The hangover from the 2008 Great Recession still lingers today, even after nearly 2 decades of relative prosperity. Its impact reshaped how many look at business risk, bringing this notion of “recession resistant businesses” to the forefront of our collective vernacular. While I was too young to experience the ramifications of 2008 directly, the downstream impacts on people I would eventually encounter professionally, left a strong impression about how economic tectonic shifts - regardless of their catalyst - reshape the world as we know it.

That takeaway feels more relevant now than ever, with Artificial Intelligence surpassing even the highest of expectations. It’s apparent that AI is currently creating a tectonic shift greater than any technological revolution or economic upheaval in modern history.

I am a staunch proponent of embracing this change; trillions of dollars in new value will be created - but it’s impossible to ignore the reality that many companies and people are on the precipice of being displaced. From software development and product design to finance and accounting - these AI models have gotten to the point where they can do the job of a skilled programer, analyst etc. with high precision and remarkable speed. What’s more, the quality of these models have progressed at a staggering pace - just look at the last 4 years of AI Will Smith eating spaghetti.

Given that AI is fundamentally changing market viability in real time, investors are grappling with separating winners from losers in a fast changing landscape. The term “recession-resistant businesses” that entered our lexicon in 2008 is now being usurped by a new one: “AI-resistant businesses.”

AI Resistant Businesses And DePIN

Infrastructure has historically been the overlooked constant of economic or technical upheaval. That dynamic is no different today. The most resilient businesses aren’t those racing to adopt AI at the risk displacement; they’re the ones whose value proposition exists independently from it.

DePIN projects represent some of the clearest examples of this principle in practice.

Take Helium, for example. The decentralized wireless infrastructure enabled by the network today, is a utility that the world will depend on regardless of AI disruption. Connectivity isn’t a feature, it’s a prerequisite.

Geodnet operates in a similar vein, providing decentralized RTK services that underpins AI-agnostic markets, like precision agriculture and drones for surveying.

Neither of these networks becomes less critical because a model got smarter. If anything, an increasingly automated world raises the demand for the physical infrastructure layer beneath it.

This is the distinguishing characteristic of a truly AI-resistant business: its utility is structural, not conditional. The question I find myself asking isn’t whether a project is AI-compatible, but whether it would matter if AI had never existed at all. For projects like these, the answer is unequivocally yes.

AI as a Growth Catalyst

Resilience, however, is only half of the equation. While many businesses are bracing against the risk of AI disruption, there are others where AI creates a direct growth catalyst. Identifying which companies or projects are impacted by the latter, is where significant investment asymmetry lies.

AI models are only as capable as the data that powers them. Projects like 375.ai sit directly in that critical supply chain, capturing the 80% of commerce data that exists offline, and has remained otherwise inaccessible for AI training. What’s unique about 375.ai in this context, is that capturing novel data sets for AI isn’t a prerequisite for their success - it’s simply a new vertical opportunity that expands their TAM.

This reality doesn’t stop at data collection. Compute is quickly becoming one of the most constrained resources on the planet, an issue further exacerbated by the growing adoption of AI. This positions networks like Akash to absorb demand that centralized providers are structurally ill-equipped to meet. Similar to 375, Akash’s value proposition exists independently from AI’s continued ascent.

This is the distinction worth reiterating: the infrastructure value for projects like 375.ai and Akash exists without AI, but their market value compounds directly with it.

Modularity In Times Of Unprecedented Change

What makes DePIN networks compelling in this conversation isn’t just their resilience, it’s their adaptability. DePINs are modular by design, enabling rapid pivots to meet demands not anticipated at their inception.

Conversely, centralized providers are burdened by rigidity that increases their risk of disruption: slow-moving procurement cycles, legacy architecture, and institutional inertia that makes rapid adaptation structurally difficult.

DePIN networks are the antithesis of this structural limitation. Their decentralized nature means capacity can expand, shift, and orient toward emerging demand without the friction that plagues their centralized counterparts.

This quality means that many DePINs aren’t just surviving the changes from AI, they’re positioned to serve it. The same modularity that insulates them from disruption is what makes them uniquely capable of meeting the infrastructure demands that AI is rapidly creating.

Final Thoughts

AI disruption isn’t coming, it’s already here. Just as 2008 forced a reckoning with economic fragility, AI is forcing one of widespread business viability. The investors who navigate this transition well won’t just be the ones that identify who benefits from AI — they’ll be the ones who understand which businesses remain indispensable regardless of its end state.

DePIN sits at a rare intersection: infrastructure that is resilient enough to withstand the disruption, and modular enough to compound directly with it. That combination is difficult to find, and even more difficult to bet against.

We’re thrilled to announce that DePIN Pulse V2 is officially live! This major update brings a comprehensive suite of tools, insights, and opportunities to help you explore and earn in the world of Decentralized Physical Infrastructure Networks.

