2026 Crypto Investment Landscape: Appchains Rise, AI Agents Take Over DeFi
TechFlow Selected TechFlow Selected
2026 Crypto Investment Landscape: Appchains Rise, AI Agents Take Over DeFi
Physical assets will achieve true on-chain adoption.
Navigating Web3 tides with focused insightsAuthor: Archetype
Translation: TechFlow
The Era of Building Dedicated Appchains Has Finally Arrived
Author: Aadharsh Pannirselvam
In short: blockchains designed, built, and optimized for applications will bring disruptive change. And next year’s best appchains will be intentionally assembled around foundational components and core principles.
The recent influx of developers, users, institutions, and capital onto chains differs from the past: they prioritize specific cultures (i.e., definitions of user experience) over abstract ideals like decentralization or censorship resistance. In practice, this demand sometimes aligns with existing infrastructure—and sometimes does not.
For consumer-focused crypto abstraction apps like Blackbird or Farcaster, certain centralized design choices—such as co-located nodes, a single sequencer, and custom databases—that might have been considered “heretical” three years ago now make perfect sense. The same applies to stablecoin chains and trading platforms like Hyperliquid* and GTE, where success often hinges on millisecond-level latency, price volatility, and optimal pricing.
But this logic doesn’t apply to all new applications.
In contrast to this acceptance of centralization is the growing institutional and retail demand for privacy. The needs and ideal user experiences of crypto applications can differ significantly, so their infrastructures should too.
Luckily, building chains from scratch to meet these specific definitions of user experience is now far less complex than it was two years ago. In fact, the process now closely resembles assembling a custom PC.
Certainly, you could handpick every hard drive, fan, and cable yourself. But if you don’t need that level of granular customization—which most people don’t—you can use services like Digital Storm or Framework, which offer pre-built custom computers tailored to different needs. If your needs fall somewhere in between, you can add your own components atop their pre-selected, compatibility-guaranteed base. This approach offers greater modularity and flexibility, eliminates unnecessary parts, and ensures the final product runs efficiently.
As applications assemble and tune core components like consensus mechanisms, execution layers, data storage, and liquidity, they are creating culturally distinct forms that reflect divergent needs (i.e., different definitions of UX), serve unique target audiences, and ultimately capture value. These differences may be as stark as those between rugged ToughBooks, business-oriented ThinkPads, powerful desktops, and sleek MacBooks—but they also converge and coexist to some extent, after all, not every computer runs its own independent operating system. Moreover, each essential component becomes a flexible “knob” that developers can tune freely without risking destabilizing changes to a parent protocol.
With Circle’s acquisition of Malachite from Informal Systems, it’s clear that ownership of customized blockspace has become a broader priority. In the coming year, I’m excited to see applications and teams define and control their chain resources using foundational components and sensible defaults offered by companies like Commonware and Delta—a model akin to HashiCorp or Stripe Atlas, but applied to blockchains and blockspace.
Ultimately, this empowers apps to directly control their cash flows and leverage the uniqueness of their build to deliver the best possible user experience on their own terms, forming a durable competitive moat.
Prediction Markets Will Keep Innovating (But Only Some Will Succeed)
Author: Tommy Hang
In this cycle, prediction markets have emerged as one of the most watched applications. With weekly trading volume across all crypto sectors hitting a record $2 billion, this category has clearly taken an important step toward mainstream consumer adoption.
This momentum has fueled a wave of related projects aiming to complement or challenge current market leaders like Polymarket and Kalshi. Yet amid the hype, distinguishing real innovation from noise will ultimately determine which projects remain worth watching by 2026.
From a market structure perspective, I’m particularly excited about solutions that narrow bid-ask spreads and deepen open interest. Although market creation remains permissioned and selective, prediction markets still suffer from relatively low liquidity for market makers and traders. Significant opportunities exist in improving optimal routing systems through products like lending, diverse liquidity models, and collateral efficiency.
Volume segmentation by category is also key to determining platform winners. For example, over 90% of Kalshi’s November trading volume came from sports markets, indicating certain platforms have inherent competitive advantages in capturing dominant liquidity. In contrast, Polymarket’s trading volume in crypto-related and political markets is 5 to 10 times higher than Kalshi’s.
