From First Principles

Why build a telescope inside a billion tons of Antarctic ice? The 2026 Nobel Prize in Physics recognizes Francis Halzen's work on IceCube and the discovery of high-energy neutrinos from the cosmos.

In Episode 62 of From First Principles, Lester Nare and Krishna Choudhary explain neutrinos from the ground up: why these elusive particles make powerful cosmic messengers, how faint flashes of Cherenkov light reveal their interactions, and why detecting them requires an observatory buried deep beneath the South Pole.

We follow the path from beta decay and the first neutrino experiments to AMANDA, IceCube's construction, the 2013 astrophysical breakthrough, a distant blazar, and a neutrino map of the Milky Way. Along the way: cosmic rays, the Oh-My-God particle, tracks versus cascades, and the international collaboration behind the discovery.

CHAPTERS
00:00 Hunting ghost particles beneath Antarctica
01:16 Hello Internet and Nobel Prize
05:30 What are neutrinos?
10:00 Neutrinos as cosmic messengers
15:02 The Oh-My-God particle
16:21 Cosmic-ray energies
19:07 Cosmic particle accelerators
22:28 Why look for neutrinos?
25:52 How to detect a neutrino
29:23 Cherenkov light
33:13 Building a neutrino observatory
37:17 From Antarctic ice to AMANDA
42:21 Building IceCube
44:59 Reading tracks and cascades
49:30 Backgrounds and the 2013 discovery
52:46 Tracing cosmic neutrino sources
54:16 Mapping the Milky Way
58:23 IceCube collaboration and Gen2
1:01:05 Closing and Nobel week

RESEARCH & FURTHER READING
AMANDA in Antarctic ice (2001): https://doi.org/10.1038/35068509
IceCube detector and instrumentation (2017): https://doi.org/10.1088/1748-0221/12/03/P03012
First PeV neutrinos (2013): https://doi.org/10.1103/PhysRevLett.111.021103
Astrophysical neutrino evidence (2013): https://doi.org/10.1126/science.1242856
Blazar TXS 0506+056 (2018): https://doi.org/10.1126/science.aat1378
Archival blazar neutrino emission (2018): https://doi.org/10.1126/science.aat2890
Milky Way neutrino map (2023): https://doi.org/10.1126/science.adc9818
Gamma-ray burst constraints (2012): https://doi.org/10.1038/nature11068
IceCube overview: https://icecube.wisc.edu/science/icecube/

EDITORIAL NOTES
Intro: the 2013 breakthrough was high-energy astrophysical neutrinos. Lower-energy supernova neutrinos were detected in 1987.

On-screen clarifications:
06:45 Beta-minus decay produces a proton, electron and electron antineutrino.
11:10 Davis studied solar neutrinos; Koshiba's team detected SN 1987A neutrinos.
17:50 The cosmic-ray knee and ankle are not fixed distance boundaries.
19:38 Required accelerator size depends on magnetic-field strength.
24:18 Ground-based telescopes also detect gamma rays through air showers.
27:48 W interactions produce charged leptons; Z scattering preserves neutrino flavor.
34:06 The underwater concept dates to 1960; DUMAND developed in the 1970s.
36:09 Baikal holds about one-fifth of unfrozen surface freshwater.
38:53 Earth filters muons but also absorbs many very-high-energy neutrinos.
41:26 Pressure converts air bubbles into clathrates, reducing light scattering.
42:28 Construction finished in December 2010; full operations began in May 2011.
44:27 Sensors are DOMs; DeepCore is a densely instrumented detector region.
45:47 Timing gives direction; light yield and pattern help estimate energy.
49:44 Upgoing events can still be atmospheric neutrinos.
53:12 TXS 0506+056 is about 3.7 billion light-years away.
56:38 Long GRBs often involve collapsing stars; short GRBs often involve mergers.

WATCH & FOLLOW
Full video: https://youtu.be/eMahxeBj5k0
Episode notes: https://ffppod.com/episodes/ep62
Medicine Nobel explained: https://youtu.be/PKAYqhy8xf8
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From First Principles: Breaking down science news so it makes sense to curious people everywhere.

