a16z 8 Trends Predictions for 2026: Stablecoins, AI, Privacy, and More Transformative Big Ideas

Author: a16z

Edited by: Deep Tide TechFlow

a16z (Andreessen Horowitz) recently released its list of “Big Ideas” that could emerge in the technology sector by 2026. These ideas were proposed by partners from its Apps, American Dynamism, Biotechnology, Cryptocurrency, Growth, Infrastructure, and Speedrun teams.

Below are some selected big ideas and insights from special contributors in the cryptocurrency field, covering topics from intelligent agents and artificial intelligence (AI), stablecoins, tokenization and finance, privacy and security, to prediction markets and other applications. If you want to learn more about the technological outlook for 2026, please read the full article.

Building the Future

Exchanges are just the starting point, not the end goal

Today, aside from stablecoins and some core infrastructure, nearly all successful crypto companies have already transitioned into or are moving toward becoming exchanges. However, what would happen if “every crypto company turns into an exchange”? The result could be a proliferation of homogeneous competition that not only distracts users but also leaves only a few winners. Companies that shift to trading too early might miss the opportunity to build more competitive and sustainable business models.

I fully understand the difficult position founders face in maintaining healthy finances, but chasing short-term product-market fit can come at a cost. This issue is especially prominent in the crypto industry because the unique dynamics around tokens and speculation often lead founders down the path of “instant gratification,” much like a “cotton candy test.”

Trading itself isn’t wrong—it is indeed an important function of market operation—but it isn’t necessarily the ultimate goal. Founders who focus on the product itself and seek product-market fit from a long-term perspective may ultimately become bigger winners.

– Arianna Simpson, General Partner of a16z Crypto Team

New Perspectives on Stablecoins, RWA Tokenization, Payments, and Finance

Thinking about Real-World Asset (RWA) Tokenization and Stablecoins in a More Crypto-Native Way

We have seen banks, fintech companies, and asset managers show strong interest in bringing US stocks, commodities, indices, and other traditional assets onto the blockchain. However, as more traditional assets are introduced to blockchain, their tokenization often takes a “physicalized” approach—based on existing real-world asset concepts without fully leveraging crypto-native features.

In contrast, synthetic assets like perpetual futures (perps) can offer deeper liquidity and are easier to implement. Perps also provide an intuitive leverage mechanism, making them perhaps the most fitting native derivatives for crypto markets today. Emerging markets stocks might be one of the most interesting asset classes to “perpify.” For example, for some stocks, the liquidity in zero-dated (0DTE) options markets often exceeds that of the spot market, making “perpification” an experiment worth trying.

Ultimately, this is a question of “perpify vs. tokenization”; regardless, we can expect to see more crypto-native real-world asset tokenization in the coming year.

Similarly, in 2026, the stablecoin space will see more “issuance innovations, not just tokenization.” Stablecoins have become mainstream in 2025, with issuance volumes continuing to grow.

However, stablecoins lacking strong credit infrastructure resemble “narrow banks”—holding specific high-liquidity, deemed extremely safe assets. While narrow banks are effective products, I don’t believe they will be the long-term backbone of on-chain economies.

We have seen many emerging asset managers, curators, and protocols push for on-chain asset-backed loans collateralized by off-chain assets. Usually, these loans are first generated off-chain and then tokenized. However, I believe this method of tokenization has limited advantages, mainly in distributing assets to on-chain users. Therefore, debt assets should be generated directly on-chain, not first off-chain and then tokenized. On-chain debt creation can reduce lending service costs, backend infrastructure costs, and improve accessibility. The challenge lies in compliance and standardization, but developers are actively working to address these issues.

– Guy Wuollet, General Partner of a16z Crypto Team

Stablecoins Drive Upgrades to Bank Core Ledgers and Open New Payment Scenarios

Today, most banks still run outdated software systems that are difficult for modern developers to recognize: as early as the 1960s and 1970s, banks were early adopters of large-scale software systems. By the 1980s and 1990s, second-generation core banking software began to emerge (e.g., Temenos GLOBUS and Infosys Finacle). However, these systems are aging, and upgrades are too slow. As a result, many critical core ledgers—key databases recording deposits, collateral, and other obligations—still run on mainframes programmed in COBOL, relying on batch file interfaces rather than modern APIs.

