Things I need my politicians to know about AI after Bernie's ban proposal

14 Sep 2026 🔖 prompt engineering security professional development
💬 EN

Despite having followed Dr. Timnit Gebru, Dr. Emily Bender, etc. back in the days before LLMs went public, I haven’t really talked about the sociopolitical side of LLM-based generative AI, other than joking about it as the “plagiarism-at-scale machine” and pointing out how short-sighted and cruel the resulting Taylorism at workplaces is.

I suppose I stayed out of it to stay in my lane:

• Dr. Gebru has a huge following and a Ph.D. in computer vision, which is a type of statistics-and-math-based computation that’s had its moment in the sun under the “AI” umbrella.
• Dr. Bender has a huge following and a Ph.D. in linguistics (which is relevant to the “language” part of “large language model,” which is what “LLM” stands for).

What on earth could I possibly add to the conversation, here on my little blog?

• I specialize in the deterministic kinds of computer programming that have traditional arithmetic and middle-school set theory under the hood.
• Not the nondeterministic kinds of computer programming that rely upon the complicated probability, statistics, and linear algebra topics whose squishiness rubbed my brain the wrong way and led to my abandoned math minor.

But now, Senator Bernie Sanders – yes, the man for whom I walked miles upon miles campaigning door-to-door because I believe single-payer healthcare is a financially efficient investment, so I’m not just some kind of hater – is DOING SOCIAL DAMAGE TO MY COMMUNITIES shilling for his enemies’ interests after they successfully propagandized him, so it seems time for me to speak up.

I had nothing to add to the Gebru/Bender-sphere conversation when it was just amongst techies.

However, it seems that now it’s time for me to spread the sane and breath-of-fresh-air work of the Gebru/Bender circle of commentators to my non-techie local elected and appointed officials, and to my frightened non-techie friends. Because otherwise, public figures are, instead, going to spread the distracting propaganda to my friends and local policymakers.

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Stay focused on the basics: environment, labor, discrimination, crime

If you are a politician or civil servant, THANK YOU if you’ve been fighting to ensure any of the following:

1. that extraordinarily large data centers can’t just go around popping up and causing harm
2. worker’s rights
3. civil/human rights (e.g. Minnesota, Lampedusa)
4. enforcement of existing criminal law against computer fraud and abuse, consumer fraud, etc.

I beg you, STAY THE COURSE.

As Dr. Bender just said:

“I’m going to go out on a limb and say that all of this acceleration in the hype is a reaction to the succesful and broadening opposition to their wasteful, polluting, noisy data centers.”

YOU ARE ALREADY DOING THE CORRECT WORK. IGNORE THE DISTRACTION coming through Sen. Sanders.

Her comment was a postscript to this larger op-ed:

“Between the [press releases] of (largely likely stolen) math breakthroughs, claims of ‘rogue AI’ (actually cybercrimes by companies, but whatevs), and doomerist ‘whistleblowers’, it feels like we’ve been subjected to a gish gallop of hype recently. And people have the gall to refer to all of this as ‘new empiral evidence’ (of ‘AI’ or ‘AGI’ or something) and demand that we respond to it. But it doesn’t matter how verifiable the math results are, nor how diligently the media repeats the anthropomorphized descriptions of the cybercrimes and breathless repetitions of existential risk narratives. None of that is ‘empirical evidence’ unless and until the public (and independent, non-AI-enthralled scientists) can review all inputs (including all training data, prompts, etc) and all actual outputs. Unfortunately, any more detailed debunking of each claim takes time (and isn’t necessarily possible, without said access, though the mathematicians whose work was stolen have made some headway). And meanwhile the AI bozos are onto their next claim, building up an illusion of ‘progress’ and ‘rapid change’ etc etc. So then ordinary people, who all have plenty to worry about now tyvm, pick up some generalized anxiety about ‘AI’ and meanwhile policymakers are introducing nonsense bills regulating unicorns (I mean ‘AGI’).”

I really can’t repeat loudly enough to my friends and policymakers that OUR AND OUR CHILDRENS’ WELLBEING depends upon you holding the line and refusing to get distracted from your TRADITIONAL environment, labor, discrimination, and crime policy-and-enforcement efforts. Put your head down on this one and KEEP GOING – it’s working. That’s WHY the propaganda’s getting so good. You were ON THE RIGHT TRACK before OpenAI attacked HuggingFace and before Bernie Sanders got involved, and you still are. STAY THERE.