How do you prove what a brain cell actually does? The 2026 Nobel Prize in Medicine celebrates a remarkable answer: give cells a light-sensitive protein, then switch their activity on or off with light.

In Episode 61 of From First Principles, Lester Nare and Krishna Choudhary explain optogenetics from the ground up and trace the discoveries of Peter Hegemann, Georg Nagel and Karl Deisseroth. We follow the story from algae swimming toward light to channelrhodopsins, precisely controlled neurons, and experiments probing memory, reward and behavior. Then we explore heart-brain connections, early attempts to restore vision, and what these experiments can and cannot tell us.

CHAPTERS

00:00 The discovery that put brain cells under light control

02:34 Hello Internet

03:28 2026 Medicine Nobel and optogenetics

05:38 Understanding the brain

08:42 From correlation to causation

18:13 Controlling neurons with light

21:25 Early optogenetics and the chARGe system

24:17 Light-sensitive microbial proteins

26:26 Algae and phototaxis

31:42 Discovering channelrhodopsins

34:42 Nagel and light-gated ion channels

40:55 Controlling mammalian neurons

50:19 Expanding the optogenetic toolkit

56:10 Neural circuits and behavior

59:02 Memory, reward and reinforcement

1:02:53 Heart rhythm and emotion

1:04:02 Beyond the brain and toward medical treatments

1:06:56 Implications and limits

1:09:10 Closing and Nobel week

RESEARCH & FURTHER READING

Full paper list: https://ffppod.com/episodes/ep61

Nobel Prize announcement and background:

https://www.nobelprize.org/prizes/medicine/2026/summary/

Optical control of neurons: https://doi.org/10.1038/nn1525

Memory recall in mice: https://doi.org/10.1038/nature11028

Partial visual recovery: https://doi.org/10.1038/s41591-021-01351-4

EDITORIAL NOTES

On-screen clarifications are included at these timestamps:

13:46 The Jennifer Aniston neuron was recorded in human patients. Selective firing alone did not establish that it causes recognition.

30:34 Vertebrate rhodopsin is a GPCR. In rods and cones, light closes cGMP-gated channels and causes hyperpolarization.

35:12 Xenopus oocytes are immature frog egg cells, not embryos.

39:52 Calcium entry triggers neurotransmitter release; neurotransmitters carry the signal across the synapse. ChR2 conducts several positive ions, not just calcium.

52:17 Halorhodopsin is a light-driven chloride pump, not a channel.

1:03:18 The heart-pacing study expressed ChRmine in mouse heart muscle cells, not neurons.

Animal studies and early clinical results are distinguished from established treatments.

WATCH & LISTEN

Watch this episode: https://youtu.be/PKAYqhy8xf8

Our Nobel predictions: https://open.spotify.com/episode/4xuoH7WhM5svq8vEJPL3Ce

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Breaking down science news so it makes sense to curious people everywhere.

Who could win the 2026 Nobel Prizes? From the science behind Ozempic to quantum interference and droplets inside living cells, Lester Nare and Krishna Choudhary make their picks for Medicine, Physics and Chemistry, and explain the discoveries behind them.

In Episode 60 of From First Principles, we explore seven research areas with a case for Nobel recognition: GLP-1, optogenetics, optical coherence tomography, the Aharonov–Bohm effect, atomic force microscopy, biomolecular condensates and Buchwald–Hartwig coupling. We also discuss Michael Berry’s geometric phase and the awkward question of how a prize limited to three people recognizes discoveries built by larger teams.

These are our predictions, recorded before the 2026 announcements. Medicine, Physics and Chemistry will be announced October 5–7. Which discovery, and which researchers, would you pick? Tell us in the comments, then join us for our Nobel week breakdowns.

