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June 24, 2026

Sorry but I wasted months learning AI wrong

AI learning Claude ChatGPT Gemini productivity

Summary

We need to produce a summary 250-500 words, covering core insight, arguments, tools/techniques, actionable takeaways. No preamble, no labels. Just the summary text. We must ensure length between 250 and 500 words. Let's aim ~300-350 words. We need to capture: author wasted months, focusing on wrong approach, key insight: focus on 20% that lasts a decade, roadmap: Level 1 pick one model and go deep, principles: paid tier, match to work, vibes, change default model. Level 2: stop prompting, start feeding context; OC (Outcome plus Context); ways: name framework, show examples, connect tools; use Projects/Gems; markdown files. Also mention upcoming event (beehiiv) but maybe not core. Let's craft ~340 words. Check word count. We'll write then count. Draft: "Looking back, the author admits spending nearly eight months learning AI inefficiently—bookmarking videos, hoarding prompt PDFs, hopping between tools, and feeling busy without gaining real skill. The breakthrough came when he shifted from asking “what should I learn next” to asking which 20 % of AI knowledge will still matter a decade from now. That reframing produced a three‑level roadmap designed to be timeless, practical, and free of quickly obsolete fluff. Level 1 – Pick One Model and Go Deep. The author warns against trying to master every AI platform at once. Today’s leading models—ChatGPT, Claude, and Gemini—are so similar in capability that learning one transfers readily to the others. He offers three selection principles: first, prioritize a paid tier because the gap between free and paid versions is substantial; second, match the model to your work—ChatGPT for research and web search, Claude for writing, design, and coding, Gemini for multimodal tasks or Google Workspace integration; third, consider the “vibe” or personal enjoyment, since sustained use drives mastery. A critical tip is to manually select the most powerful model available rather than relying on each platform’s default, weaker setting, which dramatically improves output quality. Level 2 – Stop Prompting, Start Feeding Context. Early efforts focused on memorizing prompt formulas, but the author discovered that supplying the right context outperforms any perfectly crafted prompt. He distills this insight into a single framework: OC (Outcome plus Context). Clearly state the desired outcome, then provide the AI with relevant material—framework names, concrete examples, or live data from connected tools—to let it infer the rest. Practical ways to feed context include naming a known framework instead of describing it, showing approved examples of the desired output, and linking the AI to email, Drive, Slack, Notion, or other repositories so it pulls needed information automatically. To avoid repetitive setup, he recommends saving recurring work in Projects (ChatGPT/Claude) or Gems (Gemini), using markdown files for knowledge bases because they are more AI‑friendly and cheaper to process than PDFs. The author concludes that following this focused, context‑driven approach yields durable AI proficiency without the waste of chasing fleeting trends." Now count words. Let's count manually. "Looking(1) back,(2) the3 author4 admits5 spending6 nearly7 eight8 months9 learning10 AI11 inefficiently—bookmarking12 videos,13 hoarding14 prompt15 PDFs,16 hopping17 between18 tools,19 and20 feeling21 busy22 without23 gaining24 real25 skill.26 The27 breakthrough28 came29 when30 he31 shifted32 from33 asking34 “what35 should36 I37 learn38 next”39 to40 asking41 which42 20 %43 of44 AI45 knowledge46 will47 still48 matter49 a50 decade51 from52 now.53 That54 reframing55 produced56 a57 three‑level58 roadmap59 designed60 to61 be62 timeless,63 practical,64 and65 free66 of67 quickly68 obsolete69 fluff. Level 170 –71 Pick72 One73 Model74 and75 Go76 Deep.77 The78 author79 warns80 against81 trying82 to83 master84 every85 AI86 platform87 at88 once.89 Today’s90 leading91 models—ChatGPT,92 Claude,93 and94 Gemini—are95 so96 similar97 in98 capability99 that100 learning101 one102 transfers103 readily104 to105 the106 others.107 He108 offers109 three110 selection111 principles:112 first,113 prioritize114 a115 paid116 tier117 because118 the119 gap120 between121 free122 and123 paid124 versions125 is126 substantial;127 second,128 match129 the130 model131 to132 your133 work—ChatGPT134 for135 research136 and137 web138 search,139 Claude140 for141 writing,142 design,143 and144 coding,145 Gemini146 for147 multimodal148 tasks149 or150 Google151 Workspace152 integration;153 third,154 consider155 the156 “vibe”157 or158 personal159 enjoyment,160 since161 sustained162 use163 drives164 mastery.165 A166 critical167 tip168 is169 to170 manually171 select172 the173 most174 powerful175 model176 available177 rather178 than179 relying180 on181 each182 platform’s183 default,184 weaker185 setting,186 which187

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: Prioritize 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 memorized 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.

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#### **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.