Wow who could have guessed?

The Party of Lincoln is dead

Yes, when the two parties fought tooth and nail to prevent any third voice from rising up, the people from the disassociated group decided to usurp one of the main parties, rather than try to make their own.

Literally the Republican party did this to themselves because they just couldn’t bear the thought that Trump might carve out a third party and challenge the status quo.

But the Republican party isn’t the only one this is happening too. We already see socialist entering the Democratic party. The reality should be that we have enough room to have both Democratic and Socialist party in the US. Just like we should have enough room for a Republican and Populist party.

But no, the Democrats and Republicans decided to hold onto the duopoly to the bitter end. Good riddance to both of them. It’s clear that the thing that’s eaten away the classic Republican has made the political group worse. Perhaps that which supplants the Democrats will be better than what we’ve dealt with.

Things to note.

• Gambling is a revenue stream for States.
• States up to this point have been terrible at managing revenue, gambling now gives them this glut of cash.
• Gambling has been promoted as a social activity. Know a gambling platform? Likely there’s a whole social media presence for it. And for some digital platforms that include gambling, they may even have whole social network.
• Aggressive advertising and hidden psychological factors have played a role in how people view it. “Risk-free” sign up, give the impression of harmless entertainment and some platforms deeply hide the gambling aspect.
• Low barrier to entry. Gambling usually has very little friction to get people into the platform, some even allow very low wagers, allowing “everyone” to get in.
• The escape illusion is real for the most hardcore. During periods of high inflation, stagnant wages, and high living costs, individuals look for alternative income sources, looking to escape their current situation.
• And finally, the gig market mindset where everyone feels a need to have a side hustle. Digital income streams with low entry have become popular for fulfilling this mindset.

Human beings are terrible at balancing short-term gains for long-term consequences. It’s mixed into our DNA. Our ancient ancestors, securing immediate calories or escaping a threat was a matter of life and death. Long-term planning wasn’t as critical as immediate survival. Now do note, that’s not an excuse for the people who foolish went head long into this.

This is why this struggle with the rich and powerful is eternal. It fundamentally taps on an ingrained flaw we collective fall for every single time. There is no one solution, there can never be one solution. People must forever fight themselves and the powerful from the exploitation of this fundamental flaw of humanity.

Then we are completely on the same page! I actually mentioned earlier in our thread that I hate the notion of calling these specific, highly useful operations ‘AI’ for exactly the reasons you just laid out.

My previous replies were just looking at it through a strict, literal computer science taxonomy lens. But my original comment literally points out the very things you bring up.

We’re fighting an uphill battle with the vast marketing hype and it’s good that you can understand the nuanced difference between all the things that have gotten rebranded AI. My point isn’t to try and use past technology as a way to validate what the techbros are doing but to highlight how muddy the waters have become.

I closed out my original comment with a hope that perhaps the more utility based work that’s had solid proof of usefulness doesn’t get tossed out with the bathwater so to say.

Again, I’m not apologetic of what has come from that hard work, more lamenting that everything has become the fuzzy mess it is.

By all means, head back to my original comment and let me know if there’s any part where I’ve come off indicating embracing this thing that I would say you and I agree with.

I distinctly remember the conversations about Office’s phone home system and people specifically saying “this seems problematic” and Microsoft hand waving those concerns away.

We are saying the same thing, just at different layers of the tech stack. Your description of an SGD-driven model running on an NPU is the precise low-level math and hardware that allows the Deep Reinforcement Learning (DRL) agent to function.

What’s happening here is that you’re falling into a trap many people now mistakenly believe that if software doesn’t generate text or use a Transformer, it isn’t “real AI.”

But that’s only because LLM have become the dominating conversation piece of AI.

Saying ‘that’s not AI, that’s an algorithm’ is a fundamental misunderstanding of computer science. All AI is built out of algorithms. Neural networks, stochastic gradient descent, and transformers are all algorithms.

The line between ‘traditional programming’ and ‘AI’ is machine learning the ability of an algorithm to optimize its own internal weights based on environmental feedback rather than relying on hard-coded rules written by a human.

Would you say when Google’s AlphaGo beat champions at Go that it wasn’t AI? Because it didn’t use language transformers either. By definition, a DRL agent that uses a Neural Processing Unit (NPU) to continuously calculate optimal radio frequencies via Stochastic Gradient Descent is text-book Machine Learning.

But the thing is I don’t blame you for the confusion. Marketing hype leads many to this same trap of prerequisites for particular transformation to qualify as quote/unquote AI. But technically that just isn’t true. I do enjoy this conversation we’re having as it does highlight common misconceptions.

