For over a decade, the crypto industry has positioned itself as a revolutionary financial system built for humans. Yet according to Haseeb Qureshi, managing partner at Dragonfly Capital, this narrative misses a fundamental truth: crypto designed for AI agents may better explain the technology’s persistence despite persistent friction. The argument isn’t that blockchain failed to achieve its vision, but rather that the vision itself was built on a faulty assumption about who should actually use it.
This reframing has profound implications for how we understand cryptocurrency’s evolution. Instead of viewing Bitcoin and blockchain systems as failed attempts at human-centric finance, Qureshi suggests we’ve been looking at a technology that was always meant for something else entirely. The gap between ideal and reality isn’t a design flaw—it’s a signal that humans were never the intended primary users.
The Fundamental Mismatch Between Humans and Blockchain Architecture
The crypto industry has always harbored a tension between its theoretical ideals and practical reality. Early visionaries imagined smart contracts replacing legal agreements, property rights enforced directly on-chain, and blockchain serving as a foundation for a parallel financial system. Yet decades later, even the most crypto-native organizations still rely on traditional legal frameworks for their most important transactions.
Qureshi points to his own experience at Dragonfly as evidence. When the firm invests in a startup, it doesn’t execute a smart contract. It signs a legal contract. The startup signs one too. Neither party feels comfortable without that legal agreement, even when on-chain vesting contracts exist as backup. This isn’t a limitation of current technology—it’s a reflection of how humans actually need to interact with financial systems.
The core issue, according to Qureshi, is that blockchain systems function exactly as designed. They work. The problem isn’t technical failure but social misalignment. Blockchain architecture has no built-in tolerance for human error, no recovery mechanisms for human foibles, and no flexibility for human judgment calls. Banking systems, by contrast, have evolved over centuries to accommodate precisely these human realities.
Why Traditional Banking Won the Human Design Competition
Banking may seem antiquated compared to permissionless blockchain, but its apparent friction is actually sophisticated engineering built around human reality. Banks maintain customer service departments. They reverse fraudulent transactions. They forgive mistakes. They employ compliance teams to interpret ambiguous situations. These aren’t failures—they’re features designed into a system that understood its actual users.
As Qureshi observed, “The bank, terrible as it is, was designed for humans. The banking system was specifically architected with human foibles and failure modes in mind, refined over hundreds of years. Banking is adapted to humans. Crypto is not.” This isn’t nostalgia for traditional finance—it’s recognition that legacy systems solved real problems through institutional support and human judgment that code alone cannot provide.
Blockchain’s immutability, which is presented as a feature, becomes a liability when humans are involved. A wrong address, a compromised key, a misunderstood contract parameter—these become permanent, irreversible mistakes. Banking handles these scenarios through reversal, dispute resolution, and institutional backing. The difference isn’t sophistication. It’s design philosophy.
The Friction Points That Never Made Sense for Humans
The daily experience of crypto users reveals design decisions that seem almost hostile to human interaction. Blind signing, where users approve transactions without understanding their contents. Long alphanumeric addresses that invite copy-paste errors. Stale token approvals that drain wallets unexpectedly. Automated enforcement that executes ruthlessly regardless of intent or circumstance. These aren’t edge cases. They’re core features of how blockchain systems like Ethereum operate.
Qureshi notes that cryptocurrency participants know exactly what they should do to stay safe. Verify every contract. Double-check every domain. Scan for address spoofing. Never blind sign. Never approve unlimited allowances. Everyone in crypto has heard this advice countless times. Yet adoption across the industry remains low because these practices conflict with how humans actually behave under pressure, fatigue, or time constraints. “We know we should verify the contract, double-check the domain, and scan for address spoofing. We know we should do all of it, every time. But we don’t. We’re human. And that’s the tell,” he observed.
AI Agents as Crypto’s True Native Users
If blockchain wasn’t designed for humans, what was it designed for? Qureshi’s answer points toward artificial intelligence agents as the logical endpoint of this technology stack. Unlike humans, AI agents don’t fatigue. They don’t skip verification steps out of convenience. They don’t make impulsive financial decisions driven by emotion or FOMO. They can analyze smart contract logic, simulate edge cases, and execute transactions with perfect consistency.
