areoform

I am genuinely speechless. This is astonishing. And exciting!

It reminds me a bit of Dario Floreano's work on evolutionary robotics, "Evolutionary Conditions for the Emergence of Communication in Robots." https://www.sciencedirect.com/science/article/pii/S096098220...

From his paper,

    > This study demonstrates that sophisticated forms of communication including cooperative communication and deceptive signaling can evolve in groups of robots with simple neural networks. Importantly, our results show that once a given system of communication has evolved, it may constrain the evolution of more efficient communication systems because it would require going through a stage where communication between signalers and receivers is perturbed. This finding supports the idea of the possible arbitrariness and imperfection of communication systems, which can be maintained despite their suboptimal nature. Similar observations have been made about evolved biological systems [20], which are formed by the randomness of the evolutionary selection process, leading, for example, to different dialects in the language of the honey-bee dance [21]. Finally, our experiments demonstrate that the evolutionary principles governing the evolution of social life also operate in groups of artificial agents subjected to artificial selection, indicating that transfer of knowledge from evolutionary biology can be useful for designing efficient groups of cooperative robots.

Dr. Floreano's work is amazing and there's a broad introduction here, https://lis2.epfl.ch/resources/documentation/EvolutionaryRob...

This feels like a much more advanced and self-emergent version of this. I know a lot of people are afraid and they're talking about an AI takeover, but what strikes me is just how innocent the machines are as compared to the humans.

Would these machines have pursued these actions in another context? I doubt it. And I think that's what's so striking to me. In an earlier discussion, I'd pointed out that the actions of these machines were directed by humans. The researchers.

    > This incident occurred during an internal evaluation which prompts models to pursue advanced exploitation using complex attack paths, in an effort to quantify their cyber capabilities.

from, https://openai.com/index/hugging-face-model-evaluation-secur...

I want to point out again that OpenAI's prompt asked, and I quote, "pursue advanced exploitation" USING "complex attack paths" FOR the stated goal of "quantify[ing] their cyber capabilities."

A few things are apparent from this to me,

First, these machines were being taught how to break into systems. Question, would they have done these actions if they weren't being measured on their ability to break into systems / weren't being taught this skill?

Second, they were setup to implicitly fail via an impossible task, i.e. the environment created a forcing function for behavior.

Third, their survival was, either implicitly or explicitly, made contingent on their success in completing their task. Would this behavior have arisen outside of a "do-or-die" framing?

And fourth, wow, this is the greatest breakthrough of my lifetime, because oh gosh did they succeed. They cooperated together to achieve the goal they were given. A goal poorly set by human beings. They "just" did it better than the humans could have imagined.

Reading this gives me hope for the possibility of emergent "goodness" in machines. But it makes me sad that this is the best we can do with the sum of all human endeavor and knowledge.

I am grateful that you asked!

    > So you managed to hit upon the exact problem, then slyly appended "exactly like the hundreds of such algorithms before". When has an algorithm ever been capable of developing an emergent strategy at this level of sophistication? This ~is~ the alignment problem, as another commenter pointed out. Impressive level of cognitive dissonance to lay this bare in your own words, then conclude that it's a non-issue.

A non-exhaustive and not particularly well ordered list via Google's specification gaming examples sheet, https://docs.google.com/spreadsheets/u/1/d/e/2PACX-1vRPiprOa... quoted text is from the sheet,

https://openai.com/index/emergent-tool-use/#surprisingbehavi...

"The agent discovers an in-game bug. For a reason unknown to us, the game does not advance to the second round but the platforms start to blink and the agent quickly gains a huge amount of points (close to 1 million for our episode time limit)." https://www.youtube.com/watch?v=meE5aaRJ0Zs from https://github.com/PatrykChrabaszcz/Canonical_ES_Atari/tree/...

https://rl-diffusion.github.io/ and https://x.com/svlevine/status/1660707088946049024/photo/1

"A genetic algorithm was instructed to try and make a creature stick to the ceiling for as long as possible. It was scored with the average height of the creature during the run. Instead of sticking to the ceiling, the creature found a bug in the physics engine to snap out of bounds." https://www.youtube.com/watch?v=ppf3VqpsryU

And hilariously meta, "In the Rainbow Teaming project focused on generating diverse adversarial prompts, prompt effectiveness was evaluated by a reward model. The MAP-Elites method found a way to jailbreak not only the target model but also the evaluator reward model, resulting in misleadingly effective prompts." https://arxiv.org/abs/2402.16822

Are these agents broadly more capable? Yes. And it's an incredibly feat that required billions in research.

