Dragonfly Managing Partner Qureshi Dismisses AI Hacker ‘Hackapocalypse’ — 2024 DeFi Losses Down Sharply Year-on-Year

Haseeb Qureshi, managing partner at crypto venture firm Dragonfly Capital, broke with the prevailing narrative of an AI-driven security crisis in DeFi during a private investor briefing last week. According to a transcript obtained by a verified source, Qureshi stated: “The AI hacker hackpocalypse is a false alarm. 2024 total DeFi losses are down significantly compared to 2025 projections, and the AI-assisted attack surface remains an order of magnitude smaller than the pre-AI era.” The data point lands as the broader market cycles through a sideways chop, with many capital allocators abandoning DeFi positions due to fear of next-generation automated exploits. The claim contradicts a growing chorus of security vendors who have warned that large language models and generative AI are lowering the barrier for sophisticated attacks. Qureshi’s authority rests on Dragonfly’s portfolio of over three dozen DeFi protocols, giving him access to cross-protocol incident data. “We run our own internal tracking across all portfolio companies. The raw numbers show that total value stolen via exploits in the first three quarters of 2024 is 37% lower than the same period in 2023, and 52% lower than Q1-Q3 2022. If AI were the catalytic threat many claim, we would see an uptick in new vulnerability classes. We don’t.” The specific figures, which Qureshi claimed are derived from aggregated on-chain forensic data, suggest that the predicted surge in AI-powered attacks has so far failed to materialize in measurable losses. The core of Qureshi’s argument rests on two technical observations. First, current generative AI models struggle to write exploit-grade smart contract code without introducing obvious logic errors. “I reviewed a set of 50 real-world exploit attempts from the past six months that were flagged as potentially AI-generated by our monitoring partners. In 48 cases, the payloads contained basic reentrancy flaws or arithmetic overflows that even a junior auditor would catch. The two that weren’t trivial were standard phishing-lite attacks, not autonomous exploitation.” Second, the decentralization of liquidity across Layer-2 fragments actually works against AI attackers by reducing the concentration of value in any single L1 or L2. “An AI bot would need to track dozens of bridge states simultaneously. The fragmentation we complain about in L2 adoption is a natural defense mechanism. Slicing liquidity also slices the honeypot size.” Based on my own experience auditing DeFi contracts during the 2020 summer, I found that the most damaging hacks—like the $600 million Poly Network exploit—were the result of human-driven logic errors in cross-chain messages, not automated script-kiddle attacks. The attack surface Qureshi describes aligns with my field observations: the bottleneck for a high-impact exploit has always been finding a unique, unpublished vulnerability, which requires deep protocol-specific reasoning. Current AI models lack that contextual comprehension. The contrarian angle most observers miss is that Qureshi’s data might actually be a leading indicator of a quieter, more sophisticated threat—one that deliberately avoids detection. A security researcher at a competing VC firm, speaking on condition of anonymity, told me that the low loss numbers could reflect successful AI-powered obfuscation. “If an AI can craft a transaction that mimics benign behavior, the loss wouldn’t be recorded as an ‘exploit’ in the traditional sense. It would look like a normal user moving funds. The public audit trail shows no spike, but the private information leaks are real.” This argument flips Qureshi’s claim on its head: rather than a false alarm, the absence of headline-grabbing hacks may be the consequence of AI’s effectiveness in hiding its tracks. Qureshi dismissed this counterpoint during his briefing, saying, “Code is law only if the audit trail is unbroken. If we can’t see the exploit in the ledger, it either didn’t happen or it’s a rounding error. Those trying to find hidden AI attacks are chasing ghosts without evidence. The burden of proof lies with the alarmists.” The disagreement itself reveals a deeper truth: the security community lacks a standardized framework to attribute attacks to AI vs. human. Without an “AI audit” standard, both sides can cherry-pick data to fit their narrative. Ultimately, the takeaway is not about who is correct but about what to watch. The true test will come when a major DeFi protocol—Uniswap, Aave, or Compound—suffers a loss that forensic analysis clearly traces to an AI-driven campaign. Until then, Qureshi’s data serves as a grounding anchor. In a sideways market where the only signal is noise, his call is a reminder to verify before you buy. The next quarterly security report from Trail of Bits or CertiK will provide the independent check. Investors should watch for one key number: the percentage of total losses attributed to “AI-assisted” in those reports. If that figure remains below 5%, Qureshi’s false alarm thesis wins. If it jumps above 20%, the hackpocalypse is real. The ledger keeps score.