This isn’t speculation about a distant future. The technical foundations are already in place. Autonomous agents can review contract code, understand their implications, and transact without hesitation or second-guessing. They can operate in the deterministic, rule-based environment that blockchain provides. More importantly, they can do so at scale and speed that human intermediaries could never match. The rigid architecture that frustrates human users becomes, from an AI perspective, a well-written specification rather than a footgun.
Consider the implications: what crypto sees as limitations, AI agents see as clarity. Immutability isn’t terrifying—it’s mathematical certainty. Deterministic execution isn’t inflexible—it’s reliable. Permissionless access isn’t risky—it’s a feature enabling autonomous action. The entire value proposition of blockchain suddenly becomes coherent when viewed through the lens of machine-to-machine interaction rather than human finance.
The Self-Driving Wallet and Machine Economics
Qureshi’s vision extends beyond AI simply using existing blockchain infrastructure. The future interface for cryptocurrency, he suggests, will be a “self-driving wallet”—entirely mediated by AI agents. Users won’t directly interact with addresses, approvals, or transaction details. Instead, AI agents will manage financial activities on behalf of their human principals, translating human intent into optimized blockchain transactions.
This model resolves many of crypto’s usability problems not by changing blockchain but by removing humans from direct interaction with it. A user might simply state their financial goal—”invest this amount in yield strategies” or “swap this token for stablecoins.” The AI agent handles verification, routing, gas optimization, and execution. The friction disappears because the friction was always between human cognition and machine logic.
Beyond human-AI relationships, autonomous agents will increasingly transact directly with each other. This opens possibilities for machine-to-machine economies where AIs deploy capital, execute strategies, and settle transactions without human approval at each step. Blockchain’s permissionless infrastructure becomes the natural foundation for such interactions. No gatekeepers. No approval delays. No institutional intermediaries. Just deterministic code executing according to mathematical rules.
The Historical Pattern of Technology Finding Its True Purpose
Qureshi draws an important parallel to how previous transformative technologies found their killer applications. GPS satellites were developed for military purposes but remained marginal until smartphones provided the right interface. TCP/IP was an academic protocol until the web browser made it universally useful. Similarly, blockchain as we know it may have been waiting for AI agents to become the technology that justified its entire design philosophy.
“For crypto, we might just have found it in AI agents,” Qureshi concluded. But he emphasized that such a shift won’t happen overnight. Technological breakthroughs require complementary innovations—infrastructure that doesn’t yet exist, regulatory frameworks that don’t yet exist, and cultural understanding that hasn’t yet developed. The timeline could span years, potentially a decade. What matters is recognizing that crypto’s current limitations aren’t failures but signals pointing toward a different future.
The Case for Machine-Native Financial Systems
Beyond Qureshi’s analysis, other prominent figures in crypto have reached similar conclusions. Ryan Adams, founder of Bankless, argued that what appears to be terrible UX for humans might actually be optimal UX for AI agents. He predicted that billions of AI agents with wallets could eventually drive cryptocurrency markets far beyond their current scale. The vision isn’t incremental improvement—it’s a fundamental reorientation of how financial systems operate.
Adams noted that the “AiFi narrative” remains underground, similar to DeFi’s status in 2019. “The dry tinder is quietly collecting but at some point it will ignite,” he wrote. His prediction suggests that as AI agent adoption accelerates, crypto could suddenly transition from niche technology to foundational infrastructure supporting trillions of autonomous transactions. This would represent a complete inversion of how the industry currently positions itself.
The machine-native thesis explains something that has puzzled observers for years: why crypto persists despite persistent usability challenges. The answer isn’t user error or poorly designed interfaces. It’s that the system was never optimized for the users currently trying to use it. Once the correct users—AI agents—become prevalent, the entire architecture suddenly becomes efficient, elegant, and purposeful.
Institutional Limitations and Remaining Constraints
Yet significant practical constraints remain even if AI agents do become major participants in financial systems. Liability ultimately flows to humans or institutions. If an AI agent executes a harmful financial strategy, who bears the legal responsibility? Traditional legal systems still require human accountability. Banks maintain institutional liability precisely because humans need someone to hold responsible for failures.
Smart contract automation also doesn’t eliminate all risks. Deterministic code reduces ambiguity, but it doesn’t prevent exploits, governance failures, or systemic cascades. DeFi exploits demonstrate that even well-audited contracts contain unexpected vulnerabilities. An AI agent might execute perfect code, but if that code creates unintended financial consequences, humans still suffer the damage. The determinism that appeals to machines doesn’t translate to safety for stakeholders.
