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...
That time when you got to internalise through first hand experience what good & bad look like is when you built the skill/intuition that now differentiates competent LLM wielding devs from the vibers. The problem is that expectations of juniors are inevitably rising, and they don't have the experience or confidence (or motivation) to push back on the 'why don't you just AI' management narrative, so are by default turning to rolling the dice to meet those expectations. This is how we end up with a generation of devs that truly don't understand the technology they're deploying and imho this is the boringdystopia / skynet future that we all need to defend against.
I know it's probably been said a million times, but this kinda feels like global warming, in that it's a problem that we fundamentally will never be able to fix if we just continue to chase short term profit & infinite growth.
I would say that baptism by fire _is_ where the quality of an academic education comes from, historically at least. They are the same picture.
In my experience, target schools are the only universities now that can make their assignments too hard for AI.
When my university tried that, the assignments were too hard for students. So they gave up.
If you didn't have high information density in essays you were torn into. AI was a disadvantage due to verboseness.
Most people dropped the class and prof went on sabbatical.
Education and training and entry level work build judgement.
AI is either the next wheel or abysmal doom for future generations. I see both and neither at the same time.
In corporate environment where navigating processes, politics and other non-dev tasks takes significantly longer than actual coding, AI is just a bit better google search. And trust me, all these non-dev parts are still growing and growing fast. Its useful, but not elevating people beyond their true levels in any significant way (I guess we can agree ie nr of lines produced per day ain't a good idea, rather some Dilbert-esque comic for Friday afternoon).
> I'll usually turn off AI tools for a good chunk of the day just to make sure I don't get too rusty.
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.
Basically, yes. I bought 'business tier' and I know about webdev but I'm somewhere between intern and junior, so I do a lot of discussing. One session is "I want [functionality and constraints], ask me relevant major design questions" then implementation, then me investigating and asking for fixes.
> I think the folks burning thousands of dollars of credits are unable to describe what they want.
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 perceive the AI itself as a very fast junior that I pair program with. So you basically need the seniority to be able to work with a "junior ai".
The bar for human juniors is now way higher than it used to be.
What do you think that is now? How does someone signal being 'past the bar'? If I hand wrote a toy gaussian splat renderer is that better than someone who used AI to implement a well optimized one with lots of features in vulkan?
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.
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 always asked a simple question like here is an array full of objects. Please filter out any objects where the "age" property is less than 20, or the "eye color" property is red or blue. It was meant more as a sanity check that this person can do basic programming than anything else.
Tons and tons of people failed to make basically any progress, much less solve the problem, despite saying that they worked programming day to day in that language. For a mid level role I would filter out a good 8 or 9 out of ten applicants with it.
I would consider it a non-leetcode type of question since it did not require any algorithm tricks or any optimization in time/space.
Nowadays that kind of question is trivial for AI so it doesn't seem like the best test. I'm not hiring right now,.but when I do I'm not sure what I will ask.
You're assuming the question has to even be that difficult. I've proctored sessions for senior-level webdev roles where the questions were akin to "baby's first React component" -- write a component that updates a counter when you click a button. So many candidates (who purported to be working with React for years) would fail, abysmally. Not like they were just making small mistakes; I didn't even care about best practices -- they just needed to make it work. So many failed. Lot of frauds out there.
There are so many software engineering candidates who literally cannot write the simplest code. I even had someone actually say "I don't really write code at my current job, I'm more of a thought leader." Bzzzzzt.
I've always prepared what I called level 1, level 2, and level 3 questions ready for candidates. But, I almost never even got to level 2, and never in 20 years of interviewing got to my level 3 questions.
I've been around the block for over 3 decades. I've had a number of high level positions across both IC and management tracks. These days I'm very hands on keyboard across a number of clients. If you asked me to write a basic for loop or if statement, there's a small chance I'd flub the exact syntax if writing on a whiteboard. Both because I bounce between languages all day and wires get crossed on the fly, but also the standard interview pressure type arguments. Whereas if the test is "does this person understand what a for loop is and how it works?", then yes, I can easily demonstrate I do.
