Generative AI's Existential Cringe
AI isn't cool, and it never will be.
It’s been a while. Apologies for the lack of output on my end. It… has not been the best few months. Writing has been tough, as demonstrated by my hard drive filled with half-completed, unpublished newsletters.
I’m going to get back into the swing of things, starting with this.
Jake Wood is, by all accounts, a successful actor, having performed the role of Max Branning in the British soap opera Eastenders for the past two decades or so.
At least, that’s what Wikipedia tells me. If you’re American, you probably know him as the Geico Gecko.
Anyway, the reason why I bring him up has nothing to do with his performances on stage and screen, but rather, on canvas. And the saga reveals an uncomfortable truth: that generative AI just simply ain’t cool.
In early May, Wood exhibited his Icons series at the Indelible Fine Art Gallery in Brighton, England. The exhibit included fifteen original pieces, all depicting a particular cultural or historic figure, like Dame Barbara Windsor, Winston Churchill, and Donald Trump.
One painting, of Sir David Attenborough, caught people’s attention — all for the wrong reasons.
Was it the fact that Sir David’s armchair had two left arms? Or the slightly phallic nature of Sir David’s wrinkled fingers? Perhaps it was the fact that the clasp attaching his medal to the chain was... just a little bit off?
Could it be that the British flag draped over the three-legged armchair had the wrong arrangement of colors, and looked more like a cross between the Thai, Costa Rican, and British flags than anything else?
Yeah, this was AI, and not even the fact that Wood was donating 10% of the proceeds of all prints from his Icons series were going to charity (though none of the proceeds from the originals, with the Attenborough piece having sold for £2,000) could have protected him from the onslaught that followed.
Wood initially denied using AI. In a statement published to Instagram, he said: “Just to clarify I do not use AI to generate any of my artworks. I do not use AI personally.”
“The images and photos I’ve used were already in existence and I have then collaged them (digitally or manually) and then painted over them digitally myself before printing and sticking them over a mixture of collage, spray paint and acrylic,” he added.
He would later concede that both the Attenborough painting, as well as the Donald Trump one, incorporated AI-generated imagery — though he insists that said imagery wasn’t produced by him, and “already existed prior to being used within the works.”
Indelible Fine Art Gallery would later cancel the exhibit, citing “horrid and abusive behavior,” though it had previously defended both the paintings in question, as well as the idea of using generative AI within the creative fields.
That’s the problem with generative AI. To be clear, I believe Wood when he says that he didn’t use generative AI to create the foundational images upon which he added.
I believe that, in part, because one of his paintings is based on (according to intrepid journalist and prankster, Zoe Bread), an unlicensed photo pilfered from Getty Images.
But that’s the thing — even the scent of generative AI, however passing, no matter how fleeting, is enough to put people off a piece of artwork.
It’s not so much that generative AI is deeply, deeply uncool (though it is), but rather that the technology itself is seen as completely toxic. And this isn’t a problem that can be simply engineered away with better models.
Suppose Wood’s painting didn’t include any of the telltale signs of DALL-E or Midjourney or Gemini. Suppose the fingers looked like normal fingers, and the armchair didn’t have three arms, and the British flag didn’t look like someone on /r/Vexillology wasn’t trying to create the iconography of a potential political union between the UK and Thailand. Suppose that it was a normal painting, and then, later down the line, it transpired that generative AI was used in some small, incidental way.
The result would be exactly the same.
Generative AI is toxic. It is — excluding enthusiasts and business idiots keen to find new ways to lengthen the dole queue — hated by pretty much everyone who is even tangentially aware of its existence.
Remember how people applauded when the CEO of Procreate, James Cuda, said “I really fucking hate generative AI?”
Cuda, I remind you, is the CEO of a relatively small Australian app developer, and is by no means a public figure. And his quote ended up being front-page of Reddit, and featured in the likes of CNET, Gizmodo, and VentureBeat.
And have you noticed that there hasn’t been been the same kind of reaction for anyone saying “I really fucking love generative AI?”
When Mark Zuckerberg talks about how he has an AI co-CEO, modeled on himself, the response is “this guy is fucking weird.”
When Satya Nadella brags about how he uses AI to regurgitate podcasts for him, the correct response isn’t “wow, how clever,” but to mock him as an out-of-touch business idiot who doesn’t give a shit about the actual content of the stuff he (indirectly) consumes.
