Snake oil may be a bit strong, because snake oil never works (except maybe as placebo?) whereas anything with an LLM, even though stochastic, has a pretty high chance of working.
> ... you also realize that promised productivity gains are also snake oil because reading code and building a mental model is way harder than having a mental model and writing it into code.
Not really, though it depends on the code; reading code is a skill that gets easier with practice, like any other. This is common any time you're ever in a situation where you're reading much more code than writing it (e.g. any time you have to work with a large, sprawling codebase that has existed long before you touched it.)
What makes it even easier, though, is if you're armed with an existing mental model of the code, either gleaned through documentation, or past experience with the code, or poking your colleagues.
And you can do this with agents too! I usually already have a good mental model of the code before I prompt the AI. It requires decomposing the tasks a bit carefully, but because I have a good idea of what the code should look like, reviewing the generated code is a breeze. It's like reading a book I've read before. Or, much more rarely, there's something wrong and it jumps out at me right away, so I catch most issues early. Either way the speed up is significant.
I think the placebo effect might be a decent comparison. It works most of the time, and you don't worry about it as long as you fully believe in its efficacy. However, once the illusion is shattered, the positive effects are diminished, and you can never fully trust the solution again.
for MVPs, mock ups, prototypes or in the hands of an expert coder. You can't let them go unsupervised. The promise of automated intelligence falls far short of the reality.
Not only "has a high chance of working", but you can pay more to make it more reliable. It really is striking trying to run a harness openClaw thing on a smaller or quantised model, really makes you realise how much we take for granted from SOTA models that was totally impossible just a year ago, in terms of complex, generally reliable tool use.
Indeed, and it is a complicated problem to solve. A GUI or CLI can hide footguns or make them less likely to be misused. But an AI agent is perfectly happy to use a wrecking ball to put a nail without any second thought or confirmation.
Even then. I don’t have an example off the top of my head but even perfectly clear sentences can lead the agent to strange places. Even between humans, miscommunication is easy, but then anyone sensible would ask for confirmation if their interpretation is weird. But the LLM very rarely questions the user.
I don’t think it’s fair to blame the user here. The tool must be operated by normal users.
> ... you also realize that promised productivity gains are also snake oil because reading code and building a mental model is way harder than having a mental model and writing it into code.
Not really, though it depends on the code; reading code is a skill that gets easier with practice, like any other. This is common any time you're ever in a situation where you're reading much more code than writing it (e.g. any time you have to work with a large, sprawling codebase that has existed long before you touched it.)
What makes it even easier, though, is if you're armed with an existing mental model of the code, either gleaned through documentation, or past experience with the code, or poking your colleagues.
And you can do this with agents too! I usually already have a good mental model of the code before I prompt the AI. It requires decomposing the tasks a bit carefully, but because I have a good idea of what the code should look like, reviewing the generated code is a breeze. It's like reading a book I've read before. Or, much more rarely, there's something wrong and it jumps out at me right away, so I catch most issues early. Either way the speed up is significant.