From Public Value to Token: The Bridge That Must Be Built

Price records exchange value, but what civilization truly relies on are often those values that cannot be fully recorded by price.

Written by: Fu Gui

On One Side, Sea Water; On The Other, Flames

In the crypto space of 2026, a strange period of division has emerged.

The RWA market size has surged to hundreds of billions of dollars, with Wall Street money queuing up to buy tokenized government bonds. The total market cap of memes is also bouncing between hundreds of billions to trillions, with groups still shouting for hundredfold coins at three in the morning. Stablecoins are growing day by day, ETFs have been approved, and prediction markets are bustling like a vegetable market. Everyone is calculating: TVL, fees, cash flow, P/E, NVT, DAU. These things are calculated clearly, and the capital market loves to do this.

But at the same time, a batch of things is quietly rotting away.

In February 2026, the Ethereum Foundation published a blog titled "This Is Fine (Until the Grant Runs Out)". The article introduced a plan called Project Odin, specifically designed to help teams that had previously received large grants from the EF and now do not know where the next funding will come from. By June, the EF announced layoffs of about 20%, with 54 people leaving. Vitalik voluntarily stepped back from the core decision-making team, and the foundation stated it would fully retract, abandon its core control over the ecosystem, and transform into a regular co-building node that only focuses on CROPS (Censorship Resistant, Open, Privacy, Security).

The most important blockchain ecosystem in the world can no longer afford to maintain its own public goods.

This is not an isolated case. Security researchers are looking for vulnerabilities in the entire Ethereum network, where a single vulnerability could save hundreds of millions of dollars, but the person who finds the vulnerability may not receive hundreds of millions. Open-source tools like WEBCAT, which help browsers verify code integrity, received funding from the EF in July—when asked how it makes money, no answer could be found. The more it resembles public infrastructure, the harder it is to charge each beneficiary.

Gitcoin Grants have distributed tens of millions of dollars, but GTC has dropped from its peak to a fraction of its value, with a decline of over 99% from its historical high. Helium hotspots exceeded one million, covering over 170 countries, and Helium Mobile's user base once rapidly expanded to hundreds of thousands, with a significant increase in Data Credit destruction and signs of net deflation in specific quarters—but by mid-2026, Helium Mobile was acquired by Noble Mobile, shifting its revenue focus to operator offload, and consumer subscriptions no longer directly flowed back to HNT. VitaDAO invested in 31 longevity research projects, with tens of millions of dollars in IP assets lying in the treasury, but VITA holders have never received a single cent in dividends. Friend.tech packaged attention into Keys, withdrawing tens of millions in early fees, but subsequently, TVL and activity collapsed dramatically, with countless Keys dropping to zero.

RWA is searching for cash flow, and memes are searching for traffic, but no one is looking for a price for public goods.

It’s Not That There’s No Money, But No Ruler, and No Ledger

Some might say this is a bear market problem.

It’s not! Even during bull markets, public goods still lack funding. During the big bull market of 2021, GTC still fell, and open-source developers relied on passion to sustain themselves. Some say VCs are unwilling to invest in long-term projects; VCs pursue risk-adjusted returns, which is their job, and not investing is not a mistake.

The real problems are twofold.

The first problem is: we don’t have a ruler. In traditional capital markets, when asking about a project, the list of questions is very clear: What is the revenue? What is the EBITDA? What is the cash flow? What is the TAM? What is the growth rate? What is the CAC? What is the LTV? Once these questions are answered, it becomes clear how much a company is worth. But what about public goods projects? An open-source cryptographic library that discovers a vulnerability that could cause a loss of $1 billion has its own revenue at 0. Does that mean it has no value? Clearly not. A DeSci project that funded 30 longevity studies, published papers, and opened data may extend human life by three years in ten years, but this year its protocol income is 0. A decentralized social protocol that allows users to truly own their data and social relationships, without taking a cut or selling ads, also has an income of 0. When income is zero, all flow metrics fail. P/S, P/F, DCF, MV=PQ’s numerator is all economic flow. Public goods protocols actively suppress profits by design; these methods are not "undervalued" but rather the formulas are undefined. The denominator is zero; what can you calculate for P/S?

