1kx: An in-depth analysis of the cost estimation framework for the DePIN project, how to create a rise flywheel?

Evaluating token economics for DePINs: cost estimation

Original author: Robert, 1kx

Elvin, ChainCatcher

Summary

Cost Estimation Framework:

• Step 1: Identify network contributors
• Step 2: Evaluating the cost components
• Step 3: Evaluate the differences in cost structure and summarize

Case Study

Key Points

• In order to ensure the continued participation of nodes in the decentralized physical infrastructure network (DePIN), network administrators (founders, DAO members, etc.) must consider the costs incurred by operators when operating nodes.
• In some cases, key decisions about cost optimization are obvious. For example, Livepeer's transition from Ethereum to Arbitrum in 2022 was a clear and uncontroversial choice, resulting in a reduction of over 95% in settlement costs. In other cases, DePIN managers may need external assistance to evaluate the cost of operating nodes due to limited development resources.
• If the node continues to operate at a loss, the operator will stop running the node, leading to a decrease in overall node supply. Understanding the operating costs of the DePIN network and its main driving factors can enable network operators to initiate governance discussions; at the same time, cost estimation can provide information for research and development work before the decline in network service supply, in order to reduce the costs of node operators.
• For protocol managers, estimating the operational costs of the network can be difficult due to the anonymity of contributors (these networks are typically permissionless, meaning anyone can contribute and leave at any time) and the lack of publicly available data related to costs.
• To guide managers' decision-making, we have proposed a three-step framework for estimating costs.
• Define network contributors and position them in specific roles.
• Identify the cost composition related to nodes.
• When evaluating the combination of 1 and 2, consider the differences in cost structure

In addition to the overall estimate of current costs, the framework also provides:

• Help determine the largest cost driver based on the segmentation of roles and cost components.
• Estimated changes under different assumptions and scenarios of increased demand/network capacity.

The case study will demonstrate how to apply the framework. For example, a joint investigation with the POKT network revealed the continuous efforts of node operators to further expand service nodes. Nevertheless, through decentralization, their gateway has addressed the remaining barriers to economic scalability, including demand generation.

Introduction: What is DePIN and why discuss costs

DePIN is a series of decentralized networks that provide hardware resources (physical infrastructure) for a wide range of use cases, such as computing, storage, wireless networking, or data measurement. DePINs utilize the Web3 incentive model (i.e., token reward system) to incentivize the construction of physical infrastructure networks. As of May 2024, the total market capitalization of all DePIN tokens is $29 billion.

DePINs contributes to both digital and physical resource networks:

In the Physical Resource Network (PRN), contributors deploy location-specific hardware to provide (non-replaceable) services. This includes:

• Wireless networks (e.g. Helium, World Mobile, XNET, Nodle)
• Sensor networks (e.g. Dimo, Hivemapper, Silencio, Onocoy)
• Energy network (e.g. Starpower, PowerLedger, Arkreen)

"In the Digital Resource Network (DRN), contributors guide hardware to provide (alternative) digital resources, where physical location is not the primary criterion. This includes:"

• Calculation (such as ICP, Livepeer*, Akash Network, POKT Network*, Covalent*, Lit protocol*)
• Storage (e.g. Arweave*, FIL, Sia)
• Bandwidth and privacy (e.g. NYM*, Hopr, Orchid, Mysterium, Fleek)
• AI (e.g. Bittensor, Fetch.ai, Modulus Labs)

The early DePIN project generated a lot of initial interest due to its token framework design. For example, Helium rewards contributors with HNT tokens for helping to operate the wireless network with hotspots, while Filecoin allows users to rent out their excess storage space. Although this is enough to get many DePIN projects started, token distribution may not be sufficient to ensure long-term participation of nodes in the network.

If running nodes becomes unprofitable, node operators will no longer have the incentive to operate the infrastructure. Therefore, the DePIN founding team must help node operators optimize costs.

DePIN Flywheel

The typical flywheel of the DePIN token economy is as follows:

• Establish the supply side of the service, such as storage or 5G antennas
• Inflationary token rewards incentivize node operators to provide the necessary infrastructure, although the demand is not yet sufficient to cover costs.
• As time goes by and the demand rises, monetizing network activities may increase the income of node operators, even as token rewards gradually decrease.
• Continuous monetization of network activities and increased incentives for node operators further stimulate supply, creating the DePIN flywheel

The visual presentation of DePIN's flywheel is as follows:

As described in our previous analysis of the reward issuance schedule, the USD value (token price) of these token rewards is heavily influenced by overall market sentiment. Therefore, they may appear as follows:

Or depending on when you enter the bull market, it may be like this:

So, what is the relationship between reward issuance and cost?

