DePIN Discount Rate Part 1
Quantifying The Risk Relationships Between Supply, Demand And TAM In A Tokenized Physical Infrastructure Network
This report is part one of a two-part series on our DePIN Discount Rate, a core element to the broader DePIN Valuation Model being developed by Parameter Research. Part one addresses the key considerations and components that influence the discount rate, with part two examining its calculation and contextualization in the model. While our full valuation model is coming soon, the core tenets shared in this report can be generally applied when analyzing and evaluating the prospective risks that uniquely impact decentralized physical infrastructure networks.
Discount Rates 101
In the context of financial valuation models, a discount rate serves to quantify the risk associated with an investment by specifying the required rate of return needed to adequately account for the opportunity cost of capital. All assets, whether they be stocks, bonds or cryptoassets, are measured by a combination of current and expected future value. Considering that future value is speculative and not assured, a discount rate is applied for capturing this risk. These basic principles are true whether we are looking at traditional valuation techniques, such as a discounted cash flow model, or with new methods attempting to value cryptoassets, like in Chris Burniske’s MV=PQ framework.
Simply conceptualize the discount rate as a representation for the overall risk associated with an asset, where the higher the discount rate, the greater the associated risk.
Our DePIN Discount Rate can be framed similarly to the Weighted Average Cost of Capital. However, instead of focusing on a company’s blended cost (risk) of capital, it evaluates a range of unique considerations that impact the potential viability of new decentralized physical infrastructure networks.
DePIN Primitives
DePIN’s killer use case is rooted in its novel approach to bootstrapping the supply and demand side of new infrastructure networks through the use of a native crypto token.
At the most fundamental level, these networks work as follows:
DePIN networks drive supply-side growth by incentivizing participants with a speculative network token. This could include Wi-Fi access points in a decentralized wireless network, or weather stations in a decentralized weather data network.
As supply grows, customers access or use the network with the same token, creating a feedback loop where value capture flows back to supply-side network participants. Typically, a buy-burn mechanism is in place to create deflationary pressure on the token.
What Is A DePIN Discount Rate?
Our valuation model introduces the concept of a DePIN Discount Rate, which is used alongside traditional valuation factors (ie TAM Penetration based on PMF viability, unit economics etc.) to adequately represent risk when projecting demand-side growth within the context of these multi faceted networks.
A DePIN Discount Rate can be conceptualized as a special purpose discount rate that accounts for variables uniquely impacting DePIN’s supply and demand side viability, and gets applied when computing a network’s net present value as outlined in our full valuation framework. The more risk that's applied to any one of these variables, the higher the overall discount rate for a network.
Because the DePIN Discount rate specifically evaluates these unique network risks, it is important to emphasize that it cannot be applied in a vacuum. “Traditional business” viability factors such as the competency of a team, the size of a prospective market, and the likelihood of achieving product market fit are still fundamental.
Unpacking The Components Of The DePIN Discount Rate
Two quick disclaimers as we dive in:
At the risk of being overly pedantic, we reference TAM (Total Available Market) as it relates to the unique dynamics of DePIN networks throughout the model, given its broad understanding. However, in most contexts we are really evaluating a network’s Serviceable Addressable Market, or SAM. That said, we will be using TAM in this report for the sake of simplicity.
Finally, all of these variables can be used as input factors, alongside additional components, for computing a “DePIN Viability Index” to evaluate and compare projects across the DePIN space. This concept is occasionally referenced below, and a new report on our DePIN Viability Index will be coming as a fast follow to what’s shared here.
The primary components of our DePIN Discount Rate include:
Minimum Viable Critical Mass of Supply Side Participants
Supply Sensitivity As Part Of A Marginal Propensity to Increase TAM
Demand Counterparty Risks
Output Value Theta Decay
These components are further evaluated within the context of ancillary variables including:
Supply To TAM Efficiency (TSE Ratio)
Supply To TAM Growth Efficiency (TGE Ratio)
Supply Quality Variance
Supply To TAM Multiplier
CapEx To TAM Efficiency
TAM Sensitivity To Supply Side Decentralization (TSF)
Supply To TAM Penetration Efficiency (TPE Ratio)
Metcalfe Alpha
for adequately evaluating systemic risks stemming from both the supply and demand side of these decentralized physical infrastructure networks.
Let’s unpack these further, and look at some examples.
