About 25 years ago, I was at a bbq where I spent the evening chatting with this guy named Lars Knoll. Even though I was busting my butt with my team to develop the Opera for Linux web browser, this guy had been hired by TrollTech after he had developed something called Konquerer which was becoming the default HTML rendering engine in the KDE project. He wrote the whole thing on top of the KDE libraries which were painfully slow. He rendered text and for controls using the KDE objects which were rarely more than Qt objects with a little KDE specific stuff on top. I remember thinking "there's no point thinking much about this because there's no chance it could compete with a real browser like Opera".

Shortly after, I was in Cupertino sitting across from a strange product manager type. His name was Tim Cooke. We were discussing developing the default web engine for this new Mac OS X project which was coming along. We would have to develop for Carbon because Apple hadn't implemented Objective C++ yet and we were a C++ shop and we couldn't use half the developers in the company to make a C abstraction layer for Cocoa. We came to an agreement and Apple sent us a palette full of the latest G4 macs and screens. I remember fighting over the 21" screens like children. We had an idiot project manager who insisted that we implement the browser the way Adobe was demanding. It didn't matter that we were pretty much the "experts" on making cross platform embedded browser engines. He was like "Adobe said it... it must be done this way... they are gods".

Well long story short, while we were implementing a crap Carbon based browser engine using crap tools from Metrowerks instead of the good Apple tools (which weren't that good yet). Apple put together a team which abstracted the KDE libraries and built bindings to C and then Objective-C/Cocoa. And they abstracted out everything that was platform specific otherwise from KHTML (Konquerer's underlying html widget) and wrapped it in a Cocoa wrapper and a Carbon wrapper and shipped it as Webkit and Safari. And man did it suck.

Who would have ever imagined that Lars Knoll's half assed KDE web browser would have eventually become the defacto Web standard and the default web browsing engine in every operating system... including Windows... except the strange 5 user corner case operating systems.

Don't bash a project like this. It has value. I immediately considered that I have had many cases in recent times where it could have been of value to me. That said, I wouldn't run a C based web browser engine ever. It's not that you can't write a good web browser in C. It's just that it's the wrong tool for the job. At 160,000 lines, it's a small job to port to something else. When JavaScript evolves into a JIT and the JS runtime is thunking the heck out of the DOM, then it's pretty much no longer an option. Oh, and someone will think it's a good idea to implement proper form controls. That's an insanely massive project, especially when adding WebGL as well. If this browser core catches on, which unfortunately due to the license, I don't expect it will, it will likely be 5 million lines of code almost overnight.

Yeh, no.

Three major events started "trade wars" and the government propaganda anti-China rhetoric.

1) 5G happened and Huawai was the only company with a full 5G implementation including millimeter wave and SA mode on release. Ericsson was 2-3 years behind. Nokia was 6. Huawei could deliver on day 1 and they cost much less because neither Sweden or Finland are ... well, they are terrible at competing. They simply expected China wouldn't be in the competition because... Why would they be. So, they were confused because they never had to actually compete against a company who actually did a good job.

Keep in mind, by now, nearly $2 trillion has been spent building 5G networks. Money to Sweden and Nokia recirculates. To China, it's horded and weakens the US.

2) Apple evaluated CXMT as RAM suppliers and YMTC and certified them as good enough for Apple to trust in their products and with their reputation. The US slammed the door shut on that. They are still making up excuses for why as there were no legitimate ones except that if those two companies get a foothold, the entire world semiconductor commodity trading market would collapse. As a result, non-Chinese companies can't legally license their tech to CXMT or YMTC so those companies operate patent free. But other companies have to pay license for their tech. This allows CXMT and YMTC to sell RAM and Flash for half the price. If the US lifts sanctions, in 3 years or less, Samsung, SK Hynix, and Micron could be considering bankruptcy of their RAM divisions and CXMT could aggressively dump RAM to collapse the value of the patents.

3) China focused on brands. Before Chinese brands happened, buying expensive Chinese products was a high risk, Huawei, Xiaomi and others started legitimizing Chinese premium brands. This made it so buying an $80k Chinese car was ok because it wasn't cheap Chinese no name crap.

The money floods to China and they invest in One China by spending obsessively on education and R&D. They raised 800 million people out of poverty so far. They know every yuan spent in China is worth it. They invest massively in Silk Road because they don't think 4 years at a time. Building infrastructure across Asia, South America and Africa will pay them back massively.