As mentioned above, if the token rewards and revenue from user demand are not sufficient to achieve a balance between income and expenses, node operators may decide to stop supporting the network. A large portion of DePIN's operating costs are paid in fiat currency, which makes the USD value of token rewards important and linked to the overall market performance. Despite any well-planned token issuance measures, in the worst case scenario, the situation may turn out like this:

This will lead to node operators exiting, resulting in higher latency, lower reliability, and worse user experience. Eventually, demand stagnation will shut down the flywheel.

The good news is that there are many ways to deal with this situation. One way is to make the token issuance more flexible to align with the monetization of the network (please refer to the KPI-based issuance here). Another way is to address the cost issue to make the overall network more efficient, thus less sensitive to token price drops. Our dynamic chart will be as follows:

Key Proposition: If you are aware of the cost of operating the DePIN network and its primary driving factors, you can initiate governance discussions and R&D work to reduce node operator costs before the reduction of network service supply.

Given the decentralization and permissionless nature of DePIN, assessing the cost base is not easy. While token rewards and user demand revenue are typically tracked on-chain, other costs associated with running nodes are not publicly available (such as infrastructure costs). This means that we need to make assumptions and estimates based on available data points.

In this article, we will address this challenge and introduce an estimation framework.

• Step 1: Network Contributor
• Step 2: Components of the cost
• Step 3: Evaluate the cost structure of network contributors

Framework

We propose the following framework for managers of the DePIN network as a methodology for evaluating the operating costs involved in operating infrastructure nodes.

By using this framework, the cost estimation of DePINs is decomposed into three steps:

• Identify network contributors
• Evaluation cost components (e.g. hardware, labor)
• Evaluate the above cost structure and summarize to obtain an overall cost estimate.

Step 1: Identify network contributors

Although DePINs provides a variety of services (such as computing, network coverage, mobile data, etc.), the roles required to provide these services are the same (view an overview of DePIN supply-side roles in over 30 networks here).

• Service Node/Producer: They provide services and the physical infrastructure required for them (such as servers, antennas, dashcams, etc.). For example, Filecoin's storage providers, Helium's hotspots, or Livepeer's transcoders.
• Validators/Observation Nodes/Phishers: They inspect the work completed by service nodes, directly or via the accounting layer. Then, the results of these inspections are sent to the accounting layer. For example, Filecoin's storage providers (as they also validate storage proofs from other providers) and Helium's hotspots and oracles (performing coverage proofs for other hotspots).
• Calculation Layer: Tracks the flow and status of the provided work/service and the corresponding payment. Please note that the protocol defines its own computation logic, such as how to track and store work and payments on the blockchain (which will be discussed in detail in another article). For example, Arbitrum of Livepeer or POKT-chain of the POKT network (operated by POKT validation nodes).
• Gateways: They serve as coordinators/balancers in user, service nodes, and manage access or aggregate services (such as data in sensor networks), and are also related to the accounting layer. For example, Orchestrators in Livepeer or Gateways in the POKT network.
• Delegator: can participate in the economy of the service or observe the node by collateralizing.

The roles related to the demand side (such as the sales team) are currently not common, and evaluating the costs associated with the operation of the protocol, such as governance costs, is the subject of another article.

Please note, not every DePIN has delegation and gateway, nor does it require all roles to be separated. For example, Filecoin's Storage Provider (SP) is classified as a service node and validator, and also operates the Filecoin chain, thus forming an accounting layer. Arweave miners are the same.

Step 2: Evaluate the cost components

Each of the above roles can be executed through nodes, and their costs can be divided into any one of the following four components (most of which have multiple components):

• Hardware/Infrastructure: Costs related to actual physical infrastructure, such as dashcams
• Manual: The cost associated with the time spent on setting up and operating infrastructure.
• Bandwidth, electricity, and other operating expenses: Costs related to data exchange and other operating costs, such as electricity and data center rent
• Collateral: The (opportunity) cost of not investing elsewhere.

