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> AI Moonshots: Building Revolutionary Technology with Lean Strategy
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- Fred Pope
- @fred_pope
Building Revolutionary Technology with Lean Strategy
What Clayton Christensen Would Say: Applying 'Seeing What's Next' to the AI Revolution
The Disruption Theory Lens on AI Implementation
Clayton Christensen's "Seeing What's Next" provides a powerful framework for understanding why the "one step back from bleeding edge" AI strategy isn't just safe—it's strategically brilliant. His theories of disruption, jobs-to-be-done, and value networks offer profound insights into why businesses should focus on practical AI applications rather than AGI moonshots.
Sustaining vs. Disruptive Innovation in AI
The AGI Race: A Sustaining Innovation Trap
Christensen would likely categorize the pursuit of AGI as a sustaining innovation—technology that serves existing customers in established markets with incrementally better performance. Tech giants pouring billions into AGI are fighting on the same performance trajectory, trying to make AI "smarter" in traditional ways.
Why this matters for your business: Sustaining innovations favor incumbents with deep pockets. Unless you're Google or OpenAI, you're playing a game you can't win.
Practical AI: The Disruptive Opportunity
The real disruption, Christensen would argue, comes from "good enough" AI applied to previously unserved or overserved markets. This is exactly what our blog post advocates—using proven AI capabilities to solve specific business problems.
The pattern Christensen would recognize:
- Lower performance on traditional metrics (not as "intelligent" as AGI)
- But superior on new metrics that matter to users (reliability, cost, implementation speed)
- Serves non-consumption—bringing AI to problems that couldn't afford cutting-edge solutions
The Jobs-to-be-Done Framework Applied to AI
Christensen's jobs-to-be-done theory asks: "What job is the customer hiring this product to do?" This framework reveals why practical AI implementation beats AGI pursuit:
Jobs Businesses Are Hiring AI to Do:
- Reduce decision-making time (healthcare diagnosis synthesis)
- Eliminate costly errors (manufacturing defect detection)
- Scale expertise (military targeting efficiency)
- Handle information overload (supply chain monitoring)
Jobs Businesses Are NOT Hiring AI to Do:
- Pass the Turing Test
- Achieve consciousness
- Replace human judgment entirely
- Solve problems we can't even define yet
Christensen's insight: Customers don't buy products; they hire them to do jobs. Businesses need AI that reliably does specific jobs, not AI that theoretically could do any job.
The Resources, Processes, Values (RPV) Framework
Christensen's RPV framework explains why established companies struggle with disruption—and why your AI strategy should differ from tech giants:
Tech Giants' RPV for AGI:
- Resources: Billions in capital, top researchers, massive compute
- Processes: Optimized for breakthrough research
- Values: Prioritize technological leadership and long-term bets
Your Business's RPV for AI:
- Resources: Limited budget, domain expertise, existing data
- Processes: Optimized for serving customers and efficiency
- Values: Prioritize ROI, reliability, and competitive advantage
The strategic insight: Your RPV isn't suited for the AGI race—but it's perfectly aligned for implementing practical AI solutions.
The Value Network Evolution
Christensen taught that disruptions create new value networks. In AI, we're seeing two distinct networks emerge:
The AGI Value Network:
- Customers: Researchers, governments, tech platforms
- Performance metrics: Benchmark scores, parameter counts, theoretical capabilities
- Cost structure: Billions in R&D, uncertain returns
The Practical AI Value Network:
- Customers: Businesses with specific problems
- Performance metrics: ROI, reliability, implementation speed, error reduction
- Cost structure: Predictable, scalable, immediate returns
Key insight: The practical AI value network is where new fortunes will be made, not in competing with incumbents in the AGI network.
The Non-Consumption Opportunity
Christensen's most powerful insight for our AI strategy: Focus on non-consumption.
Millions of business problems go unsolved not because AGI doesn't exist, but because existing AI solutions are:
- Too expensive
- Too complex
- Too unreliable
- Too disconnected from business needs
By implementing "good enough" AI for these underserved problems, you're not competing with Google—you're creating new markets.
The Innovator's Solution for AI
Based on Christensen's frameworks, here's your AI strategy:
1. Be Deliberately "Worse"
Don't compete on traditional AI metrics. Be worse at general intelligence but better at solving specific problems.
2. Target Non-Consumption
Find problems that aren't being addressed because cutting-edge AI is overkill.
3. Embrace Modularity
Use existing AI models as components rather than building monolithic systems.
4. Move Up-Market Gradually
Start with simple applications, then expand as the technology improves and costs drop.
5. Create Asymmetric Value
Deliver value on metrics incumbents don't prioritize: implementation speed, domain-specific accuracy, integration ease.
The Christensen Prediction
If Christensen were analyzing the AI landscape, he'd likely predict:
- The AGI leaders will overshoot customer needs, creating expensive solutions for problems that don't exist
- Practical AI implementers will capture value by solving real problems with "inferior" but adequate technology
- The disruptors will eventually move upmarket, using modular AI to challenge incumbents
- New business models will emerge around AI-as-a-service for specific industries
Your Competitive Advantage: Being "Behind"
Christensen would see being "one step back" not as falling behind but as positioning for disruption. While others chase performance metrics that don't matter to most customers, you can:
- Build solutions customers actually want
- Avoid the pioneer's arrows
- Let others subsidize R&D
- Focus resources on implementation, not innovation
The Bottom Line: Christensen's Wisdom for AI
"Seeing What's Next" teaches us to look for patterns of disruption. In AI, the pattern is clear:
- Sustaining trajectory: AGI pursuit by tech giants
- Disruptive trajectory: Practical AI solving specific jobs
- Winner's strategy: Focus on the job, not the technology
The question Christensen would ask isn't "How can we build the most advanced AI?" but "What job are customers struggling to get done that practical AI could help with?"
That's the race worth winning—and it's one where being "behind" on AGI puts you ahead on value creation.