Tuesday July 14 2026

Hacker Times

The force-feeding of AI features on an unwilling public

Discussion

FWIW, when I put <<linux create file image encrypted file system>> into Google (this was the first thing I tried, though without knowledge that it might be a tricky case I might have been less careful picking keywords) I get what look like plausible results.

At the top there's a "featured snippet" from opensource.com, allegedly from 2021, that begins with: create an empty file (this turns out to mean a file of given size with no useful data in it, not a size-0 file), then make a LUKS volume using cryptsetup, etc.

First actual search result is a question on Ask Ubuntu (the Stack Exchange site dedicated to Ubuntu) headed "How do I create an encrypted filesystem inside a file?" which unless I'm confused is at least the correct question. Top answer there (from 2017) looks plausible and seems to be describing the same steps as the "featured snippet". A couple of other links to Ask Ubuntu are given below that one but they seem worse.

Next search result is a Reddit thread that describes how to do something different but possibly still of interest to someone who wants to do the thing you describe.

Next search result is a question on unix.stackexchange.com that turns out to be about something different; under it are other results from the same site, the first of which has a cryptsetup-based recipe that seems similar to the other plausible ones mentioned above.

Further search results continue to have a good density of plausible-looking answers to essentially the intended question.

This all seems fairly satisfactory assuming the specific answers don't turn out to be garbage, which doesn't look very likely; it seems like Google has done a decent job here. It doesn't specifically turn up the LUKS manual, but then that wasn't the question you actually asked.

Having done that search to find that the relevant command seems to be cryptsetup and the underlying facility is called LUKS, searches for <<cryptsetup manual>> and <<luks documentation>> (again, the first search terms that came to mind) look to me like they find the right things.

(Google isn't my first-choice search engine at present; DuckDuckGo provides similar results in all these cases.)

I am not taking any sides on the broader question of whether in general Google can give good search results if one picks the right words for it, but in this particular case it seems OK.

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A few months ago, I needed to send an email. But when I opened Microsoft Outlook, something had changed.

Microsoft asked me to use Copilot to write my email. Copilot is my AI companion. (That’s the cute word they use.)

Hey I don’t want a companion—especially not a fake AI buddy. I never asked for this.

And what about the people receiving my emails? They don’t want this either. They want to hear from me, not a bot.

How do I turn my companion off?

After some trial-and-error, I found a way to disable Copilot. Phew!

But a few days later, Microsoft surprised me again. It wouldn’t let me save an Excel file until I had agreed to new terms for my software account.

Guess what? AI is now bundled into all of my Microsoft software.

Even worse, Microsoft recently raised the price of its subscriptions by $3 per month to cover the additional AI benefits. I get to use my AI companion 60 times per month as part of the deal.

But I don’t want to use it. I want to kill it.

As you can see, I’ve never used this service. I still have all 60 credits unused. But I’m paying for it—because it’s now embedded into Microsoft Word, Excel, etc.

This is how AI gets introduced to the marketplace—by force-feeding the public. And they’re doing this for a very good reason.

Most people won’t pay for AI voluntarily—just 8% according to a recent survey. So they need to bundle it with some other essential product.

You never get to decide.

Before proceeding let me ask a simple question: Has there ever been a major innovation that helped society, but only 8% of the public would pay for it?

That’s never happened before in human history. Everybody wanted electricity in their homes. Everybody wanted a radio. Everybody wanted a phone. Everybody wanted a refrigerator. Everybody wanted a TV set. Everybody wanted the Internet.

They wanted it. They paid for it. They enjoyed it.

AI isn’t like that. People distrust it or even hate it—and more so with each passing month. So the purveyors must bundle it into current offerings, and force usage that way.

“That’s the blight of the AI revolution. It looks like spam. It smells like spam. It tastes like spam.”

There’s another reason why huge tech companies do this—but they don’t like to talk about it. If they bundle AI into other products and services, they can hide the losses on their income statement.

