P2P team admits to betting on its own raise days after Polymarket tightened insider trading rules

ambcryptoОпубліковано о 2026-03-27Востаннє оновлено о 2026-03-27

Анотація

P2P, a crypto project, has admitted that its team placed bets on Polymarket regarding the outcome of its own $6 million fundraising campaign. The bets were made approximately 10 days before the raise concluded, using funds from the project's treasury. This activity, which generated around $23,000 in profit and loss, occurred just days after Polymarket updated its rules to explicitly prohibit insider trading, including by individuals who can influence an event's outcome. While P2P stated the bets were not based on guaranteed information and plans to return all proceeds, the case highlights the enforcement challenges decentralized prediction markets face in preventing manipulation and maintaining trust, especially from involved actors. The incident is a real-world test of how newly tightened market integrity rules are applied in practice.

A crypto project has disclosed that it placed bets on its own fundraising outcome on Polymarket, drawing attention to how newly tightened market integrity rules may apply in practice.

In a public statement, P2P.me confirmed that an account labeled “P2P Team” on-chain was controlled by its team. The account was used to bet on whether the project would reach a $6 million fundraising target.

The bets were placed roughly 10 days before the raise concluded, when the outcome had not yet been finalized.

The project stated that the capital used came from its foundation’s treasury and that all proceeds would be returned. It added that it plans to liquidate the positions and introduce internal policies governing prediction market activity.

Case emerges days after Polymarket tightened insider trading rules

The disclosure comes just days after Polymarket updated its rules on 23 March, introducing stricter definitions around insider trading and manipulation.

Among the changes, the platform explicitly prohibited trading by individuals who hold positions of influence over an outcome. That category includes participants directly involved in events tied to prediction markets.

While P2P said the bets were placed before the raise was completed and not based on guaranteed allocations, the timing of the disclosure places the case within a broader shift toward tighter oversight on prediction platforms.

On-chain activity shows active trading and profits

Data from the “P2P Team” account indicates the activity was not purely symbolic.

The account recorded roughly $149,000 in trading volume and around $23,000 in profit and loss. Individual positions generated gains of over $11,000. The figures suggest the trades were executed as active positions rather than passive signaling.

Source: Polymarket

P2P acknowledged that failing to disclose the activity at the time was a mistake. The team notes that trading on outcomes that a team can influence may erode trust, even if the result is not predetermined.

Incident highlights challenges in prediction market enforcement

The case underscores a broader challenge facing decentralized prediction markets: how to manage participation by individuals who may influence event outcomes.

Polymarket’s model relies on open participation and transparent on-chain activity. However, the presence of informed or involved actors can complicate enforcement, particularly when trades occur before outcomes are finalized.

As platforms move to formalize rules around insider activity, real-world cases like this may shape how those standards are interpreted and applied.


Final Summary

  • P2P disclosed betting on its own fundraise outcome, raising questions about insider participation in prediction markets.
  • The incident comes as platforms like Polymarket tighten rules, highlighting ongoing challenges in enforcing market integrity.

Пов'язані питання

QWhat did the P2P team admit to doing on Polymarket?

AThe P2P team admitted to placing bets on their own fundraising outcome, specifically on whether the project would reach its $6 million target.

QWhen did Polymarket update its rules regarding insider trading and manipulation?

APolymarket updated its rules, introducing stricter definitions around insider trading and manipulation, on March 23.

QWhat was the financial result of the 'P2P Team' account's trading activity?

AThe 'P2P Team' account recorded approximately $149,000 in trading volume and around $23,000 in profit and loss, with individual positions generating gains of over $11,000.

QAccording to the article, what is a key challenge for decentralized prediction markets highlighted by this incident?

AA key challenge is managing participation by individuals who may influence event outcomes, as the presence of informed or involved actors complicates enforcement, especially when trades occur before outcomes are finalized.

QWhat action did P2P say it would take following this disclosure?

AP2P stated it would liquidate the positions, return all proceeds to its foundation's treasury, and introduce internal policies governing prediction market activity.

Пов'язані матеріали

Near Returns to the AI Stage: Transformation into a Public Chain Due to 'Payroll Difficulties,' Agent and Privacy Emerge as New Growth Narratives

NEAR Returns to AI Origins: From Payroll Struggles to Blockchain, Now Focusing on AI Agents and Privacy NEAR Protocol's journey began not with grand blockchain ambitions, but from a practical hurdle: its AI startup founders, including Transformer paper co-author Illia Polosukhin, couldn't efficiently pay international developers in 2017. This led them to pivot and build a high-performance, scalable blockchain. After years navigating various crypto narratives like sharding and cross-chain interoperability, NEAR is now leveraging its AI roots to re-enter the AI arena. A key driver is its "NEAR Intents" layer, which abstracts complex cross-chain transactions. Users simply state their goal (e.g., swap BTC for ETH), and a solver network finds the optimal route. This system has processed over $20B in cross-chain volume, generating significant fee revenue. A major growth area is private transactions via "Confidential Intents/Swaps," which hide trade details until settlement to protect against MEV and front-running. Remarkably, private swaps recently accounted for over 40% of NEAR's transaction volume, highlighting strong demand but also potential regulatory scrutiny. With its AI-founder pedigree, NEAR is positioning itself at the intersection of blockchain, AI agents, and privacy, aiming to become infrastructure for the emerging agent economy while navigating the challenges of its rapid adoption.