Minimum Viable Critical Mass of Supply Side Participants:
This variable is used to attribute a risk weighting for how many node participants/users are required in order to service a network’s smallest addressable market. Generally speaking, networks that require fewer supply side participants to meet the prospective demand from an addressable market, the better.
While this factor may be perceived as one that simultaneously reduces a network's “moat”, de-risking a fledgling network's ability to bootstrap enough supply to support even the smallest level of demand should take precedence. As a network validates viability around bootstrapping the minimum amount of supply side participants to satisfy prospective demand from larger addressable markets, the weighting of this factor in a DePIN Discount Rate should decrease.
Supply Sensitivity As Part Of A Marginal Propensity To Increase TAM
In a nutshell, this phenomenon accounts for the relationship between net new supply side growth and its impact on increasing the addressable market size for a network.
Moreover, the impact of this variable should be contextualized based on:
The maturity of a network
Relationship with Supply To TAM Penetration Efficiency, when applicable
It is worth noting that the relationship between supply growth and its impact on increasing TAM != increasing TAM penetration viability, which is arguably the most important consideration in the potential success of any DePIN network (i.e. a measure for evaluating product market fit). That being said, these concepts are evaluated independently from one another, because in the majority of cases where market opportunity growth is elastic with respect to supply, and we don’t have sufficient data to evaluate penetration efficacy, understanding how supply impacts the potential for revenue generation takes precedence.
This relationship is broken down into four categories:
Linear
For every net new supply side participant that joins a network, the respective market size grows at an equivalent rate. While a linear correlation might exist between supply side growth and TAM, this does not take into account the “multiplier” relationship existing between how much demand potential is created as a result of supply. This factor is captured by a Supply to TAM efficiency ratio discussed later.
Staircase
“Unlocking” progressively larger market opportunities, occurs in tranches of supply side growth on a network. For example, if a DePIN with 10,000 nodes can service a $10 million market, but reaching a $100 million market requires at least 100,000 nodes, then effectively all supply-side growth between 11-99k nodes will yield no new potential for value capture expressed by a larger market opportunity.
This might apply to a hypothetical DePIN project capturing vehicle data, where a $10m insurance auction market can be serviced once 10k connected vehicles join the network. But servicing the full $100m vehicle insurance market is not possible until at least 100k connected vehicles join the network.
Exponential
TAM growth outpaces net new supply side costs to the network at an increasing rate. You can think of this as a form of economies of scale, but rather than improving the network’s overall operational efficiency, it relates to expanding the potential market size as the network grows.
If we consider a project like WeatherXM, their addressable market should grow exponentially as the supply side growth expands linearly.
For example, a single weather station in one city might open up a $10 million market opportunity at a cost of $5,000 to the network. When you add another weather station in a comparable second city, the market opportunity grows by an additional $10 million plus the value of an entirely new market now serviceable by collecting data between both locations, at a supply side cost that has only grown by a factor of one.
This dynamic effectively creates a growing positive externality, as the network’s potential becomes increasingly more valuable without a proportional increase in supply-side costs.
No impact
Certain DePIN projects have a TAM that is inelastic with respect to supply, where the market opportunity remains fixed regardless of how many nodes are on a network. For instance, XNET, a decentralized wireless network providing carrier offload services, has a TAM equal to the total value of all the paid offload data from AT&T, which remains constant, irrespective of XNET having 1 or 1 million nodes on the network.
Networks where TAM is inelastic to supply growth yield the least amount of risk. Therefore we can consider applying a greater weighting to additional factors that impact to final calculation of this component to sufficiently measure supply to TAM penetration potential. Those factors include variables such as TAM Sensitivity to Supply Decentralization (TSF) and Supply Quality Variance (SQV), amongst others discussed next.
3. Demand Side Counterparty Risk:
The general concept is used for evaluating a network’s demand side risk based on:
Customer Centralization
Vertical Serviceability Fragmentation
Time To Value Capture
Key-Market Novelty Beta
Third-Party Execution Risk
Customer Centralization Risk:
This point is self-evident, and can be exemplified by a network that only has a handful of prospective customers representing a specific market opportunity. In this case, the demand side counterparty risk is very high considering only a few “No’s” are required for rendering the entire network obsolete.
Computing a risk factor for this variable should be done with logarithmic decay, with the greatest weight being applied to networks suffering from high customer centralization risks.
This removes estimation biases that could over inflate the risk to a network. Computing this factor can be done on a case by case basis, or formulaically, however be warned, a formulaic approach can have undesired consequences depending on the inputs selected.
