Today’s beef with AI is brought to you by a solid few hours of swearing that started out fairly imaginative but eventually, I must admit, became quite repetitive as exasperation and exhaustion set in.

Here’s what happened: I am building some slides for a presentation that I will be giving together with a colleague. $COLLEAGUE told me he was “not good with PowerPoint”, so I told him not to worry and just send me a text outline, and I would put something together which we could work from.

Instead of doing that, he sent me a whole deck of AI-generated slop.

The output of whatever tool he used was almost, but not quite, entirely unlike our corporate template, as championed by, uh, all the other slides that will have to go in the same deck.

It wasn’t just the look of the slides, it was how they were structured. In fact, what working on these slides reminded me of most was the worst WYSIWYG tools for HTML — Microsoft Word, say. In both cases the output looks superficially okay, but it’s an absolute nightmare to edit.

Anakin and Padme meme. Anakin: "I'll just move this image around a bit" Padme: "But it won't ruin the document, right?" The bottom two panels are completely jumbled.

What do I mean? All the decoration is lots of little elements, not even grouped — so what you think is a textbox with a funky border is actually three completely separate boxes. This in turn means that you can’t scale them together or anything.

This is still relatively normal: I’ve seen people commit worse crimes against good presentation design with their own hands. But AI of course invents entirely new ways of messing up that no human would ever have conceived of. All the fonts are Arial, and so far, so good — but all the sizes are just a little bit off, 17 and 13 points, say. The colours are similar to our corporate colours but just a few hex digits off — enough to show up, especially if you put them side by side in the same deck as we will be doing. No human would choose those text sizes, and no human would go to the trouble of selecting colours outside the palette, only to pick wrong ones.

And then there’s the structure and flow of the presentation itself: it’s got that oh-so-recognisable clunky GenAI rhythm, thudding one-two-three builds where a single clear diagram would have done a better job. You might not see that in the text summary, but it’s glaringly obvious in the slides, but hey, they were “good enough” — and they took no time, or effort, or actual thought to generate! That’s good, right?

What You See Is What You… swear at and throw away

To cut a long, sad story short, the only way to integrate these sloppy inputs with the other material that I had already built with the actual corporate template was to delete everything and recreate it properly. I kept literally nothing, copying and pasting the text as plaintext, and recreating all the layout and diagrams from scratch, using our standard design language.1 This was before I could even get to the actual work of aligning the messaging, the branding, and the overall story arc of our two parts with each other and with the wider corporate position we are supposed to be presenting.

This sort of experience is why many people hate AI: it creates work for other people, under the guise of saving effort for its users. Because I care about the quality of the result, the output of the AI was worse than useless — it caused me more work than if I had just had a plain text email describing the sequence of slides I should create. But my colleague was happy: he typed out a prompt, went and did something else, and when he came back, he had something that looked “good enough”. What actually happened is that he burned a bunch of tokens to feel productive instead of just giving me the prompt that he gave the AI tool.

Slop in haste, repent at leisure

The worst of it is that he won’t just feel productive; he will look productive in the corporate dashboards that track token usage per employee. But what he has produced is not the work; it is a simulacrum of the work. This same dynamic is everywhere, as described in this wonderful piece: Appearing Productive in The Workplace.

Misunderstanding and misuse of AI in the workplace is rampant. In many of the rooms I now find myself in, expertise has been asked to look the other way: to deliver faster, produce more, integrate the tools more deeply, get out of the way of the colleagues who are “getting things done”. The artifacts are accumulating; the work is not.

From the micro to the macro

I have been talking about this shift for a while now, including in my latest Coffee Talk video: there is very little upside in applying AI to speed up steps in an existing process, without examining why the overall process operates as it does and what its outputs are. Applying AI in isolation might look like productivity, at least at the level of the individual, but the overall result is not improved by nearly the same margin, or perhaps at all.

If there is a paying business case for AI, it’s in the enterprise, because that’s where we find the systems and data already in place to make the world legible to AI. But even there, it’s not a free lunch; there is process re-engineering and systems re-architecting to do in order to take full advantage of these new capabilities.

If we don’t learn but just try to take effort out of doing the same things, at the cost of reduced quality, we are actually worse off, at a personal and a corporate level.

  1. At this point you might ask why I did not simply confront my colleague directly. Honestly, this is a problem with remote work: the conversation we would need to have is nuanced, and I don’t feel that either email or a Zoom call would be the right medium. It might still work if we had spent sufficient time together to build enough of a relationship to read each other’s emotional cues, but in this case we haven’t. This is why I say that remote work is an intentional choice, which certainly has lots of upsides, but also opens up new ways for grit to get into the gears of smooth organised work. ↩