What’s New in V2

DePIN Pulse V2 introduces several powerful features to enhance your experience:

• 🔗 Chains Dashboard: Explore DePIN activity across multiple blockchains, including Solana, Ethereum, and more. The Chains Dashboard offers a comprehensive view of DePIN projects, allowing you to track performance and trends across different networks.

• 🚀 Pre-TGE Projects: Discover early-stage DePIN projects before their token generation events (TGEs). This section provides insights into upcoming opportunities, helping you stay ahead in the DePIN space.

• ⛏️ Mining Opportunities: Find and compare DePIN mining opportunities across various projects. Whether you're interested in hardware mining or contributing resources, this section helps you identify profitable ventures.

• 🎯 Quests: Engage with DePIN projects through interactive quests. Complete tasks, earn rewards, and deepen your involvement in the ecosystem.

• 📈 Yield Opportunities: Analyze DePIN yield opportunities, including staking and lending options. This feature helps you maximize returns on your DePIN investments.

• 💰 Fundraising Tracker: Stay informed about fundraising activities within the DePIN space. Track investment rounds, funding amounts, and participating investors.

• 🧠 Compute Marketplace: Access decentralized compute resources offered by DePIN projects. This marketplace connects you with providers offering computing power for various applications.

• 📅 Events Calendar: Keep up with upcoming DePIN-related events, including conferences, webinars, and community meetups. Never miss an opportunity to connect with the DePIN community.

• 🎙️ Podcasts: Tune into podcasts featuring discussions with DePIN project leaders, developers, and industry experts. Gain insights into the latest trends and developments in the DePIN ecosystem.

What’s Coming Next

We're continuously working to enhance DePIN Pulse. Upcoming features include:

• Advanced Analytics: Deeper insights into DePIN project performance market opportunities.

• Mobile App: Access DePIN Pulse on the go with our upcoming PWA release.

• Expanded Data Sources: We’re continuously adding new projects and metrics to ensure DePIN Pulse remains the most comprehensive DePIN resource available.

• Social Metrics: Community sentiment tracking and insights.

Join Our Community

Stay updated with the latest in DePIN by joining our mailing list. Receive news, updates, and exclusive insights directly to your inbox.

Subscribe now

Explore DePIN Pulse V2 today and become an active participant in the decentralized infrastructure revolution.

In the trenches of technological disruption, the story of Bell-Northern Research (BNR) and its relationship with AT&T serves as a classic tale of innovation challenging incumbency. Today, decentralized wireless (DeWi) networks are writing a similar narrative against traditional telecom giants.

While the specifics differ, the underlying dynamics remain strikingly familiar: nimble innovators exploiting overlooked opportunities, technological shifts creating leverage, and legacy giants underestimating the potential of decentralized systems.

The Rise of Bell-Northern Research (BNR)

In the mid-20th century, AT&T held an iron grip on telecommunication infrastructure in North America. Bell Labs, its famed research arm, was synonymous with groundbreaking technological advancements, including the transistor and fiber optics. However, dominance breeds inertia, and AT&T grew increasingly bureaucratic and risk-averse.

Enter Bell-Northern Research, a Canadian R&D powerhouse founded in 1971 through a partnership between Northern Electric and Bell Canada. Unlike Bell Labs, BNR was leaner, more experimental, and focused on practical innovation. It became instrumental in pioneering digital switching systems, particularly with the DMS-100, a revolutionary digital switch that transformed telephony.

While AT&T was fixated on analog systems and incremental improvements, BNR leaped ahead with digital solutions that were more scalable, efficient, and adaptable. Northern Telecom (later Nortel) capitalized on BNR's breakthroughs, grabbing significant market share from AT&T.

Enter the DeWi Era

Fast forward to today, and we see echoes of this battle in the decentralized wireless (DeWi) movement. Projects like Helium are challenging traditional telecom providers by building decentralized wireless networks powered by blockchain technology and community participation.

DeWi leverages the inefficiencies of legacy telecom systems: massive capital expenditure, centralized infrastructure, and slow innovation cycles. Much like BNR's focus on digital switching in a world dominated by analog, DeWi focuses on lightweight, scalable infrastructure that turns everyday devices and individuals into network providers.

Where traditional telecoms rely on billion-dollar deployments of towers and fiber optics, DeWi networks harness existing infrastructure and consumer-grade hardware to create flexible, community-powered networks.

Key Parallels Between BNR vs. AT&T and DeWi vs. Telcos

1. Technological Shifts: BNR bet on digital switching, while DeWi bets on blockchain and token incentives. Both technological bets bypassed incumbent bottlenecks.