That wouldn’t be possible if they charged for AI as a standalone product. That would make its profitability (or, more likely, loss) very easy to measure.

Shareholders would complain. Stock prices would drop. Companies would be forced to address customer concerns.

But if AI is bundled into existing businesses, Silicon Valley CEOs can pretend that AI is a moneymaker, even if the public is lukewarm or hostile.

It’s like a restaurant selling granite rocks for dessert. Nobody will buy them or eat them—so the product fails miserably. But if a popular restaurant adds a dollar to the meal price, and gives every customer a rock with their bill—well, then they can say that:

Every customer gets rocks for dessert.
Every customer pays for it.
Their business is more profitable because of the tasty granite rocks.

This is how AI accounting works in Silicon Valley.

“The AI business model would collapse overnight if they needed consumer opt-in. Just pass that law, and see how quickly the bots disappear. ”

And that’s why Google does something similar to Microsoft. They add AI to search results—you don’t get to choose. It’s force-feeding all over again.

So these huge digital platforms can pretend that users have totally embraced the new tech. Shut up, buddy, and chew on your rock.

This is how AI gets implemented everywhere.

You don’t get to choose. You’re never asked. It just shows up. Now you have to deal with it.

I don’t want AI customer service—but I don’t get a choice.
I don’t want AI responses to my Google searches—but I don’t get a choice.
I don’t want AI integrated into my software—but I don’t get a choice.
I don’t want AI sending me emails—but I don’t get a choice.
I don’t want AI music on Spotify—but I don’t get a choice.
I don’t want AI books on Amazon—but I don’t get a choice.

If they gave people a choice, they would reject this tyranny masquerading as innovation.

The AI business model would collapse overnight if they needed consumer opt-in.

Just pass that law, and see how quickly the bots disappear.

Customer surveys prove that. The survey cited above presented customers with descriptions of different refrigerators. When AI was added as a feature, people showed a preference for all the other models.

The Wall Street Journal concludes that companies should “beware of promoting AI in products.”

And it’s more than just products and services. People don’t even want AI in texts or documents of any sort.

In an earlier day, customer feedback would discourage businesses from adding AI. But the dominant AI companies are huge quasi-monopolies. So they don’t ask, they don’t care—they just do it.

Every day it gets more brazen.

Frank Zappa offers a possible mission statement for Microsoft back in 1976, a few months after the company is founded.

Meta is now planning to unleash bots on you in even more aggressive ways than Google or Microsoft. They are training AI bots to send you messages, whether you want them or not. And I know (from painful personal experience) that you cannot disable Facebook Messenger without shutting down your entire Facebook account.

There’s a word for this. It’s called spam—and people hate it.

But that’s the blight of the AI revolution. It looks like spam. It smells like spam. It tastes like spam.

What happens if we decide to boycott AI? The sad truth is that we can’t. It’s already too powerful.

If I wanted to boycott AI, I would need to give up

My email account
All Google searches
My word processing software
Also Excel, PowerPoint, etc.
Amazon and many other online retailers
Spotify and any other music streamer that allows it
Contacting customer service at many companies
Etc. etc. etc.

That’s bad news—because most of us need some of those things to live and work. But it will soon get much worse. Just wait and see.

The day will eventually come when…

You want to see a doctor or nurse—but you will be forced to use the AI chatbot.
You will desperately need counseling, but only AI will be available.
You seek legal advice, and just get a bot.
You apply for a job, but they give it to a bot instead.
You need an emergency responder—police, firefighter, paramedic—but you can only reach a bot.

We already know that they won’t let us choose. That’s already decided. So now the only question is how quickly they will remove the few remaining instances where humans deal with humans.

Judging by the current situation, tech companies will move quickly. They don’t ask for permission. It just happens.

What’s most shocking is that they have done all this before making AI reliable. Every day I hear accounts of stupid and ridiculous things coming from bots. You would think they would fix this mess before forcing AI on us.

But here’s the harsh reality. They won’t fix it, because they don’t know how. Despite that, they will accelerate the force-feeding.

