Working with AI Feels More Like Leadership Than Coding

The conclusion also completely contradicts a previous point, which is that managing an LLM is not like managing a human. So the skills are, in contradiction to that LLM-ism of a conclusion, new. The author isn't using their people management skills, they're using new LLM-management skills. They think the two are similar, but didn't bother breaking down how they're the same vs where they contrast. It's just a lazy observation expanded out to a short essay that says nothing interesting.

He just accepts anything that Claude says as truth. He vibecoded over 60,000 lines of code in 3 weeks, but couldn’t get it to do what he want and made a project overrun for 3 extra months. When the pissed off stakeholders called a meeting to ask what was going on he didn’t show up and sent his junior engineer to answer questions and take the blame. Now thats leadership.

My head is in exactly the same place as coding - deep technical connection to the mental model of what is being built.

This isn’t leadership, it’s just communication. Suddenly realizing that real SWE is full of soft skills isn’t a novel epiphany.

Venkat argued that the study subjects did poorly not because LLMs were dulling their minds, but because the study subjects were freshman students with no management skills being tested on tasks that required delegation, quality gating and exception handling. The study put people in a situation that created role confusion and concluded that the poor outcomes were due to "cognitive debt" induced by LLM use.

[1]: https://contraptions.venkateshrao.com/p/prompting-is-managin...

You need to direct agents to do work worth doing and then you need to understand the output. Some of the emotional parts of management are gone since agents don't care if you tell them to throw everything away and take a different approach. Some of the therapeutic parts of development are gone since you don't need to hand craft a clever code structure.

I wouldn't say it's more like one or the other though. One of the most important jobs of a leader is finding work worth doing for their team. One of the most important jobs of a developer is ensuring system cohesion. Both of these are hard jobs.

To me, AI failure cases look like doing either of these jobs poorly:

1. Writing a big pile of tools that really provides no user value

2. Not reading the code and ending up with broken systems

Companies like Anthropic seem to understand that too. It's impressive how many CTOs and CEOs Anthropic have hired for individual contributor positions, which I think is because those leadership skills transfer surprisingly well to working with agents.

Of course, managing agents is massively easier than managing humans! You don't have to consider the agent's own desires, goals, opinions, or emotional state when telling them what to do. Humans have agency; agents (despite the name) do not.

I found a funny thing before. I'm in the 2100 block of Github IDs, meaning I was OLD SCHOOL. This got me thinking about how I was probably one of the first users of the first GPT model to be in a major product, Copilot. According to Grok, Copilot was GPT 3. I figured it as earlier because it SUCKED at generation other than auto-complete.

I'm now thinking the best way is to use these models to create the scaffolding and keep my brain in the architecture with strict reviews and small PRs. Slower, but less slop. Basically, just using AI for a bump or two above what I used Copilot for back in the day. Less running agents all day creating slop that I'll never look at. More with serious focus on what I bring my full attention to. Increased productivity, less BS.

Maybe that's just rearranging chairs on the deck on the Titanic. But it's what I'm thinking. And hitting send! ;) That's probably not the win, but I think that path could reveal it.

Its really interesting especially having different agents with different prompts and then having each one based on their reasoning, etc

The resulting decisions are fed into the coding loop with guardrails derived from those decisions. The agent one-shots features once it goes into the coding loop.

Anyone who's fallen in love with programming itself and doesn't see software production as a means to an end is not really likely to see things like this.

I see AI as an accelerator of implementing my own choices. I'm generally opposed to metaphors, designs or strategies which excessively anthropomorphize it; it seems completely wrong-headed and counterproductive.

Just astounding we decided to put a DMV in our IDEs.

Pointless, stupid article. Digital garbage, as garbage as LLM slop. So many words to say nothing.

To go anywhere serious you have to lead people, but even the ones who should be leading people are heads down talking to the LLM

They're different kinds of work, and both are interesting in their own way. But in the freelance market, LLMs have already become the baseline, so I have to use them whether I like it or not. There are both pros and cons.

It's good to be able to read code and understand its structure, but writing code and reading it to transform it into a different structure are different skills. There's definitely some decay in raw coding ability, though. So I use LLMs for professional coding and for tasks that I couldn't do before, while I keep hand-coding smaller things that feel manageable.

Honestly, I think most people who hate LLM coding actually hate being forced to use it under workplace pressure. And when LLM output looks bad, it's often because managers tend to be strict about their subordinates' work but lenient about their own. Once an LLM generates something, people tend to get attached to it and become more forgiving—since it feels like they made it.

It's tough that LLMs have made deadlines tighter. But these days, compared to the old days when I had to go through interviews and conversations to build a proposal, I actually find it more convenient that clients send me proposals written by LLMs. There are pros and cons to everything.