Author: Electric Capital

Translation: Jiahua, ChainCatcher

Trust in institutions is collapsing globally. People have lost faith in the institutions that once formed the core of economic, political, and social life: governments, banks, media, and schools. This is not a short-term trend or a reaction to a single event. It is a long-term shift in expectations. People no longer assume that institutions are neutral, reliable, or aligned with their interests.

Distributed systems and cryptography provide builders with new tools to operate without trust. These technologies are designed to function in adversarial environments: they assume that participants may have malicious intent, software must be verifiable, and the system should continue to operate even if a counterparty fails.

AI has made this shift towards "minimized trust systems" more urgent and feasible than ever. AI not only centralizes power but also lowers the cost of building. Now, an individual can create something in a few hours that previously required a team months to complete. This puts pressure on intermediaries, opens new possibilities for builders, and increases the demand for infrastructure that "empowers users."

Systems controlled by users can truly safeguard freedom. User-owned systems minimize reliance on intermediaries by returning control to users. These systems cannot be unilaterally altered. They allow people to build without needing permission. In an ideal architecture, if existing systems no longer serve users, they can freely choose to exit without losing their original functionality or data.

This article outlines 26 opportunities in key areas for 2026.

These opportunities encompass user-owned systems, globally accessible markets, entertainment built on new financial primitives, and the infrastructure prepared for a world of AI software. But they all share a common thread: they explore how power, access, and ownership should operate in a world where AI is ubiquitous and deeply embedded.

These opportunities focus on six key areas:

1. Personal Software: AI makes it possible to build customized tools tailored to individual needs, rather than just adapting to SaaS designed for the average user. Private agents, cryptographic collaboration, and locally running software are now not only feasible but increasingly necessary.

2. Agent-Oriented Infrastructure: As AI agents become the primary builders of software, existing development stacks will be disrupted. We need new primitives for testing, deploying, paying, accessing data, and coordinating between agents.

3. Fintech and Decentralized Finance: Stablecoins enable over 4 billion people to access the dollar. Now, they want yields, equity exposure, insurance, and more. The demand for global, programmable, and easily accessible financial infrastructure is accelerating.

4. Financial Entertainment: The younger generation views markets as entertainment. Trading is fast, social, and fun. This changes the nature of financial products and opens the door to new types of markets.

5. Metaverse Renaissance: World models and generative AI significantly lower the cost of building immersive, personalized environments. People will step into experiences shaped around them rather than passively consuming content. There are huge opportunities in building platforms that simplify world creation and empower users to control how their data is shared, stored, and monetized in these worlds.

6. New Cryptographic Primitives and Applications: Proof of stake and proof of work are maturing and leaving room for new consensus models. Zero-knowledge proof systems and fully homomorphic encryption are becoming practical. These primitives unlock new design spaces: consensus tied to human or physical inputs, infrastructure with default privacy, and applications built on regulated entities, energy markets, or even new jurisdictions.

Personal Software

For the first time, individuals can build customized software according to their exact needs without being limited to products from large companies. As AI agents can now handle complex workflows (like reading emails, scheduling meetings, and managing files), new demands for data privacy, data ownership, and data persistence have emerged. Cryptographically empowered systems can make these tools private, persistent, and support multi-user collaboration.

Specific ideas we want to invest in:

• Private AI Agents: People need to run AI on sensitive data securely.

  • What it might look like: An AI assistant that automatically handles your personal workflows while protecting privacy. It connects your health and financial records and provides AI insights. AI models run in trusted execution environments or compute networks, and incoming queries are anonymous. Responses are returned without enterprise providers or malicious actors seeing your data.

• Cryptographic Collaboration Spaces: People need to collaborate privately with others (whether human or intelligent agents).

  • What it might look like: A shared workspace for friends, family, or small businesses. Financials, documents, and tasks are synchronized through peer-to-peer storage solutions. Selective disclosure features authorize agents to access specific types of data. No accounts are required, and no large companies read, store, or use sensitive data for training, while supporting offline work.

• Desktop Agents: People need automation tools for their local computer data.

  • What it might look like: An agent running locally on your desktop to read emails, draft replies, create schedules, and organize your life. This idea could expand into a new type of desktop operating system in an AI-first world.

• Privacy Payment Services: People need a way to pay for software services without verifying their identity.

  • What it might look like: Purchasing VPNs, games, cloud storage, or AI computing power without an account. You pay based on usage, measured by the service provider, and settle using stablecoins through x402 or similar protocols. The service provider knows someone paid and how much, but not their identity.

Agent-Oriented Infrastructure

Smart agents will write most of our code and perform most of our cognitive work. Key impacts include: (1) Software tools need to be rebuilt from scratch because AI-generated code introduces new failure modes. (2) Development will shift in-house as custom software becomes economically viable. (3) Agents will need new tracks for trading with each other. (4) Businesses once limited by human labor can suddenly scale up. These ideas capture the opportunities arising from these second-order effects.

Specific ideas we want to invest in:

• AI-Native Computing Infrastructure: Companies need the ability to test, isolate, and roll back AI-generated changes at the infrastructure level.

