AI companies are pivoting from creating gods to building products. Good.

AI companies are collectively planning to spend a trillion dollars on hardware and data centers, but there’s been relatively little to show for it so far. This has led to a chorus of concerns that generative AI is a bubble. We won’t offer any predictions on what’s about to happen. But we think we have a solid diagnosis of how things got to this point in the first place.

In this post, we explain the mistakes that AI companies have made and how they have been trying to correct them. Then we will talk about five barriers they still have to overcome in order to make generative AI commercially successful enough to justify the investment.

Product-market fit

When ChatGPT launched, people found a thousand unexpected uses for it. This got AI developers overexcited. They completely misunderstood the market, underestimating the huge gap between proofs of concept and reliable products. This misunderstanding led to two opposing but equally flawed approaches to commercializing LLMs. 

OpenAI and Anthropic focused on building models and not worrying about products. For example, it took 6 months for OpenAI to bother to release a ChatGPT iOS app and 8 months for an Android app!

Google and Microsoft shoved AI into everything in a panicked race, without thinking about which products would actually benefit from AI and how they should be integrated.

Both groups of companies forgot the “make something people want” mantra. The generality of LLMs allowed developers to fool themselves into thinking that they were exempt from the need to find a product-market fit, as if prompting a model to perform a task is a replacement for carefully designed products or features.

OpenAI and Anthropic’s DIY approach meant that early adopters of LLMs disproportionately tended to be bad actors, since they are more invested in figuring out how to adapt new technologies for their purposes, whereas everyday users want easy-to-use products. This has contributed to a poor public perception of the technology.1

Meanwhile the AI-in-your-face approach by Microsoft and Google has led to features that are occasionally useful and more often annoying. It also led to many unforced errors due to inadequate testing like Microsoft's early Sydney chatbot and Google's Gemini image generator. This has also caused a backlash.

But companies are changing their ways. OpenAI seems to be transitioning from a research lab focused on a speculative future to something resembling a regular ...