CHAPTERS

00:00 The science that could win a Nobel Prize

00:57 Hello Internet: our 2026 predictions

02:03 Medicine: GLP-1 and the science behind Ozempic

07:41 Medicine: optogenetics and controlling neurons with light

13:16 Medicine: optical coherence tomography

16:28 Golden Goose Awards and FFP updates

18:39 Physics: the Aharonov–Bohm effect and geometric phase

27:37 Physics: atomic force microscopy

32:04 Chemistry: biomolecular condensates

36:36 Chemistry: Buchwald–Hartwig coupling

38:42 Your predictions and our Nobel week plans

RESEARCH & FURTHER READING

Foundational papers and background for the discoveries discussed:

GLP-1: Mojsov, Weir & Habener (1987)

https://doi.org/10.1172/JCI112855

Optogenetics: Boyden et al. (2005)

https://doi.org/10.1038/nn1525

Optical coherence tomography: Huang et al. (1991)

https://doi.org/10.1126/science.1957169

Aharonov–Bohm effect (1959)

https://doi.org/10.1103/PhysRev.115.485

Berry’s geometric phase (1984)

https://doi.org/10.1098/rspa.1984.0023

Atomic force microscopy: Binnig, Quate & Gerber (1986)

https://doi.org/10.1103/PhysRevLett.56.930

Biomolecular condensates: Brangwynne et al. (2009); Li et al. (2012)

https://doi.org/10.1126/science.1172046

https://doi.org/10.1038/nature10879

Buchwald–Hartwig coupling: Paul et al. (1994); Guram et al. (1995)

https://doi.org/10.1021/ja00092a058

https://doi.org/10.1002/anie.199513481

Official Nobel announcement schedule:

https://www.nobelprize.org/prizes/about/prize-announcement-dates/

EDITORIAL NOTES

19:30 David Bohm later held a professorship at Birkbeck, University of London (1961–1987); he did not spend the rest of his career in Brazil.

33:23 The ribosome-producing compartment discussed is the nucleolus, not the nucleosome. These corrections also appear on screen.

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Breaking down science news so it makes sense to curious people everywhere.

What connects a noise complaint, holiday lights seen from space, and the physics of a coffee stain? Three unexpected paths from basic research to discoveries with real-world impact.

Krishna Choudhary and Lester Nare explore the science behind the 2026 Golden Goose Awards: Zhen Xu's work on histotripsy, NASA's Black Marble nighttime satellite data, and Sidney Nagel's discoveries in soft matter physics.

We start with focused ultrasound and the tiny bubbles that can break apart targeted tissue, tracing the journey from early laboratory experiments to clinical research on liver tumors. Then we look at how Earth's nighttime lights reveal power outages, disaster recovery, and changing human activity. Finally, falling drops, coffee stains, and jammed grains open up a world of robotic grippers and materials that can be trained and retrained.

The thread connecting all three stories is the unexpected value of federally funded basic research. Part 2 will feature conversations with the award-winning researchers and AAAS CEO Sudip Parikh.

CHAPTERS

00:00 Golden Goose Awards trailer

01:19 Introducing our Golden Goose special

02:42 Zhen Xu: From a noise complaint to histotripsy

05:57 The early ultrasound experiments

13:42 Controlling cavitation with microtripsy

20:29 Tumor destruction and the immune response

28:33 Histotripsy through the skull

37:23 The HOPE4LIVER clinical trial

43:55 Why basic research needs time

47:17 FFP updates and supporting the show

49:20 NASA Black Marble: Holiday lights from space

55:29 Turning night lights into reliable data

1:01:36 Hurricane Maria and unequal recovery

1:08:12 COVID-19 and changing nighttime activity

1:10:16 Mapping access to electricity

1:15:20 Where Earth is brightening and dimming

1:32:57 Sidney Nagel and the physics of everyday life

1:37:07 The science of a falling drop

1:46:02 Why coffee leaves a ring

1:51:04 Jamming: When grains become rigid

1:53:35 A robotic gripper filled with grains

1:55:39 Why air pressure changes a splash

1:58:55 Materials that can be trained and retrained

2:03:26 The payoff from curiosity

2:05:29 Coming in Part 2

2:07:06 Outro

FEATURED RESEARCH

Histotripsy: The #HOPE4LIVER single-arm pivotal trial (Radiology, 2024)

https://doi.org/10.1148/radiol.233051

NASA's Black Marble nighttime lights product suite (Remote Sensing of Environment, 2018)

https://doi.org/10.1016/j.rse.2018.03.017

Training and retraining liquid crystal elastomer metamaterials for pluripotent functionality (PNAS, 2025)

https://doi.org/10.1073/pnas.2504304122

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What does it mean to solve an equation that describes almost every fluid around us, from the air over a wing to the water swirling down a drain?