The second problem is even more critical: the ledger is incomplete. Many projects claiming to create public goods have astonishingly low transparency. After receiving grants, where did the money go? What are the salaries of core contributors? How many off-chain assets (such as experimental equipment, clinical data, IP licenses in DeSci) are there? What is the progress of research? Were the funded papers genuinely produced or just nominal? Is the compensation for contributors fully distributed within the team? This information cannot be found on-chain, and project parties do not actively disclose it, leaving outsiders completely in the dark. DeSci is particularly severe—on-chain voting and IP-NFTs appear very transparent, but laboratory operating costs, data quality, actual contributor hours, and failure rates during research are all off-chain. Investors donate money but cannot even receive a decent quarterly report; it’s not that they don’t want to continue investing, but that they don’t dare.

What’s more troublesome is that those on-chain metrics that seem "observable" are also not clean. TVL can be self-inflated, active addresses can be witch-hunted, and trading volume can be self-circulated. High NVT does not necessarily indicate a bubble, and low NVT does not indicate undervaluation—an open-source library may be called by a hundred thousand projects, but these calls do not generate on-chain transactions, and nothing can be seen in NVT. A security tool protects the entire ecosystem; when nothing goes wrong, no one knows it exists, but when something does go wrong, everyone realizes its importance.

Thus, a very awkward gap has emerged: memes are easiest to calculate; once attention is measured, market value is established, but its social value is essentially zero. DeFi is also easy to calculate; TVL and fees are right there, but no one looks at truly important things like financial accessibility. DePIN’s revenue can be calculated, but how much remote area does it cover, and how resilient is the service? No one investigates. The token price of DeSci jumps daily, but the actual research outputs—papers, data, clinical milestones—are hardly tracked, and financial and off-chain assets are even more of a black box. The DAO’s treasury is public, but how do you calculate coordination efficiency and governance quality? The DAU of decentralized social networks has increased, but has user data sovereignty truly fallen into the hands of users? Open Source is worse; revenue is approximately 0, but how many systems rely on it is the truly important matter.

The problem has never been that value does not exist, but that the market has long been accustomed to proving value through price: what can be calculated is included in valuation, what can be monetized is included in assets, while those values shared by society as a whole but cannot directly enter cash flow are often overlooked.

ESG Is Not the Answer, But It Is an Entry Point

The traditional world has actually been solving this problem for a long time, but few in the crypto space are aware of it.

Everyone has heard of ESG—Environmental, Social, Governance. But many people’s understanding of ESG stops at "giving good companies a green label," which is too superficial. Digital Asset Research has transformed it into ESGN—adding a Network Health dimension specifically for crypto assets: energy consumption, developer distribution, token holding concentration, code quality, decentralization level, totaling over 250 indicators. ChainScore Labs even proposed a concept called Sustainability Premium—after Ethereum’s merge, energy consumption dropped by 99.9%, avoiding climate regulation and institutional blacklist risks, resulting in lower capital costs and better liquidity, which itself is a premium.

ESG addresses "besides profit, what impact does a company have on the world?" But ESG is just an entry point, far from the whole.

GRI (Global Reporting Initiative) goes further; it does not ask "does this matter make money for the company?" but rather "what significant impact does this organization have on the economy, environment, and people?" Impact Materiality and Financial Materiality are two different things. A project may earn a little less money for the company but have a significant positive impact on society, and GRI requires you to disclose this.

IRIS+ is more practical, a set of indicator dictionaries maintained by GIIN, with over 500 standard indicators. It breaks down Impact into five questions: What (what was produced), Who (who benefits), How Much (how big is the impact), Contribution (did you cause it), Risk (what risks are there). These five questions fit seamlessly into Web3—what research outcomes did a DeSci project produce, who benefited, how many people were affected, did you really cause it, is there any Impact Washing, where did the money go, all need to be clarified.