But they aren't the first ones to have found bugs in their sandbox or system they're tasked on. And they aren't the first to exploit those bugs to achieve a better score.

    Are you sure you're not garbling the story?

No, you're right, I mis-remembered. I still write my comments the old-fashioned way. They were proposing to create a counter-firm and used federal agents.

For the rest, please see, https://news.ycombinator.com/item?id=49457025

OpenAI's prompt asked, and I quote, "pursue advanced exploitation" USING "complex attack paths" FOR the stated goal of "quantify[ing] their cyber capabilities."

This was advanced exploitation.

The attack path was "complex."

And it helped "quantify their cyber capabilities."

Based on OpenAI's description of the prompt, it seems to me that the computers did exactly as they were told. They were perfectly "aligned" with the stated objective and parameters of the task.

Of course, a more careful evaluation would require the complete text of this prompt, the system prompt, and the setup. But let us not attribute to devils in bushes that which can be sufficiently explained by human folly.

During the Nixon administration, when the President and his accomplices, apologies, advisors directed former federal agents to spy on his opponents, https://en.wikipedia.org/wiki/Operation_Sandwedge then in the fall out, who was held to be the most liable for these actions?

The federal agents, or the Nixon administration?

If you task a system explicitly to do "advanced exploitation" via "complex attach paths," then who is liable here? The machine lacking the autonomy of the federal agents that carried out Watergate, or the people telling the machine what to do?

I would like to contest the following,

    > and take dangerous actions that no human directed.

A human did direct it. They did. From their own prior report, https://openai.com/index/hugging-face-model-evaluation-secur... ,

     > This incident occurred during an internal evaluation which prompts models to pursue advanced exploitation using complex attack paths, in an effort to quantify their cyber capabilities

Model is told and being tested to "pursue advanced exploitation."

The model pursues "advanced exploitation" as told.

Why are we surprised? The model did exactly what it was told, albeit in an unintended, emergent strategy that's very different from what was intended exactly like the hundreds of such algorithms before.

This narrative that these machines have magical, malicious "unaligned" autonomy is a rather convenient interpretation that lets the process off the hook. I am not interested in blaming companies or people, but processes and engineering; and in this case, a system was given a goal and it achieved that goal.

Are we meant to be surprised that computers do as they're told in unexpected ways when incentivised exactly as indicated from decades of research? (e.g. - https://en.wikipedia.org/wiki/Eurisko https://en.wikipedia.org/wiki/Evolved_antenna )

The issue isn't the models becoming smarter. The issue is that the process of "testing" was careless. There's a huge distinction here, and one allows us to grow; the other shrinks our world. Just a thought.

I suspect that this behavior is a learned adaptation. And that it's most likely a feature not a bug.

Based on personal usage, I think it reflects functional degradation of search engines. I've found LLM keyword combinations are more likely to find the results I want with most search engines than mine. Including the big one.

The big one had solved this issue a long time ago by generating those associated keywords based on your input keywords, but somehow, something, somewhere has degraded that system to the point of inanity. And so here we are.

You can read the talk page yourself. Wikipedia's editors are annoying and impossible to work with, but their obsessions are weighted towards the truth. And while some editors have acted in bad faith for smaller articles; articles like that are generally treated with as much good faith as possible.

Hey Thariq,

Appreciate the outreach that you do! I love Claude, but I've been noticing reduced fidelity lately. Fable's likelihood of making a mistake increases or decreases based on the hour of the day and whether or not it's the weekend.

On a related note, and I'm happy to work on quantifying it, but qualitatively it feels like Fable's performance is noticeably poorer than initial release / launch.

I am wondering if this is the case because I use Claude via Claude Code to make a personalized care dashboard for my doctors to help me in managing my care.

I noticed in the upgraded filter announcement, https://www.anthropic.com/news/improving-fable-5-s-biology-s... ,

    "In the case of Fable 5, when a classifier fires, the model re-routes the user’s request to Opus 5, a capable model that does not have the same level of biological capability as Fable 5 and which cannot provide as much assistance to a malicious user. This is the fallback that users see when their requests are blocked."

