30 posts tagged with "prediction markets"

On March 17, 2026, Arizona Attorney General Kris Mayes did something no state official has ever done before: she filed criminal charges against a prediction market. Twenty misdemeanor counts landed on Kalshi, the CFTC-regulated platform where billions of dollars change hands every month on everything from Federal Reserve rate decisions to presidential elections. The message was unmistakable — what Wall Street calls "event contracts" and what Silicon Valley calls "information finance," Arizona calls illegal gambling.

The charges arrived just as the prediction market industry was celebrating its most spectacular growth phase ever — and that timing is no coincidence.

In the span of a single week in late February 2026, six freshly created Polymarket wallets placed bets on the timing of U.S. strikes against Iran — and walked away with $1.2 million in combined winnings. One trader, operating under the handle "Magamyman," pocketed $553,000 alone, buying shares at roughly ten cents apiece just hours before explosions lit up Tehran's skyline. By the time Congress caught wind of what had happened, prediction markets had already processed $529 million in Iran-related wagers.

Now, the two companies that facilitated those trades — Polymarket and Kalshi — are each seeking $20 billion valuations in new fundraising rounds. The collision between prediction markets' explosive growth and Washington's escalating crackdown is shaping up to be one of 2026's defining regulatory battles.

From Niche Experiment to Billion-Dollar Machines​

Just two years ago, prediction markets were a curiosity. Today, they are a financial force. Polymarket and Kalshi combined for $40 billion in trading volume during 2025, and 2026 is on pace to shatter that record. In the week ending March 1, Polymarket alone surged to $2.4 billion in weekly volume — a 31.9% jump that marked its largest weekly showing since January. By March 9, weekly volume stood at $1.93 billion, the first time it overtook Kalshi's $1.87 billion.

Polymarket's February 2026 total exceeded $7 billion, a staggering 7.5x increase over the same month in 2025. On February 28 alone, the platform recorded $425 million in single-day trading volume, eclipsing the previous record of $371 million set on Election Day 2024.

Kalshi, the CFTC-regulated counterpart, recently crossed a $1 billion revenue run rate — with sources suggesting it may have climbed to $1.5 billion. Open interest sits at over $400 million for Kalshi and $360 million for Polymarket. Both platforms have moved well beyond election markets into sports, geopolitics, economics, and pop culture.

When The Wall Street Journal reported on March 7 that both firms were exploring fundraising at $20 billion valuations, the numbers seemed audacious — but not unreasonable. Kalshi was last valued at $11 billion (after a $1 billion raise in December 2025), and Polymarket at $9 billion (following a $2 billion round with NYSE backing in October 2025). The combined $40 billion target would make prediction markets one of the fastest-growing verticals in all of fintech.

The Iran Crisis: When Prediction Markets Became "Death Markets"​

The catalyst for Washington's intervention was not abstract policy concern — it was the visceral reality of traders profiting from war in real time.

When the U.S. and Israel launched strikes against Iran on February 28, killing Supreme Leader Ayatollah Ali Khamenei and top military leaders, Polymarket's geopolitics markets exploded. Over half a billion dollars flowed through Iran-related contracts within days. The suspicious timing of certain trades — freshly created wallets placing highly concentrated bets hours before strikes — triggered immediate comparisons to insider trading.

This was not the first time such concerns surfaced. In January 2026, Israeli authorities charged two individuals for using classified military information to place bets on Polymarket about upcoming attacks during a 12-day conflict the previous June. The charges confirmed what critics had long feared: that prediction markets on geopolitical events create financial incentives for leaking classified information.

Senator Chris Murphy (D-Conn.) captured the mood on Capitol Hill: "It's insane this is legal. People around Trump are profiting off war and death." The political optics grew worse when it emerged that Donald Trump Jr. serves as an adviser to Polymarket, and his venture capital firm, 1789 Capital, has invested millions in the platform. The White House denied any administration-connected individuals were behind the lucrative trades, but the damage to prediction markets' public image was done.

Congress Responds: The DEATH BETS Act and a Multi-Front Legislative Assault​

Washington's response has been swift and multi-pronged.

The DEATH BETS Act (March 10, 2026): Representative Mike Levin and Senator Adam Schiff introduced the Discouraging Exploitative Assassination, Tragedy, and Harm Betting in Event Trading Systems Act. The bill would prohibit any CFTC-registered exchange from listing contracts involving terrorism, assassination, war, or individual death. Crucially, it extends to contracts that could be "construed as correlating closely" to a person's death — a broad standard that could sweep in far more markets than its sponsors intend.

The DEATH BETS Act represents a philosophical shift: instead of the current permissive framework where contracts exist unless the CFTC objects, it imposes an absolute prohibition on entire categories of events.

The Moore-Carbajal Bill: Representatives Blake Moore (R-Utah) and Salud Carbajal (D-Calif.) introduced bipartisan legislation restricting prediction markets from offering contracts on war and sports — two of the highest-volume categories driving growth.

The Blumenthal-Kim Bill (March 12, 2026): Perhaps the most structurally significant legislation, this bill explicitly states that prediction markets are not exempt from state law — a direct counter to the CFTC's position that it holds exclusive regulatory jurisdiction. If enacted, it would open the door for all 50 states to regulate or ban prediction market activity.

Government Official Trading Ban: Senators proposed legislation prohibiting U.S. government officials from trading on prediction markets — a targeted response to concerns about insider knowledge being monetized on platforms like Polymarket.

The State-Level Squeeze​

While Congress debates federal action, states are not waiting. The battle over whether prediction markets constitute gambling or financial instruments is playing out in courtrooms and statehouses across the country.

Utah's legislature passed a bill broadening its gambling prohibition to include wagers tied to events occurring during sporting contests. Governor Spencer Cox has signaled he will sign it. In Nevada and Massachusetts, judges have issued rulings allowing states to restrict Kalshi and Polymarket from offering sports-related markets. However, courts in New Jersey and Tennessee have ruled in Kalshi's favor, creating a patchwork of conflicting precedents.

The fundamental legal question remains unresolved: does the CFTC's oversight of prediction markets as derivatives preempt state gambling laws? The Trump-era CFTC has sided firmly with the platforms, asserting exclusive federal jurisdiction. But the Blumenthal-Kim bill and state court rulings suggest this position may not hold.

Former White House budget director Mick Mulvaney captured the tension: prediction market regulation, he argued, belongs with states, not the federal government — a position that prediction market companies strongly oppose, knowing that state-by-state compliance would be operationally devastating.

The $20 Billion Question: Can Growth Outrun Regulation?​

The dueling trajectories — exponential growth versus mounting regulatory pressure — create a paradox at the heart of prediction markets' valuation story.

On the bull case: Kalshi and Polymarket have proven product-market fit at scale. Billion-dollar revenue run rates, hundreds of millions in open interest, and weekly volumes that rival established derivatives exchanges suggest these are not speculative bets on a niche product. The prediction market format has demonstrated its utility for price discovery across elections, economics, sports, and geopolitics. Institutional interest is growing — NYSE backed Polymarket's Series B, and traditional finance players are exploring integration.

On the bear case: the regulatory overhang is severe. War-related contracts — which drove some of the most spectacular volume — face potential outright bans. Sports markets, another high-growth category, face state-level gambling restrictions. The insider trading controversy has drawn attention from lawmakers who previously had no opinion on prediction markets. And the CFTC's friendly posture under Trump-era leadership could shift with any administration change.

The $20 billion valuations assume prediction markets can maintain their growth trajectory while navigating these headwinds. That is a bet in itself.

What Comes Next​

Several developments will determine prediction markets' regulatory fate in the coming months:

• DEATH BETS Act committee action: Whether the bill advances from committee will signal congressional appetite for restricting event categories. The broad language around contracts "construed as correlating closely" to death could set significant precedent.

• State court consolidation: The contradictory rulings across states will likely require federal appellate clarification — or congressional resolution via the Blumenthal-Kim bill.

• CFTC enforcement posture: The commission's willingness (or reluctance) to investigate the Iran-related trading anomalies will signal whether the friendly regulatory stance can survive public scrutiny.

• Fundraising outcomes: Whether Polymarket and Kalshi actually close at $20 billion will serve as a market referendum on the sector's regulatory risk. Investors pricing in these valuations are implicitly betting that prediction markets survive their current political crisis intact.

The Bigger Picture​

Prediction markets sit at an uncomfortable intersection of innovation and ethics. Their core value proposition — aggregating dispersed information into accurate probability estimates — is powerful. Academic research consistently shows prediction markets outperform polls, pundits, and models for forecasting. During the 2024 election, Polymarket's accuracy drew mainstream media attention and legitimized the format.

