Tags: generative

Bored of it · Paul Robert Lloyd

Same.

Open source devs say AI crawlers dominate traffic, forcing blocks on entire countries - Ars Technica

As it currently stands, both the rapid growth of AI-generated content overwhelming online spaces and aggressive web-crawling practices by AI firms threaten the sustainability of essential online resources. The current approach taken by some large AI companies—extracting vast amounts of data from open-source projects without clear consent or compensation—risks severely damaging the very digital ecosystem on which these AI models depend.

Go To Hellman: AI bots are destroying Open Access

AI companies with billions to burn are hard at work destroying the websites of libraries, archives, non-profit organizations, and scholarly publishers, anyone who is working to make quality information universally available on the internet.

FOSS infrastructure is under attack by AI companies

More on how large language bots are DDOSing the web:

LLM scrapers are taking down FOSS projects’ infrastructure, and it’s getting worse.

Please stop externalizing your costs directly into my face

Over the past few months, instead of working on our priorities at SourceHut, I have spent anywhere from 20-100% of my time in any given week mitigating hyper-aggressive LLM crawlers at scale.

This matches my experience with The Session. In fact, while I had this article open in a tab, I had to go deal with a tsunami of large language model bots. It’s really fucking depressing.

Please stop legitimizing LLMs or AI image generators or GitHub Copilot or any of this garbage. I am begging you to stop using them, stop talking about them, stop making new ones, just stop. If blasting CO2 into the air and ruining all of our freshwater and traumatizing cheap laborers and making every sysadmin you know miserable and ripping off code and books and art at scale and ruining our fucking democracy isn’t enough for you to leave this shit alone, what is?

Make stuff, on your own, first | Sean Voisen

AI can be incredibly useful when deployed skillfully in creative endeavors—as an ideation partner, as a scaffolding tool, by eliminating tedious tasks, etc.—but anyone making anything truly good with it is probably somebody who could already make something good first without it.

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.

Another uncalled-for blog post about the ethics of using AI | Clagnut by Richard Rutter

This is a really thoughtful piece by Rich, who’s got conflicted feelings about large language models in the design process. I suspect a lot of people can relate to this.

What I do know is that I find LLMs useful on occasion, but every time I use one I die a little inside.

In the way

This sums up my experience of companies and products trying to inject AI in to the products I use to communicate with other people. It’s always just in the way, making stupid suggestions.

“Wait, not like that”: Free and open access in the age of generative AI

Anyone at an AI company who stops to think for half a second should be able to recognize they have a vampiric relationship with the commons. While they rely on these repositories for their sustenance, their adversarial and disrespectful relationships with creators reduce the incentives for anyone to make their work publicly available going forward (freely licensed or otherwise). They drain resources from maintainers of those common repositories often without any compensation.

Even if AI companies don’t care about the benefit to the common good, it shouldn’t be hard for them to understand that by bleeding these projects dry, they are destroying their own food supply.

And yet many AI companies seem to give very little thought to this, seemingly looking only at the months in front of them rather than operating on years-long timescales. (Though perhaps anyone who has observed AI companies’ activities more generally will be unsurprised to see that they do not act as though they believe their businesses will be sustainable on the order of years.)

It would be very wise for these companies to immediately begin prioritizing the ongoing health of the commons, so that they do not wind up strangling their golden goose. It would also be very wise for the rest of us to not rely on AI companies to suddenly, miraculously come to their senses or develop a conscience en masse.

Instead, we must ensure that mechanisms are in place to force AI companies to engage with these repositories on their creators’ terms.

Hallucinations in code are the least dangerous form of LLM mistakes

The moment you run LLM generated code, any hallucinated methods will be instantly obvious: you’ll get an error. You can fix that yourself or you can feed the error back into the LLM and watch it correct itself.

Compare this to hallucinations in regular prose, where you need a critical eye, strong intuitions and well developed fact checking skills to avoid sharing information that’s incorrect and directly harmful to your reputation.

With code you get a powerful form of fact checking for free. Run the code, see if it works.

Severance Is the Future Tech Bros Want - Reactor

The tech bros advocating for generative AI to take over art are at the same level of cultural refinement as the characters in Severance. They’re creating apps to summarize books to people, tweeting from accounts with Greek statue profile pictures.

GenAI would automate Lumon’s cultural mission, allowing humans to sever themselves from the production of art and culture.

Generative AI use and human agency

You do not have to use generative AI.

AI itself cannot be held to account.

If you use AI, you are the one who is accountable for whatever you produce with it.

There are contexts in which it is immoral to use generative AI.

Correcting or fact checking generative AI may take longer than just doing a task yourself, or with conventional AI tools.

You do not have to use generative AI.

The Generative AI Con

I Feel Like I’m Going Insane

Everywhere you look, the media is telling you that OpenAI and their ilk are the future, that they’re building “advanced artificial intelligence” that can take “human-like actions,” but when you look at any of this shit for more than two seconds it’s abundantly clear that it absolutely isn’t and absolutely can’t.

