632nm

Are data centers in space physically possible, or just another overhyped idea?
In this episode, we speak with Philip Johnston, CEO of Starcloud, about the technical and economic case for putting AI infrastructure in orbit. The idea has gone viral in recent months, drawing strong criticism from science communicators like Scott Manley, Kyle Hill, and Hank Green, but rarely with detailed engagement on the underlying assumptions.
We examine whether space-based data centers can compete with terrestrial infrastructure, and what constraints actually matter: energy generation, cooling, launch costs, and manufacturing at scale. Johnston walks through the core economic model behind Starcloud, including assumptions about SpaceX’s Starship, the cost of solar power in orbit, and why removing terrestrial constraints like land use, permitting, and energy storage could fundamentally change how compute is deployed.
We discuss the physics of radiative cooling in space, the challenges of operating GPUs in a radiation environment, and how orbital systems compare to Earth-based data centers in terms of efficiency and cost structure. The conversation also explores broader questions around AI’s growing energy demands, the limits of terrestrial infrastructure, and whether shifting compute off-world is a niche solution or a long-term inevitability.
Whether you’re interested in space technology, AI infrastructure, energy systems, or the economics of large-scale computing, this episode offers a detailed look at one of the most debated ideas in modern engineering, and a rare opportunity to hear its strongest arguments laid out in full.
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Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Michael Dubrovsky: https://x.com/MikeDubrovsky
Xinghui Yin: https://x.com/XinghuiYin
Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.com
Timestamps:
00:00 - Intro
01:12 - What is Starcloud?
02:44 - Why do data centers need to go to space?
06:15 - Can’t we just build more solar panels on earth?
11:10 - Economic analysis of Starcloud
19:56 - How does Starcloud’s cooling work?
28:26 - Training an LLM in space
32:07 - Addressing critics on space Twitter
34:23 - Is Starcloud overfunded?
35:59 - Will demand for data centers keep going up?
38:11 - GPU lifespan and disposal in space
39:47 - Bus structures
41:43 - Starcloud’s origin and founders
49:29 - Fundraising, Competition, and Meeting Expectations
53:29 - Satellite size and collisions
56:29 - Manufacturing Bottlenecks
1:00:20 - Starcloud 1 tests
1:01:57 - Acceleration after YC
1:03:43 - Testing on Earth
1:05:06 - Motivations for Starcloud
1:06:45 - Data centers on the Moon
1:08:12 - Interacting with AI companies
1:08:18 - What’s next for Starcloud?
1:14:01 - Other uses for Starcloud satellites
1:17:56 - Lunar hotels and space elevators
1:24:28 - Complementary business ideas to Starcloud
1:29:51 - Philip’s competitive twin
1:32:18 - Philip and Mike’s thoughts on YC
1:34:45 - Advice for young entrepreneurs
#datacenter #aidatacenter #starlink #spacex #falcon9 #starcloud

How do you actually make quantum algorithms work on real hardware?
Build your own quantum circuits in Crumble: https://algassert.com/crumble
In this episode, we speak with Craig Gidney of Google Quantum AI, whose work focuses on the practical realities of building fault-tolerant quantum computers. Gidney explains how seemingly small implementation choices, like how you perform arithmetic, can dominate the cost of entire quantum algorithms.
We explore why factoring small numbers like 15 in Shor's algorithm can be misleadingly easy, and why scaling to larger numbers requires dramatically more resources due to operations like modular multiplication. He breaks down how quantum circuits are often dominated by classical reversible logic, and why optimizing these routines is critical for making quantum computing viable.
The conversation covers quantum error correction, including why T gates are especially expensive, how magic state factories works, and how different hardware architectures change what “cost” even means. Gidney also explains how resource estimates for breaking cryptography have dropped by orders of magnitude and what drove those improvements.
We also dive into the tools he built, including Stim, Quirk, and Crumble, which help researchers simulate noise, visualize circuits, and track how errors propagate through complex systems. Gidney shares his unconventional path into the field, the role of intuition and tooling in discovery, and how software engineering shapes modern quantum research.
Whether you’re interested in quantum computing, error correction, cryptography, or the engineering challenges behind scalable quantum systems, this episode offers a clear and grounded look at what it really takes to turn quantum algorithms into reality.
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Twitter: https://x.com/632nmPodcast
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LinkedIn: https://www.linkedin.com/company/632nm/about/
Substack: https://632nmpodcast.substack.com/
Follow our hosts!
Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Yudong Cao: https://www.linkedin.com/in/yudong-cao-25b6a929/
Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.com
Timestamps:
00:00 - Intro
01:22 - Shor’s Algorithm
04:02 - Why are Arithmetic Operations Important?
08:35 - Why are T-Gates Important for QEC?
13:47 - Motivations for Creating Crumble and STIM
18:40 - Can AI Code Quantum Simulators?
22:32 - Journey into Learning Quantum
26:50 - How to Enter the Field of Quantum Computing
31:16 - From Starcraft to Software Engineering
36:05 - Crumble Demo
53:18 - Quirk Demo
1:00:48 - Estimating Resources for Quantum Computation
1:08:58 - Optimizing Measurements for Computation
1:16:40 - How Many Qubits Do We Actually Need?
1:30:49 - Other Research Areas for Improving Fault Tolerance
1:41:23 - Elliptic Curve Discrete Logarithm Problem
1:46:55 - New Tools for Quantum Computing
1:50:23 - What Would Craig Do with Unlimited Funding?
1:52:28 - How Learning Has Changed for Craig with Experience
1:57:31 - Riding the Wave of Innovation vs Sticking to One Idea
1:59:53 - Advice for Young Scientists
#quantumcomputing #quantumphysics #computerscience #googleai #googlequantum

