What can a model’s hidden states tell us about its choices—and what can a brain map tell us about an animal’s behavior? Cameron Berg of Reciprocal Research describes experiments in which DPO preference training made existing positive and negative representations matter to OLMo’s decisions, without establishing that the model has feelings. He argues that AI safety should investigate internal activity, not rely on model outputs alone.
Phil Maher, founder of Stockfilm.com, presents TimeFly, an exploratory demo combining a published fruit fly brain map with archival footage and assumptions about movement. The conversation also looks at why AI buyers seek untouched film, how physical reels support provenance, and whether synthetic data could change demand for archives. The hosts close with wider questions about AI agents, security, model choice, and software development.
Show Notes
Cameron Berg of Reciprocal Research discusses OLMo experiments in which DPO preference training made hidden positive and negative states causally relevant to choices, while emphasizing that this is not proof of consciousness. Phil Maher of Stockfilm.com demonstrates TimeFly, a fruit fly brain simulation built around a published neural map, and considers the assumptions behind its movement, the value of untouched archival film, and the potential effects of synthetic data.
Chapters
(0:00) Hidden states and simulated fly brains
(1:20) Tesla FSD turned a Chicago day trip into office time
(2:32) An AI-made talk reset what the room expected
(4:21) If Anthropic can swap out Git, what can’t be swapped?
(7:10) The next software stack will be agents building for agents
(8:18) Could reputation be how we police AI agents?
(9:35) Should we train naive models to study eval awareness?
(11:22) Agent reputation needs agent identity, and nobody has it
(12:24) Meet Cameron Berg of Reciprocal Research
(13:36) OLMo represents valence before preference training
(14:20) The DPO step that recruits valence for choices
(15:37) Identical text hides different model choices
(20:40) The model denies experience but clears negative states
(24:39) Why Nathan and Berg now say coin flip
(26:07) Does preference training create feeling or agency?
(27:41) Could capping distress leave capabilities untouched?
(29:54) Why safety cannot rely on model outputs alone
(32:07) Meet Phil Maher of Stockfilm
(33:46) Stockfilm keeps every reel behind the footage
(35:44) TimeFly starts with a published brain map
(37:12) The fly’s movement starts with added assumptions
(39:14) Could brain maps unlock animal-perspective VR?
(40:15) A guessed face can still feel personal
(41:45) AI buyers want footage before enhancement
(43:03) Synthetic data could shrink archival demand
(45:09) Why physical reels are low background steel
(48:47) Agent directories have not delivered many buyers
(50:29) How does neural activity become fly movement?
(53:17) Vibe coding does not validate fly behavior
(54:11) Identical prompts quietly favor the model you know
(55:39) Security now beats raw performance for agents
(56:57) What would actually make you switch models?
(59:16) Opus is fast enough that you’re the bottleneck
(59:45) AI is rewriting legacy software in Rust
(1:01:02) Markets won’t let Anthropic slow down
(1:02:43) Adoption help, not bigger models, gets growth
(1:04:30) AI should carry your security, like credit cards
(1:05:37) Dying call centers could feed a scam wave
Guests
Cameron Berg — Founder and Director, Reciprocal Research (𝕏 | LinkedIn)
