Comment by CharlesW

I’m not so sure. Just think about coding assistants with MCP based tools. I can use multiple different models in GitHub Copilot and get good results with similarly capable models.

Siri’s functionality and OS integration could be exposed in a similar, industry-standard way via tools provided to the model.

Then any other model can be swapped in quite easily. Of course, they may still want to do fine tuning, quantization, performance optimization for Apple’s hardware, etc.

But I don’t see why the actual software integration part needs to be difficult.

Doubt it. Of all the issues I run into with Siri none could be solved by throwing AI slop at it. Case in point: if I ask Siri to play an album and it can't match the album name it just plays some random shit instead of erroring out.

The other day I was trying to navigate to a Costco in my car. So I opened google maps on Android Auto on the screen in my car and pressed the search box. My car won't allow me to type even while parked... so I have to speak to the Google Voice Assistant.

I was in the map search, so I just said "Costco" and it said "I can't help with that right now, please try again later" or something of the sort. I tried a couple more times until I changed up to saying "Navigate me to Costco" where it finally did the search in the textbox and found it for me.

Obviously this isn't the same thing as Gemini but the experience with Android Auto becomes more and more garbage as time passes and I'm concerned that now we're going to have 2 google product voice assistants.

Also, tbh, Gemini was great a month ago but since then it's become total garbage. Maybe it passes benchmarks or whatever but interacting with it is awful. It takes more time to interact with than to just do stuff yourself at this point.

I tried Google Maps AI last night and, wow. The experience was about as garbage as you can imagine.

I'm genuinely curious about this too. If you really only need the language and common sense parts of an LLM -- not deep factual knowledge of every technical and cultural domain -- then aren't the public models great? Just exactly what you need? Nobody's using Siri for coding.

Are there licensing issues regarding commercial use at scale or something?

> Why didn’t they just run Ollama and a public model

Same reason they switched to Intel chips in the 2000s. They were better. Then Cupertino watched. And it learned. And it leapfrogged.

If I were Google, my fear would be Apple launching and then cutting the line at TSMC to mass produce custom silicon in the 2030s.

My impression is that solar (and maybe wind?) energy have benefited from learning-by-doing [1][2] that has resulted in lower costs and/or improved performance each year. It seems reasonable to me that a similar process will apply to AI (at least in the long run). The rate of learning could be seen as a "pace" of improvement. I'm curious, do you have a reference for the deceleration of pace that you refer to?

[1] https://emp.lbl.gov/news/new-study-refocuses-learning-curve

[2] https://ourworldindata.org/grapher/solar-pv-prices-vs-cumula...

I don't think anyone really knows, because there's no objective standard for determining progress.

Lots of benchmarks exist where everyone agrees that higher scores are better, but there's no sense in which going from a score of 400 to 500 is the same progress as going from 600 to 700, or less, or more. They only really have directional validity.

I mean, the scores might correspond to real-world productivity rates in some specific domain, but that just begs the question -- productivity rates on a specific task are not intelligence.

In the current US environment, no one can survive going against Trump, and as recently evidenced, this is meant literally.

The US, for all intents and purposes, is now a kleptocracy. Rule of law, freedom of speech, even court orders, all of that doesn't matter any more in practice. There will always be some way for the federal government to strong-arm anyone into submission.