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Hyperliquid recently announced that HIP-4 will soon support permissionless deployments, similar to its successful HIP-3 model for perpetual contracts. Deployers will need to stake 500,000 HYPE (locked for 6 months) and can earn up to 50% fee share from their created markets. This move aims to replicate the "decentralized" growth strategy that propelled Hyperliquid to leadership in tokenized stocks and commodities. However, HIP-4's initial performance in the prediction market sector has been weak, with only $216M in total volume over three months and a handful of active markets. This pales in comparison to leader Polymarket, which saw $4.587B in volume last month alone. The high staking requirement—valued at around $30 million—could exclude smaller teams, potentially limiting innovation and competition. The article questions whether this strategy can succeed, noting that Hyperliquid missed the crucial timing window of the recent World Cup, a major driver for prediction markets. While permissionless deployment could increase market variety and speed, the combination of high entry barriers and missed opportunities may hinder HIP-4's ability to challenge established players like Polymarket.

Hyperliquid announced that its HIP-4 prediction markets will soon support permissionless deployments, similar to its successful HIP-3 model for perpetual contracts. This move aims to ignite growth in its underperforming prediction market segment by allowing third parties to create their own prediction markets upon staking 500,000 HYPE (worth approximately $30 million), with the potential to earn up to 50% fee shares. While this strategy mirrors the approach that propelled HIP-3 and platforms like Trade.xyz to dominance in tokenized trading, the article raises significant challenges for HIP-4. Its current performance is weak, with only $216 million in total volume over three months—pales in comparison to Polymarket's $4.6 billion monthly volume. The high staking barrier may exclude smaller teams, potentially limiting innovation and event diversity. Furthermore, Hyperliquid missed the crucial timing window of the 2026 World Cup, a major catalyst for prediction market activity, which competitors like Polymarket capitalized on heavily. The success of HIP-3 was partly due to perfect timing, such as during the U.S.-Iran conflict when traditional markets were closed. Without a similar catalytic event and facing an established leader like Polymarket, it remains uncertain whether Hyperliquid's "decentralized" strategy can successfully disrupt the prediction market landscape.

Hyperliquid announced its HIP-4 upgrade to democratize the creation of on-chain prediction markets by allowing permissionless deployment, moving away from validator-only control. Validators will vote on precise outcome templates, which qualified community deployers can then use to create their own event markets. To ensure proper settlement, deployers must stake 500,000 HYPE tokens for six months, with validators slashing these assets for failures or early exits. Despite this significant technical development, the HYPE token price saw a muted market reaction, trading around $60 with slight short-term declines. The initiative aligns with a surge in global prediction market activity, which reached a record $50.7 billion in nominal size in June, driven by major sporting events. Hyperliquid aims to position itself as a key player in this growing derivative sector.

Hyperliquid has proposed the HIP-4 upgrade to expand from perpetual futures into a broader, permissionless infrastructure layer for on-chain applications, requiring deployers to stake 500,000 HYPE to launch new markets. This shift aims to enable developers to build on its platform, a strategy backed by growing long-term investor conviction. Evidence includes a major trader staking millions in HYPE instead of taking profits, with total staked tokens reaching 43.9% of the supply, reducing available tokens and strengthening network security. Market metrics like high open interest and balanced funding rates suggest measured, non-speculative trader positioning. Sustained growth in protocol revenue, TVL, and governance participation further supports the positive outlook, contingent on continued user adoption and successful upgrade execution.

**DeepSeek Founder Liang Wenfeng's Candid Reflections on the Company's Path to AGI** DeepSeek has recently completed its first external funding round, raising over 500 billion RMB (approx. $74B) at a pre-money valuation of 3.675 trillion RMB ($543B). Founder Liang Wenfeng personally invested 200 billion RMB. This marks a strategic shift from its initial "no financing, no IPO, no commercialization" principle. In a recent investor Q&A, Liang articulated DeepSeek's core philosophy and roadmap. The company is driven by a powerful, unwritten vision for beneficial AGI rather than pure commercial maximization. He emphasizes "strategic restraint"—avoiding unnecessary conflicts, prioritizing long-term AGI success over short-term gains, and maintaining an open, cooperative stance even with competitors. Liang outlined the AGI technical roadmap: current focus on Agent capabilities, followed by solving "continual learning," which he sees as the key to unlocking models that can learn and adapt like humans. This could lead to a gradual "singularity" where AI accelerates its own research, and eventually to embodied intelligence. DeepSeek will strictly focus on this "AGI mainline," avoiding distractions like video generation which, while commercially viable, don't directly advance core intelligence. He identifies team stability as the single most critical factor for success, now bolstered by the recent funding. While talent is not a bottleneck, the primary constraint compared to the US is compute resources. Liang is optimistic about domestic AI chips, stating that Nvidia's CUDA moat is eroding and that within a year, the viability of the Chinese chip ecosystem will be proven, with Huawei's offerings being key. The main issue is production capacity. On competition, Liang believes the final differentiators will be cost, time-to-market, and user experience. He foresees Chinese companies playing a major role by offering systematically lower-cost AI services globally. DeepSeek's commercialization strategy involves offering API services at a "reasonable profit" and focusing on coding Agents. He remains committed to open-sourcing even their strongest models, seeing no downside as the barriers to effective deployment remain high. The company operates with a unique dual management structure combining top-down direction with significant bottom-up, unstructured research time for employees. Data quality and post-training are identified as major current challenges, with half of core researchers involved in data labeling efforts. Liang concludes that DeepSeek aims to be one of several trillion-dollar companies in the AI era, achieved through extreme focus on its chosen path.