I tried to think of an analogous technology that was so hated, so derided, upon its release. The closest thing I came up with was Google Glass.
And even then, the sheer visceral loathing of the general public doesn’t come quite as close — though I concede that’s probably because Google Glass was quite a niche product, and didn’t have anywhere near the same level of public awareness as generative AI does, nor was it forced into every conceivable orifice as generative AI currently is.
You wanted examples? Here’s More Examples.
If I was a normal person, I could have ended this newsletter there.
The problem is twofold:
First, I am decidedly not normal.
Second, there are far too many examples of this kind of self-inflicted, AI-generated humiliation to list, and if I just gave you one, I’d be doing you, the reader, a massive disservice.
Anyway, here’s another example, but this time, involving the written word.
For those who do not know who Matt Goodwin is, allow me to express first my sincere congratulations, followed by my deepest condolences, because I’m about to fix that.
Matt Goodwin is an academic who, at one point, wrote a bunch of serious and impactful stuff about the ascendant British far right. He wrote articles and published books where he sounded the alarm about the emergence of a breed of “new British fascism” in the post-industrial towns of the North, spearheaded by the BNP (British National Party).
The BNP, at one point, was a rising force in British politics. In 2008, it held 55 local government seats. In 2009, its leader, Nick Griffin, won a seat in the European Parliament — though this was, in no small part, thanks to the proportional representation system used in European elections.
Sidenote: To be clear, I think that proportional representation is a very good thing. But I also recognize that, occasionally, the smaller parties who benefit from it are crackpot nazis like the BNP.
In 2010, it fielded candidates in half of all parliamentary seats — and while it didn’t actually win any national representation, it managed to save its deposits in 73 of the seats where it ran, meaning it got more than 5% of the vote in each of those seats.
The BNP was founded by a guy called John Tyndall, who had previously founded a bunch of other short-lived neo-Nazi groups, including the National Labour Party and the Greater Britain Movement. Tyndall would later be ousted in favor of a new leader, called Nick Griffin, who sought to modernize the party and make it more palatable to the voting public.
To be clear, Griffin was still an unapologetic nazi and holocaust denier, and who described gay people as “creatures” and “repulsive” immediately after a former BNP member attacked a gay pub with a nail bomb. He also palled around with David Duke, the former Grand Wizard of the Knights of the Ku Klux Klan. It was just that he knew how to make himself seem less scary, less extreme, to voters — particularly those in the post-industrial towns of Northern England and Wales.
The BNP was scary, and Matt Goodwin did a lot of important work investigating both the party and the social and economic phenomena that had led to it, at one point, holding nine of the 60 seats on Stoke-on-Trent council. This is all stuff that Goodwin should be commended for.
The problem is that Goodwin eventually embraced some of the extreme politics that he once studied.
It took little more than a decade for Goodwin to transform from a student of the far right, to a candidate for the far-right Reform UK party in the Gorton and Denton by-election, where he (mercifully) lost by a narrow margin — though, in a Trumpian fashion, he did cast doubt on the integrity of the electoral process in the aftermath.
Goodwin has also been accused of sexual harassment by a fellow colleague at the Fox-like broadcaster, GB News, where he worked, and has parroted Trump’s line on Ukraine (namely, that Ukraine should make territorial concessions to Putin), leading to him being called a modern-day Chamberlain by George Monbiot.
Anyway, none of that — the accusations, the electoral loss, the pivot into swivel-eyed-loon territory — harmed his career in any meaningful way.
What did was publishing a book that, by many analyses, was produced with the use of generative AI.
In March, Goodwin self-published his latest book, Suicide of a Nation: Immigration, Islam, Identity. Later that month, Andy Twelves published an article for The Spectator that asked: “Did Matthew Goodwin use AI to write his book?”
The Spectator, I should add, is by no means a left-wing publication. Its chairman until 2024 was Andrew Neil, who was also the chairman and lead anchor of GB News upon its launch. In many cases, its writers are Goodwin’s ideological bedfellows.
Twelves tip-toes around definitively saying that Goodwin used generative AI, but points out the presence of language that is immediately familiar to anyone who’s used the Internet in the post ChatGPT era.
