I see it the opposite way actually with respect to the CS degree. If you earned your CS degree (or any degree) before 2022 or so, the value of that degree is going to grow and grow and grow until the last few people who had to learn before AI are dying out like the last COBOL developers

AI has fundamentally broken the education system in a way that will take decades for it to fully recover. Even if we figure out how to operate with AI properly in an educational setting in such a way that learners actually still learn, the damage from years of unqualified people earning degrees and then entering academia is going to reverberate through the next 50 years as those folks go on to teach...

> The best engineers can visualize the whole architecture in their head, and describe exactly what they want to an AI

I think this must be part of it. I see so many posts about people burning a thousand dollars in AI credits building a small app, and I have no idea why. I use the $20 Claude plan and I rarely run out of usage, and I make all kinds of things. I just describe what I want, do a few back-and-forths of writing out the architecture, and Claude does it.

I think the folks burning thousands of dollars of credits are unable to describe what they want.

> While this could be done by junior or senior, I think junior usually has the slight advantage in being more AI-native and knowing how to effectively prompt and work with AI, though not always.

But juniors don't (usually) have the knowledge to assess if what the AI has produced is ok or not. I agree that anybody (junior or senior) can produce something with AI, the key question is whether the same person has the skills to asses (e.g., to ask the right questions) that the produced output is what's needed. In my experience, junior + AI is just a waste of money (tokens) and a nightmare to take accountability for.

I was skeptical but I'm really starting to see the productivity benefits now.

I very much follow the pattern of having the whole architecture in my head and describe it to the AI which generates the appropriate code. So now the bottlenecks are all process related: availability of people to review my PRs, security sign offs on new development, waiting on CI builds and deployments, stakeholder validation, etc. etc.

> The majority of engineers, in my hiring experience, failed very simple tests pre-AI

Did you consider tech whiteboard / leetcode interviews are unnatural stressful environments ? Have you gone through a mid/difficult technical appraisal yourself lately ? Try it out just to get an idea how it feels on the other side...

I agree that what you're describing is the required skillset now. But two things I've been unsure of are what that looks like in terms of hiring to test for it, and for how long this remains a moat at all.

So much of tech hiring cargo culting has been built up around leetcode and other coding problems, puzzles, and more. We all pay lip service to systems thinking and architecture, but I question if even those are testing the correct things for the modern era.

And then what happens in a year when the models can handle that as well?

Largely agree, with a bit of clarification. Junior devs can indeed prompt better than some of the old timers, but the blast radius of their inexperienced decisions is much higher. High competence senior devs who embrace the new tools are gonna crush it relative to juniors.

Makes sense. You just reminded me of the article "Why Can’t Programmers... Program?" [1].

Before gen AI, I used to give candidates at my company a quick one-hour remote screening test with a couple of random "FizzBuzz"-style questions. I would usually paraphrase the question so a simple Google search would not immediately surface the answer, and 80% of candidates failed at coding a working solution, which was very much in line with the article. Post gen AI, that test effectively dropped to a 0% failure rate, so we changed our selection process.

[1] https://blog.codinghorror.com/why-cant-programmers-program/

> The best engineers can visualize the whole architecture in their head, and describe exactly what they want to an AI

I'd go a step further and say the engineers who, unprompted, discover requirements and discuss their own designs with others have an even better time. You need to effectively communicate your thoughts to coding agents, but perhaps more crucially you need to fit your ever-growing backyard of responsibilities into the larger picture. Being that bridge requires a great level of confidence and clear-headedness and will be increasingly valued.

This stupid industry doesn't have the wherewithal to actually make a good credential and training process like medicine and law, and instead lets everyone come up with their own process to vet people. We could even do it as an apprenticeship model, not like that hasn't served humanity throughout the ages.

I should have a credential I have to maintain every few years, one or two interviews, and that should get me a job.

Could you provide an example of your “very simple tests” ?

I have found in the last 3 months that there are two clear tiers of developers in the company I work at, the ones that can code with AI and the ones that can't, and the ones that can't are all going to be unemployed in 6 months.

We have a lot of people where if you gave them clear requirements, they could knock out features and they were useful for that, but I have an army of agents that can do that now for pennies. We don't need that any more. We need people who have product vision and systems design and software engineering skills. I literally don't even care if they can code with any competency.

Btw, if you think that copying and pasting a jira ticket into claude is a skill that people are going to pay you for, that is also wrong. You need to not just be able to use AI to code, you need to be able to do it _at scale_. You need to be able to manage and orchestrate fleets of ai agents writing code.

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