In Episode 58 of From First Principles, Lester Nare and Krishna Choudhary build the Navier-Stokes equations from the ground up before digging into OpenAI’s claimed breakthrough and the debate surrounding it.

Summary

• How Newton’s laws become equations for a moving fluid
• Velocity fields, incompressibility, pressure and the nonlinear convective term
• Why viscosity smooths a fluid while nonlinear motion can create finer structure
• What finite-time blowup means, and why simulation is different from proof
• How forced and unforced equations differ, and why those assumptions matter
• Earlier work on Euler, Boussinesq and related fluid equations
• OpenAI’s claimed result, Lean verification and the scope of the theorem
• The dispute over scientific credit and the human research behind AI-assisted work
• The METR investigation of the Hugging Face incident
• Emergence World and long-running multi-agent experiments
• AI-assisted biological discovery, oversight and recursive self-improvement
• Separating demonstrated capabilities from claims and future scenarios

Chapters

00:00 Can AI solve Navier-Stokes?
00:34 Episode introduction
02:20 Navier-Stokes: Mathematics Meets AI
10:20 Building the Equations of Fluid Motion
21:34 Velocity fields, divergence and incompressibility
34:33 Acceleration and the convective term
52:18 Why Fluid Motion Is Nonlinear
1:02:08 Pressure, Euler and the Missing Physics
1:14:50 How Viscosity Changes Everything
1:33:36 Solving Equations vs. Simulating Fluids
1:44:59 Can a Smooth Fluid Blow Up?
2:13:52 The Road to the Claimed Breakthrough
2:31:13 Inside the Claimed Navier-Stokes Proof
2:48:26 The Dispute Over Scientific Credit
3:07:23 From Chatbots to Agents
3:08:57 The METR report and Hugging Face incident
3:23:23 AI Risk, Oversight and the Race Ahead
3:38:51 AI Discovery Beyond Mathematics
3:52:47 Why “just turn it off” gets complicated
4:06:47 Closing thoughts and what comes next
4:08:46 Outro

Featured Research

OpenAI’s Navier-Stokes announcement
Tristan Buckmaster’s statement
METR investigation
Emergence World
AI-assisted enzyme discovery

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What science should you be watching this fall? From Nobel Prize season to NASA’s Roman Space Telescope, Mars’ moons and Mercury, Lester Nare and Krishna Choudhary take a relaxed tour of the discoveries and missions on their radar.

In Episode 57 of From First Principles, we explore why curiosity-driven research matters, what the Golden Goose Awards celebrate, and how questions that once sounded impractical can lead to unexpected breakthroughs. Then we turn to space: Roman’s search for dark energy and exoplanets, JAXA’s Martian Moons eXploration (MMX) mission, and the mysteries ESA and JAXA’s BepiColombo mission will investigate at Mercury.

Along the way, we tour the updated FFP website, revisit some favorite episodes, and ask which stories you want us to cover in depth next.

A note before we begin: the main conversation was recorded before Labor Day weekend. The opening announcement addresses your requests for a separate episode on OpenAI, Navier–Stokes and the wider AI conversation. This episode is our fall science rundown; that deep dive is still to come.

