Point-to-Pool + 100% Buyback: Deconstructing the “Flywheel Engine” of Base’s New Dark Horse, POD

Original source: Dolphin

Original compilation: Yuliya, PANews

Editor’s note: Recently, the AI main narrative on the Base ecosystem has experienced explosive growth, with the privacy-first generative AI platform Venice ($VVV) and its ecosystem projects drawing the most attention. As a co-developer of Venice’s core default model, the Dolphin network and its token $POD had an astonishing performance in May, with its market cap surging from $12.2 million to $192 million, an increase of over 14 times. This article details the unique “Peer-to-Pool” economic model of the Dolphin network, its token value capture mechanism, and the innovative design for ensuring network security through staking and slashing mechanisms. Below is a detailed breakdown and analysis of the mechanisms:

Peer-to-Pool Economic Model Design

The Dolphin network is designed as a “Peer-to-Pool” system, aiming to repurpose idle GPUs. Each AI model runs on a dedicated GPU provided within the network.

This differs from most AI DePIN projects. In other networks, buyers typically rent a node directly from a provider, establishing a one-to-one “session.”

· On the supply side, nodes running the same model form a “pool,” collaboratively processing incoming task requests. The system randomly assigns tasks based on node availability, with no direct connection between the requester and the node provider. The sole criterion for nodes to earn rewards is the amount of AI computation tasks (i.e., inference tokens) they process, with rewards paid in POD tokens from the protocol treasury.

· On the demand side, API users purchase credits directly from the protocol. The Dolphin network accepts payments in multiple 暗号currencies, including $POD, $ETH, $BTC, $USDC, $XMR, and $ZEC.

100% of all revenue received by the protocol is used to repurchase POD tokens on the open market—directly offsetting token inflation.

Buyers and sellers are decoupled, meaning the POD rewards distributed to nodes can be more or less than the POD repurchased from revenue.

For a clearer illustration, consider a specific example of running the Qwen3.6-35B model on the Dolphin network:

· Current cost of running datagen.dphn.ai: $0.50 per million tokens processed.

· Cheapest comparable competitor price on OpenRouter: $1.00 per million tokens.

· Price charged to users by Dolphin: $0.70.

· Payment to nodes by Dolphin: $0.50.

· Net buyback funds: $0.20 per million tokens generated.

In other words, the Dolphin network’s pricing is not only 30% lower than the cheapest centralized provider, but it also generates $0.20 of pure profit per million tokens generated to buy back POD on the open market.

Why This is the Best Use Case for DePIN?

This model is considered a highly promising application direction in the DePIN field, primarily for the following reasons:

· Extremely high AI inference demand: The market’s hunger for AI inference computing power is in an explosive phase.

· Vast pool of idle computing power: There is an incredibly large supply of idle gaming GPUs capable of running local AI models. This network model feels reminiscent of GPU mining (PoW), but because it outputs AI computation with genuine commercial value, its earning potential is much greater.

· Geographic independence: Unlike many DePIN networks, geographic location is irrelevant for AI inference, thus avoiding coverage issues. Due to the high geographic flexibility of AI inference, a few hundred milliseconds of latency has minimal impact on user experience. This allows the Dolphin network to connect global consumers and computing resources, greatly enhancing the scalability and utilization of each node.

· Necessity of pooled liquidity computing: This is the only way to unlock the largest supply group of GPUs (gamers and PC enthusiasts). It allows nodes to come online or go offline at any time, without the need for guaranteed fixed uptime like P2P node rentals. Previous GPU DePIN projects required one-to-one binding between consumers and nodes, which is unworkable for idle GPUs like those in gaming PCs or data center cards, as owners might want to reclaim their machines at any time. After all, no one wants to rent a virtual machine that suddenly disconnects when the GPU owner takes it back.

トークンomics and Value Accumulation

POD is the only valuable asset in the Dolphin ecosystem. 100% of all revenue generated by the network is automatically used to repurchase POD on the open market. Furthermore, Dolphin has no external equity structure based on shareholders and will never introduce one in the future.

For POD holders, staking tokens into the xPOD vault unlocks multiple exclusive benefits:

· Receive direct, auto-compounding dividends from network token buybacks.

· Gain daily AI inference credits, enabling free use of all models on the network.

