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by jgeada
314 days ago
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The other hard lesson learned during the Covid shutdowns (actually, it is learned and promptly forgotten at every major crisis): maximizing efficiency also maximizes the number of single points of failure in your system. Anything that goes wrong breaks an optimized for efficiency system. You need to have resilience and redundancy to deal with variability, but those cost money. Much better from the management perspective to ignore those risks, cash in on the cheap profits and blame "unexpected events" and get a bailout when things go wrong. They cash in on the easy money and have no downside consequences. |
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I'm wondering how many people read it to the bottom? It makes the case that productivity analysis tools already exist, maximally productive systems have some slack in them and 100% efficiency isn't just impossible, it's counterproductive (for the reason you state).
But the problem with AI is that it adds a random element which makes any kind of modelling much harder. Sometimes you get good results fasts, sometimes it wastes a lot of time and holds up the project.
You never know what you're going to get. So any kind of project planning becomes even harder.
But that's not even the main point. The subtext - which isn't stated - is that the C-suite has persuaded itself that AI is a system that is more controllable and predictable than human employees.
When in fact - as anyone working at the coal face knows - it's the opposite.
And that's a problem, for all kinds of reasons. The obvious ones, like the loss of expertise through career progression, have already been talked about.
The less obvious one being discussed here is that the more AI is used, the less predictable all kinds of projects become - both in time and quality.
And if the economy is now being designed on the assumption the opposite is true, that's not going to end well.
In previous phases of industrial revolution consistency was the bedrock benefit.
Trying to create a revolution out of inconsistency is a very risky click.