Hey
I have a real confession.
When I started learning AI, I wasted close to 8 months doing it completely wrong.
I bookmarked 50 YouTube videos. I saved threads about "secret prompts." I downloaded prompt PDFs I never opened. I jumped between ChatGPT, Claude, Gemini, and every shiny new tool that launched that week.
I felt busy. I felt informed.
I was neither.
Because here is what nobody told me: most AI content out there is either outdated within months or pure theory you will never actually use. I was collecting information, not building skill.
The turning point came when I stopped asking "what should I learn next" and started asking "what 20% of this will still matter a decade from now."
That question changed everything. And today, I am giving you the answer.
If I had to learn AI from scratch in 2026, this is the exact roadmap I would follow. Three levels. Each one builds on the last. No fluff, no theory, nothing that expires next month.
#### **Level 1: Pick One Model and Go Deep**
This is where I made my first big mistake. I tried to learn every AI tool at once.
Do not do that.
Here is why one is enough. The top models used to be far apart in capability. Today they are clustered so close together that the difference for the average user is negligible.
And because every AI company copies every other AI company, they all share the same core features. Projects, memory, file uploads, connectors. Learn them once, and the skill carries straight over to the rest.
So which one do you pick? Realistically, you have three serious choices: ChatGPT, Claude, or Gemini.
Use these three principles to decide:
Principle 1: Prioritise paid tiers. If you are on free ChatGPT but your job gives you paid Gemini, go deep on Gemini. The gap between free and paid is night and day. Do not learn AI on the weakest version of it.
Principle 2: Match the AI to your work. ChatGPT is the most mature, has the most tutorials, and is excellent at web search and research. Claude is the strongest at writing, design, and coding, and coding matters even if you are not technical because data analysis and diagrams all run on code under the hood. Gemini is the pick if you work across text, images, audio, and video, or if you live inside Google Workspace.
Principle 3: Vibes. I know it sounds unserious. It is not. Each AI has its own personality, and the more you enjoy using yours, the more you will use it, and the better you will get. Switching later is easy anyway. All three now have memory import features.
One more thing, and this single tip is worth the whole email.
Change your default model. Every AI company defaults you to their weakest model because it is the cheapest for them to run. For any real work, manually select the most powerful model you have access to. The difference is not small. The powerful models break down your request, map out steps, and catch nuances you never thought to mention.
I used the default model for months without knowing this. Do not repeat my mistake.
#### **Level 2: Stop Prompting. Start Feeding Context.**
Notice I have not mentioned prompting frameworks yet. That is on purpose.
For my first few months, I memorised prompt formulas like exam answers. Role, task, format, tone. I treated prompting like a magic spell.
Then I realized something that made all those saved prompt PDFs useless.
The right context beats the perfect prompt. Every single time.
Here is a simple example. Say you need to find a restaurant for your boss. You could spend 10 minutes writing a detailed prompt describing everything you think your boss likes. Or you could paste a list of restaurants your boss has loved in the past and let the AI figure out the pattern.
The second approach wins every time. The list is context. Context carries information you would never think to write out.
So forget the 50 frameworks. There is only one worth remembering: **OC. Outcome plus Context.**
Tell the AI what you want. Give it the material to work from. It infers the rest, often better than you would have specified it yourself.
Three ways to feed it the right context:
1. Name a framework instead of explaining it. Saying "rewrite this using the pyramid principle" carries more context than a full paragraph describing what you want. Do not know the right framework? Ask the AI for options first, then pick one.
2. Show real examples of what good looks like. Instead of describing the format and tone for a weekly update, paste the last 2 or 3 updates that got approved, add your raw notes, and say "write this week's update in the same format." Examples contain everything you forget to say.
3. Connect your tools. Your best context already lives in your email, your Drive, your Slack, your Notion. Connect them, and the AI pulls what it needs directly. No more downloading and re-uploading files like it is 2023.
And then save all of it.
This is what **Projects** are for (Claude and ChatGPT call them Projects, Gemini calls them Gems). A Project is a permanent home for recurring work. Your instructions, your reference files, your examples, all saved once. Every new conversation inside it already knows your setup. You stop repeating yourself forever.
Quick pro tip: use markdown (.md) files in your Project knowledge instead of PDFs whenever possible. They are easier for the AI to read and cheaper to process. You can even ask the AI to convert your PDFs into markdown.
#### **Level 3: Connect Everything Into an AI System**
This is the level most people have not reached yet. Honestly, I only got here recently myself.
Here is the problem with Projects: each one is a silo. My workout Project cannot see what is inside my health checkup Project, even though it obviously should.
An AI system fixes that. It does two things Projects cannot:
First, it connects the dots across everything. When I migrated my separate health, supplements, and workout Projects into one system, the AI cross-referenced my latest checkup with my training plan, flagged that I had zero cardio days despite borderline high cholesterol, and recommended adding cardio to my rest days. No single Project could have caught that.
Second, it learns from your feedback and compounds. My favorite move is called "reconcile." The AI drafts something, I edit it heavily, then I say "reconcile my final version with your initial draft." It dissects every change I made and saves the rules for next time. The more feedback I give, the fewer instructions I need. The system literally gets smarter the longer I use it.
Your three options for building one, from easiest to most powerful:
Gemini Spark is the most beginner friendly. It is already connected to Gmail, Calendar, and Drive, so setup is minimal. The tradeoff is less control.
Claude Cowork is built specifically for non-technical people. More control than Spark, with a small amount of setup. This is the sweet spot for most readers of this newsletter.
Claude Code and OpenAI Codex are Cowork on steroids. Fully customizable and extremely powerful, but you need to be somewhat comfortable around code.
A funny tell for which one fits you: look at the model selector. Codex gives you a dozen intimidating options. Cowork gives you a few. Spark gives you none. Pick the one that matches your comfort level and grow from there.
#### **The Recap and My final thoughts…**
Level 1: Pick one of the big three. Use the paid tier if you can. Always select the most powerful model. Go deep, the skills transfer.
Level 2: Stop memorizing prompts. Outcome plus context wins. Save your recurring context in Projects so you never repeat yourself.
Level 3: Connect your Projects into one AI system that spots patterns across your life and compounds every time you give it feedback.
Most people are not at level 3 yet. Honestly, most people are barely at level 1. And that is fine. There is no rush.
But here is the thing I wish I understood 8 months earlier: the gap between using AI and using AI well is invisible. You cannot see it on anyone's screen. You only see it in their results.
Now you know exactly where you stand and what comes next.
Stay curious, talk to you tomorrow.