AI didn't delete your database, you did

I have shot myself in the foot using gparted in the past by wiping the wrong disk. gparted wasn't to blame. I was.

Letting LLMs work freely without supervision sounds great but it will lead to pain. I have to supervise their work. And that is also during execution. You can try to replace a human but we see where this leads. Sooner or later the LLM will do something stupid and then the only one to blame is the person who used the tool.

Some key inherent differences with older engineering fields is that software can be more complex than physical devices and their functionality can be obfuscated because it is written as text but distributed as binaries.

However, the main problem is that software has not been subjugated to enough legal regulation. Ultimately, all law does is draw lines somewhere in the gray between black and white, but in the case of software there are few lines drawn at all, due to many political and economic reasons. Once we draw the lines, most issues will be resolved.

It's not about judging. We are socializing the losses to the public and capitalizing the profits for the already wealthy.

Second, there is a legitimate reason to destroy a database in development and automation. The biggest problem I see is often treating your development data like pets not cattle. You absolutely need to have safeguards that this cannot be run in production, but if a human has access to the credentials to run in production, the agent has access.

So, then, what do we do? In a larger organization, we can depend on the dev/ops split to maintain this. For a solo developer, or a small team, it takes a lot more discipline. Even before AI, junior and even mid-level developers didn't have the knowledge to segment. And senior devs often got complacent because they thought they knew enough.

They likely need some combination of https://www.cloudbees.com/blog/separate-aws-production-and-d..., introduction to terraform, introduction to GitHub actions, and some sort of vm where production credentials live (and AI doesn't!)

But at that point you're past vibe coding. And from what I can tell, the successful vibe coders are quickly learning that they need to go past it pretty quickly with all these horror stories.

One thing that becomes very clear from this sort of work is just how bad LLMs are. It can be invisible when you're working with them day to day, because you tend to steer them to where they are helpful. Part of game theory though is being robust. That means finding where things are bad, too, not just exploring happy paths.

To get across just how bad the failure cases of LLMs are relative to humans, I'll give the example of tic tac toe. Toddlers can play this game perfectly. LLMs though, don't merely do worse than toddlers. It is worse then that. They can lose to opponents that move randomly.

They can be just as bad as you move to more complex games. For example, they're horrible at poker. Much worse than human. Yet when you read their output, on the surface layer, it looks as if they are thinking about poker reasonably. So much so, in fact, that I've seen research efforts that were very misguided: people trying to use LLMs to understand things about bluffing and deception, despite the fact that the LLMs didn't have a good underlying model of these dynamics.

It is hard to talk about, because there are a lot of people who were stupid in the past. I remember people saying that LLMs wouldn't be able to be used for search use-cases years back and it was such a cringe take then and still is that I find myself hesitant to talk about the flaws. Yet they are there. The frontier is quite jagged. Especially if you are expecting it to be smooth, expecting something like anything close to actual competence, those jagged edges can be cutting and painful.

Its also only partially solvable through scale. Some domains have a property where, as you understand it better, the options are eliminated and constrained such that you can better think about it. Game theory, in order to reduce exploitability, explores the whole space. It defies minimization of scope. That is a problem, since we can prove that for many game theoretic contexts, the number of atoms is eclipsed by the number of unique decisions. Even if we made the model the size of our universe there would still be problems it could, in theory, be bad at.

In short, there is a practical difference between intelligence and decision management, in much the same way there is a practical difference between making purchases and accounting. And the world in which decisions are treated as seriously as they could be so much so exceeds our faculties that most people cannot even being to comprehend the complexity.