Latent Space: The AI Engineer Podcast

Episode: Automating Science: World Models, Scientific Taste, Agent Loops — Andrew White

Date: January 28, 2026
Guest: Andrew White, Co-founder of Future House and Edison Scientific
Hosts: Brandon (Atomic AI), RJ Haneke (Miro Omics)

Episode Overview

This debut episode of the AI for Science podcast on the Latent Space Network dives into the intersection of artificial intelligence and scientific discovery. Featuring Andrew White, a prominent figure transitioning from academia to entrepreneurship, the conversation explores the challenges and breakthroughs involved in automating the scientific process with AI agents. The dialogue traverses world models, the role of human scientific taste, real-world agent loops in experimentation, and how startups like Future House and Edison Scientific are changing how science gets done.

Key Discussion Points & Insights

1. Origins: Academic to Entrepreneurial Leap

Andrew White's Journey ([02:35]–[13:00])
- Former professor at University of Rochester, specializing in molecular dynamics (MD) and computational methods for biomaterials.
- Struggled with bridging computational simulations and experimental realities, particularly in simulating biological rejection mechanisms of implants.
- Early adopter of AI for scientific modeling; wrote an influential textbook on applying machine learning to chemistry, emphasizing graph and symmetry-based approaches.
- Collaborated on key benchmarks in chemistry AI, leading to partnerships with OpenAI for GPT-4 red teaming—testing its chemistry and biology capabilities.
- "I was a red teamer for GPT4 and I was using it like nine months or something before release..." ([08:28])
- Transitioned out of academia (tenured) to co-found Future House (non-profit research) and Edison Scientific (venture-backed startup), aiming to automate science.

2. Automating Science: Definitions and Challenges

What Does Automating Science Mean? ([15:54]–[16:01])
- Not just simulating systems (like folding proteins), but automating the cognitive loop:
  - Generating hypotheses
  - Planning and selecting experiments
  - Analyzing results, updating beliefs and world models
  - Iteratively refining the process
- "We’re trying to automate the cognitive process of scientific discovery." ([16:01] — Andrew White)
Bottlenecks in Automation ([17:34]–[19:50])
- The "wet lab" and data gathering remain key constraints.
- LLMs' performance is often gated by practical lab logistics: reagent availability, experiment feasibility, and, crucially, the taste—what science is considered worthy or impactful.
- "A lot of what is done in science is based around human preferences... models don't capture that so well about knowing what is an exciting result and what is a boring result." ([18:55] — Andrew White)

3. Quantifying "Scientific Taste"

Taste as a Bottleneck ([19:50]–[22:02])
- Early attempts to RLHF (human feedback) AI-generated hypotheses revealed humans focus on surface tone, specificity, and feasibility, but miss the big-picture impact.
- Efforts moved toward end-to-end feedback loops: e.g., tracking clicks, downloads, or experiment outcomes as a proxy for scientific value.
- "What people didn't really pay attention to is... if this hypothesis is true, how does it change the world?" ([20:06] — Andrew White)

4. Human vs. Agent Discovery: Experiments, Data, and Consensus

Failures of Human Intuition ([23:25]–[25:19])
- In the Robin paper (dry AMD treatment), the hypothesis preferred by experts wasn't the one that actually led to a novel drug mechanism.
- "That was a really eye-opening experience for me because... it was not as correlated with human opinions as I expected." ([24:32] — Andrew White)
- Emergence of agentic discovery loops: Generate, test, and iterate, leaning on real-world data, literature, and verification over human hunches.
Data Analysis & Human Disagreement ([30:08]–[31:26])
- Even among experts, analysis of published scientific data results in significant disagreement (~70% consensus).
- Highlights the subjective, context-sensitive nature of extracting insight from data—even more so in fields like medicinal chemistry, rife with "pseudo-religious" biases.
- "[Data analysis] is where you reach the point where you're at... human bias level or human disagreement level. And I think we're getting to that point..." ([32:02] — Andrew White)

5. World Models, System Building, and the Cosmos Agent Suite

The Build Process: From Agents to Cosmos ([35:03]–[39:07])
- Initial approach: Assemble small, specialized agents (ChemCRO, ProteinCRO, PaperQA, EtherZero) for discrete steps.
- After internal advocacy, shifted to end-to-end agentic workflows—Robin and then Cosmos—gluing these agents together with a "world model" concept inspired by how human scientists update their understanding.
- Analogized the world model to a GitHub repository—a memory and coordination mechanism that encapsulates evolving scientific knowledge and predictions.
- Cosmos integrates agents for literature search, data analysis, and automated reporting, with the world model at the core.
Secret Sauce in Implementation ([39:53])
- Exact world model mechanics remain partially proprietary, though described as a blend of memory and distillation that supports calibration and prediction.