How do neurons convert electrical signals into chemical messages in under a millisecond?
In this episode, we speak with Thomas Südhof, Stanford neuroscientist and Nobel laureate whose discoveries revealed the molecular machinery that allows neurons to communicate at synapses. Südhof explains how an electrical impulse traveling down a neuron triggers the rapid release of neurotransmitters, transforming an electrical signal into a chemical one that can be received by the next cell.
We explore the remarkable precision of synaptic transmission, including how calcium ions trigger vesicle fusion, how specialized proteins organize the release machinery, and why this entire process unfolds on the timescale of a single millisecond. Südhof walks us through the molecular components that make this possible, including the proteins that dock neurotransmitter-filled vesicles and control their release.
The conversation also examines how these discoveries reshaped modern neuroscience by revealing the fundamental mechanisms underlying neuronal communication. Südhof discusses how synapses operate as highly specialized molecular machines and how disruptions in synaptic signaling are linked to neurological and psychiatric disorders.
Whether you’re interested in neuroscience, synapses, brain signaling, neurotransmitters, or the molecular basis of thought, this episode offers a clear explanation of how neurons translate electricity into chemistry, and how this microscopic process makes brain communication possible
Follow us for more technical interviews with the world’s greatest scientists:
Twitter: https://x.com/632nmPodcast
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LinkedIn: https://www.linkedin.com/company/632nm/about/
Substack: https://632nmpodcast.substack.com/
Follow our hosts!
Mikhail Shalaginov: https://x.com/MYShalaginov
Michael Dubrovsky: https://x.com/MikeDubrovsky
Xinghui Yin: https://x.com/XinghuiYin
Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: [https://www.632nm.com](https://www.632nm.com/)
Timestamps:
00:00 - Intro
01:23 - What is a Synapse?
07:01 - History of Synapse Discovery
12:54 - How Electron Microscopy Helped Neuroscience
15:11 - Early Electrophysiological Experiments
18:31 - Why are Neurotransmitters Needed At All?
21:25 - Electrical Connections Between Cells
22:48 - How Signal Diversity is Created in Synapses
29:04 - Why are Synapses Chemical?
31:06 - How Tom Began his Neuroscience Career
39:32 - Emerging Tools that Allowed for Researching Synapses
44:16 - Discerning Protein Function
49:36 - Discovering Mechanism through Data
52:15 - Isolating Membrane Proteins
55:09 - Voltage Gates
57:50 - How Synapses Change Over Time
1:02:14 - How are Synapses Formed?
1:10:22 - The Need for New Tools
1:11:53 - Implications for Drug Discovery
1:17:07 - Exploring the Mouse Hippocampus
1:22:35 - Tom’s Work on LDL Receptors
1:26:33 - Understanding Molecular Logic
#neuroscience #neuroplasticity #nobelprize #hubermanlab #neurobiology

