Getting good results out of that team is hard, because the bottleneck is how quickly you can review their workflow and point them in new directions.
Understanding techniques like TDD, CI, linting, specification writing, research spikes etc turns out to be key to unlocking that potential. That's why experienced software engineers have such a big advantage, if they choose to use it.
First, and most important, I have actually started a number of projects that have only lived in my head historically. Instead of getting weighed down in “ugh I don’t want to write a PDF parser to ingest that data” or whatever, my attitude has become “well, why not see if an AI assistant can do this?” Getting that sort of initial momentum for a project is huge.
Secondly, AI assistants have helped me stretch outside of my comfort zone. I don’t know SwiftUI, but it’s easy enough to ask an AI assistant to put things together and see what happens.
Both these cases refer almost necessarily to domains I’m not an expert in. And I think that’s a bigger factor in side projects than in day jobs, since in your day job, it’s more expected that you are working in an area of expertise.
Perhaps an exception is when your day job is at a startup, where everyone ends up getting stretched into domains they aren’t experts in.
Anyways, my story is, of course, just another anecdote. But I do think the step function of “would never have started without AI assistance” is a really important part of the equation.
1. Learning curve: Just like any skill there is a learning curve on how to get high quality output from an LLM.
2. The change in capabilities since recent papers were authored. I started intensively using the agentic coding tools in May. I had dabbled with them before that, but the Claude 3.7 release really changed the value proposition. Since May with the various Claude 4, 4.1 and 4.5 (and GPT-5) the utility of the agentic tools has exploded. You basically have to discard any utility measure before that inflection point, it just isn't super informative.
Likewise real world architects have the skills to design a building but do not care or know how to build it, relying on engineers for that.
I think it’s important to distinguish because we’re increasingly seeing a trend towards final product over production, meaning these “vibe” people want the tool in the end and consider the steps in between to be just busywork and AI can do for them.
That’s closer to product design than to engineering. If I can imagine Monalisa and write that thought to paper, communicating that thought and getting a painter to paint it for me does not make me Da Vinci.
Da Vinci himself likely had dozens of nameless assistants laboring in his studio on new experiments with light and color, new chemistry, etc. Da Vinci was Da Vinci because of his vision and genius, not because of his dexterity with his hands.
Clearly I’m not in marketing.
Regardless, I’m delighted that this has gotten people to ‘independently discover’ software engineering best practices on their own.
> The AI adds a ton. It really is like having a whole team of extra coders available, all of which can type faster than you.
Funny thing is, the least time consuming aspect of making programs is encoding solutions in source form. For example, a reasonable typist can produce thousands of text lines per workday if they know what must be typed (such as transcribing documents).
What takes longest when producing programmatic solutions is understanding what must be typed in the first place. After that, the rest is just an exercise in typing and picking good file/type/variable names.