  • What it might look like: An AWS or GCP rebuilt for agents. Agents write code in a sandbox, safely test against production data, and deploy with automatic rollbacks in case of issues. The entire process assumes the code comes from agents rather than humans.

• End-to-End Product Development Tools: Non-technical employees need to go from ideas to runnable software.

  • What it might look like: A platform where users specify business goals, data sources, and expected outcomes. The system generates plans, designs, code, and a working product. This system eliminates the need for technical translation, allowing non-technical users to transition directly from "idea" to "deployed product" in hours instead of months.

• Agent-Powered Commerce: Agents need to autonomously buy and sell without human identities or bank accounts.

  • What it might look like: An API marketplace where agents can purchase services from other agents. Discovery, negotiation, and pay-per-call usage through protocols like x402, with instant settlement using stablecoins.

• Data Networks and Markets: AI needs data infrastructure that can compensate contributors and give them control over their usage.

  • What it might look like: A network where users share medical records, consumption patterns, investment behaviors, or creative works for AI training. Contributors set permissions and get rewarded as their data improves models. AI companies obtain the financial data they need with clear provenance.

• Scalable Professional Services: Service-oriented businesses need AI-native operations to surpass the scaling limits of human labor.

  • What it might look like: A law firm where each lawyer has an AI assistant handling research, drafting, and document review. A company that once served 1,000 clients now serves 100,000 clients. Any customer service profession—lawyers, architects, marketers, accountants, financial advisors—can be restructured around AI.

Fintech and DeFi

Over 4 billion people facing currency risk and millions of businesses are actively seeking dollar channels through stablecoins, representing the largest expansion of the dollar network effect in decades. As stablecoins provide individuals worldwide access to dollars—growing from $3 billion in 2019 to over $300 billion today—millions of new dollar holders need more than just digital cash. They need yields, investment opportunities, and financial services. Opportunities are increasing in financial products that empower users with ownership and global access.

Specific ideas we want to invest in:

• Yield Unrelated to Cryptocurrency: Stablecoin holders need a yield that does not decline when Bitcoin prices drop.

  • What it might look like: A platform that brings real-world infrastructure yields to stablecoin holders. Yields could come from data center project bonds, solar facilities, and electric vehicle charging networks, which have predictable cash flows and are uncorrelated with cryptocurrency.

• Globally Accessible Equity: Global investors need low-friction, low-cost direct ownership of foreign opportunities.

  • What it might look like: A financial product that replicates equity ownership with price exposure, no funding rates, and no expiration date. Traders in the Philippines build portfolios of U.S. tech stocks; Canadians build exposure to South Korean semiconductors.

• New Types of Insurance: Businesses need quick, transparent underwriting for operational risks that traditional insurance cannot cover.

  • What it might look like: A platform that creates new insurance products using prediction markets. Chain hotels can purchase hurricane protection for their properties in Florida. Ski resorts can hedge against warm winter risks. Capital providers offer liquidity in exchange for uncorrelated yields.

• On-Chain Commodity Markets: Commodities need markets with 24/7 trading, instant settlement, and global access.

  • What it might look like: A market trading energy storage capacity. Battery storage capacity is a potential starting point, as data centers need reliable power and are investing in storage to reduce reliance on the grid and integrate renewable energy. Data centers with excess storage capacity can sell storage to nearby facilities during peak demand. Grid operators can trade capacity based on seasonal demand.

• Protected DeFi Assets: Institutions need to deploy assets into DeFi while ensuring security even in the event of a hack.

  • What it might look like: A wrapped version of ETH that can be rolled back if the protocol is hacked. A trusted committee reviews exploits and can reverse GuardedETH without the underlying ETH moving. Legitimate transactions proceed as normal.

Financial Entertainment

The younger generation views financial markets as an alternative to traditional paths. When they engage with the markets, they reimagine what markets look like and turn them into entertainment. They trade like they play games: seeking high-adrenaline trades with fast feedback loops in easy-to-learn and accessible markets. Fast-turnaround products like zero-day-to-expiration (0DTE) options, which can settle in hours, now exceed 55% of the trading volume of S&P 500 options. Easy-to-enter markets like prediction markets, where anyone can bet on news headlines, are projected to reach $44 billion in trading volume by 2025, growing fivefold from the previous year. They also turn their trading into content: discussing positions live on Discord, sharing wins and losses on TikTok, and reviewing portfolios on Twitch. When markets become entertainment, there is an opportunity to create new platforms that treat financial data as engaging, participatory content as perceived by their users.

Specific ideas we want to invest in:

• Audience Capital: Live-stream audiences need a way to economically participate in outcomes.

  • What it might look like: A platform that allows audiences to stake on live content. Participation makes watching more engaging, but currently, audiences are limited to tipping and subscriptions. The platform allows reality show viewers to predict who will be eliminated or lets audiences follow trades as streamers share trading sessions.

• Opinion Markets: Prediction market platforms need markets that settle based on collective beliefs rather than just event outcomes.