The last point refers to the cost of capital: it is almost impossible to obtain information on the debt/financing costs associated with these operations on a wide scale. However, there is a part of the content related to the cost of capital that we can evaluate: many DePINs follow a pattern of mortgaging to gain access to working tokens and require node operators to mortgage some tokens to be allowed to contribute. Obtaining these tokens is an investment, and even if we assume that the amount can be recovered when leaving the network, holding these tokens has opportunity costs compared to investing capital elsewhere.

Our assessment of the composition of costs would be incomplete if it did not involve costs related to accounting layer transactions. Assessing this is not straightforward and depends on several variable factors. In general, the network determines to what extent accounting is outsourced off-chain. However, for settlement layer records and on-chain transactions, there are three options available.

• Proprietary L1: The network runs its own blockchain. Examples include Arweave, Filecoin, and POKT Network. Typically, service nodes and validator nodes also cover this role, which is why the associated costs are included (however, we will try to separate them if possible - see the example of POKT Network).
• Proprietary L2, more commonly known as application-specific Rollup: The cost of Rollup infrastructure (sequencers, etc.) and adjacent infrastructure (block explorer, wallet integration, etc.) can usually be mapped to these four components. In cases where it is unclear, such as when using Rollup-as-a-service providers (RaaS), it will be mapped to bandwidth and other costs.
• Public L1/L2: These outsource the settlement layer, which means there are no hardware and labor costs for the network. However, service nodes, validation nodes (as well as users/payers) pay directly (based on usage). Assessing the network-related costs of these transactions presents some challenges, so there are also some limitations: not all transactions are related to the accounting layer, such as exchanges or other DeFi transactions, but it is usually not easy to separate these transactions. We map these costs to bandwidth and other costs.

To create a cost estimate, it is a challenging task to integrate all these elements. We not only need to estimate each cost component for each role in the network as shown in the diagram below, but we also need to consider that not all node operators have the same cost structure. Determining the overall cost estimate is more complex than simply multiplying the number of network node operators by an estimate for one node operator.

Step 3: Evaluate cost structure

When we talk about cost structure, we are referring to the key differences that affect costs. These key differences make assumptions crucial. Of course, this is a trade-off: making assumptions simplifies the process but may sacrifice accuracy. In other words, considering how many factors are involved, certain assumptions must be made to arrive at a viable theory.

When evaluating the cost structure, there are three main factors to consider.

• Differences in setup: A typical example is an operator using bare metal servers, while another runs on the cloud (purchase vs. leasing). When we know the corresponding shares in the entire network, we can usually take these differences into account. This also involves capital costs in leasing or financing agreements. Assuming no capital costs, we suggest ignoring these differences.
• Another cost difference is related to the purchase time (purchasing storage becomes cheaper over time, purchasing H 100 s may not) or operation location. We recommend considering the time aspect by using the current price. For labor costs, location is important: DePIN can recruit contributors from all over the world, and there is a big difference in local wage levels, and the time spent on these jobs is difficult to evaluate. Nevertheless, we have made a simplified assumption that in our framework version, the hourly wages of all node operators are the same.
• Efficiency difference: Node operators can have the same settings, but if one runs more of the same nodes, they may have lower costs per node due to economies of scale. In our framework, we need to first evaluate the node distribution of each node operator to address these impacts. Then, to understand and estimate cost impacts, surveys are needed with both larger and smaller operators or other available data points (e.g. bulk discounts for promotions).
• Another example is the long-term supporters of the network, who progress faster on the learning curve and therefore operate more efficiently compared to those who have just joined. We will ignore this aspect unless we have direct data points from the survey.
• Differences in attribution and calculation: Although node operators are equal in the first two points, they may view their contributions from different cost bases, so the final costs may differ. For example, one person may consider their participation as part-time and not track any time spent, while another person may consider it as their main business and pay wages based on time spent on the project. We consider this difference by providing a wider margin of error for the "part-timers" side (as they are usually underestimated), but assume that the time investment of each node operation is the same (also see economies of scale).

This is related to the benefits of the sharing economy, which is common for DePIN: operators can use the same settings in multiple networks (thus also hardware, labor, and operating expenses such as bandwidth and electricity), such as Livepeer with Ethereum and FIL operation, io.net with Render, FIL, and other GPU networks. In the case of hardware being crucial to operations, we do not consider cost savings related to the sharing economy. They are not only difficult to identify but also difficult to quantify which network benefits the most in terms of costs and how the savings are allocated. In accounting, we will decompose the total cost into monthly amounts. For simplification, we assume that we amortize the total amount over the same term throughout the entire lifecycle and allocate the same amount to each month for all node operators.