Most assets worldwide are still stored in these decades-old core ledgers. While these systems have been tested over time, gained regulatory trust, and are deeply embedded in complex banking operations, they also hinder innovation. For example, adding real-time payment features can take months or even years, compounded by significant technical debt and regulatory complexity.

This is where stablecoins come into play. Over the past few years, stablecoins have found product-market fit and successfully entered mainstream finance. This year, traditional financial institutions (TradFi) have embraced stablecoins at a new level. Financial tools like stablecoins, tokenized deposits, tokenized government bonds, and on-chain bonds enable banks, fintechs, and financial institutions to develop new products and serve more customers. More importantly, these innovations do not require rewriting legacy systems—despite their age, these systems have operated stably for decades. Stablecoins thus offer a new avenue for institutional innovation.

– Sam Broner

On the Future of Intelligent Agents and AI

Using AI to Perform Substantive Research Tasks

As a mathematical economist, earlier this year I found it very difficult to get consumer-grade AI models to understand my workflows; by November, I could give the models abstract instructions akin to a PhD student… and sometimes they would return entirely new and correctly executed answers. Moreover, we are beginning to see AI used more broadly in research—especially in reasoning, where AI models now not only assist in discovery but can autonomously solve Putnam problems (perhaps the hardest university math exams in the world).

What remains unclear is which areas will benefit most from this research assistance and how. I expect AI’s research capabilities will foster and inspire a new “polymath” research style: one that tends to hypothesize relationships between ideas and quickly deduce from more speculative answers. These answers may not be perfectly accurate but can point in the right direction within certain logical frameworks. Ironically, this approach is somewhat like harnessing the “hallucination” power of models: when these models become sufficiently “smart,” allowing them to explore freely in the abstract space—even if they sometimes produce nonsense—can sometimes lead to breakthroughs, much like human creativity when breaking free from linear thinking and clear directions.

Thinking this way requires a new AI workflow—not just a “model-to-model” proxy pattern, but a more complex “agent-wrapping-agent” pattern—where different layers of models assist researchers in evaluating earlier models’ proposals and gradually distill valuable insights. I have used this method to write papers, while others have used it for patent searches, inventing new art forms, and (regrettably) discovering new attack vectors for smart contracts.

However, to run this “wrapped reasoning agent” research mode, better interoperability between models and a way to identify and fairly compensate each model’s contribution are needed—and these are precisely problems that encryption technology can help solve.

– Scott Kominers, Member of a16z Crypto Research Team, Professor at Harvard Business School

The Invisible Tax AI Agents Impose on the Open Web

With the rise of AI agents, an “invisible tax” is pressing down on the open web, fundamentally disrupting its economic foundation. This interference stems from the increasing asymmetry between the web’s contextual layer and execution layer: currently, AI agents extract data from ad-supported content sites (the contextual layer), providing convenience to users while systematically bypassing the revenue sources that support content creation (such as ads and subscriptions).

To prevent further decline of the open web (and to protect the diversity of content fueling AI), we need large-scale deployment of technological and economic solutions. These might include next-generation sponsored content, micro-attribution systems, or other innovative funding models. Existing AI licensing protocols have proven to be only short-term stopgaps, often compensating content creators only a small fraction of the revenue lost to AI traffic.

The web needs a new economic model where value can flow automatically. The most critical transition next year will be from static licensing to real-time usage-based compensation. This involves testing and scaling systems—possibly leveraging blockchain-supported micro-payments (nanopayments) and complex attribution standards—to automatically reward all entities contributing valuable information for AI agents to complete tasks successfully.

– Liz Harkavy, a16z Crypto Investment Team

Privacy as the Ultimate Moat

Privacy Will Become the Most Important Moat in Crypto

Privacy is one of the key features driving the global on-chain finance movement. Yet, it remains a significant element lacking in almost all current blockchains. For most blockchains, privacy issues are often considered an afterthought.

Today, privacy itself has become a critical differentiator for blockchains. More importantly, privacy can create a “chain lock-in” effect—or a privacy network effect. Especially in an era where performance competition is no longer sufficient for advantage, privacy becomes even more crucial.

