Institutional Monetization Through Offchain Data.” The idea is to expand beyond purely on-chain DeFi endpoints and deliver market data to traditional financial (TradFi) workflows via paid subscriptions. Douro Labs, a major contributor to Pyth, is spearheading the proposal.
Phase 2 is not just about generating revenue, but about restructuring how value is captured and shared: between publishers, users, token holders, and the broader network. Importantly, existing DeFi usage and on-chain data flows are intended to continue unchanged; the subscription model is additional.
Why Phase 2: The Need & Market Opportunity
The current market data industry is massive — estimated at $50+ billion annually. Institutions (banks, asset managers, brokers, media firms etc.) spend tons on legacy providers like Bloomberg, Refinitiv, etc. The Phase 2 proposal argues that this industry is expensive, fragmented, and inefficient — rife with redundancies, markups, regional silos, and downstream intermediaries taking large margins.
Pyth already has strengths: 600+ protocol integrations, coverage of hundreds of assets including real-world ones, high frequency and high quality price feeds, on many blockchains, with more than $1.6 trillion in cumulative transaction volume over dApps using its feeds.
Institutions are expressing demand for offchain usage: risk models, compliance, settlement, accounting, display, regulatory workflows etc. These use cases demand data that is cleaner, more accessible, less siloed, historically rich, maybe higher frequency or lower latency than some current options.
So, Phase 2 aims to tap this demand and push Pyth into being not only a DeFi oracle but an enterprise market data provider — capturing more value and distributing it back via governance.
What the Proposal Actually Says: Key Elements
Here are the main components of what Douro Labs is proposing with Phase 2:
1. Institutional Subscription Product
A paid, high-tier data offering for institutions. This would deliver premium features: more asset coverage, higher frequency updates, deeper historical data, regulatory and compliance friendly tools. Enterprises could integrate this data into risk analytics, settlement and accounting systems, compliance workflows, display terminals, etc.
2. Payment Flexibility
Institutions would be able to pay in multiple ways: USD, stablecoins, or PYTH token. This allows TradFi clients to use familiar payment methods, while also integrating on-chain economics.
3. Distribution Network & Distributors
Since running institutional subscription business is operationally heavy (billing, SLAs, client relationships), the proposal suggests using distributors or partners like Douro Labs to handle those tasks. Distributors would take a share of revenue; the rest goes to DAO.
4. Revenue Allocation & DAO Mechanisms
The proposal envisions that revenues from these subscriptions flow to the Pyth DAO treasury. Then the DAO can decide how to use them: possible options include token buybacks, revenue sharing with publishers/providers, incentives for stakers, holder rewards, and other ways to strengthen network infrastructure.
5. Protection of Existing DeFi Functionality
The proposal is careful to say that existing on-chain core services aren’t being replaced: DeFi users who depend on Pyth’s standard feeds will continue to have them. The subscription product is an overlay, not a replacement.
6. Phased Rollout, Governance Steps
There are several steps required before DAO approval: community discussion, agree on fee structure & terms, select distributors, define how proceeds are allocated, then follow-up with formal proposals and voting.
How This Delivers Community Value: What the DAO Is Being Asked
It’s not just “bring in revenue”; Phase 2 is proposing that the DAO consider how value flows back to community stakeholders. Here are the possibilities:
Token Buybacks / Token Deflation: Using revenue to buy back PYTH tokens, perhaps burn them or otherwise reduce supply, enhancing value for holders.
Rewards for Publishers / Data Providers: Publishers (exchanges, market makers, etc.) supply first-party data. Under Phase 1, some rewards exist, but Phase 2 would allow better or more direct compensation for providers, especially those contributing high-frequency / high-value feeds.
Staker Incentives: Those staking PYTH in order to secure/validate data might receive a share of DAO revenue or special rewards, improving security and network resilience.
Holder / Token Utility Enhancements: The PYTH token’s utility may increase: subscriptions accept PYTH, token holders get governance power, maybe discounted subscription tiers, etc., meaning token isn’t just speculative.
DAO & Governance Participation: By having community input in how revenue is allocated, how fees are structured, how distributors are chosen, etc. the DAO is being asked to deliver transparency and alignment. The network is asking: do you, the community, prefer buybacks, or more rewards for data providers, or investment in expansion?
