Whaddya think?

Crawlers

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.

Dan wrote an interesting post with a somewhat clickbaity title; This Competition Exposed How AI is Reshaping Design:

I watched two designers go head-to-head in a high-speed battle to create the best landing page in 45 minutes. One was a seasoned pro. The other was a non-designer using AI.

If you can ignore the title (and the fact that Dan still actively posts on Twitter; something I find very hard to ignore), then there’s a really thoughtful analysis in there.

It’s less about one platform or tool vs. another more than it is a commentary on how design happens, and whether or not that’s changing in a significant way.

In particular, there’s a very revealing graph that shows the pros and cons of both approaches.

There’s no doubt about it, using a generative large language model helped a non-designer to get past the blank page. But it was less useful in subsequent iterations that rely on decision-making:

I’ve said it before and I’ll say it again: design is deciding. The best designers are the best deciders.

Dan finishes by saying that what he’d really like to see is an experienced designer/decider using these tools to turbo-boost their process:

AI raises the floor for non-designers. But can it raise the ceiling for designers?

Meanwhile, Matt has been writing about Vibe-designing. Matt is an experienced designer, but he’s not experienced with Figma. He’s found that he can work around that using a large language model:

Where in the past 30 years I might have had to cajole a more technically adept colleague into making something through sketches, gesticulating and making sound effects – I open up a Claude window and start what-iffing.

The “vibe” part of the equation often defaults to the mean, which is not a surprise when you think about what you’re asking to help is a staggeringly-massive machine for producing generally-unsurprising satisfactory answers quickly. So, you look at the output as a basis for the next sketch, and the next sketch and quickly, together, you move to something more novel as a result.

Interesting! Just as Dan insisted, the important work is making the decision and moving on to the next stage. If the actual outputs at each stage are mediocre, that seems to be okay, as long as they’re just good enough to inform a go/no-go decision.

This certainly seems more centaur-like than the usual boring uses of large language models to simply do what people are already doing.

Rich gets at something similar when he talks about using large language models for prototyping, where it’s okay if the code is kind of shitty:

If all you need is crappy code to try out a concept or a solution, then an LLM might well enable you (the designer) to do that.

Mind you, even if you do end up finding useful and appropriate ways to use these tools, you’re still using a tool built on exploitation and unfairness:

It’s hard (and reckless) to ignore the heartfelt and cogent perspective laid out by Miriam on the role of AI companies in the current geopolitical crisis:

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.