As ex-FTC-chair Lina Khan just said:

• (click expander icon at left – it’s a rightward-facing triangle – if the quote below is not showing up)

Law enforcers already have authority to charge companies and their CEOs for creating and releasing dangerous, unvetted, or defective products. We shouldn’t let discussions about new legal regimes distract from the fact that there’s no AI exemption from laws already on the books — a point @FTC emphasized repeatedly during my tenure.

1. There is an extensive set of laws that govern dangerous and defective products. For example, releasing unvetted AI models or agents can violate consumer protection laws. Shipping flawed AI tools without implementing adequate measures to detect and stop rogue or defective AI agents can be an “unfair or deceptive” act or practice under the FTC Act (and analogous state laws). And some state AGs are already exploring holding AI firms and their CEOs criminally liable when their models participate in criminal activity.

2. Existing laws also prohibit “unfair methods of competition.” This covers instances where AI firms appropriate the competitively sensitive information of their customers, including through tracking their use of various tools. It can also cover instances where firms pursue dangerous behavior, aware that doing so may compel rivals to do the same.

As the Supreme Court has noted: “A method of competition which casts upon one’s competitors the burden of the loss of business unless they will descend to a practice which they are under a powerful moral compulsion not to adopt, even though it is not criminal, was thought to involve the kind of unfairness at which the [unfair methods of competition] statute was aimed.”

1. The highly concentrated and interconnected structure of these markets could be creating major risks and conflicts of interest. We had started investigating these partnerships and cross-investments across the stack (and released a preliminarily overview of some findings: https://www.ftc.gov/news-events/news/press-releases/2025/01/ftc-issues-staff-report-ai-partnerships-investments-study).

Both federal and state enforcers should be scrutinizing these opaque relationships and inter-dependencies. We are already seeing how these relationships could undermine accountability. For example, OpenAI could face liability given the Hugging Face incident, but Hugging Face being bought up by Nvidia means that we’re unlikely to see it file a lawsuit over this — given Nvidia’s strong incentive to see OpenAI continue full speed ahead.

1. As AI tools dramatically change the landscape of cybersecurity risks and hacks, all businesses should be doubling down on having core security protections in place. Firms that fail to invest in adequate data security measures or fix known vulnerabilities can also be breaking the law. A recent analysis showed that around 1/3 of Fortune 100 companies do not even have a way to notify them about security issues. During my @FTC tenure, we sued firms for poor data security practices and held CEOs liable when they were personally responsible.

https://this.weekinsecurity.com/dozens-of-americas-largest-companies-have-no-simple-way-to-report-security-flaws/

https://www.ftc.gov/news-events/news/press-releases/2022/10/ftc-takes-action-against-drizly-its-ceo-james-cory-rellas-security-failures-exposed-data-25-million

1. As policymakers consider new legal regimes, we should be looking to lessons from prior efforts to govern major sectors, such as banking and other networks, platforms, and utilities. Tools like structural separations, nondiscrimination, and supervision could be key, and there’s a rich history of what works and what doesn’t. But we can and must pursue any new efforts alongside enforcing existing laws.

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Who benefits? Maybe not your 401(k)

I’ve been trying to cut back my social media doomscrolling by uninstalling apps and logging out of web sites each time I’m done, but the one guilty pleasure I haven’t managed to pull myself away from, because it doesn’t require an app or account to see, is the front page of Reddit.

For whatever Reddit-subcultural reason, subreddits like /r/stocks and /r/WallStreetBets always seem to bubble to the top of /r/All.

I find it fascinating that the Gebru/Bender circles don’t always say too much about what’s going on in retail-investor-enthusiast circles (they seem a bit more focused on the eugenecist links in “AGI” circles – yikes). And nor retail-investor-enthusiast circles really seem to care much what the Gebru/Bender circles have to say about tech. And yet, they definitely seem to be overlapping about “IPOs” (initial public offerings) right now: there seem to be some rich people trying to stay rich by lying about how much their assets are worth, getting their assets listed on public stock exchanges, selling their overvalued shares to pension funds and 401(k)s, and laughing all the way to the bank when all those institutional investors finally get around do doing the math about how little the assets were really worth.

My journey to noticing:

About a year ago (1, 2, 3, 4, 5, 6, 7, 8), I saw Reddit stock enthusiasts starting to worry about whether the S&P 500’s upward run was a bubble of misdirecting bookkeeping by NVIDIA, Oracle, OpenAI, Microsoft, Google, Anthropic, etc. (With them not actually doing anything that causes new value or money to happen, yet their “expected profits” and “valuations” rising, but if you look at their forecasts, it all just seems to be based on them having timed exactly who’s going to pay whom when, and which details about “as long as this little part of the circle doesn’t collapse in value” potentially get ignored.)

