a16z's 8 Trend Predictions for 2026: Stablecoins, AI, Privacy, and More Transformative Big Ideas
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a16z's 8 Trend Predictions for 2026: Stablecoins, AI, Privacy, and More Transformative Big Ideas
Privacy will become the most important moat in the crypto space.
Navigating Web3 tides with focused insightsAuthor: a16z
Compiled by: TechFlow
a16z (Andreessen Horowitz) recently released its list of potential "big ideas" for the technology landscape in 2026. These ideas were contributed by partners from its Apps, American Dynamism, Bio, Crypto, Growth, Infrastructure, and Speedrun teams.
Below is a selection of big ideas from the crypto space, along with insights from some special contributors. They cover a wide range of topics from smart agents & AI, stablecoins, tokenization & finance, privacy & security, to prediction markets and other applications. For more on the 2026 tech outlook, read the full article.
Building the Future
Exchanges Are a Starting Point, Not the Endgame
Today, almost every successful crypto company besides stablecoins and some core infrastructure has pivoted to or is pivoting towards becoming an exchange. But if "every crypto company becomes an exchange," what's the endgame? A proliferation of homogeneous competition that fragments user attention and likely leaves only a few winners. Companies that pivot to trading too early may miss the chance to build more competitive, durable business models.
I deeply empathize with founders struggling to make their companies financially viable, but there's also a cost to chasing short-term product-market fit. This is especially acute in crypto, where the unique dynamics around tokens and speculation often steer founders towards "instant gratification," like a marshmallow test.
There's nothing wrong with trading—it's an important market function—but it's not necessarily the end goal. Founders who focus on the product itself and take a long-term view to finding product-market fit may end up being bigger winners.
– Arianna Simpson, General Partner, a16z Crypto
New Thinking on Stablecoins, RWA Tokenization, Payments & Finance
Think More Crypto-Native for RWA Tokenization & Stablecoins
We've seen banks, fintechs, and asset managers keen to bring U.S. equities, commodities, indices, and other traditional assets on-chain. However, as more traditional assets are brought onto blockchains, they are often tokenized "skeuomorphically"—based on existing real-world asset concepts—without leveraging crypto-native properties.
In contrast, synthetic forms like perpetual futures (perps) can offer deeper liquidity and are simpler to implement. Perps also provide an easy-to-understand leverage mechanism, making them perhaps the most crypto-native derivative today. Emerging market equities might be one of the most interesting asset classes to "perpify." For some stocks, for instance, the zero-days-to-expiry (0DTE) options market is often deeper than the spot market, making perpification an interesting experiment.
Ultimately, it's a question of "perpify vs. tokenize"; either way, we should expect to see more crypto-native RWA tokenization in the year ahead.
Similarly, in 2026, stablecoins will see more "issuance innovation, not just tokenization." Stablecoins went mainstream in 2025, and issuance continues to grow.
However, stablecoins without strong credit infrastructure are more like "narrow banks"—holding specific, highly liquid, perceived-as-ultra-safe assets. While narrow banks are a valid product, I don't think they will be the long-term backbone of the on-chain economy.
We've seen many emerging asset managers, curators, and protocols pushing for on-chain asset-backed loans collateralized by off-chain collateral. Often, these loans are generated off-chain and then tokenized. However, I think the benefits of this tokenization are limited, perhaps only for distribution to users already on-chain. Instead, debt assets should be generated on-chain, not off-chain and then tokenized. Generating debt on-chain reduces loan servicing costs, back-office structuring costs, and increases accessibility. The challenge is compliance and standardization, but builders are working on it.
– Guy Wuollet, General Partner, a16z Crypto
Stablecoins Drive Core Ledger Upgrades, Unlock New Payment Use Cases
Today, most banks still run on legacy software systems that would be unrecognizable to modern developers: Banks were early adopters of large software systems in the 1960s and 70s. In the 80s and 90s, second-generation core banking software emerged (e.g., Temenos's GLOBUS, InfoSys's Finacle). However, these have aged and are upgraded too slowly. So many of banking's critical core ledgers—the databases that record deposits, collateral, and other obligations—still run on mainframe computers using the COBOL programming language, relying on batch file interfaces rather than modern APIs.
