In this episode of the AI Briefing, Tom challenges the LLM hype cycle and explains why traditional machine learning models and statistical approaches often outperform large language models for data processing tasks. Learn when to use LLMs appropriately versus more efficient, cost-effective alternatives.

Episode Show Notes

Key Topics Covered

The LLM Hype Cycle Reality Check

Why LLMs aren't always the answer for data processing
The hidden costs of using LLMs for inappropriate tasks
Understanding when simpler solutions outperform complex AI

Traditional AI & ML Still Matter

Statistical models and their advantages over LLMs
Machine learning frameworks that have existed for decades
Why efficiency matters in production environments

The Data Science Knowledge Gap

Why you can't skip understanding data science fundamentals
The risks of asking LLMs to generate models without validation
How to determine if your model matches your question type

Making Smart Technology Choices

Evaluating total cost of ownership for AI solutions
Balancing innovation with practical efficiency
Questions to ask before implementing LLMs in your pipeline

Main Takeaways

Not every problem needs an LLM - Traditional machine learning models and statistical approaches often work better for structured data analysis
Know your fundamentals - Understanding data science basics is crucial, even when using AI assistants to generate code
Consider total cost - LLMs can be expensive to run at scale; evaluate whether simpler solutions offer better ROI
Use the right tool - Match your technology choice to your specific use case, not to current trends
Avoid the hype trap - Don't implement AI just because management wants "AI-powered" solutions

Resources Mentioned

PyTorch (ML framework)
Claude AI
GitHub Copilot/Codex

Contact

Need help evaluating your AI strategy? Tom is available for consultations on choosing the right tools for your data pipeline.

This is the AI Briefing with Tom - practical insights on AI implementation without the hype.

Chapters

0:00 - Introduction: Beyond the LLM Hype
0:37 - The Problem with Using LLMs for Everything
1:01 - Traditional ML Models: Better Solutions for Structured Data
1:38 - The Data Science Knowledge Requirement
2:25 - Making Smart AI Technology Choices
3:15 - Cost Considerations and Final Thoughts

What is The AI Briefing?

The AI Briefing is your 5-minute daily intelligence report on AI in the workplace. Designed for busy corporate leaders, we distill the latest news, emerging agentic tools, and strategic insights into a quick, actionable briefing. No fluff, no jargon overload—just the AI knowledge you need to lead confidently in an automated world.

Hi folks happy Thursday my name is Tom

and this is the AI briefing.

Today I want to have a quick chat about fact.

I didn't know there's been an awful lot

of different types of AI been

around for many many years.

Today I just want to touch upon like

some of the obvious things that people during

this LLM hype cycle may have missed but

also because there's different use cases and different

reasons you would use different stuff inside of

your data processing pipeline.

Now in the year of 2026 and everything

being LLMs of course it's easy to chuck

an LLM a whole bunch of data and

ask it to be able to process that

data come up with a framework to be

able to deal with it etc etc etc.

Now that works pretty well to a degree

but there's also like better ways of just

dealing with data.

So for example if you're trying to get

insights from regular data an

LLM is not necessarily the best way to do it.

There has been many models stats models machine

learning models a bit around the decades that

are far more effective and efficient than getting

the data you're getting the answers from the

data that you require.

Now of course you need to have

some sort of comprehension as to whether or

not the machine learning model you're writing actually

makes any sense and is giving you the

answers that you need and that requires some

element of data science.

You cannot do these things without having some

semblance of knowing what you're doing because even

if you said okay Claude or okay Codex

here's some data.

Now create me a model that allows me

to analyze this data on a regular basis

and I would like to have it written in PyTorch.

There is no actual it's

not determined that what you're going to get

out the far end is even the right

type of model for the right type of

question you're asking because at the end of

day it's an LLM who's writing code so

how do you determine the data that you're

like actually processing is the correct data and

so when it comes to jumping

a ball of the hypestrain getting up to

speed with LLMs make sure that you're using

LLMs for the right reasons and not just

because it's a shortcut to get something done

because if you're doing the latter you're probably

going to come a cropper at some point

and you're gonna have to make that investment

in data science and data analytics anyway.

So bear that in mind and next time

you start spinning up a data processing environment

ask yourself I'm a paying too much because

I'm using an LLM to do processing is

an LLM really the right tool for the

job here or is there actually something that's

a bit more boring a bit more traditional

a bit more like a stats model or

a database or something sensible that I could

actually use to get this done and a

total cost of ownership over a period of

time ends up being demonstratively lower.

Just something to think of course I do

enjoy LLMs I do enjoy everything that's going

on at the moment so I'm not this

isn't a poo -poo on LLMs I just

want to make sure that people use them

for the right reasons and not just because

everything is powered by AI these days and

that is the requirement from their bosses to

go and build out.

Bear that in mind have a think about

it if you need any help or support

feel free to reach out I'd love to

have a chat.

My name is Tom this has been the

AI briefing and I will see you next time.