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Retail Tradin
2026-09-14 00:00:00

Tulip King argues retail trading could define the next 20 years of market structure

A PANews article compiled from commentary by Tulip King frames the rise of retail trading as more than a reaction to economic stress or speculative desperation. The piece argues that what looks like chaos on the surface may actually be the turning point of a much longer cycle, one in which open networks, creator culture and crypto-native financial rails push individual participants closer to the center of global markets. To make that case, the article compares crypto with YouTube. Just as YouTube lowered the cost of publishing and distribution, allowing creators to challenge television, newsrooms, consumer brands and even Hollywood, crypto is described as doing something similar for trading and finance: always on, permissionless, global and cheap to access. The article points to Bitcoin, Zcash, stablecoins, Ethereum, Solana, Hyperliquid, prediction markets, perpetual futures and flash loans as signs that crypto has already built new monetary and market infrastructure. Tulip King also argues that the next stage may revolve around social trading. Streamers with visible track records, onchain performance data and creator-style distribution could shape how traders are followed, how capital is allocated and how talent is hired. Even so, the article does not claim everyone will make money. Its narrower point is that future market volume may become far more retail-driven, even if profits remain concentrated among the most skilled participants.

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Tulip King argues retail trading could define the next 20 years of market structure
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MEXC
2026-09-02 04:03:18

MEXC product chief outlines zero-fee strategy, RWA expansion and a three-stage AI roadmap

MEXC Product Director Vivien Lin used a lengthy interview with BlockBeats to lay out how the exchange thinks about product design, retail user acquisition, real-world asset access and AI-driven trading. Her core argument is that the next phase of exchange competition will not be decided by simply listing more assets or piling on more features, but by reducing the distance between users and tradeable opportunities. Lin said MEXC’s zero-fee approach was designed to cut both direct trading costs and the psychological barrier to entering the market. Combined with liquidity and low-slippage execution, she described that mix as a key part of the platform’s retail-focused edge. She also said the exchange now serves more than 40 million users worldwide. On listings, Lin said MEXC mainly looks at two factors: user participation and liquidity. On market expansion, she said MEXC has already launched more than 350 RWA-linked instruments, including single stocks, indexes, gold, silver and crude oil, and that its Real Stock product connects directly to licensed traditional brokerage infrastructure to give users 1:1 exposure to underlying assets. Lin also mapped out MEXC’s AI plan in three steps: Assistant, Copilot and, eventually, a Financial Operating System. She said the company is now moving from the second stage toward the third while working through computing, algorithm and performance constraints.

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MEXC product chief outlines zero-fee strategy, RWA expansion and a three-stage AI roadmap
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Whale Movemen
2026-08-27 06:30:00

Former Foresight Ventures co-founder pitches Mojo as an AI trading product built around trader behavior

WhiteForest, a former co-founder of Foresight Ventures, has published a lengthy critique of the current AI trading market while introducing Mojo, a product he says is designed to confront retail traders with their own repeated mistakes instead of feeding them more signals. Drawing on his time overseeing five business lines at Bitget and reviewing trading data from hundreds of thousands of retail users, he argues that retail losses are not mainly caused by weak judgment or a lack of information. In his view, the bigger problem is execution at critical moments and the absence of systems that can surface a trader’s own prior rules, comments, position limits, and repeated behavior before another trade is placed. The article groups most AI trading offerings into four categories: strategy cards, copy-trading agents, AI analysis chatboxes, and products that integrate large language models but reduce them to rigid, hard-coded workflows. WhiteForest says those products optimize distribution, engagement, or data collection rather than decision quality. He uses that framework to present Mojo as a system that can show a user what they did over the prior 12 months, what they previously said they would do, and whether they are simply repeating a pattern. He also makes broader claims about how user behavior data could be captured and monetized by proprietary trading firms if the market keeps moving in its current direction.

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