That equation works as follows:
Where:
Max Weight Factor = The upper limit weighting this variable represents within the category (ie 25%)
C = Estimated number of customers.
Decay Rate = the rate at which the risk factor decays
A higher decay rate results in a faster decay.
A lower decay rate results in a slower decay.
Vertical Serviceability Fragmentation:
This variable can be viewed as a bit of a catch-22, because on one hand, a network's ability to service multiple markets de-risks the overall chance of failure, but can also introduce execution risks by way of needing larger project teams, with wider ranges of subject matter experts, and oftentimes more tooling for effectively taping into these “unrelated” market segments. Given the potential variance here, this factor will require additional discretion when applying it to a network valuation model.
Our recommendation is to evaluate this factor independently from the customer centralization risk variable, on a project specific basis where the weighting applied grows at a faster rate as market fragmentation increases linearly. This should ideally create a blended risk factor between customer centralization and resource constraints created by too much potential fragmentation.
Applying a risk weighting here can be done on a case by case evaluation, or formulaically.
Where:
Risk Factor: The final value representing the risk associated with the number of market segments, which increases exponentially as more uncorrelated segments are added.
Max Weight Factor: The upper limit weighting this variable represents within the category (ie 25%)
E: The risk factor grows exponentially with the number of market segments.
M: The number of market segments in the network. As M increases, the Risk Factor increases exponentially.
Lambda: A constant that controls the rate of exponential growth of the Risk Factor. The larger the value of lambda, the more rapidly the risk increases with each additional market segment.
A higher lambda value causes the risk to grow faster as the number of segments increases.
A lower lambda value causes the risk to grow slower as the number of segments increases.
Time To Value Capture:
Some projects may be affected by long sales or payment cycles, where demand side value capture does not immediately materialize within a network. Networks subjected to this consideration may have high demand viability, but the downstream effects from this variable impact supply side participants as a potential risk.
This factor can be a result of deals that take long periods of time to complete, or payment terms that are not immediately reflected as revenue onchain.
Key-Market Novelty Beta:
One of the best aspects to DePIN as a category is that many of these decentralized physical infrastructure networks are able to improve upon and bring new forms of value to easily quantifiable, well established markets that have weathered countless economic cycles.
Crypto markets, on the other hand, can be very volatile where demand viability becomes heavily correlated to market cycles. While servicing sectors like the crypto market is exciting, and can yield potentially high returns when the market is frothy, DePIN networks that primarily service novel markets brings increased risk.
Evaluating overall product market fit, or the impact of macro cycles as it relates to project viability within different customers segments, is done independently of this consideration, as those concerns impact all businesses, not just DePIN networks. However, considering the intersections between the crypto market and DePIN, it is prudent to ensure this factor is measured as part of a network's overall risk profile.
If you are familiar with the concept behind a key-man clause, this factor borrows on that general premise, but instead of having risks that are correlated to one maverick founder, this metric evaluates risks to servicing a novel but equally risky market. DePIN Networks that aim to service crypto customers exclusively have greater associated risks as a result.
The most straightforward way to evaluate this risk factor is on a binary scale, as a percentage of the total Demand Counterparty Risk weighting.
Third-Party Execution Risk:
While DePIN Networks are fundamentally comprised of supply and demand side participants, it would be disingenuous to skip over the fact that there is almost always a third-party who is consequential in overseeing or shepherding the network’s success. We evaluate this risk factor on the basis of how much team involvement is required for generating demand side revenue. For example, platform networks like Dimo have the potential to generate revenue independently of their team going out and “striking deals” which yields less systemic risk to the viability of the network as it relates to demand side value capture.
4. Output Value Theta Decay:
This variable captures the potential risk created between the delta in time and the output of whatever is “created” by a DePIN network. The concept of output value theta decay is most relevant with data DePIN networks, but can remain applicable across the space as a whole. Networks with a higher output value theta decay require more assurance from continual supply side participation.
The impact of theta decay on DePIN projects (especially the ones that produce data) can affect both the TAM and market penetration growth potential.
We apply this variable to account for 2 key considerations within a network:
Is there an immediate binary value exchange relationship between production and consumption, creating a direct zero sum value correlation between the two. (Think of energy that is produced in a basic decentralized energy network. If that energy cannot be stored, and is not consumed at the point of creation, its ability to generate revenue for the network almost immediately goes to zero.
If an immediate binary value relationship does not exist, over what time horizon does a network’s output lose value over time.