SROI (Social Return on Investment) is more direct; it asks: for every $1 of social capital invested, how much social value is generated? A typical statement is "for every £1 invested, £4.28 of social value is generated." For many Web3 public goods, Financial ROI is close to 0, but Social ROI is far greater than 0.

The UK Green Book defines social value more broadly: economic prosperity, justice, security, climate, environment, health, well-being, distribution effects, all count. Monetizable aspects are calculated as Net Present Social Value, while non-monetizable aspects are compared using multiple standards; not everything needs to be converted into dollars.

There is also a Digital Public Goods Standard used by the Digital Public Goods Alliance, which judges whether a digital project is a public good based on nine dimensions: SDG relevance, open-source licensing, ownership, platform independence, documentation, privacy, security, etc. This system is already in operation.

The traditional world not only has rulers but also requires companies to disclose according to those rulers. Publicly listed companies must disclose annual reports, ESG reports, and social responsibility reports, with auditors signing off, and fraud being penalized. Public goods projects in crypto do not even have a standard format for disclosure. Teams that receive grants write a blog post after spending the money and consider it done; those that do not receive grants do not even write a blog. It is not that they are required to be audited like public companies, but at the very least, basic categorical disclosures—financial, impact, governance, risk—must have a standard format; otherwise, investors and funders cannot even make horizontal comparisons.

Since the traditional world has begun to assess digital public goods, why does crypto still rely on Twitter buzz, TVL, and token price to judge everything?

Social Value Does Not Equal Token Value

But here, we must pour a bucket of cold water, or else it will turn into another pile of garbage crypto narratives.

Do not think that giving a project a high ESG score means its token should rise. Social value does not equal enterprise value, enterprise value does not equal protocol value, and protocol value does not equal token value. This is a long chain of transmission; if any link in the middle breaks, the value cannot be transmitted.

A DeSci project that generates $100 million in social value in a year—promoting open research, shortening research cycles, and increasing collaboration. But if users do not need to hold its token, researchers do not need to stake, the token does not participate in service payments, the treasury has no value capture, the token has no governance budget rights, and the token is not a data access right—then what does this $100 million in social value have to do with the token market value?

α≈0. The project is an excellent public good, but it is not an excellent token investment target.

Conversely, consider a DePIN network where social demand increases, service usage increases, token staking demand increases, token burn increases, node security demand increases, and treasury increases—this is when social value truly transmits to token demand. This chain is visible in a normally functioning DePIN, but in situations like Helium Mobile being acquired and subscription revenue no longer flowing back to HNT, α and β can be structurally severed.

So the key question is not "Does this project have social value?" but rather three questions: How much social value does it create? How much of this value is internalized by the protocol (α, Public Value Capture Ratio)? And how much of the protocol value is actually carried by the token (β, Token Capture Ratio)?

These two coefficients are an order of magnitude more important than the ESG score itself.

But it must be made clear: α and β cannot be precisely calculated in reality. The selection of shadow prices, the delineation of attribution boundaries, and the differences in counterfactual assumptions can cause the α valuation of the same project to differ by 2 to 3 times, and β can also fluctuate significantly due to subtle differences in token mechanism design. Anyone claiming "I have precisely calculated α=0.37, β=0.62" either does not understand or is selling something. What can be done in reality is to provide a range, add a risk discount, and then continuously calibrate with real data. The range may be wide, but a wide range is better than an incorrect precise value.

Why is VitaDAO's α low? VITA holders have never received dividends, and VitaDAO's own guidelines state that VITA is not an economic tool. Because the cycle of scientific research is ten years, while the liquidity cycle of tokens is six months, there is a mismatch. Why does Friend.tech's β seem high but ultimately collapsed? Because it directly packaged "followers" into speculative shares, essentially turning social relationships into zero-sum games, and the public good attributes were destroyed by financialization. Farcaster and Lens both chose not to issue tokens, which precisely indicates that social graphs as public goods are harmed by forcibly issuing tokens.

