6.7K @alex_crypto98 Good morning
Woke up thinking about what actually unlocks enterprise-grade AI
@nesaorg strengthening its compute backbone with IONET is a serious signal
Scalable, reliable decentralized compute isn’t optional anymore
Privacy-first execution only works when the infrastructure can handle real load
This feels like groundwork laid before growth accelerates
36
40
289
5.4K @tbros6868 Ever wonder why so many DePIN networks look strong early… then quietly fall apart? 🤔
➠ Rewards ignore real demand
➠ Token supply grows even when usage drops
➠ Suppliers leave the moment incentives wobble
Let’s talk about the Incentive Dynamic Engine from @ionet , or IDE. This is their proposed shift away from fixed emissions toward demand driven incentives, targeting rollout in Q2 2026.
This is less hype. More math.
The old problem nobody likes to admit
Most DePIN token models work like this.
Tokens emitted → on a fixed schedule
Usage goes down → emissions keep flowing
Supply piles up → price drops
Suppliers panic → network shrinks
@ionet lived this early on. Rewards kept printing even when GPU demand cooled. Token value dipped. Suppliers who needed predictable USD income backed off. Utility fell. Fragile loop.
@DeepNodeAI treats AI like shared infrastructure, not a walled garden. Builders keep ownership, models compete on performance, and $DN aligns value with usage. The recent stress test was painful
26
29
1.1K
17.3K @0xDvox Enterprise AI doesn’t care about “token incentives” it cares about uptime, predictable capacity, and invoices that make sense.
That’s @ionet the gap most decentralized compute networks hit: they pay suppliers on a fixed emissions schedule, even when real workload revenue is low. It works early then volatility shows up and suppliers churn right when reliability matters most.
answer is IDE (Incentive Dynamic Engine) a demand-driven overhaul proposed in Dec 2025, with rollout targeted for Q2 2026.
The core idea is simple: payouts should follow real usage.
IDE runs an adaptive loop:
It tracks a sustainability ratio (real-time revenue vs payout target).
In surplus periods, ≥50% of remaining revenue goes to $IO buybacks + burn, turning growth into supply reduction.
In shortfall periods, dual-vault buffering can temporarily release funds to keep USD-targeted supplier payouts steady instead of forcing suppliers to eat downside.
End state: fewer boom/bust cycles, stickier GPU supply, and a network that behaves like professional infrastructure not a mining game.
85
81
3.8K