With cross-chain bridge protocols, as long as all information is public, migrating between chains is straightforward. But once privacy is introduced, this convenience disappears: transferring tokens across chains is easy, but transferring privacy is extremely difficult. Users moving from a privacy chain to a public chain—or between privacy chains—face risks, as observers of on-chain data, mempools, or network traffic might infer their identities. Crossing the boundaries between privacy and public chains, or between different privacy chains, can leak metadata such as transaction timing and amounts, making user tracking easier.

Compared to many homogeneous new chains, these chains’ transaction fees may be driven down to near zero due to competition, while privacy-enabled blockchains can generate stronger network effects. The reality is, if a “general-purpose” blockchain lacks a mature ecosystem, killer apps, or unfair distribution advantages, there is little reason for users to choose to build or stay on it, let alone develop loyalty.

On public blockchains, users can easily transact with others on different chains—they join whichever chain they prefer. But on private blockchains, the choice of which chain to join is critical, because once joined, users are less likely to transfer to other chains to avoid privacy risks. This creates a “winner-takes-all” dynamic. Since privacy is vital for most real-world applications, a few privacy chains may ultimately dominate the crypto space.

– Ali Yahya, General Partner of a16z Crypto Team

Other Industries and Applications

Prediction Markets Will Become Larger, Broader, and Smarter

Prediction markets are gradually entering the mainstream, and in the coming year, with the intersection of crypto and AI, they will grow bigger, be applied more broadly, and become smarter, bringing new challenges for developers.

First, more contracts will be listed in prediction markets. This means we can not only get real-time odds on major elections or geopolitical events but also forecast more detailed outcomes and complex cross-events. As these new contracts uncover more information and integrate into news ecosystems (a trend already underway), they will raise important societal questions, such as how to balance information value and how to design these markets to be more transparent and auditable—problems that can be addressed with crypto technology.

To handle the surge in new contracts, we need new ways to reach consensus on real-world events to settle these contracts. Centralized solutions (e.g., confirming whether an event actually occurred) are important but have limitations, as shown by contentious cases like Zelensky’s lawsuit market and Venezuela’s election market. To address these edge cases and help prediction markets expand into more practical applications, decentralized governance mechanisms and large language model (LLM) oracle systems can assist in determining the truth of disputed outcomes.

AI’s potential isn’t limited to LLM-driven oracles. For example, active AI agents on these platforms can gather signals worldwide to gain short-term trading advantages. This can help us view the world from new perspectives and more accurately predict future trends. (Projects like Prophet Arena have already generated excitement in this field.) Besides serving as complex political analysts providing insights, these AI agents may also reveal fundamental predictive factors behind complex social events as we study their emerging strategies.

Will prediction markets replace polls? No. Instead, they will improve polling (and poll data can be fed into prediction markets). As a political economy professor, I am most excited about prediction markets working synergistically with a vibrant polling ecosystem—but this will depend on new technologies, such as AI, to improve survey experiences, and encryption to verify that survey participants are human, not bots.

– Andy Hall, a16z Crypto Research Advisor, Professor of Political Economy at Stanford University

Cryptography Will Expand Beyond Blockchain into New Applications

For years, SNARKs (Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge, a cryptographic proof that can verify the correctness of computations without re-executing them) have primarily been used in blockchain. This is because their computational overhead is enormous: proving a computation might be 1 million times more expensive than just running it directly. In scenarios requiring thousands of verifiers, this overhead is justified, but elsewhere it is impractical.

This situation is about to change. By 2026, zkVM (Zero-Knowledge Virtual Machine) proof systems will reduce the computational overhead to about 10,000 times, with memory usage only a few hundred megabytes—fast enough to run on smartphones and cheap enough for widespread application. There is a reason why “10,000 times” might be a critical threshold: high-end GPUs have a parallel throughput roughly 10,000 times that of a laptop CPU. By the end of 2026, a single GPU will be able to generate real-time proofs of CPU computations.

This will unlock visions from early research papers: verifiable cloud computing. If you’re already running CPU workloads in the cloud (because your tasks are not GPU-accelerated or you lack expertise, or due to historical reasons), you’ll be able to obtain cryptographic proofs of computation correctness at reasonable costs. Moreover, proof systems are already optimized for GPUs, requiring no additional code adjustments.

– Justin Thaler, Member of a16z Crypto Research Team, Associate Professor of Computer Science at Georgetown University

—— a16z Crypto Editorial Team