The AI Model Built for What LLMs Can't Do

The AI Model Built for What LLMs Can't Do

From AI and I by Dan Shipper

April 15, 2026 · 54 min

About this episode

Eve Bodnia discusses her alternative to LLMs and the limitations of current AI architectures.

Most AI companies are racing to build bigger LLMs. Eve Bodnia thinks that's the wrong approach. Eve is the founder and CEO of Logical Intelligence, which is developing an alternative to the transformer-based models dominating the industry. Her argument: LLMs’ architecture makes them fundamentally unsuited for some mission-critical tasks. A system that generates output one token at a time, with no ability to inspect its own reasoning mid-process or guarantee its results, shouldn't be trusted to design chips, analyze financial data, or even fly a plane. Her alternative is the energy-based model (EBM), a form of AI rooted in the physics principle of energy minimization, not language prediction. Rather than guessing the next probable word, an EBM maps every possible outcome across a mathematical landscape, where likely states settle into valleys and improbable ones sit on peaks. Dan Shipper talked with Bodnia for AI & I about why she believes LLM progress is plateauing, what it means for AI to actually understand data rather than just pattern-match across it, and how her team is building toward formally verified code generated in plain English—no C++ required. If you found this…

People in this episode

Host: Dan Shipper

Guest: Eve Bodnia

Topics covered

  • AI models
  • LLMs
  • energy-based models
  • machine learning
  • data understanding

Keywords

  • AI
  • LLMs
  • energy-based model
  • machine learning
  • data analysis
  • chip design
  • verified code

Sponsors

Granola

Mentioned in this episode

Organizations: Logical Intelligence

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