  • What it might look like: A platform that generates ranked lists of markets. Users stake on how they think others will rank these items. The platform creates lists like "Best Pizza in New York," "Top Wines Under $20," "Most Influential Movies of the Past Decade," or "Best AI Developer Tools," with all rankings determined by the market. Lists settle weekly based on staked-weighted rankings.

• Short Series Release Platform: As individual creators can produce series more cheaply than studios, they need funding and distribution platforms.

  • What it might look like: A short series UGC (user-generated content) platform. Creators use AI video tools to produce series: Mafia Boyfriend Legends, Secrets of Billionaires, Revenge Thrillers. Fans use tokens to unlock episodes and tip creators directly. Creators earn income based on viewership. ReelShort generated over $700 million in revenue in Q1 2025 with low-budget, studio-produced short series. The platform combines YouTube's UGC content with ReelShort's video format.

Metaverse Renaissance

Immersive digital worlds are now economically viable to build. In the past two years, AI models for images, videos, and simulations have rapidly advanced, significantly lowering the cost of creating assets and environments. Individual creators can now build what previously required entire game studios. Meanwhile, the demand for personalized, interactive content is accelerating: Dispatch, a "choose your own path" TV/game hybrid, sold 3.3 million copies in three months, generating $85 million in revenue with a 98% approval rating. In Q3 2025 alone, Roblox's daily active users grew by 70% year-over-year, paying creators $428 million. Personalized, AI-driven character chat applications like Character AI also show strong early demand for personalized entertainment. These new environments not only entertain users but also generate rich structured interaction data for world models and robotics.

Specific ideas we want to invest in:

• World Compiler: Individual creators without professional skills need tools to convert natural language into fully interactive 3D environments.

  • What it might look like: A platform that transforms natural language into fully interactive 3D worlds. Building 3D environments still requires expertise in modeling, physics, and NPC behavior. AI can break this barrier. Creators describe a world, and the system builds it. Assets, physics, NPC logic, and memory are all handled automatically. Individual creators can publish rich virtual environments in days instead of years.

• Procedural Narrative Engine: Players need stories that adapt to them and never end.

  • What it might look like: A platform that generates player-specific stories in real-time. Linear stories have endings. Players want experiences that adapt to them and continue indefinitely. Users enter a detective universe where each case is unique to them. Characters remember past interactions. Plot twists respond to their choices. The story never runs out.

• "World as Dataset" Platform: World models and robotic systems need diverse interaction data. Consumer immersive environments continuously generate this data, but currently, no one captures it.

  • What it might look like: A VR game where each player's interactions are instrumented and recorded. How users navigate rooms, pick up objects, and interact with characters all become training data for robots. Users opt in, set permissions for shared data, and receive compensation. AI companies gain access to real human behavior data that they cannot generate synthetically.

New Cryptographic Primitives and Applications

Cryptographic primitives are no longer theoretical. Proof of stake and proof of work have both proven resilient at scale. Zero-knowledge proofs are moving out of the research phase and into production systems. Fully homomorphic encryption is becoming faster and more available. As these foundational technologies mature, they unlock new opportunities for builders to create systems that prioritize privacy, embed real-world inputs into consensus, and support coordination of legacy systems like energy markets or governments.

Specific ideas we want to invest in:

• Human Time as Consensus: Blockchain networks need consensus mechanisms anchored in human effort rather than just capital.

  • What it might look like: Proof of useful work, where consensus requires completing tasks of external value, such as labeling data or verifying real-world events. Participation rights stem from proven capabilities rather than staking.

• Physical Resource Networks: Small-scale infrastructure operators need coordination systems that make their contributions economically viable.

  • What it might look like: An energy network where production or storage serves as consensus weight, combining grid stability with network security. Sensor networks anchored in physical measurements, such as weather, water, or infrastructure monitoring.

• Privacy-Native L1: Healthcare, enterprises, regulated finance, and many other industries need blockchains with default privacy.

  • What it might look like: Confidential state machines that compute on encrypted data by default. Current chains are transparent by default, but many entities (like healthcare, enterprises, and regulated finance) cannot legally operate on transparent chains. Validators use ZK-native architectures or FHE-based execution to verify transaction content without viewing it.

• Use-Case Specific FHE (Fully Homomorphic Encryption): Institutions often need to collaborate on data without revealing it to each other.

  • What it might look like: Banks detect suspicious patterns across institutions without sharing customer data. Each bank runs FHE queries on encrypted data from other banks. They can identify accounts that have interacted with the same suspicious entity without disclosing customer lists to each other.

• Energy Contract Settlement: Traditional markets need cryptographic rails to create 24/7 settlement between different parties. Deregulated energy markets are a great starting point as they are outdated and increasingly strained with rising AI-driven energy demands.

  • What it might look like: A shared settlement layer for energy contracts in deregulated markets. Delivery data triggers automatic payments. Suppliers see cash flow in real-time. Brokers receive commissions instantly. No single party controls the ledger.

• Crypto-Native Jurisdictions: Economic zones and frontier jurisdictions need to rethink governance and financial infrastructure.

  • What it might look like: A new jurisdiction that adopts cryptographic rails from day one. On-chain identities, programmable courts, tokenized capital markets, and smart contract-based regulatory logic.

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