Filecoin did something very smart in September 2026: ProPGF Batch 3, where 19 projects received about $1.4 million in funding. This is not simply throwing money around; they introduced a framework called Filecoin Kernel, dividing public goods into Essential (things the network cannot do without, such as client diversity, RPC, indexing, documentation) and Important (things whose silent failure would cause cascading effects, such as coordination and settlement). They began to answer a cold question: If this project disappears, will the Filecoin network really be hurt?

This is the kind of thinking that mature protocols should have: public goods are not a moral issue; they are a budget issue, a governance issue, a priority issue, and a disclosure issue.

Six-Layer Framework: From Cash Flow to Market Pricing

So what we really need is not another ESG scorecard, but a system that engineers, audits, and transmits "non-economic value" to tokens. Moreover, this system must come with disclosure standards from day one—if you do not disclose in this format, no one can give you a valuation.

Call it PIVF or PVATV; the name is not important. The core structure is six layers. There are strict transmission relationships between the six layers; if the previous layer cannot transmit, the next layer does not need to be calculated.

The first layer is Economic Value. Cash flow, revenue, and use value. What is the annual revenue? What are the protocol fees? How much is the treasury income? What is the PSRF (Protocol Sustainable Revenue Floor)—the total needed for core contributor salaries, key infrastructure expenditures, security audit reserves, and inflation buffers to survive for a year? If annual revenue is above PSRF, the project can survive; if below PSRF, there is a risk of supply interruption. This layer is the foundation; not making money is not shameful, but pretending the building is high while the foundation is empty is shameful. All projects must disclose quarterly: income details, expenditure details, treasury balance, and PSRF coverage ratio. This is the most basic transparency requirement.

The second layer is Public / Social Value. Externalities, public goods, and social impact. How many downstream projects rely on an open-source library? How many real ongoing studies are funded by a DeSci project? How many areas without traditional infrastructure does a DePIN network cover? How many users' real data does a privacy tool protect? This layer uses the Theory of Change causal chain: inputs → activities → outputs → outcomes → impacts. Customize auditable indicators from the IRIS+ metrics by track: DAOs look at active contributor addresses, proposal execution rates, and fund usage reports; DeSci looks at the number of funded projects, IP-NFT counts, open-access papers, experimental progress reports, and off-chain asset lists; DePIN looks at active nodes, geographic coverage, real data utilization rates, and service SLAs; Open Source looks at the number of downstream dependencies, key vulnerability fixes, and code reuse frequency. Each indicator must have a corresponding data source—on-chain verifiable contract addresses, off-chain original document links, or third-party audit reports. Indicators that do not disclose data sources do not count.

The third layer is Impact Attribution. This layer is the easiest to skip and also the one most exploited by impact washing. Value exists, but is it really caused by this project? Five questions must be answered one by one: What exactly happened? Who exactly benefited? How much is net of deadweight? What is your contribution margin versus other actors? What are the negative side effects? Discount witch addresses, deduct the parts that would happen anyway (deadweight), do not count parts transferred from other projects (displacement), and divide accounts by contribution ratios in multi-party collaborations; serious negative impacts cannot be offset by public welfare points. This layer is essentially a fraud prevention layer. If "number of funded projects" is directly linked to token buybacks, the project party will definitely inflate quantity over quality; if "number of papers" becomes an impact indicator, there will definitely be inflated papers; if "active addresses" count towards scores, witch addresses will proliferate. Therefore, this layer must require: multi-dimensional indicator cross-validation, independent third-party audit sampling, dMRV (digital measurement, reporting, and verification) original data on-chain proof, challengeable and slashing. Without this layer, the entire framework is just a narrative tool in a new bottle.

The fourth layer is Protocol Capture. Social value exists, attribution is clear, how much can the protocol internalize? This is α. α depends on mechanism design: Is service payment mandatory in tokens? Do validators need to stake? Does the treasury have a continuous income source? Does the burn mechanism work effectively? Can governance rights influence real economic parameters? Whether α is high or not depends on the mechanism, not on sentiment. α is not a fixed number; it is a range that changes with protocol parameters. Sequencer earnings, MEV distribution, protocol fee switches—all of these are regulators of α.