There should be laws against this.

There should be transparency laws. There should be opt-in laws. There should be liability laws. There should be IP laws.

We’re lucky that AI bots don’t write the laws—at least not yet. We should take action before that happens. And if politicians won’t act, let’s turn to voter initiatives (which are binding in many places) or class-action suits or take other steps.

I have a hunch that juries will be sympathetic to our plight. After all, they are getting force-fed too.

Let me address a final question—which is the frequently mentioned argument that the US needs to develop AI as fast as possible to get there before the Chinese.

I’m not sure where there is. But I’m happy to let China or other countries arrive at that unhappy destination while I wait behind and watch.

I’m absolutely certain that getting there will be a matter of great regret. There might even be the last place you would want to be. So I’d rather it happened as far away from here as possible.

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ToucanLoucan•about 1 year ago

> Any time a new technology makes people uncomfortable, someone pulls the CO₂ card. We've seen this with cryptocurrencies, electric cars, even the internet itself.

I actually don't recall people "pulling the CO2 card" for the Internet. I do recall people doing it for cryptocurrency; and they were correct to do so. Even proof of stake is still incredibly energy inefficient at handling transactions. VISA handles thousands for what a proof-of-stake chain takes to handle a handful, and they do it faster to boot.

Electric cars don't contribute much CO2, so I don't recall much of that either. They do however have high particulate pollution amounts due to weighing considerably more (especially American-centric models like Teslas and the EV Hummer/F-150 Lightning) which aren't nothing to consider, and more to the point, electric cars do not solve the ancillary issues with infrastructure, like traffic congestion and cars effectively being a tax on everyone in a car-centric society who wants to be able to live. The fact that we all have to spend thousands every year on metal boxes we don't much care about just to be able to get around and have that box sit idle the vast majority of the time is ludicrously inefficient.

> But curiously, the same people rarely question the CO₂ footprint of things like gaming, streaming, international sports, live concerts, political campaigns, or even large-scale scientific research.

I have to vehemently disagree here. All scientific research, for starters, has to take environmental impact into account. Among other things that's why nobody in Vegas is watching nuclear tests anymore.

For another, people have long criticized numerous pop celebrities for being incredibly cavalier with the logistics for their concerts, and political figures have received similar criticism.

International sports meanwhile have gotten TONS of bad press for how awful it is that we have to move the stupid olympics around each year, both in the environmental sense, and the financial one since hosting practically renders a non-western country destitute overnight. Not even going into Qatar's controversial labor practices in building theirs.

> If we're serious about CO₂, then we need consistent standards — not just selective outrage. Either we cut fairly across the board, or we focus on making electricity cleaner and more sustainable, instead of trying to shame specific technologies into nonexistence (which, by the way, never happens).

No we don't. We can say, collectively, that the cost of powering gaming PC's, while notable, is something we're okay with, and conversely, powering plagiarism machines is not. Or, as people are so fond of saying here, let the market decide. Charge for AI services what they actually cost to provide plus profit, and see if the market will bear it. A lot of the interest right now is based on the fact that most of it is completely free, or being bundled with existing software, which is not a stable long-term solution.

noduerme•about 1 year ago

I'm not angry, I'm just extremely skeptical. If a programming language varied from version to version the way LLMs do, to the extent that the same input could have radically different consequences, no one would use it. Even if the "compiled code" of the LLM's output is proven to work, you will need to make changes in the "source code" of your higher level natural language. Again it's one thing to divorce memory management from logic; it's another to divorce logic from your desire for a working program. Without selecting the logic structures that you need and want, or understanding them, pretty much anything could be introduced to your code.

The point of coding, and what developers are paid for, is taking a vision of a final product which receives input and returns output, and making that perfectly consistent with the express desire of whoever is paying to build that system. Under all use cases. Asking questions about what should happen if a hundred different edge cases arise, before they do, is 99% of the job. Development is a job well suited to students of logic, poorly suited to memorizers and mathematicians, and obscenely ill suited to LLMs and those who attempt to follow the supposed reasoning that arises from gradient descent through a language's structure. Even in the best case scenario, edge case analysis will never be possible for AIs that are built like LLMs, because they demonstrate a lack of abstract thought.

