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The problems that keep scientists up at night ✅ Broken incentive systems excluding value creators ✅ Fragmented ecosystems preventing collaboration ✅ Attribution nightmares in complex AI workflows ✅ Technical barriers to cross-domain research

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Technical Problem Focus ❌ Why can't a climate scientist easily use a biomedical AI model to study environmental health impacts? Technical incompatibility, inconsistent standards, and zero interoperability. The fragmentation problem is killing innovation. Thread 👇 1/4 🧵

Sticks explains ..

#DeepIntelligenceMoney - “A balance sheet for intelligence” DeSci on Hetu 1/ Zoom out: civilization has a design bug. We reward expenditure, not contribution. Budgets spent, energy burned, time consumed → all monetizable. But the things that actually move science forward —

#DeepIntelligenceMoney - In the name of Proof of Causal Work, we wish you a semantic aligned Thanksgiving 🍁

In the name of the #ScienceOfConsensus — after years assembling a frontier research team — we enter a new era: #ResearchAsCivilization Upgrading to Advaita Research means building the substrate where- consensus generates intelligence, intelligence generates science, and science

#ScienceOfConsensus Recent LLM advances keep pushing reasoning forward — but they expose a deeper structural limit: a solitary model, no matter how strong, cannot reliably solve the hardest reasoning problems. Difficult math, compositional logic, open-ended analysis — these