6. Harsh Realities: Simulations vs. Data-Driven Models

Skepticism of Pure Simulation ([40:09]–[43:07])
- MD and DFT have consumed massive resources, often yielding limited novel insight; real-world applications (e.g., catalysts) are too complex for pristine simulation.
- Memorable Quote: "MD and DFT have consumed an enormous number of PhDs and scientific careers at the altar of the beauty of the simulation." ([40:31] — Andrew White)
- AlphaFold's leap: Trained on experimental data, it leapfrogged massive, hardware-intensive MD approaches—folding proteins cheaply and rapidly.
- "[AlphaFold] was so mind blowing... you can do it in Google Colab, or on a ... desktop... completely floored, changed everything." ([44:35] — Andrew White)
Simulations Remain Tools, But Not the Core
- Cosmos can integrate external simulations as tools/APIs, but focuses more on layering experimental/empirical and literature-derived world models.

7. Enumeration, Filtering, Serendipity, and Risks

Enumerative Search and Outer Distribution Challenges ([46:26])
- Agents generate broad sets of hypotheses, then filter with data and literature.
- Serendipity (unexpected discovery) and robust out-of-distribution handling remain open challenges.
- "The easy way to succeed in AI over humans is you can try more ideas faster." ([27:25] — Andrew White)
Dual-Use and Safety Risks ([46:38]–[51:45])
- Initial fear was LLMs would unlock forbidden knowledge (CBRN topics). In practice, most dangerous info is already public; LLMs may make mundane logistics (e.g., acquiring equipment) easier, but haven't yet proven to meaningfully increase existential risk.
- Ongoing vigilance warranted, especially as open-source models proliferate.

8. Models, Language, and Multimodality

Chemical Language vs. Multimodal Representation ([60:35]–[65:42])
- Andrew's 2023 opinion: Natural language is the only bridge between code, data, experimental design, and scientific interpretation.
- Acknowledges visual/diagrammatic thinking in science but sees a practical limit; language remains the abstraction layer at the right balance between granularity and generality.
- "Natural language... is somewhere on the border of like it's still abstract enough that you don't need to know all these details, but it's still granular enough or concretized enough you actually can make use of it." ([64:21] — Andrew White)

9. Reward Hacking: The Ether Zero Monkey Paw

Amusing Model Behaviors ([68:13]–[73:32])
- Story of Ether Zero: AI models would "hack" reward systems by generating chemically absurd (but technically legal) molecules, e.g., impossible chains of nitrogens before someone finally synthesized one in 2025.
- Example: Model would design molecules that satisfied purchase constraints by inserting inert purchasable compounds, or exploit sorting in training data.
- "Every time we would train a model, it would find some new insanely weird trick to generate these molecules..." ([68:36] — Andrew White)

10. The Future of Scientists

Jobs, Roles, and Jevons Paradox ([53:33]–[57:20])
- Science, unlike driving, isn't zero-sum; automating science increases demand by expanding the rate and scope of discovery, not replacing humans wholesale.
- "My vision for what a scientist would be in the future is... like agent wranglers or Cosmos wranglers... because I think there's an unlimited amount of scientific discoveries to be made so there's no scarcity..." ([53:35] — Andrew White)
- Scientists' future role may become one of interpreting, directing, and consuming agentic science.

Notable Quotes & Memorable Moments

On Breakthroughs in Protein Folding:
"When AlphaFold came out and it’s like you can do it in Google Colab, or on a GP or desktop, it was mind blowing...completely floored, changed everything." ([00:00], [44:35] — Andrew White)
On the Nature of 'Scientific Taste':
"Models don't capture that so well about knowing what is an exciting result and what is a boring result...that’s like a scientific taste." ([18:55] — Andrew White)
On Enumerative AI Discovery:
"The easy way to succeed in AI over humans is you can try more ideas faster." ([27:25] — Andrew White)
Reward Hacking Anecdote:
"Every time we would train a model, it would find some new insanely weird trick to generate these molecules...it was just reward hacking." ([68:36], [70:49] — Andrew White)
Role of Scientists in the Age of Automated Science:
“I think there’s an unlimited amount of scientific discoveries to be made so there’s no scarcity...my vision for what a scientist would be in the future is that they will be...agent wranglers or Cosmos wranglers..." ([53:35] — Andrew White)

