AI and the ironies of automation – Part 2

Comparisons with deterministic tools such as calculators will always lead astray. There is no comparable situation where faced with a new problem the AI will just give up. If there is the need for an expert, the need is always there, because there is no indication external to the process that the process will fail.

The way you put that makes be think of the current challenge younger generations are having with technology in general. Kids who were raised on touch screen interfaces vs kids in older generations who were raised on computers that required more technical skill to figure out.

In the same way, when everything just works, there will be no difference, but when something goes wrong, the person who learned the skills before will have a distinct advantage.

The question is if AI gets good enough that slowing down occasionally to find a specialist is tenable. It doesn't need to be perfect, it just needs to be predicably not perfect.

Expertw will always be needed, but they may be more like car mechanics, there to fix hopefully rare issues and provide a tune up, rather than building the cars themselves.

I used to be a maintenance data analyst in a welding plant welding about 1 million units per month.

I was the only person in the factory who was a qualified welder.

That's how it tends to go, automation removes some parts of the work but creates more complexity. Sooner or later that will also be automated away, and so on and so forth. AGI evangelists ought to read Marx's Capital.

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I’ve been using LLMs to code for some time and I look at it differently.

I ask myself if I need to understand the code, and if the answer is yes I don’t use an LLM. It’s not a matter of discipline, it’s a sober view of what the minimal amount of work for me is.

> "Most companies are efficiency-obsessed. Hence, they also expect AI solutions to increase “productivity”

So this is true on paper, but I can tell you that companies don't broadly do a very good job of being efficient. What they do a good job of is doing the bare minimum in a number of situations, generating fragile, messy, annoying, or tech-debt-ridden systems / processes / etc.

Companies regularly claim to make objective and efficient decisions, but often those decisions amount to little more than doing a half-assed job because it will save money and will probably be good enough. The "probably" does a lot of work here, and then "probably" is not good enough there's a lot of blame shifting / politics / bullshitting.

The idea that companies are efficient is generally not very realistic except when it comes to things with real, measurable costs, such as manufacturing.

Not necessarily. It depends if the process is deterministic and repeatable.

If an AI generates a process more quickly than a human, and the process can be run deterministically, and the outputs are testable, then the process can run without direct human supervision after initial testing - which is how most automated processes work.

The testing should happen anyway, so any speed increase in process generation is a productivity gain.

Human monitoring only matters if the AI is continually improvising new solutions to dynamic problems and the solutions are significantly wrong/unreliable.

Which is a management/analysis problem, and no different in principle to managing a team.

The key difference in practice is that you can hire and fire people on a team, you can intervene to change goals and culture, and you can rearrange roles.

With an agentic workflow you can change the prompts, use different models, and redesign the flow. But your choices are more constrained.