2. Agility vs. Inertia: BNR operated with a nimble, innovative structure compared to AT&T's bureaucracy. Similarly, DeWi projects iterate rapidly and pivot effectively compared to risk-averse telecom giants.

3. Resource Efficiency: BNR's digital switches reduced infrastructure costs. DeWi minimizes the need for centralized infrastructure by incentivizing community contributions.

4. Underdog Advantage: AT&T, blinded by its market dominance, underestimated BNR's innovation. Telcos today similarly overlook the long-term potential of decentralized networks.

The Challenges Ahead for DeWi

However, it's not all smooth sailing. Nortel, despite BNR's successes, ultimately collapsed due to poor management and market missteps. DeWi networks face their own existential challenges:

• Regulatory Hurdles: Telecom is one of the most heavily regulated industries globally, and decentralized networks operate in a gray area.

• Scalability: While community-driven networks are efficient, scaling them to meet global demand remains a challenge.

• Interoperability: Fragmented DeWi networks risk becoming silos without robust standards for interoperability.

What Can DeWi Learn from BNR?

1. Focus on Practical Innovation: BNR's success lay in solving immediate problems with digital switches. DeWi must similarly prioritize real-world utility over speculative tokenomics.

2. Partnerships Matter: Bell Canada’s support gave BNR legitimacy. DeWi networks should seek partnerships with progressive telecom providers.

3. Guard Against Complacency: Nortel's downfall stemmed from poor long-term strategy. DeWi must remain agile even as they grow.

The Road Ahead

Just as BNR reshaped telecom infrastructure, DeWi has the potential to redefine connectivity in the 21st century. Whether it’s providing affordable connectivity in underserved regions, enabling IoT networks, or creating privacy-preserving wireless infrastructure, DeWi represents a fundamental shift.

The question remains: will traditional telcos learn from AT&T's historical missteps, or will they, once again, be disrupted by the little guy with a bold vision and better technology? Only time will tell, but history suggests that the incumbents should be watching their rear-view mirrors very closely.

1. Burn & Mint Equilibrium

2. Buyback And Burn

Exact implementation specifics vary, but by and large, here’s an overview on how these models works:

Burn and Mint Equilibrium:

When a customer purchases 10 units of a resource generated by a DePIN, tokens representing the value of those 10 units, priced in an external currency (e.g., fiat), are burned (removed from circulation) and minted (distributed) to supply-side participants.

Buyback and Burn:

Revenue generated from the DePIN network is used to purchase and remove tokens from supply. Unlike Burn and Mint, the mechanism for issuing rewards to supply participants varies across projects.

Despite their prevalence, these models aren’t flawless. DePIN participants are growing increasingly aware of the challenges facing DePIN token economic models today, primarily:

• Inflation risks

• Network tokens serving different functions for multiple stakeholders, which changes dynamically based on the maturity of the network

Resulting in long-term sustainability uncertainty and challenges impacting valuation models.

Before unpacking the specific issues stemming from these approaches, it’s important to contextualize DePIN networks today while considering what they might look like in the future, specifically around their different stages of growth, how demand value capture occurs, and the role of tokens across these stages and stakeholders.

Level Setting the State of DePIN Networks Today

DePIN networks in their current state (and likely for the foreseeable future) have 2 primary phases of growth:

• Pre-revenue supply participation

• Fiat based revenue generation enabled by supply participants

One of the most compelling aspects of DePIN is its focus on massive TAMs, largely comprised of Web2 customers paying to access these networks with fiat. Given the nature of these customers, it's reasonable to expect fiat-based revenue will remain the dominant form of value capture for the next 5 to 10 years.

On Tokens:

DePIN tokens should be used to align incentives. The challenge is incentives differ across network stakeholders at different stages of network growth. This becomes exceptionally hard to balance when a single network token functions as a medium of exchange, unit of account and store of value.

On Token Value and Network Valuations:

The relationship between token/network valuations and tokenomic models are deeply intertwined.

Because tokens serve as more than just a medium of exchange, in order for a network’s value to grow, participants need a genuine reason to hold the token, over and above clever financial engineering mechanisms which force that behavior.

There are two primary primary drivers behind why someone would hold a network token, both of which are directly impacted by tokenomics

1. The market has underpriced the value of a network

2. There will be future value accrual to the token

These factors are certainly related, but evaluated independently given that tokenomic dynamics vary across projects.

Network Valuations:

In traditional financial markets, we have well understood frameworks for valuing different asset classes. Tokens are not stock, but to some degree, a project's market cap should reflect a combination of current and future value generated by the network, which can be used to derive the price of an individual token, (while considering their additional roles as a medium of exchange and unit of account). Evaluating whether or not the market has mispriced a network does require a new type of valuation framework, however, despite the exact approach used, a tokenomics model must enable straightforward forecasting of key data points—such as tokens outstanding at various revenue targets across all growth stages—that serve as inputs for potential valuation models.

