🚨Gamma hit a $2.1B valuation with $100M ARR—profitably—showing AI-native productivity apps can dethrone entrenched tools like PowerPoint. Expect more categories where creation, formatting, and distribution collapse into one AI-first flow.

Everyone wants to build the "next big thing."

Here's a truth that'll save you months: build things that are already successful and repeat what they've done using AI tools.

The easiest way to win is to replicate.

I'm not talking about copying someone's product and slapping your logo on it. I'm talking about using proven patterns as your starting point instead of inventing from scratch.

That Notion template that organized your life? Build a custom version for your specific workflow with Bolt. That onboarding flow that converted you into a customer? Replicate that pattern in your product using Cursor. The dashboard from your last job that everyone loved? Rebuild it in a weekend with Lovable.

You're not stealing -- you're learning the same way artists learn by copying masters.

Most original ideas fail because they're solving problems nobody has. Or solving real problems in ways users don't recognize as solutions.

Replicated ideas with a twist for your specific market? Those ship fast because the pattern is proven. Users understand them immediately because they've seen similar things before.

The "innovation" comes from applying proven patterns to new contexts, not reinventing basic UX from first principles.

A healthcare startup wanted to build "a revolutionary new patient portal." They spent 5 months designing custom workflows.

Users hated it. Too unfamiliar, too much learning curve.

They rebuilt it in 3 weeks by replicating the exact pattern that Stripe's dashboard uses. Navigation, layout, interactions -- all borrowed. Just different data.

Adoption went from 12% to 67% because it felt familiar.

You can literally show Cursor a screenshot and say "build me this layout but for customer data instead of financial data." It understands the pattern and adapts it.

Same with Bolt, Replit, Lovable, v0 -- they're all trained on thousands of existing UIs. Describing something that already exists is way easier than describing something novel.

"Make it look like Linear's project view" gets you 90% of the way there in minutes.

... is about patterns vs. pixels. Copying the pattern (sidebar nav, card-based layout, hover states) is learning. Copying the exact visuals, copy, and branding is theft.

Most successful products are just better executions of existing patterns anyway. Notion didn't invent documents or databases. Stripe didn't invent payment processing. They just did it better than what existed.

What's next: Expect AI tools to build pattern libraries where you can say "build me a dashboard in the style of Retool" or "create a landing page using the Webflow agency template pattern."

BTW: Y Combinator's advice has always been "copy something that works in one market and bring it to another market." AI tools just made that process 100x faster.

There is no better time to learn how to use AI to build solutions businesses want. I’m hosting a workshop that will show you, step-by-step, how to build tools and solutions using AI without having to know how to code.

If you’re interested, reply back to this email with the keyword BUILD and we’ll send you more info.

We are announcing the dates this week. If you replied BUILD, we will send you a personal email to join us.

The Recap: SoftBank and OpenAI’s 50-50 Japan joint venture is the clearest example yet of AI’s circular funding loop: a major investor partnering with its portfolio company to distribute enterprise AI under one roof. With the launch delayed from its original 2025 summer target and SoftBank approving a final $22.5B leg of its broader OpenAI investment contingent on corporate restructuring, the question is whether this structure creates net-new value—or just recycles capital and narrative.

Unpacked:

Bottom line: AI’s biggest deals increasingly bundle capital, distribution, and story. That can accelerate adoption—but it can also obscure whether anyone built a defensible business versus a finely tuned financial machine.

Wikipedia demands AI companies pay for API access instead of scraping—foundational infrastructure is pivoting business models as AI traffic replaces humans.

Lovable reaches 8M users with half of the Fortune 500 adopting—non-technical employees are becoming primary AI development users.

German court rules OpenAI cannot use copyrighted lyrics without licenses—first major EU precedent setting training-data boundaries.

Google’s confusing Gemini Home rollout reveals the pain of migrating legacy smart homes to LLM agents—consumer-scale infrastructure transitions are messy.

Apple reportedly delays second-gen iPhone Air after scaling back production—hinting at limits to forcing new form-factor adoption.

European scientists publicly tell the EU Commission to stop overhyping AI—pushing back on corporate narratives shaping policy.

Samsung adds Siri Shortcuts to SmartThings routines—ecosystem cooperation over walled gardens in the smart home race.

Contrarian perspective on the AI bubble argues for nuanced evaluation—separating durable value creation from speculation.

P.S. Reply back to this email and let us know what area of AI you are struggling with the most. We are going to be hosting some free trainings and want your input.

Best of AI™ Team

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