Visa Head of Crypto: Eight Major Evolution Directions for Crypto and AI by 2026

Original Author: Cuy Sheffield, Vice President and Head of Crypto at Visa

Original Compilation: Saoirse, Foresight News

As cryptocurrency and AI gradually mature, the most important transformation in these two fields is no longer "theoretically possible" but "reliably implementable in practice." Currently, both technologies have crossed critical thresholds, achieving significant performance improvements, yet their practical adoption rates remain uneven. The core development trend for 2026 stems precisely from this "performance vs. adoption" gap.

Below are several key themes I have been following long-term, along with preliminary thoughts on the direction of these technologies, areas of value accumulation, and "why the ultimate winners may be completely different from the industry pioneers."

Theme 1: Cryptocurrency is Shifting from a Speculative Asset Class to a High-Quality Technology

The first decade of cryptocurrency development was characterized by "speculative advantage"—its market is global, continuous, and highly open, with extreme volatility making cryptocurrency trading more dynamic and attractive than traditional financial markets.

However, simultaneously, its underlying technology was not ready for mainstream adoption: early blockchains were slow, expensive, and lacked stability. Apart from speculative scenarios, cryptocurrency almost never surpassed existing traditional systems in terms of cost, speed, or convenience.

Now, this imbalance is beginning to reverse. Blockchain technology has become faster, more economical, and more reliable. The most attractive application scenarios for cryptocurrency are no longer speculation but infrastructure—particularly in settlement and payments. As cryptocurrency gradually becomes a more mature technology, the core role of speculation will diminish: it will not disappear completely but will no longer be the primary source of value.

Theme 2: Stablecoins are a Clear Achievement of Cryptocurrency's "Pure Utility"

Stablecoins differ from previous cryptocurrency narratives; their success is based on specific, objective criteria: in certain scenarios, stablecoins are faster, cheaper, and have broader coverage than traditional payment channels, while also seamlessly integrating into modern software systems.

Stablecoins do not require users to view cryptocurrency as an "ideology" to believe in; their applications often occur "implicitly" within existing products and workflows—this also allows institutions and enterprises that previously considered the crypto ecosystem "too volatile and opaque" to finally clearly understand its value.

It can be said that stablecoins help cryptocurrency re-anchor itself to "utility" rather than "speculation" and set a clear benchmark for "how cryptocurrency can succeed in practical implementation."

Theme 3: When Cryptocurrency Becomes Infrastructure, "Distribution Capability" is More Important than "Technical Novelty"

In the past, when cryptocurrency primarily played the role of a "speculative tool," its "distribution" was endogenous—new tokens only needed to "exist" to naturally accumulate liquidity and attention.

As cryptocurrency becomes infrastructure, its application scenarios are shifting from the "market level" to the "product level": it is embedded into payment processes, platforms, and enterprise systems, often without end-users being aware of its presence.

This shift greatly benefits two types of entities: first, enterprises with existing distribution channels and reliable customer relationships; second, institutions with regulatory licenses, compliance systems, and risk prevention infrastructure. "Protocol novelty" alone is no longer sufficient to drive the large-scale adoption of cryptocurrency.

Theme 4: AI Agents Have Practical Value, and Their Impact is Extending Beyond Coding

The practicality of AI Agents is becoming increasingly prominent, but their role is often misunderstood: the most successful agents are not "autonomous decision-makers" but "tools that reduce coordination costs in workflows."

Historically, this has been most evident in software development—agent tools accelerate coding, debugging, code refactoring, and environment setup. However, in recent years, this "tool value" is rapidly spreading to more fields.

Taking tools like Claude Code as an example, although positioned as a "developer tool," its rapid adoption reflects a deeper trend: agent systems are becoming the "interface for knowledge work," not limited to programming. Users are beginning to apply "agent-driven workflows" to research, analysis, writing, planning, data processing, and operational tasks—these tasks lean more towards "general professional work" than traditional programming.

The truly crucial aspect is not "ambient coding" itself, but the core pattern behind it:

• Users delegate "goal intent," not "specific steps";
• Agents manage "contextual information" across files, tools, and tasks;
• The work mode shifts from "linear progression" to "iterative, conversational."