Despite the hype, the marketing, the tens of thousands of media articles, the trillions of dollars in market capitalization, none of this feels real, or at least real enough to sustain this miserable, specious bubble.

We are in the midst of a group delusion — a consequence of an economy ruled by people that do not participate in labor of any kind outside of sending and receiving emails and going to lunches that last several hours — where the people with the money do not understand or care about human beings.

Their narrative is built on a mixture of hysteria, hype, and deeply cynical hope in the hearts of men that dream of automating away jobs that they would never, ever do themselves.

Generative AI is a financial, ecological and social time bomb, and I believe that it’s fundamentally damaging the relationship between the tech industry and society, while also shining a glaring, blinding light on the disconnection between the powerful and regular people. The fact that Sam Altman can ship such mediocre software and get more coverage and attention than every meaningful scientific breakthrough of the last five years combined is a sign that our society is sick, our media is broken, and that the tech industry thinks we’re all fucking morons.

AI is Stifling Tech Adoption | Vale.Rocks

Want to use all those great features that have been in landing in browsers over the past year or two? View transitions! Scroll-driven animations! So much more!

Well, your coding co-pilot is not going to going to be of any help.

Large language models, especially those on the scale of many of the most accessible, popular hosted options, take humongous datasets and long periods to train. By the time everything has been scraped and a dataset has been built, the set is on some level already obsolete. Then, before a model can reach the hands of consumers, time must be taken to train and evaluate it, and then even more to finally deploy it.

Once it has finally released, it usually remains stagnant in terms of having its knowledge updated. This creates an AI knowledge gap. A period between the present and AI’s training cutoff. This gap creates a time between when a new technology emerges and when AI systems can effectively support user needs regarding its adoption, meaning that models will not be able to service users requesting assistance with new technologies, thus disincentivising their use.

So we get this instead:

I’ve anecdotally noticed that many AI tools have a ‘preference’ for React and Tailwind when asked to tackle a web-based task, or even to create any app involving an interface at all.

Tech continues to be political | Miriam Eric Suzanne

Being “in tech” in 2025 is depressing, and if I’m going to stick around, I need to remember why I’m here.

This. A million times, this.

I urge you to read what Miriam has written here. She has articulated everything I’ve been feeling.

I don’t know how to participate in a community that so eagerly brushes aside the active and intentional/foundational harms of a technology. In return for what? Faster copypasta? Automation tools being rebranded as an “agentic” web? Assurance that we won’t be left behind?

What happens to what we’ve already created? - The History of the Web

We wonder often if what is created by AI has any value, and at what cost to artists and creators. These are important considerations. But we need to also wonder what AI is taking from what has already been created.

Is it okay?

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

It always annoys me when a politician is accused of “flip-flopping” when they change their mind on something. Instead of admiring someone for being willing to re-examine previously-held beliefs, we lambast them. We admire conviction, even though that’s a trait that has been at the root of history’s worst attrocities.

When you look at the history of human progress, some of our greatest advances were made by people willing to question their beliefs. Prioritising data over opinion is what underpins the scientific method.

But I get it. It can be very uncomfortable to change your mind. There’s inevitably going to be some psychological resistance, a kind of inertia of opinion that favours the sunk cost of all the time you’ve spent believing something.

I was thinking back to times when I’ve changed my opinion on something after being confronted with new evidence.

In my younger days, I was staunchly anti-nuclear power. It didn’t help that in my younger days, nuclear power and nuclear weapons were conceptually linked in the public discourse. In the intervening years I’ve come to believe that nuclear power is far less destructive than fossil fuels. There are still a lot of issues—in terms of cost and time—which make nuclear less attractive than solar or wind, but I honestly can’t reconcile someone claiming to be an environmentalist while simultaneously opposing nuclear power. The data just doesn’t support that conclusion.

Similarly, I remember in the early 2000s being opposed to genetically-modified crops. But the more I looked into the facts, there was nothing—other than vibes—to bolster that opposition. And yet I know many people who’ve maintainted their opposition, often the same people who point to the scientific evidence when it comes to climate change. It’s a strange kind of cognitive dissonance that would allow for that kind of cherry-picking.

There are other situations where I’ve gone more in the other direction—initially positive, later negative. Google’s AMP project is one example. It sounded okay to me at first. But as I got into the details, its fundamental unfairness couldn’t be ignored.

I was fairly neutral on blockchains at first, at least from a technological perspective. There was even some initial promise of distributed data preservation. But over time my opinion went down, down, down.

Bitcoin, with its proof-of-work idiocy, is the poster-child of everything wrong with the reality of blockchains. The astoundingly wasteful energy consumption is just staggeringly pointless. Over time, any sufficiently wasteful project becomes indistinguishable from evil.

Speaking of energy usage…

My feelings about large language models have been dominated by two massive elephants in the room. One is the completely unethical way that the training data has been acquired (by ripping off the work of people who never gave their permission). The other is the profligate energy usage in not just training these models, but also running queries on the network.

My opinion on the provenance of the training data hasn’t changed. If anything, it’s hardened. I want us to fight back against this unethical harvesting by poisoning the well that the training data is drawing from.