· Obtain premium subscription status in Dolphin’s web chat interface, bots, and other ecosystem applications.

In its tokenomic design, Dolphin synthesizes the best elements from numerous successful DeFi projects, deeply integrating them into the framework most suitable for decentralized AI inference and training:

· Inspired by ETH mechanism: Node operators and validators must post a bond, which is deducted (slashed) if malicious behavior is detected.

· Inspired by CRV mechanism: Provides reward acceleration for node operators. Locking POD can potentially double rewards. Based on the bond-to-earnings ratios of other platforms, an acceleration multiplier of 1.5x to 2x is highly competitive in the market.

· Inspired by xSUSHI/yCRV mechanism: Introduces an auto-compounding staking vault. Users don’t need to manually claim rewards, meaning xPOD (the staked state of the Dolphin token) can be directly used as collateral for node operator bonds.

· Inspired by stAAVE mechanism: Implements a reasonable withdrawal cooldown period and withdrawal time window to ensure network fund stability.

· Inspired by vlCVX/veCRV mechanism: Establishes a “bribe market” for daily unused xPOD computing credits. Users can sell their unused credits to earn higher staking yields.

Bonding, Slashing, and Reward Multiplier Mechanism

Cheating is undeniably the biggest threat in a decentralized computing network. If unchecked, node operators could secretly switch to smaller, crippled, or even fake AI models while still claiming rewards. This would cause output quality to crash, buyers would leave, and the ecosystem’s flywheel would never spin.

To address this challenge, the Dolphin network introduces a “slashable bond” mechanism, deeply aligning the interests of node operators with the value of the POD token. If malicious cheating is confirmed, a node’s bond, equivalent to 4 weeks of earnings, is directly slashed. This makes cheating economically unviable.

By default, node operators earn POD in a “bonded state.” Once a node accumulates a bonded POD amount equivalent to 4 weeks of earnings, they can choose during the weekly settlement whether to continue receiving bonded POD or claim liquid POD that is freely tradable.

Choosing to claim liquid POD incurs a 20% fee. This fee is directly deposited into the xPOD staking vault, effectively distributing it to other stakers and node operators who maintain their bonds.

Nodes can also deposit xPOD directly into the bonding contract, which not only boosts their rewards but also qualifies them to verify other nodes in the network.

The POD reward multiplier determines how much extra a node earns on top of its base rewards. This mechanism is inspired by Curve Finance’s liquidity provider (LP) acceleration, but Dolphin has specifically adapted it for decentralized AI networks, incorporating usage-based reward distribution, unified account-level bonding, and slashing penalties.

In simple terms:

· Nodes earn base rewards by completing AI computation, verification work, and related protocol tasks.

· The system multiplies your node rewards based on the amount of bonded tokens in your account and your earnings ratio.

· The earnings ratio calculation uses a smoothed average of your base rewards over the past few weeks, employing a “fast-rise, slow-decline” algorithm: your average earnings metric rises quickly when you take on more computation tasks, but declines slowly when idle.

· If your account maintains a bond equivalent to over 3 months of earnings, and your active bond is at least 50,000 POD, you qualify as a verifier.

· If your bonded amount is equivalent to 6 months (26 weeks) of your earnings, the system guarantees a minimum reward multiplier of 1.5x.

· If your bonded amount exceeds 6 months of earnings, your reward multiplier can reach up to 2x. The specific multiplier depends on your relative proportion compared to other over-bonded participants and the absolute amount exceeding the 6-month target.

All calculations use only the amount of POD; the reward system does not involve any fiat price oracles. Bonds are calculated per account (wallet), and the calculated reward multiplier applies to all nodes under that account. If you add more nodes, your account’s total earnings increase, so you need to proportionally increase your active bond to maintain the same reward multiplier.

Finally, the Dolphin network is set to release a paper titled “Encrypted Live-Weight Proofs for Decentralized Inference” tomorrow. This paper will detail a lightweight verification system capable of verifying that nodes across various hardware types are running the correct model, surpassing the limitations of standard TEE verification which is only usable on enterprise NVIDIA GPUs.

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この記事はインターネットから得たものです。 Point-to-Pool + 100% Buyback: Deconstructing the “Flywheel Engine” of Base’s New Dark Horse, POD

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