However, on-chain prediction markets still have a long way to go before achieving true mass adoption. A useful benchmark is the 2025 Super Bowl, which generated $23 billion in off-chain betting volume in a single day—more than ten times the current combined daily volume of all on-chain markets.
Bridging this gap will require sharp, insightful teams focused on solving core problems in prediction markets. Over the next year, I’ll be closely watching these potential industry players.
Agent-Based Curators Will Drive DeFi Expansion
Author: Eskender Abebe
The curation layer in DeFi currently exists in two extremes: fully algorithmic (hard-coded interest curves, fixed rebalancing rules) or fully human-dependent (risk committees, active managers). Agent-based curators represent a third path: AI agents (LLMs + tools + loops) managing curation and risk policies in vaults, lending markets, and structured products. This goes beyond executing fixed rules—it involves reasoning and decision-making around risk, returns, and strategy.
Consider the role of curators in Morpho markets. Someone must define collateral policies, loan-to-value (LTV) limits, and risk parameters to generate yield products. Today, the human element in this process is a bottleneck. Intelligent agents can scale this. Soon, agent-based curators will directly compete with both algorithmic models and human managers.
So when will DeFi have its "Move 37" moment?
When I speak with crypto fund managers about AI, I typically get one of two extreme responses: either LLMs will automate every trading desk, or these tools are just "hallucinating toys" incapable of handling real markets. Both views miss a crucial architectural shift: intelligent agents can bring emotionless execution, systematic policy adherence, and flexible reasoning into domains where humans are noisy and pure algorithms are too brittle. They’re more likely to oversee or orchestrate low-level algorithms rather than replace them entirely. In this context, LLMs act more like “architects” designing safety frameworks, while deterministic code continues to handle high-latency-sensitive core tasks.
When deep reasoning costs mere cents, the most profitable vaults won’t belong to the smartest humans, but to those with the greatest computational power.
Short Video Becomes the New "Storefront"
Author: Katie Chiou
Short video is rapidly becoming the default interface for discovering (and eventually purchasing) favorite content. TikTok Shop achieved over $20 billion in GMV in the first half of 2025, nearly doubling year-over-year, quietly transforming global entertainment consumption habits into a "storefront" experience.
In response, Instagram has transformed Reels from a defensive feature into a revenue engine. This short-video format not only drives more visibility but also captures an increasing share of Meta’s projected 2025 ad revenue. Meanwhile, Whatnot has proven that conversion rates based on live streams and personal charisma surpass what traditional e-commerce can achieve.
The core logic is simple: people make decisions faster when watching content in real time. Every swipe becomes a decision point. Platforms understand this well—hence the line between recommendation feeds and checkout flows is disappearing. Feeds are becoming new points of sale, and every creator is becoming a distribution channel.
AI further accelerates this trend. It reduces video production costs, increases output volume, and allows creators and brands to test new ideas in real time. More content means more conversion opportunities, and platforms respond by optimizing purchase intent within every second of video.
Crypto plays a critical role here. Faster content demands faster, more efficient payment rails. As shopping becomes seamless and embedded directly into content, we need a system capable of handling micropayments, programmatically distributing revenue, and tracking contributions across complex influence chains. Crypto is built for this fluidity. It's hard to imagine a hyper-scale commercial era natively built on streaming without crypto support.
Blockchains Will Enable New Laws of AI Scaling
Author: Danny Sursock
For years, AI development has centered on a multi-billion-dollar arms race between hyperscale enterprises and startup giants, forcing decentralized innovators to explore in the shadows.
Yet as mainstream attention shifts, several crypto-native teams have made major progress in decentralized training and inference. This quiet revolution is moving from theory into testing and production environments.
Teams like Ritual*, Pluralis, Exo*, Odyn, Ambient, and Bagel are now ready for their moment. This new generation of competitors is poised to unleash explosive, disruptive impact on the fundamental trajectory of AI development.
By training models in globally distributed environments and leveraging novel asynchronous communication and parallelization methods already being validated at production scale, the scaling limits of AI will be completely broken.
Meanwhile, the combination of new consensus mechanisms and privacy primitives makes verifiable and confidential inference a realistic option in the on-chain developer toolkit.
Furthermore, revolutionary blockchain architectures will enable smart contracts to integrate highly expressive computational structures, simplifying the operation of autonomous AI agents using cryptocurrency as a medium of exchange.
The foundational work is done.