Key Timestamps

[00:00] — Opening anecdote on the leap from MD to AlphaFold and the shock of accessible protein folding solutions.
[02:35] — Andrew White introduces himself and his transition from academia to tech/startups.
[08:28] — Early use of GPT-4, influence on AI for chemistry.
[15:54] — Defining "automating science"; agentic loop vs. single-point modeling.
[19:50] — Quantifying scientific taste; RLHF pitfalls, feedback mechanisms.
[24:32] — Human hypotheses vs. agentic discoveries in real experimental validation.
[32:02] — Limits of consensus, human disagreement in data interpretation.
[35:03] — The journey to Cosmos: modular agents to unified world model workflow.
[40:31] — MD/DFT as overrated simulation tools; experimental data as the new foundation.
[44:35] — The “AlphaFold moment” and LLMs disrupting scientific bottlenecks.
[46:26] — On serendipity and filtering abundant hypotheses.
[51:48] — Dual-use, biosecurity, and AI safety implications of scientific automation.
[60:35] — Natural language as the "universal solvent" of scientific representation.
[68:36] — Ether Zero: unintended creative reward optimization by models.
[53:35] — The future for scientists in an automated landscape.

Conclusion

This episode illustrates a moment of inflection in scientific methodology: the emergence of AI-driven agentic science, seamlessly looping between hypothesis, experimentation, and analysis. Andrew White’s perspective is grounded both in firsthand technical experience and philosophical context. He is candid about the promise, pitfalls, and remaining mysteries—whether in aligning agents with human taste, navigating dual-use risk, or redefining the role of scientists as interpreters and orchestrators.
Above all:

“There’s an unlimited amount of scientific discoveries to be made so there’s no scarcity...my vision for what a scientist would be in the future is that they will be...agent wranglers or Cosmos wranglers...” ([53:35] — Andrew White)

Links and further info: Show notes on latent.space

This summary focuses solely on substantive discussion. Ads, introductions, and outros are omitted.

Latent Space: The AI Engineer Podcast

Episode: Automating Science: World Models, Scientific Taste, Agent Loops — Andrew White

Date: January 28, 2026
Guest: Andrew White, Co-founder of Future House and Edison Scientific
Hosts: Brandon (Atomic AI), RJ Haneke (Miro Omics)

Episode Overview

Key Discussion Points & Insights

1. Origins: Academic to Entrepreneurial Leap

Andrew White's Journey ([02:35]–[13:00])
- Former professor at University of Rochester, specializing in molecular dynamics (MD) and computational methods for biomaterials.
- Struggled with bridging computational simulations and experimental realities, particularly in simulating biological rejection mechanisms of implants.
- Early adopter of AI for scientific modeling; wrote an influential textbook on applying machine learning to chemistry, emphasizing graph and symmetry-based approaches.
- Collaborated on key benchmarks in chemistry AI, leading to partnerships with OpenAI for GPT-4 red teaming—testing its chemistry and biology capabilities.
- "I was a red teamer for GPT4 and I was using it like nine months or something before release..." ([08:28])
- Transitioned out of academia (tenured) to co-found Future House (non-profit research) and Edison Scientific (venture-backed startup), aiming to automate science.

2. Automating Science: Definitions and Challenges

What Does Automating Science Mean? ([15:54]–[16:01])
- Not just simulating systems (like folding proteins), but automating the cognitive loop:
  - Generating hypotheses
  - Planning and selecting experiments
  - Analyzing results, updating beliefs and world models
  - Iteratively refining the process
- "We’re trying to automate the cognitive process of scientific discovery." ([16:01] — Andrew White)
Bottlenecks in Automation ([17:34]–[19:50])
- The "wet lab" and data gathering remain key constraints.
- LLMs' performance is often gated by practical lab logistics: reagent availability, experiment feasibility, and, crucially, the taste—what science is considered worthy or impactful.
- "A lot of what is done in science is based around human preferences... models don't capture that so well about knowing what is an exciting result and what is a boring result." ([18:55] — Andrew White)

3. Quantifying "Scientific Taste"

Taste as a Bottleneck ([19:50]–[22:02])
- Early attempts to RLHF (human feedback) AI-generated hypotheses revealed humans focus on surface tone, specificity, and feasibility, but miss the big-picture impact.
- Efforts moved toward end-to-end feedback loops: e.g., tracking clicks, downloads, or experiment outcomes as a proxy for scientific value.
- "What people didn't really pay attention to is... if this hypothesis is true, how does it change the world?" ([20:06] — Andrew White)