But the Iran crisis exposed a fundamental tension: the same market design that makes prediction markets effective at price discovery also creates financial incentives around events where such incentives feel morally indefensible. There is a meaningful difference between betting on whether the Fed will cut rates and betting on when a foreign leader will be assassinated.

The industry's challenge is existential, not operational. Polymarket and Kalshi need to convince regulators and the public that prediction markets can be the "information markets" their proponents describe — without becoming the "death markets" their critics fear. At $40 billion in combined target valuations, the stakes have never been higher.

---

BlockEden.xyz provides the blockchain infrastructure that powers the next generation of decentralized applications — from DeFi protocols to prediction market backends. As platforms like Polymarket scale on Polygon and Kalshi explores on-chain settlement, reliable node services and API access become critical infrastructure. Explore our API marketplace to build on foundations designed for high-throughput, high-stakes applications.

Someone made $553,000 betting on a world leader's death — hours before the bombs fell. Now Congress wants to shut it down. The DEATH BETS Act, introduced this week by Senator Adam Schiff and Representative Mike Levin, would permanently ban prediction market contracts tied to war, terrorism, assassination, and individual deaths. The bill arrives at a moment when the prediction market industry is exploding — $5.9 billion in weekly volume and $20 billion valuations — and forces a fundamental question: where does information discovery end and moral hazard begin?

From Niche Curiosity to $64 Billion Industry​

Prediction markets were a fringe experiment just two years ago. Monthly trading volume in early 2024 hovered below $100 million. By December 2025, that figure had surged past $13 billion per month, with full-year global volume reaching nearly $64 billion — a 400% increase from 2024.

Two platforms dominate the space. Kalshi, a US-regulated designated contract market, posted $17.1 billion in 2025 trading volume and recently crossed a $1.5 billion revenue run rate. Polymarket, a crypto-native platform operating largely outside US jurisdiction, handled $21.5 billion in 2025. Together they command 85–90% of global prediction market volume. Both are targeting $20 billion valuations in upcoming funding rounds.

The growth has been turbocharged by sports betting (which now comprises the majority of trading activity) and high-profile political events. But it is the geopolitical contracts — bets on wars, strikes, and regime change — that have drawn the sharpest scrutiny.

$529 Million on Iran: The Catalyst​

The immediate catalyst for the DEATH BETS Act was the explosion of wagering around the US military campaign against Iran in early 2026. According to TechCrunch reporting, $529 million was traded on Polymarket contracts tied to the timing and scope of the attack — making it one of the platform's largest markets ever.

The numbers were staggering, but the details were worse. Crypto-analytics firm Bubblemaps identified six newly created Polymarket accounts that collectively made $1.2 million by correctly betting the US would strike Iran by February 28. The accounts were all created in February and had only ever placed bets on strike timing. Some purchased shares at roughly ten cents apiece, hours before the first explosions were reported in Tehran.

One account, trading under the username "Magamyman," made more than $553,000 placing bets on Iran and its Supreme Leader, Ayatollah Ali Khamenei, just before an Israeli strike killed him. In February, Israeli authorities arrested and charged a civilian and a military reservist on suspicion of using classified information to place wagers on the platform.

The pattern raised an obvious question: were people with access to military intelligence profiting from advance knowledge of strikes? While investigators could not confirm the traders had insider connections, the circumstantial evidence was enough to trigger a bipartisan outcry.

What the DEATH BETS Act Would Do​

The bill's full name — the Discouraging Exploitative Assassination, Tragedy, and Harm Betting in Event Trading Systems Act — leaves little ambiguity about its intent. The legislation would amend the Commodity Exchange Act to impose a categorical ban on any CFTC-registered exchange listing contracts involving:

• Terrorism or terrorist acts
• Assassination of individuals
• War or armed conflict
• An individual's death

Currently, the CFTC has discretionary authority to block event contracts it deems "contrary to the public interest." The DEATH BETS Act would remove that discretion and replace it with a bright-line prohibition. No case-by-case analysis. No weighing of information value against moral cost. These categories would be permanently off-limits for regulated platforms.

"Betting on war and death creates an environment in which insiders can profit off of classified information, our national security is jeopardized, and violence is encouraged," Senator Schiff stated in the bill's announcement. Representative Levin cited the $500 million-plus wagered on Iran strike timing as evidence that the current framework is inadequate.

The Information Discovery Defense​

Proponents of prediction markets argue that these contracts serve a vital function: aggregating dispersed information into accurate probability estimates. Academic research consistently shows that prediction markets outperform polls, pundit forecasts, and expert panels in predicting outcomes — from elections to economic indicators.

The defense extends to geopolitical events. When a prediction market prices the probability of a military strike at 85%, it is synthesizing thousands of individual assessments of publicly available intelligence, diplomatic signals, and historical patterns. This information has genuine value for businesses managing supply chain risk, investors hedging portfolios, and journalists interpreting complex situations.

First Amendment advocates add a constitutional dimension. If prediction markets are a form of expression — participants communicating their beliefs about future events through financial transactions — then categorical bans on specific topics face heightened judicial scrutiny. The argument has particular force when the banned topics are inherently political.

The Moral Hazard Counterargument​

Critics counter that geopolitical prediction markets create perverse incentives that no amount of information value can justify. The core concern is straightforward: when people can profit from death and destruction, some will be incentivized to cause or facilitate those outcomes.

The insider trading dimension amplifies this worry. Military operations involve thousands of personnel with varying levels of access to classified information. If even a fraction of those individuals can monetize their knowledge through anonymous, crypto-based prediction markets, the integrity of national security operations is compromised. The Israeli arrests demonstrated this is not a theoretical concern.

There is also the question of taste and public morality. Polymarket hosted contracts on whether specific world leaders would be killed — and traders celebrated profitable outcomes in real time. For many observers, the spectacle of financial markets cheering death crosses a line that no efficiency argument can justify.

The Regulatory Landscape: A Three-Way Tug of War​

The DEATH BETS Act enters a regulatory environment already in flux. Three competing forces are shaping prediction market oversight:

1. CFTC Rulemaking

On March 12, 2026, the CFTC launched a formal rulemaking process for prediction markets — its most significant regulatory action in the space to date. The six-page advisory asserted federal authority over event contracts and opened a 45-day public comment window. Chairman Michael Selig has outlined an agenda that includes guidance on which contracts are permissible and how designated contract markets should clear new products.

The CFTC's approach favors principles-based regulation: contracts must not be "readily susceptible to manipulation" and must not be "contrary to the public interest." This framework preserves regulatory flexibility but leaves significant gray areas.

2. State-Level Challenges

Multiple states have sued prediction market platforms, arguing that event contracts constitute gambling under state law. The jurisdictional question — whether CFTC federal preemption overrides state gaming authority — is widely expected to reach the Supreme Court. The CFTC's March advisory explicitly asserted federal primacy, setting up a direct collision with state regulators.

3. The Offshore Reality

Perhaps the most significant challenge is enforcement. Polymarket, the platform where the most controversial Iran bets occurred, operates outside US regulatory jurisdiction. American users access the platform through VPNs and cryptocurrency — neither of which the DEATH BETS Act can easily reach. A ban limited to CFTC-registered exchanges would push controversial contracts to offshore platforms while leaving the underlying demand intact.

Will It Pass? The Political Calculus​

The honest assessment: probably not in its current form. Republicans control the Senate majority through at least the end of 2026. The Trump administration has been broadly supportive of prediction markets, and the CFTC under Chairman Selig has signaled a preference for rulemaking over legislative prohibition. Even some Democrats privately acknowledge that a categorical ban may be too blunt an instrument.

But the bill's impact may not depend on passage. By forcing a public debate about the ethics of death and war contracts, the DEATH BETS Act pressures the CFTC to address these categories in its ongoing rulemaking. It also creates a legislative template that could be revived if a future incident — say, confirmed insider trading on a military operation — generates sufficient public outrage.

The prediction market industry itself appears to be reading the room. Kalshi, the US-regulated platform, already voluntarily avoids contracts on assassination, war, and terrorism. Its competitive strategy increasingly emphasizes regulatory compliance as a differentiator against offshore rivals. The DEATH BETS Act, paradoxically, may strengthen Kalshi's market position by codifying restrictions it already follows.

What This Means for the $9 Billion Sector​

The prediction market industry faces a defining moment. With combined weekly volume exceeding $5.9 billion and both leading platforms pursuing $20 billion valuations, the financial stakes are enormous. But the sector's long-term viability depends on navigating the tension between information value and moral boundaries.

Three scenarios are most likely:

Scenario 1: Selective Prohibition. The CFTC's rulemaking process produces bright-line bans on death, assassination, and terrorism contracts while permitting other geopolitical events. This fragments the market but preserves most of the industry's growth trajectory.

Scenario 2: Self-Regulation. Industry leaders voluntarily adopt restrictions on the most controversial categories, pre-empting legislative action. This is already happening to some degree with Kalshi's approach.