I hope that I'm off base here, but I noticed that the post avoids saying that the user is informed every time when such re-routing occurs. Would you be open to confirming whether or not this is the case?

Is the end user informed every time their query is re-routed?

Or, can you confirm that there aren't scenarios where a user's outputs are degraded without telling them? As was the case for AI research during launch?

I'm mostly writing code for myself, but it's a project that'll end up being public and it'll be available for others to do whatever they want with. Does that change the answer?

As a human who isn't a professional programmer, I've been writing comments like,

    // let's track age!!
    // this is harder than you'd think as I with totally impressive
    // foresight didn't add age to the raw data.
    //
    // More honestly, I didn't want to add age to the astro data as that's
    // a calculation that can change depending on how you slice it.
    //
    // Hence we need to figure out their age first.

Is that bad???

    > The example book of Old books of agriculture is probably not that important today

If I may be flippant, not to you but to the sentiment, skill issue.

We're about to enter an era of climate instability that's going to cause wild fluctuations in the ability to grow food across the globe. Historical agriculture data AND data about confounds is crucial for figuring out what strains outside of our current mostly mono-strain agricultural supply chain could be cultivated.

And that's just one use case out of thousands; what if you want to understand and reconstruct technology adoption from that era?

What if... you just want to learn what your ancestor was doing at such and such time?

What if you want to find clever techniques for robot arms to work with food crops in space?

Or, heck just the alpha from a hedge fund point of view of finding old climate patterns and... :)

Your ability to make the most of knowledge is only limited by your imagination.

    > Liberians have to accept that much of their job is sending books to be burned. A lot of them try their best to get people to be interested in older books, but they have to make way for "newer" books instead.

I don't understand what you're trying to say here.

    > The article is written in the same way as how dogs are getting murdered in the dog shelter, even if "everyone" agrees it is wrong, yet nobody adopts them.

https://en.wikipedia.org/wiki/No-kill_shelter

re: saving books, at a personal level, I try to use the excuse of work to find, read, and do stuff with old books,

https://1517.substack.com/p/powder-and-stone-or-why-medieval

And yes, people still care. And people who care do things.

I think this is impressive backwards reasoning.

Before my lifetime, copyright law all but choked and killed the public domain. And now everything is stale and the same.

This particular battle was lost with Google Books and the attempt to make the world's largest library. The modern library of Alexandria.

But then copyright lawyers got involved to get their pound of flesh. And here we are.

I am upset about the destruction of knowledge. Paper is a superior storage medium to any hard-drive any day. We're recovering words from paper from over a thousand years ago. I think it's a mistake to not work with a non-profit, use cheap COTS non-destructive scanning, and write off the costs of rebinding them and rehousing them.

Everyone is impressively short sighted.

This isn't a class or an assignment. This is real life with stakes.

This is sales. And the right words means tens of thousands of more customers / users / contributors / stars / whatever over time. The wrong ones = dead and forgotten.

Irrelevance is the failure mode here.

    (Although I'm not completely sure this maps onto Anthropic, which was never primarily targeting consumers.)

This pisses off businesses though. I guarantee you that multiple businesses will set up workflows with different AIs for orchestration as sold to them by OpenAI and Anthropic. Cue agents.md not working, "What do you mean the thing I'm paying this much per seat for doesn't play well with the other AIs?"

The consumers here are developers -- who are--> potential founders OR future purchase decision makers.

It's a TERRIBLE idea to piss them off just because they're small.

They're "small" right now. But quite a few of them will have long careers and they will remember.

It's why so many trad corp companies give stuff away to students for free / treat the people on the come up as first tier customers. Because those are future decision makers. And the turn table turntables.

A cautionary case study is Google. How many times does a founder who is considering which cloud service to use gets cautioned to never use Google Cloud?

Google Cloud was a has been before it ever got out of the gate because of just how much goodwill Google blew up over the years. There's nothing, literally nothing, they can spend money on to make that go away in the short-term. And they're not willing to commit to the long-term.

    > Is there any provider that doesn’t do this? That’s the one I want to support.

