The graveyard metaphor is a useful one, because it suggests a certain finality. But the list of failed AI projects, from Apple's repeatedly delayed Siri to OpenAI's messy super app launch, is less a cemetery and more a construction site. Things are being torn down, yes, but the point is that something new is supposed to go up in their place. The narrative that failure here is a dead end misses the more interesting reality: it is a cost of doing business in a space where the difference between a bold bet and a fatal miscalculation is often just timing.
What stands out to us is not that projects miss expectations. That is true in every engineering discipline. What matters is that we are finally seeing the market punish a specific kind of hubris: the belief that a large language model alone is a product. Apple's Siri struggles are a perfect case study. The technology has been technically competent for years, but the product experience has been a series of broken promises. This is not a data problem or a compute problem. It is a design problem, a workflow problem, and a trust problem. As we explored in Verify Your AI's Understanding: A Simple Check for Tax Season, the real value is not in what the model knows, but in whether you can rely on it to act correctly when it matters. A delayed Siri is not a failure of AI; it is a failure to define what a successful outcome actually looks like.
For our readers, the practical takeaway is not to abandon hope in the technology. It is to stop treating vendor roadmaps as if they were physics. The messy launch of a super app is the market's way of saying that aggregation is not intelligence. The winners will not be the ones with the most parameters, but the ones who can map a model's capabilities to a specific, painful job. That is why we keep coming back to the fundamentals, like the distributed systems work in Unlock LLM Training: A Practical Guide to Distributed Algorithms. The graveyard is full of projects that scaled the compute but not the understanding.
The honest reaction to this list is not "AI is over." It is "product management is hard." The graveyard is a reminder that the barrier to entry has shifted. It is no longer enough to train a model. You have to ship a tool that fits into a human workflow without breaking it. The real signal to watch is not which companies shut down, but which ones learn to iterate with the same speed they train. If you are building on this stack, ask yourself one question: are you building a feature that happens to use AI, or are you building a solution to a problem that you actually understand? The former is a project. The latter is a product. The graveyard is full of the former, and the door is still open for the latter. The next few quarters will separate the ones who treated this as a gold rush from the ones who treated it as a discipline.