How social media encourages the worst of AI boosterism
The Dark Side of AI Boosterism: How Social Media Encourages the Worst of AI Hype
Demis Hassabis, CEO of Google DeepMind, recently summed up the current state of AI boosterism in three damning words: "This is embarrassing." His comment was in response to an overexcited post by Sébastien Bubeck, a research scientist at rival firm OpenAI, announcing that two mathematicians had used OpenAI's latest large language model, GPT-5, to find solutions to 10 unsolved problems in mathematics. "Science acceleration via AI has officially begun," Bubeck crowed.
But, as we'll explore in this article, Bubeck's enthusiasm was misplaced. The mathematicians didn't actually solve the problems; they simply found existing solutions that Bubeck wasn't aware of. This incident highlights the dangers of AI boosterism, where the hype surrounding AI advancements often overshadows the actual achievements and creates unrealistic expectations.
The Erdős Problems: A Perfect Example of AI Hype
To understand the context of Bubeck's post, let's take a look at the Erdős problems. Paul Erdős, one of the most prolific mathematicians of the 20th century, left behind hundreds of puzzles when he died. To help keep track of which ones have been solved, Thomas Bloom, a mathematician at the University of Manchester, UK, set up erdosproblems.com, which lists more than 1,100 problems and notes that around 430 of them come with solutions.
When Bubeck celebrated GPT-5's breakthrough, Bloom was quick to call him out. "This is a dramatic misrepresentation," he wrote on X. Bloom explained that a problem isn't necessarily unsolved if this website does not list a solution. That simply means Bloom wasn't aware of one. There are millions of mathematics papers out there, and nobody has read all of them. But GPT-5 probably has.
The Hype Overshadows the Actual Achievement
The incident highlights the dangers of AI boosterism, where the hype surrounding AI advancements often overshadows the actual achievements and creates unrealistic expectations. In this case, the actual achievement was not the solving of the problems, but rather the ability of GPT-5 to find existing solutions that Bubeck wasn't aware of.
The Importance of Literature Search
Mathematicians are very interested in using LLMs to trawl through vast numbers of existing results, François Charton, a research scientist who studies the application of LLMs to mathematics at the AI startup Axiom Math, told me when I talked to him about this Erdős gotcha. But literature search is dull compared with genuine discovery, especially to AI's fervent boosters on social media.
Other Examples of AI Hype
This incident is not an isolated example of AI hype. In August, a pair of mathematicians showed that no LLM at the time was able to solve a math puzzle known as Yu Tsumura's 554th Problem. Two months later, social media erupted with evidence that GPT-5 now could. "Lee Sedol moment is coming for many," one observer commented, referring to the Go master who lost to DeepMind's AI AlphaGo in 2016.
But Charton pointed out that solving Yu Tsumura's 554th Problem isn't a big deal to mathematicians. "It's a question you would give an undergrad," he said. "There is this tendency to overdo everything."
The Need for a Deeper Dive
More sober assessments of what LLMs may or may not be good at are coming in. At the same time that mathematicians were fighting on the internet about GPT-5, two new studies came out that looked in depth at the use of LLMs in medicine and law (two fields that model makers have claimed their tech excels at).
Researchers found that LLMs could make certain medical diagnoses, but they were flawed at recommending treatments. When it comes to law, researchers found that LLMs often give inconsistent and incorrect advice. "Evidence thus far spectacularly fails to meet the burden of proof," the authors concluded.
The Consequences of AI Hype
The consequences of AI hype are far-reaching. It creates unrealistic expectations, leads to overinvestment in AI research, and distracts from the actual challenges and limitations of AI. It also creates a culture of fear and anxiety, where people are afraid to question the capabilities of AI or to point out its limitations.
Conclusion
The incident highlights the dangers of AI boosterism and the need for a more nuanced and realistic understanding of AI's capabilities. We need to move beyond the hype and focus on the actual achievements and limitations of AI. We need to create a culture of critical thinking and skepticism, where people are encouraged to question the capabilities of AI and to point out its limitations.
Only then can we truly harness the potential of AI and create a future where AI is used to benefit humanity, not to create unrealistic expectations and to distract from the actual challenges and limitations of AI.