The fifth layer is Token Capture. Of the value captured by the protocol, how much can the token carry? This is β. β depends on token design: Does the token have value capture rights? Will treasury income flow back to holders? Is the buyback and burn mechanism written into the contract? Is the staking yield derived from real income rather than inflation subsidies? Can governance tokens truly determine budget allocations? Similarly, β is a range, not a single point. Many projects' token designs seem to have capture, but in reality, treasury multi-signatures are controlled by the team, buybacks can be canceled at any time, and staking yields are all inflation—this kind of β should be heavily discounted.

The sixth layer is Market Calibration. After calculating the first five layers, a basic value range for the token is obtained. Only then does the market come into play: NVT, MVRV, treasury NAV, market cap / active developer ratio, net deflation state—these on-chain native indicators are retained as calibrators. If the value calculated by PIVF differs from market cap by more than 50%, there are three possibilities: the market has not recognized it (underestimation opportunity), your shadow price assumptions are too optimistic (model risk), or the project has fatal risks you have not seen (such as team collapse, regulatory crackdown). Always respect the market, but do not blindly follow it.

The final output is not an exact price but a range. Additionally, three diagnostic indicators are attached: PSRF coverage ratio (greater than 1 is sustainable), impact / market cap ratio (significantly greater than 1 may be underestimated, but requires αβ transmission chain verification), and comprehensive risk discount rate (the higher, the more cautious). All input data must be labeled with sources, disclosure assumptions, and confidence intervals. This is the responsible way to treat investors.

How to Build Bridges and Deal with Free Riders

The framework has been discussed, but the most critical question has not been answered: How to "inject" non-economic value into tokens? You cannot just say there is social value, and the token will automatically rise in price. Bridges must be built piece by piece, and relying solely on goodwill is not enough; there must also be a system.

The first bridge is the funding flow for public goods. QF (Quadratic Funding) and RetroPGF (Retrospective Public Goods Funding) have been operating on a large scale. Gitcoin has allocated tens of millions of dollars using QF, and Optimism's RetroPGF has iterated multiple rounds, distributing over a hundred million dollars before its pause. OSO (Open Source Observer) has unified GitHub, npm, and on-chain deployments into an auditable impact metric pipeline, allocating tens of millions of OP before the RetroPGF pause. The money goes into project treasuries, which can buy tokens for burning, issue contributor rewards, or enhance product adoption to increase economic value. Tokens here serve as both funding currency and a measure of value.

However, there is a fundamental dilemma that cannot be bypassed: the free-rider problem. Even if all institutions agree that a certain open-source security library has created a social value of one billion dollars, as long as it is free, no single institution has the economic incentive to contribute—"others pay for maintenance, I use it for free, why not?" This is not a moral issue; it is a classic prisoner's dilemma in game theory. Relying on the goodwill of capital and ESG consciousness cannot solve this problem. A strong mechanism for internalizing externalities is necessary: protocol-level taxes and Pigouvian subsidies. A simple example is that L2 ecosystems can mandate that a fixed percentage of Sequencer profits (for example, 10% to 20%) be automatically injected into the RetroPGF fund pool through preset contracts, without requiring anyone to vote or show goodwill; the code is written in stone, and taxes are paid simultaneously with transactions. Ethereum's EIP-1559 burn is a form of protocol tax, as are L2 Sequencer fee distributions and MEV smooth distributions. The maintenance costs of public goods should be proportionately borne by the economic activities that benefit from them, rather than relying on individuals like Vitalik to dig into their pockets.

The second bridge is impact certificates. Hypercerts, developed by Protocol Labs, are ERC-1155 semi-fungible tokens that record "who did what, for whom, when, and what impact was generated," turning completed beneficial work into tradable, divisible, and attributable on-chain assets. The accumulated Hypercerts of a project become collateral for reputation, and the impact market allows social value to be speculated and allocated, feeding back to builders. Impact certificates are the first time "social value" has taken on an asset form. dMRV and dynamic oracles are crucial here: impact cannot rely on self-reporting by the project party; there must be a continuous, auditable data flow fed in.

