大型语言模型（LLM）需要耗费数百万美元，这使得只有少数科技巨头（如OpenAI、Google、Meta、Anthropic）能够负担得起，对初创公司而言是“烧钱”行为。

💡 建议创业者另辟蹊径：Taylor鼓励AI领域的创业者将精力集中在开发基于现有AI模型的服务、应用场景和工具，而非试图从零开始构建自己的前沿模型，他将这些工具比作淘金热中的“挖矿工具”。

🌐 市场趋势与反例：Taylor认为高昂的资本门槛将导致AI模型市场走向整合。然而，中国公司DeepSeek的案例表明，通过更高效的芯片利用和成本控制，中小型企业也有可能开发出有竞争力的AI模型，并取得市场成功，引发了关于行业内模型开发成本的重新审视。

🚀 未来AI公司形态：Taylor预测，未来AI领域的SaaS（软件即服务）应用将演变为“代理公司”，即更智能、自主地为用户提供服务的AI驱动型企业，这为创业者提供了新的发展方向。

OpenAI chairman Bret Taylor said that companies trying to train their own LLMs will "burn through millions of dollars.""There's ones you can lease, there's open source ones," Taylor said on the Minus One podcast. "Don't do it."Taylor said the only companies that could handle the high training costs were OpenAI, Anthropic, Google, and Meta.

There's a scene in "The Dark Knight" where the Joker sets ablaze a massive pile of money. Deciding to develop your own frontier AI model today may be a similar exercise in burning cash — just ask OpenAI chairman Bret Taylor.

Taylor, who has worked for three companies that have trained LLMs, including Google, Facebook, and OpenAI, called training new AI models a "good way to burn through millions of dollars."

On a recent episode of the Minus One podcast, Taylor advised AI founders to build services and use-cases, but not new frontier models entirely.

"Unless you work at OpenAI or Anthropic or Google or Meta, you're probably not building one of those," said Taylor, who also cofounded Sierra AI. "It requires so much capital that it will tend towards consolidation."

That high bar of capital has stopped any "indie data center market" from forming, Taylor said, because it simply costs too much.

Taylor advised that founders work with the AI juggernauts instead — which, it's worth noting, is something that AI giants like OpenAI, where he's chairman, would directly benefit from. OpenAI sells "tokens" to access its API, which developers can build into their applications and programs.

While the American LLM market remains largely consolidated, international players have tested Taylor's theory. In January, DeepSeek released its R1 reasoning model and a corresponding chatbot. DeepSeek used fewer, less advanced chips to build its LLM, minimizing capital costs.

The Chinese AI app shot to No. 1 on the App Store charts, surpassing ChatGPT and igniting a debate in tech and on Wall Street about whether tech giants were overspending on AI model development.

In his podcast interview, Taylor laid out other paths that entrepreneurs could take in the AI market, rather than training a new model. One was the "AI tools market."

"This is the proverbial pickaxes in the gold rush," Taylor said. "It's a dangerous space because I think there's a lot of things that are scratching an itch today that the foundation model providers might do tomorrow."

Entrepreneurs could also try to build what Taylor called an "applied AI company."

"What were SaaS applications in 2010 will be agent companies in 2030, in my opinion," Taylor said.

Building a model from scratch, though, is a sure-fire way to "destroy your capital," Taylor said. He called handmade models "fast-depreciating assets," and not cheap ones either, costing the builder millions of dollars.

"There's ones you can lease, there's open source ones," Taylor said. "Don't do it."