
Insights from recent episode analysis
Audience Interest
Podcast Focus
Publishing Consistency
Platform Reach
Insights are generated by CastFox AI using publicly available data, episode content, and proprietary models.
Most discussed topics
Brands & references
Total monthly reach
Estimated from 44 chart positions in 44 markets.
By chart position
- 🇺🇸US · Mathematics#11M to 3M
- 🇬🇧GB · Mathematics#11M to 3M
- 🇨🇦CA · Mathematics#13300K to 1M
- 🇦🇺AU · Mathematics#13300K to 1M
- 🇩🇪DE · Mathematics#24100K to 300K
- Per-Episode Audience
Est. listeners per new episode within ~30 days
1.8M to 5.4M🎙 Daily cadence·186 episodes·Last published 2w ago - Monthly Reach
Unique listeners across all episodes (30 days)
6M to 18M🇺🇸17%🇬🇧17%🇨🇦6%+41 more - Active Followers
Loyal subscribers who consistently listen
1.8M to 5.4M
Market Insights
Platform Distribution
Reach across major podcast platforms, updated hourly
Total Followers
—
Total Plays
—
Total Reviews
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* Data sourced directly from platform APIs and aggregated hourly across all major podcast directories.
On the show
From 21 epsHosts
Recent guests
Recent episodes
What Actually Makes Something Alive? with Melanie Challenger
Aug 19, 2026
48m 46s
Why Uncertainty Is Science's Greatest Strength with Stuart Firestein
Aug 6, 2026
43m 04s
Robot Proof: Why Better AI Starts With Better People with Vivienne Ming
Jul 25, 2026
59m 25s
Why Nothing Works: Robber Barons, Algorithms & Governing AI
Jul 10, 2026
44m 02s
Can Math Save Journalism?: Julia Angwin on Proof, Power, and Amazon's Algorithm
Jul 2, 2026
52m 49s
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| Date | Episode | Topics | Guests | Brands | Places | Keywords | Sponsor | Length | |
|---|---|---|---|---|---|---|---|---|---|
| 8/19/26 | What Actually Makes Something Alive? with Melanie Challenger | What does it mean to be alive? In this episode of Breaking Math, Autumn and Noah speak with Melanie Challenger, author of Alive, about one of the most profound questions in science and philosophy: how do we define life?Challenger argues that life is not simply a machine-like process or a bundle of genetic instructions. Living beings are embodied, purposeful agents. From single-celled organisms to sequoia seeds, from animals to human beings, life is marked by an astonishing capacity to work to keep itself alive.Chapters08:12 The concept of purpose in living beings09:14 The scientific view of purpose and agency11:52 The importance of purpose and meaning in life13:19 The danger of ignoring organism agency in science14:34 Living beings as purposeful agents15:35 Comparing purpose in a Roomba and a single-celled organism18:03 Autopoetic vs allopoetic systems20:03 Free will, agency, and the universe23:24 The physical basis of life and energy28:38 Aristotle's concept of psyche and purpose33:46 The importance of understanding what life truly is37:56 Material integration and the difference between machines and living beings38:15 The concept of self and embodiment in life41:09 The whole body as the agent, not just the brainFollow Melanie Challenger on her website:(https://www.melaniechallenger.com/) Subscribe for more on math, AI, technology, and the systems running the world. Follow Breaking Math onSubstack (https://breakingmath.substack.com/)X (https://x.com/breakingmathpod)Instagram (https://www.instagram.com/breakingmathmedia/)Bluesky (https://bsky.app/profile/breakingmath.bsky.social)Website (https://www.breakingmath.io/)YouTube (https://www.youtube.com/@BreakingMathPod)Follow Noah onInstagram (https://www.instagram.com/profnoahgian/)X (https://x.com/ProfNoahGian)Bluesky (https://bsky.app/profile/profnoahgian.bsky.social)Follow Autumn onX (https://x.com/1autumn_leaf)Bluesky (https://bsky.app/profile/1autumnleaf.bsky.social)Instagram (https://www.instagram.com/1autumnleaf/)Substack (https://substack.com/@1autumnleaf)email: [email protected] | 48m 46s | ||||||