The third bridge is the ESG compliance premium. High ESG ratings lead to compliant access for institutional capital, better liquidity, and lower discount rates. The global ESG asset pool exceeds 30 trillion dollars, making this a key channel for low-profit projects to exchange "non-economic compliance" for "economic liquidity." ChainScore Labs' concept of Sustainability Premium is not unfounded—why are institutions more willing to allocate after Ethereum's merge? The green premium is tangible.

The fourth bridge is attention-driven demand. However, it must be validated real attention, not the kind that directly securitizes "followers" like Friend.tech. Real attention → user and developer adoption → network effects → token demand (gas/governance/staking). dMRV, oracles, and OSO validate attention as "real adoption," preventing pure meme churn.

Regarding monetization, an additional technical detail must be added: the anchor of shadow prices cannot be static fiat currency values. If shadow prices are denominated in dollars, fiat inflation and crypto bull/bear cycles will cause the monetization results of impact to diverge sharply from the capital supply in the token market. In a bear market, when tokens drop by 80%, the "social value" calculated in dollars remains unchanged, but the project party cannot obtain corresponding levels of funding. A more robust approach is to use algorithmic dynamic oracles, combined with a basket of external actual purchasing power for calibration—such as the hourly wage of a mid-level developer, the cost of a standard security audit, or the marginal cost of 1GB/year of decentralized storage—anchoring with the relative prices of real production factors rather than fixating on a dollar figure. This way, shadow prices will automatically adjust with productivity changes and crypto cycles, preventing public goods from being overvalued in bear markets with no buyers, and ensuring that truly important things are not sold cheaply in bull markets.

The design of tokens themselves also needs to change. Governance rights cannot be nominal; they must influence real economic parameters—Uniswap's proposal to switch fees to burn 100 million UNI in December 2025 received a 99.9% approval rate, with the first burn amounting to hundreds of millions of dollars in value, making governance decisions a source of value. Work utility must generate real on-chain demand—payments for computing power/storage/network services in DePIN must use tokens, validators must stake, and burn mechanisms must create deflation; this is the hardest support for token value. The utility of impact is a new concept: impact tracing dividends, impact buybacks (a fixed percentage of income used for buybacks and burns, with buyback amounts linked to multidimensional impact output metrics, and must undergo independent audits, not just single metrics), and impact certification (third-party audit results on-chain).

Not all projects are suitable for token issuance. Farcaster operates well with 40,000 to 60,000 DAU without a token, Lens has yet to issue a token, and VitaDAO's VITA is deliberately designed as a non-economic tool. There is an inherent tension between the attributes of public goods and the capture of token value; projects with strong public goods attributes, unclear revenue paths, and free-rider problems that cannot be solved through mechanism design may be better served by funding/donations/treasury/protocol taxes. Forcing token issuance will ultimately lead to either memes, team disbandment, or becoming tools for impact washing.

Good Words Are Done, Now for the Bad

Whenever a new system emerges, there are always those who exploit it. This set of ideas is no exception, and in crypto, the speed of bad actors' innovation often outpaces that of good ones.

The first pitfall is impact washing. Once social value enters token valuation, projects will start buying third-party certifications, inflating user numbers, inflating research citations, inflating DAO votes, inflating DePIN devices, and creating false public welfare activities. Once "number of funded projects," "number of papers," and "active addresses" are directly linked to token buybacks or treasury allocations, inflating metrics will almost inevitably occur—this is not a question of if, but when. The solution is not to eliminate metrics, but to treat the Impact Score itself as a protocol that needs to be attacked and audited; witch addresses should use identity clustering and sampling verification; inflating reading counts should only count interactions with behavioral consequences using QAA; inflating paper citations should use the DORA principle and expert sampling; short-term airdrop participation should filter for 30/90/365 days of continuity; scoring sources should use multi-party oracles with stakes, challenges, and slashing; repeated calculations must clarify contribution chain boundaries; serious negative impacts should have red lines that cannot be offset by public welfare scores. Multi-dimensional cross-validation is essential, independent third-party audits are necessary, and anyone should be allowed to submit evidence to challenge a particular impact claim—otherwise, the new system will be gamified faster than the old narrative system.

