Problem-Driven AI Learning

Shaan Puri shares his approach to learning about AI by focusing on practical application rather than constant information consumption.

Problem-Driven AI Learning Approach

• Focuses on applying AI to solve actual problems rather than abstract learning
• Uses an "intermittent fasting" style model for AI information consumption
• Avoids the "constant whiplash" of reacting to every new demo, model, or tool
• Makes AI useful by incorporating it into his solution process for real problems

Key Elements of This Approach

• Adds AI to his "solution list" when facing a problem
  • "When I have a problem I now add it into my sort of solution list like 'oh do I think AI could solve this'"
  • Research becomes "just in time" for actual problems rather than theoretical
• Learns incrementally through practical application
  • Each problem-solving attempt teaches something valuable about AI capabilities
  • Avoids "intellectual jacking off" of trying to keep up with everything

Dedicated Learning Periods

• Carves out specific times for deep AI learning (similar to "think weeks")
  • Clears calendar for dedicated AI exploration
  • Creates focused immersion without constant distraction
• Compares this to batch-checking email rather than constant checking
  • "If you just batch your email and check it once at noon and once at 8pm, you're totally up to date but didn't have this nervous energy constantly doing it"

AI's Market Potential

• Views AI as replacing both software and labor
• Believes AI will create entirely new markets by making services affordable that weren't before
  • "Things I wouldn't have otherwise hired people for, I'm willing to pay AI a little bit for"
• Compares AI's potential to how Uber expanded beyond the taxi market by creating new use cases

Balancing Knowledge and Overwhelm

• Recognizes the risk of under-investing time in AI learning
• Avoids information overload by being selective about what to learn
• Focuses on practical applications that deliver immediate value