Token Value Accrual:

Even if a DePIN project has a “reasonably priced market cap”, token economics directly impact individual token value accrual which affects holding incentives. For example, a network’s market cap may represent a fair value today, but holders can independently expect token price appreciation over time even if market cap growth stagnates, due to specific burn mechanisms, providing a strong reason to hold.

For those interested in DePIN token valuations, the Parameter Research team is developing a framework for assessing token value, starting with the introduction of the DePIN Discounted Rate.

The Problems With Today’s Models

Burn and Mint Equilibrium:

Assuming true equilibrium exists, the burn and mint model works moderately well once a DePIN network has demand, as tokens emitted to reward supply participants are directly proportional to what customers pay for resources on the network. This 1-1 relationship creates a quasi-quantifiable emission schedule (where token price variability impacts how many tokens are burned/emitted), but with a direct financial relationship between tokens and network revenue that can be used to reasonably forecast and estimate token value.

Emissions and Inflation Issues:
To incentivize supply participants before demand is established (e.g., in pre-revenue stages or early revenue phases with insufficient demand), networks must emit tokens that cannot be burned, leading to perpetual inflation.

Since revenue-based token burns are the only way to reduce emissions, all tokens generated outside of demand capture are effectively an externality as they impact the price, which impacts marginal token burn.

While some inflation may be acceptable to bootstrap the network (and can be “priced in” to valuation models), estimating the target inflation rate ahead of sufficient demand is a significant assumption that will rarely be accurate. This has led many networks to adopt fluid mechanisms for issuing early-stage supply rewards, increasing runaway inflation risks and complicating forecasting due to a lack of token float change rate assurances.

Buyback and Burn:

The Buyback and Burn model faces even greater risks of runaway inflation and highly variable forecasting.

Emissions and Inflation:

For the Buy and Burn model to be sustainable long-term, there must eventually be near equilibrium between the fiat-denominated value of emitted tokens and the revenue captured by the network. Otherwise, inflation will continuously erode token value.

This means if early-stage token rewards exceed the revenue they generate, the network must eventually issue rewards at a fiat-denominated rate lower than the revenue generated to avoid perpetual inflation. Balancing this relationship is additionally dicey when tokens ideally represent some combination of current and future value accrual.

Fixed Resource Denominated Reward Emissions:

Networks with a fixed reward rate tied to resources provided by supply participants are particularly vulnerable to runaway inflation, as these reward-rates must be continually evaluated and adjusted based on token price.

For example, if a network issues 1 token per unit of resource over a fixed 1-year period, and has a current token price of $1.50 with USD-denominated revenues of $1 per unit, achieving inflation equilibrium requires eventually emitting a proportional number of tokens at a USD-denominated value of $0.50 (assuming 100% of revenue is used to buyback and burn tokens). Now, if the token price rises to $3 during the year, the equivalent number of tokens must be emitted at a USD-denominated value of $0.25 to maintain parity.

This introduces a significant amount of counter-party risk that is exceptionally prone to short-term decision making

Runaway inflation can be mitigated to some degree, by using a fixed reward pool divided amongst supply participants based on their proportional contribution, however, long-term value accrual risks to individual tokens still persist.

Valuations:

Beyond inflation risks, the Buyback and Burn model complicates valuations (even when a network’s emission rate is predictable) as multiple assumptions are required for network forecasting and modeling the delta in individual token price over time.

This is because emissions and revenue are independent from one another, with token burns serving as the sole value driver, and given token price and the ability to burn are inversely correlated, the number of tokens removed from supply in the future is an independent variable, affecting token price.

It’s worth noting that burn and mint networks which emit tokens independently from demand face similar challenges.

From an analyst’s perspective, this forces one of two trade-offs: assume the market is always perfectly efficient (where future token burn is commensurate to a fair market cap based on expected revenue), or attempt to account for potential “market-pricing inefficiencies" with variable token burn rates based on expected vs actual value while simultaneously modeling potential revenue growth over time.

Requirements For Developing An Alternative Model:

DePIN networks need a mechanism to reward supply side participants independently from how Demand is “represented” on the network , while maintaining a financial relationship between the two.

Core tenants of a great model:

• Decouple the direct emission relationship between supply and demand for both token creation and removal while preserving a quantifiable link between the two.

• Ensure controlled, predictable emissions that incentivize supply-side participants in both pre-revenue and post-revenue stages, without introducing perpetual inflation or externalities

Proposed Idea: “Stake & Stable”:

Supply side emissions are programmatic, similar to Bitcoin, and issued as a reward pool per epoch. Supply emissions should decrease logarithmically over time.