In real life I'm not going to take an interview where there's not already that degree of trust so if that questions comes up something is already wrong. But I'm sure there are interviewers in the world who'd fail someone for that.
One of the worst guys took 20 minutes, with me having to coach him through it the entire time. It was a true exercise in patience, but I don't mind helping people learn new things. When he got his rejection email, he actually complained to the recruiter because he thought he did really well. Dude...
Half of the people I screen fail it. It's crazy.
https://blog.codinghorror.com/why-cant-programmers-program/
Most interviewees failed fizzbuzz, and that was 20 years ago.
It’s been well over a decade that I’ve had to do the coding interview monkey dance and I actually turned down an offer where I did pass a coding interview because I found it insulting and took a job for slightly less money where the new to the company director was interested in a more strategic hire (2016). That was the same thing that happened before in 2014 and after in 2018 - a new manager/director/CTO looking for a strategic hire.
In fact even my job at BigTech -AWS ProServe (full time blue badge RSU earning employee) as a customer facing consultant specializing in app dev was all behavioral as well as my next full time job as a staff consultant in 2023.
I’m 51 years old and was 40 in 2014. If I’m still trying to compete based on my ability to reverse a b tree on the whiteboard even at 40, I have made some horrible life decisions.
(Well actually I did make a horrible life decision staying at my second job too long until 2008 and becoming an expert beginner. But that’s another story)
I can never get over how this became a thing. Was listening to a Brian Cox video on YouTube the other night (something about his voice helps me sleep). He said "I don't memorize formulas, it's easy to look them up."
If you ever need to reverse a b tree (in 30+ years of writing code, I never have) it's easy to look that up. It tells me nothing about your ability as a developer of real software that you spent time memorizing trivia before an interview.
It's a contrived scenario, but the whole point is that it measures min(a,b) where `a` is your ability to think, and `b` is your ability to prepare (and memorize answers ahead of time). (I'd personally try to find ways to measure `a` instead of `b`, maybe by asking questions people wouldn't have heard before.)
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?
Let them use their preferred setup and AI to the full extent they want, and evaluate their output and their methodology. Ask questions of "why did you choose X over Y", especially if you're skeptical, and see their reasoning. Ask what they'd do next with more time.
It's clear when a candidate can build an entire working product, end-to-end, in <1 day vs. someone who struggles to create a bug-free MVP and would take a week for the product.
In addition to the technical interview, hiring them on a trial basis is the absolute best if possible.
Taste and technical understanding of goals and implementation to reach those goals is the biggest differentiator now. AI can handle all the code and syntax, but it's not great at architecture yet - it defaults to what's mid if not otherwise instructed.
I do feel like there's something *different* about the required skillset now, and it's not something that all engineers have even experienced ones. But I can't put my finger on what exactly it is. If I'm right though, classic interview techniques won't select for it because they never were intended to do so.
Either the machines exterminate us or we become glorified pets.
Hope the AIs prefer us to cats (even though that's a long shot).
An amateur with a chess engine that blunders 10% of the time will hardly play much better than if they didn't use it. They might even play worse. Over the course of a game, those small probabilities stack up to make a blunder a certainty, and the amateur will not be able to distinguish it from a good move.
However, an experienced player with the same broken engine will easily beat even a grandmaster since they will be able to recognise the blunder and ignore it.
I often find myself asking LLMs "but if you do X won't it be broken because Y?". If you can't see the blunders and use LLMs as slot machines then you're going to spend more money in order to iterate slower.
I guess? I don't really see why that would be the case. Being a senior is also about understanding the requirements better and knowing how/what to test. I mean we're talking about prompting text into a textarea, something I think even an "old timer" can do pretty well.
I'm not sure why junior engineers would be any better at that though, unless it's just that they're approaching it with less bias and reaping beginners luck.
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/
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.
I should have a credential I have to maintain every few years, one or two interviews, and that should get me a job.
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.