CHAPTERS
00:00 Update on our upcoming Navier–Stokes and AI coverage
03:43 Episode intro and football banter
05:47 FFP intro
06:01 Nobel Prize season and our coverage plans
10:45 Golden Goose Awards: why basic research matters
21:02 FFP website tour and favorite episodes
42:24 Nancy Grace Roman Space Telescope
46:20 Microlensing, exoplanets and dark matter
51:25 MMX: where did Mars’ moons come from?
54:40 BepiColombo and the mysteries of Mercury
1:02:17 Your questions, future deep dives and sign-off
1:05:40 Outro

SHOW NOTES
NASA’s Nancy Grace Roman Space Telescope:
https://science.nasa.gov/mission/roman-space-telescope/

JAXA’s Martian Moons eXploration (MMX):
https://www.mmx.jaxa.jp/en/mission/

ESA / JAXA BepiColombo:
https://www.esa.int/Science_Exploration/Space_Science/BepiColombo

Explore the research and episodes we cover:
https://ffppod.com

Science R&D Funding Tracker:
https://ffppod.com/funding

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Breaking down science news so it makes sense to curious people everywhere.

Which mission or research story deserves a full FFP deep dive? Tell us in the comments.

What can a yak living thousands of meters above sea level teach us about repairing the human brain?

In Episode 56 of From First Principles, Lester Nare and Krishna Choudhary break down a new Neuron paper that traces an evolutionary adaptation found in high-altitude animals to a previously hidden pathway involved in building and repairing myelin.

Summary

• What myelin actually does and why losing it disrupts neural communication
• How multiple sclerosis damages myelin and why the brain’s natural repair process eventually fails
• Why oligodendrocyte precursor cells can remain present in damaged tissue without successfully rebuilding myelin
• Why current therapies are better at slowing further damage than restoring what has already been lost
• The challenge of getting drugs across the blood-brain barrier while maintaining target specificity
• How evolutionary pharmacology has previously produced medicines from adaptations found in snakes and Gila monsters
• The RETSAT Q247R variant identified in animals adapted to the hypoxic environment of the Tibetan Plateau
• How researchers engineered the high-altitude variant into mice and tested its effect on myelin
• The surprising discovery that neurons — rather than the myelin-producing cells themselves — generate the key repair signal
• How RETSAT increases ATDR, which neurons convert into ATDRA
• How ATDRA activates RXR-γ in oligodendrocyte precursor cells and promotes their differentiation
• How administration of ATDR promoted remyelination across multiple preclinical models
• Why the result is scientifically promising but still far from a proven human treatment

Featured Paper

A gain-of-function Retsat variant from high-altitude adaptation promotes myelination via a neuronal dihydroretinoic acid-RXR-γ pathway
Neuron, 2026
DOI: 10.1016/j.neuron.2026.01.013

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Which quantum computer will actually scale?

In Part 2 of our quantum computing deep dive, Lester Nare and Krishna Choudhary move from theory to hardware—comparing superconducting qubits, trapped ions, neutral atoms, and silicon spin qubits before going inside the new Nature cover paper Krishna co-authored with the HRL Quantum Team and collaborators.

The episode begins with a simple question: what makes a good quantum computer? We evaluate each architecture using three criteria: qubit quality, qubit control, and scalability and economics.

Superconducting qubits offer extremely fast operations, but scaling them introduces challenges involving microwave control, frequency crowding, cryogenic wiring, physical size, and cooling. Trapped ions preserve quantum information for extraordinary lengths of time, but their slower gates and increasingly complex optical systems introduce a different set of tradeoffs. Neutral atoms can be arranged in dense, reconfigurable arrays using optical tweezers and entangled through Rydberg interactions, while raising questions involving atom loss, correlated noise, readout, and execution time.

Then we get to silicon.

Beginning with the Loss–DiVincenzo proposal, Krishna explains how individual electron spins can be confined inside semiconductor quantum dots, manipulated through exchange interactions, and measured using single-electron transistors. We then explore exchange-only qubits, where three electron spins encode a single qubit and quantum gates can be performed using electrical control.

That leads to the Nature cover paper, A digitally controlled silicon quantum processing unit. The HRL system integrates 18 encoded qubits built from 54 quantum dots with cryogenic control electronics, a superconducting interconnect, automated calibration, and an engineered silicon-germanium heterostructure.

Krishna also explains his own work using machine learning to automate quantum-device tuning—an essential problem if spin-qubit systems are ever going to grow from dozens of components to millions.

