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Apple's accidental moat: How the "AI Loser" may end up winning

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Re: Apple's accidental moat: How the "AI Loser" may end up winning

#151

Post by root_axis »

am17an wrote: post #75
mh- wrote: post #64

Earlier quoted context omitted.

A lot of people are making the mistake of noticing that local models have been 12-24 months behind SotA ones for a good portion of the last couple years, and then drawing a dotted line assuming that continues to hold. It simply.. doesn't. The SotA models are enormous now, and there's no free lunch on compression/quantization here. Opus 4.6 capabilities are not coming to your (even 64-128gb) laptop or phone in the pop…

Don’t underestimate the march of technology. Just look at your phone, it has more FLOPS than there were in the entire world 40 years ago.

Yeah, but that's the current state of the art after decades of aggressive optimizations, there's no foreseeable future where we'll ever be able to cram several orders of magnitude more ram into a phone.

Re: Apple's accidental moat: How the "AI Loser" may end up winning

#153

Post by spicymaki »

Krutonium wrote: post #92
codeptualize wrote: post #76

Earlier quoted context omitted.

I think their M chips are a good example. They ran on intel for so long, then did the impossible of changing architecture on Mac, even without much transition pain. Obviously that was built upon years of iPhone experience, but it shows they can lag behind, buy from other vendors, and still win when it becomes worth it to them.

It's also notably not the first time they switched. They did the Motorola (I think MIPS?) Archictecure, then IBM PowerPC, then Intel x86 (for a single generation, then x86_64) and now Apple M-Series.

Motorola chip was called 68000.

Re: Apple's accidental moat: How the "AI Loser" may end up winning

#155

Post by tuyiown »

rtpg wrote: post #19
Gigachad wrote: post #14

Earlier quoted context omitted.

For consumers AI has anti hype right now. It's off-putting to see consumer products slapped with a hundred AI labels. I see people talk about how you can turn off all of Apple Intelligence with one toggle rather than hundreds on Samsung. Firefox is also marketing how easy it is to disable AI.

I think a lot of people are not hype about AI in their toaster, but... I don't think people are generally turned off form deeper integration in their OS itself. Especially when for some people this is representing ideas similar to how programmer-types get excited about Shortcuts. Decently accessible automation and discovery, without having to go figure out a bunch of stuff

You're right, there is plenty of space for features that require AI to work but that are undistinguishable from "classical" feature. Better autocompletion is a proven one for example.

Re: Apple's accidental moat: How the "AI Loser" may end up winning

#156

Post by hurfdurf »

lern_too_spel wrote: post #33
m463 wrote: post #27

Earlier quoted context omitted.

Quietly they are doing things on-device. The OCR + copy/paste is genuine goodness - modestly functional.

That's also literally years behind the competition. https://www.androidpolice.com/2018/05/09/android-ps-new-rece...

Remember when Google added Car Crash Detection to Pixel in early 2020? Nobody does.

But when Apple added it in iPhone 14 (2022)...

Re: Apple's accidental moat: How the "AI Loser" may end up winning

#157

Post by yorwba »

zarzavat wrote: post #124
blcknight wrote: post #105

Earlier quoted context omitted.

There is a cognitive ceiling for what you can do with smaller models. Animals with simpler neural pathways often outperform whatever think they are capable of but there's no substitute for scale. I don't think you'll ever get a 4B or 8B model equivalent to Opus 4.6. Maybe just for coding tasks but certainly not Opus' breadth.

The only thing that we are sure can't be highly compressed is knowledge, because you can only fit so much information in given entropy budget without losing fidelity. The minimal size limits of reasoning abilities are not clear at all. It could be that you don't need all that many parameters. In which case the door is open for small focused models to converge to parity with larger models in reasoning ability. If that…

I think you underestimate the amount of knowledge needed to deal with the complexities of language in general as opposed to specific applications. We had algorithms to do complex mathematical reasoning before we had LLMs, the drawback being that they require input in restricted formal languages. Removing that restriction is what LLMs brought to the table.

