What can be stated without evidence can be dismissed without evidence. It is IMO pretty clear to me there is no substance to this post, without knowing anything about the author.
In general most such claims today are without substance, as they are made without any real metrics, and the metrics we actually need we just don't have. I.e. we need to quantify the technical debt of LLM code, how often it has errors relative to human-written code, and how critical / costly those errors are in each case relative to the cost of developer wages, and also need to be clear if the LLM usage is just boilerplate / webshit vs. on legacy codebases involving non-trivial logic and/or context, and whether e.g. the velocity / usefulness of the LLM-generated code decreases as the codebase grows, and etc.
Otherwise, anyone can make vague claims that might even be in earnest, only to have e.g. studies show that in fact the productivity is reduced, despite the developer "feeling" faster. Vague claims are useless at this point without concrete measurements and numbers.
> We’ll unpack why identical tools deliver ~0% lift in some orgs and 25%+ in others.
At https://youtu.be/JvosMkuNxF8?t=145 he says the median is 10% more productivity, and looking at the chart we can see a 19% increase for the top teams (from July 2025).
The paper this is based on doesn't seem to be available which is frustrating though!
DHH has long past the point where anyone should be caring about his technical opinions. This is a 0 substance post.
> DHH has long past the point where anyone should be caring about his technical opinions. This is a 0 substance post.
Can you elaborate?
What can be stated without evidence can be dismissed without evidence. It is IMO pretty clear to me there is no substance to this post, without knowing anything about the author.
In general most such claims today are without substance, as they are made without any real metrics, and the metrics we actually need we just don't have. I.e. we need to quantify the technical debt of LLM code, how often it has errors relative to human-written code, and how critical / costly those errors are in each case relative to the cost of developer wages, and also need to be clear if the LLM usage is just boilerplate / webshit vs. on legacy codebases involving non-trivial logic and/or context, and whether e.g. the velocity / usefulness of the LLM-generated code decreases as the codebase grows, and etc.
Otherwise, anyone can make vague claims that might even be in earnest, only to have e.g. studies show that in fact the productivity is reduced, despite the developer "feeling" faster. Vague claims are useless at this point without concrete measurements and numbers.
This study does a good job of measuring the productivity impact. It found 1% uplift in dev productivity from using AI.
https://youtu.be/JvosMkuNxF8?si=J9qCjE-RvfU6qoU0
Actually it didn't
From the video summary itself:
> We’ll unpack why identical tools deliver ~0% lift in some orgs and 25%+ in others.
At https://youtu.be/JvosMkuNxF8?t=145 he says the median is 10% more productivity, and looking at the chart we can see a 19% increase for the top teams (from July 2025).
The paper this is based on doesn't seem to be available which is frustrating though!
Great example of something that actually has some substance beyond meaningless anecdotes.
The claim was > DHH has long past the point where anyone should be caring about his technical opinions.
I asked for evidence, you are replying to something else.
I’ve seen the same change in the last 6 months.
So have I. Opus 4.5 still needs close monitoring and code review, but it is now good enough for most of my day to day tasks.
Can we please stop taking this guy seriously...