Watch/Read Part 1 here

Shrinath V chats with Sangeet Paul Choudary, author of Reshuffle: Who wins when AI Restacks the Knowledge Economy

Choudary, a leading thinker on platform economics and digital ecosystems, is Senior Fellow at UC Berkeley, a Dartmouth Scholar, and advisor to global C-suites.

Shrinath leads The Salient Advisory, mentors startups through Google for Startups, and contributes to Founding Fuel.

8 Key Takeaways 

1. Redefining "Where to Play and How to Win": The fundamental strategic questions shift dramatically with AI. Traditional industry boundaries collapse as capabilities from different sectors can now be combined.

• Auto manufacturers are no longer just in the “auto” industry—car-generated data can be used by insurance companies, driving schools, and energy providers. Cars can also serve as energy generators and providers, as demonstrated when Nissan Leaf cars powered entire neighbourhoods during Japan's nuclear disaster.

2. Asset Repurposing and New Business Models: AI enables assets to be repurposed across industry boundaries into fundamentally new use cases.

• Retailers converting stores into dark stores for new fulfillment models
• Power industry transformation where power generation assets now provide data on asset performance, changing maintenance contracts between owners and operators
• Usage-based car insurance in India where companies offer premiums based on actual driving data rather than fixed payments

3. Knowledge as Building Blocks: AI transforms tacit knowledge trapped in people's heads into modular, reusable building blocks—bringing together multiple capabilities to design a new solution.

• Bose’s failed attempt to apply audio expertise (damping vibrations) to car suspension systems for a better driving/passenger experience is an example of a company reimagining an existing capability for a new context.
• Kodak vs. Fujifilm offers an example of domain expertise available as building blocks for others to use: while Kodak failed to pivot, Fujifilm took the same chemical expertise and successfully applied it to cosmetics and other industries. Fujifilm still moved as a component provider and somebody else who knows the context and the complimentary technologies and capabilities, needed to make a solution out of that.

4. Build vs. Buy vs. Partner Framework: The framework depends on two key factors:

• Centrality to differentiation: How critical is the technology to your competitive advantage?
• Path to commoditization: How quickly will the technology become commoditized?
  • Example: Using Stripe for payments works because payments aren't your primary differentiation—you're not competing on payment processing.

5. Personal Strategy in the AI World: The common refrain “AI won't take your job, but someone using AI will” is insufficient. Technology changes entire systems, not just tasks.

• The Typist Lesson: When word processors arrived, typists thought they just needed to learn the new technology. But the entire profession disappeared—not because technology took over typing (we still type today), but because typing became so easy that everyone could do it. The real value of typists was managing the cost of editing on typewriters, which became irrelevant with word processors.

6. Three Sources of Job Value

Jobs derive value from three factors:

• Rare skills that are difficult to find
• Coordination abilities—managing workflows across stakeholders
• Risk assumption—taking responsibility for decisions and outcomes

7. Career Strategy for Different Stages

Early Career Professionals: The linear career path is dead. Instead, adopt a continuous cycle of learning, signaling, and applying skills. Don't just chase the highest-paying job—prioritize opportunities that offer learning and future signaling power.

Mid-Career Professionals: Focus on understanding how systems around you are changing—which teams are gaining power, which jobs are becoming more valuable, and why.

8. Portfolio Career Approach: The arbitrage game of jumping between companies for salary increases (common from late 1990s to 2010s) won't work in the rapidly changing AI landscape. Think entrepreneurially about your career as a portfolio that balances:

• Learning new relevant skills—embrace continuous adaptation

• Signaling your capabilities to the market

• Monetizing current abilities

Follow the full package here for Part 1, Shrinath's insights from the book, and an exract