I'm not hostile to LLMs so much as toward the implication that they do anything remotely similar to what we do as developers. But you're welcome to live in a fantasy world where they "make apps". I suppose it's always obnoxious to hear someone tout a quick way to get rich or to cook a turkey in 25 minutes, no knowledge required. Just do be aware that your intetnet fame and fortune will be no reflection on whether your method will actually work. Those of us in the industry are already acutely aware that it doesn't work, and that some folks are just leading children down a lazy pied piper's path rather than teaching them how to think. That's where the assumption comes from that anyone promoting what you're promoting is selling snake oil.

obirunda•about 1 year ago

LLMs do not revoke ambiguity from the English language.. Look, if you prefer to use this as part of your workflow and you understand the language and paradigms being chosen by the LLM on your behalf, and can manage to produce extensible code with it, then that's a matter of preference and if you find yourself more productive that way, all power to you.

But when you say English as a programming language, you're implying that we have bypassed its ambiguity. If this was actually possible, we would have an English compiler, and before you suggest LLMs are compilers, they require context. Yes, you can produce code from English but it's entirely non-deterministic, and they also fool you into thinking because they can reproduce in-training material, they will be just as competent at something actually novel.

Your point about waiting on an engineer for a PR is actually moot. What is the goal? Ship a prototype? Build maintainable software? If it's the latter, agents may cost less but they don't remove your personal cognitive load. Because you can't actually let the agent develop truly unattended, you still have to review, validate and approve. And if it's hot garbage you need to spin it all over and hope it works.

So even if you are saving on a single engineer's cost, you have to count your personal cost of baby sitting this "agent". Assuming that you are designing the entire stack this can go better, but if you "forget the code even exits" and let the model also architect your stack for you then you are likely just wasting token money on proof-of-concepts rather than creating a real product.

I also find interesting that so many cult followers love to dismiss other humans in favor of this technology as if it already provides all the attributes that humans possess. As far as I'm concerned cognitive load can still only be truly decreased by having an engineer who understands your product and can champion it foward. Understanding the goal and the mission in real meaningful ways.

DocTomoe•about 1 year ago

Calling “whataboutism” is often just a way to derail those providing necessary context. It’s a rhetorical eject button — and more often than not, a sign someone isn’t arguing in good faith. But just on the off-chance you are one of "the good ones": Thank you for the clarification - and fair enough, I appreciate that you're applying the same standard broadly. That's more intellectually honest than most.

Now, do you also act on that in your private life? How beneficial, for instance, is your participation in online debate?

As for this phrase — "most people paying attention" — that’s weasel wording at its finest. It lets you both assert a consensus and discredit dissent in a single stroke. People who disagree? They’re just not paying attention, obviously. It’s a No True Scotsman — minus the kilts.

As for your question: evaluating AI's long-term benefit versus long-term climate cost is tricky because the landscape is evolving fast. But here’s a rough sketch of where I currently stand.

Short-term climate cost: Yes, significant - especially in training large models and the massive scaling of data centers. But this is neither unique to AI nor necessarily linear; newer models (like LoRA-based systems) and infrastructure optimizations already aim to cut energy use significantly.

Short-term benefit: Uneven. Entertainment chatbots? Low direct utility — though arguably high in quality-of-life value for many. Medical imaging, protein folding, logistics optimization, or disaster prediction? Substantial.

Long-term benefit: If AI continues to improve and democratize access to knowledge, diagnosis, decision-making, and resource allocation — its potential social, medical, and economic impact could be enormous. Not just "nice-to-have" but truly transformative for global efficiency and resilience.

Long-term harm: If AI remains centralized, opaque, and energy-inefficient, it could deepen inequalities, increase waste, and consolidate power dangerously.