Technically, yes, it’s an algorithm but all AI software is built out of algorithms. The critical difference is that traditional algorithms are fixed, static instructions written step-by-step by human engineers. Deep Reinforcement Learning (DRL) is a self-learning algorithm. Instead of a developer programming exactly how to handle every single wireless interference scenario, the DRL model acts like an AI agent. It continuously learns, adapts, and teaches itself the absolute best optimization paths purely through real-world trial and error.

Modern dense networks face a ton of unpredictable interference and variable traffic patterns. Wifi is a victim of it’s own success. It’s literally everywhere and thus all of these sources clobber the airwaves around them. This makes the traditional methods for traffic management and resource allocation of the airwaves too complex to fully implement.

However, your usage of LLM isn’t correct here. Wifi 7 doesn’t use a large language model, it uses what is called a Deep Reinforcement Learning (DRL) model. Wifi 7 isn’t trying to be generative, it’s being administrative. It’s looking at the airwaves as they are, and attempting to find an optimization for the current situation it is in.

In most cases the wifi coverage is not such that the NPU needs to step in. Traditional methods for transmission can be used, but in cases where you’re walking in a mall or in an apartment complex. You have tons of APs vying for the same resource. AI is used here to listen to what’s going on out in the world and come up with a method to target the highest bandwidth that can be achieved.

The intellectual poverty extends to the economics. Acemoglu has found that only 4.6 percent of tasks in the economy are currently cost-effective to automate with AI. His estimate for AI’s total productivity impact over the next decade: 0.66 percent. Goldman Sachs projected seven percent in 2023, before we began to see the shape of this thing. McKinsey projects between 0.5 and 3.5 percent annually.

Yeah this is the thing I keep saying and everyone on social media keeps saying “Boo, AI bad.”

The reality is that AI presents a very real, very useful thing. A combination of linear algebra, calculus, and probability. There’s lot of use cases for it. But putting it in all those use cases is not undoing the entire economy. We don’t need anywhere near the AI data centers that techbros keep saying we need.

Things like Bayes theorem have uses, I hate that it’s gotten lumped into AI. Optimization via loss function is incredibly useful in a lot of domains. But none of them can one-to-one replace human beings and it’s wild watching all these “captains of industry” lose their collective shit.

Someone is catastrophically wrong, and the people spending the money are not the ones with the Nobel Prize.

Yeah, it’s the techbros. They’re taking really useful mathematical operations and functions and doing neat albeit useless tricks with it. Anyone who understands the actual fundamental math behind these models and doesn’t get too carried away with it will tell you, the reason…

Over ninety percent of firms surveyed in 2025 reported no measurable impact on employment or productivity despite a quarter-trillion dollars in AI investment.

is because people are being handed something that they have no idea how to use and the way they’re being told to use it, is actually wrong.

Engineers who did the spec for 802.11be (wifi 7) understood the nature of a channel matrix operation in MU-MIMO. Singular value decomposition benefits from vector dot multiplication and gradient descent minimization. This is absolutely perfect for AI, it’s dang near what you’d want to use it for. And that’s why Wifi 7 routers come with an embedded model and NPU to run the model onboard. 4096-QAM benefits from linear transformations through a Euclidean space. Mass matrix operations to perform those transformations are ideal with AI.

Which is why I hate this notion that we’ve called these specific, highly useful operations, AI. Because they have way more application than neural networks, but since you have the hardware, Wifi 7 LDPC uses GAANs, because you’ve got the hardware. MLO and studying the interference in a particular space are also perfect for neural networks.

There are all these uses and honestly it’s crazy watching this insanity that is people like OpenAI, Claude, and so on. There’s no way they’re going to make good on their promises of being able to fire everyone. It just doesn’t make any logical sense when you look at the various domains of math that underpin AI.

And maybe that’s because, the people who fly off the handle with AI, are people who take this math and see the human brain in those formulas. I think that’s wild take, but my understanding of biology is limited. But I feel our brains are bit more complex than a two year study in College Calculus and Linear Algebra. But that’s just my, not very well studied in biology, opinion. But I think that’s where these people fly off the handle, they see activation equations, ANN layer transformation equations, and what not and think “human brain”. And it’s that thinking that’s drove them to this insanity.

There’s no way the AI industry as it is can keep up. It is bound for collapse. But in all of that, the underlying math is still very important and very useful, and maybe that will get relabeled to neural networking or linear optimization? But what we are seeing is a party trick that can be done with these equations and it’s apparently a trillion dollar party trick.

The issue is, I don’t think the generative is here to stay. But it’s clear the industrial stuff has solidified. Wifi 7 has AI on an NPU as part of the spec. Because when it can learn the radio signals in the air and the interference it needs to avoid, you get vastly better wifi. BGP is a great protocol for routes between ASes, but it’s dumb protocol, it relies on static rules and metrics. AI overlays are making better routing choices based on learned patterns of traffic, ISPs have seen gains by better optimization.

