Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

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

adlrocha.substack.com

Post reply on HN
Print view View original

131–140 of 402 posts

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

#131

Post by dandaka »

10keane wrote: post #93

there are always three elements in the equations of business model: 1. marginal cost 2. marginal revenue 3. value created for llm providers, i always believe the key is to focus on high value problems such as coding or knowledge work, becaues of the high marginal cost of having new customers - the token burnt. and low marginal revenue if the problem is not valuable enough. in this sense no llm providers can scale lik…

I am super bullish on Google, they are my best bet to earn from models. Mostly because they are vertically integrated (other revenue streams) + open to provide services to other companies (Apple deal).

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

#132

Post by bitpush »

Gigachad wrote: post #15
worthless-trash wrote: post #5

Don't worry, when apple introduce it, it'll be revolutionary and 10% thinner.

Apple will just drip feed locally running models that enable minor conveniences. They will probably drop the Apple Intelligence label later and just have things with their own names like "magic eraser".

Apple have had Siri for decades without any meaningful movement. If you think Apple is suddenly going to get better, that's just wishful thinking. Apple neither has the expertise nor the capability to do any of that. They'd hvae demonstrated that with Siri long time back.

What Apple does it build beautiful hardware. The software has been shambles for a really long time.

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

#133

Post by dwedge »

sky2224 wrote: post #82
nielsbot wrote: post #78

> I am actually of the opinion that without some kind of bailout, OpenAI could be bankrupt in the next 18-24 months, but I am horrible at predictions I find this intriguing.. Does anyone here have enough insight to speculate more?

It's probably one of the biggest headlines right now. OpenAI has about $96 billion in debt and they don't have a revenue generating product yet.

I might be wrong but should you not have said profit generating? I pay them $20 a month so they have at least $20 of revenue

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

#134

Post by MagicMoonlight »

socalgal2 wrote: post #56
grtteee wrote: post #2

This is the classic apple approach - wait to understand what the thing is capable of doing (aka let others make sunk investments), envision a solution that is way better than the competition and then architect a path to building a leapfrog product that builds a large lead.

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

The Vision Pro is the best AR/VR product ever created.

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

#135

Post by flir »

swazzy wrote: post #73
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…

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

Assuming improvements in LLMs follow a sigmoid curve, even if the cloud models are always slightly ahead in terms of raw performance it won't make much of a difference to most people, most of the time.

The local models have their own advantages (privacy, no -as-a-service model) that, for many people and orgs, will offset a small performance advantage. And, of course, you can always fall back on the cloud models should you hit something particularly chewy.

(All IMO - we're all just guessing. For example, good marketing or an as-yet-undiscovered network effect of cloud LLMs might distort this landscape).

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

#136

Post by oarsinsync »

crote wrote: post #118
moondev wrote: post #91

Earlier quoted context omitted.

How is changing the architecture of a platform that only you make hardware for doing the impossible? They could change the architecture again tonight, and start releasing new machines with it. The users will adopt because there is literally no other choice. Every machine they release will be fastest and most capable on the platform, because there is no other option

The hard part is doing so without completely ruining the existing app ecosystem. Rosetta 2 is genuinely impressive.

Rosetta 1 delivered 50-80% of the performance of native, during the PPC->Intel transition. It turns out, you can deliver not particularly impressive performance and still not ruin your app ecosystem, because developers have to either update to target your new platform, or leave your platform entirely.

You can also voluntarily cut off huge chunks of your own app ecosystem intentionally, by giving up 32bit support and requiring everything to be 64bit capable.

...because users have no other choice when only one vendor controls the both the hardware+software. They can either use the apps still available to them, or they can leave. And the cost of leaving for users is a lot higher.

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

#137

Post by adamddev1 »

46493168 wrote: post #16

Apple is almost 2 years out from their announcement of Apple Intelligence. It has barely delivered on any of the hype. New Siri was delayed and barely mentioned in the last WWDC; none of the features are released in China. In other news, people keep buying iPhones, and Apple just had its best quarter ever in China. AAPL is up 24% from last year.

It's almost like people don't actually want LLMs all over their core tools...

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

#138

Post by idle_zealot »

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…

> and only calling out to large models when they actually need the extra knowledge

When would you want lossy encoding of lots of data bundled together with your reasoning? If it is true that reasoning can be done efficiently with fewer parameters it seems like you would always want it operating normal data searching and retrieval tools to access knowledge rather than risk hallucination.

And re: this discussion of large data centers versus local models, do recall that we already know it's possible to make a pretty darn clever reasoning model that's small and portable and made out of meat.

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

#139

Post by adrian_b »

jurmous wrote: post #98
Gigachad wrote: post #58

Earlier quoted context omitted.

We still aren't going to be putting 200gb ram on a phone in a couple years to run those local models.

We don’t need 200gb of RAM on a phone to run big models. Just 200 GB of storage thanks to Apple’s “LLM in a flash” research. See: https://x.com/danveloper/status/2034353876753592372

Yes, I agree that this is the right solution, because for a locally-hosted model I value more the quality of the output than the speed with which it is produced, so I prefer the models as they were originally trained, not with further quantizations.

While that paper praises the Apple advantage in SSD speed, which allows a decent performance for inference with huge models, nowadays SSD speeds equal or greater than that can be achieved in any desktop PC that has dual PCIe 5.0 SSDs, or even one PCIe 5.0 and one PCIe 4.0 SSDs.

Because I had also independently reached this conclusion, like I presume many others, I have just started to work a week ago on modifying llama.cpp to use in an optimal manner weights stored on SSDs, while also batching many tasks, so that they will share each pass through the SSDs. I assume that in the following months we will see more projects in this direction, so the local hosting of very large models will become easier and more widespread, allowing the avoidance of the high risks associated with external providers, like the recent enshittification of Claude Code.

Post reply on HN