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Architect of AI’s Foundation Declares It’s Time to Move On
In a remarkable critique from within, Llion Jones, the co-author of the seminal 2017 paper “Attention Is All You Need” and co-founder of Sakana AI, has publicly stated he is “absolutely sick” of transformers, the very technology he helped create that now powers virtually every major AI system. According to reports from his talk at the TED AI conference in San Francisco, Jones argued that the artificial intelligence field has become dangerously narrow, potentially blinding researchers to the next major breakthrough.
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The Paradox of Plenty: More Resources, Less Creativity
Jones painted a concerning picture of the current AI research landscape, describing what analysts suggest is a paradox where unprecedented investment and talent have led to a reduction in creative exploration. “Despite the fact that there’s never been so much interest and resources and money and talent, this has somehow caused the narrowing of the research that we’re doing,” Jones told the audience, according to the report. He attributed this narrowing to the “immense amount of pressure” from investors demanding returns and researchers competing in an overcrowded field.
The report states that Jones compared the situation to the AI concept of “exploration versus exploitation,” where a system that exploits known paths too much fails to discover superior alternatives. “We are almost certainly in that situation right now in the AI industry,” he argued, warning that the community is overly concentrated on permuting a single architecture while a breakthrough might be just around the corner.
From Freedom to Pressure: The Story of the Transformer’s Birth
To underscore his concerns, Jones contrasted today’s high-pressure environment with the conditions that allowed the transformer to emerge. Sources indicate he described the original project as “very organic, bottom up,” born from casual conversations and whiteboard scribbles. Critically, he noted that the team had the freedom to explore without management pressure to publish papers or push specific metrics., according to emerging trends
This freedom, he suggested, is largely absent today, even for researchers commanding astronomical salaries. “Do you think that when they start their new position they feel empowered to try their wild ideas and more speculative ideas, or do they feel immense pressure to prove their worth and once again, go for the low hanging fruit?” he asked, according to the conference account.
Betting on Research Freedom Over Incremental Gains
At Sakana AI, Jones is reportedly attempting to recreate that earlier, more exploratory environment. His proposed solution is to deliberately turn up the “explore dial” and foster a culture of open sharing, even at a competitive cost. He offered a guiding mantra from engineer Brian Cheung: “You should only do the research that wouldn’t happen if you weren’t doing it.”
This philosophy was exemplified by Sakana’s “continuous thought machine” project. The report states that an employee who pitched the brain-inspired idea said he would have faced skepticism at previous employers. At Sakana, Jones gave him a week to explore, and the project eventually gained recognition at a major AI conference. Jones reportedly believes that such freedom is a powerful tool for attracting top talent, arguing that ambitious people naturally seek out environments where they can pursue speculative ideas.
A Collaborative Path Forward
Jones was careful to clarify that he is not dismissing the value of ongoing transformer research, noting that “there’s still plenty of very important work to be done on current technology.” However, he contends that given the vast resources in AI, the field can afford to invest significantly more in exploratory work.
His ultimate message was one of collaboration. “Genuinely, from my perspective, this is not a competition,” Jones concluded, according to the report. “We all have the same goal… So if we can all collectively turn up the explore dial and then openly share what we find, we can get to our goal much faster.” His warning arrives as the industry grapples with evidence of diminishing returns from simply scaling up existing models, suggesting that architectural innovation may be the key to future progress.
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References
- https://chatgpt.com/
- https://claude.ai/
- https://scholar.google.com/citations?user=_3_P5VwAAAAJ&hl=en
- https://arxiv.org/abs/1706.03762
- https://tedai-sanfrancisco.ted.com/
- https://hbr.org/2025/10/is-ai-a-boom-or-a-bubble
- https://sakana.ai/
- https://sakana.ai/ctm/
- https://neurips.cc/virtual/2025/poster/115192
- https://hai.stanford.edu/ai-index/2025-ai-index-report/economy
- http://en.wikipedia.org/wiki/Chief_technology_officer
- http://en.wikipedia.org/wiki/Transformer
- http://en.wikipedia.org/wiki/Artificial_intelligence
- http://en.wikipedia.org/wiki/Creativity
- http://en.wikipedia.org/wiki/Attention
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