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AI subscriptions are a ticking time bomb for enterprise

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Re: AI subscriptions are a ticking time bomb for enterprise

#211

Post by harrall »

Traubenfuchs wrote: post #207
harrall wrote: post #190

Earlier quoted context omitted.

You can now buy 128 GB unified memory computers from AMD as commodity. They’re still pricey, the world is still scaling up memory production, and a lot of code isn’t yet built for AMD, but we went from the Wright’s brothers first airplane to jet engines in 27 years. I’m not sure “it’s only a few years away” but we are sure moving there fast.

I believe the same thing but keep repeating the question: Then what are all the datacenters for?

I print documents and photos at home regularly but I still contract out to dedicated print shops.

The print shop can’t replicate the practicality of local printing and I can’t replicate their scale of investment. Both coexist perfectly.

Re: AI subscriptions are a ticking time bomb for enterprise

#212

Post by GolfPopper »

nate8bit wrote: post #159

The entire problem with "AI" is that it's easy to do without. The AI companies know it, the users know it - even the most pro AI agent manager knows it. Thought experiment: remove AI from the world right now, all of it - what do you have? Business as usual. This article doesn't do enough to underscore that - dreaded be the day I need to get an actual engineer to review a PR, right?

In my opinion, that's likely a large part of why it's being pushed so hard. Not to drive honest revenue, but to get AI products so deeply embedded that 'just removing AI' won't be seen as an option, even when keeping it has higher and higher costs, up to and beyond airline-style bailouts from the government. An entirely new layer of wealth-extracting intermediary, being sold under false promises.

Re: AI subscriptions are a ticking time bomb for enterprise

#214

Post by janderland »

dools wrote: post #192
alecco wrote: post #167

Earlier quoted context omitted.

> Local modals are 6 months to 18 months behind frontier. I wish this was true but it is not. And I am working on open source models so if anything, I would have a bias towards agreeing with you. Frontier closed models (GPT/Claude) are gaining distance to everybody else. Even Google, once the king. Your claim is a meme coming from benchmark results and sadly a lot of models are benchmaxxed. Llama 4, and most notably…

Kimi k2.6 is about on par with GPT 5.2 so I’d say open weight models are about 6 months behind.

Has Kimi found a way to vastly reduce the amount of VRAM required without running at 3 tokens per second? That’s the real concern.

Re: AI subscriptions are a ticking time bomb for enterprise

#215

Post by ac29 »

nixon_why69 wrote: post #155
slashdave wrote: post #140

Earlier quoted context omitted.

We are discussing how rapid development has been, and now you want to freeze your model in silicon?

Why not have a bunch of SRAM and various operations like "Q4 matmul" in silicon? Model weights and even architectures could still evolve on a platform like that.

Doesnt "a bunch of SRAM" top out at maybe a few gigs per chip (with zero area used for logic)? You'd need an order of magnitude more to fit even a fairly weak general purpose LLM model.

Re: AI subscriptions are a ticking time bomb for enterprise

#217

Post by comfysocks »

slashdave wrote: post #133
evo_9 wrote: post #79

Every AI subscription is a ticking time bomb for the frontier provider; within a few years we will be running local models as good as today’s frontier models with almost no cost burden. The floor will fall out of the enterprise market for all the frontier companies.

> within a few years we will be running local models as good as today’s frontier models I seriously doubt it. Scaling is already strained (don't buy into the "exponential" hype). And, in any case, the competition will be against the frontier models that will exist in two years.

> I seriously doubt it. Scaling is already strained (don't buy into the "exponential" hype). And, in any case, the competition will be against the frontier models that will exist in two years.

But even if scaling plateaus for the frontier models, maybe distillation will improve to the point where smaller more manageable models can reach the same plateau. That would be great for local.

Re: AI subscriptions are a ticking time bomb for enterprise

#218

Post by throwa356262 »

nixon_why69 wrote: post #155
slashdave wrote: post #140

Earlier quoted context omitted.

We are discussing how rapid development has been, and now you want to freeze your model in silicon?

Why not have a bunch of SRAM and various operations like "Q4 matmul" in silicon? Model weights and even architectures could still evolve on a platform like that.

I belive that is what NPUs are.

The issue is the very huge amount of DRAM and high bandwidth these model require.

Re: AI subscriptions are a ticking time bomb for enterprise

#219

Post by cbg0 »

dools wrote: post #192
alecco wrote: post #167

Earlier quoted context omitted.

> Local modals are 6 months to 18 months behind frontier. I wish this was true but it is not. And I am working on open source models so if anything, I would have a bias towards agreeing with you. Frontier closed models (GPT/Claude) are gaining distance to everybody else. Even Google, once the king. Your claim is a meme coming from benchmark results and sadly a lot of models are benchmaxxed. Llama 4, and most notably…

Kimi k2.6 is about on par with GPT 5.2 so I’d say open weight models are about 6 months behind.

The Q4 quantization requires about 600GB of RAM without context, not exactly consumer hardware friendly.

Re: AI subscriptions are a ticking time bomb for enterprise

#220

Post by aleqs »

lukeschlather wrote: post #135
alsetmusic wrote: post #115

Earlier quoted context omitted.

Local modals are 6 months to 18 months behind frontier. Even if the performance of a cloud model is faster, it's clear that local is catching up.

It is not getting easier to obtain hardware that can run models which are sufficiently useful to undercut frontier models, if anything the cost of such hardware has gone up by 25% or more just in the past 6 months.

I think hardware prices will come back down once we start seeing more efficiency improvements in models and hardware, and once more people and companies self-host models (which seems to be happening more and more these days). I think the massive infra/hardware expenditures of OpenAI and the like are going to end up unnecessary, leading to hardware price drops.
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