At the time, I didn’t think much about this as “Oh, bummer, another possible bubble-pop to weather; good thing I’m still young enough for my 401(k) to have a while.”

Then Elon Musk’s SpaceX IPO’ed, and those same stock-enthusiast communities (1, 2) started making the front page of Reddit with their worries that the people running NASDAQ had been propagandized at best – incentivized at worst – to change their rules for inclusion on the NASDAQ in a way that was meant to let SpaceX’s private investors make an expedited sale to NASDAQ-tracking mutual funds and exchange-traded funds – the kinds pensions & 401(k)s & retail investors go heavily into as “safe” bets – so that when SpaceX plummeted to its true value, it’d happen while us commoners were the owners of the stock shares, not while the original private investors still held the shares. (In the past, NASDAQ required a company that just went public to prove its worth on the open market for a while before taking its “worth” – or “market capitalization” – at face value. The S&P 500 stuck to their guns, in fact, and did not change that rule. But NASDAQ changed their rule and allowed SpaceX to be listed as a “huge company” based on their right-at-IPO hype-driven sales & “valuation,” not based on how things shook out after the dust settles, which was traditionally required to prove you were actually a NASDAQ-worthy company.)

Again, this just struck me as sad trivia about the state of our current SEC, FEC, etc. and worried about my own retirement (I mean, can I count on my 401(k) to outlast market crashes long-term if the public market’s getting money siphoned out of it and into private trading at this deregulated of a fast pace?!), but it didn’t seem to have anything to do with AI.

And then OpenAI attacked HuggingFace. And yes, I am using that as a direct transitive verb. When it happened, I would’ve said “OpenAI accidentally attacked HuggingFace,” but since then, I’ve heard enough experienced programmers, on the tech podcasts I’ve listened to for years while folding laundry, point out that this falls under the old military aphorism “there are no accidents.” Any data scientist or software developer getting paid $400,000+/year to work on cutting-edge OpenAI research has years of experience knowing exactly how these things work, and how to properly ensure that their experimental machines have no internet access so that they can’t break anything. I heard multiple programmers on multiple podcasts more or less laugh:

Yeah, come on, you don’t really believe a company of that talent just forgot to disable internet access, do you? It’s well-known their private investors want to IPO the company soon. This seems like a ‘no press is bad press’ situation to build brand awareness, pre-IPO, by getting into the news by committing cyber-crime and playing ‘oopsie’ / playing hero ‘helping’ clean it up, so that lots of the shares would get bought post-IPO because everyone thinks they’re a big-deal company who’s on the cutting edge of important and amazing things.

Again, depressing, and making me scared for my 401(k), because who knows if NASDAQ would do another “sure, come on in!” leaving my institutional funds holding the share-price-drop bag, instead of leaving OpenAI’s original private investors still holding the share-price-drop bag, when the market figures out they’re not as important as their mythmaking and tanks share prices. Or if the S&P 500 would cave in like NASDAQ did. But it was just a generic cynicism and grief.

Then about 2 weeks ago, a LinkedIn post reviewing Gil Duràn’s book “The Nerd Reich” crossed my feed. I bookmarked it to read later with the notes “On Peter Thielism? / Curtis Yarvinism? in American capitalism, and its recent impact on American entrepeneurs’ expectations?” (e.g. Inculcating expectations that it’s natural that entrepreneurs of a certain wealth-level wouldn’t be accountable to anyone else – that they wouldn’t have a line ending at someone else’s nose when it comes to their right to swing their fist.) It struck a chord against all of that Reddit “what on earth are people doing to the public stock market?” talk I’d been seeing, and against all of that “baloney, OpenAI didn’t know what they were doing” joking I’ve been hearing on my respected tech podcasts.

And suddenly, I wanted to know what Dr. Timnit Gebru had to say. I hadn’t followed anyone ever since the big jump from Twitter to BlueSky/Mastodon, because I figured it was a good opportunity to wean myself off of doomscrolling-masquerading-as-staying-abreast-of-tech. But I jumped back on, just to look at what she was up to lately. And boom – there she is, also talking about IPOs, too! (Last time I followed her, pre-jump, she was still focusing on more of the “Yo, tech companies, stop releasing stuff dictators are going to use for propaganda and surveillance without bothering to make it not do that, even though I know you want to self-aggrandize and feel important by rushing” angle. Which is the same angle, really, just not yet involving the stock market, so not yet getting U.S. senators’ attention.)

