On April 2, 2026, East 8 Time, YZi Labs and Susquehanna Crypto jointly announced a strategic additional investment in the on-chain prediction market platform Predict.fun. This is not a round of financing from scratch, but an increase based on existing equity and partnership, marking the first systematic bet by traditional quantitative funds on the on-chain prediction track. Unlike the common practice of "project raising rounds, institutions gaining exposure," this transaction chose not to disclose any amount or valuation details, forming a stark contrast between the official statement of "rapid growth" and the highly vague financing information. The intertwining of a high-growth track, the initial involvement of a traditional institution, and a low transparency financing structure becomes the core contradiction behind this transaction.

Traditional Quantitative Entry Susquehanna's Attitude Shift

Susquehanna Crypto is backed by a traditional quantitative and market-making group, with its parent company long focused on traditional financial derivatives, option pricing, and high-frequency trading, making it a typical "data-model-trading" closed-loop institution. However, prior to this, it had maintained a wait-and-see approach in the niche direction of on-chain prediction markets and was more inclined to trade mainstream assets and more mature crypto derivatives. The long absence was not due to insufficient technical capability but rather a proactive delay decision based on an insufficient understanding of the track structure, regulation, and counterparty quality.

This entry into Predict.fun as a joint investor is not just a simple financial layout but signifies a clear attitude shift in the market: from "researching targets from a distance" to being a participant "willing to take on equity risk." For quantitative institutions known for their sophisticated risk-return assessments, making a move itself is a qualitative signal—at least at this current moment, it believes the structural opportunities of the on-chain prediction track are clear enough to warrant offsetting the previous "missed risk" with real capital.

From the perspective of traditional institutions, the appeal of prediction markets lies not only in speculative returns but in the combination of arbitrage, hedging, and data value: on one hand, the odds discrepancies between different platforms and different assets inherently provide material for cross-platform and cross-market arbitrage strategies; on the other hand, the price of prediction markets themselves can be seen as "continuous polling," helping institutions build hedging combinations on macro events, regulatory directions, and even black swan risks. Going deeper, if these event odds data can be systematically collected and cleaned, it would constitute a potential new data pipeline for quantitative institutions reliant on factor mining and alternative data.

From Incubation to Increased Investment The Growth Trajectory of Predict.fun

Predict.fun was initially part of the YZi Labs incubation project, entering its incubation program EASY Residency, completing key stages from product prototype, market validation to initial operations in a relatively closed and high-density environment. The "graduation point" of EASY Residency signifies that the project has passed internal screenings concerning product usability, team execution, and initial market feedback and can transition from the internal accelerator scene to open market competition.

Official information indicates that Predict.fun has grown rapidly since "graduating" from EASY Residency, but specific metrics such as order volume, trading scale, or active users have not been provided. Based on commonalities in product forms and industry paths, its expansion is more likely reliant on a few main lines: one is the combination of event and price betting aimed at crypto-native users, driving natural diffusion via hot narrative; the second is embedding predictions into a broader ecosystem as an "interactive gameplay" through partnerships with KOLs, communities, and other protocols; the third is attempting to lower participation barriers, bringing experiences and liquidity closer to traditional centralized betting platforms, rather than staying at the level of niche geek tools.

Against this backdrop, YZi Labs' decision to continue increasing investment is itself an internal "vote" on the quality of project growth. For incubators holding first-hand post-investment data, judgment standards often do not rest on the spectacular increase of a single indicator but rather focus on several dimensions: is growth healthy and sustainable, are returns and risks matched, do users and transactions exhibit a certain degree of stickiness and retention, and has the platform maintained basic compliance and risk control baseline amidst sensitive regulatory and public opinion events? Continuing to increase investment appears more to be a phased positive response to these questions.

The Mystery of Amounts The Silent Choice Not to Disclose Financing Figures

From the publicly available information, the financing amount and valuation range for this round of investment in Predict.fun have not been disclosed, with the only confirmations from the outside being the nature of "strategic additional investment" along with investor composition and timing. Due to the concentration of information sources in a single official channel, media and research institutions can hardly verify amounts and valuations, and can only acknowledge the objective reality of "information absence."

This contrasts sharply with the common financing narratives in the crypto industry: over the past few years, project parties and institutions have often been inclined to report exaggerated rounds and valuation figures in hopes of gaining greater volume in social media and industry news, even if the numbers behind these figures involve complex structures, harsh terms, or extreme difficulty in fulfillment. In this context, Predict.fun's choice to remain silent on amounts and valuations appears somewhat “counter-cyclical,” resembling traditional venture capital's emphasis on "terms quality over headline figures."

As for the motivation behind this silence, it is hard to provide a single definitive explanation. One possibility is based on valuation gaming considerations: in a high-volatility track, prematurely anchoring public valuations may impose constraints on project parties and early investors in subsequent rounds and secondary market expectation management. It may also involve competitive defense, avoiding letting competitors deduce current progress and funding ammunition from public valuations and rounds during a phase when the prediction market landscape has not yet stabilized. There are also regulatory sensitivities involved; certain jurisdictions pay particular attention to financing disclosures involving specific event predictions or gambling-like businesses, leading projects and institutions to downplay public exposure. These explanations all hold some reasonableness but are insufficient to reach a definitive conclusion on their own, remaining as parallel hypotheses.

