In the short term, I have seen a lot of managers and other higher-ups talk about how we do not need to worry about low-level details anymore. In their minds, we are now all designers and architects, so we do not think about the small implementation details that ultimately do matter for performance and reliability.
People who know what they are talking about worry about all of the details from the big picture down to the small scales. We can still operate using abstractions like designers and architects, but we must know enough to choose the right abstractions that account for the concrete details properly. I've discussed this point earlier this year [1] using the tree swing diagram [2].
But what will their day to day look like? Meetings?
Previously, I'd have to work days undisturbed to get important stuff out of the door. There was effort involved to reach an elegant solution that fit business need.
Now I'm a meat bag pressing enter on a "recommended" option Claude already figured out was the best approach.
And how long can that last? We’re expensive meat bags…
If you are constantly thinking claudes approach is the best then perhaps you were not that good of an engineer to start with.
I'm very interested in systems as well, but being less sure about the future (on whether this is something I really need to think about, or whether I'll get opportunities to work much at this level), I started reading about such topics a fair bit less.
It doesn't yet know when I brushed my teeth last, what specific foods in what quantities give me heartburn/indigestion, what that funky smell from my running shoes might be.
It can write pretty good code on a recursive loop when its provided a clear target. It can write better code when it has someone who understand architecture guiding it. It can review code reasonably well as well.
ChatGPT tied to robotics might even be able to do more interesting things!
It does pretty poor on highly specific knowledge where a RAG better supports -- something like Agent Search at GCP or AI Search at Cloudflare. But it can synthesize.
In effect, we've built an amazing library registry and need to up our librarian skills and the skills of people or systems that can use the information the librarian and their system can find.
Knowledge has been available just by asking Google for decades now. The LLM makes it easier but it's a difference of degree not of kind
Until the models are 100% reliable knowledge will be required in order to quickly spot issues and work efficiently with the model to address them.
I do wonder though about the optimizations within it, what could be the most optimal way to achieve such room (ie. knowing which questions to ask)
Yes, learning and tinkering is still really the greatest way to achieve that
but I think what I am talking about can be better simplified with the analogy of a gym: previously what used to be necessary (manual work/labour) but when most people got into information work, even then there was/is a need for it (physical work), then we saw the evolution of machines specifically designed to optimize for it and we got machines specifically designed for this training, which helped push people's body to their absolute limits.
I do wonder if an hyper-optimized environment of learning and for asking questions (or more so knowing the know how on which questions to ask), this whole process might be optimized for it and what that process might would look like is a source of curiosity to me.
A relevant video which talks about similar topics: Bodybuilding for the mind: https://www.youtube.com/watch?v=o0DtxUJ6rAc
I'm not sure how it plays out in 5 or 10 years, but that's how it is now.