AI Was Supposed to Help Juniors Shine. Why Does It Mostly Make Seniors Stronger?

But my important point in it was the « identify and name » the elements of your problem [piece of music, or whatever]

Learning process has usually been, as you mention, to follow the path first, then eventually you can name things afterwards. Which is a highly uncomfortable process.

Another path that AI might force us to follow is to quick-identifyAndName the proper concepts, ahead of practical experience.

Man I don't miss that place or those people. Glad AI's basically destroyed it.

I don't think this is necessarily a massive moat for senior programmers. I feel it's a not a massive jump to teach AI architecture patterns and good data modelling?

I feel that anthropic etc al. just haven't got to that training stage yet.

That then leaves you with the mental model problem. Yes, there then a large context problem, but again I was wondering if setting up an MCP that presented the AI a meaningful class map or something might help.

Essentially give the AI a mental model of the code. I personally find class maps useless as they tend to clash with my own mental model. But it might work with AI. The class map can obviously be built without AI, but then you might even get AI to go through the code function by function and annotate the class map with comments about any oddities of each function. The MCP server could even limit the size of the map, depending on what part of the code it's looking to change (working on the email sending, don't bother sending them the UI later).

I'm guessing someone's already tried it given some of the ridiculous .Claude folders I've seen[1] but I've seen no-one talking about whether it works or not yet in the discussions I follow.

[1] That I suspect are pointlessly over complicated and make CC worse not better

Or until it does not. On numerous occasions I've observed LLMs get stuck in the endless loop of fix: one thing, break the other. Senior is capable of fixing it themselves and juniors may not even have a clue how the code works.

For instance, I've been working on an app recently with some social share icon logos in svg.

Whenever I get it to tweak bits of code elsewhere in the same file, 80% of the time it goes and changes those svg icons, completely corrupting some of the logos, curiously consistent in how it does it. Several times I've had that slip through and had to go and undo it again, at which point it starts to feel like the superfast junior dev you're using has malign intent!

I have this pattern while driving.

Using the main roads, when there is little to no traffic, the commute is objectively, measurably the fastest.

However, during peak hours, I find myself in traffic jams, so I divert to squiggly country roads which are both slower and longer, but at least I’m moving all the time.

The thing is, when I did have to take the main road during the peak traffic, the difference between it and squiggly country roads was like two to three minutes at worst, and not half an hour like I was afraid it would be. Sure, ten minutes crawling or standing felt like an hour.

Maybe coding with LLMs makes you think you are doing something productive the whole time, but the actual output is little different from the old way? But hey, at least it’s not like you’re twiddling your thumbs for hours, and the bossware measuring your productivity by your keyboard and mouse activity is happy!

I think the mixed reports on utility have a lot to do with the very different ways the tool is used and how much 'magic' the end-user expects versus how much the end-user expects to guide the tool to do the work.

To get the best out of it, you do have to provide significant amount of scaffolding (though it can help with that too). If you're just pointing it at a codebase and expecting it to figure it out, you're going to have mixed results at best. If you guide it well, it can save a significant amount of manual effort and time.

But I think, and this is just conjecture, that if you measure over a longer timespan, the ai assisted route will be consistently faster.

And for me, this is down to momentum and stamina. Paired with the ai, I’m much more forward looking, always anticipating the next architectural challenge and filling in upcoming knowledge and resource gaps. Without the ai, I would be expending much more energy on managing people and writing code myself. I would be much more stop-and-start as I pause, take stock, deal with human and team issues, and rebuild my capacity for difficult abstract thinking.

Paired with a good ai agent and if I consistently avoid the well known pitfalls of said agent, development feels like it has the pace of cross country skiing, a long pleasant steady and satisfying burn.

If I were to attack the same system myself without any LLM assist, I'd make a lot of choices to optimize for my speed and knowledge base. The code would end up much simpler. For something that would be handed off to another person (including future me) that can be a win. But if the system is self contained then going bigger and fancier in that moment can be a win. It all depends on the exact goals.

All in all, there's a lot of nuance to this stuff and it's probably not really replacing anyone except people who A) aren't that skilled to start with and B) spend more time yelling about how bad AI is than actually digging in and trying stuff.

And if this is true, you will have to coach AI each time whereas a person should advance over time.

Even if you do it by yourself, you need to do the same thinking and iterative process by yourself. You just get the code almost instantly and mostly correctly, if you are good at defining the initial specification.

On a tangent; that study is brought up a lot. There are some issues with it, but I agree with the main takeaway to be weary of the feeling of productivity vs actual productivity.

But most of the time its brought up by AI skeptics, that conveniently gloss over the fact it's about averages.

Which, while organizationally interesting, is far less interesting than to discover what is and isn't currently possible at the tail end by the most skillful users.

There have been many more studies showing productivity gains across a variety of tasks that preceded that one.

That study wasn't necessarily wrong about the specific methodology they had for onboarding people to use AI. But if I remember correctly it was funded by an organization that was slightly skeptical of AI.

I don't need a study to tell me that five projects that have been stuck in slow plodding along waiting for me to ever have time or resources for nearly ten years. But these are now nearing completion after only two months of picking up Claude Code. And with high-quality implementations that were feverdreams.

My background is academic science not professional programming though and the output quality and speed of Claude Code is vastly better than what grad students generate. But you don't trust grad student code either. The major difference here is that suggestions for improvement loop in minutes rather than weeks or months. Claude will get the science wrong, but so do grad students.

(But sure technically they are not finished yet ... but yeah)

It takes an LLM 2-20 minutes to give me the next stage of output not 1-2 days (week?). As a result, I have higher context the entire time so my side of the iteration is maybe 10x faster too.

That's a significant difference. There are a lot of tasks that can be done by a n00b with some advice, especially when you can say "copy the pattern when I did this same basic thing here and here".

And there are a lot of things a n00b, or an LLM, can't do.

The study you reference was real, and I am not surprised — because accurately gauging the productivity win, or loss, obtained by using LLMs in real production coding workflows is also not junior stuff.

I don't have to worry about managing the noob's emotions or their availability, I can tell the LLM to try 3 different approaches and it only takes a few minutes... I can get mad at it and say "fuck it I'll do this part myself", the LLM doesn't have to be reminded of our workflow or formatting (I just tell the LLM once)

I can tell it that I see a code smell and it will usually have an idea of what I'm talking about and attempt to correct, little explanation needed

The LLM can also: do tons of research in a short amount of time, traverse the codebase and answer questions for me, etc

it's a noob savant

It's no replacement for a competent person, but it's a very useful assistant