Is Hegseth going to be tested? Because all this is, is him indicating that everyone a hint over 30 will be fired.

As for the testosterone replacement, that is just irony that he’s looking for gender affirming care for the military, when they just said they weren’t for that.

Thom Yorke has literally tried everything for selling music. Though it all he’s seen the music industry eventually consume it.

You can’t invent something new without it eventually turning into what was the old. We can always keep moving the tech, we can always keep inventing some new platform, but we never get to keep to ourselves forever.

The reason why this keeps happening is because people keep trying to apply a solution that doesn’t apply to the problem. We don’t need some NEW method of delivery. We just need to get rid of the OLD system of industry.

It’s rich people. AI is the current excuse. I hate this notion that if we got rid of AI we solve the issue. No, the rich people are still there and they’ll find a new thing to suck everyone’s lives with.

Wow who could have guessed?

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.

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

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.

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.

Man, the issue is FAA ATC rules indicate that you have to be under 31 years of age and you must retire at age 56. Add on top of that a requirement of four years of schooling and if you don’t make the choice to be part of ATC by age 27, you just are never going to be one.

I’m pretty sure there’s reasons behind the mandatory retirement age. But dang, it’s hard convincing anyone under 30 that they should pick up a pretty stressful job.

Many local government’s aren’t on the home rule, they follow some form of the Dillon Rule. This applies to utilities and land use. For some local areas they are required by some degree to follow the State’s allocation and billing of utilities to remain classified as a public utility in the State.

In many areas our legal framework at the State and local level were never made to handle what’s coming down the pipe with new advances. This is why I always indicate that data centers and their impact need to be addressed at the local level. That’s why I think Federal regulation is the wrong step for the building part of AI. This is very much a local and/or State level that needs to desperately be answered there.

The good news is that we see more people who are involved with their local government with this issue. But this underlying issue has been one since the 1970s, it’s just that these companies have hired firms that are incredibly well versed in the shortcomings of local ordinances and State law. It’s super difficult to patch up flaws in the laws when they’re being exploited at rapid fire pace.

That supply is constrained artificially for particular markets. There’s nothing that stops Samsung, Hynix, or Micron from indicating particular runs for different sectors. And if those three had not removed other competition, we would have producers to increase that supply.

Again, this doesn’t absolve the AI industry in the least. But we have makers that are only making limited selections of product for pure gain and are able to do that via their manipulation of the market. We don’t always have to have a good guy and a bad guy, it can bad guys all around.

We are paying more for a PlayStation so that idiots can use ChatGPT to mislead people on dating apps – something is rotten in the state of gaming

I need people to understand, AI is the current “thing”. We have an industry that produces memory for this planet that is a functional monopoly. Today their excuse is AI. But their excuse for that sudden increase changes roughly every four years. And we continue to let them get away with it, because we collectively blame the consumer.

And do know, I’m not saying AI companies get pass from me. That’s not the point here. The large AI companies and us regular people are consumers of the exact same product that only three companies provide. Those three companies have been legally found guilty in several courts of law across the world of colluding to increase prices, and because there’s not really any other alternative, they chalk the fines up as the cost of business and we write it off as a necessary evil.

But when we blame AI (which there’s lots to blame AI companies for, again that’s beside the point here) we are just blaming a consumer of a product. We are basically saying “Why do they get that thing I wanted. I should be the one who gets it, not them.” Now there’s a lot of industry regulation and international treaties that ensure we’re at the bottom of the list and AI companies pay into keeping that status quo. But let’s be real here, if it wasn’t them, it would be someone else.

When we say the reason computers and technology is getting expensive is because of AI, we are actually avoiding the real culprit here. A tightly controlled market, not unlike say the diamond business of old. And should AI fade away (which math equations that represent ways to optimize pattern matching are something we’ve found to be incredibly helpful) that tightly controlled, highly colluded, industry remains. And then we eventually find ourselves right back where we left off and are convinced to blame something else.

Again, this isn’t trying to absolve the sins of AI companies. But it’s to point out that this isn’t an “AI has done all of this all by itself.” And when we do that, we’re providing cover for an industry that largely runs corrupt with impunity.