Where:
Max Weight: The upper limit weighting this variable represents within the category (i.e. 25%)
Lambda: The estimated decay rate (controls how output value decreases over time).
T: Time, measured in months or years (depending on the timescale you're working with).
The lambda value selected will be project specific.
Variables Used For Further Evaluation
These variables are either applied directly to the components above, or as a fifth weighting category (depending on the project) as outlined in the second part of this report.
Supply To TAM Multiplier (TSM):
We use this variable to understand how much value a single node represents in terms of bringing demand side potential.
Applying this ratio requires knowing the estimated addressable market size, and the estimated max number of supply side nodes for that given market, and can be further evaluated with a Supply Quality Variance variable discussed shortly. (If no SQV exists, then all nodes will have an operating efficiency of 1).
This ratio should remain constant in networks with a linear relationship between supply and addressable market growth, (barring additional supply side variance considerations) and will serve as a “snapshot” for networks where supply has an exponential or a stair step relationship with TAM growth (ie this ratio will continue to change for networks in this category). This variable does not apply to networks where TAM is inelastic with supply changes.
The higher this multiple, the more opportunity a net new supply node brings to a network.
Supply To TAM Efficiency (TSE):
This variable allows for comparison between networks, by capturing how supply impacts supporting a specific addressable market. The higher the ratio, the less supply is required.
Applying this ratio requires knowing the estimated addressable market size, and the estimated max number of supply side nodes for that given market.
This ratio should remain constant in networks with a linear relationship between supply and addressable market growth, (barring additional node variance considerations).
This ratio will work as a “snapshot” for networks where supply has an exponential or a stair step relationship with TAM growth.
For networks where TAM is inelastic with supply, this ratio does not directly apply. however, it can be used to see how much a single node moves the needle for TAM penetration.
The Supply To TAM Efficiency ratio is calculated by:
We can also represent the supply side CapEx efficiency as it relates to TAM for comparing different networks with:
Supply To TAM Growth Efficiency (TGE):
Networks where TAM is elastic with supply growth, and a non-linear relationship exists, a network’s addressable market can grow at an increasing multiple of supply, and is represented by a Supply to TAM Growth Efficiency ratio.
This ratio helps us understand how efficiently new supply side growth impacts TAM growth for networks who have a exponential or stair step relationship between
Networks with a linear relationship will be a fixed rate at all points of the network growth cycle
Where:
Mi: the initial market size for market i
Mi′:the final market size for market i
Ni: the initial number of supply side nodes for market i
Ni′: the final number of supply side nodes for market i
k: the total number of markets
Then:
Market Growth for Market
i:ΔMi=Mi′−Mi
Node Growth for Market
i:ΔNi=Ni′−Ni
Supply To TAM Penetration Efficiency (TPE):
This variable is important for evaluating how effectively a network is capturing revenue within their addressable market.
That being said, in networks where TAM is elastic with supply, this metric will be a “lagging indicator”, and should be evaluated in the context of a growth rate when applying it to future revenue capture projections.
This ratio is calculated by:
Where:
R = actual revenue
M = Estimated market size
SC is defined as Supply/Capacity
Outputs are represented as a percentage.
Supply Quality Variance
In certain DePIN networks (such as in decentralized wireless and compute networks) not all supply-side nodes are equal in terms of their overall impact in bringing value. This means that the impact each node has on key metrics like TAM penetration and TAM growth can vary dramatically. These factors will be project specific, but might include node location, utilization, or accessibility.
Because this factor varies from network to network, it is difficult to algorithmically calculate a ratio based on a set of inputs. We will show how this can be estimated given the existence of historical data, and how to approximate a plug figure. Either approach used to compute this variable can be applied to a DePIN Discount Rate, or a DePIN Viability index. Be cautious when using historical data for estimates, as there's a proclivity for it to skew results in an unintended manner.
Approximation
If supply quality variance does not exist, apply a weighting of 1.
If supply quality variance does exist, apply a weighting factor between .3-.7, depending on how severe that variability can impact a network, where the more estimated risk, the lower the weighting factor.
