Cut unverified GPT-4/Gemini-2 ranking claim; remove stale footer

README.md

@@ -30,7 +30,7 @@ The dominant assumption in AI development is that GPU acceleration is a prerequi
 
 We present evidence that this assumption is wrong for an important and growing class of models: sparse, geometrically-grounded architectures under two billion parameters.
 
-Our evidence is empirical, not theoretical. Over six months (September 2025 through March 2026), we trained 15 models across four distinct architecture families on a single AMD EPYC 9454P processor (48 cores, 96 threads, $3,450 retail). Total compute expenditure: $24. Average cost per model: $1.60. These models are publicly available on HuggingFace, accumulating organic downloads from real users. One model in a related family (SymbioticAI-1B) ranked directly below GPT-4 and Gemini 2 on contemporaneous benchmarks.
+Our evidence is empirical, not theoretical. Over six months (September 2025 through March 2026), we trained 15 models across four distinct architecture families on a single AMD EPYC 9454P processor (48 cores, 96 threads, $3,450 retail). Total compute expenditure: $24. Average cost per model: $1.60. These models are publicly available on HuggingFace, accumulating organic downloads from real users.
 
 We do not claim parity with frontier models trained on trillions of tokens across thousands of GPUs. We claim something more consequential: the marginal capability gained per dollar spent is orders of magnitude higher with our methodology than with standard GPU training. The efficiency gap is so extreme that it challenges the economic model underlying the entire AI industry.
 
@@ -436,6 +436,4 @@ Colca, R. (2025-2026). DISC: Discrepancy Calculus. Convergent Intelligence LLC:
 
 **Total Portfolio: 41 models | 2,781 total downloads**
 
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-*Last updated: 2026-03-28 12:58 UTC*
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