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OnAir Post: AI Science onAir
News
This episode is a bit different in that it’s a solo episode! I spent this week visiting the Institute of Advanced Study at Princeton, and one meeting in particular shook me so much I felt compelled to make this special episode. To support this podcast and our research lab, head to https://coolworldslab.com/support
CHAPTERS
0:00 Teaser
0:36 My IAS Visit
1:30 IAS Context
2:27 Why This Setting Matters
3:38 Meeting Brief
4:47 Coding Supremacy
6:55 Analytic Supremacy
10:15 Surrendering Control
12:31 Accelerating Adoption
13:40 Ethic Be Damned
15:05 Skill Atrophy
17:01 Use It Else You’re Cooked
18:47 Learning to Use AI
20:41 Human Oversight
23:40 Cost Concerns
26:47 Patents & IP
29:38 Who Wins?
34:04 Who Loses?
40:26 Grad Admissions
45:44 Collaborations
48:40 Tenured Faculty
51:58 Me & AI
58:43 Public Backlash
1:04:41 Historical Significance
1:05:20 Democratization of Science
1:09:00 Science as a Human Endeavour
1:12:15 Final Thoughts
1:14:20 Credits
Understanding AI, – January 28, 2026
Protein-folding models are the success story in AI for science.
In the late 2010s, researchers from Google DeepMind used machine learning to predict the three-dimensional shape of proteins. AlphaFold 2, announced in 2020, was so good that its creators shared the 2024 Nobel Prize in chemistry with an outside academic.
Yet many academics have had mixed feelings about DeepMind’s advances. In 2018, Mohammed AlQuraishi, then a research fellow at Harvard, wrote a widely read blog post reporting on a “broad sense of existential angst” among protein-folding researchers.
The first version of AlphaFold had just won CASP13, a prominent protein-folding competition. AlQuraishi wrote that he and his fellow academics worried about “whether protein structure prediction as an academic field has a future, or whether like many parts of machine learning, the best research will from here on out get done in industrial labs, with mere breadcrumbs left for academic groups.”
Industrial labs are less likely to share their findings fully or investigate questions without immediate commercial applications. Without academic work, the next generation of insights might end up siloed in a handful of companies, which could slow down progress for the entire field.
Understanding AI, – January 28, 2026
Protein-folding models are the success story in AI for science.
In the late 2010s, researchers from Google DeepMind used machine learning to predict the three-dimensional shape of proteins. AlphaFold 2, announced in 2020, was so good that its creators shared the 2024 Nobel Prize in chemistry with an outside academic.
Yet many academics have had mixed feelings about DeepMind’s advances. In 2018, Mohammed AlQuraishi, then a research fellow at Harvard, wrote a widely read blog post reporting on a “broad sense of existential angst” among protein-folding researchers.
The first version of AlphaFold had just won CASP13, a prominent protein-folding competition. AlQuraishi wrote that he and his fellow academics worried about “whether protein structure prediction as an academic field has a future, or whether like many parts of machine learning, the best research will from here on out get done in industrial labs, with mere breadcrumbs left for academic groups.”
Industrial labs are less likely to share their findings fully or investigate questions without immediate commercial applications. Without academic work, the next generation of insights might end up siloed in a handful of companies, which could slow down progress for the entire field.
About
Overview
1. Accelerated Research and Discovery:
Faster Data Analysis:
AGI could process and analyze vast amounts of data far more quickly and efficiently than humans, leading to quicker identification of patterns and insights.
Hypothesis Generation and Experiment Design:
AGI could formulate new hypotheses and design experiments at an unprecedented scale, accelerating scientific breakthroughs across various fields.
Simulation and Modeling:
AGI could simulate complex systems and scenarios, allowing scientists to explore possibilities and test theories in ways that were previously impossible.
Drug Discovery and Development:
AGI could simulate and analyze thousands of chemical interactions in moments, predicting outcomes that would take human researchers much longer to ascertain, potentially leading to faster development of new drugs and treatments.
Materials Science:
AGI could help in the design and discovery of new materials with specific properties, leading to advancements in various technologies.
2. Interdisciplinary Integration:
Bridging Silos:
AGI could seamlessly integrate knowledge from diverse fields, leading to holistic advancements that human researchers might miss due to the limitations of siloed disciplines.Cross-Field Collaboration:
AGI could facilitate collaboration between scientists from different disciplines, fostering a more integrated approach to scientific research.
3. Technological Advancements:
New Technologies:
AGI could drive the creation of new technologies and the enhancement of existing ones, leading to advancements in various fields.Robotics and Automation:
AGI could enable robots to perform complex tasks with human-like precision and adaptability, leading to advancements in manufacturing, space exploration, and other areas.Healthcare:
AGI could revolutionize healthcare by improving patient outcomes and reducing costs through more accurate diagnoses, personalized treatment plans, and drug discovery.
4. Solving Complex Problems:
Climate Change:
AGI could help tackle some of the world’s most pressing challenges, such as climate change, by analyzing vast amounts of data and simulating different scenarios to identify the most effective strategies.
Resource Management:
AGI could help optimize resource management and distribution, leading to more sustainable practices.
Global Issues:
AGI could contribute to solving other complex global problems, such as poverty, disease, and conflict
5. Ethical Considerations:
Bias and Fairness:
It’s crucial to ensure that AGI systems are developed and used in a way that is fair and unbiased, avoiding the perpetuation of existing societal inequalities.
Transparency and Explainability:
Scientists and policymakers need to understand how AGI systems make decisions, ensuring that they are accountable and transparent.
Safety and Security:
It’s important to develop safety protocols and mechanisms to ensure that AGI systems are used responsibly and do not pose a risk to human safety or security.
Source: Google AI + Curators
