AI Wild Ride Trajectory Simulator

2026–2035 Horizon
Insp. by @kimmonismus: "We are in for a wild ride I guess."
Scenario Archetypes
Dynamic Control Levers
Recursive Feedback Rate (λ) 0.78
Compute Scaling Exponent 3.4x/yr
Energy Grid Capacity 45 GW
Inference Efficiency Mult. 5.2x
Institutional & Safety Drag 0.35
Non-Linear Phase Portrait (Runge-Kutta 4th Order) SOLVING ODE
Effective Capability (Index)
Grid Power Demand (GW)
Recursive Agent Density
Physical Grid Ceiling
Timeline Scrubber Inspection Year 2030.0 (Q1)
Calculating systemic dynamics across the decade...
Inspection Telemetry (@ 2030)
Frontier ELO Equivalency
4,120
Power Draw
38.4GW
Synthetic Data Share
84.2%
Agent Autonomy Ratio
68.5%
Bottleneck Cascade Status
⚡ Power Generation Wall CLEAR (38.4/45 GW)
🔬 High-NA Litho & CoWoS NOMINAL (+140% Cap)
🛡️ Verification Horizon STABLE (Margin +1.2)
🏛️ Economic Absorption FRICTION MODERATE
System dynamics differential model resolves non-linear state vector: dC/dt = α·C·(1 + λ·A)·(1 - P/P_max) - δ·D with Runge-Kutta integration.
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