High-Speed Track Low Transparency The Structural Gap in Prediction Markets

In the mainstream narrative of Web3, on-chain prediction markets are often viewed as a high-growth track that combines "high financialization, strong playability, and rich data value"; from "information markets," "decentralized polling" to "event derivatives," it can almost find its place in every new narrative round. The investment in and increase in Predict.fun partially bets on the realization of this long-term story.

However, when shifting the perspective from stories to fundamentals, the industry's shortcomings in real transactions, market depth, and user structure disclosures are glaringly obvious. Most platforms only provide partial market snapshots or vague growth curves, lacking verifiable historical transaction and order book data; institutions find it difficult to accurately assess counterparty composition, concentration of leading players, and liquidity elasticity under extreme market conditions, let alone quantitatively assess whether the betting table is “fair.” This data-level "fog" directly limits the entry of larger-scale capital and stricter risk control systems.

With blurred lines around oracle, odds generation, and the role of the bookmaker, how to judge the credibility of a prediction platform becomes a necessary issue for institutions and users. On the oracle level, it is important to examine whether its pricing and event result acquisition rely on a single source, if it has multi-source verification and anomaly correction mechanisms; regarding odds generation, it's necessary to distinguish between purely liquidity pool-based automated market-making and subjective human interventions; concerning the role of the bookmaker, one must beware of scenarios where the platform simultaneously acts as both "rule maker" and "large counterparty," as a lack of publicly verifiable risk exposure and hedging mechanisms would amplify the bookmaker's advantage infinitely. For traditional institutions, only when these key mechanisms can be audited and quantified can the prediction track possess real asset allocation significance.

Accelerator Sample YZi Labs' Closed Loop and Intermediary Role

Using Predict.fun as a sample, one can clearly see the "closed-loop path" of YZi Labs from incubation to strategic increase: first, refining early product development and team evaluation during the EASY Residency phase, then helping market cold starts through resource and brand introduction, and subsequently leveraging post-investment tracking and data feedback based on the project's demonstrated "rapid growth after graduation," collaborating with external institutions for additional investment. This top-down closed loop allows YZi Labs not only to serve as a filter for early projects but also as a command center for subsequent capital and resource allocation.

The expression of "continued increase" reveals YZi Labs' preference in post-investment empowerment and project selection: on one hand, it favors stacking resources on projects with existing deep interactions and data reserves, rather than pursuing one-time exposure; on the other hand, it seems to place more importance on sustainable growth and strategic execution capability, rather than just short-term surges in certain indicators. This preference also shapes its incubation system in reverse—projects wanting to receive follow-up strategic resources must provide sufficiently "hard" performance in real operational data, compliance risk control, and team responsiveness rather than merely telling a bigger story.

From a more macro perspective, Web3 accelerators are becoming a vital intermediary between early projects and traditional institutional funds. For traditional quantitative institutions like Susquehanna Crypto, performing due diligence directly on early on-chain projects is costly and laden with information noise; collaborating with accelerators possessing filtering capabilities and situational resources allows for outsourcing early filtering and incubation, concentrating attention on "assets that have already passed the first round of natural selection." The case of Predict.fun, to some extent, showcases this new division of labor model: the accelerator is responsible for identification and cultivation, while traditional quantitative funds enter and amplify leverage at a later stage.

A Divergence Point After a Non-Disclosed Financing

Overall, the joint additional investment by YZi Labs and Susquehanna Crypto in Predict.fun has symbolic significance on multiple levels: for the project itself, this is dual endorsement from both the existing incubators and traditional quantitative institutions, as well as a supplement to competitive chips for the next stage; for the on-chain prediction track, this marks a portion of Wall Street-style funds transitioning from observers to holders, bringing more complex arbitrage, hedging, and data play to the track; for the overall entry rhythm of traditional quantitative institutions, this transaction may just be a starting point, but it clearly delineates a path from accelerators to institutional funds.

Meanwhile, the fact that this financing remains completely opaque in terms of amount and valuation also exposes obvious shortcomings in the industry regarding financing and operational data disclosures. For tracks aiming to attract larger-scale institutions and stricter compliance funds, how to provide adequate verifiable data and terms information while protecting business secrets and competitive advantages will directly impact the rhythm and confidence of the next round of institutional entry. Lacking verifiable figures, stories ultimately find it difficult to gain high weight in the face of capital with stringent risk control models.

If Susquehanna's move represents a structural bet by traditional quantitative institutions on the prediction track, then the key question moving forward is: can prediction markets transition from storytelling to "verifiable growth"? This not only means needing more transparent transaction and order book data but also requires providing auditable and quantifiable answers regarding oracle mechanisms, odds formation, bookmaker risk exposure, and compliance frameworks. Only when these infrastructures mature can on-chain prediction markets genuinely attract capital from Wall Street and the wider institutional world, rather than merely becoming another short-cycle conceptual stage.

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