The industrial grade AI has an objectively proven track record. People can downvote me all they want, none of that matters in the light of fact. AI is in a lot of programming. The stuff that’s proven is the boilerplate. The industrial AI. The generative AI where you ask a few words and get a wbesite, yeah that’s smoke and mirrors. Bridges aren’t useful while they’re being built, they’re only useful after they’re built.

Just like we saw wizards to churn code out back in the day, we’re going to see that with AI in coding. Is the AI going to code at least 50% of the program? Not likely. But having a ban on even 1% AI in code is just unrealistic. One, it denies the reality that we’re already using some AI in tech and coding. Two, you better believe that bad actors are going to be using AI to punch holes in software. And three, it’s completely unenforceable. Flathub lacks the staff to actually police that policy and so it’s going to devolve into Flathub chancing rumors and “hints” on which program has AI in it or not.

And it’s silly because when we have tools and use them correctly, they make our lives easier. Is the 100% generative AI garbage at coding, absolutely. But things like technical documentation, generating API docs, commenting DDL, and so on. Things that we programmers aren’t paid enough for. We talk about commenting our code, who here has time to do that properly? We keep trying to invent all kinds of new ways to “auto-doc”. But now we have a generic documentation generator.

That is an absolutely terrible standard. In fact, that’s technically not even a standard. The “I know it when I see it” measure is literally the logic used in censorship. It allows cognitive biases to seep in with no check. A lack of hard metrics means that there’ zero ways there can be any objective consistency. And finally, this kind of rationale makes the biggest sin that I can think of, “non-falsifiability”.

Whatever people’s opinion on AI are or are not. This logic should wholly be rejected in every instance it is brought forth. It is literally the antithesis of rational thought.

AI’s demand for memory is pretty difficult to really get across because there’s a lot of complex factors, but whatever you can imagine is the demand, it’s higher than that.

You can look at pre and post AI to get a slightly better picture, but then the numbers don’t look terrible and so the demand isn’t as clear.

2020-2023 primary customers were smartphones, laptops, PC. Data centers were eating about 32% of the global market for RAM. Monolithic DDR4/DDR5 was the main product and High Bandwidth Memory (HBM) was about 8%. Total memory set being sold was like 16GB kits to 64GB kits, obviously server kits were going out, just the majority was those mostly for PCs.

2025 hits and the primary customer is AI Data Centers. To put it at scale, you have literally everything that uses memory (and I mean literally every fucking thing on this planet) and AI Data Centers. And the break between those two bins are 30% and 70%. AI data centers are consuming more than twice the memory of literally everything combined that uses RAM that isn’t an AI data center.

The primary RAM being made now is HBM, which is way more complex. 23% of all the wafers that will be used to make integrated circuits will be HBM RAM. And by wafers, I mean all the chips that will be made this year, lock, stock, and barrel. If you randomly picked up a wafer out of a fab you have a almost 1 in 4 chance to pick up RAM. And finally the average kit going out is 1TB to 2TB kits, which is a lot more than the old 16GB to 64GB kits.

Now I mention HBM because it eats more wafer, that’s because unlike DDR4/5 RAM, HBM RAM is a three-dimensional circuit. 12 to 16 layers of silicon is stacked on top of each other. So HBM consumes about 300% more silicon than other memory (not every layer is one-to-one in size). So you don’t just have one fab making chips, you have several fabs making the layers.

The next thing is that building fabs is complex. I hate trying to explain the complexity, but you can’t do it overnight. Usually you have to build these things over the course of five years. Just to give you some idea of how technical the construction is. If you had a road within 500 feet of a chip fabricator sitting on a regular concrete floor, the car driving on the road would create enough shakiness in the Earth to cause the chip fabricator to bounce around too much. So when they build the place that have to literally isolate the small earth quakes humans walking around inside the place cause. This requires very complex floor building. And this is just the floor, not to mention how clean the place has to be kept, isolated as much as possible atmosphere, literally specific sections are under vacuum. It’s massively complex to build ONE of these.

The complexity comes with a price tag. Average cost to build one memory making factory is around $15B to $20B. It’s serious cash, but even if you have 5 years and $20B, there’s a specific bottleneck. ASML. ASML is the only company on the entire face of the Earth that makes the chip making machines. They’ve indicated that if you ordered a machine today, you can expect it roughly 1½ to 2 years from now. That’s how many people have put in an order for the machines to make memory.

