How a Tweeted Breakthrough Claim Sparked Backlash and Revealed AI’s Real Strengths in Research
- OpenAI researchers hyped GPT-5 as solving long-unsolved math problems, only to retract amid swift criticism from experts like DeepMind’s Demis Hassabis and mathematician Thomas Bloom.
- The incident highlights the dangers of sloppy communication in a high-stakes AI field flooded with hype, raising questions about OpenAI’s internal pressures and verification processes.
- Beneath the embarrassment, GPT-5 shines as a powerful tool for literature reviews, accelerating math research by surfacing overlooked papers—proving AI’s value as an assistant rather than a solo genius.
In the fast-paced world of artificial intelligence, where breakthroughs are announced with the fanfare of fireworks and billions in funding hang in the balance, a single tweet can ignite a firestorm. That’s exactly what happened recently when OpenAI, the powerhouse behind ChatGPT, teased a monumental achievement in mathematics. Leading researchers claimed that their latest model, GPT-5, had cracked solutions to 10 previously unsolved problems posed by the legendary mathematician Paul Erdős—problems that had stumped experts for decades. The excitement was palpable, painting a vision of AI not just mimicking human thought, but pioneering new discoveries. Yet, in a twist worthy of a tech thriller, the claim unraveled almost as quickly as it spread, exposing the perils of overzealous announcements in an industry prone to exaggeration.
The saga began with a now-deleted post from OpenAI manager Kevin Weil on X (formerly Twitter). In a burst of enthusiasm, Weil declared that GPT-5 had “found solutions to 10 (!) previously unsolved Erdős problems” and was making headway on eleven more. He emphasized their longstanding mystery, framing the development as a game-changer for number theory and generative AI’s potential to drive novel research. Other OpenAI team members quickly echoed the sentiment, amplifying the buzz across social media. To the uninitiated, this sounded like science fiction becoming reality: an AI independently generating mathematical proofs for some of the field’s toughest enigmas, potentially unlocking doors to major advances in understanding prime numbers, combinatorics, and beyond.
But the math community wasn’t buying it. Mathematician Thomas Bloom, who maintains the comprehensive site erdosproblems.com—a go-to resource cataloging over 1,000 of Erdős’s open questions—responded almost immediately with a sharp rebuke. Bloom labeled the statements a “dramatic misinterpretation,” clarifying that the “open” status on his site simply meant he personally hadn’t resolved or fully documented the problem—not that it was unsolved in the broader mathematical world. In reality, GPT-5 hadn’t conjured original proofs from thin air. Instead, it had surfaced existing research papers that Bloom had overlooked amid the vast, scattered literature. What OpenAI touted as a breakthrough was, at best, an efficient literature search; at worst, a case of the AI “discovering” solutions already known to specialists.
The backlash escalated swiftly, drawing fire from AI heavyweights. DeepMind CEO Demis Hassabis, whose organization has long competed with OpenAI in the race for AGI, called the episode “embarrassing,” pointing to the sloppy communication that fueled unnecessary hype. Meta AI chief Yann LeCun piled on with a witty jab, accusing OpenAI of being “hoisted by their own GPTards”—a play on “retards” that underscored how the company had fallen victim to its own buzzword-driven narrative. Even OpenAI researcher Sébastien Bubeck, who was involved in the GPT-5 evaluations, later admitted the ambiguity in phrasing like “found solutions,” despite knowing the model’s actual contributions were far more modest. The original tweets vanished from X, and the team issued retractions, acknowledging the error. Yet, the damage was done, leaving observers to wonder: in a field where perception often trumps precision, why would seasoned researchers broadcast such bold claims without double-checking the facts?
This isn’t just a one-off gaffe; it feeds into a growing narrative of OpenAI operating under intense pressure. As the frontrunner in generative AI, the company faces sky-high expectations from investors, regulators, and the public. With billions at stake—think Microsoft’s multi-billion-dollar backing and the global AI arms race—every announcement carries weight. The incident raises uncomfortable questions about internal rigor: How does a team of elite minds, including those who’ve pushed boundaries in language models, let hype override verification? In an era where AI ethics and transparency are under scrutiny, such missteps erode trust and amplify skepticism. Critics argue it reflects a broader cultural issue in Silicon Valley, where speed-to-announce often outpaces substance, especially when competing against rivals like DeepMind and Meta.
Yet, amid the embarrassment, there’s a silver lining that’s getting overshadowed: GPT-5’s genuine utility as a research assistant. Far from solving the unsolvable, the model excelled at a more practical task—scouring academic literature to connect dots in fragmented fields like number theory. Erdős problems, with their inconsistent terminology and sprawling references across decades of journals, are a perfect test case for this. GPT-5 didn’t invent math; it acted as a tireless librarian, pulling up obscure papers that even experts like Bloom had missed. This capability is transformative for researchers drowning in information overload, where hours spent hunting citations can delay real progress.
Renowned mathematician Terence Tao, a Fields Medal winner often called the “Mozart of Math,” offers a grounded perspective on AI’s role here. Tao has long advocated for tools that “industrialize” mathematics, speeding up rote tasks to let human ingenuity focus on creativity. He views generative AI’s most immediate impact not in cracking the hardest open problems—though there have been isolated examples of progress on benchmarks like IMO competitions—but in mundane yet essential work like literature reviews. As Tao notes, AI can classify results, flag inconsistencies, and suggest connections, but human oversight remains irreplaceable for validation and integration. GPT-5’s performance aligns with this: it’s a time-saving ally, not a replacement for the deep intuition that defines mathematical discovery.
This episode underscores AI’s evolving place in science. While the hype around autonomous breakthroughs persists, the real revolution lies in augmentation—empowering researchers to tackle bigger challenges faster. OpenAI’s misstep serves as a cautionary tale: in pursuing the next big thing, don’t neglect the fundamentals of clear communication and factual grounding. As the field matures, incidents like this could push for better standards, ensuring that when the next true advancement comes, it’s celebrated on merit, not mirage. For now, GPT-5 reminds us that even in failure, AI is quietly reshaping how we explore the unknown—one overlooked paper at a time.