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README.md

@@ -10,13 +10,11 @@ on 2/6/2026.
 
 ## Abstract
 
-AI governance conversations tend to focus on "frontier" models—systems assumed to be far ahead in performance and capabilities,
-implicitly the largest and most expensive.
-Recent developments challenge this framing: open-weight models trained for significantly less now match the priciest flagship commercial offerings benchmarks,
-and even smaller alternatives approach these on specific applications. 
-Admittedly, these numbers don't capture everything: users continue choosing proprietary systems over cheaper options.
-The most likely explanation is the trillions of tokens of user data commercial providers amass; and use to further train their models.
-Consequently, addressing "advanced" AI risks will require focusing on the incentives and consequences of these data practices rather than just model scale and evaluation metrics.
+𝘈𝘐 𝘨𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦 𝘤𝘰𝘯𝘷𝘦𝘳𝘴𝘢𝘵𝘪𝘰𝘯𝘴 𝘵𝘦𝘯𝘥 𝘵𝘰 𝘧𝘰𝘤𝘶𝘴 𝘰𝘯 "𝘧𝘳𝘰𝘯𝘵𝘪𝘦𝘳" 𝘮𝘰𝘥𝘦𝘭𝘴—𝘴𝘺𝘴𝘵𝘦𝘮𝘴 𝘢𝘴𝘴𝘶𝘮𝘦𝘥 𝘵𝘰 𝘣𝘦 𝘧𝘢𝘳 𝘢𝘩𝘦𝘢𝘥 𝘪𝘯 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦 𝘢𝘯𝘥 𝘤𝘢𝘱𝘢𝘣𝘪𝘭𝘪𝘵𝘪𝘦𝘴, 𝘪𝘮𝘱𝘭𝘪𝘤𝘪𝘵𝘭𝘺 𝘵𝘩𝘦 𝘭𝘢𝘳𝘨𝘦𝘴𝘵 𝘢𝘯𝘥 𝘮𝘰𝘴𝘵 𝘦𝘹𝘱𝘦𝘯𝘴𝘪𝘷𝘦.
+𝘙𝘦𝘤𝘦𝘯𝘵 𝘥𝘦𝘷𝘦𝘭𝘰𝘱𝘮𝘦𝘯𝘵𝘴 𝘤𝘩𝘢𝘭𝘭𝘦𝘯𝘨𝘦 𝘵𝘩𝘪𝘴 𝘧𝘳𝘢𝘮𝘪𝘯𝘨: 𝘰𝘱𝘦𝘯-𝘸𝘦𝘪𝘨𝘩𝘵 𝘮𝘰𝘥𝘦𝘭𝘴 𝘵𝘳𝘢𝘪𝘯𝘦𝘥 𝘧𝘰𝘳 𝘴𝘪𝘨𝘯𝘪𝘧𝘪𝘤𝘢𝘯𝘵𝘭𝘺 𝘭𝘦𝘴𝘴 𝘯𝘰𝘸 𝘮𝘢𝘵𝘤𝘩 𝘵𝘩𝘦 𝘱𝘳𝘪𝘤𝘪𝘦𝘴𝘵 𝘧𝘭𝘢𝘨𝘴𝘩𝘪𝘱 𝘤𝘰𝘮𝘮𝘦𝘳𝘤𝘪𝘢𝘭 𝘰𝘧𝘧𝘦𝘳𝘪𝘯𝘨𝘴' 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦 𝘢𝘤𝘳𝘰𝘴𝘴 𝘣𝘦𝘯𝘤𝘩𝘮𝘢𝘳𝘬𝘴,
+𝘢𝘯𝘥 𝘦𝘷𝘦𝘯 𝘴𝘮𝘢𝘭𝘭𝘦𝘳 𝘢𝘭𝘵𝘦𝘳𝘯𝘢𝘵𝘪𝘷𝘦𝘴 𝘢𝘱𝘱𝘳𝘰𝘢𝘤𝘩 𝘵𝘩𝘦𝘴𝘦 𝘰𝘯 𝘴𝘱𝘦𝘤𝘪𝘧𝘪𝘤 𝘢𝘱𝘱𝘭𝘪𝘤𝘢𝘵𝘪𝘰𝘯𝘴. 𝘈𝘥𝘮𝘪𝘵𝘵𝘦𝘥𝘭𝘺, 𝘵𝘩𝘦𝘴𝘦 𝘯𝘶𝘮𝘣𝘦𝘳𝘴 𝘥𝘰𝘯'𝘵 𝘤𝘢𝘱𝘵𝘶𝘳𝘦 𝘦𝘷𝘦𝘳𝘺𝘵𝘩𝘪𝘯𝘨: 𝘶𝘴𝘦𝘳𝘴 𝘤𝘰𝘯𝘵𝘪𝘯𝘶𝘦 𝘤𝘩𝘰𝘰𝘴𝘪𝘯𝘨 𝘱𝘳𝘰𝘱𝘳𝘪𝘦𝘵𝘢𝘳𝘺 𝘴𝘺𝘴𝘵𝘦𝘮𝘴 𝘰𝘷𝘦𝘳 𝘤𝘩𝘦𝘢𝘱𝘦𝘳 𝘰𝘱𝘵𝘪𝘰𝘯𝘴.
+𝘛𝘩𝘦 𝘮𝘰𝘴𝘵 𝘭𝘪𝘬𝘦𝘭𝘺 𝘦𝘹𝘱𝘭𝘢𝘯𝘢𝘵𝘪𝘰𝘯 𝘪𝘴 𝘵𝘩𝘦 𝘵𝘳𝘪𝘭𝘭𝘪𝘰𝘯𝘴 𝘰𝘧 𝘵𝘰𝘬𝘦𝘯𝘴 𝘰𝘧 𝘶𝘴𝘦𝘳 𝘥𝘢𝘵𝘢 𝘤𝘰𝘮𝘮𝘦𝘳𝘤𝘪𝘢𝘭 𝘱𝘳𝘰𝘷𝘪𝘥𝘦𝘳𝘴 𝘢𝘮𝘢𝘴𝘴; 𝘢𝘯𝘥 𝘶𝘴𝘦 𝘵𝘰 𝘧𝘶𝘳𝘵𝘩𝘦𝘳 𝘵𝘳𝘢𝘪𝘯 𝘵𝘩𝘦𝘪𝘳 𝘮𝘰𝘥𝘦𝘭𝘴.
+𝘊𝘰𝘯𝘴𝘦𝘲𝘶𝘦𝘯𝘵𝘭𝘺, 𝘢𝘥𝘥𝘳𝘦𝘴𝘴𝘪𝘯𝘨 "𝘢𝘥𝘷𝘢𝘯𝘤𝘦𝘥" 𝘈𝘐 𝘳𝘪𝘴𝘬𝘴 𝘸𝘪𝘭𝘭 𝘳𝘦𝘲𝘶𝘪𝘳𝘦 𝘧𝘰𝘤𝘶𝘴𝘪𝘯𝘨 𝘰𝘯 𝘵𝘩𝘦 𝘪𝘯𝘤𝘦𝘯𝘵𝘪𝘷𝘦𝘴 𝘢𝘯𝘥 𝘤𝘰𝘯𝘴𝘦𝘲𝘶𝘦𝘯𝘤𝘦𝘴 𝘰𝘧 𝘵𝘩𝘦𝘴𝘦 𝘥𝘢𝘵𝘢 𝘱𝘳𝘢𝘤𝘵𝘪𝘤𝘦𝘴 𝘳𝘢𝘵𝘩𝘦𝘳 𝘵𝘩𝘢𝘯 𝘫𝘶𝘴𝘵 𝘮𝘰𝘥𝘦𝘭 𝘴𝘤𝘢𝘭𝘦 𝘢𝘯𝘥 𝘦𝘷𝘢𝘭𝘶𝘢𝘵𝘪𝘰𝘯 𝘮𝘦𝘵𝘳𝘪𝘤𝘴.
 
 Cite as:
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