Famed iPhone, Sony Hacker Says AI Coding Agents Are a Disaster Waiting to Happen

In brief - George Hotz, the hacker behind the first iPhone jailbreak and PlayStation 3 crack, published a blog post Sunday calling AI coding agent adoption "one of the most costly mistakes in the field's history." - His core argument: high performers can spot bad agent output, but weaker engineers can't—and it's the weaker engineers producing ten times the volume, degrading average code quality at scale. - The post arrived five days after Andrej Karpathy joined Anthropic's pre-training team with the opposite view, marking a clear split among serious engineers on whether AI agents actually work. George Hotz—the hacker who first cracked the iPhone at age 17 and reverse-engineered the PlayStation 3 before Sony sued him for it—published a blog post Sunday arguing that mass adoption of AI coding agents will end in disaster, or at least close to it. “I’m calling it now, the adoption of AI agents into software development will be one of the most costly mistakes in the field’s history,” Hotz wrote. “Agents cannot program, and it’s taking longer and longer to realize that they can’t.” “The output is broken, but in a way that’s getting harder and harder to detect. Which is exactly what you’d expect from an increasingly accurate statistical model.” The post, titled "The Eternal Sloptember," arrives five days after Andrej Karpathy, one of AI's most prominent researchers, joined Anthropic's pre-training team with the explicit view that AI agents have already transformed software development. The two men now represent opposite poles of a debate the industry hasn't settled—and both have actual credibility to stake a position. Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time. — Andrej Karpathy (@karpathy) May 19, 2026 Hotz didn't reach his conclusion from the sidelines. He spent six months using agents on real projects: parts of Tinygrad, his open-source deep learning framework, and a complete firmware reverse-engineering of a USB-PCIe chip. "The agent frontloads all the progress," he writes, then hands you what he describes as a slot machine lever—you pull it and hope the finishing work gets done. It never quite does. Not about ego Hotz anticipates the obvious pushback: a programmer who defines part of his identity through his craft would naturally resist tools that threaten to replace him. He takes the objection seriously and dismisses it on the merits. “I thought more about the self worth preservation thing. Google’s AFL found more bugs than LLMs and nobody felt that way about it. Chess and Go are more popular than ever,” Hotz wrote. And he’s right in the sense that Chess AI has dominated humans for decades and the game only grew more popular. So, his concern isn't about being replaced. It's about what happens to code quality when everyone is using these tools at once, especially when Big Tech and Wall Street are constantly pushing for the mass use of these tools. “I almost think this is some kind of psyop to sell agents,” Hotz argues. “Fear of loss is one of the only ways to make big companies move. Though I think in that fear they are making a big mistake.” His central argument is organizational. High performers have tight enough feedback loops to catch agent-generated problems before they ship. They read the code, spot the errors, and calibrate when to trust the tool. "The bottom performers won't have that self check," he writes—and they're the ones using agents to produce 10 times their previous output. At a large company, that math produces something specific: faster degradation of average code quality, masked by sheer volume. The outcome, he argues, will be "a golden era for buckets and buckets of slop, and a dark age for gems of quality." As a concrete example, he points to reports that Apple is pushing AI coding tools across its entire engineering organization, then asks simply: "Do you think macOS will get better or worse in the next 2 years?" Where the camps stand Hotz now places himself in what he calls the "LeCun/Marcus camp"—referring to Yann LeCun, Meta's chief AI scientist, and Gary Marcus, a longtime LLM skeptic. Both have argued that language models are fundamentally sophisticated pattern-matchers: They can imitate the distribution of existing code, but can't reason through genuinely new problems from first principles. Vibe coding—describing what you want in plain language and letting AI generate the implementation—has exploded over the past year, and the major labs have positioned agent-based coding as a flagship product. Microsoft transformed GitHub Copilot into a full agentic system in 2025, with CEO Satya Nadella describing it as a platform-level shift comparable to the move to cloud. The pushback to Hotz's position isn't abstract. Karpathy, who had been skeptical of agents earlier in 2025, reversed his position after new model releases and joined Anthropic's pre-training team on May 19—five days before Hotz published. He described the next few years at the frontier as "especially formative." Anthropic CEO Dario Amodei said in Davos that some Anthropic engineers have already stopped writing code themselves, letting models handle it while they review the output. Hotz, for his part, says he tried to do the same thing and found himself reaching for the manual fix every time.