I don't think this is a fair characterization of Humane. I don't and haven't worked at Humane, but I did interview there and have some friends who work there now. They are notoriously secretive about their product (founders are ex-Apple, and they try to keep an Apple like secrecy culture) but I do know a bit about the evolution of the product.
Humane was founded in 2018, well before ChatGPT was released in November 2022. If you look online you can find some articles about patent applications they made well before ChatGPT was released that give you an idea about their idea for the product at the time, e.g. https://9to5google.com/2022/01/07/humane-android-ar-wearable...
Developing the hand tracking, laser projection system, voice recognition, etc. is very hard, especially considering the power constraints on the device. They spent years working on this and when LLMs hit the scene they realized that the original product idea was going to be severely lacking if they didn't integrate this technology. This caused a big internal pivot to more closely integrate with these LLMs. I'm not sure which they're using, presumably they're paying for GPT-4 access or something like that. It's understandable why they felt like they had to do this, and why it feels like a rushed integration. The bottom line is that they were way too optimistic with the hardware capabilities when they started working on the product, and the last minute rush to integrate with LLMs to at least improve the software capabilities to kind of close the gap is what we're left with. It's not a great situation, but I also think it's unfair to characterize it as a "cash grab".
I work at Alphabet and I recently went to an internal tech talk about deploying large language models like this at Google. As a disclaimer I'll first note that this is not my area of expertise, I just attended the tech talk because it sounded interesting.
Large language models like GPT are one of the biggest areas of active ML research at Google, and there's a ton of pretty obvious applications for how they can be used to answer queries, index information, etc. There is a huge budget at Google related to staffing people to work on these kinds of models and do the actual training, which is very expensive because it takes a ton of compute capacity to train these super huge language models. However what I gathered from the talk is the economics of actually using these kinds of language models in the biggest Google products (e.g. search, gmail) isn't quite there yet. It's one thing to put up a demo that interested nerds can play with, but it's quite another thing to try to integrate it deeply in a system that serves billions of requests a day when you take into account serving costs, added latency, and the fact that the average revenue on something like a Google search is close to infinitesimal already. I think I remember the presenter saying something like they'd want to reduce the costs by at least 10x before it would be feasible to integrate models like this in products like search. A 10x or even 100x improvement is obviously an attainable target in the next few years, so I think technology like this is coming in the next few years.
Humane was founded in 2018, well before ChatGPT was released in November 2022. If you look online you can find some articles about patent applications they made well before ChatGPT was released that give you an idea about their idea for the product at the time, e.g. https://9to5google.com/2022/01/07/humane-android-ar-wearable...
Developing the hand tracking, laser projection system, voice recognition, etc. is very hard, especially considering the power constraints on the device. They spent years working on this and when LLMs hit the scene they realized that the original product idea was going to be severely lacking if they didn't integrate this technology. This caused a big internal pivot to more closely integrate with these LLMs. I'm not sure which they're using, presumably they're paying for GPT-4 access or something like that. It's understandable why they felt like they had to do this, and why it feels like a rushed integration. The bottom line is that they were way too optimistic with the hardware capabilities when they started working on the product, and the last minute rush to integrate with LLMs to at least improve the software capabilities to kind of close the gap is what we're left with. It's not a great situation, but I also think it's unfair to characterize it as a "cash grab".