Bitcoin has fallen below $70,000, and only 7 of the top 100 cryptocurrencies by market cap are still trading above their 200-day moving average. The contrast in the source material is sharp: 53% of Nasdaq 100 constituents remain above the same level. In a weak market, that gap matters. It strips away much of the narrative premium and leaves projects facing a harder question—do they solve a real problem or not?
The article frames survivability in crypto around practical viability. It is less interested in broad visions or technical complexity for its own sake, and more focused on whether a project addresses an immediate market pain point, is built for near-term adoption, or is laying infrastructure that an industry may eventually depend on. Hyperliquid, Canton Network, and Kite AI are presented as examples of those three routes. They operate in different time frames, but the common thread is simple: each is tied to a concrete use case rather than an abstract story.
Hyperliquid targets friction in crypto trading
Hyperliquid is positioned around a very current problem. Centralized exchanges have long been the default venue for traders, yet periods of stress have repeatedly exposed a mismatch between platform incentives and user interests. Decentralized exchanges offered an alternative, but poor user experience and weaker performance have often limited adoption.
According to the source, Hyperliquid brought key features traders expect from CEXs into an onchain environment: high leverage, fast execution, and stable liquidity through the HLP mechanism. Some of its early traction was linked to demand around the $HYPE airdrop. That alone would not say much. What the article emphasizes instead is that user activity remained high after the airdrop ended, which it treats as evidence that platform performance, not only token incentives, helped sustain usage. In that reading, Hyperliquid’s resilience comes from addressing a lasting complaint with centralized venues.
Canton Network focuses on privacy and compliance for institutions
Canton Network is aimed at a different window of demand. As interest in real-world assets, or RWA, keeps rising, financial institutions are increasingly treating blockchain as potential financial infrastructure rather than just a public network. That shift comes with constraints. Institutions do not want absolute transparency by default; they need systems that can support regulatory compliance while protecting sensitive transaction data.
Canton’s answer is a selective privacy model. Using the smart contract language DAML, it allows data disclosure to be configured around the needs of specific participants. The source argues that this structure lets institutions keep transaction confidentiality while sharing information only where necessary. Instead of forcing institutions into a framework designed from the perspective of a technology vendor, Canton is described as building around institutional requirements from the start.
The article also points to early institutional cooperation as a key signal. It highlights Canton’s work with DTCC, which is intended to create a channel for assets managed in traditional financial systems to move into the Canton environment. The source notes that DTCC processes roughly $3.7 quadrillion in transactions each year. That figure is used to support a narrow point: Canton is being discussed in connection with infrastructure that already matters at scale.
Kite AI is building for an AI-agent economy that has not fully arrived
Kite AI stands apart because its immediate real-world usage is still limited. Even so, the source argues that its internal logic remains credible if viewed through the lens of a future in which AI agents act as economic participants. In both Web2 and Web3, the article says, there is broad agreement that AI agents may eventually handle tasks such as hotel bookings or grocery orders on behalf of users.
If that future takes shape, current payment rails may not be enough. Existing transaction systems were designed around transfers between humans, not autonomous software entities initiating and completing payments on their own. Kite AI is presented as a project building for that gap. Its core components include Agent Passport for identity verification and the x402 protocol for automated payments. The article does not argue that this stack is already ready for mass deployment; its point is narrower. If AI agents do become active economic actors, infrastructure of this type could become necessary.
Three questions the article uses to judge durability
Across all three cases, the source returns to the same screening method. First, what exact problem is the project trying to solve, and is it a genuine pain point rather than a need invented to justify a technology? Second, is the proposed solution structurally sound across technical design, compliance, and economics? Third, does the team have the industry access, technical depth, and delivery record needed to execute in the real world?
The closing argument is restrained. Weak market conditions do not mean crypto experimentation has stopped, and new projects will keep appearing. But in a downturn, the standard gets tougher and clearer at the same time: projects that address real operational needs are more likely to retain credibility, while those built mainly on optimistic narratives face a much harder test.