In various types of knowledge work, agents excel at gathering context, executing bounded tasks, reducing handoffs, and accelerating iteration efficiency, but they still have shortcomings in "open-ended judgment," "accountability," and "error correction."

Therefore, most agents currently used in production environments still need to be "bounded, supervised, and embedded in systems," rather than operating completely independently. The practical value of agents stems from the "restructuring of knowledge workflows," not from "replacing labor" or "achieving full autonomy."

Theme 5: AI's Bottleneck Has Shifted from "Intelligence Level" to "Trustworthiness"

The intelligence level of AI models has rapidly improved. The limiting factor today is no longer "singular linguistic fluency or reasoning ability" but "reliability in practical systems."

Production environments have zero tolerance for three types of issues: first, AI "hallucinations" (generating false information); second, inconsistent outputs; third, opaque failure modes. Once AI is involved in customer service, financial transactions, or compliance, "mostly correct" results are no longer acceptable.

Establishing "trust" requires four foundations: first, traceability of results; second, memory capability; third, verifiability; fourth, the ability to proactively expose "uncertainty." Before these capabilities are sufficiently mature, AI's autonomy must be constrained.

Theme 6: Systems Engineering Determines Whether AI Can Be Deployed in Production Scenarios

Successful AI products treat "models" as "components" rather than "finished products"—their reliability stems from "architectural design," not "prompt optimization."

Here, "architectural design" includes state management, control flow, evaluation and monitoring systems, as well as failure handling and recovery mechanisms. This is also why AI development today increasingly resembles "traditional software engineering" rather than "cutting-edge theoretical research."

Long-term value will accrue to two types of entities: first, system builders; second, platform owners who control workflows and distribution channels.

As agent tools expand from coding to research, writing, analysis, and operational processes, the importance of "systems engineering" will become even more prominent: knowledge work is often complex, state-dependent, and context-intensive, making agents that can "reliably manage memory, tools, and iterative processes" (not just those that generate outputs) more valuable.

Theme 7: The Contradiction Between Open Models and Centralized Control Raises Unresolved Governance Issues

As AI systems become more capable and integrate deeper into the economic sphere, the question of "who owns and controls the most powerful AI models" is creating a core contradiction.

On one hand, R&D at the AI frontier remains "capital-intensive" and is increasingly concentrated due to influences from "compute access, regulatory policies, and geopolitics." On the other hand, open-source models and tools continue to iterate and improve, driven by "extensive experimentation and convenient deployment."

This "coexistence of centralization and openness" has triggered a series of unresolved issues: dependency risk, auditability, transparency, long-term bargaining power, and control over critical infrastructure. The most likely outcome is a "hybrid model"—frontier models drive technological breakthroughs, while open or semi-open systems integrate these capabilities into "widely distributed software."

Theme 8: Programmable Money Gives Rise to New Agent Payment Flows

When AI systems play a role in workflows, their need for "economic interaction" is increasing—for example, paying for services, calling APIs, compensating other agents, or settling "usage-based interaction fees."

This demand has brought "stablecoins" back into focus: they are seen as "machine-native money," programmable, auditable, and capable of transfer without human intervention.

Taking "developer-facing protocols" like x402 as an example, although still in early experimental stages, the direction they point to is clear: payment flows will operate as "APIs," not traditional "checkout pages"—this enables "continuous, granular transactions" between software agents.

Currently, this field remains nascent: transaction volumes are small, user experience is rough, and security and permission systems are still being perfected. But infrastructure innovation often begins with such "early exploration."

It's worth noting that its significance is not "autonomy for autonomy's sake," but rather "when software can execute transactions through programming, new economic behaviors become possible."

Conclusion

Whether it's cryptocurrency or artificial intelligence, the early development stages favored "eye-catching concepts" and "technical novelty." In the next stage, "reliability," "governance capability," and "distribution capability" will become more important competitive dimensions.

Today, the technology itself is no longer the primary limiting factor; "embedding the technology into practical systems" is the key.

In my view, the defining characteristic of 2026 will not be "a single breakthrough technology" but the "steady accumulation of infrastructure"—these facilities, while operating silently, are quietly reshaping "how value flows" and "how work gets done."