The challenge now is scaling this infrastructure to production levels and proving that blockchains can drive fundamental AI innovation beyond philosophy, ideology, or superficial funding experiments.
Real World Assets Will Achieve True On-Chain Adoption
Author: Dmitriy Berenzon
For years, we’ve heard about asset tokenization. Now, with mainstream stablecoin adoption, smooth and robust fiat-to-crypto onramps and offramps, and increasingly clear regulatory support worldwide, real world assets (RWAs) are finally going mainstream. According to RWA.xyz*, the total value of tokenized assets across categories has surpassed $18 billion—up from just $3.7 billion a year ago. I expect this trend to accelerate further by 2026.
It’s important to note that tokenization and vaults represent two distinct RWA design patterns: tokenization brings representations of off-chain assets on-chain, while vaults create bridges between on-chain capital and off-chain yields.
I’m excited to see both tokenization and vaults provide on-chain access to a wide range of physical and financial assets—from commodities like gold and rare metals, to private credit for working capital and payment financing, to private and public equities, and more global currencies. Let’s even get creative! I’d love to see eggs, GPUs, energy derivatives, wage advances, Brazilian government bonds, yen, and more go on-chain!
But make no mistake: this isn’t just about putting more things on-chain. It’s about upgrading global capital allocation via public blockchains. Blockchains can transform opaque, slow, and siloed markets into transparent, programmable, and liquid ones. Once these assets are on-chain, we gain massive advantages through composability with existing DeFi primitives.
Finally, many of these assets will inevitably face challenges around transferability, transparency, liquidity, risk management, and distribution during on-chain transition—making the development of infrastructure to address these issues equally important and exciting!
The Agent-Driven Product Renaissance Is Coming
Author: Ash Egan
The future web will be shaped less by social platforms we scroll through and more by intelligent agents we converse with.
Today, bots and agents already account for a rapidly growing portion of online activity. Rough estimates suggest this share is already around 50%, including both on-chain and off-chain actions. In crypto, bots are increasingly involved in trading, curation, assistance, contract scanning, and performing tasks on our behalf—such as token trading, fund management, smart contract audits, and game development.
This marks the arrival of a programmable, agent-driven web era. While we’ve been in this phase for some time, 2026 will be a turning point—crypto product design will increasingly serve intelligent agents, not just humans (in a positive, liberating, and non-dystopian way).
This vision is beginning to take shape. Personally, I want to spend less time clicking between websites and more time managing on-chain agents through chat-like interfaces. Imagine Telegram-style chats, but with agents dedicated to specific apps or tasks. These agents could devise and execute complex strategies, search the web for information most relevant to me, and return trade outcomes, risks, opportunities, and filtered insights. I’d simply state a goal, and they’d find opportunities, filter out noise, and act at the optimal moment.
The on-chain infrastructure is already ready. By combining open data graphs by default, programmable micropayments, on-chain social graphs, and cross-chain liquidity rails, we already have everything needed to support a dynamic agent ecosystem. Crypto’s plug-and-play nature means fewer bureaucratic hurdles and dead ends for agents. Compared to Web2 infrastructure, blockchains offer ideal conditions for this agent-first evolution.
This may be the most important point: this isn’t just automation—it’s liberation from Web2 silos. Liberation from friction. Liberation from waiting. We’re already seeing this shift in search: today, about 20% of Google searches generate AI overviews, and data shows users are significantly less likely to click traditional search results when they see an AI summary. Manually scrolling through pages is becoming unnecessary. A programmable agent network will extend this trend across the apps we use—and I believe this is a positive development.
This era will reduce mindless scrolling and panic trading. Time zone barriers will fade (no more “waiting for Asian markets to wake up”). Interactions with the on-chain world will become simpler and more expressive—for both developers and everyday users.
As more assets, systems, and users come on-chain, this cycle will amplify:
More on-chain opportunities → Deploy more intelligent agents → Unlock more value. Repeat.
But what we build now, and how we build it, will determine whether this agent network becomes a thin layer of noise and automation—or ignites an empowering, vibrant product renaissance.
*Note: Some companies mentioned are part of Archetype’s portfolio.
Join TechFlow official community to stay tuned
Telegram:https://t.me/TechFlowDaily
X (Twitter):https://x.com/TechFlowPost
X (Twitter) EN:https://x.com/BlockFlow_News
@archetypevc
