everything will be able to talk to anything else, for real this time
it will be like the internet of things only some asshole will call it "intelligence of things"
again, there will be no S for Security in this new IoT
your thermostat will one day start fucking with you. when you run a diagnostic llm on it, it turns out it's keeping around 5 different viral copies of personality files around, that were left behind by llm botnets/openclaw-like memetic replicators/your grandpa leaving behind easter eggs before his death.
the future will be pretty evenly distributed, and full of weird shit
> Generally, models intended to be run locally will be much smaller, such as Muse Glimmer or Qwen3 Coder.
That is such an interesting set of models to use as examples here. One being essentially obsolete on release a month ago, and the other being completely ancient in LLM time. I really wonder how they landed on those two.
I just stopped reading at that, for anyone else, Please find a better source and take everything in here with a grain of salt.
IMO
The number one improvement that mattered for local AI was llama.cpp, partial offloading to system cpu/ram. The next was quants, being able to take fp16 and turn it to q8, q4 etc. The next IMHO is unsloth dynamic quant, that have been able to do mixed precision so we have UDq1/q2 that is actually pretty damn coherent. Allowing individuals to drive K3 locally even if it's at Q1/Q2. Then MoE changed everything for everyone, cloud and local. The other is integrated GPU, Apple, Strix Halo, DGX Spark. Then all the extra improvements like MTP, DSpark, etc. Of course there's many other additional things that have mattered too
Then we have these articles that say tokens will get so cheap that labs won’t know how to make profit.
Who is correct?
Token pricing was a small minority of customers up until this year, when all the labs started trying to force customers onto token-based billing. Within the last week, Anthropic repriced my team's plan from a temporary "50% extra tokens" to 25%: https://support.claude.com/en/articles/15910845-claude-code-...
The fact that all this is ongoing within such a short timeframe should make you suspicious of any analysis that claims to be observing "statistical trends" like they've discovered a new Moore's Law out of 6 months of pricing data from 2 companies.
Asking Claude for a rough estimate based on publicly available throughput and cost data for open weight models on modern GPUs suggests serverless, pay-as-you-go inference is profitable on owned GPUs with reasonable utilization (30-50%).
Also, it's impossible that they become cheaper than specialized software. Or even as cheap as them. It's still possible that they become cheap enough that it doesn't matter.
AI is not a bubble, IMO, though we may see a retrench and some companies with sky-high valuations will crash to more reasonable ones. But data center demand is probably a bubble, and the main driver will be reduction in the actual amount of power and data center space required to serve escalating demand.
I think hardware and model improvements will pace or maybe outrun demand and then when demand starts to saturate will keep going and leave a lot of orphaned data centers.
When people say "AI is a bubble", they mean economically as a whole, which includes data centers.
Perhaps we need better terminology for "product useful; numbers nonsensical"
Not so sure about this. There’s always a potential threshold. After all, we don’t all use the most powerful computers, the latest phones, the highest resolution cameras, the fastest or best cars.
I am already not interested in cloud LLMs and I don’t even use the best (on paper) model that I can run locally. I prefer a model that people insisted (here) was “dead on arrival” but appears to work better for me.
The edge is somewhere else.
You can't simply wave Jevon's paradox at things. Thousands of miles of canals were dug in the UK that couldn't be sustained and were abandoned. Thousands of miles of railways were laid that could be sustained and were abandoned. And those are potentially durable investments, unlike cheap walls, pillars and roofs laid over a levelled concrete slab full of fast depreciating IT equipment.
But yes, if sold for a negative margin Jevon eventually stops because the decreasing supply will drive up prices.
> things are made that will sell for less than the cost of construction
Price is set at the marginal cost. Capital costs aren't in marginal costs.
You'll need a better counter-example than UK railways which suffered from Parliament price-fixing.
I'm so glad the tide here is turning on this talking point, brought on by exactly the same people beating us over the head with it for months while no progress is made towards it materializing.
Many, many people who post here are capable neither of real analysis nor distinguishing real analysis from memes. They aren't hackers, they are adherents of a cult that happens to focus on the same subject matter as hackers.