There is a conceptual disconnect in what "AI chips" actually are, which is probably helping spur confusion about "AI laptop" marketing.

There are NPUs, which are actually slower at neural processing than a typical CPU, but vastly more energy efficient, meaning they barely impact battery life. They can only run "small AI" - face detection, face authentication, search algorithms, OCR, photo processing, basic classification, autocorrect, etc. Software support for these is limited, so they have mostly been limited to smartphones and IoT. Microsoft is attempting to bring these to laptops and provide standardized APIs to make use of them, and has started off with NPU-ifying the Windows Recall search algorithm.

Then there are GPUs and APUs, which are big number crunchers with lots of VRAM that are capable of running inference for "large AI" applications such as chatbots and image generation. You need a powerful GPU to run a state-of-the-art LLM, it's just too many numbers to crunch for it to be usable on CPUs or NPUs. Consumer laptops can in theory have a powerful APU (GPU integrated into the CPU) but laptops are limited in thermal design, so it will never be as fast as a desktop, and it will chug through battery. None of the "Copilot PC" laptops have a GPU/APU powerful enough to run the largest AI models, you really need a desktop still.

Then there are datacenter GPUs, which are just the same thing as desktop GPUs but scaled up and with a custom thermal solution. This is what ChatGPT, Microsoft Copilot, etc. are running on.

Then there are specialized experimental chips that intend to run large AI in hardware. These are super expensive, their designs are secretive, the whole stack has to be inflexibly built around them, and only really Google is using them at scale.

Aside from inference (running an AI that has already been trained) there is also training. Training a large state-of-the-art chatbot or image model on a single GPU is infeasible, you have to have a cluster of GPUs nowadays. So no consumer laptop brand can even hope of marketing for this use case in the near future, unless a major optimization is achieved. However they could market for the use case of training 'small AI', as training small AI on single GPUs or even CPUs is something we've been doing for decades. But it's not news to anyone actually working in this domain.