Reward pool emissions are distributed to node operators differently, based on the state of the network. This could be thought of as KPI based emissions, where the KPI used to determine when the reward distribution mechanism changes is revenue, creating two stages of rewards for nodes:

• Pre-revenue: network nodes earn tokens emitted per epoch, based on their “contribution” to the network. E.g., with 100 nodes online, 1 node would earn 1% of an epoch reward pool (assuming all nodes are equal).

• At the transition point between these two stages of the network, some percent the tokens previously emitted to supply side participants must be staked for nodes to continue participating in the network. For example, 80% of the median number of tokens distributed during this time might be required for continued participation.

  • This increases the incentive to hold, while ensuring that willing node participants have the ability to continue running the network

• Post-revenue: Nodes earn tokens from the epoch reward pool based on their contribution to revenue generation. For example, a node generating 10% of the network’s revenue earns 10% of the epoch emissions.

  • Introducing some type of UBI or PoC rewards is still possible with this approach, where a fixed percentage of rewards are distributed to all online nodes per epoch. Projects can also use earning multipliers to incentivize nodes based on location or other relevant factors from this fixed reward pool.

As demand enters the network, fiat-revenue is issued as a type of stable coin, and placed into its own “pool”.

• A fixed percentage of this pool (e.g., 5%) is distributed to node operators on a pro-rata basis based on their staked tokens, incentivizing nodes to earn and stake more tokens by providing resources. This distribution occurs on a fixed schedule, such as every epoch.

• Token holders can also choose to burn their tokens in exchange for a pro-rata share of the stable pool.

What is accomplished with this approach:

• Supply emissions are separate from Demand capture, but a programmatic and financial relationship persists.

• Early participants are incentivized without the uncontrolled emission or externality issues that exist with current models.

• The stable pool serves as collateral for the network token which is issued programmatically, enabling straightforward valuations using existing models (e.g. discounted cash flow/gordon growth model) by providing:

  • Known emissions and token float at all stages of the network

  • Simplified demand value capture forecasting

  • Fixed distributions to staked tokens

As demand grows, the price of the network token increases exponentially, given emissions are reduced over time.

Even if demand plateaus (assuming the network’s market cap is fairly valued), the token price should still appreciate over time because net-new emissions continually decrease. Given a token’s value is computed by tokens outstanding divided by the book value of the stable pool + future rewards, the denominator will outpace numerator growth ensuring price appreciation in this case.

If demand ceases, token holders can burn tokens to access the pro-rata “book value” of the stable pool.

It’s worth noting that as long as participants believe future demand capture is likely, few will burn tokens, ensuring tokens trade at some multiple of fundamentals.

What is missing from this approach:

Although most value in DePIN networks will likely continue to be captured in fiat for now, it's important to consider the impact if demand-side users begin spending the native protocol token, instead of using fiat within the network. A burn and mint style model could work here because:

• Previously emitted tokens are, and continue to be collateralized by a growing stable pool

• Which ensures that any tokens minted through this mechanism are proportional to the value removed from the network by demand side participants

Drawbacks:

• Implementing a hybrid “Stake and Stable” + Burn and Mint emission mechanism would be technically complicated and will likely require some novel implementation approach

• Potential implementation complexity for networks who generate and sell data

• Perception challenges around Fully Diluted Market Cap Vs Market Cap

• Many people in the DePIN space view token float reduction as a key indicator of value, necessitating a shift in how value is perceived to focus on other key indicators

• Potential regulatory challenges

Conclusion:

The "Stake and Stable" model offers a potential alternative to traditional DePIN tokenomics, tackling inflation risks and valuation challenges inherent in current approaches. Continued innovation in tokenomic design will be essential to ensure long-term sustainability and unlock DePIN’s full potential in capturing massive Web2 markets as the ecosystem evolves.

Browser Extensions: The Trailblazers

Browser extensions have long been a staple of DePIN. By integrating directly into a user’s browser, these apps offer a seamless way to participate in networks. Users can earn points or tokens by leveraging their existing online activities and network resources. This model has set the stage for more accessible network participation, reducing the barriers to entry traditionally associated with DePIN operations.

While impactful, browser extensions primarily cater to users who are already familiar with Web3 ecosystems and are largely speculative in regard to their profitability. Their growth has been explosive, but in most cases these extensions are only compatible with desktop and laptop computers, and require a fixed internet connection, leaving room for other location-based mechanisms to broaden the reach of DePIN networks.

Some relevant examples of DePIN Browser Extensions:

• Grass Lite Node - 2M+ Users

• Dawn Validator - 1M+ Users

• Bless Network - 1M+ Users

• Gradient Sentry Node - 1M+ Users

• BlockMesh Network - 500K+ Users

• Pipe Guardian Node - 300K+ Users (Currently Unavailable)

Network Companions: Utilities for DePIN Operators

Network Companions focus on managing and operating existing DePIN networks, these apps enhance user experience by providing tools for monitoring, controlling, or optimizing network participation. Unlike browser extensions, these apps are tailored for users who already have a stake in a geographically distributed DePINs, such as wireless and sensor networks.

