We are serious and transparent. Here is a simplified Python version of the skeleton model.

Help us make this a reality.

import numpy as np
import matplotlib.pyplot as plt

Initialize Parameters

years = 50 # Simulation period
P = 5 # AI Progress Rate (1-10 scale)
C = 10 # Computational Power Growth
S = 2 # Self-Improvement Factor
A = 1 # AI Autonomy Level
X = 8 # AI Safety & Control Measures (higher = more control)
threshold_ASI = 100 # ASI readiness score
w = [0.3, 0.2, 0.3, 0.2, 0.5] # Weights for ASI score calculation

Data storage

ASI_scores = []

for year in range(years):
# Update AI parameters (simple exponential growth model)
P += np.log1p(P) * 0.5
C += np.log1p(C) * 0.3
S += np.log1p(S) * 0.4
A += np.log1p(A) * 0.2
X -= np.log1p(X) * 0.1 # Control weakens over time unless reinforced

# Compute ASI Score
ASI_score = w[0]*P + w[1]*C + w[2]*S + w[3]*A - w[4]*X
ASI_scores.append(ASI_score)

if ASI_score >= threshold_ASI:
    print(f"ASI Achieved in Year {2025 + year}")
    break

Plot ASI readiness over time

plt.plot(range(len(ASI_scores)), ASI_scores, label=”ASI Readiness Score”)
plt.axhline(y=threshold_ASI, color=’r’, linestyle=’–‘, label=”ASI Threshold”)
plt.xlabel(“Years from 2025”)
plt.ylabel(“ASI Readiness Score”)
plt.legend()
plt.title(“ASI Readiness Over Time”)
plt.show()