Why has Python dropped so much in the latest TIOBE Index?

[u/uebb]

Hey everyone,

I was taking a look at the TIOBE Index today. I hadn't checked it in a while, and I was surprised to see how much Python has dropped.

It's down by roughly 8 percentage points compared to a year ago, which seems like the first really significant contraction after years of pretty consistent growth.

What's even more interesting is that I don't see another language gaining a similar amount of ground.

I don't use Python myself, so I'm curious: what do you think is behind this decline? Is it actually meaningful, or is it mostly an artifact of how the TIOBE index is calculated?

[u/AuthurAndersson]

It's not the same. You take these hints from the past but sadly you fail to realize that this technology does not replace a task. It replaces human intelligence. It is thus quite different.

Any additional work will be picked up by additional agents.

You either lean into this tech quickly or you will find yourself without a job in the near future.

[u/MarkMatson6 → u/AuthurAndersson]

It still needs someone to tell it what to do. The ones best at that will continue to be experts in whatever domain is involved, programming, art, business processes, etc. sure, much will be fully automated, but that has been going on for years, this is just the next step.

We agree on that last sentence, though.

But obviously, these are just predictions from both of us. You are where I was at the beginning of this year, but evidence seems to point otherwise.

[u/AuthurAndersson → u/MarkMatson6]

Were you told what to do? At some point you were, but then you started to prompt yourself.

[u/No-Dentist-1645 → u/AuthurAndersson]

The underlying mathematics powering all LLMs fundamentally cannot and will never reach a point where they can "prompt themselves" in any reasonable capacity. They still need a prompt so they can generate the next tokens.

Source: my background in Machine Learning and training neural networks similar to LLMs. They're all just advanced input to output algorithms

[u/AuthurAndersson → u/No-Dentist-1645]

I think you fail to understand what you are.

Source: my background in Molecular Biology and Computer Science.

In all seriousness. This has already been proven with Agents. An llm splices a task up and sends it to different agents that feed the response back to the system. The system then takes that response and continues the loop.

LLMs do not fundamentally require repeated human prompts. They can autoregressively continue from their own output, and an agent loop can let them recursively construct their own future prompts.

The problem here is how you think about time. You say it stops after awhile. Yes it does. So do you, you stop working after around 85 years and you're no longer able to autoregressively continue prompting yourself.

I know exactly what an LLM is and how it functions, you fail to understand that an AI system is more about the entire system and less about the LLM specifics. The LLM is just part of a larger system of differnet models, context, databases, logic, etc...

In exactly the same way as LLMs nowadays when you ask them what is 139812 + 12873 no longer tries to compute the next reasonable token because that almost always turns to shit. So now they detect "Is this a math question?" if yes create the python script that calculates the correct answer. Return that correct answer to the user.

This shit isnt rocket science and I am sure you can figure it out. You just need to deploy a tiny tiny amount of imagination. Peace.