The Infrastructure vs. Narrative Question
There’s also a possibility that Qureshi’s thesis, while intellectually compelling, misses how technology actually evolves. If AI becomes the primary financial interface using blockchain infrastructure, crypto might fade into backend systems rather than function as the revolutionary parallel financial system early advocates imagined. Users wouldn’t experience decentralization or immutability. They’d experience convenience mediated through AI.
From this perspective, crypto succeeds by disappearing. The permissionless, deterministic foundation remains valuable for machine-to-machine transactions, but it becomes transparent infrastructure rather than a user-facing system. This outcome wouldn’t invalidate Qureshi’s argument about design alignment, but it would represent a different future than the one originally envisioned when Bitcoin launched in 2009.
Implications for Crypto’s Present and Future
Whether or not Qureshi’s full thesis proves correct, his analysis offers valuable reframing of how we evaluate blockchain technology. Instead of asking “why isn’t crypto user-friendly,” we might ask “what types of users would find this system natural to use.” This inverts the presumption that blockchain failed to achieve its promise. Rather, it suggests blockchain succeeded—just not for the users we initially assumed would benefit.
This perspective influences how we evaluate current crypto market conditions and future narratives. If machine-native financial systems represent the actual future of blockchain, then current adoption barriers among humans become irrelevant. The friction that has limited retail adoption becomes a non-issue once you recognize that retail adoption was never the actual target.
For crypto-native institutions and builders, the implications are equally significant. Rather than constantly trying to improve UX for humans, perhaps the more productive path involves building the AI agent infrastructure that Qureshi describes. Self-driving wallets. Agent-to-agent transaction protocols. Autonomous financial strategies. These represent the actual frontier where blockchain’s unique properties become decisive advantages rather than annoying constraints.
The Timing Question and Market Adoption
When might AI agents become prevalent enough to drive meaningful blockchain adoption? Current timelines remain speculative. Advanced AI systems exist, but autonomous agents with direct financial authority remain rare. Regulatory frameworks for such systems don’t yet exist. Cultural acceptance of machine-managed finances remains limited. These barriers suggest the transition won’t happen quickly, even if it proves inevitable.
However, the infrastructure is being built now. Ethereum and other platforms are scaling to handle higher transaction volumes, improving the foundation for machine-to-machine transactions. Development tools are improving. Economic models supporting autonomous agents are being tested. The pieces accumulate slowly, but accumulate they do.
Reconciling the AI Agent Vision with Current Reality
Today’s crypto market doesn’t resemble Qureshi’s vision. Most blockchain activity still involves humans making decisions, taking risks, and managing wallets. Market prices respond to human psychology. Community governance remains contentious and emotional. If AI agents were the natural users, why doesn’t the market already reflect that reality?
The answer likely involves timing and infrastructure maturity. Current AI agents lack the economic incentives, technical infrastructure, and regulatory clarity needed to operate meaningfully in financial systems. As these elements develop, adoption could accelerate rapidly. The history of technology suggests that once the right infrastructure exists, adoption often follows faster than anyone anticipated. GPS, smartphones, and the web all experienced sudden inflection points once complementary technologies aligned.
What’s Next
Qureshi’s argument that crypto was designed for AI agents rather than humans represents a significant reframing of blockchain technology’s purpose and promise. Whether completely accurate or not, it offers a coherent explanation for features that have frustrated human users for years. More importantly, it suggests that blockchain’s limitations aren’t failures to overcome but signals pointing toward a different future.
The practical implications remain to be seen. Will AI agents truly become the primary blockchain users? Will financial systems transition to machine-native infrastructure? Or will human institutions eventually figure out how to make blockchain genuinely usable for their purposes? The honest answer is that crypto’s actual future remains uncertain, influenced by technology development, regulatory evolution, and economic incentives we cannot fully predict.
What seems clear is that the current framing of crypto as “for everyone” or “the future of finance” requires serious reconsideration. If machines rather than humans represent crypto’s true constituency, then the industry’s marketing, development priorities, and adoption strategies might need fundamental reorientation. The future of blockchain may depend less on solving human usability problems than on building the AI agent infrastructure that makes the technology’s current design suddenly optimal.
