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Polymarket Under Fire: Allegations Of Paid, Misleading Prediction Market Videos

Investigation Uncovers Coordinated Promotional Tactics

A recent investigation by The Wall Street Journal has revealed that Polymarket may have been compensating online creators to produce misleading content. The investigation analyzed more than 1,100 videos that showcased what appeared to be lucrative bets on its prediction market.

Manufactured Content And Social Media Amplification

The findings indicate that many of these videos featured near-perfect replicas of the Polymarket website, complete with staged trades and fictitious winnings. Further intensifying the effort, a specialized marketing contractor reportedly deployed a “social-media army” to amplify these videos, thereby enhancing the deceptive narrative surrounding the platform.

Creator Agreements Under The Microscope

Notably, the Wall Street Journal’s report highlights that creators were instructed not to disclose their financial ties to Polymarket. Despite these guidelines, several creators began including the handle “@polymarket partner” in their bios after journalists raised questions. Razeen Khan, a college student and former creator who collaborated with Polymarket until March, compared the practice to overly polished commercials that misrepresent fast food, emphasizing that the videos did not accurately depict real-life outcomes.

Polymarket’s Commitment To Transparency

In response to the allegations, Polymarket stated that it remains “committed to maintaining accurate, fair, and transparent markets” and announced plans to audit its promotional content. This move is expected to address investor concerns and bolster confidence in the platform’s integrity.

Mirendil Signs $100 Million Google Cloud Deal To Advance Self-Improving AI

AI startup Mirendil has signed a multi-year agreement worth more than $100 million with Google Cloud to secure computing infrastructure for its self-improving AI research.

The partnership reflects growing competition among AI companies to lock in access to high-performance computing, while cloud providers race to attract promising startups developing next-generation AI models.

Backing The Next Stage Of AI Research

Mirendil plans to use Google’s Tensor Processing Units (TPUs), Nvidia GPUs and managed training infrastructure to develop AI systems capable of improving their own performance over time.

Known as recursive self-improvement, the concept focuses on building AI that can refine its knowledge and capabilities with minimal human intervention. The technology is attracting growing interest across the industry, with several startups and leading AI labs exploring similar approaches.

According to co-founder and Chief Executive Behnam Neyshabur, the long-term goal is to develop AI that can automate scientific research and accelerate discoveries in fields such as medicine, biology and materials science.

Compute Capacity Becomes A Strategic Asset

Training increasingly advanced AI models requires enormous computing resources, making long-term infrastructure agreements a critical competitive advantage.

Mirendil said Google’s combination of TPUs and GPUs allows workloads to be matched with the most suitable hardware, improving efficiency while reducing costs for customers.

For Google Cloud, the agreement strengthens its position in the race to provide infrastructure for frontier AI developers, while giving the company exposure to one of the industry’s emerging approaches to next-generation artificial intelligence.

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