Is it okay?

Robin takes a fair and balanced look at the ethics of using large language models.

Is it okay?

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A couple of days ago I linked to a post by Robin Sloan called Is it okay?, saying:

Robin takes a fair and balanced look at the ethics of using large language models.

That’s how it came across to me: fair and balanced.

Robin’s central question is whether the current crop of large language models might one day lead to life-saving super-science, in which case, doesn’t that outweigh the damage they’re doing to our collective culture?

Baldur wrote a response entitled Knowledge tech that’s subtly wrong is more dangerous than tech that’s obviously wrong. (Or, where I disagree with Robin Sloan).

Baldur pointed out that one side of the scale that Robin is attempting to balance is based on pure science fiction:

There is no path from language modelling to super-science.

Robin responded pointing out that some things that we currently have would have seemed like science fiction a few years ago, right?

Well, no. Baldur debunks that in a post called Now I’m disappointed.

(By the way, can I just point out how great it is to see a blog-to-blog conversation like this, regardless of how much they might be in disagreement.)

Baldur kept bringing the receipts. That’s when it struck me that Robin’s stance is largely based on vibes, whereas Baldur’s viewpoint is informed by facts on the ground.

In a way, they’ve got something in common. They’re both advocating for an interpretation of the precautionary principle, just from completely opposite ends.

Robin’s stance is that if these tools one day yield amazing scientific breakthroughs then that’s reason enough to use them today. It’s uncomfortably close to the reasoning of the effective accelerationist nutjobs, but in a much milder form.

Baldur’s stance is that because of the present harms being inflicted by current large language models, we should be slamming on the brakes. If anything, the harms are going to multiply, not magically reduce.

I have to say, Robin’s stance doesn’t look nearly as fair and balanced as I initially thought. I’m on Team Baldur.

Michelle also weighs in, pointing out the flaw in Robin’s thinking:

AI isn’t LLMs. Or not just LLMs. It’s plausible that AI (or more accurately, Machine Learning) could be a useful scientific tool, particularly when it comes to making sense of large datasets in a way no human could with any kind of accuracy, and many people are already deploying it for such purposes. This isn’t entirely without risk (I’ll save that debate for another time), but in my opinion could feasibly constitute a legitimate application of AI.

LLMs are not this.

In other words, we’ve got a language collision:

We call them “AI”, we look at how much they can do today, and we draw a straight line to what we know of “AI” in our science fiction.

This ridiculous situation could’ve been avoided if we had settled on a more accurate buzzword like “applied statistics” instead of “AI”.

There’s one other flaw in Robin’s reasoning. I don’t think it follows that future improvements warrant present use. Quite the opposite:

The logic is completely backwards! If large language models are going to improve their ethical shortcomings (which is debatable, but let’s be generous), then that’s all the more reason to avoid using the current crop of egregiously damaging tools.

You don’t get companies to change their behaviour by rewarding them for it. If you really want better behaviour from the purveyors of generative tools, you should be boycotting the current offerings.

Anyway, this back-and-forth between Robin and Baldur (and Michelle) was interesting. But it all pales in comparison to the truth bomb that Miriam dropped in her post Tech continues to be political:

When eugenics-obsessed billionaires try to sell me a new toy, I don’t ask how many keystrokes it will save me at work. It’s impossible for me to discuss the utility of a thing when I fundamentally disagree with the purpose of it.

Boom!

Maybe we should consider the beliefs and assumptions that have been built into a technology before we embrace it? But we often prefer to treat each new toy as as an abstract and unmotivated opportunity. If only the good people like ourselves would get involved early, we can surely teach everyone else to use it ethically!

You know what? I could quote every single line. Just go read the whole thing. Please.

# Friday, February 14th, 2025 at 5:07pm

Related links

Vibe code is legacy code | Val Town Blog

When you vibe code, you are incurring tech debt as fast as the LLM can spit it out. Which is why vibe coding is perfect for prototypes and throwaway projects: It’s only legacy code if you have to maintain it!

The worst possible situation is to have a non-programmer vibe code a large project that they intend to maintain. This would be the equivalent of giving a credit card to a child without first explaining the concept of debt.

If you don’t understand the code, your only recourse is to ask AI to fix it for you, which is like paying off credit card debt with another credit card.

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Vibe coding and Robocop

The short version of what I want to say is: vibe coding seems to live very squarely in the land of prototypes and toys. Promoting software that’s been built entirely using this method would be akin to sending a hacked weekend prototype to production and expecting it to be stable.

Remy is taking a very sensible approach here:

I’ve used it myself to solve really bespoke problems where the user count is one.

Would I put this out to production: absolutely not.

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Keeping up appearances | deadSimpleTech

Looking at LLM usage and promotion as a cultural phenomenon, it has all of the markings of a status game. The material gains from the LLM (which are usually quite marginal) really aren’t why people are doing it: they’re doing it because in many spaces, using ChatGPT and being very optimistic about AI being the “future” raises their social status. It’s important not only to be using it, but to be seen using it and be seen supporting it and telling people who don’t use it that they’re stupid luddites who’ll inevitably be left behind by technology.

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In 2025, venture capital can’t pretend everything is fine any more – Pivot to AI

Here is the state of venture capital in early 2025:

  • Venture capital is moribund except AI.
  • AI is moribund except OpenAI.
  • OpenAI is a weird scam that wants to burn money so fast it summons AI God.
  • Nobody can cash out.

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What I’ve learned about writing AI apps so far | Seldo.com

LLMs are good at transforming text into less text

Laurie is really onto something with this:

This is the biggest and most fundamental thing about LLMs, and a great rule of thumb for what’s going to be an effective LLM application. Is what you’re doing taking a large amount of text and asking the LLM to convert it into a smaller amount of text? Then it’s probably going to be great at it. If you’re asking it to convert into a roughly equal amount of text it will be so-so. If you’re asking it to create more text than you gave it, forget about it.

Depending how much of the hype around AI you’ve taken on board, the idea that they “take text and turn it into less text” might seem gigantic back-pedal away from previous claims of what AI can do. But taking text and turning it into less text is still an enormous field of endeavour, and a huge market. It’s still very exciting, all the more exciting because it’s got clear boundaries and isn’t hype-driven over-reaching, or dependent on LLMs overnight becoming way better than they currently are.

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