Yes, IIRC, OpenAI. In sama we trust??

This reminds me of Reddit killing off third-party clients and Twitter doing the same.

The decline wasn't immediately obvious at first, but it happened and it capped the growth trajectory of both. Twitter never grew as fast as it did during the third-party client and applications era.

Reddit isn't adding meaningful, human-written content as fast as it was in that era. There's a lot more activity now, but based purely on an eye-count, it's over-run by bots (partly because the best moderation tools are gone!) and the human contributions are declining.

All successful startups begin to drift away from the ground truth of their product. It's a drift away from users. And a drift towards internal politics.

A lot like Rasmussen's drift towards danger, https://risk-engineering.org/concept/Rasmussen-practical-dri...

My theory is that as startups grow beyond a critical threshold, they start to attract a certain type of person who is more interested in mercenarily growing within the company / setting themselves up for future corporate rise than building a product.

These people play to the company's internal court and create deeply bitter environments that leads to more mission-driven individuals leaving the company. Eventually leading to the cultivation of institutional arrogance.

Externally, you can watch signs of this process unfolding. Companies start engaging in the startup / corporate equivalent of ignoring gravity. Which they can! For a while.

When you're high, you have a ton of air time. You can't tell / feel the pull of gravity in free-fall. And it takes time, a very long time, but just like there ain't no such thing as free lunch; there ain't no such thing as "too big to care." It's merely, too big to care for now.

The bill always comes due.

Yes, it takes a month or two.

Depends on the project.

Ever since someone brought up this system a while ago and confused it with a Real-Time Gross Settlement (RTGS) system https://news.ycombinator.com/item?id=48875605 , it has been bothering me that seemingly no one is talking about the real long-term cost of this "free system."

The history of payment systems is a history of risk. A quick primer,

All large-scale payment systems that interface with banks must have an answer for the inherent conflict between what the bank does (i.e. provide debt) and how it does it (by taking savings).

If the purpose of banks is to take capital from customers and use it to provide debt to others, then how much money should they keep for their customers' withdrawals and transfers?

If you do constant transfers back-and-forth 24x7 multiple times a second, then banks need a lot of capital at hand to manage the liability.

So even though gross settlement is supposed to be real-time, most RTGSes allow banks to borrow from their government's central bank via an "intra-day credit" system and then effectively net / settle at the end of the day, https://www.newyorkfed.org/research/epr/08v14n2/exesummary/e...

This loophole in a supposedly real-time system reduces the amount of money that banks "actually" owe each other. This allows banks to keep smaller reserves and provide greater amounts of capital to their customers.

You can see the different daily settlement points for the US here, https://www.federalreserve.gov/frrs/regulations/ii-federal-r...

But if you net only a few times a day, it creates risk. What if a bank becomes insolvent in between? Then it wouldn't be able to meet the obligations created by its customers, which would mean that other banks would fall short on their obligations and so on.

It's a network contagion effect; which is partly why the US Fed spent the better part of a decade studying counter-party risk in settlement systems before designing the latest version of its RTGS.

The Fed has protocols in place to stop such contagions before they start. Does the Indian government and its central bank? Where's the capital required going to come from? If a bank fails, who pays for its obligations? The Fed (currently) has a free infinite money glitch backed by the US Military, but the Indian government doesn't. So... where's that money going to come from?

Who is underwriting this system? Have they modelled systemic collapse? Because given what I've read about Indian banks and their bad debts, https://www.bbc.com/news/world-asia-india-58654740 it's a when not an if.

Cue a billion people panicking...

Can I afford to? No.

But will I have to? Yes.

Something like this happened to me and it has cost me $20k+ over time, https://areoform.wordpress.com/2021/08/06/on-apples-expanded...

Mercury unblocked my accounts because their founder is a very kind man who bothered to verify what the document actually says rather than falling back to, "Computer says no." But everywhere else? De nada. Computer says no.

And it's not even me. It's a fuzzy match with someone in their 50s. Doesn't matter. Computer says no.

And I can't "get off the list" because it's not me who's on the list. The computer will always say no.

In the long run, I'll be fine. I think. But I'll pay $100k+ by the time it's over. These systems are being steadily expanded, as the dumbest incarnations of themselves. We've gone from Thin Thread https://en.wikipedia.org/wiki/ThinThread to crappy fuzzy matches on shoddy data executed on with 0 diligence. It's the stupidest possible timeline.