marsbit48 хв тому

Near Returns to the AI Stage: Transformation into a Public Chain Due to 'Payroll Difficulties,' Agent and Privacy Emerge as New Growth Narratives

marsbit48 хв тому

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

In recent discussions, Vitalik Buterin has frequently emphasized the concept of "CROPS," a framework defining core values for Ethereum's development. CROPS stands for Censorship Resistance, Capture Resistance, Open Source, Privacy, and Security. Initially outlined in the Ethereum Foundation's "EF Mandate," it represents a commitment to user sovereignty, ensuring that the network resists external control, remains open, protects privacy, and prioritizes security. The relevance of CROPS extends beyond Ethereum's foundational principles, becoming crucial in the context of AI integration. As AI agents begin handling wallet operations and automated transactions, the risk increases that users may cede control over their digital assets, privacy, and intentions to centralized AI service providers. A "CROPS AI" would therefore emphasize local execution where possible, privacy-preserving remote model calls (e.g., using zero-knowledge proofs), and transparent, verifiable processes to maintain user agency. Vitalik highlights a significant convergence between "CROPS Ethereum access layer" and "CROPS AI." Both address the same fundamental challenge: how users can access powerful services—be it blockchain data via RPCs or AI models—without exposing sensitive information or relinquishing ultimate control. This intersection points toward a future digital entry point that is more private, secure, and user-controlled. Ultimately, CROPS is not merely an abstract ideal but a practical guidepost. It steers development—from protocol resilience and wallet design to AI agent safety—towards a future where users retain self-sovereignty even as digital systems grow more complex and powerful. In an era of accelerating AI adoption, these "slow variables" of censorship resistance, openness, privacy, and security may define Ethereum's enduring value.

marsbit58 хв тому

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

marsbit58 хв тому

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

Silicon Valley investor and "Godfather of Startups" Steve Hoffman warns that combining Web3 with AI is likely a trap, not a promising venture. In an interview, Hoffman argues that while AI is a foundational technology touching all industries, Web3 adds complexity, friction, and regulatory risk without solving mainstream consumer or business needs. He advises founders to focus on deep, specialized applications where startups can out-iterate giants, rather than on generic features easily replicated by large tech companies. Hoffman observes that Silicon Valley will lead foundational AI research, while China excels at rapid, large-scale application and commercialization, particularly in robotics. He stresses that AI-driven autonomous agents capable of collaborative, multi-step tasks are 2-4 years away, which will cause significant job displacement. The solution is not to slow AI but to redesign business models around human-AI collaboration and reform social systems like education and retraining. For startups, Hoffman recommends focusing on vertical, expertise-heavy domains to build defensibility. He sees major opportunities in AI fraud detection and cybersecurity. Key founder mindsets include systemic thinking over feature-focus, relentless customer centricity, building adaptive teams, and deeply understanding AI's capabilities and limits. Hoffman is also leading a non-profit initiative to establish university centers aimed at training future leaders in responsible, human-value-aligned AI innovation.

marsbit2 год тому

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

marsbit2 год тому

Token Inefficient, Economy Tokenless

The article "Tokens Aren't Economical, Economics Aren't Tokenized" analyzes a pivotal shift in the AI industry from a technology-driven narrative to one dominated by capital efficiency. It highlights two concurrent trends: a severe capital shortage due to the exorbitant and recurring costs of compute (e.g., OpenAI's high burn rate) and a wave of corporate spin-offs where major tech companies are separating their AI units (like Kuaishou's Kling and Baidu's Kunlunxin). The core argument is that AI's "anti-internet" business model, where user growth increases costs rather than profits, has created a disconnect between high valuations and actual cash flow. Spin-offs address this by allowing AI assets to be valued independently. Within a parent company, they are seen as cost centers, but as standalone entities, they are priced based on their growth potential and scarcity in the primary market, leading to massive valuation premiums (e.g., Kling's estimated value tripling post-spin-off). The industry is at an inflection point, moving from "model worship" to "value realization." The competition is evolving from a pure compute (GPU) race to a broader focus on systemic efficiency and full-stack engineering (involving CPUs and orchestration) to achieve viable commercialization. The year 2026 is framed as a critical moment where the industry must definitively answer how to economically translate AI capability into tangible business value, reshaping the sector's future power structure.

marsbit2 год тому

Token Inefficient, Economy Tokenless

marsbit2 год тому

Торгівля

Спот
Ф'ючерси
活动图片