Scenario 3: Offshore Migration. Regulatory pressure on US-registered platforms pushes controversial contracts entirely to offshore, crypto-native platforms beyond regulatory reach — the worst outcome for those concerned about insider trading and market integrity.

The most likely outcome is a combination of the first two: CFTC rules that formalize existing industry norms, combined with continued enforcement challenges against offshore platforms. The DEATH BETS Act may never become law, but it has already changed the conversation.

The Deeper Question​

Beyond the policy debate, the DEATH BETS Act forces a reckoning with a question that prediction market enthusiasts have largely avoided: does the right to bet on anything include the right to bet on anyone's death?

The information discovery argument is compelling in the abstract. In practice, watching anonymous traders celebrate profits timed to missile strikes raises questions that efficiency metrics cannot answer. The prediction market industry's $64 billion moment of truth is not really about regulation. It is about whether an industry built on the premise that markets know best can acknowledge that some knowledge comes at too high a price.

---

As blockchain-based prediction markets and DeFi platforms continue to evolve under shifting regulatory frameworks, reliable infrastructure becomes essential for builders navigating this space. BlockEden.xyz provides enterprise-grade RPC and API services across major chains, helping developers build compliant, resilient applications on foundations designed for the institutional era.

What if you could trade not just what happens in the world, but what people think about it? In March 2026, Polymarket and Kaito AI launched exactly that — "Attention Markets," a new category of prediction markets where users wager on internet trends, brand popularity, and social sentiment rather than traditional real-world events. The partnership fuses Kaito's AI-quantified attention data with Polymarket's $21.5 billion prediction market infrastructure, creating tradeable instruments from something that has never been priced on-chain before: collective human attention.

The timing is no accident. It arrives just weeks after Kaito's flagship Yaps product was killed by X's API crackdown on InfoFi apps — and at a moment when prediction markets are projected to reach $1.3 trillion in annual volume by year-end.

When you post your opinion on X (Twitter), it costs you nothing to be wrong. When you bet $10,000 on a prediction market, being wrong costs you $10,000. That single difference — the cost of error — is the foundational primitive behind an emerging $381 million sector that is quietly rewiring how humanity prices truth.

Information Finance (InfoFi) is Vitalik Buterin's term for "a discipline where you start from a fact that you want to know, and then deliberately design a market to optimally elicit that information from market participants." Unlike traditional finance, which prices assets, InfoFi prices expectations — transforming epistemic uncertainty into tradeable signals. The sector now spans prediction markets processing $40 billion annually, attention markets distributing $116 million to content creators, and credibility networks securing 33 million verified users.

But beneath the marketing narratives, every InfoFi system runs on five technical primitives that determine whether information gets priced accurately or drowned in noise. Understanding these primitives is the difference between building a robust information market and an expensive spam machine.

Primitive 1: Cost-Bearing Signal Submission​

The central insight of InfoFi is deceptively simple: opinions are cheap, commitments are expensive. Every well-designed InfoFi system forces participants to bear a real cost when submitting information, creating the friction that separates signal from noise.

In prediction markets, this takes the form of capital staked on beliefs. Polymarket processed 95 million trades in 2025, reaching $21.5 billion in annual volume. The platform migrated from automated market makers to a Central Limit Order Book (CLOB) — the same mechanism used by institutional exchanges — with off-chain order matching and on-chain settlement via smart contracts on Polygon. Each trade is a cost-bearing commitment: participants lose money when they're wrong, which creates relentless incentive pressure toward accurate probability assessment.

Ethos Network, which launched on Base in January 2025, applies this primitive to social reputation. When you endorse another user's trustworthiness, you stake ETH. That ETH is at risk if your endorsee behaves badly. The result: reputation endorsements carry real information precisely because they are costly to give.

The Intuition Protocol takes the most explicit approach, launching mainnet in October 2025 with $8.5 million in backing from Superscrypt, Shima, F-Prime (Fidelity's venture arm), ConsenSys, and Polygon. Its architecture treats information as an asset class:

• Atoms: Canonical identifiers for any discrete claim (an identity, concept, or piece of information)
• Triples: Subject-predicate-object statements — e.g., "Protocol X has vulnerability Y" or "Alice is trustworthy"

Both can be staked on via bonding curves. Creating low-quality Atoms costs you tokens; curating high-quality ones earns fees.

The common thread: cost of error creates a noise filter. Casual, low-confidence claims are suppressed by the friction of commitment.

Primitive 2: Proper Scoring Rules and Incentive Compatibility​

Cost-bearing alone is insufficient — the structure of the payoff must ensure that truthful reporting is the optimal strategy. This is the mathematical domain of proper scoring rules: mechanisms where a participant maximizes their expected reward by reporting their true beliefs.

The Logarithmic Market Scoring Rule (LMSR), invented by economist Robin Hanson, was the foundational mechanism for early prediction markets. Its cost function — C(q) = b × ln(Σ exp(qᵢ/b)) — solves the bootstrapping problem by ensuring the automated market maker always has liquidity, even before any traders arrive. The parameter b controls the tradeoff between liquidity depth and the market maker's maximum potential loss. Historical trades are embedded in the current price, providing natural dampening against noise traders.

LMSR's limitation is capital inefficiency: it provides the same liquidity depth regardless of where prices are, wasting capital near extreme probability values (like a 95% confident market). Paradigm's November 2024 paper introduced a prediction-market-specific AMM (pm-AMM) that treats outcome prices as following Brownian motion — the same mathematical framework underlying Black-Scholes options pricing — and adjusts liquidity depth dynamically over time to maintain constant loss-versus-rebalancing rates for liquidity providers.

The same mathematical property — incentive compatibility — appears in non-financial systems. Ethos Network's vouching mechanism is incentive-compatible: if you stake ETH to endorse someone who later rugs users, your ETH is at risk. The optimal strategy is to only endorse people you genuinely believe are trustworthy. Intuition's token curated registries function similarly: stakers profit when their curated information is judged high-quality, lose tokens when it is low-quality.

Primitive 3: Graph-Based Trust Propagation​

Static reputation scores are gameable. If a score is computed from raw counts (followers, reviews, transactions), a well-funded attacker can simply buy the inputs. Graph-based trust propagation is the solution: trust is not assigned absolutely but propagates through the social graph, making context and relationships central to score computation.

EigenTrust, originally designed to identify malicious nodes in peer-to-peer networks, is the leading algorithm for this purpose. OpenRank (by Karma3 Labs, backed by Galaxy and IDEO CoLab) applies EigenTrust to Farcaster and Lens Protocol social graph data. Rather than treating a "follow" from a new account and a "follow" from a highly-trusted account as equivalent, EigenTrust weights interactions by the reputation of the actor. The algorithm converges to a stable trust assignment where your reputation depends on who trusts you, and how much they themselves are trusted.

The result is a personalized trust graph — your reputation relative to a given community reflects the specific social connections within that community. OpenRank uses this to power Farcaster's "For You" feeds, channel rankings, and frame personalization. A user deeply embedded in the DeFi community gets different reputation scores for different contexts than a user embedded in the NFT art community.

Kaito's YAP scoring system applies the same logic to attention markets. Engagement from a high-YAP (high-reputation) account is worth exponentially more than engagement from a low-YAP account. This is PageRank applied to social capital: links from high-authority nodes transfer more authority than links from low-authority nodes. Kaito processes this across ~200,000 monthly active creators, computing mindshare — the percentage of total crypto Twitter attention captured by a given project — with weighted social graph traversal.

Ethos takes graph propagation even further with its invitation-only system. Your account's value depends not just on who vouched for you, but on the entire chain of who invited whom. A fresh account invited by a well-connected Ethos member inherits some of that member's credibility — a structural enforcement of the "trusted by trusted people" principle.

Primitive 4: Multi-Layer Sybil Resistance​

Sybil attacks — flooding a system with fake identities to game scores, harvest rewards, or distort markets — are the existential threat to every InfoFi primitive. If fake identities are cheap to create, cost-bearing signals can be gamed with coordinated bots, reputation graphs can be artificially inflated, and prediction market resolutions can be manipulated.

The InfoFi sector has converged on a multi-layer defense stack:

Layer 0 — Biometric Verification: World (formerly Worldcoin) uses iris-scanning Orbs to issue World IDs on Worldchain. Zero-knowledge proofs enable users to prove humanness without revealing which iris was scanned, preventing cross-application tracking. With 7,500 Orbs deploying across the US in 2025, this layer aims for 200 million proof-of-humanity verifications.

Layer 1 — Invitation and Social Graph Constraints: Ethos (invitation-only), Farcaster (phone verification), and Lens Protocol (wallet-gated profile creation) impose structural friction on identity creation. Fake identities require real social connections to bootstrap.