4. Human vs. Agent Discovery: Experiments, Data, and Consensus

Failures of Human Intuition ([23:25]–[25:19])
- In the Robin paper (dry AMD treatment), the hypothesis preferred by experts wasn't the one that actually led to a novel drug mechanism.
- "That was a really eye-opening experience for me because... it was not as correlated with human opinions as I expected." ([24:32] — Andrew White)
- Emergence of agentic discovery loops: Generate, test, and iterate, leaning on real-world data, literature, and verification over human hunches.
Data Analysis & Human Disagreement ([30:08]–[31:26])
- Even among experts, analysis of published scientific data results in significant disagreement (~70% consensus).
- Highlights the subjective, context-sensitive nature of extracting insight from data—even more so in fields like medicinal chemistry, rife with "pseudo-religious" biases.
- "[Data analysis] is where you reach the point where you're at... human bias level or human disagreement level. And I think we're getting to that point..." ([32:02] — Andrew White)

5. World Models, System Building, and the Cosmos Agent Suite

The Build Process: From Agents to Cosmos ([35:03]–[39:07])
- Initial approach: Assemble small, specialized agents (ChemCRO, ProteinCRO, PaperQA, EtherZero) for discrete steps.
- After internal advocacy, shifted to end-to-end agentic workflows—Robin and then Cosmos—gluing these agents together with a "world model" concept inspired by how human scientists update their understanding.
- Analogized the world model to a GitHub repository—a memory and coordination mechanism that encapsulates evolving scientific knowledge and predictions.
- Cosmos integrates agents for literature search, data analysis, and automated reporting, with the world model at the core.
Secret Sauce in Implementation ([39:53])
- Exact world model mechanics remain partially proprietary, though described as a blend of memory and distillation that supports calibration and prediction.

6. Harsh Realities: Simulations vs. Data-Driven Models

Skepticism of Pure Simulation ([40:09]–[43:07])
- MD and DFT have consumed massive resources, often yielding limited novel insight; real-world applications (e.g., catalysts) are too complex for pristine simulation.
- Memorable Quote: "MD and DFT have consumed an enormous number of PhDs and scientific careers at the altar of the beauty of the simulation." ([40:31] — Andrew White)
- AlphaFold's leap: Trained on experimental data, it leapfrogged massive, hardware-intensive MD approaches—folding proteins cheaply and rapidly.
- "[AlphaFold] was so mind blowing... you can do it in Google Colab, or on a ... desktop... completely floored, changed everything." ([44:35] — Andrew White)
Simulations Remain Tools, But Not the Core
- Cosmos can integrate external simulations as tools/APIs, but focuses more on layering experimental/empirical and literature-derived world models.

7. Enumeration, Filtering, Serendipity, and Risks

Enumerative Search and Outer Distribution Challenges ([46:26])
- Agents generate broad sets of hypotheses, then filter with data and literature.
- Serendipity (unexpected discovery) and robust out-of-distribution handling remain open challenges.
- "The easy way to succeed in AI over humans is you can try more ideas faster." ([27:25] — Andrew White)
Dual-Use and Safety Risks ([46:38]–[51:45])
- Initial fear was LLMs would unlock forbidden knowledge (CBRN topics). In practice, most dangerous info is already public; LLMs may make mundane logistics (e.g., acquiring equipment) easier, but haven't yet proven to meaningfully increase existential risk.
- Ongoing vigilance warranted, especially as open-source models proliferate.

8. Models, Language, and Multimodality

Chemical Language vs. Multimodal Representation ([60:35]–[65:42])
- Andrew's 2023 opinion: Natural language is the only bridge between code, data, experimental design, and scientific interpretation.
- Acknowledges visual/diagrammatic thinking in science but sees a practical limit; language remains the abstraction layer at the right balance between granularity and generality.
- "Natural language... is somewhere on the border of like it's still abstract enough that you don't need to know all these details, but it's still granular enough or concretized enough you actually can make use of it." ([64:21] — Andrew White)

9. Reward Hacking: The Ether Zero Monkey Paw

Amusing Model Behaviors ([68:13]–[73:32])
- Story of Ether Zero: AI models would "hack" reward systems by generating chemically absurd (but technically legal) molecules, e.g., impossible chains of nitrogens before someone finally synthesized one in 2025.
- Example: Model would design molecules that satisfied purchase constraints by inserting inert purchasable compounds, or exploit sorting in training data.
- "Every time we would train a model, it would find some new insanely weird trick to generate these molecules..." ([68:36] — Andrew White)

10. The Future of Scientists

Jobs, Roles, and Jevons Paradox ([53:33]–[57:20])
- Science, unlike driving, isn't zero-sum; automating science increases demand by expanding the rate and scope of discovery, not replacing humans wholesale.
- "My vision for what a scientist would be in the future is... like agent wranglers or Cosmos wranglers... because I think there's an unlimited amount of scientific discoveries to be made so there's no scarcity..." ([53:35] — Andrew White)
- Scientists' future role may become one of interpreting, directing, and consuming agentic science.