But even if AI causes twice the CO₂-output it causes today, and would only be used for ludicrous reasons, it pales to the CO₂ pollution causes by a single day of average American warfighting ... while still - differently from war fighting - having a net-positive outcome to AI users' lives.

So to answer directly:

Right now, AI is somewhere near the threshold. It’s not obviously "worth it" for every observer, and that’s fine. But it’s also not a luxury toy — not anymore. It’s a volatile but serious tool, and whether it tips toward benefit or harm depends entirely on how we build, govern, and use it.

Let me turn the question around: What would you need to see — in outcomes, not marketing — to say: "Yes. That was worth the carbon."?

handfuloflight•about 1 year ago

You're mischaracterizing my position from the start. I never claimed LLMs "revoke ambiguity from the English language."

I said I'm doing everything a programmer does except writing syntax. So your argument about English being "ambiguous" misses the point. ⍵[⍋⍵]}⍨?10⍴100 is extremely precise to an APL programmer but completely ambiguous to everyone else. Meanwhile "generate 10 random integers from 1 to 100 and sort them in ascending order" is unambiguous to both humans and LLMs. The precision comes from clear specification, not syntax.

You're conflating oversight with "babysitting." When you review and validate code, that's normal engineering process whether it comes from humans or AI. If anything, managing human developers involves actual babysitting: handling office politics, mood swings, sick days, ego management, motivation issues, and interpersonal conflicts. CTOs or managers spend significant time on the human element that has nothing to do with code quality. You're calling technical review "babysitting" while ignoring that managing humans involves literal people management.

You've created a false choice between "prototype" and "production software" as if natural language programming can only produce one or the other. The architectural thinking isn't missing, it's just expressed in natural language rather than syntax. System design, scalability patterns, and business requirements understanding are all still required.

Your assumption that "cognitive load can only be decreased by an engineer who understands your product" ignores that someone can understand their own product better than contractors. You're acting like the goal is to "dismiss humans" when it's about finding more efficient ways to build software, I'd gladly hire other natural language developers with proper vetting, and I actually have plans to do so. And to be sure, I would rather hire the natural language developer who also knows syntax over one who doesn't, all else being equal. Emphasis on all else being equal.

The core issue is you're defending traditional methods on principle rather than engaging with whether the outcomes can actually be achieved differently.

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obirunda•about 1 year ago

You keep sidestepping the core issue with LLMs.

If all that you are really doing is writing your code in English and asking the LLM to re-write it for you in your language of choice (probably JS), then end of discussion. But your tone really implies you're a big fan of the vibes of automation this gives.

Your repeated accusations of "conflating" are a transparent attempt to deflect from the hollowness of your own arguments. You keep yapping about me conflating things. It's ironic because you are the one committing this error by treating the process of software engineering as a set of neatly separable, independent tasks.

You've built your entire argument on a fragile, false dichotomy between "strategic" and "mechanical" work. This is a fantasy. The "mechanical" act of implementation is not divorced from the "strategic" act of architecture. The architectural insights you claim to get from "running code and testing behavior" are a direct result of the specific implementation choices that were made. You don't get to wave a natural language wand, generate a black box of code, and then pretend you have the same deep understanding as someone who has grappled with the trade-offs at every level of the stack.

Implementation informs architecture, and vice versa. By offloading the implementation, you are severing a critical feedback loop and are left with a shallow, surface-level understanding of your own product.

Your food processors and compiler analogy—are fundamentally flawed because they compare deterministic tools to a non-deterministic one. A compiler or food processor doesn't get "creative." An LLM does. Building production systems on this foundation isn't "transformative"; it's reckless.

You've avoided every direct question about your actual workflow because there is clearly no rigor there. You're not optimizing for results; you're optimizing for the feeling of speed while sacrificing the deep, hard-won knowledge that actually produces robust, maintainable software. You're not building, you're just generating.

handfuloflight•about 1 year ago

You're still avoiding the conflation argument because you can't defend it. You conflated "architectural feedback from running code" with "architectural feedback from typing syntax." These are fundamentally different cognitive processes.

