The REAL AI gap isn’t between models it’s between users…

Between o1 Pro and o3-mini, DeepSeek R1, and Gemini 2.0 Flash Thinking, there’s been a lot of new AI tools launched in the last few months.

Sure, some perform better here or there on certain benchmarks, but generally speaking, there’s been a leveling of the playing field between the top providers. And with Claude’s new model coming soon, we think that’s likely to remain the case.

That’s why I think it's worth double-clicking on this piece from The Algorithmic Bridge that argues the performance gap between AI experts and novices is growing faster than the gap between AI models themselves.

Here’s the key points:

• The performance gap between good and bad AI users is growing with each new model.
• Basic prompting gets basic results, even from advanced models.
• But expert prompting can squeeze 2-10x more value from the same AI.
• Your ability to ask good questions will soon matter more than the model you use.

This part is REALLY relatable—these days, most of us hit an “intelligence ceiling” where we can't even think of hard enough questions for new AI models to test.\

Think of it like this: Twenty years ago, everyone got access to Google. But the people who really learned how to search, filter, and verify information had a massive advantage (we still think this is true, TBH. That’s why using Perplexity, ChatGPT search, and Deep Research are huge resources—IF you know how to verify their results).

Now, the same thing is happening with AI—except this time, the gap between experts and novices is even bigger.

The good news? There's still time to get ahead of the curve. Most people are still using AI like a basic calculator when it’s actually a supercomputer. Don't be one of them.

Now here's the key insight: Ethan Mollick argues that while everyone's waiting for artificial general intelligence (AI as smart as humans in all domains), narrow AI is quietly revolutionizing specific fields.

For professionals, this means the opportunity isn't in becoming an “AI generalist”—it's in mastering how AI can enhance your specific domain expertise. The most successful AI users aren't trying to automate everything; they're becoming dramatically better at specific, high-value tasks.

What this means for you:

1. Learn Prompt Engineering Now:
   1. Start with basic templates and frameworks.
      1. Here’s some templates for Claude and tips for ChatGPT.
      2. We’ve got an intro to ChatGPT course that we’ll update soon.
   2. Practice giving your AI clear context and instructions.
   3. Learn what techniques work best for each model, and what model is best for your specific needs.
      1. For instance, GPT 4o and Claude Sonnet still need chain-of-thought and step-by-step prompting, where o1 and other reasoning models don’t.
2. Focus on Your Domain:
   1. Build prompting skills specific to your field.
   2. Consider all of your repeated tasks. Which ones could AI handle?
   3. Combine AI outputs with your professional judgment.
3. Track What Works:
   1. Keep a “prompt library” of your best AI interactions—you can use ChatGPT Projects or Claude Projects to do this.
   2. Note which approaches work best for different tasks.
   3. Share techniques with colleagues—even if you’re using AI in secret.