We’re in the era of incompetence & cybercrimes headlined as ‘unprecedented model capabilities gone rogue.’ So OpenAI & Anthropic are trying to one up each other with such incompetence because the ‘press release as a service’-performing media & clueless politicians parrot pre-IPO CEO talking points.

Something seems fishy with the financials.

Also, as “Boxo McFoxo’s” reply to Dr. Bender’s thread pointed out:

“I think it’s that, but also the IPOs, and another regulatory capture push for moat-building.”

I’m definitely smelling a “follow the money” / “who benefits?” / “cui bono?” angle to the fact that someone bothered to personally propagandize Bernie Sanders, specifically. Clearly, there are well-funded people who want to really scale up the reach of vague messages that DON’T EVEN FIT how the underlying probability, statistics, and linear algebra mathematics under the hood of how LLM-based generative AI works!!

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LLMs don’t do that

I just made a pretty big assertion – that LLMs don’t even work like the “doomers” are getting Sen. Sanders to say they do.

• I wish I could give a simple explanation, but along the lines of that aphorism “if you can’t explain it to a six-year-old, you don’t really understand it that well,” I’ll be honest, I understood what I read just well enough to become reassured, myself, but not well enough to explain it.
• I wish I could cite my sources, but unfortunately, I was just curiously reading about how LLMs work, a year ago, when they started getting fun at helping me write code. I didn’t save anything off that I can now re-read.

All I’ll say is that, despite LLM-based agents being very good at accomplishing a lot of tasks because they combine certain features of human language (and imagery/video/music, which apparently we sort of make in similar ways to the way we string words together, so you can more or less do useful things against them all with the same mathematical representations) with the speed of computers, there are other tasks they’re not so good at, or ways in which they’re not so good at the things they’re overall pretty good at. As I said in my post “LLMs, Rubber Ducks, and Doubt, they’re a bit of a few-trick pony.

Now, what Dr. Gebru, Dr. Bender, and all the other names I don’t follow but they follow and cite have been pointing out since at least the late 2010s is that you can do a lot of HARM with just what they are. That’s what they were focused on when I first encountered them. I remember Dr. Gebru, back in the day, begging Silicon Valley not to start letting people play with LLMs until they figured out how to also govern the technology’s ability to give people what they ask for, if they ask for something bad (ahem – which, since they didn’t bother to listen, governments like Minnesota’s are having to start forcing their hands on with laws like the anti-nudification bill). Why? She argued that, for example, sure, even if people are able to use LLMs to make more beautifully wordsmithed propaganda in English than they personally were capable of authoring, okay, well, hopefully some recipients will notice and Snopes will publish fact-checks. But she worried that not enough people would be watching and correcting the equivalent Tigrinya-language propaganda, and that giving people more elegant wordsmithing of lies could, for example, harm Ethiopian and Eritrean people, where there is already active fighting on the ground, a lot faster than perhaps native English speakers imagined that “wordsmithing tools” could harm.

Such AI ethicists – think, for example, the 2016 book “Weapons of Math Destruction” and “Algorithms of Oppression” – had already been pointing out the harms of “read-only-mode” statistics-based technologies like computer vision or predictive analytics. (Remember the articles about soap dispensers being bad at detecting that dark-skinned hands are beneath them?)

When the “generative” “AI” (LLM) technology started coming out, the ethicists’ main concern about its “write/create-mode” capabilities was, again, that the things it could write/create could themselves cause harm (e.g. more beautiful propaganda).

What I remember those ethicists being very clear about at the time, though, was that it did not inherently mean that LLMs would be able to “write/create” every type of harm imaginable. They were still going to be extremely limited to being able to help people hurt each other in a very tool-specific limited set of ways. They were concerned about the intensity of the harm, but they were very clear that the flavor of the harm was limited to the underlying mathematics. In fact, they got a bit famous for trying to counteract Silicon Valley bigwigs clearly propagandizing potential investors via inflating LLMs’ importance and mocking LLMs as mathematically nothing but a “stochastic (that is, probability-based) parrot.”

(I hate to use the comparison, because the phrase is normally used to fight sensible legislative proposals, but “stochastic parrot” struck me as a bit of a “guns don’t kill people; people kill people” message, meant to reality-check and counterbalance tech owners’ lies forecasting that all of their guns were going to suddenly come alive and start firing autonomously in random directions, and the lies that only investing trillions of dollars immediately into tech owners’ pockets could save humanity from that fate.)