| 8/6/26 | Why Uncertainty Is Science's Greatest Strength with Stuart Firestein | Neuroscientist Stuart Firestein (Columbia University) joins Breaking Math to make an extravagant claim: uncertainty isn't a weakness in science — it's the defining feature that makes progress possible. In this episode, we break down why the "one right answer" myth is one of the most damaging ideas in science, why real experts are often the most uncertain people in the room, and why authority and expertise pull in opposite directions, covering two fundamentally different kinds of probability, why Darwin never erased a 300-year-old classification system built on an assumption he disproved, why AI is exceptional at prediction but not built for causation, and why pseudoscience always has a confident answer while real science rarely does — plus the philosophical difference between hope and optimism, and why Voltaire had to invent the word "optimism" in 1759 to describe it. Chapters03:00 Predictability and the sea of uncertainties04:08 Science as a search for probabilities and multiple solutions06:16 Biological classification and the dynamic nature of species09:10 The optimistic view of a branching universe12:41 Probability as the language of optimism16:48 Two types of probability and their roles17:50 AI, probabilistic models, and the future of certainty21:40 Science and the creation of better ignorance23:21 The importance of asking questions over giving answers27:21 Authority versus knowledge in science30:04 Pluralism and multiple solutions in science32:46 Science in the gray area of uncertainty35:39 The brain and randomness in thought39:44 Science as a source of hope and optimismFollow Breaking Math onSubstack (https://breakingmath.substack.com/)X (https://x.com/breakingmathpod)Instagram (https://www.instagram.com/breakingmathmedia/)Bluesky (https://bsky.app/profile/breakingmath.bsky.social)Website (https://www.breakingmath.io/)YouTube (https://www.youtube.com/@BreakingMathPod)Follow Noah onInstagram (https://www.instagram.com/profnoahgian/)X (https://x.com/ProfNoahGian)Bluesky (https://bsky.app/profile/profnoahgian.bsky.social)Follow Autumn onX (https://x.com/1autumn_leaf)Bluesky (https://bsky.app/profile/1autumnleaf.bsky.social)Instagram (https://www.instagram.com/1autumnleaf/)Substack (https://substack.com/@1autumnleaf)email: [email protected] | 43m 04s | ||||||
| 7/25/26 | Robot Proof: Why Better AI Starts With Better People with Vivienne Ming | Neuroscientist, entrepreneur, and author Dr. Vivienne Ming joins Autumn and Noah to make the case that if we want better AI, we need to build better people first. We get into why AI tutors that hand students answers make learning worse, not better; what her research on "hybrid intelligence" reveals about the human traits — not the AI model — that predict elite human-AI collaboration; a wild experiment running Dungeons & Dragons with Claude and Gemini as dungeon masters to expose the gap between knowing and understanding; her case for "fiduciary AI," legal duty-of-care standards for tutors, hiring tools, and diagnostic models; and the real story of a hiring algorithm that learned to discriminate against women after every explicit gender marker was stripped out.Chapters02:20 Why build this book now? The importance of human qualities04:16 AI in education and the concept of robot-proofing06:37 The median student and AI personalization09:31 The limitations of AI understanding and theory of mind11:30 Building better people with AI and human interaction14:23 Hybrid intelligence and the role of human-AI collaboration23:56 Case study: AI in Dungeons & Dragons30:42 AI's strengths and limitations in understanding and cognition37:34 The science of purpose and its impact on life and society44:44 The collective intelligence of humans versus AI46:54 Key takeaway: Build better people for better Follow Vivienne Ming on X (https://x.com/neuraltheory) Get Vivienne's book, Robot Proof: (https://amzn.to/3Tz21aP) Follow Breaking Math onSubstack (https://breakingmath.substack.com/)X (https://x.com/breakingmathpod)Instagram (https://www.instagram.com/breakingmathmedia/)Website (https://www.breakingmath.io/)YouTube (https://www.youtube.com/@BreakingMathPod)Follow Noah onInstagram (https://www.instagram.com/profnoahgian/)X (https://x.com/ProfNoahGian)Bluesky (https://bsky.app/profile/profnoahgian.bsky.social)Follow Autumn onX (https://x.com/1autumn_leaf)Bluesky (https://bsky.app/profile/1autumnleaf.bsky.social)Instagram (https://www.instagram.com/1autumnleaf/)Substack (https://substack.com/@1autumnleaf)email: [email protected] | 59m 25s | ||||||
