Written by: Aadharsh Pannirselvam, Tommy Hang, Eskender Abebe, Katie Chiou, Danny Sursock, Dmitriy Berenzon, Ash Egan, Archetype
Compiled by: JW, Techub News
Looking ahead to 2026, the Archetype team is focusing on trends, forces, and structural changes.
Building application chains has finally become meaningful.
The core judgment is actually very clear: truly competitive blockchains will increasingly be created for 'specific applications.'
It's not about having a universal chain first and then forcing various applications to adapt; rather, it is designed and built around the needs of the applications from the very beginning and continuously adjusted. This type of chain will show very strong performance in the coming year.
The reason is that the new wave of developers, users, institutions, and funds entering the crypto world is fundamentally different from the early days. They have clear cultural preferences and specific requirements for user experience, no longer preoccupied with abstract value propositions. In reality, these demands can sometimes be met by existing infrastructure, but more often they cannot.
Taking applications like Blackbird and Farcaster, which significantly reduce the perception of crypto and are aimed at ordinary users, as examples, some design choices that were considered 'unacceptable' three years ago, such as centralized node deployment, single orderers, or even fully customized data systems, have now become reasonable solutions for enhancing user experience.
For applications related to trading or stablecoins, such as Hyperliquid and GTE, this is even more pronounced. The competition among these systems fundamentally depends on latency, matching efficiency, and price quality. In scenarios where milliseconds can determine life and death, many 'principle issues' will naturally give way to the experience itself.
Of course, not all applications are suitable for this path.
An important counterbalance that is forming is the clear rise in privacy demands from institutions and retail investors. Different applications face vastly different user groups, use cases, and risk preferences, and therefore, the infrastructure they rely on should also manifest in varied forms.
The good news is that customizing a chain for applications is no longer a high-threshold project. Compared to two years ago, this process now resembles assembling a custom computer.
You can choose to configure each component entirely on your own, or adjust based on mature solutions. The models provided by Digital Storm or Framework essentially allow for replacing or streamlining certain modules on a proven combination basis, ensuring performance while avoiding unnecessary complexity. This approach brings higher modularity and controllability. Applications can retain only the components they truly need while ensuring overall system stability and scalability.
When foundational modules such as consensus mechanisms, execution layers, data storage, and liquidity become primitives that can be freely combined and adjusted, applications themselves will naturally form highly differentiated 'chain forms'. These forms will continually reflect their understanding of user experience and serve very specific target groups.
This difference is akin to various types of computing devices: ToughBook, ThinkPad, desktop, or MacBook; they appear very different, yet share a substantial amount of common logic underneath. The key is that each component becomes an adjustable parameter rather than a necessary limitation. From Circle's acquisition of Informal Systems' Malachite, a trend can be clearly seen: the emphasis on the sovereignty of exclusive blockchain space is becoming a consensus.
In the coming year, we may see roles akin to 'HashiCorp or Stripe Atlas in the blockchain space' emerge, with teams like Commonware and Delta providing standardized primitives and default configurations, allowing applications to define and control their chain resources more easily.
Ultimately, this model will enable applications to truly achieve one thing: directly own their blockchain space and cash flow, making the chain itself a part of their long-term competitive advantage.
Prediction markets will continue to evolve.
In this cycle, prediction markets are undoubtedly one of the most watched application categories.
When the weekly trading volume of all crypto prediction market platforms surpasses 2 billion dollars, this track has already proven with data that it is no longer just a niche experiment.
As the heat rises, numerous projects attempting to replicate, replace, or even directly challenge leading platforms like Polymarket and Kalshi emerge. But beyond emotions, the truly important question remains: which teams are addressing core structural issues, and which are merely riding the wave.
From the perspective of market structure, the most noteworthy aspect remains how to compress price spreads and enhance open interest. Even though market creation currently tends to be permissioned, the overall liquidity on the market-making and trading sides of prediction markets is still relatively thin.
Whether through better order routing mechanisms, more suitable liquidity models, or improving capital utilization through lending, there are significant areas for improvement that also determine whether products can truly scale. The structure of trading categories will also directly influence platform competitiveness. For example, Kalshi saw over 90% of its trading volume in November come from sports markets, indicating a natural advantage in specific liquidity structures. Meanwhile, Polymarket's trading volume in crypto and politically related markets is significantly ahead, reaching several times that of Kalshi. Even so, on-chain prediction markets still face a considerable gap from achieving true mainstream scale.
The Super Bowl in 2025 is an intuitive comparison: in just one day, the trading volume of traditional off-chain betting platforms reached 23 billion dollars, far exceeding the total daily volume of all on-chain prediction markets.
To narrow this gap, it is not through marketing or narrative, but through teams capable of genuinely solving structural issues. This is also the most worthy aspect to continue observing in the coming year.
Agent-type curators will push DeFi toward scaling.
The asset management layer of DeFi has two extreme models: pure algorithm (hard-coded interest rate curves, fixed rebalancing rules) or pure manual (risk committees, active fund managers). Agent-type curators represent a third model: they do not simply execute preset rules, but continuously assess risk, return, and strategy through AI Agents (LLM + tools + feedback mechanisms) and participate in parameter formulation.
