It’s a matter of fact that the larger the market, the greater the demand for efficient resource allocation because the stakes scale with it. The market opportunity for DeAI is still massively underserved, even before factoring future trajectory growth. To put in perspective: @ChatGPTapp adoption hit 100M users in just 2 months, and it took 14% of the time it took smartphones to reach 40% penetration. Today, centralized hyperscalars still command 61%+ of the market, leaving plenty of room for externalized market penetration. But let’s be real, the “DeAI Thesis” also comes with tough challenges: 1⃣ Security + Regulatory Gaps: Data exposure risk + lack of compliance-ready frameworks. 2⃣ Weak Pull Incentives: Open models lack closed loops & token-driven incentives face sustainability questions. 3⃣ Cold Start Credibility: Low trust + shallow market presence slows early adoption. Still, like any emerging sector, maturity takes time. Fortunately imo, there are tailwinds DeAI can lean...
Your AI “cost problem” isn’t compute.
It’s coordination.
Idle GPUs sit at 12-18% utilization, DeAI turns these into fuel:
@ionet up to 90% cheaper
@rendernetwork up to 90% lower
@Filecoin ~78% less for storage
Build the network, not the bottleneck.
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