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The cost of AI slop could cause a rethink that shakes the global economy in 2026 (www.theguardian.com)

submitted 4 days ago by HellsBelle@sh.itjust.works to c/world@lemmy.world

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The US dictionary Merriam-Webster’s word of the year for 2025 was “slop”, which it defines as “digital content of low quality that is produced, usually in quantity, by means of artificial intelligence”. The choice underlined the fact that while AI is being widely embraced, not least by corporate bosses keen to cut payroll costs, its downsides are also becoming obvious. In 2026, a reckoning with reality for AI represents a growing economic risk.

Ed Zitron, the foul-mouthed figurehead of AI scepticism, argues pretty convincingly that, as things stand, the “unit economics” of the entire industry – the cost of servicing the requests of a single customer against the price companies are able to charge them – just don’t add up. In typically colourful language, he calls them “dogshit”.

Revenues from AI are rising rapidly as more paying clients sign up but so far not by enough to cover the wild levels of investment under way: $400bn (£297bn) in 2025, with much more forecast in the next 12 months.

Another vehement sceptic, Cory Doctorow, argues: “These companies are not profitable. They can’t be profitable. They keep the lights on by soaking up hundreds of billions of dollars in other people’s money and then lighting it on fire.”

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[–] Aceticon@lemmy.dbzer0.com 5 points 3 days ago* (last edited 3 days ago)

In my experience one needs to be a senior developer with at least some experience with their own code having gone through a full project lifecycle (most importantly, including Support, Maintenance and even Expansion stages) to really, not just intellectually know but even feel in your bones, the massive importance in reducing lifetime maintenance costs of the very kind of practices in making code that LLMs (even with code reviews and fixes) don't clone (even when cloning only "good" code they can't do things like for example consistency, especially at the design level).

Inexperienced devs just count the time cost of LLM generation and think AI really speeds up coding.
Somewhat experienced devs count that plus code review costs and think it can sometimes make coding a bit faster
Very experience devs looks at the inconsistent multiple-style disconnected mess (even after code review) when all those generated snippets get integrated and add the costs of maintaining and expanding that codebase to the rest, concluding that "even in the best case in six months this shit will already have cost me more time in overall even if I refactor it, than it would cost for me doing it properly myself in the first place".

It's very much the same problem with having junior developers do part of the coding, only worse because at least junior devs are consistent and hence predictable in how they fuck up so you know what to look for and once you find it you know to look for more of the same, and you can actually teach junior developers so they get better over time and especially focus on teaching them not to make the worst mistakes they make, whilst LLMs are unteachable and will never get better plus they're mistakes are pretty much randomly distributed in the error space.

You give coding tasks in a controlled way to junior devs whilst handling the impact of their mistakes because you're investing in them, whilst doing the same to an LLM has an higher chance of returning high impact mistakes and yields you no such "investment" returns at all.