Goodhart's law always holds: when a metric becomes a target, it ceases to be a good metric. This is why the third layer of attribution must exist independently, and why outputs must be ranges rather than single points.

The second pitfall is the subjectivity of shadow prices. Experience from SROI and IWA shows that different valuation methods for the same externality can vary by more than three times. There is almost no ready monetization coefficient for open-source public goods, and the parameter selection of dynamic oracles (the proportion of each element in the basket, update frequency) will also introduce new subjectivity. Therefore, we must acknowledge: what we can do is converge on an estimated range, not eliminate uncertainty. If someone tells you, "this project is worth exactly $3.72," they are likely deceiving you. If someone tells you, "this project's impact range is X to Y, with confidence Z, and the main assumptions are A and B," they are serious.

The third pitfall is the attribution dilemma. The impact of public goods is often the result of multiple parties working together; an open-source tool may be integrated by multiple projects, and a security upgrade of a foundational library protects the entire ecosystem, making precise attribution to a single protocol extremely difficult. SROI's attribution discount discipline can alleviate this but cannot eliminate it. Counterfactual estimates ("what would happen if this project did not exist") are inherently unprovable assumptions.

The fourth pitfall is market cognitive lag. The current crypto market is primarily driven by speculation, and the valuation premium for non-economic indicators may not be recognized by the market for a long time. Research by Jin et al. shows that pricing effects for environmental dimensions only manifest after specific events. There is almost no event study proving that S and G dimensions have been priced. Practitioners and academia are disconnected—frameworks from people like Burniske, Woo, and Samani are often found in blogs and industry reports, not in peer-reviewed literature. Once a framework is established, the market may take two to three years to react, and project parties need to be mentally prepared.

The fifth pitfall is governance capture. The definitions and weights of impact metrics, the selection of third-party auditing agencies, and the decision-making mechanisms for challenges and slashing may be captured by whales or core teams, turning into tools for "scoring their own projects high." Voices that are not token-weighted (citizen juries, DID-based reputation), rotation systems for auditing agencies, and governance of impact parameters should require supermajority votes and time locks.

The sixth pitfall is transparency fraud. The third layer of attribution requires original data to be recorded on-chain, but off-chain data can be fabricated—labs can manipulate images, nodes can run virtual machines, and users can hire companies to inflate metrics. dMRV is not a panacea; it can only ensure that "the data recorded on-chain has not been tampered with," not that "the uploaded data is true." Therefore, physical world verification nodes, random sampling, reputation system linkage, and severe penalties for fraud are necessary.

However, these pitfalls are not reasons to refrain from action. The ESG data infrastructure of the 2010s was also poor; MSCI and Sustainalytics were gradually developed, and many "greenwashing" scandals occurred in the process. The problem is that crypto has not even begun to take this step. EF contraction, DeSci black box operations, and public goods teams cannot even produce a standardized disclosure report—these issues will not disappear just because you pretend not to see them.

A Ruler and Language Are Both Necessary

Ultimately, what crypto lacks now is neither money nor projects.

It lacks two things. One is language, and the other is a ruler.

Traditional finance has a mature language: Revenue, EBITDA, DCF, P/E, WACC, Beta. When these terms are mentioned, investors, analysts, auditors, and regulators around the world understand them, can compare, and can trade. This language allows capital to flow efficiently. Traditional finance also has a set of standards: accounting principles, disclosure requirements, auditing processes, and regulatory penalties. How to write an annual report for a listed company, what must be disclosed, what constitutes fraud, and the penalties for fraud are all written in the rules. The combination of language and standards enables the capital market to function.

Public goods-type Web3 projects currently lack both. When project teams say, "We are doing something very valuable," investors ask, "What is your Revenue?" and the conversation cannot proceed—because there is no common language. Even if there were a common language, if the project team does not disclose, discloses irregularly, or discloses without audit, or if the audit is not comparable—because there are no standard metrics. Project teams can only rely on narratives, visions, memes, and endorsements from big names to raise funds. Narratives are not comparable, auditable, or reviewable; during a bull market, any narrative can find believers, but during a bear market, the first to fail are the narrative-driven projects.