Formulaic approach based on historical data (assumes no change moving forward, and we have a separate report to account for how TAM growth can change this formula in non-linear cases if readers are interested)
Network where Supply Side Growth as a part of a marginal propensity to increase TAM has a direct relationship:
Data Requirements:
Actual Revenue per Node (Ri)
Max Revenue per Node: The maximum revenue each node could theoretically generate. (TAM / minimum number of fully utilized nodes)
Steps:
Performance Each Node:
Average Performance:
\(\text{Average Performance Ratio} = \frac{\sum \text{Performance Ratio}_i}{N} \)Standard Deviation of Performance:
\(\text{SD of PR} = \sqrt{\frac{\sum (\text{PR}_i - \text{AP})^2}{N}} \)SQV Calculation:
Where Max Possible SD could be set based on data or theoretically:
Interpretation:
SQV = 1: All nodes perform at maximum potential with no variability.
SQV = 0: Extreme variability.
Network where TAM is inelastic with respect to supply side growth:
Steps:
Calculate the Average Revenue Across All Nodes
Standard Deviation Of Supply Revenue:
Coefficient of Variation (CV):
SQV Calculation:
\(\text{SQV} = \frac{1}{1 + \text{CV}}\)
Interpretation:
SQV = 1: No variance across nodes.
SQV closer to 0: Significant performance variability across nodes.
These variables can then be mapped directly to a discount rate risk factor weight through the following formula:
Where:
SQV Impact: the potential impact from supply variance, with values closer to 1 reducing risk and values closer to 0 increasing risk.
Max Risk Factor the upper limit of risk within the overall model
TAM Sensitivity To Supply Side Decentralization
This ratio accounts for the fact that certain networks have a direct relationship between the decentralization of supply side participants and a serviceable addressable market. Calculating this ratio generally requires an understanding of how a network works as the supply side number is “backed into” given:
1. the number of locations (hexes)
2. node capacity per hex required for an addressable market.
The easiest way to conceptualize this TAM sensitivity ratio is:
Where:
TSF = TAM Sensitivity Factor
L= # of hexes in a network / max # nodes per hex
N= total number of nodes required to saturate TAM
Networks unaffected by supply side decentralization have a ratio of 1, as they are inelastic. As TSF approaches zero, the greater a TAM is affected by the decentralization of supply in a network.
Lower TSF != less valuable networks per se but is used to quantify the relationship between decentralization and its impact on demand side potential as a risk consideration. Networks with a low TSF may be disproportionately affected by large concentrations of supply. This metric is particularly beneficial when used in the context of other factors outlined above.
Metcalfe Alpha:
Many DePIN networks function as platforms which have the potential to grow exponentially, as new, oftentimes unforeseen markets and opportunities are created from their novel developments. This consideration should not be ignored, but also evaluated in a manner that doesn’t artificially over estimate the impact on capturing demand value.
We propose that this concept be evaluated independently from supply sensitivity as part of a marginal propensity to increase TAM, and specifically evaluated on the basis of whether the potential impact on demand side growth risk is reduced or remains neutral (unchanged).
The rationale here is that modeling potential future network effects in new value chains is both speculative, and often happens as a result of unanticipated factors. Therefore, evaluating this point on the basis of an ability to increase TAM/revenue is a risky proposition. However, recognizing that new markets may be created should be factored into the overall network viability as it relates to demand side growth risk.
Impact On Future Demand Side Growth Through A Metcalfe Alpha Coefficient 1:
For example, consider a simple version of the Helium Network that was built to go after a $1 Billion smart home market. A perspicacious builder may recognize this network's potential in powering a new, multi-billion dollar RTK network, which in turn is used to support 5 new multi-billion dollar networks. This value capture as it relates to this example may have been initially unexpected, but the potential has existed all along, creating a slight reduction in measuring demand side growth risk.
Impact On Future Demand Side Growth Through A Metcalfe Alpha Coefficient of 0:
Now consider a simplified version of the Helium Mobile Network, which is purpose built to only service the carrier-offload market. In this scenario, the “limited” nature of the network restricts the likelihood that future, unforeseen market opportunities with growing network effects will be created, and therefore the overall risk to demand side growth potential remains unchanged.
Additional network ratios that may be worth considering:
CapEx to network value ratio
Supply To CapEx Ratio
Supply Stickiness
Sunk cost impact, activity requirement, opportunity cost
Part 1 of this report outlines the individual components of the DePIN discount rate, where part 2 will take a closer look at how this figure gets computed and applied when modeling a project’s value in our upcoming DePIN Valuation Model.
It is worth repeating that everything outlined above is simply part of a framework for evaluating viability as it relates to supply and demand growth in these novel DePIN networks. But the uniqueness that comes along with each project requires discretion as part of that evaluation process.