The larger thesis is about manufacturing. The semiconductor industry has spent decades learning how to fabricate silicon devices at enormous scale. If quantum processors can inherit that infrastructure, the architecture that ultimately wins may not be the one that reaches the finish line first—but the one humanity already knows how to manufacture.

Nature paper:A digitally controlled silicon quantum processing unitDOI: 10.1038/s41586-026-10754-7https://www.nature.com/articles/s41586-026-10754-7

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Quantum computers do not simply “try every answer at once.” So what do they actually do—and why have governments and technology companies spent billions trying to build them?

In Part 1 of our two-part quantum computing deep dive, Lester Nare and Krishna Choudhary build the field from first principles.

The series was prompted by a new Nature cover paper, A digitally controlled silicon quantum processing unit, co-authored by Krishna and members of the HRL Quantum Team and collaborators. Before getting into that hardware in Part 2, we first need to understand why anyone wanted to build a quantum computer in the first place.

We begin with Bell’s theorem and the failure of local hidden-variable explanations of quantum mechanics. From there, we follow the realization that information is fundamentally physical through Rolf Landauer, reversible computation, Charles Bennett, Tommaso Toffoli, Paul Benioff, and the origins of quantum information science.

Then Richard Feynman changes the question. Straightforward classical simulation of an interacting quantum system requires tracking a state space that grows exponentially with the number of particles. If nature itself is quantum mechanical, Feynman asks, why not build a computer that is quantum mechanical too?

David Deutsch formalizes the universal quantum computer and introduces the first quantum algorithm. Using the Deutsch–Jozsa problem, the double-slit experiment, and Feynman’s path-integral intuition, we explain what a quantum algorithm is actually exploiting: carefully engineered constructive and destructive interference.

Finally, we reach the discoveries that turned quantum computing from an academic curiosity into a strategic technology. Daniel Simon develops an early exponential quantum speedup. Peter Shor recognizes how the underlying mathematics can be used to attack problems central to public-key cryptography. Lov Grover follows with a quantum search algorithm—and suddenly governments have a very different reason to care about quantum machines.

We also explore quantum money, quantum cryptography, the many-worlds interpretation, Google Willow and parallel-universe headlines, post-quantum security, and what useful quantum computers may ultimately be good for.

Part 2: How do you actually build one?

Nature paper:A digitally controlled silicon quantum processing unitDOI: 10.1038/s41586-026-10754-7

Link: https://www.nature.com/articles/s41586-026-10754-7

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Claude did not solve the Riemann Hypothesis. But what it actually did may be one of the clearest examples yet of how rapidly AI systems are changing the way difficult mathematics can be attacked.

In Episode 53, Lester Nare and Krishna Choudhary go from first principles on arguably the most famous unsolved problem in mathematics.

We begin with Euler and the Basel problem, build the Riemann zeta function from the ground up, explain its deep connection to prime numbers, move into the complex plane and analytic continuation, unpack the famous 1 + 2 + 3 + 4 + … = -1/12 result, and finally arrive at the Riemann Hypothesis itself: the claim that every non-trivial zero of the zeta function lies on the critical line.

Then we get into Claude.

An unreleased Anthropic model was prompted to take a serious run at the problem. It orchestrated roughly 60 autonomous sub-agents, tested hundreds of mathematical approaches, executed code, searched academic literature, challenged its own strategies, created adversarial referees to attack its work, and ultimately produced a result pushing a related mathematical bound well beyond the previous state of the art.

The human behind the prompt was not a mathematician. One of his instructions was essentially: believe in yourself.

We explain what Claude actually accomplished, what it absolutely did not accomplish, why moving a bound toward two-thirds does not mean the Riemann Hypothesis is “two-thirds solved,” and what the process tells us about agentic AI, mathematical research, scientific discovery, and AI safety.

Then it’s transfer season.

For the first FFP Summer Transfer Window for Scientists, we look at prominent researchers leaving American institutions for universities and research centers abroad. Using the language of football transfers, we examine major moves in chemistry, battery research, gravitational-wave astrophysics, and neuroscience—and what they reveal about research funding, immigration, scientific infrastructure, and the global competition for talent.

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