How do you turn a flat piece of nanostructured material into a secure biometric sensor?

In this episode, we speak with Rob Devlin, co-founder and CEO of Metalenz, about how metasurfaces are transforming optics and enabling a new generation of biosecure sensing. Devlin explains how engineers can control light at the subwavelength scale to replace bulky lens stacks with a single flat surface, and why the real breakthrough isn’t just miniaturization, but the ability to mass-produce optics in semiconductor fabs.

We explore how Metalenz scaled metasurfaces from academic prototypes into millions of devices, and what it takes to design optics for manufacturing. Devlin breaks down the transition from building one perfect device in a cleanroom to producing millions that all meet tight specifications.

The conversation focuses on polarization imaging as a new information channel in consumer devices. Unlike traditional cameras that capture only intensity and color, polarization reveals material properties. This enables a new approach to facial recognition that is both more secure and more compact than existing systems.

Rob also shares the story behind Metalenz, from its origins in a Harvard lab to partnerships with major semiconductor manufacturers, and how the company navigated the challenges of finding product-market fit, scaling fabrication, and building a new sensing stack from scratch.

Whether you’re interested in optics, nanofabrication, consumer electronics, or the future of biometric security, this episode explores how controlling light at the nanoscale is opening entirely new possibilities for sensing and identity verification.

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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
01:22 - Making Metalenses Mass-Producible
10:58 - Metasurfaces for Polarimetry
17:10 - Face ID Security and Pitfalls
24:47 - Polar ID Principles
29:02 - Polar ID Demo
39:58 - Meeting Federico Capasso
50:43 - Developing Metasurface Fabrication Techniques
55:58 - Founding Metalenz
1:11:44 - Future of Metalenz and Metasurfaces

#photonics #faceid #biometrics #metasurface #biosecurity #optics

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.

Follow us for more technical interviews with the world’s greatest scientists:
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Substack: https://632nmpodcast.substack.com/

Follow our hosts!
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.

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://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