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

adlrocha.substack.com

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

#391

Post by jacobgold »

tim333 wrote: post #339
jacobgold wrote: post #314

Earlier quoted context omitted.

"Google had the best search engine, it became a centre of gravity..." Almost no one made serious attempts at competing with Google. And not because of network effects or any other hard blocker. In the early 2000s, the industry just wasn't mature enough to heavily fund serious competition. By the 2020s the industry has funding and founders ready to jump on any huge opportunity that presents itself. There are of course…

Microsoft had a good go with Bing.

They did as well competing with Google on search as they have competing with Apple on smartphones.

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

#392

Post by pojzon »

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…

Like you say, Google is not playing the game to win. They play it to make others lose.

Looking at current advancements - this is the horse I would bet my money on.

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

#393

Post by GeekyBear »

crena wrote: post #371
GeekyBear wrote: post #319

Earlier quoted context omitted.

Apple's Neural Engine and the CoreML framework to leverage it are almost a decade old now. Apple Intelligence was a rebrand, and Apple has made some unique decisions rolling it out. For instance, the new chatbot version of Siri's hallucinations were seen as unacceptable, so its release was delayed. Is a chatbot that provides false information regularly really an advancement? Apple chose not to do photorealistic gener…

That is definitely true, but some time ago Apple’s marketing team has also put out some pretty cringey commercials to the contrary. It’s wild how they seemed to be encouraging people to cheat and hide their ineptitudes, rather than just being honest about it.

As far as I'm concerned, I would rather see the industry prioritize AI that doesn't provide false information over pushing slop out as quickly as possible.

Hell, there are chatbots out there trying to convince kids that suicide is a great idea.

How is prioritizing pushing out slop as quickly as possible with no consideration of the consequences acceptable?

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

#394

Post by ponector »

blitzar wrote: post #340
ponector wrote: post #324

Earlier quoted context omitted.

It has an excellent reputation of having no reported accidents!

My regular tow truck driver tells me he sees all sorts; fords, audis, mercedes, teslas, even the odd exotic car like a lambo - but never once an apple car would you believe it.

Invisible car.

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

#395

Post by shermantanktop »

jstummbillig wrote: post #323
dns_snek wrote: post #276

Earlier quoted context omitted.

We're not talking about writing assembly by hand here. If your software has a million daily users and wastes a minute of their day, that's about 9 work-years of labour wasted every single day. In a 5-year lifecycle that's about 10,000 years of human labour wasted. Yes, I had to quadruple-check this myself. Does it take 10,000 work-years of effort, per project, to train its developers to write reasonably performant co…

What world are you living in where the median piece of software has a million users? Or even a hundredth of that?

I work on software with millions of users..? Sure, it’s not the median, but it exists and there’s a lot of it.

Unfortunately the number of users and the collective value of their wasted time doesn’t make arguing for efficiency and performance any easier.

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

#396

Post by rldjbpin »

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…

> If a couple more iterations of this, say gemma6 is as good as current opus and runs completely locally on a Mac, I won’t really bother with the cloud models.

> That’s a problem.

While improvements should continue rolling in, and might even match current SOTA in benchmarks down the line, is it "good enough"?

Hard to believe we have reached that stage with current models, which would continue to stretch beyond what we can economically run. Call it skill issue, or try to fix it with a revolutionary harness, it seemingly takes a village to get it all working. Maybe by then we will have good enough ecosystem in these layers too, but if current capabilities is the benchmark, it might need more time in the oven.

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

#397

Post by rldjbpin »

the core thesis of the article and comparison with non-"losers" seem vague at best. comparing model providers with hardware vendor with software ecosystem is like differentiating between apple to oranges.

if hardware moat was to be discussed, then compare with nvidia, amd and google's tpu division perhaps. in-house intelligence is best left alone for apple. they are relying on the "peers" for underlying capabilities as is. [1] [2]

outside of inference and (pro/con)sumer space, there is little to offer for the enterprise or the people developing the lowest end of the stack. even the recent tinygrad egpu is shockingly slow [3]. which might made gb10 look much more capable for in-house training.

regardless, most of the industry "moat" does not appear sustainable at best. only time will tell how it will turn out for everyone but on a positive note, apple does not put all its eggs in this basket, which is probably wiser.

[1] https://news.ycombinator.com/item?id=40636980

[2] https://news.ycombinator.com/item?id=46589675

[3] https://www.youtube.com/watch?v=C4KWsmezXm4

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

#398

Post by ethbr1 »

jacobgold wrote: post #390
ethbr1 wrote: post #321

Earlier quoted context omitted.

Yahoo? MSN Search?

There were lots of search engines but very few that made serious well-funded attempts at competing head-to-head with Google.

I'd consider both of those well-funded.

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

#399

Post by herewulf »

jstummbillig wrote: post #323
dns_snek wrote: post #276

Earlier quoted context omitted.

We're not talking about writing assembly by hand here. If your software has a million daily users and wastes a minute of their day, that's about 9 work-years of labour wasted every single day. In a 5-year lifecycle that's about 10,000 years of human labour wasted. Yes, I had to quadruple-check this myself. Does it take 10,000 work-years of effort, per project, to train its developers to write reasonably performant co…

What world are you living in where the median piece of software has a million users? Or even a hundredth of that?

The one we're in where "software" doesn't just mean an app that someone downloads from a website or an app store. Software includes lots of server side components, etc, etc.

I once noticed my name in the Chromium OS credits due to a patch I had submitted to a library that's on every Chromebook. 1 million would be a small number for Chromebooks alone.

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

#400

Post by herewulf »

Gigachad wrote: post #58
colechristensen wrote: post #41

Earlier quoted context omitted.

Local models seem somewhere between 9 and 24 months behind. I'm not saying I won't be impressed with what online models will be able to do in two years, but I'm pretty satisfied with the prediction that I won't really need them in a couple of years.

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

My phone connected via mesh VPN to a server at home is local enough model for me.
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