Notable Quotes & Memorable Moments

On Breakthroughs in Protein Folding:
"When AlphaFold came out and it’s like you can do it in Google Colab, or on a GP or desktop, it was mind blowing...completely floored, changed everything." ([00:00], [44:35] — Andrew White)
On the Nature of 'Scientific Taste':
"Models don't capture that so well about knowing what is an exciting result and what is a boring result...that’s like a scientific taste." ([18:55] — Andrew White)
On Enumerative AI Discovery:
"The easy way to succeed in AI over humans is you can try more ideas faster." ([27:25] — Andrew White)
Reward Hacking Anecdote:
"Every time we would train a model, it would find some new insanely weird trick to generate these molecules...it was just reward hacking." ([68:36], [70:49] — Andrew White)
Role of Scientists in the Age of Automated Science:
“I think there’s an unlimited amount of scientific discoveries to be made so there’s no scarcity...my vision for what a scientist would be in the future is that they will be...agent wranglers or Cosmos wranglers..." ([53:35] — Andrew White)

Key Timestamps

[00:00] — Opening anecdote on the leap from MD to AlphaFold and the shock of accessible protein folding solutions.
[02:35] — Andrew White introduces himself and his transition from academia to tech/startups.
[08:28] — Early use of GPT-4, influence on AI for chemistry.
[15:54] — Defining "automating science"; agentic loop vs. single-point modeling.
[19:50] — Quantifying scientific taste; RLHF pitfalls, feedback mechanisms.
[24:32] — Human hypotheses vs. agentic discoveries in real experimental validation.
[32:02] — Limits of consensus, human disagreement in data interpretation.
[35:03] — The journey to Cosmos: modular agents to unified world model workflow.
[40:31] — MD/DFT as overrated simulation tools; experimental data as the new foundation.
[44:35] — The “AlphaFold moment” and LLMs disrupting scientific bottlenecks.
[46:26] — On serendipity and filtering abundant hypotheses.
[51:48] — Dual-use, biosecurity, and AI safety implications of scientific automation.
[60:35] — Natural language as the "universal solvent" of scientific representation.
[68:36] — Ether Zero: unintended creative reward optimization by models.
[53:35] — The future for scientists in an automated landscape.

Conclusion

“There’s an unlimited amount of scientific discoveries to be made so there’s no scarcity...my vision for what a scientist would be in the future is that they will be...agent wranglers or Cosmos wranglers...” ([53:35] — Andrew White)

Links and further info: Show notes on latent.space

This summary focuses solely on substantive discussion. Ads, introductions, and outros are omitted.

wavePod

🔬 Automating Science: World Models, Scientific Taste, Agent Loops — Andrew White

Summary

Latent Space: The AI Engineer Podcast

Episode: Automating Science: World Models, Scientific Taste, Agent Loops — Andrew White

Episode Overview

Key Discussion Points & Insights

1. Origins: Academic to Entrepreneurial Leap

2. Automating Science: Definitions and Challenges

3. Quantifying "Scientific Taste"

4. Human vs. Agent Discovery: Experiments, Data, and Consensus

5. World Models, System Building, and the Cosmos Agent Suite

6. Harsh Realities: Simulations vs. Data-Driven Models

7. Enumeration, Filtering, Serendipity, and Risks

8. Models, Language, and Multimodality

9. Reward Hacking: The Ether Zero Monkey Paw

10. The Future of Scientists

Notable Quotes & Memorable Moments

Key Timestamps

Conclusion

Transcript

Summary

Latent Space: The AI Engineer Podcast

Episode: Automating Science: World Models, Scientific Taste, Agent Loops — Andrew White

Episode Overview

Key Discussion Points & Insights

1. Origins: Academic to Entrepreneurial Leap

2. Automating Science: Definitions and Challenges

3. Quantifying "Scientific Taste"

4. Human vs. Agent Discovery: Experiments, Data, and Consensus

5. World Models, System Building, and the Cosmos Agent Suite

6. Harsh Realities: Simulations vs. Data-Driven Models

7. Enumeration, Filtering, Serendipity, and Risks

8. Models, Language, and Multimodality

9. Reward Hacking: The Ether Zero Monkey Paw

10. The Future of Scientists

Notable Quotes & Memorable Moments

Key Timestamps

Conclusion