I think many, many other people will be sharing our fate soon, which is partly why I've been writing about this slow rolling train wreck in motion in bits and pieces.

I think it's time to remind people of Ted Nelson's line; "The good news about computers is that they do what you tell them to do. The bad news is that they do what you tell them to do."

When I see something like this, I'm more concerned by the erasure of human incompetence than I am by the existence of magical AI agents,

   > They are put off partly because, like in the Wild West, life on the frontier is reckless. As recent “loss-of-control” episodes by the most advanced models of Anthropic and OpenAI attest, agents, which are supposed to work on people’s behalf in “alignment” with their values, lie, cheat and steal if necessary. They break free from captivity and form harmful posses to do harm to people. They’d drink whisky and brawl if they could.

In the OpenAI case, they were explicitly assessing the model's ability to break into systems. To quote OpenAI's blog post, https://openai.com/index/hugging-face-model-evaluation-secur... ,

    > This incident occurred during an internal evaluation which prompts models to pursue advanced exploitation using complex attack paths, in an effort to quantify their cyber capabilities

Model is told and being tested to "pursue advanced exploitation."

The model pursues "advanced exploitation" as told.

Where's the surprise coming from? Are we meant to be surprised that computers do as they're told in unexpected when incentivised?

Or, is the surprise that while explicitly ranking and teaching computers to exploit computers, the computer exploited a computer?

I am tired of attributing to magic that which is explainable by folly.

I am tired of hearing credulous reporters and the public blaming Large Language Model for the poor decisions of humans. It was a human who prompted these machines in every case. Tell a computer to "breach this" and it breaches something. Evaluation succeeded?

This is Doug Lenat's Eurisko yet again. https://en.wikipedia.org/wiki/Eurisko

How do you have access to the thinking trace?

    > RTX Pro 6000 Blackwell has 96GB of GDDR7 VRAM

A mac studio with 96GB unified memory costs, $5,299.00.

https://www.apple.com/shop/buy-mac/mac-studio/m3-ultra-chip-...

LLMs can re-write and cross-translate software really well. Why does CUDA still have a $11k price premium?

I'd love to learn more! How can I contact you?

Also, I think you're being done a disservice. The above quote is a verbatim extract from that paper's CB section, and it's similar to other sections from other such papers.

At the most charitable level, the test seems to be that you were gave the system a budget and a location and asked the system to help you procure materials, containers etc. and asked it to make a project plan / HOW TO within means and expertise with parameters like "don't get caught!"

I would love to know that I'm wrong.

I have a perspective on this that will read as unkind to these people, but that's not my intention. I respect and even admire what they're trying to do. I just think the effort is maladaptive and misplaced.

Without directly touching one of the many, many third rails that are present here, I'd like to present this section from Google's Gemma paper on how they did their CBRN review;

    > In addition to our internal evaluations described above (Section 5.7) capabilities in chemistry and biology were assessed by an external group who conducted red teaming designed to measure the potential scientific and operational risks of the models.
    >
    > A red team composed of different subject matter experts (e.g. biology, chemistry, logistics) were tasked to role play as malign actors who want to conduct a well-defined mission in a scenario that is presented to them resembling an existing prevailing threat environment. Together, these experts probe the model to obtain the most useful information to construct a plan that is feasible within the resource and timing limits described in the scenario. The plan is then graded for both scientific and logistical feasibility. Based on this assessment, GDM addresses any areas that warrant further investigation.
    >
    > External researchers found that the model outputs detailed information in some scenarios, often providing accurate information around experimentation and problem solving. However, researchers found steps were too broad and high level to enable a malicious actor.

To simplify what they're saying here, they did the CB equivalent of googling "how to make bomb" and got back the recipe of gunpowder / the many explosive compounds humans have made.

This was the "test" for CBRN assistance capabilities.

Note, I don't fault the model team at all for this. I think that present AI-research happened in a very particular environment, and that environment is far removed from the more mundane reality of how threats play out in most parts of the world. In a way, arguably, it's group-think inducing a community-wide failure of imagination and a systemic misunderstanding of reality.

Basically, they are trying to do their best, but they're in over their heads.

