Yes to all of this. Especially the part about having to babysit the agent except for the simplest of problems. Anything that involves even the smallest of departures from the most common case become tedious slogs of cajoling the damn agent over and over.
I am required to use Anthropic corporate subscription tokens where I work. It produces wrong results about half the time. Of course, it is difficult to tell if those results are incorrect without fully inspecting the generated code and reading all the explanatory output, and of course by also checking if unit tests are both passing and coverage has not altered. Half the time it runs existing unit tests to ensure change fidelity, but half the time it skips this step even though it is specifically instructed to run such tests after making changes. I also ask for the agent to generate automated smoke testing with a report of test results that I can run myself. This is a very tedious and time consuming process, and I wind up throwing away about two thirds of the generated code including smoke test automation per issue.
This process does not take too much longer than my regular workflow. But reviewing explanatory output from the language model which narrates the “reasoning” behind any change more complex than a simple field or function rename is overwhelmingly tedious because of the bizarre linguistic turns of phrase generated by the inference. So most of the time it actually winds up being more mentally exhausting than just debugging, or searching a code base in my editor and making changes myself.
Listening to myself complain about this new workflow I keep hearing the apologizers arguments in my head, like – this is just the beginning, man! We’re on the second rung of the exponential ladder!
I don’t know about that. I am skeptical. It doesn’t seem like it is getting significantly better, only the agent software has gotten pretty good at automating some of the stuff software developers do, like code searches and stack tracing.
So if the argument is that agent software will improve generated code quality exponentially, I really doubt that. Or, if the argument is that the models’ output quality will improve exponentially, that seems highly unlikely without devastating operating costs externalized to ecosystems or non-user populations.
Jevon’s paradox prevents any actual efficiency gains from outstripping usage rate increases (if the actual output quality improves sufficiently to make an expanding user base unavoidable) which would make externalized costs continue to grow at an also exponential (though derivitive) rate.
Either way I slice it, my sense is that most outcomes from large language model usage are morally suspect in outcomes. At best, they will supplant reasoning skills and coding skills that took me decades to develop, but will produce software that is worse for at least the next year or so, until output quality stabilizes around A grade level or better.
What I mean by that last part is that current models and agentic engines get graded by quality tests where problems are presented and solutions are graded. It seems that the quality grading winds up putting most systems at a place on a bell curve compared to humans just around C-grade quality. So, if this is just lower quality because the tech is still in its infancy, that’s fine I guess, but will all the results implemented and deployed into production systems using these subpar results be unwound when the systems capable of better quality results finally Moore’s Law their way onto our laptops? I also doubt that.
Meanwhile, if Capitalists wind up overcoming the psychosis bug and instead let the skeptic take over the narrative, it then seems unavoidable that costs could skyrocket for users of current best-of-class systems, and we’ll all be left using D-grade or worse models backing the agent software, or accepting incredibly large wait times for responses from slow local or overloaded/throttled datacenter-hosted inference processors.
So if current trends continue, I predict a bubble implosion typical of over-exuberant capital, and a drought of model-assisted code work for years afterward, if not a decade. And within that era, many people will be employed to correct sub-par software developed by model-backed agent systems that were basically merged and deployed into production systems that only “seemed” to work and pass gating unit tests, but wound up making maintenance by less available model-backed agent systems a serious challenge in a post-bubble world.
People forget about the S-curve inherent in all new technology adoption and growth. It only looks exponential at first. Eventually there’s always a plateau, and fickle Capitalists used to quarterly profits or liquidation of holdings get skittish until the next wave.
In our case, the next wave may never come without a fundamental set of improvements in underlying energy and cooling infrastructure technology. And if those improvements never arrive (and there’s a real chance such improvements are walled off by hard physics limits) then we’ll either see ecological collapse or, energy market collapse, or just simple linguistic inference quality limits. Any of which could disable further development of this technology tree.
Leaving us in the lurch to get ourselves out of the hole we find ourselves in.
Who could have guessed.
Trump will intervene and pardon Tim Burger for his crimes, of that I am certain.