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The Acceleration Paradox

Why moving faster with AI might be taking us in the wrong direction

Richard M. Thompson · 29 Apr 2026 · 6 min read

Flat editorial illustration in the style of 1843 magazine: a red open-top car speeds toward a black brick wall just a car-length ahead while its driver flings an arm up in delight, oblivious to the impact.

This, like all my posts, was written by me. I used AI for research and to improve the overall structure.

Scroll through any social media platform and you'll be immediately pummelled with a particular set of marketing messages relating to AI and Software Engineering.

"I vibe coded my way to $10,000 MRR in 2 weeks"

"I've built more in the last month than I built in the last 2 years"

"Use these methods to 10x / 100x your work rate"

"I'm vibe coding apps through my phone while waiting in line at the grocery store"

"AI does all my marketing"

"I used AI to copy this person's website and business, and now am making almost as much money as them."

"We replaced 10 junior devs on our 20-dev team. We now have 10 devs doing the work that 20 did last year."

The above are all real tweets I have seen, and hey, for some people, maybe these claims are true.

To me though, they seem unrealistic. If you 10x your work rate, that means you can do almost 3 months' worth of work in a week. If you 100x it, you do a whole year's work in that week.

10x and 100x sounds cool, but I don't think we're there yet.

Other claims seem morally bankrupt. I wouldn't be advertising the firing of half of my employees in favour of AI. I'd be ashamed. Another trend that's recently arisen is "cloning someone else's entire SaaS" — copying someone's business and then using AI to try and steal their clients. Call me old fashioned, but that seems downright dishonest.

But marketing is as marketing does, and it's designed to do two things:

  1. Make you feel inadequate,
  2. Sell you something that promises to fill that void.

In the 1990s, Greed was Good. Now in the 2020s, Speed is Good.

We don't know where we're going, but at least we're getting there fast!

The Series

I originally published this as one long essay. I've since broken it into seven parts, because each argument deserved more room — and because the thread that ties them together comes from cognitive science, where I spent years before turning to software. The short version of that thread: conscious attention inherently lacks ecology. It's a narrow beam — good at optimising the one variable it's pointed at, blind to the system absorbing the cost. The acceleration story is what happens when that beam gets an engine.

  1. Acceleration as the Product — the promise of velocity, and the questions the marketing never asks.
  2. Felt Faster, Measured Slower — the METR study, and why the feeling of speed can't be trusted.
  3. The Engineering Productivity Paradox — feature delivery is up; so are duplication, complexity, and technical debt.
  4. Some Things Shouldn't Be Fast — learning, judgement, and the case for the human bottleneck.
  5. Hidden Costs — slop, devalued effort, and the fight for your own cognitive functioning.
  6. Don't Pull the Ladder Up — if AI replaces junior developers, who becomes a senior developer?
  7. When the Builders Are Scared — what the people building AI say under oath about their own creations.

Conclusion

Mario Zechner, creator of the Pi coding agent harness — whose talk "Building Pi in a World of Slop" threads through this whole series — concludes it the following way:

"Slow down. Think about what you're building and why. Don't just build because your agent can do it. Learn to say no. This is your most valuable capability at the moment. Fewer features, but the ones that matter. Then use your agents to polish the shit out of that."

"If you do anything important, write it by hand... And all of this requires discipline and agency. And all of this still requires humans."

Personally, I am struggling to balance the automated building pipelines with the manual work. Doing things manually seems slow, and I notice myself becoming lazy on the computer. We will all need to find our own pathway through all this.

I think AI could truly help us improve our society, and maybe help us solve some of the problems that would otherwise make humans go extinct. But if we don't use it consciously, with awareness, and balance out all the great other stuff we've learned until this point — if we let it just take over and "do everything for us" — that way lies madness and destruction.

Now, more than ever, we need to exert our willpower over our own habits, our attention, and our awareness. We need to learn to limit the scope of what AI knows, and what it does in our lives, just like we stop ourselves from scrolling on Instagram or Facebook all day — despite how difficult it is to tear yourself away once you're in a dopamine doom-scroll loop.

I think my intention with this series was to provide some reasoning for the way I'm trying to work: slower and more purposeful, while also leveraging AI to do the amazing things that it can do. I believe there's a middle path and I'm not saying I've found it; just that I think we should all be searching for it before the no-speed-limit-AI-development-autobahn kills us.

Some Other Interesting Articles About Similar Stuff

The illustrations in this series were generated with OpenAI's gpt-image-2, directed through a locked style system — one style block pasted verbatim into every prompt, with hand-written captions. The full prompts are here. Each part carries its own sources; the list above is further reading.