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

ambcryptoPublicado a 2026-03-27Actualizado a 2026-03-27

Resumen

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.

Preguntas relacionadas

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.

Lecturas Relacionadas

Dan Koe: The Counterintuitive Truth—You Don't Need to Remember Everything You Read

Dan Koe: The Counterintuitive Truth — You Don't Need to Remember Everything You Read The central idea is that deliberately trying to remember information is often misguided. True learning isn't about memorizing facts but about having important knowledge surface naturally when needed through use. Most forgetting is normal, not a failure. The article reframes learning using a control theory framework—a four-step feedback loop: having a clear Goal, accurately Sensing your current state, Comparing the gap, and Acting to close it. Most learning stalls because people only do step 2 (blind input) without a goal to create the necessary "error signal" for focused action. The most effective method is to start with output, not input. Begin a meaningful personal project first, and learn only what's necessary to complete it. This project-driven, "just-in-time" learning ensures knowledge is contextual and retained. The concept of a "Second Brain" often fails because it becomes a digital graveyard—over-collected and under-utilized. The goal should be building a "Second Subconscious"—a dynamic system that proactively surfaces relevant ideas during creation, not a static storage vault. Tools like Obsidian+Claude or Eden can help by automating organization and enabling semantic search, but their value depends on linking knowledge to active projects. Ultimately, what matters is not what you store, but what you filter and internalize. Focus on ideas that shape your worldview, use projects as filters, and transform collected material through writing and sharing. AI should be used to reduce friction in research and editing, not to formulate your core views. In conclusion, remembering is a byproduct, not the goal. Knowledge that sticks comes from pursuing personal goals, applying it in real projects, and digesting it through creation. The tools are merely aids; the crucial step is to start doing meaningful work and let the necessary knowledge find you.

marsbitHace 51 min(s)

Dan Koe: The Counterintuitive Truth—You Don't Need to Remember Everything You Read

marsbitHace 51 min(s)

A New Era: The Fundamental Transformation of China's Entrepreneurs

A profound generational shift is underway among Chinese entrepreneurs. The wealth and influence once dominated by real estate and internet giants is now being claimed by a new wave of founders driving breakthroughs in AI, semiconductors, and robotics. This change is vividly reflected in 2026's wealth rankings. Figures like Zhang Yiming (ByteDance), Liang Wenfeng (DeepSeek), Chen Tianshi (Cambricon), and Wang Xingxing (Unitree Robotics) are ascending. Their wealth stems not from traditional business models but from market expectations for future technological competitiveness, with AI, chips, and smart hardware becoming the primary engines of wealth creation. Their common trait is a foundational focus on technology, often starting from the laboratory rather than a business plan. Examples include Chen Tianshi's decade-long push in AI chips, Liang Wenfeng's core algorithmic innovations at DeepSeek with a compact team, and Zhu Yiming's "no salary until profitable" 9-year journey to build Changxin Memory into a global DRAM player. This transition marks a fundamental shift in China's economic imperative: from commercial expansion and learning to indigenous innovation and deep industrial capability. While the previous generation built the foundational market and infrastructure, this new cohort is tasked with achieving global leadership in core technologies, moving China from "keeping pace" to pioneering original, breakthrough innovations that are industrialized at scale. The baton is being passed to those competing on the world stage through technological originality.

marsbitHace 51 min(s)

A New Era: The Fundamental Transformation of China's Entrepreneurs

marsbitHace 51 min(s)

Once-Popular Web3 Enters Wave of Layoffs

The once-hot Web3 industry is experiencing a severe wave of layoffs. While many companies attribute job cuts to AI-driven restructuring, the primary reason is often financial pressure. The Web3 sector, at the intersection of tech and finance, has been hit particularly hard. Employees at major cryptocurrency exchanges report sudden, impersonal layoffs—often with system access revoked overnight—and minimal or no severance. Common tactics include setting impossible performance targets or terminating employees for minor policy violations. The working atmosphere has become toxic, marked by intense monitoring, excessive meetings, and management obsessed with control and internal politics rather than product innovation. The industry's core business model is collapsing. Exchange revenue from trading fees and listing charges has plummeted due to a decline in quality projects and retail investor exodus. Events like the massive forced liquidation on October 10th further shattered confidence. Competition from on-chain derivatives platforms and prediction markets is intensifying the downturn. As layoffs continue, displaced workers struggle to find new opportunities. Many transition to the AI sector, but face significant bias from traditional finance and even some AI firms, which view crypto industry experience with suspicion. The current downturn appears more structural than cyclical, driven by unsustainable practices, internal strife, and a failure to innovate, raising questions about the industry's future trajectory.

marsbitHace 1 hora(s)

Once-Popular Web3 Enters Wave of Layoffs

marsbitHace 1 hora(s)

Trading

Spot
活动图片