Anybody who has used Chat-GPT will recognise the telltale signs of possible AI writing, such as the ‘it’s not X, it’s Y’ comparisons and the strange obsession with things being quiet or silent.
Twelves also pointed out several statistics and quotes for which there is no evidence of them existing outside of Goodwin’s book, suggesting that they may, in fact, be the product of a hallucinating LLM.
Take Goodwin’s claims about British schools. He cites reports that in one Bradford classroom, only four out of 28 pupils spoke English as a first language, with teachers reduced to mediating ‘dozens of languages.’ I can find no reporting that backs up this claim and Goodwin provides no source for it in his book. The case sounds suspiciously like the response when you type, ‘find me an alarming case of no English in a primary school’ into ChatGPT and hit enter.
And:
The sloppiness does not end here. Goodwin seems to have created quotes by Cicero, Hayek, Roger Scruton, Livy, Noah Webster, James Burnham and Walker Connor – an impressive feat, in a sense. ‘The most dangerous experiments are those conducted on entire societies’, is a quote that Goodwin attributes to Hayek, despite there being no record of it elsewhere. It seems the most dangerous experiment is publishing a book without any fact-checking.
Oh, most damning of all, the book included ChatGPT links in the footnotes — something that, realistically, would only happen if you used ChatGPT.
Goodwin later defended his work on GB News, where he engaged in a live debate with Twelves, which... did not go well for him.
The nickname MattGPT — itself a stroke of genius — will follow him to the grave. Even his fellow GB News personalities were dunking on him.
This episode was enlightening, if not for one reason: it revealed that while a depressingly large section of the British public will tolerate election denialism, the shameless appeasement of autocrats, and language that resembles that of the far right groups that Goodwin once studied, it won’t tolerate AI slop.
Or, said another way: (Allegedly) using AI to write your book is a greater transgression than cozying up to the Hungarian far-right, espousing ethnonationalist rhetoric, or simply being a sore fucking loser.
Claude Can’t Code
Remember how, at the end of March, Anthropic accidentally leaked the source code to its Claude Code tool?
Just a few months prior, on December 27, the lead developer of Claude Code, Boris Cherny, said that “100%” of his contributions were written by Claude Code itself.
I believe Cherny was telling the truth, because every bit of analysis of the Claude Code codebase has revealed that it’s a dog’s dinner.
Quoting Denis Stretskov of TechTrenches:
A file called print.ts contained a single function spanning 3,167 lines with 486 branch points and 12 levels of nesting. One HN commenter catalogued what lived inside that function: the agent run loop, SIGINT handling, rate limiting, AWS authentication, MCP lifecycle management, plugin loading, team-lead polling via a while(true) loop, model switching, and turn interruption recovery. His verdict: this should be 8 to 10 separate modules. Nobody disagreed.
And:
Bad structure is one thing. You can argue it’s style. But the leaked source also showed what happens when code like this runs at scale.
The leaked source contained a comment in autoCompact.ts that became a symbol: “1,279 sessions had 50+ consecutive failures (up to 3,272) in a single session, wasting ~250K API calls/day globally.”
The fix was three lines of code. Set a maximum failure threshold, then disable compaction for the session. Three lines to stop burning a quarter million API calls daily. Someone knew about the problem. Someone wrote the comment documenting it. Then they shipped it anyway.
There are a lot of posts that analyze the Claude Code leak, but the reason why I’m quoting Stretskov is because he makes an interesting point.
Vibe coding requires a certain ethos that doesn’t care about quality — which I’d argue is because quality is something that an LLM is innately incapable of producing. The Claude Code team ethos is one where problems aren’t necessarily caused by faulty outputs generated by LLMs, but inadequate prompting, and thus, the solution is to re-prompt until you get something that works.
Kind-of.
Quoting Stretskov one last time:
The response to leaking code with a 3,167-line function, a regex for sentiment analysis, and bugs that basic integration tests would catch is not to add tests. Not to add code review. Not to add process. It’s to go faster. Regenerate. And have Claude check Claude’s work.
This is the ouroboros. The snake eating its own tail. AI writes the code. AI reviews the code. AI checks the deployment. When it breaks, the answer is more AI. The loop has no exit condition.
I believe that part of the reason why AI-generated code doesn’t incite the same public display of loathing as, say, AI-generated text or imagery is because code is fundamentally different.