Of course, there are more subtle differences, and we will explore them in more detail in the DePIN repository.

This adds a third dimension to our "execution plan" and creates 60 different combinations to consider.

In general, although this formula is very comprehensive and provides multiple options for cost structures, the most useful application is to apply it to multiple different time points rather than a static time point. The most powerful model is the one that links operating costs with network capacity. This allows us to understand the extent to which costs vary with changes in capacity or utilization. Network capacity is related to the services provided by the network, such as the number of RPC requests in Pocket, the storage volume of Arweave or Filecoin, or the percentage of road network mapping in Hivemapper.

Please note that this formula requires a large amount of publicly available information. We recommend obtaining this information through documents provided on the internet, forum/Discord posts, and if possible, through investigation.

Conclusion and next steps

As DePIN develops at an increasing pace, estimating the cost components for various DePIN is challenging. In addition to the known power law of hardware costs and capacity over time, estimating cryptocurrency-specific costs, such as gas on the settlement layer and throughput capacity, is not a simple matter.

Understanding the relationship between current costs and the issuance of rewards and demand-side income, how the largest cost drivers change with assumed variations, and how costs increase with increasing demand are all useful indicators.

To assist in guiding governance decisions about the DePIN economic design, cost estimates need to be associated with reward issuance and revenue use. While I plan to provide more examples of cost estimates for DePINs, I welcome feedback on the proposed framework, its assumptions and summaries, as well as potential improvements to the provided cost estimates.

Appendix - Example Illustration Framework

Livepeer

Livepeer provides decentralized video infrastructure for real-time and on-demand streaming. Recently, Livepeer has started enabling idle GPU resources for AI model training use cases (see here for more details).

Here is the process for applying the framework step by step. Most cost estimates are based on surveys with node operators (Orchestrators) conducted in the summer of 2023 and community information (such as here).

The estimated total cost of operating the Livepeer network is approximately $85,000 per month. A detailed breakdown of the average cost shows that hardware and labor account for roughly the same share (about 40%). If we take into account the uncertainty in the labor cost estimate described in the table, the monthly cost of the network's 100 Orchestrators, their transcoders, and settlement costs on Arbitrum is approximately $40,000, at the lower end of the estimated range. It is worth noting that the monthly cost of $40,000 is not far from the current fee income of about 5-10 ETH per month (corresponding to an ETH price of $3,000-4,000). However, Orchestrators do not have negative profits as a larger portion of their income actually comes from staking rewards.

It is worth noting that due to the settlement of Livepeer transactions on Arbitrum, the cost of settlement layer is within the range of 0.5-2 ETH per month. This saves over 95% of costs compared to the first quarter of 2022 before the Arbitrum migration. In addition, as of today, transactions on Livepeer have increased 2-3 times. In relative terms, the accounting layer now accounts for about 5% of the total cost, compared to a major cost driver before the migration (about 80% of the total cost).

Recently, it adjusted the algorithm that determines the work allocation method, paying more attention to the price per pixel provided by Orchestrator. This puts downward pressure on transcoding prices, which may help stimulate demand, but discussions on the forum indicate that price levels need to be further reduced. On the other hand, the recently launched AI-subnets may help add additional monetization channels to the network.

In estimating a potential scenario in the spreadsheet, it is noteworthy that tripling the demand for transcoding minutes would only increase the overall cost by 20%. Bandwidth is the primary driver of cost increases.

If we assume a similar price level (with 1 ETH at $3,000), this should be enough to bring the network into the breakeven range. However, if the transcoding price drops by 50%, the network-level fee income will be about $45,000 per month, which is below the lower limit of the cost estimate. The dynamics of costs and income on the Livepeer network will change with the emergence of new use cases (such as AI video generation), thereby increasing monetization opportunities. This remains to be observed.

POKT

At its core, the POKT network provides decentralized remote procedure call (RPC) endpoints. Recently, the POKT network announced its expansion to more use cases related to AI model inference. The phased approach is as follows. Most cost estimates are based on a survey conducted with node operators in the summer of 2023, as well as subsequent interviews with these node operators and gateway operators.

Based on about 15,000 nodes and four gateway operators providing RPC endpoints, we estimate that the cost of the POKT network is currently about $200,000 per month (± $80,000) to serve approximately 500 million relays per day. The largest portion currently is the service nodes (approximately 75% of the cost).

