I've been thinking about why AI seems to accelerate some teams dramatically while leaving others mostly unchanged. This post is an attempt to articulate what I think is missing: not better tools, but better routing of work, context, and ownership. Curious how this resonates (or doesn't) with others.
The main problem that I'm seeing is that software design is underappreciated and underestimated. To the extent there is AI hype it is driven by this blind spot. Software isn't just a bunch of text. Software is logical structures that form moving parts that interlock and function based on a ton of requirements and specs of the target hardware.
So far AI has shown it cannot understand this layer of software. There are studies of how LLMs derive their answers to technical questions and it is not based on the first principals or logical reasoning, but rather sparse representations derived from training data. As a result it could answer extremely difficult questions that are well represented in the training data but fail miserably on the simplest kinds of questions, i.e. some simple addition of ten digits.
This is what the article is talking about with small teams with new projects being more productive. Chances are these small teams have small enough problems and also have a lot more flexibility to produce software that is experimental and doesn't work that well.
I am also not surprised the hype exists. The software industry does not value software design, and instead optimize their codebases so they can scale by adding an army of coders that produce a ton of duplicate logic and unnecessary complexity. This goes hand-in-hand with how LLMs work, so the transition is seamless.
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[ 2.8 ms ] story [ 17.8 ms ] threadSo far AI has shown it cannot understand this layer of software. There are studies of how LLMs derive their answers to technical questions and it is not based on the first principals or logical reasoning, but rather sparse representations derived from training data. As a result it could answer extremely difficult questions that are well represented in the training data but fail miserably on the simplest kinds of questions, i.e. some simple addition of ten digits.
This is what the article is talking about with small teams with new projects being more productive. Chances are these small teams have small enough problems and also have a lot more flexibility to produce software that is experimental and doesn't work that well.
I am also not surprised the hype exists. The software industry does not value software design, and instead optimize their codebases so they can scale by adding an army of coders that produce a ton of duplicate logic and unnecessary complexity. This goes hand-in-hand with how LLMs work, so the transition is seamless.