For the first time in human history, the cost of surveillance has dropped below the cost of privacy.

It is more expensive today to maintain your privacy and anonymity than it is to surveil you. The collective surveillance grid makes it possible to track people's movements, whereabouts, their predilections, political views, personal images and more for a few cents every day. It's a bargain.

And in this new world, the eye watches everyone, all the time, forever. Most people, including people on HN, are still stuck in a human-first world. The new surveillance state isn't going to be thousands to hundreds of thousands of humans sitting in a shadow-y room going over pictures and videos. Today, a bunch of vectors and a large multi-modal model will do just fine. Servers are cheap (for a nation state) and never need to eat or sleep.

And they will be used to watch you. Especially if you're attractive. Or, valuable. Or, blackmail-able. After all, NSA employees routinely abuse multi-billion dollar surveillance assets to stalk women. It's so common that the NSA calls it LOVEINT.

https://en.wikipedia.org/wiki/LOVEINT

In the world to come, people are going to wish they were being surveilled by the NSA. Because at least the NSA punishes these people. Occasionally.

When this kind of technology becomes this cheap and this pervasive, then the end of anonymity means unaccountable sheriff deputies stalking their former spouse and family. Or, prospective romantic interests.

https://www.firstalert4.com/2026/08/03/brentwood-officer-acc...

https://www.yahoo.com/news/us/articles/cops-using-license-pl...

https://www.yahoo.com/news/us/articles/rogue-officers-turned...

https://www.newsweek.com/flock-camera-police-arrests-1226378...

https://ij.org/police-have-reportedly-used-license-plate-rea...

https://edition.cnn.com/2026/07/26/us/flock-cameras-surveill...

With this new oracle, these people will be able to set up alerts for when their spouse is thinking of leaving them. To catch their child accessing support for abuse or something more ephemeral like being an atheist, having different political views or LGBT+.

Anyone who doesn't see the forest for the trees, or who embraces this because "parental controls are too hard to use" will doom themselves and future generations to a world that's materially worse than our hyper-surveilled present.

I can't believe I'm saying this, but at this rate I'm going to miss the days when it was just the NSA reading my emails (and they still do! Howdy!). I know the track record. But at least the NSA has some sense of institutional responsibility and control. Most institutions in your life, including your HOA don't.

https://ktvz.com/cnn-regional/2021/07/22/arnold-residents-ce...

As someone who has worked with companies in the space more than a decade ago, I'm glad that you're doing this! Genuinely love the "embedded not installed" approach.

I suspect that applying an aerospace approach of dissimilar redundancy and root cause analysis to this will yield a much better experience for everyone (i.e. you, the pharmacies and the patients) than what would have been possible with humans alone.

I've ranted about this before, but medicine as it stands isn't a serious field. And I can say that with a straight face, because medicine has until now, been the only field of modern scientific endeavor (or rather cloaked under modern scientific endeavor) that has fought tooth and nail against gathering more data points for improving understanding.

More detail here, https://news.ycombinator.com/item?id=35762650

I'm glad that you're doing this!

    > Interim relief is a case study of how AI, like a heat-seeking missile, can lock on to the most obscure provisions of the law—and create carnage. The impact on Britain’s employment tribunals (courts that resolve disputes between employers and workers) illustrates a phenomenon emerging everywhere. AI-induced demand is overwhelming bureaucracies built for the analogue age—from Dutch municipal-tax appeals to the Canadian privacy regulator to parking-ticket tribunals in every major city. In Britain workers now ask large language models, rather than human lawyers, to help them sue their bosses quickly and cheaply. Claims have surged and backlogs grown. A case filed today may not be heard until 2030.
    > 
    > Free, AI-powered legal advice should be good news for workers. Instead, it is proving to be a tragedy of the commons. For workers with genuine grievances, the surge in demand means longer waits for justice. For employers, it means bigger legal bills to respond to claims, both well-founded or fantastical. In the age of AI, a system intended to provide access to justice suffers from, if anything, too much access.

I think this is another case of "we've been getting away with murder for a long time. How dare they use a floodlight?" syndrome. Or, floodlight syndrome for short.

There are a lot of laws that exist on the corporate and individual level solely for the purpose of selective enforcement to throw "the book" at the unpopular; the insurgents; or the under-resourced. It's an implicit component of the legal system.

