I’m a systems architect who uses agentic coding to get around some executive dysfunction challenges with writing my own code. I learned about the dysfunction late in life; maybe meds will eventually get me to a point where I can consistently work on a personal code project without a looming deadline or social contract in the loop. For now I am part of the problem. Hi!

I look at agentic coding as a firearms problem:

• it lowers the barrier to execution: anyone with sloppy aim can point and pull the trigger, and do a lot of harm in the process
• both perform better in the hands of people with discipline and experience, but that doesn’t change the fact that LLMs and guns have massively proliferated and any idiot can get their hands on one (or that shitty people benefit from mass producing them)
• the industrial complex producing guns profits from murder, and the industrial complex producing datacenters profit from murder of the environment and attempts to eliminate jobs
• the same countries who treat gun control as an anathema tend to view AI control as an anathema
• analogies suck, they’re never perfect but they get the conversation started

That said, most of the people who hate AI also don’t know a damn thing about how they’re used correctly. It’s also not their fault. Sure, they’re under no obligation to be an expert about the thing that’s harming them. But there’s a bigger reason: the companies pushing this shit benefit from a world stage where people use them wastefully and inefficiently.

The short list of things most people don’t get:

Spoiler'd because most people also won't give a fuck.

• A LLM is like a ten year old with a doctorate who wants to impress you. It can answer random trivia off the top of its head, but it’s going to make shit up when it doesn’t know the answer.
• Never trust a LLM answer that was produced without a web search or web fetch. Pretend you’re that obnoxious algebra teacher who rolled their eyes at your calculator and told you to show the work during exams. Work for a LLM is tool calls: web searches and webpage fetches. Tell it to research documentation and best practices before you start. If you don’t, the answer you get will be dated by 1-2 years in the best case, and a confidently wrong hallucination at worst. (unfortunately you’re still at risk of being fucked because frontier labs use webpage summarization in these tool calls, and that summarization adds another vector for hallucinated shit; lovely)
• The baseline skills of the operator are more important than anyone gives credit. Your results scale to your own knowledge level and your use of the LLM to extend into adjacent subject matter. Employers don’t want to hear this: they want employees to go away. Frontier LLM labs don’t want employers to hear this: they need employers to buy into the delusion of eliminating their work force, and also financially benefit from people using the tools wastefully. (token burn = profit = the heat death of the universe as an afterthought, but that’s the next generation’s problem) Casual LLM critics don’t want to hear this: confidently incorrect echo chambers are the comfort food of anyone with an axe to grind about what they hate.
• Consistent results require controlling the probability space so that your pipeline swats the hand of the LLM when it’s lazy or generating slop. For coding, that means using languages or tooling that prevent major classes of problems to begin with (Rust, Go, Typescript) or toolchains that bitch about sloppy code (pytest-ruff + pytest-ty for Python). It means extensive unit tests that cover expressions of functionality, and knowledge of how to prompt out bad behaviors. (don’t rewrite tests to conform to “preexisting bugs”, always align tests to higher standards, etc.)

I could add another ten bullet points here, but I’m not here to prove that LLMs are capable of being useful. I’m here to demonstrate that it requires knowledge of a problem domain to get the best results. That means you can’t get rid of employees familiar with the knowledge domain. You need senior employees to get the best results, and you need junior employees so that they can eventually become senior employees. Same as it ever was. But because we’ve got useless fucking politicians who are too busy lining their own pockets instead of, I don’t know, regulating a worldwide memory shortage caused by a handful of companies, the whole thing is cannibalization of human society all the way down.

We could have scaled this to operate in the consumer space at a fraction of the energy costs (Apple SOC style versus energy inefficient hyperscaling) and used it as something to generate value by augmenting the capabilities of individual workers instead of trying to replace them wholesale. It only makes sense that it should have benefitted everyone considering this tech was built on shoving libraries worth of human achievement into a training pipeline (often without permission), right?

Nah. They profit more if we don’t own our own compute and everyone is encouraged to burn energy on treating the LLMs like all-knowing oracles instead of research tooling. They control the access to creations built on stolen knowledge. But hey, if everything else gets burned down in the process at least they come out on the other side rich and bailed out.