Although developers care about code quality (or, rather, did), the end-users only really care about whether it works. Code, to most people, is a mystery.
And there isn’t really an incentive for people to dig into a codebase and see whether it has the indicative smells of a large language model. The only reason why the Claude Code leak attracted so much scrutiny is because:
a.) It’s a major product from one of the largest (by valuation) startups in the world. b.) Its release came under deeply embarrassing circumstances. c.) Its lead developer said that it was created wholly with AI — and that, in itself, is an invitation to see how good it really is.
Vibe coding’s comparative lack of toxicity is because developers love to find new ways to work faster, or better, and LLMs theoretically promise both of those things. And while they compromise code quality, we’re in a weird situation where devs are being incentivized to not even look at the shit that their models produce.
If you think that’s a bold assertion, allow me to point out that, in February, Spotify co-CEO Gustav Söderström said that the company’s most senior engineers haven’t written a single line of code in months.
“As a concrete example, an engineer at Spotify on their morning commute from Slack on their cellphone can tell Claude to fix a bug or add a new feature to the iOS app,” he told investors in an earnings call.
“And once Claude finishes that work, the engineer then gets a new version of the app, pushed to them on Slack on their phone, so that he can then merge it to production, all before they even arrive at the office.”
I imagine that a few companies have gone in this direction, tasking AI with more than simply writing code, but validating it and deploying it. And I think it’ll work well — right until the point where it doesn’t.
Maybe their apps get bigger, more broken, or more bloated. Maybe shit starts to break. Maybe the codebase leaks and people start doing their own (human-driven) code reviews.
And at that point, the mockery will begin.
The Odious Stench of AI
We’re at a weird moment in tech history.
In the past, tech companies made products that people wanted to use, and the success of these companies was determined on how appealing, how useful their products were.
Generative AI, by contrast, is something that is actively being forced down our throats — both at home and at work.
Need to find something on Google? Here’s a helpful summary from Gemini, which may or may not (but probably is) be wrong. Want to see how your family is doing on Facebook? I can’t help you, but here’s an AI-generated emotion-bait story, complete with a computer-synthesized illustration to really tug at those heart strings.
In my last corporate job, I was forced to use an AI tool to write the copy for webinar invitations and shit, and it fucking sucked. The copy was bland, vapid, and fixing it took longer than actually writing something from scratch.
Sidenote: That tool, by the way, was Jasper.AI. Honestly the biggest piece of shit I’ve ever had the misfortune to use at a job. And I once administered Sharepoint.
Generative AI is built upon theft — from artists and creatives, but also the theft of opportunity, with hundreds of billions of dollars going into hyperscaler capex spending, all to build data centers for two companies, Anthropic and OpenAI, that cannot and will not ever become profitable.
Companies whose CEOs salivate at the idea of making hundreds of millions of people jobless, and destroying the idea of a dignified middle class existence.
We’re told to believe that generative AI is so essential that we must allow it to destroy our careers, our communities, to steal our water and our work, and to pollute the atmosphere.
And at the same time, generative AI is giving us books with hallucinated quotes, pictures of three-armed armchairs, and whatever the fuck we’re calling the Claude Code codebase.
This is why generative AI is so repulsive to many. And why, I believe, that in the long run, it will die.
Yes, the economics are bad. Fatal, even.
But there’s a lot of dumb money in this world, and plenty of dumbfuck CEOs like Mark Zuckerberg and Satya Nadella and Sundar Pichai who are willing to continue burning money on GPUs and data centers.
But I can’t imagine the broader public sentiment surrounding generative AI will change any time soon.
For that to happen, people would have to be content to allow themselves and their neighbors to become jobless — or to lose the jobs that allow them to comfortably provide for their families and be forced to compete with millions of others from the formerly-professional class for lower-paid, more precarious work.
It would require that people be okay with rampant theft from creators, authors, artists, and software developers. The public would have to embrace, whole-heartedly, the idea of massive data centers blotting the landscape, all powered by fume-spewing gas turbines that poison their kids.
It would require that people lower their standards and accept the soulless, meaningless, and inevitably broken dross that AI has to offer.
And it would require us to accept that Satya Nadella, Sundar Pichai, and Mark Zuckerberg know what we want better than we do.
It’s a hard sell.