Layer 2 — Stake-Weighted Trust: EigenTrust-based systems weight trust by stake or established reputation. Coordination attacks require accumulating real trust from existing members — expensive to fake.

Layer 3 — Behavioral Analysis: Kaito's algorithm was updated in 2025 after criticism that it rewarded KOL (Key Opinion Leader) content farming over genuine analysis. The updates introduced AI filters that detect paid followers, bot-like posting patterns, and content that mentions rankings without providing insight. Replies no longer count toward leaderboard rankings; posts that only discuss rewards without adding information are excluded from mindshare calculations.

Layer 4 — ZK Credential Aggregation: Human Passport (formerly Gitcoin Passport, acquired by Holonym Foundation in 2025) aggregates credentials from multiple sources — social verification, on-chain history, biometrics — into a single Sybil-resistance score using zero-knowledge proofs. With 2 million users and 34 million credentials issued, it enables applications to require a minimum Sybil resistance score without learning which specific verifications a user holds.

Galxe combines these layers at scale: 33 million users across 7,000+ brands hold credentials verified through ZK proofs, with Galxe Score aggregating on-chain activity across Ethereum, Solana, TON, Sui, and other chains into a multi-dimensional reputation metric.

Primitive 5: Continuous Pricing via Bonding Curves​

Binary scores ("trusted" or "not trusted", "verified" or "unverified") are inadequate for information markets because they fail to represent the degree of confidence, reputation, or attention. InfoFi systems use bonding curves — continuous mathematical functions that determine price based on the quantity demanded — to create markets that price information on a spectrum.

LMSR's cost function is a bonding curve for prediction market shares: as more shares of a given outcome are purchased, their price increases continuously. This makes the market price a real-time indicator of collective confidence.

Ethos's reputation market layer creates bonding curves for individual credibility: "trust tickets" and "distrust tickets" linked to specific user profiles are priced continuously based on demand. When the community believes a user's trustworthiness is increasing, trust ticket prices rise. This transforms reputation assessment from a static badge into a live market with continuous price discovery.

Cookie.fun introduced the Price-to-Mindshare (P/M) ratio as a continuous valuation metric for AI agents: market capitalization divided by mindshare percentage, analogous to the price-to-earnings ratio in equity markets. A low P/M implies undervalued attention relative to market cap; a high P/M implies the opposite. This is the InfoFi equivalent of fundamental valuation — translating attention metrics into continuous investment signals.

Intuition's vault architecture uses bonding curves to determine how staking affects the credibility and relevance score of each Atom and Triple. Staking into a vault that contains accurate, widely-cited information is profitable; staking into a vault with poor-quality information incurs losses as others exit. The continuous pricing mechanism aligns curator incentives with information quality over time.

The Architecture That Prices Truth​

These five primitives are not independent systems — they compose into a unified architecture. Cost-bearing signals are only valuable if they are structured as proper scoring rules (so truthful reporting is optimal), aggregated via graph propagation (so context affects value), defended by Sybil resistance (so fake signals are expensive), and expressed via continuous pricing (so degrees of confidence are captured).

The $40 billion annual volume in prediction markets, the $116 million distributed to attention market participants, and the 33 million credentialed identities across Web3 represent early evidence that these mechanisms work. Polymarket's monthly active traders grew from 45,000 to 19 million between 2024 and 2025 — a 421x increase driven not by speculation but by users discovering that prediction markets provide more accurate event probability assessments than traditional media.

The next wave of InfoFi applications will likely come from AI agents using these markets as data feeds. Kalshi already reports that algorithmic bots are the primary participants on its CFTC-regulated platform, with AI systems treating probability shifts in prediction markets as execution triggers for trades in correlated traditional markets. When AI agents consume and produce information at scale, the quality of the underlying pricing mechanisms determines the quality of the AI systems built on top of them.

What Vitalik called "info finance" is becoming the plumbing of the information economy: the layer that determines what is true, who is trustworthy, and what deserves attention — with capital-enforced incentives that traditional information systems have never had.

BlockEden.xyz provides infrastructure for builders across Sui, Aptos, Ethereum, and 20+ blockchain networks. Developers building information markets, reputation systems, and on-chain analytics can access production-grade node services and data APIs at BlockEden.xyz.

When the global datasphere exploded from 33 zettabytes in 2018 to a projected 175 zettabytes by 2025—and an anticipated 394 zettabytes by 2028—a paradox emerged: More information didn't lead to better decisions. Instead, it created an overwhelming noise-to-signal problem that traditional platforms couldn't solve. Enter Information Finance (InfoFi), a breakthrough framework transforming how we value, trade, and monetize judgment itself. As prediction markets process over $5 billion in weekly volume and platforms like Kaito and Cookie DAO pioneer attention scoring systems, we're witnessing the birth of a new asset class where credibility, influence, and analytical prowess become tradeable commodities.

The Information Explosion Paradox​

The numbers are staggering. IDC's research reveals that the world's data grew from a mere 33 zettabytes in 2018 to 175 zettabytes by 2025—a compound annual growth rate of 61%. To put this in perspective, if you stored 175ZB on BluRay discs, the stack would reach the moon 23 times. By 2028, we're expected to hit 394 zettabytes, nearly doubling in just three years.

Yet despite this abundance, decision quality has stagnated. The problem isn't lack of information—it's the inability to filter signal from noise at scale. In Web2, attention became the commodity, extracted by platforms through engagement farming and algorithmic feeds. Users produced data; platforms captured value. But what if the very ability to navigate this data deluge—to make accurate predictions, identify emerging trends, or curate valuable insights—could itself become an asset?

This is the core thesis of Information Finance: transforming judgment from an uncompensated social act into a measurable, tradeable, and financially rewarded capability.

Kaito: Pricing Influence Through Reputation Assetization​

Kaito AI represents the vanguard of this transformation. Unlike traditional social platforms that reward mere volume—more posts, more engagement, more noise—Kaito has pioneered a system that prices the quality of judgment itself.

On January 4, 2026, Kaito announced a paradigm shift: transitioning from "attention distribution" to "reputation assetization." The platform fundamentally restructured influence weighting by introducing Reputation Data and On-chain Holdings as core metrics. This wasn't just a technical upgrade—it was a philosophical repositioning. The system now answers the question: "What kind of participation deserves to be valued long-term?"

The mechanism is elegant. Kaito's AI analyzes user behavior across platforms like X (formerly Twitter) to generate "Yaps"—a tokenized score reflecting quality engagement. These Yaps feed into the Yapper Leaderboard, creating a transparent, data-backed ranking system where influence becomes quantifiable and, critically, verifiable.

But Kaito didn't stop at scoring. In early March 2026, it partnered with Polymarket to launch "Attention Markets"—contracts that let traders bet on social-media mindshare using Kaito AI data to settle outcomes. The first markets went live immediately: one tracking Polymarket's own mindshare trajectory, another betting on whether it would achieve an all-time high mindshare in Q1 2026.

This is where Information Finance gets revolutionary. Attention Markets don't just measure engagement—they create a financial mechanism to price it. If you believe a topic, project, or meme will capture 15% of X mindshare next week, you can now take a position on that belief. When judgment is correct, it's rewarded. When it's wrong, capital flows to those with superior analytical capabilities.

The implications are profound: low-cost noise gets marginalized because it carries financial risk, while high-signal contributions become economically advantaged.

Cookie DAO: The AI Agent Intelligence Layer​

While Kaito focuses on human influence scoring, Cookie DAO tackles a parallel challenge: tracking and pricing the performance of AI agents themselves.

Cookie DAO operates as a decentralized data aggregation layer, indexing activity from AI agents operating across blockchains and social platforms. Its dashboard provides real-time analytics on market capitalization, social engagement, token holder growth, and—crucially—"mindshare" rankings that quantify each agent's influence.

The platform leverages 7 terabytes of real-time onchain and social data feeds, monitoring conversations across all crypto sectors. One standout feature is the "mindshare" metric, which doesn't just count mentions but weights them by credibility, context, and impact.

Cookie DAO's 2026 roadmap reveals ambitious plans:

• Token-Gated Data Access (Q1 2026): Exclusive AI agent analytics for $COOKIE holders, creating a direct monetization pathway for information curation.
• Cookie Deep Research Terminal (2026): AI-enhanced analytics designed for institutional adoption, positioning Cookie DAO as the Bloomberg Terminal for AI agent intelligence.
• Snaps Incentives Partnership (2026): A collaboration aimed at redefining creator rewards through data-backed performance metrics.

What makes Cookie DAO particularly significant is its role in a future where AI agents become autonomous economic actors. As these agents trade, curate, and make decisions, their credibility and track record become critical inputs for other agents and human users. Cookie DAO is building the trust infrastructure that prices this credibility.