Most of the world's assets still sit on these decades-old core ledgers. While these systems are battle-tested, trusted by regulators, and deeply embedded in complex banking workflows, they also stifle innovation. Adding critical features like real-time payments can take months or years, wrestling with technical debt and regulatory complexity.
This is where stablecoins come in. Over the past few years, stablecoins found product-market fit and entered mainstream finance. And this year, traditional finance (TradFi) institutions embraced stablecoins at a new level. Instruments like stablecoins, tokenized deposits, tokenized treasuries, and on-chain bonds enable banks, fintechs, and financial institutions to develop new products and serve more customers. Crucially, they can do so without forcing a rewrite of their legacy systems—which, while old, have run stably for decades. Stablecoins thus offer institutions a new path to innovate.
– Sam Broner
On the Future of Smart Agents & AI
Using AI to Do Substantive Research
As a mathematical economist, at the beginning of the year, I found it difficult to get consumer AI models to understand my workflows; by November, I could give them abstract instructions as I would a PhD student… and they would sometimes return novel and correctly executed answers. Moreover, we started seeing AI used in broader research—especially in reasoning, where models can now not only directly aid discovery but also autonomously solve Putnam problems (perhaps the world's hardest undergraduate math exam).
What remains unclear is where and how this research assistance will be most helpful. But I expect AI's research capabilities will enable and incentivize a new "polymath" research style: one that leans into speculating about relationships between ideas and quickly reasoning from more hypothetical answers. These answers may not be fully accurate, but they point in the right direction within some logical framework. Ironically, this approach is a bit like harnessing the power of model "hallucinations": when models become "smart" enough, letting them roam in abstract space, while sometimes producing nonsense, can also yield breakthrough discoveries—much like humans are most creative when freed from linear thinking and clear directions.
Thinking this way requires a new AI workflow—not just "agent-to-agent" but more complex "agent-wrapping-agent," where layers of models help researchers evaluate earlier model proposals and iteratively distill value. I've used this to write papers; others use it for patent searches, inventing new art forms, and even (unfortunately) discovering new smart contract attacks.
But running this "wrapped reasoning agent" research requires better interoperability between models and a way to identify and properly compensate each model's contributions—problems crypto can help solve.
– Scott Kominers, a16z Crypto Research, 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 bearing down on the open web, fundamentally disrupting its economics. This disruption stems from a growing asymmetry between the internet's context layer and its execution layer: today, AI agents extract data from ad-supported content sites (the context layer) to serve users convenience, while systematically bypassing the revenue streams (ads, subscriptions) that fund content creation.
To prevent further decay of the open web (and protect the diverse content that fuels AI), we need technical and economic solutions deployed at scale. This could include next-generation sponsored content, micro-attribution systems, or other novel funding models. Existing AI licensing deals have proven to be stopgaps, often compensating content providers for only a fraction of the revenue lost to AI traffic diversion.
The web needs a new techno-economic model where value flows automatically. The most critical shift next year will be moving from static licensing to compensation based on real-time usage. This means testing and scaling systems—likely using blockchain-enabled nanopayments and sophisticated attribution standards—to automatically reward every entity whose information contributed to an AI agent successfully completing a task.
– Liz Harkavy, a16z Crypto Investing
Privacy as a Moat
Privacy Will Be Crypto's Most Important Moat
Privacy is one of the key properties that will drive global finance on-chain. Yet, it's a critical element missing from almost all blockchains today. For most blockchains, privacy is often an afterthought.
But today, privacy alone is enough to be a key differentiating property for a blockchain. More importantly, privacy also enables a "chain lock-in," or a privacy network effect. This is especially important in an era where performance competition is no longer a sufficient advantage.
