TFA is directionally correct, though it repeats a few cliches which are no longer accurate. E.g. people and some empirical data report improved productivity even on large, brownfield codebases, with the caveat that effectiveness seems to be related more to the quality processes around the code rather than the code itself.
However, this TFA is absolutely correct about the point that it takes a long time to master this technology.
A second, related point is that the users have to adapt themselves to the technology to fully harness it. This is the hardest part. As an example, after writing OO code for my entire career, I use a much more of a functional programming style these days because that's what gets the best results from AI for me.
In fact, if you look at how the most effective users of AI agents do coding, it is nothing like what we are used to. It's more like a microcosm of all the activities that happen around coding -- planning, research, discussions, design, testing, review, etc -- rather than the coding itself. The closest analogy I can think of is the workstyle of senior / staff engineers working with junior team members.
Similarly, organizations will have to rethink their workflows and processes from the ground-up to fully leverage AI. As a trivial example, tasks that used to take days and meetings can now take minutes, but will require much more careful review. So we need support for the humans-in-the-loop to do this efficiently and effectively, e.g. being able to quickly access all the inputs that went into the AI's work product, and spot-check them or run custom validations. This kind of infra would be specific to each type of task and doesn't exist yet and needs to be built.
Just foisting a chatbot on employees is not helpful at all, especially as a top-down mandate with no guidance or training or dedicated time to experiment AND empowerment to shake things up. Without that you will mostly get poor results and resentment against AI, which we are already seeing.
It's only 3 years since ChatGPT was released, so it is still very early days. Given how slow most organizations move, I'm actually surprised that any of them are reporting positive results this early.
However, this TFA is absolutely correct about the point that it takes a long time to master this technology.
A second, related point is that the users have to adapt themselves to the technology to fully harness it. This is the hardest part. As an example, after writing OO code for my entire career, I use a much more of a functional programming style these days because that's what gets the best results from AI for me.
In fact, if you look at how the most effective users of AI agents do coding, it is nothing like what we are used to. It's more like a microcosm of all the activities that happen around coding -- planning, research, discussions, design, testing, review, etc -- rather than the coding itself. The closest analogy I can think of is the workstyle of senior / staff engineers working with junior team members.
Similarly, organizations will have to rethink their workflows and processes from the ground-up to fully leverage AI. As a trivial example, tasks that used to take days and meetings can now take minutes, but will require much more careful review. So we need support for the humans-in-the-loop to do this efficiently and effectively, e.g. being able to quickly access all the inputs that went into the AI's work product, and spot-check them or run custom validations. This kind of infra would be specific to each type of task and doesn't exist yet and needs to be built.
Just foisting a chatbot on employees is not helpful at all, especially as a top-down mandate with no guidance or training or dedicated time to experiment AND empowerment to shake things up. Without that you will mostly get poor results and resentment against AI, which we are already seeing.
It's only 3 years since ChatGPT was released, so it is still very early days. Given how slow most organizations move, I'm actually surprised that any of them are reporting positive results this early.