Do AI models hallucinate more than humans? Anthropic CEO Dario Amodei says no—and he’s betting on it. Speaking at the company’s inaugural Code with Claude developer event in San Francisco, Amodei claimed that advanced AI models, including those developed by Anthropic, now hallucinate—i.e., make up information and present it as fact—less often than people do. This bold assertion not only reframes how we perceive AI accuracy but also highlights Anthropic’s confidence in its trajectory toward Artificial General Intelligence (AGI).
AI Hallucination: A Barrier—or a Benchmark?
The term AI hallucination has become a hot topic across the tech world, particularly as companies race toward building reliable AI systems for critical use cases like legal research, healthcare, and finance. While hallucinations are widely recognized as one of the core limitations of large language models (LLMs), Amodei downplayed their significance. “It really depends how you measure it,” he said, “but I suspect that AI models probably hallucinate less than humans, though in more surprising ways.”
This view stands in contrast to industry skepticism. Google DeepMind CEO Demis Hassabis recently emphasized the frequency of hallucinations as a serious issue, calling attention to persistent “holes” in AI logic. For instance, just weeks prior, a lawyer representing Anthropic had to publicly apologize after Claude—Anthropic’s AI assistant—generated inaccurate legal citations during a court filing.
The Path to AGI: No Hard Stops Ahead?
Despite high-profile missteps, Amodei remains optimistic. He told reporters there are no insurmountable barriers preventing AI from reaching AGI—systems that match or exceed human intelligence across a wide range of tasks. “Everyone’s always looking for these hard blocks on what [AI] can do,” he said. “They’re nowhere to be seen. There’s no such thing.”
His confidence is backed by measurable progress. Amodei referenced a widely circulated paper he authored in 2024, predicting that AGI could arrive as soon as 2026. At the developer event, he reaffirmed that prediction, noting that AI capabilities are “steadily rising,” using the metaphor: “The water is rising everywhere.”
Measuring Hallucinations: AI vs. Human Fallibility
Quantifying hallucination rates remains challenging. Most current benchmarks evaluate AI against other models, not against human performance. However, emerging data suggests that advanced systems like OpenAI’s GPT-4.5 and Anthropic’s Claude are improving significantly on standard evaluation metrics.
Several techniques are proving effective at reducing hallucinations—chief among them is augmenting AI models with tools like live web search and structured memory.
A Turning Point for Trust in AI?
As the AI industry continues to evolve, claims like Amodei’s signal a shift from fearing AI errors to managing and contextualizing them. Whether or not current AI models truly hallucinate less than humans is still up for debate, but one thing is clear—Anthropic is staking its future on that possibility. For developers, businesses, and investors navigating the booming AI economy, understanding the real-world implications of hallucinations is crucial for deploying AI in high-value sectors like legal tech, healthcare AI platforms, and AI-powered productivity tools.
This latest statement from Anthropic’s CEO may spark fresh discussion, but it also invites a closer look at how we define—and measure—truth in the age of intelligent machines.
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