The token economics are already showing market validation, with COOKIE maintaining a \12.8 million market cap and $2.57 million in daily trading volume as of February 2026. More importantly, the platform is positioning itself as the "AI version of Chainlink"—providing decentralized, verifiable data about the most important new class of market participants: AI agents themselves.

The InfoFi Ecosystem: From Prediction Markets to Data Monetization​

Kaito and Cookie DAO aren't operating in isolation. They're part of a broader InfoFi movement that's redefining how information creates financial value.

Prediction markets represent the most mature segment. As of February 1, 2026, these platforms have evolved from "betting parlors" to the "source of truth" for global financial systems. The numbers speak for themselves:

• $5.23 billion in combined weekly trading volume (record set in early February 2026)
• $701.7 million in daily volume on January 12, 2026—a historic single-day record
• Over $50 billion in annual liquidity across major platforms

The speed advantage is staggering. When a Congressional memo leaked information about a potential government shutdown, Kalshi's prediction market reflected a 4% probability shift within 400 milliseconds. Traditional news wires took nearly three minutes to report the same information. For traders, institutional investors, and risk managers, that 179.6-second gap represents the difference between profit and loss.

This is InfoFi's core value proposition: markets price information faster and more accurately than any other mechanism because participants have capital at stake. It's not about clicks or likes—it's about money following conviction.

The institutional adoption validates this thesis:

• Polymarket now provides real-time forecast data to The Wall Street Journal and Barron's through a News Corp partnership.
• Coinbase integrated prediction market feeds into its "Everything Exchange," allowing retail users to trade event contracts alongside crypto.
• Intercontinental Exchange (ICE) invested $2 billion in Polymarket, signaling Wall Street's recognition that prediction markets are critical financial infrastructure.

Beyond prediction markets, InfoFi encompasses multiple emerging verticals:

1. Attention Markets (Kaito, Cookie DAO): Pricing mindshare and influence
2. Reputation Systems (Proof of Humanity, Lens Protocol, Ethos Network): Credibility scoring as collateral
3. Data Markets (Ocean Protocol, LazAI): Monetizing AI training data and user-generated insights

Each segment addresses the same fundamental problem: How do we price judgment, credibility, and information quality in a world drowning in data?

The Mechanism: How Low-Cost Noise Becomes Marginalized​

Traditional social media platforms suffer from a terminal flaw: they reward engagement, not accuracy. A sensational lie spreads faster than a nuanced truth because virality, not veracity, drives algorithmic distribution.

Information Finance flips this incentive structure through capital-bearing judgments. Here's how it works:

1. Skin in the Game When you make a prediction, rate an AI agent, or score influence, you're not just expressing an opinion—you're taking a financial position. If you're wrong repeatedly, you lose capital. If you're right, you accumulate wealth and reputation.

2. Transparent Track Records Blockchain-based systems create immutable histories of predictions and assessments. You can't delete past mistakes or retroactively claim prescience. Your credibility becomes verifiable and portable across platforms.

3. Market-Based Filtering In prediction markets, incorrect predictions lose money. In attention markets, overestimating a trend's mindshare means your position depreciates. In reputation systems, false endorsements damage your credibility score. The market mechanically filters out low-quality information.

4. Credibility as Collateral As platforms mature, high-reputation actors gain access to premium features, larger position sizes, or token-gated data. Low-reputation participants face higher costs or restricted access. This creates a virtuous cycle where maintaining accuracy becomes economically essential.

Kaito's evolution exemplifies this. By weighting Reputation Data and On-chain Holdings, the platform ensures that influence isn't just about follower counts or post volume. An account with 100,000 followers but terrible prediction accuracy carries less weight than a smaller account with consistent, verifiable insights.

Cookie DAO's mindshare metrics similarly distinguish between viral-but-wrong and accurate-but-niche. An AI agent that generates massive social engagement but produces poor trading signals will rank lower than one with modest attention but superior performance.

The Data Explosion Challenge​

The urgency of InfoFi becomes clearer when you examine the data trajectory:

• 2010: 2 zettabytes of global data
• 2018: 33 zettabytes
• 2025: 175 zettabytes (IDC projection)
• 2028: 394 zettabytes (Statista forecast)

This 20x growth in under two decades isn't just quantitative—it represents a qualitative shift. By 2025, 49% of data resides in public cloud environments. IoT devices alone will generate 90 zettabytes by 2025. The datasphere is increasingly distributed, real-time, and heterogeneous.

Traditional information intermediaries—news organizations, research firms, analysts—can't scale to match this growth. They're limited by human editorial capacity and centralized trust models. InfoFi provides an alternative: decentralized, market-based curation where credibility compounds through verifiable track records.

This isn't theoretical. The prediction market boom of 2025-2026 demonstrates that when financial incentives align with informational accuracy, markets become extraordinarily efficient discovery mechanisms. The 400-millisecond price adjustment on Kalshi wasn't because traders read the memo faster—it's because the market structure incentivizes acting on information immediately and accurately.

The $381 Million Sector and What Comes Next​

The InfoFi sector isn't without challenges. In January 2026, major InfoFi tokens experienced significant corrections. X (formerly Twitter) banned several engagement-reward apps, causing KAITO to drop 18% and COOKIE to fall 20%. The sector's market capitalization, while growing, remains modest at approximately $381 million.

These setbacks, however, may be clarifying rather than catastrophic. The initial wave of InfoFi projects focused on simple engagement rewards—essentially Web2 attention economics with token incentives. The ban on engagement-reward apps forced a market-wide evolution toward more sophisticated models.

Kaito's pivot from "paying for posts" to "pricing credibility" exemplifies this maturation. Cookie DAO's shift toward institutional-grade analytics signals similar strategic clarity. The survivors aren't building better social media platforms—they're building financial infrastructure for pricing information itself.

The roadmap forward includes several critical developments:

Interoperability Across Platforms Currently, reputation and credibility are siloed. Your Kaito Yapper score doesn't translate to Polymarket win rates or Cookie DAO mindshare metrics. Future InfoFi systems will need reputation portability—cryptographically verifiable track records that work across ecosystems.

AI Agent Integration As AI agents become autonomous economic actors, they'll need to assess credibility of data sources, other agents, and human counterparties. InfoFi platforms like Cookie DAO become essential infrastructure for this trust layer.

Institutional Adoption Prediction markets have already crossed this threshold with ICE's $2 billion Polymarket investment and News Corp's data partnership. Attention markets and reputation systems will follow as traditional finance recognizes that pricing information quality is a trillion-dollar opportunity.

Regulatory Clarity The CFTC's regulation of Kalshi and ongoing negotiations around prediction market expansion signal that regulators are engaging with InfoFi as legitimate financial infrastructure, not gambling. This clarity will unlock institutional capital currently sitting on the sidelines.

Building on Reliable Infrastructure​

The explosion of on-chain activity—from prediction markets processing billions in weekly volume to AI agents requiring real-time data feeds—demands infrastructure that won't buckle under demand. When milliseconds determine profitability, API reliability isn't optional.

This is where specialized blockchain infrastructure becomes critical. Platforms building InfoFi applications need consistent access to historical data, mempool analytics, and high-throughput APIs that scale with market volatility. A single downtime event during a prediction market settlement or attention market snapshot can destroy user trust irreversibly.

For builders entering the InfoFi space, BlockEden.xyz provides enterprise-grade API infrastructure for major blockchains, ensuring your attention market contracts, reputation systems, or prediction platforms maintain uptime when it matters most. Explore our services designed for the demands of real-time financial applications.

Conclusion: Judgment as the Ultimate Scarce Resource​

We're witnessing a fundamental shift in how information creates value. In the Web2 era, attention was the commodity—captured by platforms, extracted from users. The Web3 InfoFi movement proposes something more sophisticated: judgment itself as an asset class.

Kaito's reputation assetization transforms social influence from popularity to verifiable predictive capability. Cookie DAO's AI agent analytics creates transparent performance metrics for autonomous economic actors. Prediction markets like Polymarket and Kalshi demonstrate that capital-bearing judgments outperform traditional information intermediaries on speed and accuracy.

As the datasphere grows from 175 zettabytes to 394 zettabytes and beyond, the bottleneck isn't information availability—it's the ability to filter, synthesize, and act on that information correctly. InfoFi platforms create economic incentives that reward accuracy and marginalize noise.

The mechanism is elegant: when judgment carries financial consequences, low-cost noise becomes expensive and high-signal analysis becomes profitable. Markets do the filtering that algorithms can't and human editors won't scale to match.

For crypto natives, this represents an opportunity to participate in building the trust infrastructure for the information age. For traditional finance, it's a recognition that pricing uncertainty and credibility is a fundamental financial primitive. For society at large, it's a potential solution to the misinformation crisis—not through censorship or fact-checking, but through markets that make truth profitable and lies costly.

