io.net unveils the Incentive Dynamic Engine (IDE), a demand-driven tokenomics overhaul that cuts 150M $IO, adds buybacks, and aims to stabilize GPU payouts.io.net unveils the Incentive Dynamic Engine (IDE), a demand-driven tokenomics overhaul that cuts 150M $IO, adds buybacks, and aims to stabilize GPU payouts.

io.net Unveils Incentive Dynamic Engine (IDE) to Stabilize DePIN Tokenomics

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io.net

io.net, the decentralized physical infrastructure network (DePIN) focused on AI compute, has proposed a major overhaul of its tokenomics with the launch of the Incentive Dynamic Engine (IDE), a demand-driven model designed to anchor long-term stability for suppliers, users and token holders. The redesign, presented this week in a litepaper and backed by a third-party report, moves io.net away from fixed token emissions and toward an automated system that adjusts emissions, buys back tokens and burns a material portion of revenue to reduce inflationary pressure.

The announcement comes after io.net’s initial inflationary incentives helped the network scale quickly: since launching in June last year, the platform says it has processed more than $20 million in verifiable compute leases, bootstrapping a global pool of GPUs and proving real usage demand. As the project transitions from a bootstrapping phase into a foundation for enterprise and research workloads, the team argues the old fixed-emissions model risks ongoing inflation and unstable supplier incomes, problems IDE aims to solve.

At its heart, IDE replaces a supply-driven rewards schedule with a real-time, demand-driven control system. The mechanism uses two counter-cyclical vaults and a “sustainability ratio” to automatically balance payouts and reserves: when revenue is strong the system keeps tokens in reserve and absorbs circulation, and when demand softens it can release tokens to stabilize USD-equivalent supplier payouts. That coupling of supply adjustments to actual compute usage, the litepaper explains, is intended to make supplier income predictable and the network resilient across market cycles.

From Bootstrap to Stability

IDE also introduces a substantial, built-in deflationary mechanism. After suppliers are paid, at least 50 percent of the remaining revenue will be used to purchase $IO and permanently burn those tokens; the proposal targets removing 150 million or more $IO from circulation over time. The litepaper frames this not as a one-off stunt but as a structural shift: routing client fees into buybacks and burns aligns investor incentives with real utility rather than pure speculation.

The team behind io.net has made the proposal public for community review: a litepaper and supporting documentation are available on the project website, and an open feedback period runs through late February, after which the final design will be published ahead of a planned Q2 2026 rollout to the live network. The company says it has engaged independent auditors and economists to stress-test IDE and that a third-party report from CryptoEconLab (CEL) supports the framework’s core assumptions.

Gaurav Sharma, CEO of io.net, said: “We’re at a crucial juncture for AI: continue with centralised hyperscalers underpinned by obscure, circular finance, or build decentralised, open markets for compute. Blockchain can be the solution, but DePINs in their current form are not fit for purpose. IDE introduces a unique approach, one that enables enterprises to adopt decentralized compute by avoiding a fixed emissions model, and is the foundation for the long-term growth of io.net. GPU providers, users and investors will benefit from the first reliable and open compute network, accurately aligning incentives. Following its implementation next year, IDE will enable startups, researchers and enterprises to develop and deploy AI systems on io.net for the long term.”

If implemented as designed, the shift would be a notable experiment in DePIN tokenomics: instead of continual, schedule-based token issuance, io.net would operate a feedback-controlled economy in which emissions, burns and reserves respond to measurable network activity. The firm argues this model reduces income volatility for providers, rewards real world utility, and creates a more durable market for large-scale AI workloads, from startups to enterprise deployments.

The litepaper, IDE technical appendix and the community feedback form are linked from io.net’s tokenomics page for anyone who wants to dig into the math and governance considerations. io.net says it will actively review community comments through the consultation window, publish a final version at the end of March, and then move to implement the redesigned tokenomics in the second quarter of 2026.

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