Tools like Gitcoin, Optimism RetroPGF, Hypercerts, OSO, dMRV, SourceCred, ESGN, IRIS+, and SROI have already proven their feasibility at different stages. OSO uses impact metrics to determine the allocation of tens of millions of OP tokens, Hypercerts turns impact into tradable assets, and Gitcoin QF allows 100 people each to donate $1 to outweigh one person donating $100. These are not fantasies; they are operational systems. Discussions on Filecoin Kernel, L2 sequencer fee distribution, and the EIP-1559 burn mechanism are also moving in the right direction.

What is missing is the layer that connects them into a standard pipeline: a complete framework that goes from economic value anchoring, to public value identification, to impact attribution and anti-cheating, to protocol capturing α, to Token carrying β, to market calibration. This framework is not meant to eliminate financial metrics but to fill in the gaps when financial metrics are zero or distorted; it is not meant to replace auditing but to embed disclosure norms from day one—what data must be disclosed, in what format, where the data sources are, who will audit, and how fraud will be penalized. It allows things that "cannot make money but are essential" to have a verifiable, comparable, and capital-priced language, as well as a metric that can measure, compare, and hold accountable.

Coase mentioned in 1960 that when transaction costs are so high that private markets cannot internalize externalities, new mechanisms are needed. Blockchain itself is a device that reduces transaction costs. Second-order financing is a public goods preference optimal aggregation mechanism mathematically proven by Buterin, Hitzig, and Weyl. Libertarianism is not a utopia; it has been running on Gitcoin for years. The Crypto-Coase theorem states that blockchain is a "feasible mechanism" that outperforms centralized regulation when transaction costs are low—but the premise is that you must have a way to quantify, secure rights to, trade, disclose, and audit externalities.

Hoffmann and others calculated that the demand-side value of open-source software is about $8.8 trillion. Every $1 invested in MLOSS corresponds to at least $100 of global economic value. This value is currently "ownerless," enjoyed freely by society, but no one maintains it, funds it, prices it, or even clearly articulates how much it is worth. In 2026, the Ethereum Foundation shrinks, security researchers rely on grants to survive, DeSci projects have ten-year cycles that do not align with six-month liquidity cycles, and decentralized social networks hesitate to issue tokens—these issues all stem from the same problem: value is created, but it does not return to the creators; value exists, but no one can articulate or measure it in a way that everyone understands and trusts.

The Filecoin Kernel framework poses a good question: If this project disappears, will the network really be harmed? The entire crypto industry needs to ask the same question: If these public goods projects all die, will Web3 really be harmed?

The answer is yes. RWA needs underlying protocol security, memes need public chains to run, ETFs need nodes not to crash, and AI agents need decentralized storage and computing power. All the glamorous commercial applications are built on "non-profitable" things like open-source code, public infrastructure, security research, and open data. If the foundation is not solid, the higher the building, the more dangerous it becomes.

If Web3 truly wants to become the next generation of public infrastructure, it must learn to establish a language and metrics for those values that cannot directly generate profits. It is not about replacing valuation with sentiment, not about using narratives to cover up, not about substituting new ESG rhetoric for old meme rhetoric, but about using verifiable data, auditable metrics, comparable frameworks, and executable disclosure standards to translate social value into language that capital can understand, internalize the costs of free-riding into protocols, and lock the risks of fraudulent metrics into mechanism design. This is not about doing good; it is a survival issue for the infrastructure itself.

Price records exchange value. But the things that civilization truly relies on—open-source code, security research, open science, decentralized communication, privacy protection—their value has never been just exchange value. By supplementing the measurement of these values, establishing disclosure rules, and solving the free-riding problem, public goods will no longer need to wait until grants run out to realize they cannot survive, builders will not need to choose between idealism and survival, and this industry will not just be a huge casino.

Ultimately, a mature industry is not defined by how many times it can multiply, but by whether it can honestly sustain the things that keep it running—and whether there is a mechanism to ensure that no one can pretend these things do not exist.