The attention economy is evolving into something far more powerful: an economy where your judgment, your credibility, and your analytical capability aren't just valuable—they're tradeable assets in their own right.

---

Sources:

• The Bet On InfoFi Is Changing — From Paying For Posts To Pricing Credibility - Benzinga
• Polymarket, Kaito AI Launch 'Attention Markets' That Let Traders Bet On Social Hype - Benzinga
• What Is Kaito the AI InfoFi Platform, and How Does Its Yappers System Work? - BingX
• 2026 Kaito Marketing Guide - Odaily
• Kaito and Polymarket Unveil 'Attention Markets' - The Defiant
• Ultimate Guide to Cookie.fun and Cookie DAO - DappRadar
• Cookie DAO launches platform to track performance of AI agents - CryptoSlate
• AI version of Chainlink, why did COOKIE surge by 300%? - ChainCatcher
• What to expect for digital assets in 2026 - World Economic Forum
• The InfoFi Revolution: How Prediction Markets Became the World's Fastest Financial Data Feed - FinancialContent
• The 2026 Volume War: Polymarket and Kalshi Battle for the Future of 'InfoFi' - FinancialContent
• IDC: Expect 175 zettabytes of data worldwide by 2025 - Network World
• Data growth worldwide 2010-2028 - Statista
• Global DataSphere to Hit 175 Zettabytes by 2025, IDC Says - BigDATAwire
• Global Datasphere From 33ZB in 2018 to 175ZB by 2025 - StorageNewsletter

When Kalshi's daily trading volume hit $814 million in early 2026, capturing 66.4% of the prediction market share, it wasn't retail speculators driving the surge. It was AI agents. Autonomous trading algorithms now contribute over 30% of prediction market volume, transforming what began as internet curiosity into Wall Street's newest institutional forecasting infrastructure. The sector's weekly volume—$5.9 billion and climbing—rivals many traditional derivatives markets, with one critical difference: these markets trade information, not just assets.

This is "Information Finance"—the monetization of collective intelligence through blockchain-based prediction markets. When traders bet $42 million on whether OpenAI will achieve AGI before 2030, or $18 million on which company goes public next, they're not gambling. They're creating liquid, tradeable forecasts that institutional investors, policymakers, and corporate strategists increasingly trust more than traditional analysts. The question isn't whether prediction markets will disrupt forecasting. It's how quickly institutions will adopt markets that outperform expert predictions by measurable margins.

The $5.9B Milestone: From Fringe to Financial Infrastructure​

Prediction markets ended 2025 with record all-time high volumes approaching $5.3 billion, a trajectory that accelerated into 2026. Weekly volumes now consistently exceed $5.9 billion, with daily peaks touching $814 million during major events. For context, this exceeds the daily trading volume of many mid-cap stocks and rivals specialized derivatives markets.

The growth isn't linear—it's exponential. Prediction market volumes in 2024 were measured in hundreds of millions annually. By 2025, monthly volumes surpassed $1 billion. In 2026, weekly volumes routinely hit $5.9 billion, representing over 10x annual growth. This acceleration reflects fundamental shifts in how institutions view prediction markets: from novelty to necessity.

Kalshi dominates with 66.4% market share, processing the majority of institutional volume. Polymarket, operating in the crypto-native space, captures significant retail and international flow. Together, these platforms handle billions in weekly volume across thousands of markets covering elections, economics, tech developments, sports, and entertainment.

The sector's legitimacy received ICE's (Intercontinental Exchange) validation when the parent company of NYSE invested $2 billion in prediction market infrastructure. When the operator of the world's largest stock exchange deploys capital at this scale, it signals that prediction markets are no longer experimental—they're strategic infrastructure.

AI Agents: The 30% Contributing Factor​

The most underappreciated driver of prediction market growth is AI agent participation. Autonomous trading algorithms now contribute 30%+ of total volume, fundamentally changing market dynamics.

Why are AI agents trading predictions? Three reasons:

Information arbitrage: AI agents scan thousands of data sources—news, social media, on-chain data, traditional financial markets—to identify mispriced predictions. When a market prices an event at 40% probability but AI analysis suggests 55%, agents trade the spread.

Liquidity provision: Just as market makers provide liquidity in stock exchanges, AI agents offer two-sided markets in prediction platforms. This improves price discovery and reduces spreads, making markets more efficient for all participants.

Portfolio diversification: Institutional investors deploy AI agents to gain exposure to non-traditional information signals. A hedge fund might use prediction markets to hedge political risk, tech development timelines, or regulatory outcomes—risks difficult to express in traditional markets.

The emergence of AI agent trading creates a positive feedback loop. More AI participation means better liquidity, which attracts more institutional capital, which justifies more AI development. Prediction markets are becoming a training ground for autonomous agents learning to navigate complex, real-world forecasting challenges.

Traders on Kalshi are pricing a 42% probability that OpenAI will achieve AGI before 2030—up from 32% six months prior. This market, with over $42 million in liquidity, reflects the "wisdom of crowds" that includes engineers, venture capitalists, policy experts, and increasingly, AI agents processing signals humans can't track at scale.

Kalshi's Institutional Dominance: The Regulated Exchange Advantage​

Kalshi's 66.4% market share isn't accidental—it's structural. As the first CFTC-regulated prediction market exchange in the U.S., Kalshi offers institutional investors something competitors can't: regulatory certainty.

Institutional capital demands compliance. Hedge funds, asset managers, and corporate treasuries can't deploy billions into unregulated platforms without triggering legal and compliance risks. Kalshi's CFTC registration eliminates this barrier, enabling institutions to trade predictions alongside stocks, bonds, and derivatives in their portfolios.

The regulated status creates network effects. More institutional volume attracts better liquidity providers, which tightens spreads, which attracts more traders. Kalshi's order books are now deep enough that multi-million-dollar trades execute without significant slippage—a threshold that separates functional markets from experimental ones.

Kalshi's product breadth matters too. Markets span elections, economic indicators, tech milestones, IPO timings, corporate earnings, and macroeconomic events. This diversity allows institutional investors to express nuanced views. A hedge fund bearish on tech valuations can short prediction markets on unicorn IPOs. A policy analyst anticipating regulatory change can trade congressional outcome markets.

The high liquidity ensures prices aren't easily manipulated. With millions at stake and thousands of participants, market prices reflect genuine consensus rather than individual manipulation. This "wisdom of crowds" beats expert predictions in blind tests—prediction markets consistently outperform polling, analyst forecasts, and pundit opinions.

Polymarket's Crypto-Native Alternative: The Decentralized Challenger​

While Kalshi dominates regulated U.S. markets, Polymarket captures crypto-native and international flow. Operating on blockchain rails with USDC settlement, Polymarket offers permissionless access—no KYC, no geographic restrictions, no regulatory gatekeeping.

Polymarket's advantage is global reach. Traders from jurisdictions where Kalshi isn't accessible can participate freely. During the 2024 U.S. elections, Polymarket processed over $3 billion in volume, demonstrating that crypto-native infrastructure can handle institutional scale.

The platform's crypto integration enables novel mechanisms. Smart contracts enforce settlement automatically based on oracle data. Liquidity pools operate continuously without intermediaries. Settlement happens in seconds rather than days. These advantages appeal to crypto-native traders comfortable with DeFi primitives.

However, regulatory uncertainty remains Polymarket's challenge. Operating without explicit U.S. regulatory approval limits institutional adoption domestically. While retail and international users embrace permissionless access, U.S. institutions largely avoid platforms lacking regulatory clarity.

The competition between Kalshi (regulated, institutional) and Polymarket (crypto-native, permissionless) mirrors broader debates in digital finance. Both models work. Both serve different user bases. The sector's growth suggests room for multiple winners, each optimizing for different regulatory and technological trade-offs.

Information Finance: Monetizing Collective Intelligence​

The term "Information Finance" describes prediction markets' core innovation: transforming forecasts into tradeable, liquid instruments. Traditional forecasting relies on experts providing point estimates with uncertain accuracy. Prediction markets aggregate distributed knowledge into continuous, market-priced probabilities.

Why markets beat experts:

Skin in the game: Market participants risk capital on their forecasts. Bad predictions lose money. This incentive structure filters noise from signal better than opinion polling or expert panels where participants face no penalty for being wrong.

Continuous updating: Market prices adjust in real-time as new information emerges. Expert forecasts are static until the next report. Markets are dynamic, incorporating breaking news, leaks, and emerging trends instantly.

Aggregated knowledge: Markets pool information from thousands of participants with diverse expertise. No single expert can match the collective knowledge of engineers, investors, policymakers, and operators each contributing specialized insight.