The one thing which will cause China's greatest growth will be when they start opening Chinese universities to the masses. It will be way bigger than any other Silk Road initiative. And it will devastate the US economy. The US cannot afford to lose access to the world's top talent and can't afford to lose control of who gets an education and who doesn't.

Did you know that China's defense against American anti-Chinese propaganda is simple pro-China marketing? Which do you think is working better long term globally?

There are people who see what's happening and prepares for the changes. There are fools who make idiotic comments like yours.

No one is forcing him to spend money he doesn't have to produce a product no one will pay for.

He has irresponsibly played a game and even helped make the rules. Now he realizes other people are better at it than he is, but he keeps spending borrowed money for 'pay to play' and all the other players except openai (his twin) are figuring out how to play for much less.

His biggest competition are the bastards at companies like Alibaba, Microsoft, and Google who actually have products, services, and paying customers who will stick with them even if their AI is a generation or three behind.

See, it's perfectly ok to slow down. The others have done it. But they cheated... They actually had business plans.

Should these companies complain about their only paying customers?

Tl;dr but did you just go after hpe by referring to hp products lines?

Qwen 3.8 27b runs with mtp and a 72k context window on two RTX 5060 Ti 16GB at about 35tps (in MTP, that compares to 75tps non mtp). And, yesterday, it nearly matched GLM 5.2 running on 8xH100 in every task I tossed it. (My glm rig is rate limited to 30rps). My comparison is "ability to handle long complex tasks".

So, the trick is to use memory, search, fetch, vector database.

The purpose of a model is to reason. It needs enough training to perform further research. Context is very-short term memory, vector databases are their long term memory, rag is the books they read, and search and fetch are their libraries.

I would LOVE to switch back to two 3090 cards for 48GB (they do image and video now), but when I run qwen 3.8 27b fp8 with 256k context, I find the quality is higher in some cases, but drops because the model then favors short term memory iver research.

Memory bandwidth is much more exciting. 500tps when running on an HPC is very very nice.

So, if it were me measuring, a single 64GB HBM3e GPU is really where we should aim as this should be consumer cost friendly by 2030. (now I have to try on an A100 later today)

Are they going to try selling a mediocre chatbot to wall street as AGI?

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.

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.

https://www.cs.wisc.edu/2025/0...

And DARPA has a whole program for it:

https://www.darpa.mil/research...

most of those companies have serious issues zoning data centers and getting power sorted out.

Right now, we're living in a world where for operations, all the companies need new modern data centers with the power grid and additional infrastructure to support them. By spending on AI, they get the land, the leases, the power, the infrastructure, etc... all because in the name of national security, the geriatrics... I mean congress and the president will just throw money at making it happen. They don't really need the data centers, they need the zoning and infrastructure for the data centers.

When AI bubble pops, the government will have to step in and bail them out and they will continue with business as usual. But these companies are much smarter than this. They know this is a bubble, they know it will pop. Unlike Anthropic or OpenAI, they all have products and long term customers and will be just fine.

Musk will use the data centers to handle self-driving, robots, space operations, etc.... this is money well spent for him. The other companies will continue to deliver cloud services to their customers and AI will still be a major part of it, just not he headline. OpenAI and Anthropic and ... all the rest of the "AI is our product" companies will just fizzle as if they never existed.

Please please please never use occam's razor in any way like this again. Occam is a terrible tool employed generally by morons to make themselves sound intelligent when they're simply spouting shit. I get it, it sounds really cool... like DeMorgan's theorem or Gödel’s Incompleteness Theorems but unlike these, Occam's Razor has no foundation in science, math or anything else.

And that said, while Bill doesn't have a photographic memory, I expect that like myself, he can recall nearly verbatim every conversation he has ever had.... ever

So, I assume that this is a long term support OS? So if I install it, the system will receive proper updates and patches for years?

Or it it just someone who told OpenCode make me a distro and then for some utterly inexplicable reason, recorded a who youtube video with his face on it?

It seems to me that computer skills should make it so people can adapt.

Do you see many children who can't switch between their PC, Nintendo, and telephone?

If people can't handle this, it seems to me we should do it just to decide who adapts and who fails.

Switching between SAP and PeopleSoft is a much bigger problem