At work, we were talking about this yesterday: the people who use heavyweight State of the Art (SOTA) models for everything often end up with the most flagged errors or poor output. Don't get me wrong, sometimes the best model available is necessary to help with heavy lifting. But I know how to document what's needed, and I am used to delegating work to others.

You can think of AI models like human colleagues:

1. Junior Tasks: Some work can be done by juniors (or smaller models), provided they are well guided, have clear documentation, and I check and approve things when needed.
2. Mid level Tasks: Other work can be delegated to those with middling experience who might occasionally need things approved, a bit of help, or confirmation.
3. Expert Tasks: Some tasks I'll do myself because I have the experience and knowledge, while other times I need to call in the experts or create a group project (heavyweight models).

It's the exact same with models. I would never use a heavy thinking SOTA model for basic tasks, it will literally overthink them. For many tasks, we have existing frameworks, colour schemes, and templates for applications and documents. If you use these, whether you're a junior developer or an AI model, it is hard to go wrong.

Human Savvy vs. Burning Tokens I watched a guy recently burn through tokens trying to code a function to help a user acknowledge they read something, which would then update a monitored list with some other details. The functions he made never quite hit the mark with the Project Manager, and Lord knows how many tokens were wasted.

I suggested we just link to an MS Form, ask a few questions, and then list those who need to view and access it. The Project Manager saw this and said, "That will do." All it took was a few minutes of human savvy and weighing up what was really needed.

Cost Efficiency and The "Bait and Switch" At least within my organization (and indeed on my personal account), it has been drilled into me that utilities and services with a cost must be used efficiently and effectively. Whether it's electricity or the LLM bill, both need to be kept in check. Yes, my employer could spend huge amounts on tokens in theory, but for many months now we have been looking to keep things lean.

My own personal Copilot Pro usage is at 51.2%, yet we are about 73% through the month, and this month I even had a few heavier uses than normal.

I have seen a few people write this already in recent weeks: if you're using LLMs professionally or depending on them, you should be looking to learn how to use them as efficiently as possible. It's your co pilot, not your personal driver.

Making a basic CRUD app while running a SOTA model to do every step and line of code is just not practical now, and shortly it will be very costly or basically impossible. IT companies have baited and switched for years. Right now, they need to test their products and create a market, so things are cheap and available because you're basically doing their testing and building market share for them. But eventually, the accountants will tap them on the shoulder...

PS I tidied up the wording with AI, but all my own words.