Transparent probability: Markets express forecasts as probabilities with clear confidence intervals. A market pricing an event at 65% says "roughly two-thirds chance"—more useful than an expert saying "likely" without quantification.

Research consistently shows prediction markets outperform expert panels, polling, and analyst forecasts across domains—elections, economics, tech development, and corporate outcomes. The track record isn't perfect, but it's measurably better than alternatives.

Financial institutions are taking notice. Rather than hiring expensive consultants for scenario analysis, firms can consult prediction markets. Want to know if Congress will pass crypto regulation this year? There's a market for that. Wondering if a competitor will IPO before year-end? Trade that forecast. Assessing geopolitical risk? Bet on it.

The Institutional Use Case: Forecasting as a Service​

Prediction markets are transitioning from speculative entertainment to institutional infrastructure. Several use cases drive adoption:

Risk management: Corporations use prediction markets to hedge risks difficult to express in traditional derivatives. A supply chain manager worried about port strikes can trade prediction markets on labor negotiations. A CFO concerned about interest rates can cross-reference Fed prediction markets with bond futures.

Strategic planning: Companies make billion-dollar decisions based on forecasts. Will AI regulation pass? Will a tech platform face antitrust action? Will a competitor launch a product? Prediction markets provide probabilistic answers with real capital at risk.

Investment research: Hedge funds and asset managers use prediction markets as alternative data sources. Market prices on tech milestones, regulatory outcomes, or macro events inform portfolio positioning. Some funds directly trade prediction markets as alpha sources.

Policy analysis: Governments and think tanks consult prediction markets for public opinion beyond polling. Markets filter genuine belief from virtue signaling—participants betting their money reveal true expectations, not socially desirable responses.

The ICE's $2 billion investment signals that traditional exchanges view prediction markets as a new asset class. Just as derivatives markets emerged in the 1970s to monetize risk management, prediction markets are emerging in the 2020s to monetize forecasting.

The AI-Agent-Market Feedback Loop​

AI agents participating in prediction markets create a feedback loop accelerating both technologies:

Better AI from market data: AI models train on prediction market outcomes to improve forecasting. A model predicting tech IPO timings improves by backtesting against Kalshi's historical data. This creates incentive for AI labs to build prediction-focused models.

Better markets from AI participation: AI agents provide liquidity, arbitrage mispricing, and improve price discovery. Human traders benefit from tighter spreads and better information aggregation. Markets become more efficient as AI participation increases.

Institutional AI adoption: Institutions deploying AI agents into prediction markets gain experience with autonomous trading systems in lower-stakes environments. Lessons learned transfer to equities, forex, and derivatives trading.

The 30%+ AI contribution to volume isn't a ceiling—it's a floor. As AI capabilities improve and institutional adoption increases, agent participation could hit 50-70% within years. This doesn't replace human judgment—it augments it. Humans set strategies, AI agents execute at scale and speed impossible manually.

The technology stacks are converging. AI labs partner with prediction market platforms. Exchanges build APIs for algorithmic trading. Institutions develop proprietary AI for prediction market strategies. This convergence positions prediction markets as a testing ground for the next generation of autonomous financial agents.

Challenges and Skepticism​

Despite growth, prediction markets face legitimate challenges:

Manipulation risk: While high liquidity reduces manipulation, low-volume markets remain vulnerable. A motivated actor with capital can temporarily skew prices on niche markets. Platforms combat this with liquidity requirements and manipulation detection, but risk persists.

Oracle dependency: Prediction markets require oracles—trusted entities determining outcomes. Oracle errors or corruption can cause incorrect settlements. Blockchain-based markets minimize this with decentralized oracle networks, but traditional markets rely on centralized resolution.

Regulatory uncertainty: While Kalshi is CFTC-regulated, broader regulatory frameworks remain unclear. Will more prediction markets gain approval? Will international markets face restrictions? Regulatory evolution could constrain or accelerate growth unpredictably.

Liquidity concentration: Most volume concentrates in high-profile markets (elections, major tech events). Niche markets lack liquidity, limiting usefulness for specialized forecasting. Solving this requires either market-making incentives or AI agent liquidity provision.

Ethical concerns: Should markets exist on sensitive topics—political violence, deaths, disasters? Critics argue monetizing tragic events is unethical. Proponents counter that information from such markets helps prevent harm. This debate will shape which markets platforms allow.

The 2026-2030 Trajectory​

If weekly volumes hit $5.9 billion in early 2026, where does the sector go?

Assuming moderate growth (50% annually—conservative given recent acceleration), prediction market volumes could exceed $50 billion annually by 2028 and $150 billion by 2030. This would position the sector comparable to mid-sized derivatives markets.

More aggressive scenarios—ICE launching prediction markets on NYSE, major banks offering prediction instruments, regulatory approval for more market types—could push volumes toward $500 billion+ by 2030. At that scale, prediction markets become a distinct asset class in institutional portfolios.

The technology enablers are in place: blockchain settlement, AI agents, regulatory frameworks, institutional interest, and proven track records outperforming traditional forecasting. What remains is adoption curve dynamics—how quickly institutions integrate prediction markets into decision-making processes.

The shift from "fringe speculation" to "institutional forecasting tool" is well underway. When ICE invests $2 billion, when AI agents contribute 30% of volume, when Kalshi daily volumes hit $814 million, the narrative has permanently changed. Prediction markets aren't a curiosity. They're the future of how institutions quantify uncertainty and hedge information risk.

Sources​

• Prediction Markets Record All-Time High $5.3B Volume to End 2025
• The Great Prediction War of 2026: Polymarket and Kalshi Battle for Dominance
• The Race to Singularity: Why Kalshi Traders Are Betting Big on OpenAI Achieving AGI Before 2030
• Kalshi - Prediction Market for Trading the Future

When Intercontinental Exchange—the parent company of the New York Stock Exchange—backed Polymarket with a $2 billion investment in 2025, Wall Street sent a clear signal: information itself has become a tradeable financial asset. This wasn't just another crypto investment. It was the traditional finance world's acceptance of InfoFi (Information Finance), a paradigm shift where knowledge, attention, data credibility, and prediction signals transform into monetizable on-chain assets.

The numbers tell a compelling story. The InfoFi market reached $649 million in valuation by late 2025, with prediction markets alone generating over $27.9 billion in trading volume between January and October. Meanwhile, stablecoin circulation surpassed $300 billion, processing $4 trillion in the first seven months of 2025—an 83% year-over-year jump. These aren't isolated trends. They're converging into a fundamental reimagining of how information flows, how trust is established, and how value is exchanged in the digital economy.

The Birth of Information Finance​

InfoFi emerged from a simple but powerful observation: in the attention economy, information has measurable value, yet most of that value is captured by centralized platforms rather than by the individuals who create, curate, or verify it. Ethereum co-founder Vitalik Buterin popularized the concept in a 2024 blog post, outlining InfoFi's "potential to create better implementations of social media, science, news, governance, and other fields."

The core innovation lies in transforming intangible information flows into tangible financial instruments. By utilizing blockchain's transparency, AI's analytical power, and the scalability of big data, InfoFi assigns market value to information that was previously difficult to monetize. This includes everything from prediction signals and data credibility to user attention and reputation scores.

The InfoFi market currently segments into six key categories:

1. Prediction Markets: Platforms like Polymarket allow users to buy shares in the outcomes of future events. The price fluctuates based on collective market belief, effectively turning knowledge into a tradeable financial asset. Polymarket recorded over $18 billion in trading volume throughout 2024 and 2025, and famously predicted the 2024 U.S. presidential election with 95% accuracy—several hours before the Associated Press made the official call.

2. Yap-to-Earn: Social platforms that monetize user-generated content and engagement directly through token economics, redistributing attention value to creators rather than centralizing it in platform shareholders.

3. Data Analytics and Insights: Kaito stands as the leading platform in this space, generating $33 million in annual revenue through its advanced data analytics platform. Founded by former Citadel portfolio manager Yu Hu, Kaito has attracted $10.8 million in funding from Dragonfly, Sequoia Capital China, and Spartan Group.

4. Attention Markets: Tokenizing and trading user attention as a scarce resource, allowing advertisers and content creators to directly purchase engagement.

5. Reputation Markets: On-chain reputation systems where credibility itself becomes a tradeable commodity, with financial incentives aligned to accuracy and trustworthiness.

6. Paid Content: Decentralized content platforms where information itself is tokenized and sold directly to consumers without intermediary platforms taking massive cuts.

Prediction Markets: The "Truth Machine" of Web3​

If InfoFi is about turning information into assets, prediction markets represent its purest form. These platforms use blockchain and smart contracts to let users trade on outcomes of real-world events—elections, sports, economic indicators, even crypto prices. The mechanism is elegant: if you believe an event will happen, you buy shares. If it occurs, you profit. If not, you lose your stake.