With bridge protocols, it's trivial for users to move between chains as long as everything is public. But with privacy, that's no longer the case: it's easy to bridge tokens, but it's extremely hard to bridge privacy. Users risk exposure when moving on or off a privacy chain, whether to a public chain or another privacy chain, because observers of chain data, mempools, or network traffic might infer their identity. Crossing the boundary between a privacy chain and a public chain, or even between two privacy chains, leaks various metadata, like timing and amount correlations, which can make it easier to track users.
Compared to many homogeneous new chains whose fees might be driven to near-zero by competition, privacy-enabled blockchains can form stronger network effects. The reality is, if a "general-purpose" blockchain doesn't have an established ecosystem, a killer app, or an unfair distribution advantage, there's little reason for users to use it, build on it, or be loyal to it.
On public blockchains, users can easily transact with users on other chains—it doesn't matter which chain they join. On private blockchains, however, the chain a user joins matters a lot, because once they join, they are unlikely to move to another chain to avoid privacy exposure. This creates a "winner-take-most" dynamic. And since privacy is critical for most real-world use cases, a handful of privacy chains could end up dominating crypto.
– Ali Yahya, General Partner, a16z Crypto
Other Industries & Applications
Prediction Markets Will Get Bigger, Broader, Smarter
Prediction markets have been going mainstream, and in the coming year, as they intersect with crypto and AI, they will get bigger, broader, smarter—and present new, important challenges for builders.
First, there will be many more contracts listed in prediction markets. This means we'll have real-time odds not just for major elections or geopolitical events, but for nuanced outcomes and complex cross-events. As these new contracts unearth more information and become integrated into news ecosystems (a trend already underway), they will raise important societal questions, like how to value information and how to better design these markets to be more transparent, auditable, etc.—questions crypto can help solve.
To handle the influx of new contracts, we'll need new ways to reach consensus on real-world events to settle them. Centralized platform solutions (e.g., confirming whether an event happened) are important, but disputed cases like the Zelenskyy lawsuit market and Venezuela election market expose their limits. To handle these edge cases and help prediction markets scale to more practical applications, new decentralized governance mechanisms and LLM oracles can help adjudicate the truth of disputed outcomes.
AI's potential extends beyond LLM-powered oracles. For example, AI agents active on these platforms could gather signals globally for short-term trading advantages. This could help us see the world in new ways and predict future trends more accurately. (Projects like Prophet Arena are already making this space exciting.) Beyond serving as sophisticated political analysts offering insights, these AI agents, as we study their emergent strategies, could reveal the underlying predictors of complex social events.
Will prediction markets replace polls? No. Instead, they'll make polls better (and poll information can be fed into prediction markets). As a political economy professor, I'm most excited about the potential for prediction markets to work alongside the rich ecosystem of polls—but we'll need to rely on new technologies, like AI, which can improve the survey experience; and crypto, which can offer new ways to verify that survey and poll participants are human, not bots.
– Andy Hall, a16z Crypto Research Advisor, Professor of Political Economy at Stanford
Crypto Will Expand to New Applications Beyond Blockchains
For years, SNARKs (succinct non-interactive arguments of knowledge, cryptographic proofs that verify a computation's correctness without re-running it) have been used primarily for blockchains. This is because their computational overhead was too large: proving a computation could be a million times more work than running it. That overhead is worthwhile when amortized across thousands of verifiers, but impractical elsewhere.
That's about to change. By 2026, zkVM (zero-knowledge virtual machine) prover overhead will drop to roughly 10,000x, with memory footprints of just a few hundred megabytes—fast enough to run on phones and cheap enough for many use cases. Here's why "10,000x" might be a key threshold: high-end GPUs have roughly 10,000x the parallel throughput of a laptop CPU. By the end of 2026, a single GPU will be able to generate proofs of CPU executions in real-time.
This unlocks a vision from early research papers: verifiable cloud computing. If you're already running CPU workloads in the cloud (because your compute isn't amenable to GPU acceleration, or you lack expertise, or for legacy reasons), you'll be able to get cryptographic proofs of computational correctness at reasonable cost overhead. And since provers are optimized for GPUs, your code needs no changes.
– Justin Thaler, a16z Crypto Research, Associate Professor of Computer Science at Georgetown
– a16z Crypto Editorial
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