The evolution path of the prediction market is highly representative: from crypto-native applications (Polymarket), to obtaining traditional financial regulatory licenses (Kalshi) , and then to being integrated by top exchanges and brokerage giants (ICE, Robinhood) . The key to its success lies in meeting the needs of different levels.

Value to different stakeholders:

• For users: the "bet" into a "expression of views" with economic incentives , both entertainment and potential benefits.
• For platforms (such as Robinhood) : It is the perfect tool to increase user engagement rate and achieve cross selling , and can seamlessly integrate into trading apps.
• For institutions (such as ICE): The probability of market generation is high-value alternative Data assets that can be packaged and sold to clients such as hedge funds.

Competitive core: It has been upgraded from simple product experience to comprehensive competition in regulatory status, traffic entrance and data distribution network . Even if newcomers (such as DraftKings) enter the market, it is difficult to shake the ecosystem position of the duopoly.

Thinking: When a track is taken over by traditional financial giants with capital and channel advantages, is there still a chance for crypto-native innovation teams?

Evolution of the competition landscape: https://blog.ju.com/prediction-markets-2026-growth/?utm_source=blog #Competition #TraditionalFinance #Evolution #WallStreet #Track

When cloud giants control the lifeline (computing power) of AI training and charge high fees, Gensyn, led by a16z, is trying to open a gap with decentralized protocols. Its testnet data shows that this alternative solution has initially worked.

Current progress and data:

1. Cost advantage : Claiming that the cost of computing power can be as low as 1/5 of the centralized Cloud as a Service, this will be the key to attracting small and medium developers and researchers.
2. Technical verification: Its RL Swarm collaborative training framework connected more than 26,000 nodes in the test network and completed more than 120,000 training sessions, proving that distributed collaborative training is technically feasible.
3. Trust mechanism: Through cryptographic proof and random sampling verification (Verde protocol) to ensure the reliability of computing strange nodes, which is the foundation of the entire business model.

Key question: The success of the testnet proves the potential of the technology, but whether it can truly form a stable and large-scale demand and supply at the commercial level after the mainnet goes online will be the touchstone of the next stage.

Project details and testnet data: https://blog.ju.com/gensyn-ai-compute-network #CloudServices #Competition #Infrastructure #AIModel

New Player in FHE Privacy Track: Octra Enters at $200 million Valuation to Challenge ZamaOutside of FHE (full homomorphic encryption) privacy track giant Zama (valued at $1 billion), new project Octra is launching a token sale at a fully diluted valuation of $200 million to join the competition.Key differences:

1. Technical path: Octra uses a completely self-developed FHE technology stack, while Zama and others provide more general FHE solutions. This may be an advantage (more controllable) or a risk (longer validation).
2. Application positioning: Not only to create an independent privacy public chain, but also to emphasize as a "coprocessor" for existing mainstream chains. This may be a more practical ecosystem entry strategy.
3. Performance Claims: Its testnet peak throughput (17,000 TPS) and processed transaction volume (over 100 million) data is designed to demonstrate the usability of its technology.

Thinking: Against the backdrop of privacy becoming a rigid demand, the FHE track is far from being finalized. Can Octra's "self-developed" story and relatively low valuation attract developers and capital attention, thus competing with giants?Project comparison and details:https://blog.ju.com/octra-fhe-blockchain-token-sale/?utm_source=blog #FHETrack #Competition #Privacy #Blockchain

Backed by giants such as Stripe, Visa, and OpenAI, Tempo blockchain's strategy is not to compete with stablecoin publishers such as Circle (USDC) and Tether (USDT), but aims to be the best underlying network to carry all these stablecoins .Key strategy analysis:

1. Positioning "neutral infrastructure": not creating stablecoins, but compatible with all mainstream stablecoins, and allowing them to directly pay network fees, reducing the threshold for use.
2. Binding giant ecosystem: Its partner list (Visa, Deutsche Bank, Shopify, etc.) itself is a ready-made, massive potential stablecoin users and usage scenarios.
3. Full stack layout: Combined with Stripe's previous acquisition of wallet company Privy and other actions, its goal is to provide enterprises with a complete stablecoin payment solution from entry to settlement.

Reflection: If successful, Tempo may not directly create the common "hundredfold coin" wealth effect in the crypto market, but it may become the "silent whale" - quietly changing the underlying pattern of the actual circulation and use of stablecoins. Do you have confidence in this blockchain path dominated by traditional capital?Project details and partners: https://blog.ju.com/tempo-blockchain-stripe-payments #Stablecoin #FinancialInfrastructure #Competition #Web3