In the last few days plug-in solar became legal for Britons. How expensive are they ? One company will "sell 460W and 890W kits, starting at £599 and £999 respectively, and it plans to launch another four models in late September, with prices starting from £499". Expect them to repay their cost in 4 to 10 years. I expect the purchase price to come down as volume sold goes up.

pay the orange one a sufficiently large bribe to ensure that he was not charged.

The intellect of toddlers improves as time passes. Trump's appears to be going in the opposite direction.

Correct. When I am driving my attention should be looking at the car in front, not peering at a display trying to find the menu item to change air-con fan speed. These screens are dangerous, you think that you will find what you want with a quick glance and suddenly realise that you have had your eyes off the road for 10 seconds - which at 70mph is over 200 yards.

I guess that BMW is now struck off the list when I next look for a car to buy; along with Tesla and others which are just computer screens with a vehicle attached.

Inference is neural network limited. On systems with entirely separated data and compute, this is a memory bus constraint. In quantized systems, ALUs and FPUs are illogical. A simple 16 cell LUT is suitable for noise free multiplication. A neural network circuit with a dedicated LUT and addressable buffers more or less eliminates bandwidth constraints. But this requires neural networks more similar to FPGA cells rather than classic compute.

That said, separating inference from data drops network depth considerably. And in transformers depth increases memory access exponentially which is why nearly identical MoE and dense models perform so differently. We of course need data centers for models with massive numbers of active parameters. But massive numbers of active parameters weaken models as that means depending on training rather than looking up facts. Huge active parameter sets will never be a good design. A real world analogy is that huge models are like Jeopardy champions who have crap loads of partial trivia facts in their heads. Smaller models are like librarians who don't have a trillions useless facts memorized but knows how to find the data for the researchers who will use the librarian repetitively to follow through to the answer. The model only needs enough training to use the data.

What really matters is, how fast it can find the data.

Inference suffers greatly when you treat weights as data. And bigger models have more trivia like facts memorized from their training. This means more active parameters and greater network depths with higher memory bandwidth needs. Even now, you see the chatbots are improving drastically because they rely substantially more on data and way less on inference.

I am not sure which part you consider gibberish, but it makes perfect sense to me. But it might sound better in my head.

P.S. I actually have considerable data to backup many of my claims. At work I'm sitting on about 50MW of compute. We build in shipping crates in a repurposed mine. There is a group of us where our goal is to avoid spinning up more compute. We need to deliver inference to 200,000 users eventually. (not customers, employees) and we could throw millions at NVidia and end up hosting some crap model, but we focus instead on cutting that memory bandwidth need. And yes, I'm far from being the smart guy on the team.

My point was to stop a vendor saying that the product lifetime is 3 months and thus using this as a get out from having to provide updates over a reasonable period. Quite how long is another debate.

Also: this need not apply to all s/ware and all bugs equally. Some which, for example, can lead to a security failure exploitable from a remote location if more important than a local cosmetic issue. Deciding a classification is a bigger can of worms than it seems at first sight.

and s/ware vendors had to provide patches/... for free. Declaring a short product lifetime would not be allowed. Would we have better quality s/ware, written secure by design ?

To work Linus' Law needs many eyeballs, unfortunately these are not always around. It seems that AI is helping - which is good. I assume/hope that the current deluge will abate once the current/historic CVEs get fixed and that the only new ones are for bugs in new code.

This seems smart.

The federal government needs
  a) more tax money to reduce their dependence on more bonds
  b) more tax money to redistribute to startups
  c) reshoring to avoid just being too far behind.

So, taxing imports is a great idea. It doesn't matter if the trickle down effect works or not. The government has bills to pay. The government is owned by the people. Therefore, the people need to pay their bills. These taxes are good... we need to reward Trump for finally bringing new socialism to America. And what is best is that this really will steal from the rich and give to the poor.

BTW... Little secret, if TSMC spun down there business right now over the next 12 months, we'd be fine. We might have to rewind our tech a year or two and it might take some time to grow capacity, but it really wouldn't matter. TSMC just gives us a 12-18 month boost over their competitors. If they scale down as Intel, GF, Samsung, SMIC and a few others scale up... it really wouldn't matter.