But my opinion on the energy usage might just be swaying a little.

Michael Liebreich published an in-depth piece for Bloomberg last month called Generative AI – The Power and the Glory. He doesn’t sugar-coat the problems with current and future levels of power consumption for large language models, but he also doesn’t paint a completely bleak picture.

Effectively there’s a yet-to-decided battle between Koomey’s law and the Jevons paradox. Time will tell which way this will go.

The whole article is well worth a read. But what really gave me pause was a recent piece by Hannah Ritchie asking What’s the impact of artificial intelligence on energy demand?

When Hannah Ritchie speaks, I listen. And I’m well aware of the irony there. That’s classic argument from authority, when the whole point of Hannah Ritchie’s work is that it’s the data that matters.

In any case, she does an excellent job of putting my current worries into a historical context, as well as laying out some potential futures.

Don’t get me wrong, the energy demands of large language models are enormous and are only going to increase, but we may well see some compensatory efficiencies.

Personally, I’d just like to see these tools charge a fair price for their usage. Right now they’re being subsidised by venture capital. If people actually had to pay out of pocket for the energy used per query, we’d get a much better idea of how valuable these tools actually are to people.

Instead we’re seeing these tools being crammed into existing products regardless of whether anybody actually wants them (and in my anecdotal experience, most people resent this being forced on them).

Still, I thought it was worth making a note of how my opinion on the energy usage of large language models is open to change.

But I still won’t use one that’s been trained on other people’s work without their permission.

I went to the UX Brighton conference yesterday.

The quality of the presentations was really good this year, probably the best yet. Usually there are one or two stand-out speakers (like Tom Kerwin last year), but this year, the standard felt very high to me.

But…

The theme of the conference was UX and “AI”, and I’ve never been more disappointed by what wasn’t said at a conference.

Not a single speaker addressed where the training data for current large language models comes from (it comes from scraping other people’s copyrighted creative works).

Not a single speaker addressed the energy requirements for current large language models (the requirements are absolutely mahoosive—not just for the training, but for each and every query).

My charitable reading of the situation yesterday was that every speaker assumed that someone else would cover those issues.

The less charitable reading is that this was a deliberate decision.

Whenever the issue of ethics came up, it was only ever in relation to how we might use these tools: considering user needs, being transparent, all that good stuff. But never once did the question arise of whether it’s ethical to even use these tools.

In fact, the message was often the opposite: words like “responsibility” and “duty” came up, but only in the admonition that UX designers have a responsibility and duty to use these tools! And if that carrot didn’t work, there’s always the stick of scaring you into using these tools for fear of being left behind and having a machine replace you.

I was left feeling somewhat depressed about the deliberately narrow focus. Maggie’s talk was the only one that dealt with any externalities, looking at how the firehose of slop is blasting away at society. But again, the focus was only ever on how these tools are used or abused; nobody addressed the possibility of deliberately choosing not to use them.

If audience members weren’t yet using generative tools in their daily work, the assumption was that they were lagging behind and it was only a matter of time before they’d get on board the hype train. There was no room for the idea that someone might examine the roots of these tools and make a conscious choice not to fund their development.

There’s a quote by Finnish architect Eliel Saarinen that UX designers like repeating:

Always design a thing by considering it in its next larger context. A chair in a room, a room in a house, a house in an environment, an environment in a city plan.

But none of the speakers at UX Brighton chose to examine the larger context of the tools they were encouraging us to use.

One speaker told us “Be curious!”, but clearly that curiosity should not extend to the foundations of the tools themselves. Ignore what’s behind the curtain. Instead look at all the cool stuff we can do now. Don’t worry about the fact that everything you do with these tools is built on a bedrock of exploitation and environmental harm. We should instead blithely build a new generation of user interfaces on the burial ground of human culture.

Whenever I get into a discussion about these issues, it always seems to come back ’round to whether these tools are actually any good or not. People point to the genuinely useful tasks they can accomplish. But that’s not my issue. There are absolutely smart and efficient ways to use large language models—in some situations, it’s like suddenly having a superpower. But as Molly White puts it:

The benefits, though extant, seem to pale in comparison to the costs.

There are no ethical uses of current large language models.

And if you believe that the ethical issues will somehow be ironed out in future iterations, then that’s all the more reason to stop using the current crop of exploitative large language models.

Anyway, like I said, all the talks at UX Brighton were very good. But I just wish just one of them had addressed the underlying questions that any good UX designer should ask: “Where did this data come from? What are the second-order effects of deploying this technology?”

Having a talk on those topics would’ve been nice, but I would’ve settled for having five minutes of one talk, or even one minute. But there was nothing.

There’s one possible explanation for this glaring absence that’s quite depressing to consider. It may be that these topics weren’t covered because there’s an assumption that everybody already knows about them, and frankly, doesn’t care.

To use an outdated movie reference, imagine a raving Charlton Heston shouting that “Soylent Green is people!”, only to be met with indifference. “Everyone knows Soylent Green is people. So what?”