99% of people are using Claude, Codex, Gemini and GPT like it's still 2023

Paying $20/month and getting $2 of value.

I've watched hundreds of devs and founders use these tools. The gap between the top 1% and everyone else isn't the model. It's how they drive it.

Here's the playbook the power users actually run.

What the 99% are doing wrong

One line prompts and praying ("write me a landing page"). Starting a fresh chat for every task, killing all context. Copy pasting code in and out like it's 2023. Treating Claude like Google with vibes. Never giving it the codebase, the goal, or the constraints. Accepting the first answer instead of pushing back. Using one model for everything. That's not using AI. That's tipping a genius $20 to fetch coffee. What the top 1% actually do

1. They write a project brief before they prompt.

Not "build me an app." They hand the model a one page spec: goal, user, constraints, success criteria, tech stack, and what NOT to do. Every senior engineer I see keeps a CLAUDE.md or AGENTS.md in their repo. It 10x's output overnight.

2. They use planning mode before code mode.

Ask the model to plan first. Critique the plan. Approve it. Then let it execute. You skip 80% of the rewrites.

3. They run the right model for the right job.

Claude Opus 4.6 and 4.7 for long horizon coding, refactors, agentic loops. GPT-5 and Codex for tight algorithms, math heavy work, fast iterations. Gemini 3.1 Pro for million token context, whole repo reviews, video and PDFs. Antigravity for autonomous multi file edits, browser and terminal in one loop. Grok for real time web reasoning and research. One tool for everything is the slowest possible workflow.

4. They chain tools like a pipeline.

Gemini reads the entire repo. Claude writes the diff. Codex runs the tests. ChatGPT explains it to the team. Stop loyalty to one chatbot. Start running a stack. Try different tools for different tasks and find which fits better for you.

5. They treat context like fuel.

Context engineering beats prompt engineering now. Every power user is doing some version of this: pinned project files, a system prompt that defines the AI's role, voice and rules, examples of good and bad output, the actual codebase loaded in (not pasted in chunks), and memory of past decisions so it stops repeating mistakes.

6. They push back instead of accepting.

"That's wrong. Here's why. Try again with X constraint." "Give me 3 versions and rank them." "What would a staff engineer say about this approach?" The model gets sharper the harder you push. Most people fold on the first try.

7. They use sub agents and parallel runs.

Spawn 3 agents. One writes. One reviews. One tests. Merge the best parts. Claude Code, Codex CLI and Antigravity all support this now. Almost nobody uses it.

8. They keep a prompt library.

The best builders probably have 20+ reusable prompts saved. Specs, reviews, refactors, naming, debugging, PR descriptions, cold emails, tweets. If you're rewriting the same prompt twice, you're losing. The actual mindset shift These models are not search engines. They are not autocomplete. They are junior staff with infinite patience and zero ego. Your job is to be the manager. Set the goal. Give the context. Review the output. Iterate. The people getting 10x results aren't smarter. They just stopped treating a $200B brain like a magic 8 ball. The play starting today

Write a CLAUDE.md or AGENTS.md for your main project this week. Pick 2 models, learn their edges, stop tab hopping. Plan before you prompt, always. Save your top 10 prompts somewhere you can find them. Push back twice before accepting any output.

Screenshot this. In 6 months the gap between people who do this and people who don't will look like the gap between people who learned to Google in 2005 and people who didn't.

The tools are ready. Most people aren't.