The absolute best data centers today should be written into history as a dark dark stain on the progression of humanity. It is a sign that no matter how stupid we have been up until now, we managed to find that hole has no bottom.

Consider this... The reason we need data centers that massive today is because the tech isn't ready.

NVidia B300 filled racks using 100kw each are barely capable of running modern frontier models. Even if we designed more powerful chips that could do the job, there isn't enough silicon wafers produced in the world to do the job. Even the most amazing semiconductor technologies on earth are not good enough. Consider that even if we could build 1TB HBM4 NPUs, the time it would take to generate a token on a transformer that large would be slow.

What we know

Transformers will leave the data center between 2030-2032 as it will never make sense to run transformer inference in racks. It's at least 1000x less green than local inference.

Intel, AMD, NVidia, Apple and every RISC-V/ARM chip supplier on earth is 100% focused on making local inference the thing.

We will soon stop training massive models that start becoming obsolete the moment the last parameter is fed. Even now, Qwen 3.8 27b is good enough for a year at least (first model to do this).

For LLMs, we'll just stop wasting cycles on huge networks and will focus more on stronger harnesses attached to better data sources.

Cloud AI will be more about acting as data providers. Models will become reasoning engines, the "smarts" will come from things like vector databases.

Vector databases will use kilowatts, not megawatts. Data storage will be much smaller.

Things like Intel's dead Optane product will become the future of datacenters. Google Search, Microsoft Bing, maybe Baidu are the best positioned companies for what comes next and they don't need 100KW per rack to deliver. If I were to enter this segment, I would pair a customized KLV object query engine onto a 1024 bit wide ecc memory bus and connect it with low cost networking (think Intel's Ethernet fabric tech). Then add a super capacitor to flush to flash on power loss... Or just hope Optane comes back. Superfast, massive, token based vector databases are the future of cloud LLM

What's next. In 3-5 years, every single rack in every single AI data center is trash

This is fact.

Even now H100 racks should be heading to scrap.

The chips are slow, they use old heat spreader tech, they get almost no performance per watt compared to B300. They are much harder to cool. They have so little VRAM it takes piles of them to run newer models. PCIe data center GPUs were always irresponsible purchases. SXM GPUs only run on special systems Noone but high end data centers can even run.

Consider that every single B300 or older equipped rack on earth is landfill in 3-5 years.

By then, the world will have overprovisioned RAM, we'll have moved 2-3 generations of cooling forward, we'll have designed denser racks, started employing all the awesome tech China built to work around lack of EUV... No really, Korean companies are already licensing CXMT tech for wafer stacking. We will have moved to lower fresh water depen....

You know what? It is going to just be cheaper to bankrupt the shell corporations who own the current data centers than to upgrade them to new tech. Besides, the environmental issues will be such a mess. Imagine trying to dispose of that much fr4 epoxy without causing environmental disasters? It's probably at least as much epoxy as the total mass of the twin towers.

I can keep ranting but I promise this, there isn't a single piece of tech in the most modern AI data center on earth that is worth using in 5 years... Not even the racks. It's all garbage.

Because scientists follow the evidence to draw conclusions, they sometimes say things that contradict the ideology. Climate change is a good example of this.

yet people still claim (and some maybe even believe) that climate change is a hoax. Do something before it is too late. I used to think that it would affect my grandkids, not me (I am 71), but I am now not so sure - this Summer has been hot!

It's a solid start, but it still leaves plenty of room for improvement in light of advances like sodium batteries for grid storage to always fill up reserves when there's an oversupply. And generating green hydrogen for industrial use off that will become even more important in the decades to come too.

The user does not have any opportunity to negotiate a change to any of the clauses, they are 'terms of business', deliberately written so that most people do not read them and even fewer understand them. Amazon is not really any worse than most of the on-line retailers.

What is needed are a set of agreements that are drawn up to be understandable to non lawyers and to be fair to both parties. There need to be a set of them as the requirements of a business selling clothes is different from one selling pizzas. The agreement must be easily checkable by the user that it has not been modified and there should be a short schedule that the business can specify defined terms, eg: business name, delivery charge and timescale, return address, ...