Taking the Morpho market as an example, to build a sustainable revenue product, it is essential to clarify the collateral policy, LTV cap, and risk parameters. Currently, this process heavily relies on human judgment, which naturally presents scalability bottlenecks. The introduction of Agents is essentially an attempt to solve this problem.
Next, we are likely to see Agent-type curators directly competing with traditional algorithmic models and manual managers in the same market.
Regarding the role of AI in trading and asset management, market opinions often swing to two extremes: either it is believed to quickly replace human traders, or it is thought to be completely incapable of handling the uncertainties of the real market.
But the real change does not lie in 'replacement' itself, but in the adjustments at the architectural level. Agents are more likely to take on the roles of strategy design, constraint formulation, and portfolio management rather than directly participating in latency-sensitive underlying executions. As inference costs continue to decline, computing power itself will become a new competitive factor.
In such an environment, the most advantageous DeFi products may not come from the smartest individuals, but from teams capable of scaling smart decision-making systems.
Short videos are becoming the new entry point for transactions.
Short videos are becoming the primary entry point for people to discover, understand, and ultimately purchase content.
TikTok Shop achieved over 20 billion dollars in GMV in the first half of 2025 and continues to grow rapidly, which itself demonstrates the strength of the trend.
Instagram is also gradually transforming Reels from a defensive feature into a core business engine. Whatnot's practices further demonstrate the advantages of real-time, personalized content in conversion efficiency, which significantly exceeds traditional e-commerce models.
The logic behind this is not complicated. When watching real-time content, it is easier to make quick decisions. As recommendation streams and settlement processes gradually merge, the content itself becomes the trading interface, and creators naturally evolve into distribution nodes. The addition of AI further accelerates this process. The production costs of content continue to decrease, testing frequency increases, and platforms begin optimizing conversion efficiency for every second of video.
In such an environment, payment systems must be fast enough, cheap enough, and highly composable. Micropayments, automated revenue sharing, and contribution attribution will all become fundamental capabilities.
This is precisely a scenario where the crypto system is inherently compatible. In a business system where streaming media is the native form, it is hard to imagine without crypto as the underlying settlement and incentive tool.
Blockchain is driving new AI expansion paths.
In the past few years, AI's attention has mainly focused on the competition between large cloud vendors and leading startups. But at the same time, a group of crypto-native teams is making significant progress in distributed training and inference.
These attempts have gradually moved from theoretical stages to testing and even into production environments. Teams like Ritual, Pluralis, Exo, Odyn, Ambient, and Bagel are at the forefront of this exploration. By training models in a globally distributed environment and combining asynchronous communication with parallel mechanisms, traditional scalability bottlenecks are being redefined.
At the same time, new consensus mechanisms and privacy technologies have made verifiable and confidential inference realistically feasible. Furthermore, some new blockchain architectures are attempting to genuinely integrate smart contracts with more general computing structures, providing a foundation for autonomous Agents to operate.
The foundational capabilities are already in place.
The key going forward is whether it can scale to production level and prove that this path is not just a conceptual experiment, but a genuine way to promote the evolution of AI capabilities.
RWA is moving towards real-world scale.
Regarding RWA, the industry has been discussing it for many years. However, with the popularization of stablecoins, the maturation of deposit and withdrawal channels, and the gradual clarification of the regulatory environment, tokenization has finally begun to enter a scaling phase.
According to data from RWA.xyz, the current scale of tokenized assets issued on-chain has exceeded 18 billion dollars, whereas a year ago, this figure was less than 4 billion.
It is essential to clearly differentiate between the two modes.
Tokenization is mapping off-chain assets onto the chain; while Vault allows on-chain capital to directly participate in off-chain revenue. In the future, the types of assets on-chain will broaden, ranging from commodities, private credit, to stocks, foreign exchange, and even some non-traditional assets.
But the focus is not just on 'the variety of asset types'.
The real significance lies in making the originally inefficient and opaque capital allocation process more programmable and liquid through blockchain.
Of course, this process will still face challenges such as transfer restrictions, insufficient liquidity, and risk management, and thus, the corresponding infrastructure is also worth paying attention to.
The era of Agent-driven product cycles is about to arrive.
The interactive core of the next generation internet is shifting from 'platforms' to 'Agents'. Whether on-chain or off-chain, automated Agents have already taken on a considerable proportion of online activities. In the crypto space, they participate in trading, asset management, information filtering, contract auditing, and even content production.
The year 2026 is likely to be a clear turning point.
The design of crypto products will start to prioritize Agents over human interfaces. The ideal form is not more buttons, but fewer operations. Users only need to issue goals through a conversational interface, with the Agent responsible for information filtering, strategy execution, and result feedback. The infrastructure supporting all of this already exists: open data, programmatic payments, on-chain identities, and cross-chain liquidity.
Compared to Web2, blockchain is more friendly to Agents because they face open interfaces rather than closed systems. This not only improves efficiency but also represents a shift in interaction methods. As searching, trading, and execution gradually come under the control of Agents, humans can focus their energy on higher-level judgments.
As more assets and activities go on-chain, this cycle will continually amplify: opportunities increase, Agents multiply, and value is released.
The only real question left is: is the system we are building now amplifying value or amplifying noise?
