You Can't Compete on Cheap Models Anymore
The Paradox of AI: Better Tools, Same Results
Introduction to the Issue
- Observations indicate that while AI tools are improving and becoming cheaper, outputs from various users appear increasingly similar.
- This phenomenon is not merely a tooling issue; it stems from a shift in where value is derived in the execution process.
Case Study: Hashimoto's Experiment
- Mitchell Hashimoto, co-founder of HashiCorp, tested different AI models for common engineering tasks.
- Despite varying costs and times, all models produced comparable results for standard tasks, leading to discussions about routing work to cheaper models.
The Value Shift
- As execution becomes commoditized with cheaper models, the critical question shifts to where value moves next.
- Hashimoto's second test involved a complex task that only the more expensive model could handle effectively, highlighting the importance of imagination in task selection.
Imagination vs. Execution
Understanding Task Assignment
- Unique tasks arise not from existing processes but from an expert's insight into new possibilities enabled by advanced tools.
- AI can execute predefined tasks but lacks the ability to determine which tasks are worth pursuing; this limitation emphasizes the need for human imagination.
The Role of Execution
- While execution is crucial and should be optimized, it must be paired with imaginative thinking to unlock true potential.
- A strong execution strategy should incorporate targeted applications of advanced models for transformative questions rather than just routine tasks.
Convergence of Outputs
Commonality in Tasks
- Many users rely on shared prompts and public playbooks leading to converging outputs across different platforms.
- This convergence indicates that differentiation relies heavily on human creativity rather than solely on tool capabilities.
Historical Context: Blackberry vs. Apple
- Blackberry excelled at executing known smartphone features but failed due to lack of innovative vision compared to Apple's reimagining of what a phone could be.
The Importance of Imagination
Evaluating Your Task List
- Reflect on whether your task list has evolved recently or if you are simply optimizing old routines with new tools.
Defining Imagination
- Imagination isn't an exclusive trait; it's about understanding capabilities through hands-on experience with tools like AI models.
Exploring New Possibilities
Example Use Case: Fable 5 Application
- Fable 5 can analyze geographic data for marketing purposes by identifying properties suitable for specific enhancements based on sunlight exposure.
Complexity and Feasibility
- While initial idea generation may require advanced models, subsequent executions can often utilize cheaper alternatives once concepts are validated.
Redesigning Processes
Historical Analogy: Electrification in Factories
- Just as factories struggled initially after electrification due to outdated layouts, companies today risk missing out by sticking with traditional workflows when integrating AI technologies.
Stripe’s Success Story
- Stripe successfully migrated extensive code changes rapidly due to prior investments in infrastructure and team training tailored for such transformations.
Cultivating Imagination Within Teams
Empowering Employees
- Organizations must ensure that employees who understand context have permission and resources necessary to explore innovative uses for technology.
Conclusion: Shifting Focus from Cost Savings
- True transformation comes not just from cost savings but also from fostering an environment where imaginative problem-solving thrives alongside efficient execution strategies.