So all that aside, there’s one more bottleneck. HBM has to be stacked in layers, there are very few people on this planet that can do that, and they have years long backlog. And even then, most times the stacking fails. About 30% to 50% of all HBM is trashed because the layers fell apart. And the people who stack are entirely different people than the layer makers. But they’re the same people that take that DDR4/5 wafer and cap it into that little black rectangle you see on your sticks of memory. So they have pretty much ~100% of their employees doing nothing but stacking layers of memory together.

Another thing is economic prioritization, HBM is about 500% more than DDR4/5’s price tag per GB. A fab producing wafers of DDR4/5 is making about $x.xx. A fab producing a couple of the layers for HBM is making about 500% × $x.xx on average (it’s complicated because of the layers), even with the stacking issues. And the profit margin on HBM is 70% versus DDR4/5 before AI which was fingernail thin. SK Hynix was actually taking a loss on production of DDR5 at about -1.6%. So going from -1.6% to 70% profit has created a crowding out effect. Not to mention that since there was a bit of a bleeding out period after COVID, some literally stopped making RAM. Which has made the issue even worse.

The last thing before I run out of characters is the AI growth. AI needs about 300% more memory every ten months. That’s how fast these models are growing. That’s caused a panic buying and also caused a rushing to fulfill. The industry is losing it’s collective mind because the money to be made is big and so lots think it can’t last and trying to get their cut before the gravy train derails.

It’s this anticipatory self‑sabotage that always makes it a self-fulfilling prophecy. The reality is that the DNC has changed. While it’s not perfect, it’s far from where it was at. Hell the run in TN-7 shows that. Republicans designed the district to be R+22 and came up in that election as R+9. Democrats were able to get a ground game going under the nose of the Republicans.

What happens is that people don’t see ENOUGH change and just go full tilt doomer. Nothing changes overnight. People need to get over themselves. If Democrats do what they did for the special election in Tennessee for at least ten years straight, then Republicans are doomed. But there’s this propensity that if it doesn’t happen in the next twenty-four hours then it’ll never happen.

I really strongly encourage Democrats to give up on the doomer act here. Change is difficult, actual change takes time.

Says guy who tanked immigration reform so that it could be an election issue.

And?

They are never going to remove him from office. Full stop.

Ish.

The issue is that it isn’t a straight shot as a lot of people paint. Call Centers work off of User Interfaces, AI can’t see or use those, so those UIs suddenly have to be retooled in a way that the AI understands, which that’s not easy. Additionally there’s business logic that is complex and there’s a lot of siloed knowledge, all of that is hard to extract and put into a model that’s usable.

The thing is that these LLM and AI companies were thinking the rest of the world is as structured as the data models they trained their AIs on and that’s just not the case. The LLMs can absolutely do the task if given the task correctly, it just that it’s near impossible to give the task they need to perform correctly in 100% of the situations. Hell, even humans fail this, people get written up at call centers all the time.

To put it simple, you ever hear the joke, “we don’t have to worry about AI taking the programmers jobs because then the CEO would have to accurately explain the problem they’re trying to solve/sell”? It’s IRL that, that’s holding up a ton of the LLMs in call centers. Like there’s two VERY narrow processes that the company I work for has implemented AI for, and those are really basic situations where explaining the full scope is pretty easy.

But take what I have to say with a grain of salt. I can’t say the company I work for has ever really been that gung-ho about AI to begin with. But I can tell you that it’s WAY, WAY, WAY more work to deploy AI than the tech bros like to paint it. Like you can just hit the button and “go”, but it’s going to crash and burn. Like to get it right is way more work than the AI industry let’s on.

I’m still trying to figure out how systemd has anything to do with this. Any configurable level user database could have implemented this. /etc/passwd has your “Real Name” in it as well. Finger protocol could have been selected to expose this. Literally there’s dozens of different places the age thing could have been implemented. The maintainers of systemd decided to be the first. Hell, MidnightBSD just added a daemon to implement it.

Likewise if you’re informed enough you also know there’s a flag to explicitly block it and ways to patch it out. While I’m typically a Slackware and sysvinit type of person, there’s nothing unique about systemd that enabled the shitty law California passed. And fi you really care about privacy, how about less gloat and more information about ways around it, like this fork.

You’ll go a lot further educating folks how to get around the things you perceive as bad rather than whatever your original comment was. The entire point is to get people … on your side.

It’s located on the Ruby Pipeline which will serve as the primary source of energy in the short term. Additionally, the data center being classified as a national security site, is located near the Utah Test and Training Range.

Longer term the facility is looking at nuclear facilities for power and the possibility for a runway and aviation facilities.

The primary customer of this facility will be the United States military.

Good luck. The data center is classified as a national security site and has the Utah Test and Training Range base nearby.

They’re building it with the intention of military security.