Some relevant examples of Mobile DePIN Network Companions:

• Helium Wallet - 100k+ Downloads (Google Play Store)

• DIMO Mobile - 50k+ Downloads (Google Play Store)

• Helium Mobile Builder - 5k+ Downloads (Google Play Store)

• Sourceful Energy - Just launched, no public user data yet

Mobile Apps: The Game Changers

The sudden influx of mobile apps marks a significant turning point for DePIN. With the ubiquity of smartphones, these apps have made it possible for anyone to contribute to decentralized networks using devices they already own and carry every day, or unused devices that may be collecting dust.

Standalone DePIN Apps: These iOS and Android applications allow users to mine or earn points/tokens by performing specific tasks, such as:

• Capturing and sharing location data.

• Completing actions at designated intervals.

• Engaging in behaviors aligned with network goals.

Mobile apps stand out because they democratize access to DePIN participation. Users no longer need to invest in standalone hardware, instead contributing directly from their smartphones. This innovation significantly lowers the barrier to entry and broadens the user base.

Some relevant examples of DePIN mobile apps:

• Helium Mobile - 120K+ Users (Flipside)

• Natix - 100K+ Downloads (Google Play Store)

• UpRock - 2.6M+ Users (Google Play Store)

• Chirp (Chirp Tracker & Kage) - 110k+ Users (Google Play Store)

• 375go - 100k+ Users (Google Play Store)

• Acurast Processor Lite - 10k+ Users (Google Play Store)

• Teleport - 15k+ Signups (TRIP Explorer) (Recently Deprecated)

• AmbiGo - 25K+ Users (Ambient Explorer)

• Silencio - 830k+ Users (Silencio on X)

• Wifi Map - 193M+ Users (Wifi Map on X)

User Contributions Without Hardware Costs

One of the most compelling aspects of this app-driven growth is the ability for users to participate in networks without incurring hardware costs. By leveraging existing devices, these apps eliminate the need for traditional servers, specialized sensors/radios, and other equipment typically associated with DePIN networks.

Users contribute by performing tasks that often align with their daily routines, such as:

• Browsing the internet.

• Walking or commuting.

• Capturing environmental data like air quality or noise levels.

• Allowing background services to use resources when the device is idle.

This model not only simplifies participation but also aligns incentives—users earn rewards while contributing to the growth and functionality of the network. The result is a symbiotic relationship where networks benefit from increased participation, and users gain tangible value.

Why Mobile App Growth Is Outpacing Browser Extensions

While browser extensions and network companions have paved the way, mobile apps are experiencing unprecedented growth due to their accessibility and convenience. Key factors driving this trend include:

• Ubiquity of Smartphones: Nearly everyone owns a smartphone, making it the ideal gateway for onboarding new users.

• Ease of Use: Mobile apps are user-friendly and intuitive, appealing to a broader audience.

• Low Barrier to Entry: Without the need for specialized hardware, mobile apps attract users who might otherwise be excluded from participating in DePIN networks.

• Diverse Use Cases: Mobile apps can cater to a wide range of activities, from data collection to network management, making them versatile tools for network operators.

• Referral Systems: Mobile apps benefit from incentive programs focused on allowing existing users to onboard friends and family, often increasing potential earnings for both the referrer and referred.

Challenges With Mobile Apps

While DePIN apps bring a new, highly scalable entry point for onboarding, they aren’t without drawbacks and specifically face challenges related to user expectations and direct competition, creating strain on network growth post-TGE.

• Value Add Variance: User telemetry can have a high degree of variance in regard to the value of the aggregated dataset.

• Lack of Moat: The decreased barrier to entry present in the current generation of DePIN apps can lead to data generation becoming easily commoditized, resulting in reduced business model defensibility.

• Misaligned Expectations: Mobile-first DePIN networks have historically seen substantial user growth, followed by a sharp decline after the token generation event due to unrealistic expectations in the value of user contributions.

• Constrained Hardware Capabilities: Mobile DePIN apps are ultimately as capable as the devices they’re running on, generally resulting in a lower efficacy when compared to desktop apps or dedicated physical hardware devices.

Conclusion

The rise of app-driven participation in DePIN represents a new era of direct participation. Browser extensions and network companions have laid a strong foundation, but the rapid adoption of mobile apps is set to redefine how users interact with decentralized networks. By eliminating barriers to entry and leveraging existing devices, these apps accelerate user contributions to DePIN ecosystems.

