Hang in there while we get back on track
Borrowing Disesdi’s logic in their arithmetic (which is hard to argue against), after four years of open source LLMs, we should have three AirBnBs,1two Stripes,2and three Dropboxes3thanks to the power of AI.
Or else admit this is a dopamine game that makes you feel like The Universe’s Most Special Programmer™️ when it’s really just gamified mass-scale intellectual dependency.
Disesdi’s poststruck a chord with me because I’ve witness the same underwhelming output of software during this AI rage. These tools, in the hands of non-technical people who have not put in the time and effort to learn about the nerdy stuff, are simply not able to generate working apps that will persist and be security-hardened against run-of-the-mill attacks. And when experienced software engineers use them, the impact feels like a limp handshake.
The massive blindspot here is that writing lines of code isn’t the part of software development that drains the most time. This may seem like I’m contradicting myself on the argument that we should have at least a handful of AirBnBs or Dropboxes by now. But there isn’t a contradiction because leaders of companies and startupstruly believethat LLMs have solved the bottlenecks in product delivery. It’s the perception that all technical issues in software development no longer exist because you can “just get Claude to do it”.4
I recently attended a tech conference where there was a panel discussion on the main stage that was titled something like “No code, No problem”. The panel comprised four startup founders who vibe coded their way through MVP and, assumedly, are taking money from customers.5The moment that made me walk out of the session was when one of the founders said he didn’t have, and won’t ever need, a CTO because he can just use Claude to solve his technical problems.
Imagine that. You don’t need software expertise in your software company. You don’t need a structural engineer to design the structure of an office building. You don’t need a surgeon to perform surgery.
I’ve been told by people who are much, much better developers than I am that they’ve sped up their productivity in writing software by using Claude and other coding agents. So I thought, what the heck? When you run a consultancy, you can actually convert that type of development speed into money! I had already used a few different AI agents to help write the boring stuff – boilerplate code, repetitive SQL, etc. But I wanted to see if I can get in on this 10x magic. I decided to put my money where my mouth was and I bought $10 worth of DeepSeek credits to use with a project I was working on.6
The process was incredibly infuriating. The chatbot recommended some of the dumbest shit you could possibly do. And let’s be clear here:the code DeepSeek wrote would run, but it was a clown car rolling around with wheels held on by duct tape. Now, you might be thinking, “Well this fucking guy doesn’t know how toprompt. If he was just better atprompting, he wouldn’t suck so much.” And you might be right. But I’ve alsoshipped a product before, which is infinitely more than most people accomplish in their entire engineering career. So maybe I do suck at prompting. Or maybe something else sucks.
Untilvery, veryrecently, the way you would find the answer to something you didn’t know was by Googling it, reading a bunch of different opinions, and filtering and combining those thoughts to generate your own position. And before Googling was the go-to method for finding the answer to something, you’d have to travel to a monstrous building full of dusty humans so you can search, aisle by aisle, for the right collection of tomes that each contained a portion of the thing you wanted to understand better (I like to call it “doing itin the stacks” whenever I visit a library). That’s all been replaced with a text war between you and a robot that spews whatever shitsome asshole on Reddit posts. Just take a second to digest this stat!!!:
Reddit outranks financial experts 176% of the time when ChatGPT answers finance questions, despite YMYL guidelines prioritizing authoritative sources.
Too many people don’t think for themselves enough in this post-GenAI world. The curiosity hasn’t left us – which is very encouraging – but instead of thinking critically about a problem or question, there’s an app for that. The issue is that the “app for that” can’t reason. Its source of information is a range of differing opinions mostly from non-experts that aren’t verified for legitimacy.