TWO TEAMS SOLVE QUANTUM CRYPTO PROBLEM THREE HOURS APART

The near-simultaneous discoveries raise fundamental questions about what constitutes independent research in an era of advanced AI systems. Both teams leveraged GPT-5.6 Sol Ultra to tackle a longstanding open problem in quantum cryptography. According to researchers involved, the convergence wasn't coincidental—when facing unsolved problems, checking whether the latest AI model can solve them has become standard practice. "If someone mentions an open problem, the first thing is to see if GPT solves it," one researcher noted. The tight timeline underscores how AI systems have become critical tools in scientific discovery. Rather than representing dueling efforts by separate groups, the three-hour gap reflects how quickly researchers can now move from problem identification to solution verification using the same computational resources. This development highlights a shift in research methodology. Where scientific breakthroughs once hinged on individual insight or team-specific approaches, researchers now operate from a shared technological foundation. Both teams accessed identical models, training data, and algorithmic capabilities. The incident prompts broader questions about the definition of independent discovery. Historically, simultaneous discoveries by separate researchers were rare enough to merit historical notation—think Newton and Leibniz with calculus. When researchers follow nearly identical methodological paths using identical tools, the notion of independence becomes harder to defend. It also raises practical considerations for academic institutions and funding bodies. Should simultaneous AI-assisted solutions be treated differently than traditional discoveries? How should credit and priority be assigned when the bottleneck shifts from insight to computational speed? The quantum cryptography problem itself remains significant regardless of these meta-questions. The solution demonstrates GPT-5.6 Sol Ultra's capability in highly specialized mathematical domains, suggesting AI systems are now viable approaches to previously intractable problems. As AI systems become more sophisticated, researchers expect more overlapping discoveries. The field may need to recalibrate how it measures original contribution when the methodology becomes standardized across teams.