Here's my thesis on what went wrong with generative AI from a business perspective and how that's changing now. 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 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. (A point we make in the AI Snake Oil book: amazon.com/Snake-Oil-Artific…) 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 self-owns due to inadequate testing like Microsoft's early Sydney chatbot and Google's Gemini image generator. This has also caused a backlash. But things are changing. OpenAI and Anthropic seem to be transitioning from research labs focused on a speculative future to something resembling regular product companies. If you take all the human-interest elements out of the OpenAI boardroom drama, it was fundamentally about the company's shift from creating gods to building products. Google and Microsoft are slower to learn, but my guess is that Apple will force them to change. Last year it was seen as a laggard on AI, but it seems obvious in retrospect that the slow and thoughtful approach that Apple showcased at WWDC is more likely to resonate with users. Still, we shouldn't expect changes overnight. There are unsolved research challenges when it comes to making LLM-based AI assistants that actually work: aisnakeoil.com/p/new-paper-a… Enterprise adoption will likely be even slower. the barriers include integrating it into existing products and workflows and training people to use it productively while avoiding its pitfalls. We should expect this to happen on a timescale of a decade rather than a year. If you want clear analyses of AI that look past the hype, subscribe to the AI Snake Oil newsletter. aisnakeoil.com/