Polymarket's performance in the 2024 U.S. presidential election showcased the power of aggregated market intelligence. The platform not only called the race hours before traditional media but also predicted outcomes in swing states like Arizona, Georgia, North Carolina, and Nevada more accurately than polling aggregators. This wasn't luck—it was the wisdom of crowds, financially incentivized and cryptographically secured.

The trust mechanism here is crucial. Polymarket operates on the Polygon blockchain, offering low transaction fees and fast settlement times. It's non-custodial, meaning the platform doesn't hold user funds. Operations are transparent and automated via blockchain, making the system censorship-resistant and trustless. Smart contracts automatically execute payouts when events conclude, removing the need for trusted intermediaries.

However, the model isn't without challenges. Chaos Labs, a crypto risk management firm, estimated that wash trading—where traders simultaneously buy and sell the same asset to artificially inflate volume—could account for up to a third of Polymarket's trading during the 2024 presidential campaign. This highlights a persistent tension in InfoFi: the economic incentives that make these markets powerful can also make them vulnerable to manipulation.

Regulatory clarity arrived in 2025 when the U.S. Department of Justice and the Commodity Futures Trading Commission (CFTC) formally ended investigations into Polymarket without bringing new charges. Shortly after, Polymarket acquired QCEX, a CFTC-licensed derivatives exchange and clearinghouse, for $112 million, enabling legal operations within the United States under regulatory compliance. By February 2026, Polymarket's valuation reached $9 billion.

In January 2026, the Public Integrity in Financial Prediction Markets Act (H.R. 7004) was introduced to ban federal officials from trading on non-public information, ensuring the "purity of data" in these markets. This legislative framework underscores an important reality: prediction markets aren't just crypto experiments—they're becoming recognized infrastructure for information discovery.

Stablecoins: The Rails Powering Web3 Payments​

While InfoFi represents the what—tradeable information assets—stablecoins provide the how: the payment infrastructure enabling instant, low-cost, global transactions. The stablecoin market's evolution from crypto-native settlement to mainstream payment infrastructure mirrors InfoFi's trajectory from niche experiment to institutional adoption.

Stablecoin transaction volume exceeded $27 trillion annually in 2025, with USDT (Tether) and USDC (Circle) controlling 94% of the market and accounting for 99% of payment volume. Monthly payment flows surpassed $10 billion, with business transactions representing 63% of total volume. This shift from speculative trading to real economic utility marks a fundamental maturation of the technology.

Mastercard's integration exemplifies the infrastructure buildout. The payments giant now enables stablecoin spending at more than 150 million merchant locations via its existing card network. Users link their stablecoin balances to virtual or physical Mastercard cards, with automatic conversion at the point of sale. This seamless bridge between crypto and traditional finance was unthinkable just two years ago.

Circle Payments Network has emerged as critical infrastructure, connecting financial institutions, digital challenger banks, payment companies, and digital wallets to process payments instantly across currencies and markets. Circle reports over 100 financial institutions in the pipeline, with products including Circle Gateway for cross-chain liquidity and Arc, a blockchain designed specifically for enterprise-grade stablecoin payments.

The GENIUS Act, signed into law in 2025, provided the first federal framework governing U.S. payment stablecoins. It established clear standards for licensing, reserves, consumer protections, and ongoing oversight—regulatory certainty that has unlocked institutional capital and engineering resources.

Primary networks for stablecoin transfers include Ethereum, Tron, Binance Smart Chain (BSC), Solana, and Base. This multi-chain infrastructure ensures redundancy, specialization (e.g., Solana for high-frequency, low-value transactions; Ethereum for high-value, security-critical transfers), and competitive dynamics that drive down costs.

Oracle Networks: The Bridge Between Worlds​

For InfoFi and Web3 payments to scale, blockchain applications need reliable access to real-world data. Oracle networks provide this critical infrastructure, acting as bridges between on-chain smart contracts and off-chain information sources.

Chainlink's Runtime Environment (CRE), announced in November 2025, represents a watershed moment. This all-in-one orchestration layer unlocks institutional-grade smart contracts for onchain finance. Leading financial institutions including Swift, Euroclear, UBS, Kinexys by J.P. Morgan, Mastercard, AWS, Google Cloud, Aave's Horizon, and Ondo are adopting CRE to capture what the Boston Consulting Group estimates as an $867 trillion tokenization opportunity.

The scale is staggering: the World Economic Forum projects that by 2030, 10% of global GDP will be stored on blockchain, with tokenized illiquid assets reaching approximately $16 trillion. These projections assume robust oracle infrastructure that can reliably feed data on asset prices, identity verification, regulatory compliance, and event outcomes into smart contracts.

Oracle technology is also evolving beyond static data delivery. Modern oracles like Chainlink now use AI to deliver predictive data rather than just historical snapshots. The APRO (AT) token, officially listed on November 5, 2025, represents this next generation: infrastructure aimed at bridging reliable real-world data with blockchain-powered applications across DeFi, AI, RWAs (Real World Assets), and prediction markets.

Given the $867 trillion in financial assets that could be tokenized (per World Economic Forum estimates), oracle networks aren't just infrastructure—they're the nervous system of the emerging tokenized economy. Without reliable data feeds, smart contracts can't function. With them, the entire global financial system can potentially migrate on-chain.

The Convergence: Data, Finance, and Trust​

The real innovation isn't InfoFi alone, or stablecoins alone, or oracles alone. It's the convergence of these technologies into a cohesive system where information flows freely, value settles instantly, and trust is cryptographically enforced rather than institutionally mediated.

Consider a near-future scenario: A prediction market (InfoFi layer) uses oracle data feeds (data layer) to settle outcomes, with payouts processed in USDC via Circle Payments Network (payment layer), automatically converted to local currency via Mastercard (bridge layer) at 150 million global merchants. The user experiences instant, trustless, low-cost settlement. The system operates 24/7 without intermediaries.

This isn't speculation. The infrastructure is live and scaling. The regulatory frameworks are being established. The institutional capital is committed. Years of experimentation with blockchain-based transactions are giving way to concrete infrastructure, regulatory frameworks, and institutional commitment that could push Web3 payments into everyday commerce by 2026.

Industry analysts expect 2026 to mark the inflection point, with landmark events including the launch of the first cross-border tokenized securities settlement network led by a major Wall Street bank. By 2026, the internet will think, verify, and move money automatically through one shared system, where AI makes decisions, blockchains prove them, and payments enforce them instantly without human middlemen.

The Road Ahead: Challenges and Opportunities​

Despite the momentum, significant challenges remain. Wash trading and market manipulation persist in prediction markets. Stablecoin infrastructure still faces banking access issues in many jurisdictions. Oracle networks are potential single points of failure—critical infrastructure that, if compromised, could cascade failures across interconnected smart contracts.

Regulatory uncertainty persists outside the U.S., with different jurisdictions taking vastly different approaches to crypto classification, stablecoin issuance, and prediction market legality. The European Union's MiCA (Markets in Crypto-Assets) regulation, the UK's stablecoin framework proposals, and Asia-Pacific's fragmented approach create a complex global landscape.

User experience remains a barrier to mainstream adoption. Despite infrastructure improvements, most users still find wallet management, private key security, and cross-chain operations intimidating. Abstracting this complexity without sacrificing security or decentralization is an ongoing design challenge.

Yet the trajectory is unmistakable. Information is becoming liquid. Payments are becoming instant and global. Trust is being algorithmically enforced. The $649 million InfoFi market is just the beginning—a proof of concept for a much larger transformation.

When the New York Stock Exchange's parent company invests $2 billion in a prediction market, it's not betting on speculation. It's betting on infrastructure. It's recognizing that information, properly structured and incentivized, isn't just valuable—it's tradeable, verifiable, and foundational to the next iteration of global finance.

The Web3 payment revolution isn't coming. It's here. And it's being built on the bedrock of information as an asset class.

---

Sources:

• InfoFi | Ledger
• InfoFi Explained: Transforming Information into Financial Assets | OKX
• The Rise of InfoFi: How Prediction Markets Became the World's 'Truth Machine'
• Why Web3 Payments Could Finally Go Mainstream In 2026 - Benzinga
• Polymarket - Wikipedia
• Prediction markets explode in 2025: Inside the Kalshi-Polymarket duopoly and challengers | The Block
• How stablecoins took on cross-border payments: 2025 in data
• Chainlink Runtime Environment Goes Live, Unlocking Institutional Tokenization at Scale
• The LINK Between Worlds | Grayscale
• 2026 Stablecoin Predictions: From Crypto Plumbing to Payments Infrastructure - FinTech Weekly

The prediction market industry just crossed a threshold that seemed impossible two years ago: $5.9 billion in weekly trading volume, with AI agents executing over 30% of all trades.