Jaël retweetledi
As an engineer, you should have some depth. AI will probably help you if you let it.
Here is how the story of progress goes in people’s minds.
Our ancestors coded in machine language, then in assembler, then in C, then in C#, then in JavaScript. And now we prompt Claude. Each time, we drop the previous layer.
This is another instance of linear thinking about how progress works. We have seen this linear thinking applied to industrial policies: we no longer need to make steel, we can just order it online.
If you view technology as a sequence of new layers where only the last layer matters, you are shallow. You could say, “Why would Elon Musk need to know how rockets work? He can just outsource these details to people he pays.”
It amounts to saying that once you have the abstraction, you no longer need access to grounded reality. The problem with abstractions is that they are leaky. Here is Spolsky’s law:
Non-trivial abstractions are leaky to some degree. Abstractions fail. Sometimes a little, sometimes a lot. There is leakage. Things go wrong. It happens all over the place when you have abstractions.
By the way, people working for you are abstractions too, they are social abstractions.
If you don't realize that abstractions are leaky, they you will be mislead constantly. The same applies to your own work when you build the architecture of your system. You are pushed in one direction only: more and more abstraction. But your abstractions are leaky. Things go wrong, and you often cannot understand from the highest level what is happening.
We see the effect at the political level. Much of the West today is infected by technocratic thinking. A few smart people sitting in offices believe they can run the world because they have models running on Excel spreadsheets.
I have this same problem as a professor teaching computer science. Why would anyone learn to write a for loop? ChatGPT can do for loops just fine.
As a result, we have an increasing number of students who finish our introduction to programming course without any actual practical knowledge. We fail them and they don't understand why we do so. Why can't they just prompt their AI, isn't it the same as a coding?
Here is what I think a good engineer ought to be able to do: work at different levels.
It does not mean that everyone ought to regularly read machine language or understand the layouts of transistors on the CPU, but you should have some depth as an engineer.
I am reading a lot more assembly today than I did 20 years ago, by a wide margin.
In part because it is much easier.
The same is true at different levels. I can much more easily explore how the microarchitecture of my CPU impacts my code. The information is more accessible. AI can more easily index this information for you.
What this tells us about AI is that it will make the job of programmers more challenging when it is applied, not less. You will need better trained people, not less sophisticated people. Because, at some point, the abstractions will leak. And you'll need a good engineer. Trust me, you will.
Casey Muratori@cmuratori
IDK, I "review" compiler output all the time. A good debugger makes this cheap and easy. The disassembly is always there, and it jumps out at you when there are codegen anomalies, which happens more often than you might think in optimized builds.
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