There's a lot of noise in the AI space right now. Too many people who don't know what they're doing are overcomplicating things, selling courses, and trying to intimidate you into buying their latest offering.
Forget that noise.
If you genuinely want to make money with AI on the side-whether through automation projects, consulting, or building AI-powered products-you don't need to be overwhelmed. You need clarity. And it turns out, there are really only 5 core concepts that matter.
1. Know Which Models Work Best for Which Tasks
Not all models are created equal. Some excel at text generation, others at image creation, some at reasoning, and others at code. Understanding the strengths and weaknesses of different models-whether that's GPT-4, Claude, Gemini, or specialised open-source alternatives-is fundamental.
This knowledge alone separates people who build things from people who fumble around hoping something works.
2. Master Cost/Benefit Analysis Across Models
Here's the business part: not every task needs the most expensive model.
Sometimes Claude Opus 4.5 is overkill. A cheaper model like GPT-3.5 or Claude's instant tier might do the job just fine and cut your costs in half. Learning to analyse which model gives you the best results for the lowest cost is how you actually build sustainable, profitable automations.
3. Know When to Start New Chats vs. Rely on Existing Context
This is a tactical skill that most people miss. Context windows matter. When does it make sense to maintain a long conversation thread vs. starting fresh? When should you include previous outputs in the context vs. reference them separately?
Getting this right dramatically improves both your outputs and your efficiency.
4. Master Consistent Output Formats for Workflows
This is where automation becomes possible. If you can get models to return structured, consistent data-JSON, markdown tables, CSV format, whatever your workflow needs—you can actually automate downstream processes.
This is the foundation of any serious AI workflow. Without it, you're manually processing outputs like it's 2015.
5. String Together Models (Output as Input)
The final piece is chaining models together. One model's output becomes another's input. This is the basis of agents-systems that can break down complex tasks, delegate them to specialised models, and combine the results.
This is where the real power lies. This is where you stop thinking like someone running a prompt and start thinking like an engineer.
Learn from People Who Actually Know Their Stuff
There's plenty of hype, but there are also genuinely smart people sharing real knowledge. If you want to skip the BS and learn from people who actually understand this space, here are some voices worth following:
The Bottom Line
Making money with AI isn't as complicated as the marketing world wants you to believe. Master these 5 concepts, follow people who actually know what they're talking about, and you'll be ahead of 95% of people trying to figure this out.
The noise is loud, but the signal is clear.
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