It is important to note that many of the Web3 focused passive income generation opportunities on the market today are ultimately extractive in regards to end user value. Mobile apps like Pi Network have pioneered a stickiness factor relating to “mobile mining” in which users login to click a button once per day to continue accruing points/tokens that do not and may never exist, ultimately deriving revenue through users being required to watch ads or share personal info with data brokers. DePIN apps, on the other hand, provide a fair exchange of value. In short, users transparently share specific data with the issuing protocol, and are compensated at regular intervals directly correlated to the amount and quality of information generated.

As mobile apps continue to gain traction, they promise to unlock new levels of engagement and utility, driving the next wave of innovation in DePIN. This shift not only accelerates the growth of decentralized networks but also democratizes access, ensuring that anyone with a smartphone can participate in building the infrastructure of tomorrow.

Make Sunsets is a bold climate tech startup with a mission to mitigate global warming by deploying reflective aerosol particles into the stratosphere, mimicking the cooling effects of volcanic eruptions. Their technology focuses on leveraging sulfur dioxide (SO₂)-filled balloons, released about 20km from Earth’s surface, to reflect sunlight and reduce planetary temperatures. While their work has sparked debates over geoengineering ethics and efficacy, Make Sunsets has already gained significant traction as a pioneer in this space.

One of the most compelling aspects of Make Sunsets’ approach is its efficiency: 1 gram of SO₂ offsets 1 ton of CO₂ emissions for one year, equating to one Cooling Credit. This staggering ratio highlights the potential for small-scale interventions to yield outsized climate benefits.

Traction and Progress

Make Sunsets is far from a mere concept; they’ve actively deployed their technology, scaling efforts to launch balloons globally. Here’s a snapshot of their traction:

• Community Participation: The company encourages individuals to join their balloon launches, providing a hands-on opportunity to participate in planetary cooling. DIY balloon kits and Cooling Credit subscriptions allow supporters to engage directly in the process.

• Media and Thought Leadership: Make Sunsets has been featured in major publications like Inc. and discussed in forums like Uncharted Territories and Keep Cool, sparking public and expert conversations about their approach.

• Focus on Transparency: Monthly updates about deployments and plans position them as a disruptive player in the geoengineering space.

Additionally, targeted deployments in high-impact regions such as Southeast Asia, where global warming effects are particularly pronounced, could amplify their impact. Beyond planetary cooling, Make Sunsets could present alternative revenue streams for developing countries, incentivizing participation in climate initiatives and fostering local engagement.

Why Make Sunsets Makes Sense as a DePIN Network

Decentralized Physical Infrastructure Networks (DePINs) are reshaping industries by coordinating physical activities through decentralized technologies. Make Sunsets embodies many characteristics of a DePIN, even if unintentionally:

1. Decentralized Deployment:

• The distributed nature of balloon launches aligns well with the DePIN model, where a network of participants contributes to deploying infrastructure. By tokenizing Cooling Credits, Make Sunsets could enable a global community to fund and execute launches collectively.

2. Community Ownership:

• DePINs thrive on shared ownership and incentives. Introducing a governance model could empower participants to decide on launch locations, technologies, and scale. Democratizing geoengineering could increase public trust and transparency.

3. Scalability Through Incentives:

• Incentivizing balloon launches through tokenized rewards could accelerate deployment. Participants could earn and trade Cooling Credits representing their contribution to planetary cooling, fostering engagement and scalability.

4. Data as a Network Utility:

• Make Sunsets generates valuable atmospheric data. By integrating this into a decentralized network, they could create a data marketplace, attracting researchers, governments, and corporations to optimize climate interventions.

5. Alignment with Climate Web3 Innovations:

• Blockchains enhance transparency and trust through smart contracts and verifiable data oracle systems. Launch milestones, cooling credit issuance, and funding allocation could all be automated and verifiable.

Additional DePIN Applications

Geoengineering at scale requires coordination, funding, and legitimacy—challenges DePINs are uniquely suited to solve. Expanding on the model:

• Emission-Based Token Model: A declining emissions curve tied to the “point of no return” for climate change could guide token issuance. For example, fewer tokens could be emitted as global temperature trends stabilize, incentivizing participation when most impactful.

• Geographic Reward Multipliers: A multiplier system could prioritize launches in high-impact regions near the equator or areas experiencing acute climate risks.

• Streamlined User Experience: Cooling credit buyers could pay with traditional methods like credit cards while backend systems handle crypto conversions. Existing infrastructure in the crypto space already supports this level of abstraction.

• Incentive Adjustment for Climate Trends: Tokens could be tied to global warming metrics. If temperature trends stabilize, emissions reduce, creating a feedback loop that rewards actions when they’re most impactful. This creates interesting synergies with decentralized weather data collection networks like WeatherXM and Nubila, and decentralized environmental monitoring data networks like Ambient however, safeguards would be necessary to prevent gaming the system.

