alt Hacker NewsUnderstanding is the new bottleneck
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I hate these X is the new bottleneck.
Coding was never a bottleneck, except when it was, and when it was, it still is.
Understanding is not a new bottleneck, except when it is, and when it is, it always was.
Do other industries do this? When somebody brings a nail gun to a framing job do carpenters say: “hammering was never a bottleneck” or do they say: “measuring is the new bottleneck”? The answer is neither. And in fact my analogy is flawed, we are talking about cabinet makers who just went to IKEA bought a ready made set in flat packaging and are now proudly claiming that “assembly is the new bottleneck”.
There was no single bottleneck to programing, and there is no single bottleneck to programing. If you have to pick one, user demand is perhaps the only real bottlneck. Creating software that users saw value in using is just as hard with AI or without it (arguably harder with AI... when all you have is a hammer and all that).
TFA almost reaches this conclusion at the end when they claim (in speech pattern which is suspiciously AI-like): “The point was always to augment, not just automate.”. If we are augmenting the user experience we are doing a good job and people may actually use the software we write... if no, well it doesn’t matter how well we understand or how fast we write the code (or have AI write it for us).
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This is a temporary bottleneck. AI is moving so fast that this will change. Wait six months and this article is no longer relevant.
About a year ago most people were still typing code. Having an agent do ALL code was crazy.
Within a year or two years at most, a lot of people will stop trying to understand code. The onus will shift to testing and QAing.
I know this is hard to hear but that’s the trendline. That’s where all of this is converging. Everyone’s to busy trying to lock themselves down as an expert of the new “paradigm” but it’s all moving so fast that the paradigm now won’t be the paradigm of tomorrow.
Understanding is expensive. And hence valuable.
LLMs usually points to the most idiotic future trajectory on my work, and I have to curse it inorder to let it keep up with my refined understanding.
But what else would one expect from a probabilistic weighted next token predictor, other than to conduct probabilistic search which are 99.99% deadends.
But LLMs can pave the way towards constructing resilient and correct architecture which can be iterated fast by a human.
Architecture and determinism is where my money is in.