How do engineers solve problems that seem to violate the laws of physics?
In this episode, we speak with Dan Gelbart, a prolific inventor and precision engineer, about what it really means to work at the limits of physical law. From lasers and optical systems to ultra-precision manufacturing and semiconductor tools, Gelbart has spent decades designing systems where nanometers, noise, and nonlinearities matter, and where small misunderstandings of physics can block real progress.
We discuss the story of the first working laser, built by Theodore Maiman, and why it succeeded only after questioning widely accepted assumptions. Gelbart explains how many “impossible” engineering problems aren’t forbidden by physics at all: they’re constrained by measurement errors, incomplete models, or failure to explore edge cases like pulsed operation, material effects, and boundary conditions.
We explore precision metrology, high-resolution imaging for satellite systems, the culture of engineering education, and the difference between a true physical limit and a design constraint. Gelbart reflects on why mastering fundamentals, mechanics, optics, electromagnetism, matters more than chasing trends, and how breakthroughs often come from carefully re-examining what others assume cannot be done.
Whether you’re interested in physics, engineering, semiconductor manufacturing, lasers, or the philosophy of technological innovation, this conversation offers a rigorous look at how engineers operate at the edge of what nature allows, and sometimes push beyond what others think is possible.
Follow us for more technical interviews with the world’s greatest scientists:
Twitter: https://x.com/632nmPodcast
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LinkedIn: https://www.linkedin.com/company/632nm/about/
Substack: https://632nmpodcast.substack.com/
Follow our hosts!
Mikhail Shalaginov: https://x.com/MYShalaginov
Michael Dubrovsky: https://x.com/MikeDubrovsky
Xinghui Yin: https://x.com/XinghuiYin
Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: [https://www.632nm.com](https://www.632nm.com/)
Timestamps:
00:00 - Intro
01:35 - The World’s First Laser
07:53 - Solving Impossible Problems
23:37 - Underestimated Problems
39:36 - Dan’s Backstory
43:33 - How to Teach Yourself Anything
47:03 - Shortcomings of Modern Education
53:19 - Developing the Optical Tape Recorder
1:01:39 - Machine Obsolescence
1:08:04 - Why are Scientists Often Bad Businessmen?
1:15:17 - Developing Medical Devices
1:24:52 - Untapped Potential of Materials Science
1:30:47 - Accidental Inventions
1:35:37 - Surviving Bureaucracy
1:42:27 - Humanoid Robots
1:44:11 - Managing an Engineering Team
1:50:06 - Developing the First Good Mobile Data Terminal
1:54:15 - Building an Environment for Solving Problems
2:02:18 - Why Aren’t We Inventing New Things?
#machining #cnc #precisionengineering #metrology #machineshop

How does experience rewire the brain—and why is vision the ideal system for understanding neuroplasticity?
In this episode, we speak with Mark Bear, MIT neuroscientist and a pioneer in the study of experience-dependent plasticity. Bear explains how the visual cortex became a model system for uncovering the synaptic mechanisms that allow the brain to change, adapt, and learn, especially during early development.
We explore how visual experience shapes neural circuits, why the brain undergoes critical periods of heightened plasticity, and what classic experiments in visual deprivation revealed about how connections are strengthened or lost. Bear walks us through the discovery of binocular vision in the cortex, the role of inhibition in closing critical periods, and how these ideas reshaped our understanding of learning and memory.
The conversation also covers modern views of cortical plasticity, including perceptual learning, visual recognition memory, and how the brain distinguishes familiar from novel stimuli. Bear discusses how insights from vision extend to broader questions about brain development, neurological disorders such as amblyopia, and whether adult plasticity can be reopened.
Whether you’re interested in neuroscience, brain development, neuroplasticity, learning and memory, or the biology of vision, this episode offers a clear and authoritative look at how experience shapes the brain at the level of neural circuits and synapses.
Follow us for more technical interviews with the world’s greatest scientists:
Twitter: https://x.com/632nmPodcast
Instagram: https://www.instagram.com/632nmpodcast?utm_source=ig_web_button_share_sheet&igsh=ZDNlZDc0MzIxNw==
LinkedIn: https://www.linkedin.com/company/632nm/about/
Substack: https://632nmpodcast.substack.com/
Follow our hosts!
Mikhail Shalaginov: https://x.com/MYShalaginov
Michael Dubrovsky: https://x.com/MikeDubrovsky
Xinghui Yin: https://x.com/XinghuiYin
Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.com
Timestamps:
00:00 - Intro
00:54 - Neuroplasticity in the Visual Cortex
05:45 - Critical Periods for Neuroplasticity
16:50 - Brain Development in Blind People
19:25 - Hallucinations and Sensory Deprivation
25:36 - How Mark’s Vision Disorder Led Him to a Career in Neuroscience
31:35 - Intro to the Visual System
35:52 - Visual System Processing
40:52 - Pop Science Neuroplasticity
45:00 - Memory Enhancing Pharmaceuticals
50:18 - Other Ways of Modifying the Visual Cortex
1:14:50 - Declarative vs Procedural Memory
1:22:36 - Neural Networks and Memory Degradation
1:25:16 - Neuron Transplants and Neurogenesis
1:28:58 - Brain-Machine Interfaces
1:33:46 - Most Prominent Issues in the Field
1:40:47 - Fragile X Syndrome
1:51:10 - Advice for Young Scientists
#neuroplasticity #neuroscience #hubermanlab #braindevelopment #brainplasticity