Episode
232 : Why most predictions go wrong ? #bitcoin #agenticAI #flyingCars - to name a few
- Podcast
- Deep Dive with Gemini
- Published
- May 5, 2026
- Duration seconds
- 2117
- Processing state
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Summary
Research #OrthogonalPresent the core concept that the "Now" is not a point on a simple linear timeline, but a high-dimensional coordinate where millions of trajectories from the past and future converge. Because multiple forces act on this single point, they create a resultant reality that is orthogonal to the vectors that created it, making the present inherently unknowable in real-time. #TemporalPhysics This refers to the analytical framework explaining why visionary and open-ended predictions consistently fail to materialize on schedule . It highlights that properly analyzing our timeline requires acknowledging both the "pull" of the future and the heavy, immovable reality of the past. #PastVector This symbolizes the massive momentum or "freight train" of realized history moving toward the future. Forecasting models frequently fail because they ignore this powerful inertia—such as 100 years of urban zoning, organizational culture, or 500 years of financial history—which heavily constrains current motion. #FutureVector This stands for the pull of potentiality moving toward the past . It embodies the trajectory of what could happen, driven by human desires, mathematical scarcity (like Bitcoin halvings), and technological breakthroughs. Analysts often accurately identify this vector but mistakenly assume it will shift reality without resistance. #DoubleConeModel This describes the structural mapping of time where every concept, social movement, or technology exists as a pair of spiraling cones meeting at the present . The future cone radiates outward into potentiality, while the past cone radiates backward to represent the historical path dependencies and ancestral elements that lock in current constraints.