--- schema: 1 type: video title: The AI Model Built for What LLMs Can't Do description: >- 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 episode interesting, please like, subscribe, comment, and share! Head to http://granola.ai/every and get 3 months free with the code EVERY To hear more from Dan Shipper: Subscribe to Every: https://every.to/subscribe Follow him on X: https://twitter.com/danshipper Timestamps: 00:00:51 - Introduction 00:02:09 - Why correctness and verifiability matter in AI 00:09:33 - What an energy-based model is 00:14:21 - How EBMs construct energy landscapes to understand data 00:19:00 - Why modeling intelligence through language alone is a flawed approach 00:26:54 - What it means for a model to "understand" data 00:37:21 - How EBMs solve the vibe coding problem and enable formally verified code 00:43:21 - Why LLM progress is plateauing 00:49:54 - Mission-critical industries haven't adopted LLMs, and how EBMs could fill that gap canonical: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc' markdown: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc.md' llms: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc/llms.txt' parent: 'https://liquidgold.cc/channel/everyinc.md' channel: everyinc updated: '2026-10-24T01:04:34.059Z' language: null children: [] videoId: Q-i8ZSUCtIc durationSeconds: 3218 publishedAt: '2026-04-15T15:00:53.000Z' searchChunks: - ordinal: 0 text: null startSeconds: 0 endSeconds: 11 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=0' heldReason: rights - ordinal: 1 text: null startSeconds: 11 endSeconds: 126 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=11' heldReason: rights - ordinal: 2 text: null startSeconds: 137 endSeconds: 225 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=137' heldReason: rights - ordinal: 3 text: null startSeconds: 236 endSeconds: 362 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=236' heldReason: rights - ordinal: 4 text: null startSeconds: 362 endSeconds: 373 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=362' heldReason: rights - ordinal: 5 text: null startSeconds: 507 endSeconds: 605 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=507' heldReason: rights - ordinal: 6 text: null startSeconds: 615 endSeconds: 748 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=615' heldReason: rights - ordinal: 7 text: null startSeconds: 758 endSeconds: 847 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=758' heldReason: rights - ordinal: 8 text: null startSeconds: 1046 endSeconds: 1144 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=1046' heldReason: rights - ordinal: 9 text: null startSeconds: 1156 endSeconds: 1245 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=1156' heldReason: rights - ordinal: 10 text: null startSeconds: 1256 endSeconds: 1382 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=1256' heldReason: rights - ordinal: 11 text: null startSeconds: 1392 endSeconds: 1502 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=1392' heldReason: rights - ordinal: 12 text: null startSeconds: 1514 endSeconds: 1601 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=1514' heldReason: rights - ordinal: 13 text: null startSeconds: 1612 endSeconds: 1703 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=1612' heldReason: rights - ordinal: 14 text: null startSeconds: 1713 endSeconds: 1800 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=1713' heldReason: rights - ordinal: 15 text: null startSeconds: 1810 endSeconds: 1909 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=1810' heldReason: rights - ordinal: 16 text: null startSeconds: 1919 endSeconds: 2011 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=1919' heldReason: rights - ordinal: 17 text: null startSeconds: 2022 endSeconds: 2135 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=2022' heldReason: rights - ordinal: 18 text: null startSeconds: 2146 endSeconds: 2283 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=2146' heldReason: rights - ordinal: 19 text: null startSeconds: 2283 endSeconds: 2294 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=2283' heldReason: rights - ordinal: 20 text: null startSeconds: 2294 endSeconds: 2428 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=2294' heldReason: rights - ordinal: 21 text: null startSeconds: 2440 endSeconds: 2520 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=2440' heldReason: rights - ordinal: 22 text: null startSeconds: 2644 endSeconds: 2655 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=2644' heldReason: rights - ordinal: 23 text: null startSeconds: 2746 endSeconds: 2844 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=2746' heldReason: rights - ordinal: 24 text: null startSeconds: 2956 endSeconds: 3078 watch: 'https://liquidgold.cc/watch/Q-i8ZSUCtIc?t=2956' heldReason: rights --- # The AI Model Built for What LLMs Can't Do 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 episode interesting, please like, subscribe, comment, and share! Head to http://granola.ai/every and get 3 months free with the code EVERY To hear more from Dan Shipper: Subscribe to Every: https://every.to/subscribe Follow him on X: https://twitter.com/danshipper Timestamps: 00:00:51 - Introduction 00:02:09 - Why correctness and verifiability matter in AI 00:09:33 - What an energy-based model is 00:14:21 - How EBMs construct energy landscapes to understand data 00:19:00 - Why modeling intelligence through language alone is a flawed approach 00:26:54 - What it means for a model to "understand" data 00:37:21 - How EBMs solve the vibe coding problem and enable formally verified code 00:43:21 - Why LLM progress is plateauing 00:49:54 - Mission-critical industries haven't adopted LLMs, and how EBMs could fill that gap Canonical: https://liquidgold.cc/watch/Q-i8ZSUCtIc · Channel: [Every](https://liquidgold.cc/channel/everyinc.md) > Search chunk text is held until public-source provenance is verified. ## Document chunks > Missing: no stored document chunks are available.