Are Reasoning Models Worth It?

Dr. Ryan Ries here once again, and today I want to chat about one of the biggest reality checks the AI industry has gotten in a while.

While everyone's been losing their minds over "reasoning" AI models (the ones that supposedly think through problems step-by-step like humans), Apple just dropped a research bomb that changes everything.

Their scientists put these fancy new reasoning models head-to-head against regular ChatGPT-style AI. The results are pretty shocking.

The Great Reasoning Model Reality Check

Here's what Apple discovered when they actually tested these reasoning models:

On simple problems: Regular AI models actually performed better than the expensive "reasoning" ones.

On medium-complexity tasks: The reasoning models showed only marginal improvements, hardly worth the premium price tag.

On truly difficult problems: Both model types completely fell apart, but here's the kicker - the reasoning models often just gave up entirely when they had plenty of computational power left.

It's like buying a Ferrari that can't handle highway speeds. Sure, it looks impressive in your driveway, but when you actually need performance, it leaves you stranded.

What This Really Means

Apple’s research exposed a dirty secret. These "reasoning" models aren't actually reasoning at all. 

They're just really sophisticated pattern-matching machines that break down the moment they encounter something genuinely novel.

Think about it. True reasoning would get better as problems get more complex, not worse. But these models show the opposite pattern. They ramp up their "thinking" to a point, then completely collapse despite having all the resources they need.

The Business Impact You Need to Know

Here's where this gets expensive for companies jumping on the reasoning model bandwagon:

You're paying premium prices for models that perform worse on the hard problems you actually need solved.

The infrastructure costs for these "thinking" models are astronomical compared to standard LLMs.

ROI calculations that looked promising on paper are falling apart in real-world applications.

When Reasoning Models Actually Make Sense

Look, I'm not here to completely trash reasoning models. They do have their place, and Apple's study revealed some interesting sweet spots.

Medium-complexity problems with clear logical steps - Examples would be financial analysis, legal document review, or complex troubleshooting workflows where you need the model to show its work.

High-stakes applications where explainability matters - When you need to audit the AI's decision-making process, reasoning models' step-by-step approach can be invaluable.

Specialized domains with established reasoning patterns - Mathematical proofs, code debugging, or scientific hypothesis testing, where the reasoning path is as important as the answer.

But here's the key - even in these scenarios, you need to test rigorously. Don't assume the reasoning model will automatically perform better just because it "thinks" through the problem.

My Take: Match the Tool to the Job

Here's my practical take: for 90% of business applications, you're better off with a well-prompted standard model. But for that critical 10% where you need genuine step-by-step reasoning, the investment might make sense.

The real magic happens when you master the fundamentals first:

1. Clear problem definition - know exactly what output you need
2. Smart prompting frameworks - structure your requests for maximum effectiveness
3. Proper testing and iteration - validate performance on your actual use cases

The Framework That Actually Works

Instead of automatically defaulting to a certain model, try this decision framework:

• Start with standard models - they're cheaper, faster, and often more reliable for most tasks 
• Identify true reasoning needs - does your use case require step-by-step logical analysis? 
• Test both approaches - measure performance on YOUR specific problems, not generic benchmarks 
• Consider the explainability factor - do you need to show your work for compliance or trust? 
• Calculate total cost of ownership - including infrastructure, fine-tuning, and maintenance 
• Scale gradually - prove value in pilot projects before making big infrastructure investments

We've built hundreds of successful AI applications for clients using this approach, and the results speak for themselves.

What's Next?

This Apple study is a wake-up call for the entire AI industry. We've been so caught up in the reasoning hype that we forgot to ask the basic question: do these models actually solve real problems better?

The answer, for most use cases, is no.

My prediction: Companies that focus on solid fundamentals with standard models will outperform those who bet big on reasoning model hype. 

Have you been considering reasoning models for your business? Let me know - I'm curious to hear what specific problems you're trying to solve. Might save you some serious budget headaches.

Until next time,
Ryan

Now, time for our AI-generated image.

This week, I tried out two different models to turn an image of myself into a muppet. Grok didn’t quite understand the prompt... Great shirt though.