Trade-Offs and Risks: What Needs Careful Consideration
Of course, this kind of expansion into institutional subscription data has risks and trade-offs. For the DAO, stakeholders, token holders, publishers, etc., here are what to watch:
1. Competition vs. Expectations
Legacy providers like Bloomberg, Refinitiv etc. have established relationships, trusted datasets, regulatory compliance, expensive SLAs. To win, Pyth’s product needs to match or beat their reliability, latency, currency coverage, etc. The expectation vs the execution gap could hurt reputation if not carefully managed.
2. Infrastructure Costs & Scalability
Serving institutional customers implies stricter SLAs, better uptime, more support, more historical / deep data, likely more infrastructure redundancy, more staff, etc. These come with cost. The revenue model must reliably cover costs plus yield profit to be sustainable. Otherwise, it can strain DAO resources.
3. Token Demand & Payment Mix
The proposal allows payment via USD, stablecoins, or PYTH tokens. If too many subscriptions use USD/stablecoins, then PYTH may not capture value (i.e. less demand for token utility). On the other hand, insisting on too much token usage may reduce appeal to institutions. It’s a balancing act.
4. Governance Complexity & Speed
DAO decisions (fee structure, distributor contracts, allocation of revenue) can be slow, contentious. Delays can harm momentum. Also, fairness demands good transparency in how revenue is reported, how distributors are chosen. If not done well, trust could suffer.
5. Maintaining Core Values and DeFi Users
There’s risk that focusing too much on institutionals could lead to higher costs, or shifting priorities away from DeFi users and builders. Ensuring that DeFi-oriented price feeds remain affordable and high-quality is important. Some community feedback emphasizes this.
What to Watch For: Key Metrics & Decision Points
As the DAO considers this proposal, here are some of the concrete metrics, events, and decisions to monitor:
Metric / Decision Why It Matters
Subscription pricing & tiers Determines how competitive Pyth can be vs incumbents, and how accessible for smaller institutions.
Percentage of revenue allocated to DAO vs distributors Impacts how much accrues for community use, and whether distributors are incentivized fairly.
PYTH token usage in payment vs USD/stablecoins Affects token demand, utility, and token value.
Growth in number of institutions subscribing Validates market demand.
Increase in symbol/asset coverage, update frequency & latency for subscription product Institutional customers care deeply about latency, historical data, depth; better technical specs = higher reputation & willingness to pay.
Governance votes and community feedback Whether DAO is aligned, transparent, responsive. Poor governance could undermine trust.
Quality of distributor agreements and service levels Ensures institutions get reliable service; mistakes here can damage Pyth’s reputation.
Broader Impact: Why This Could Be Transformative
If Phase 2 succeeds, it could have several positive systemic effects:
Sustainability: Recurring institutional revenue helps move Pyth from being reliant largely on token speculation or DeFi integrations alone toward a stable, diversified business model.
Alignment of Incentives: As more publishers (those providing raw feeds), stakers, token holders, and even users see economic benefit, they’ll be more motivated to maintain data quality, uptime, and ecosystem health.
Democratization of Market Data: Pyth’s vision is to make financial market data more accessible, less expensive, less regionally siloed. If institutions adopt Pyth’s offering, it could reduce cost barriers and increase transparency in financial markets.
Competitive Pressure on Legacy Providers: Big incumbents may be forced to lower prices, improve latency, or open up more flexible licensing in response. That could benefit a wide segment of the financial services industry.
Growth of the On-Chain / Off-Chain Bridge: Pyth’s expansion into offchain workflows means more institutions will interact (or consider interacting) with on-chain data providers, pushing further convergence of TradFi and DeFi.
Conclusion: What the DAO Is Being Asked to Do
The Phase 2 proposal from Douro Labs is more than just a product launch; it is a strategic inflection point. The DAO is being asked:
To sanction entering the institutional subscription market.
To set policy around how revenue will be split and distributed.
To decide how much emphasis to place on token utility (e.g. premium tiers, discount for paying in PYTH, staking revenue shares).
To ensure that the core DeFi users don’t lose out maintain affordability & quality for existing feeds.
To pick distributors/partners carefully, with performance, trust, and transparency in mind.
If the DAO executes well, this could be a step toward making PYTH one of the foundational infrastructure layers for global finance not only a leader among oracles, but a credible source of professional, subscription-grade market data. But execution risk is real: pricing, infrastructure, governance, token economics all need careful calibration.
