A good chunk of engineering is putting together reliable systems from unreliable parts.
You build in safeguards, redundancy, defense in depth, recovery systems. You build models of the system and prove characteristics about it.
Software is fundamentally automation. LLMs enable automating the construction of software itself. They're much faster and cheaper than people, and they're more unreliable. (People are unreliable too!)
The immediate challenge of these times is figuring out how to reliably construct reliable software in the large, over the longer term, reliably. This is an engineering challenge, and the only way we'll get to the other side of it is by trying to do it. Things will be rough, there will be a Cambrian explosion of techniques, most approaches will fail, and many more won't survive as models improve on quality and capability. But we'll figure it out.
Making things by hand, as in the time before agentic coding, can be engineering too, but it is not the core challenge of these times, and it will soon be a hobby, or possibly a kind of luxury good. You will no more want hand-written software than you'll want a hand-made car. It will not have the precision, performance or reliability of machine-made software.
Strong agree. I think the fundamental challenge of working in fields that increasingly become AI-enabled will be the ability to understand and direct large or intricate systems without prior knowledge/the advantage of having built the model as implemented. That’s already how it works in complex domains or large businesses.
It does require a different kind of ego/abilities than before. My (negative) framing of the whiplash effect is that it’s a reckoning of “process fetishism”/a bad kind of careerism in the tech hiring market (because for the labor market to work, candidates need to be evaluable and sortable by businesses, and many people build an identity/optimize for legibility around “best practices” or very particular “technologies” which might get them a job).
Ultimately, you need to know and learn/be responsible for stuff, and be able to help people with your labor, not be “a type of person” that isn’t effective at the task of helping. But at the same time knowing things and being able to take accountability/help people remains critical, especially because that’s what people will want to pay for even as “time spent typing it in” decreases.
Personally, I think it will be a good thing because software and “tech” will become a more strongly domain-driven/enabling medium for real-world or specialized things. IE it is the end to “software for its own sake” or “willingness to type it in and play with Jira/jenkins/frameworks” and the beginning of something that is more applicable or knowledge-building rather than “being the X for Y at Z”. Harder but more fun :)
Cars use AI for steering control (et al) and technology very similar to RLVR (hold the RL), eg property-based testing and formal verification, to prove the soundness of their embedded systems. Most of us in San Francisco trust Waymo with our lives more than human uber/Lyft drivers
As long as you can verify/test and take accountability for the thing you put your name on there’s no reason not to treat it as a process or search problem rather than one you assemble yourself by hand. The only problem is that it’s ironically much harder and more engineering than most “software engineers” are willing or able to do.
I spent several years working on permutation testing/experimentation and creating e2e verification of infrastructure because at scale, or when reliability/correctness are critical, you cannot rely on a single person’s mental model, or for the world to not drift around a system as it works now. That kind of system is what allows you to use LLMs or engineers who don’t know everything about it to improve or change it. It’s more science than art, which is often (but not always) what you want
You build in safeguards, redundancy, defense in depth, recovery systems. You build models of the system and prove characteristics about it.
Software is fundamentally automation. LLMs enable automating the construction of software itself. They're much faster and cheaper than people, and they're more unreliable. (People are unreliable too!)
The immediate challenge of these times is figuring out how to reliably construct reliable software in the large, over the longer term, reliably. This is an engineering challenge, and the only way we'll get to the other side of it is by trying to do it. Things will be rough, there will be a Cambrian explosion of techniques, most approaches will fail, and many more won't survive as models improve on quality and capability. But we'll figure it out.
Making things by hand, as in the time before agentic coding, can be engineering too, but it is not the core challenge of these times, and it will soon be a hobby, or possibly a kind of luxury good. You will no more want hand-written software than you'll want a hand-made car. It will not have the precision, performance or reliability of machine-made software.