So, again, I apologize that I no longer remember enough about the details of the linear algebra that so fascinated me a year ago explaining how my cool coding assistant worked under the hood, and which, as a side effect, helped me see what the reasonable “stochastic parrot” scientists had been trying to say about the limits of the underlying math.

But I want you to know that there are people who do know enough, and I hear them all the time in my tech-deep-dive sources, pointing that out even to to this day. A power saw definitely has a role in carpentry and also in crime like breaking-and-entering; but also, it really isn’t at all relevant to, say, teaching kindergarten. Depth of harm/benefit potential isn’t the same as breadth. The notion of “Artifical General Intelligence” (“AGI”) that Sen. Sanders is helping private investors push onto the rest of us is as mathematically ridiculous, about LLMs, as it would be to say that power saws are on the brink of reading books aloud.

But, that said, the smart folks understand a power saw isn’t meant to, say, replace a kindergarten teacher, are still very focused on “DON’T COMMIT CRIMES WITH THAT POWER SAW; THAT’S A BAD THING TO DO!” as pointed out about the OpenAI-knew-better HuggingFace incident. And I hope you will be, too!

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Haste makes waste

Hence my plea to friends who are out there doing political action, and to policymakers – please hold the line. Focus on the use of the technology as it actually is, not on the vague pre-IPO talking points.

1. Focus on regulating what is and isn’t allowed to be done with our environment for any potentially-useful purpose. Whether that’s mining, ScotchGuard, wordsmithing-at-scale, or image-recognition-at-scale. If humanity can’t make ScotchGuard without polluting water, don’t let humanity make ScotchGuard until it figures out how to do it more safely. We’ve known how to regulate human activity for environmental reasons for a long time now. It’s no different with “AI.” If humanity can’t make funny fake cat videos without giving people heart disease from lack of sleep from noise pollution or without making electricity grids fail that keep people’s ventilators running , don’t let humanity make funny fake cat videos until it figures out how to do it more safely.
2. Focus on enforcing existing laws against crimes and against rights violations, like Lina Khan and all my tech podcasters are begging policymakers to do (or like legislatures like Minnesota’s is ensuring more explicit laws make it crystal-clear enforcers should do).

I know those are boring, but you’re doing a great job, and those are still all humanity needs from you. Humans have known that well-designed constraints and structures and guardrails actually help people innovate faster, not slower, for a long time. The late-2000s “DevOps” movement I specialize in right now was founded on that principle. Cars can turn at appropriate traffic-flow-preserving speeds when we regulate highway construction to require that curves be banked. Heck, ancient Roman emperor Augustus Caesar said “festina lente” – “hurry slowly” – 2,000 years ago.

You’re doing the right thing when you make sure we have clean water, sleepable sound levels, and stable grids right here, right now. We’ll all get our silly cat videos when we’re good and ready to do it cleanly thanks to your legislation and enforcement. (And yes, in the meantime, that might slow down a bubble just enough that investors aren’t able to push their lies onto our 401(k)s. Oh, darn, too bad for them. Sorry, y’all, but you just shouldn’t be able to swing your fist into others’ noses. You’re not that special.)

Keep up the good fight on the real things that matter about regulating “AI.”

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Related links

• “Not All AI Is Generative: Understanding the Difference” by Girls Who Code
• “As soon as it works, no one calls it AI anymore.”
  • (As I heard on, I believe it was the .NET Rocks podcast, once we know what it’s “for,” we instead give it a name like “machine learning,” “natural language processing,” “optical character recognition,” “automatic speech recognition,” “data mining,” “heuristic search,” etc. And then “AI” gets recycled to describe the next thing we don’t quite understand.)
• Stories Are Weapons by Annalee Newitz
• How Data Happened by Chris Wiggins & Matthew L Jones
• “Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People,” created by Dr. Alondra Nelson
• One last great Timnit Gebru quote

  “Bless their soul but who is going to listen? I want to break it to them how long we’ve been warning about these cults funded by unlimited amounts of money being behind these companies which themselves claimed to be created for ‘AI safety’ 🙄 AND how the press has been doing stenography for so long.”

• And a reply to it:

  “I feel that you’ve been debunking the same lies ever since the anti AI / pro AI regulation online space started. They haven’t changed their rhetoric; people, even well meaning, believe them.”