Once the difficult problem of figuring out what the input is supposed to mean was somewhat solved, bolting on reasoning was easy in comparison. It basically fell out with just a bit of prompting, "let's think step by step."

If you want to remove that knowledge to shrink the model, we're back to contorting our input into a restricted language to get the output we want, i.e. programming.

Re: Apple's accidental moat: How the "AI Loser" may end up winning

#158

Post by dathinab »

blcknight wrote: post #105
amazingamazing wrote: post #22

Gemma4 in my view is good enough to do things similar to Gemini 2.5 flash, meaning if I point it code and ask for help and there is a problem with the code it’ll answer correctly in terms of suggestions but it’s not great at using all tools or one shooting things that require a lot of context or “expert knowledge” If a couple more iterations of this, say gemma6 is as good as current opus and runs completely locally o…

There is a cognitive ceiling for what you can do with smaller models. Animals with simpler neural pathways often outperform whatever think they are capable of but there's no substitute for scale. I don't think you'll ever get a 4B or 8B model equivalent to Opus 4.6. Maybe just for coding tasks but certainly not Opus' breadth.

except you don't want knowledge in the model, and most of that "size" comes from "encoded knowledge", i.e. over fitting. The goal should be to only have language handling in the model, and the knowledge in a database you can actually update, analyze etc. It's just really hard to do so.

"world models" (for cars) maybe make sense for self driving, but they are also just a crude workaround to have a physics simulation to push understanding of physics. Through in difference to most topics, basic, physics tend to not change randomly and it's based on observation of reality, so it probably can work.

Law, health advice, programming stuff etc. on the other hand changes all the time and is all based on what humans wrote about it. Which in some areas (e.g. law or health) is very commonly outdated, wrong or at least incomplete in a dangerous way. And for programming changes all the time.

Having this separation of language processing and knowledge sources is ... hard, language is messy and often interleaves with information.

But this is most likely achievable with smaller models. Actually it might even be easier with a small model. (Through if the necessary knowledge bases are achievable to fit on run on a mac is another topic...)

And this should be the goal of AI companies, as it's the only long term sustainable approach as far as I can tell.

I say should because it may not be, because if they solve it that way and someone manages to clone their success then they lose all their moat for specialized areas as people can create knowledge bases for those areas with know-how OpenAI simple doesn't have access to. (Which would be a preferable outcome as it means actual competition and a potential fair working market.)

Re: Apple's accidental moat: How the "AI Loser" may end up winning

#159

Post by throwaw12 »

shermantanktop wrote: post #87
swazzy wrote: post #73

Earlier quoted context omitted.

similar vibes as "640k ought to be enough for anybody"

Well you can do a lot with 640k…if you try. We have 16G in base machines and very few people know how to try anymore. The world has moved on, that code-golf time is now spent on ad algorithms or whatever. Escaping the constraint delivered a different future than anticipated.

> you can do a lot with 640k…if you try.

it is economically not viable to try anymore.

"XYZ Corp" won't allow their developers to write their desktop app in Rust because they want to consume only 16MB RAM, then another implementation for mobile with Swift and/or Kotlin, when they can release good enough solution with React + Electron consuming 4GB RAM and reuse components with React Native.

Re: Apple's accidental moat: How the "AI Loser" may end up winning

#160

Post by jeroenhd »

Krutonium wrote: post #94
socalgal2 wrote: post #56

Earlier quoted context omitted.

Yea, they nailed that with the Newton, Apple Pippin, and the Apple Vision Pro

The Vision Pro was a Development Kit; Just like the first generation Apple Watch. It's not meant for the consumers, it's meant for the developers among the consumers. We will see if they ever release a new VisionOS device, but it's not the first time they did that; see also the Apple Watch.

You can explain away every failed product launch with "it's a developer product", not meant for consumers.

This wasn't like HoloLens or Google Glass. They marketed these devices to consumers and then sold these devices to consumers.

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