When I refer to English as a programming language, I mean using English to express programming logic and requirements while automating the syntax translation. I'm not claiming we've eliminated the need for actual code, but that we can express the what and why in natural language while handling the how of implementation mechanically.

Your "100% equivalent" standard misses the point entirely. Abstractions work by letting you operate at a higher conceptual level. Assembly programmers could have made the same arguments about C: "you don't really understand what's happening at the hardware level!" Web developers could face the same critique about frameworks: "you don't really understand the DOM manipulation!" Are you writing assembly, then? Are your handcoding your DOM manipulation in your prancing purity? Or using 1998 web tech?

The value of any abstraction is whether it enables better problem-solving by removing unnecessary cognitive overhead. The architectural insights you value don't come from the physical act of typing brackets, semicolons, and variable declarations; they come from understanding system behavior, performance characteristics, and design tradeoffs, all of which remain fully present in my workflow.

You're defending the mechanical act of keystroke-by-keystroke code construction as if it's inseparable from the intelligence of system design. It's not.

You've confused form with function. The syntax is just the representation of logic, not the logic itself. You can understand a complex algorithm from pseudocode without knowing any particular language's syntax. You can analyze system architecture from high-level diagrams without seeing code. You can identify performance bottlenecks by profiling behavior, not by staring at semicolons. You've elevated the delivery mechanism above the actual thinking.

obirunda•about 1 year ago

First of all. I never said that typing brackets and semicolons is what I'm arguing the benefits will come from. That's a very reductionist view of the process.

You have really strawmanned that and positioned my point as stemming from this concept of typing language specific code as being sacrosanct in some way. I'm defending that, because it's not my argument.

I'm arguing that you are being dishonest when you claim to be using English as the programming language in a way that actually expedites the process. I'm saying this is your evidence-free opinion.

I'm also confused by what your involvement is in the implementation and the extent of your specifications. When you write your specifications in English is all pseudo-code? Or are you leaving a lot for the LLM to deduce and implement?

By definition, if you are allowing some level of autonomy and "creative decision making" to the model, you are using it as an abstraction. But this is a dangerous choice, because you cannot guarantee it's reliably abstracting, especially if it's the latter. If it's the former, then I don't see the benefit of writing requirements so detailed as to pseudo-code level to have it write in compilable code for you just so you don't have to type brackets and semicolons.

LLMs aren't good enough yet to deliver reliable code in a project where you can actually consider that portion fully abstracted. You need to code review and test anything that comes out of it. If you're also considering the tests as being abstracted by LLMs then you have a proper feedback loop of slop.

Also, I'm not suggesting that it's impossible for you to understand, conceptually what you're trying to accomplish without writing the code yourself. That's ludicrous, I'm strictly calling B.S, when you are claiming to be using English as a programming language as if that has been abstracted. Whatever your "workflow" is, you're fooling yourself into thinking you have arrived at some productivity nirvana and are just accumulating technical debt for the future you.

handfuloflight•about 1 year ago

module Obirunda where

import Control.Monad.State

import Control.Monad.Writer

import Data.Functor.Identity

type Argument = String

type Evidence = Maybe String

type Competence = Int

data ObirundaState = ObirundaState {

    arguments :: [Argument],
    evidence :: Evidence,
    competence :: Competence

} deriving (Show)

obirundaLoop :: StateT ObirundaState (Writer [String]) ()

obirundaLoop = do

    modify $ \s -> s { arguments = ["tradition", "syntax sacred"] }
    tell ["demanding proof from others"]
    modify $ \s -> s { evidence = Nothing }
    tell ["providing none myself"]
    obirundaLoop

runObirunda :: ObirundaState -> ((), [String])

runObirunda = runWriter . execStateT obirundaLoop

-- ghci> runObirunda (ObirundaState [] Nothing 0)

-- Never terminates. Pattern recognition, anyone?