Challenges and Opportunities

While Make Sunsets focuses on SO₂ for cooling, questions remain about its classification as a product or byproduct in today’s economy. Additionally, the feasibility of widespread hydrogen and helium gas production for balloon deployment and the dissonance between public climate narratives and actionable short-term solutions present areas for further exploration.

Conclusion

Make Sunsets stands at the forefront of an urgent and innovative effort to combat climate change. By embracing decentralized principles and leveraging blockchain technology, they could accelerate their mission, engage a global audience, and solidify their role as a leader in climate tech. Geoengineering at scale could become a reality through DePIN networks—and Make Sunsets is already halfway there.

Follow Make Sunsets on X

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:

1. 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.

2. 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:

1. Minimum Viable Critical Mass of Supply Side Participants

2. Supply Sensitivity As Part Of A Marginal Propensity to Increase TAM

3. Demand Counterparty Risks

4. 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.

1. 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.

2. 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:

1. The maturity of a network

2. 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:

1. 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.

2. 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.

3. 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.

4. 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:

1. Customer Centralization

2. Vertical Serviceability Fragmentation

3. Time To Value Capture

4. Key-Market Novelty Beta

5. 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:

1. 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.

2. 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:

1. Performance Each Node:

2. Average Performance:
   

3. Standard Deviation of Performance:
   

4. SQV Calculation:

5. 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:

1. Calculate the Average Revenue Across All Nodes

2. Standard Deviation Of Supply Revenue:

3. Coefficient of Variation (CV):

4. SQV Calculation:
   

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.

At Parameter Research, we are embarking on a mission to advance the adoption and understanding of blockchain technology through real-world applications. Our collective focuses on Decentralized Physical Infrastructure Networks (DePIN), a revolutionary sector that bridges the gap between blockchain and physical infrastructure. Today, we’re thrilled to share everything we’ve accomplished so far and outline our vision for the future.

What We’ve Built So Far

DePIN Pulse

DePIN Pulse is our opportunities aggregator for DePIN projects. It’s a comprehensive resource designed to showcase APYs, hardware ROIs, quests, activities, staking, and more. Whether you’re an investor, developer, or enthusiast, DePIN Pulse provides actionable insights to help you navigate this emerging sector.

Unleashing DePIN Podcast

Our industry podcast, Unleashing DePIN, highlights notable founders and teams building groundbreaking projects in the space. Through in-depth conversations, we aim to educate, inspire, and foster a sense of community within the DePIN ecosystem.

Eclipse XRAY

Eclipse XRAY is an open-source blockchain explorer for the Eclipse ecosystem. By providing transparent and accessible data, XRAY empowers users to dive deeper into blockchain activity and understand the mechanics of this cutting-edge technology.

Research Insights

Our team has conducted deep-dive analyses into the Web3 ecosystem, with a focus on infrastructure and real-world utility. These insights are foundational to understanding the trajectory of the industry and identifying opportunities for growth and innovation.

What’s Next

We’re just getting started. Here’s a preview of what we’ll be focusing on in the coming months:

- Expanding DePIN Pulse: We’re adding more features, data sources, and interactive tools to make DePIN Pulse the go-to platform for anyone involved in DePIN.

- New Research Initiatives: Our team will publish a series of reports diving into emerging trends in Web3 infrastructure, tokenomics, and decentralized governance.

- DePIN Financial Model: We’re working on a framework for evaluating DePIN network valuations under a unified model, with plans to apply this methodology to a range of DePINs in the coming months.

- Community Building: Through meetups, webinars, and collaborations, we aim to bring together a vibrant community of builders, researchers, and enthusiasts.

- Open Source Development: Beyond Eclipse XRAY, we’re exploring additional open-source tools and frameworks to support the DePIN ecosystem.

About Us:

Tyler Boscolo serves as Head of Product at 3NUM Labs, a new telephony protocol working to develop globally routed onchain mobile numbers and secure communication network. His work at Parameter comprises of financial research/analysis and software development.

Nick Carpinito leads Ecosystem and Research efforts at Mycelium Networks, an incubator and accelerator focused on DePIN and blockchain infrastructure. His work at Parameter comprises software development and data driven research efforts.

To-date, Nick and Tyler have released 40 episodes of the Unleashing DePIN podcast, highlighting some of the brightest minds in the DePIN sector.

Join Us on This Journey

Parameter Research is driven by a belief in the transformative power of Web3. Our work is guided by the principles of transparency, collaboration, and innovation. As we continue to grow, we invite you to join us on this journey. Whether it’s through engaging with our content, contributing to our projects, or participating in our community events, there’s a place for everyone at Parameter Research.

Stay tuned for more updates, and thank you for being part of this exciting adventure.