Comment by dataviz1000

This is what I am thinking about this morning. I just woke up, made a cup of coffee, read the financial news, and started exploring the code I wrote yesterday.

My first thought was that I can abstract what I wrote yesterday, which was a variation of what I built over the previous week. My second thought was a physiological response of fear that today is going to be a hard hyper focus day full of frustration, and that the coding agents that built this will not be able to build a modular, clean abstraction. That was followed by weighing whether it is better to have multiple one off solutions, or to manually create the abstraction myself.

I agree with you 100 percent that the poor performance of models like GPT 4 introduced some kind of regularization in the human in loop coding process.

Nonetheless, we live in a world of competition, and the people who develop techniques that give them an edge will succeed. There is a video about the evolution of technique in the high jump, the Western Roll, the Straddle Technique, and finally the Fosbury Flop. Using coding agents will be like this too.

I am working with 150 GB of time series data. There are certain pain points that need to be mitigated. For example, a different LLM model has to be coerced into analyzing or working with the data from a completely different approach in order to validate. That means instead of being 4x faster, each iteration is 4x faster, and it needs to be done twice, so it still is only 2x faster. I burned $400 in tokens in January. This cannot be good for the environment.

Timezone handling always has to be validated manually. Every exploration of the data is a train and test split. Here is the thing that hurts the most. The AI coding agents always show the top test results, not the test results of the top train results. Rather than tell me a model has no significant results, it will hide that and only present the winning outliers, which is misleading and, like the OP research suggests, very dangerous.

A lot of people are going to get burned before the techniques to mitigate this are developed.

Overfitting has always been a problem when working with data. Just because the barrier of entry for time series work is much lower does not mean that people developing the skill, whether using old school tools like ARIMA manually or having AI do the work, escape the problem of overfitting. The models will always show the happy, successful looking results.

Just like calculators are used when teaching higher math at the secondary level so basic arithmetic does not slow the process of learning math skills, AI will be used in teaching too. What we are doing is confusing techniques that have not been developed yet with not being able to acquire skills. I wrack and challenge my brain every day solving these problems. As millions of other software engineers do as well, the patterns will emerge and later become the skills taught in schools.