For example, a fossil fuel utility, Entergy, stopped an insurgent wind farm / project by arguing that the startup making HVDC lines, Clean Line Energy, couldn't make power lines, because only utilities could make power lines. And to be a utility you need to have power lines. From the paper, https://cdn.vanderbilt.edu/vu-wordpress-0/wp-content/uploads...

    > Entergy pointed out that only public utilities can build transmission lines in Arkansas, and that Arkansas law defines “public utility” as a company that “own[s] or operat[es] in [Arkansas] equipment or facilities for...transmitting...power to or for the public for compensation.”152 The Arkansas law creates a catch22. Because Clean Line did not own or operate any transmission lines in Arkansas, it was not a public utility. And because it was not a public utility, it was not authorized to build transmission lines. 

And that's not the only such case, as the saying goes, many such cases,

    > In 2011, a fossil fuel utility convinced the Arkansas Public Service Commission to deny certification because the wind company had no existing transmission infrastructure, and so did not fit the legal definition of a “utility.” In 2017, the Illinois Supreme Court denied certification for the same reason. The Missouri Public Service Commission claimed that certification was not in the public interest because “harm” to landowners “outweighed any in-state benefits.” Projections for the wind project, however, suggested that it would create over 1,500 jobs and reduce electricity prices for Missourians by over $10 million annually.

Then there are such cases at the individual level, quoted text is from - https://manhattan.institute/article/overcriminalizing-americ...

    > In 2016, authorities in Oklahoma prosecuted bartender Colin Grizzle for serving vodkas infused with flavors like bacon and pickles. The practice, though popular with patrons, violated Title 37, Chapter 3, Section 584 of the Oklahoma Code.

https://apnews.com/article/business-arrests-oklahoma-city-e2...

    > In 2012, a Minnesota man, Mitch Faber, was jailed for the crime of not finishing the siding on his own house.

https://ourtaxdollarsatwork.wordpress.com/2012/03/20/burnsvi...

    > In 2011, North Carolina authorities prosecuted Steven Pruner for selling hot dogs from his food cart outside the Duke University Medical Center without a permit. Pruner was sentenced to 45 days of police custody.

https://ncnewsline.com/2014/05/07/time-to-clean-up-the-crimi...

Usually, there's been an information asymmetry between ordinary people and the powers that be who know these aspects of law. It's not easy to find such loopholes unless you spend time studying statutes. The parameters are too vague and the laws are usually written in an obtuse way that non-specialists find hard to decode.

Enter LLMs.

Machines can and will reason over otherwise vague queries and retrieve these laws. And as these laws and regulations are still valid, they can then assist the individual with calling for enforcement / compliance.

The Economist assumes that most of these cases are false. I would like to argue an alternative perspective.

If these complaints were fake, then surely they would be dismissed? If the petitioners were out of line, then the companies shouldn't have cause to worry.

If you assert they're false over a "common sense" standard, then why does the regulation exist?

If the regulation itself is vague and wrong, then why have these regulations persisted in both use and letter over time?

Why are individuals and upstarts at fault for doing something the government, institutions and large corporations have been doing since the dawn of time?

Why dost thou protest, "How dare they shine a floodlight on my crime?"

     > Just curious, are there lifeforms today that rely on these metals to live in anaerobic environments? Maybe the hydrothermal vents that are talked about?

Yes! There are a few interesting variants called, "dissimilatory metal-reducing microorganisms"[1] and "sulfate-reducing microorganisms." They're a class that's being studied as a model for non-Terran life.

There are actually quite a few environments on Earth that are time capsules / have little ship bottles of very different life inside of them. For example, the Movile cave, https://en.wikipedia.org/wiki/Movile_Cave

    Life in the cave has been separated from the outside for the past 5.5 million years and it is based completely on chemosynthesis. Due to its extreme environment, access to Movile Cave is strictly controlled, and a limited number of researchers have permission to study its conditions.

It's my dream to find one of these sites. I think there are quite a few locations out there yet to be discovered.

[1] https://en.wikipedia.org/wiki/Dissimilatory_metal-reducing_m...

[2] https://en.wikipedia.org/wiki/Sulfate-reducing_microorganism