The justification of the slippery slope is pretty simple.

They ask to add in a DoB field that must be filled out and reported at all times. So we add it into our systems and say no big deal. If you hate it put down your birthday as 1900-01-01 and call it a day.

But what is the problem with a self reported, unconfirmed field like this? It is utterly useless BECAUSE it is a self reported, unconfirmed field. It doesn’t solve any problem AND it doesn’t provide any real personal information. So why even ask for it?

The two options are malicious intent and stupidity that tech can’t be worked around.

We can skip the latter as stupid people will always be stupid. So the former, malicious intent. When they point out that this new law isn’t actually fixing things because of the fact people are lying about their age they will inevitably say we need government IDs added to the system. They will not only make sure you are the correct age for content, but know WHO is viewing such content and they will be tracking it.

Now you might say, wait there is a third option, benevolent people actually wanting safety. Creating a system where personal information is mandatory to your interaction with the internet creates a security target that we all know cannot be covered. And we also know that all tech can be broken so kids will find a way around this stuff. Using your parent’s ID, a globally shared fake ID, hacking the protocol for certification. they will get around it.

The slope is slippery because the only options are

• Stupid => which falls down the hill on accident
• Benevolent => which falls down the hill because its a vertical cliff face that can’t be scaled
• Malicious => they push you down the hill because they are assholes

This is literally how killer computers come into existence.

The issue is two fold.

First the scope of the project is very important. When I am working on a web app the most complicated project is still 90% boilerplate stuff. You write some RESTful code on some framework using CRUD and make a UI that draws based on data. No matter what you are making, lets be honest, it’s not novel. This is why vibe coding can exist. Most of your unit tests can be derived from the types in your functions. Do a little bit of tracing through functions and AI can easily make your code less fragile.

When you are working on anything more complicated making code better requires you to actually grok the business requirements. Edge cases aren’t as simple. The reasons for doing things a specific way aren’t so superficial. Especially when you start having to write optimizations the compilers don’t do automatically.

The second issue is learning matterial. The majority of the code we write is buggy. Not just in range testing but in solution to problems. There is a reason why we don’t typically write once and never go back to our code.

Now think about when you, as a human, go back over old code. The commit log and blame usually don’t give a great picture of why the change was needed. Not unless the dev was really detailed in their documentation. And even then it requires domain knowledge and conceptualization that AI still can’t do.

When teaching humans to be be better at development we suck at it even when we can grok the language and the business needs. That is a hurdle we still need to cross with AI.