As we can obtain historical data on the number of active nodes in the network and have data points with different cost components over time, we can estimate the network cost on a timeline, showing three major cost reduction milestones that have been addressed.

• After entering the bear market in mid-2022 and reducing token rewards (especially USD-based token rewards), the nodes integrate
• Improved within the network, such as Geomesh and LeanPOKT, significantly reducing operating costs, as well as individual improvements by node operators.
• By adding simpler gateway settings, the decentralized gateway role has reduced bandwidth costs.

Due to our cost framework, which links cost estimation to network capacity and demand, we can assess changes in the cost structure. For example, if the demand rises from the current 500 million per day to, for example, 2.5 billion relays per day, the gateway will account for 60% of the total cost base, approximately $400,000 per month (currently about $200,000). Please note that this is double the cost, while the demand has risen by a factor of 5. This is because the service node is able to improve its setup, thus being able to meet the growing demand on a basically similar cost base.

If we further assume that the share of new gateways operating on a lower-cost basis increases to, for example, 50% (currently 30%) of the total number of relay nodes, then the overall network cost will be $300,000 per month.

With the decentralization of gateways, gateway operators can define their own price points. Assuming an average price of $4 per million requests, the POKT network as a whole would earn $300,000 per month, thus achieving a basic break-even point.

Dfinity/ICP

Dfinity/Internet Computer Protocol (ICP) is designed as "the blockchain of blockchains" to provide computational resources for executing smart contracts (referred to as canisters) that are organized in subnets (details). The pillar is to provide storage, computation, and bandwidth for replicating all canisters, states, and subnet computations on node machines.

The framework for gradual application is shown here. Most cost estimates are based on data from documents and forum posts.

ICP is one of the few networks that incorporate the cost based on legal tender into the token reward mechanism, making cost assessment easier. Currently, about 85 operators run approximately 1400 node machines. For the economies of scale of larger operators, we do not have data points, so our overall estimate range is quite wide: the monthly cost of operating the ICP network is about $400,000 to $900,000, with an average of about $600,000.

Although a proper income assessment deserves a separate article, we estimate that the current monthly income is about $25,000. This seems low compared to the estimated costs, but it is due to low utilization: with only 559 active node machines, we estimate that the current demand (expressed as a periodic burn rate) is about 2% of the total capacity. This means that the network can withstand a demand increase of 25 times, for example, without increasing the current cost basis. A forum post actually estimates that future demand will increase by 15-25 times over the next two years, which would then (under the same conditions) result in ICP earning these fees per month.

DIMO

DIMO is a decentralized network that empowers drivers to manage their vehicle data. At the same time, DIMO enables businesses and developers to build innovative mobility-related applications (and profit from them). Data measurement is done through special devices (Autopi, Macaron) or applications. While the above DePIN example is a digital resource network, DIMO is the first example of a physical resource network included in this analysis.

The framework for phased application is as follows. Most cost estimates are based on online (device) price information, Dune data, and forum posts.

For the settlement layer, we assume that half of the average cost per connected vehicle in Q1 2024, ranging from $0.6 to $1.5, can be attributed to DIMO operations. For the gateway, we assume a monthly hardware cost of approximately $4,000 and labor costs associated with the aforementioned operations of approximately $11,000 per month. Overall, this amounts to approximately $180,000 in monthly expenses, as shown in the table below. The majority of the costs are related to bandwidth and other expenses, with approximately 1/3 associated with settlement costs on Polygon and the remaining 2/3 associated with monthly cost allocation for smart car integration.

We have no clues about the actual revenue from the network, but estimates based on the global automotive data market and related automotive data revenue show that the current revenue per car is around $150 to $185, which could rise to $500 to $600 by 2030. If DIMO can obtain 10-15% of the revenue from it, the generated income range will be $110,000 to $180,000 per month, covering the operating costs.

However, the monetization of data itself does not seem to be the actual goal of the protocol; instead, DIMO focuses on providing infrastructure for building applications on top of the network, as reflected in the recent discussions about DIMO nodes and token upgrades. The changes discussed may affect the aforementioned cost structure.

Special thanks to my contributors: Mihai (Messari), Raullen (IoTeX), Nodies Team, Grove Team, Pocket Network Foundation, DIMO team, Diana Biggs, and Christopher Heymann for their contributions and feedback.

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