| 7/10/26 | AI governanceantitrust+4 | Marc Dunkelman | Financial TimesThe Economist+3 | America | AI safetycongestion pricing+3 | — | 44m 02s | ||
| 7/2/26 | journalismmathematical proof+5 | Julia Angwin | Proof NewsWall Street Journal+5 | — | journalismmathematics+7 | — | 52m 49s | ||
| 6/24/26 | mathematicsformal verification+4 | Kevin Hartnett | Quanta Books | — | Leanformal proofs+5 | — | 45m 38s | ||
| 6/10/26 | data sciencesocial justice+4 | Chad Topaz | Breaking MathUnlocking Justice | Rikers Island | data scienceinjustice+5 | — | 40m 06s | ||
| 6/2/26 | AI and employmentautomation+4 | Martin Ford | Breaking MathRise of the Robots | — | AIautomation+5 | — | 44m 24s | ||
| 5/29/26 | radio astronomyspace exploration+4 | Dr. Emma Chapman | Square Kilometre Arraylunar radio telescopes+1 | — | radio wavesAI+5 | — | 47m 03s | ||
| 5/23/26 | AI in mathematicsmathematical conjectures+5 | Daniel Litt | OpenAIErdős Problem | — | AImathematics+6 | — | 29m 38s | ||
Want analysis for the episodes below?Free for Pro Submit a request, we'll have your selected episodes analyzed within an hour. Free, at no cost to you, for Pro users. | |||||||||
| 5/21/26 | addictiondopamine+4 | Maia Szalavitz | Breaking Math Podcast | — | addictiondopamine+5 | — | 50m 15s | ||
| 5/14/26 | statisticsAI+4 | Ron Wasserstein | American Statistical Association | — | statisticsAI+5 | — | 47m 33s | ||
| 5/5/26 | ransomwarecybercrime+5 | Anja Shortland | King’s College LondonDark Screens | — | ransomwarecybercrime+8 | — | 41m 57s | ||
| 4/28/26 | huge numberslimits of human intuition+4 | Richard Elwes | Breaking Math | — | huge numbersexponential growth+5 | — | 56m 37s | ||
| 4/26/26 | AI transformationjob skills+4 | Sheamus McGovern | Open Data Science ConferenceTensorFlow+2 | — | AIjob loss+7 | — | 35m 33s | ||
| 4/15/26 | mathematicsmagic+4 | Matt Baker | Breaking Math Podcast | — | mathmagic+5 | — | 50m 58s | ||
| 4/7/26 | scientific frauddata transparency+4 | Thomas PlümperEric Neumayer | Breaking MathLinkedIn+4 | — | fraud in researchp-values+3 | — | 38m 35s | ||
| 3/27/26 | Women in HistoryAfrican-American history+4 | Dr. Victoria Bateman | — | — | Mary T. Washington WylieCPA+5 | — | 8m 01s | ||
| 3/24/26 | mathematicsAI+5 | Hortensia Soto | Mathematical Association of America | — | mathematicsAI+5 | — | 40m 20s | ||
| 3/20/26 | data visualizationhealthcare reform+3 | Dr. Victoria Bateman | — | — | Florence Nightingaledata-driven reform+5 | — | 12m 55s | ||
| 3/17/26 | gerrymanderingmathematics in politics+4 | Karen Saxe | American Mathematical Society | Washington, D.C. | gerrymanderingmathematics+7 | — | 35m 18s | ||
| 3/13/26 | economicswomen in history+3 | Dr. Victoria Bateman | — | — | Anna Schwartzeconomics+3 | — | 14m 20s | ||
| 3/11/26 | mathematical researchfederal grants+4 | Lauren K. Williams | Harvard UniversityMacArthur Fellowship | — | mathematicsgrants+6 | — | 25m 46s | ||
| 3/6/26 | financial literacywomen empowerment+4 | Dr. Victoria Bateman | Breaking Math PodcastEngland | — | Priscilla Wakefieldfinancial literacy+4 | — | 9m 44s | ||
| 8/23/18 | 31: Into the Abyss (Part Two; Black Holes) | Black holes are objects that seem exotic to us because they have properties that boggle our comparatively mild-mannered minds. These are objects that light cannot escape from, yet glow with the energy they have captured until they evaporate out all of their mass. They thus have temperature, but Einstein's general theory of relativity predicts a paradoxically smooth form. And perhaps most mind-boggling of all, it seems at first glance that they have the ability to erase information. So what is black hole thermodynamics? How does it interact with the fabric of space? And what are virtual particles? | 56m 49s | ||||||
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Chart history for Breaking Math Podcast
Peaked at #1 in United States, top 10 in 29 of 50 tracked markets, currently #1 in United States.
| Market | Genre | Peak | Current | Trend |
|---|---|---|---|---|
| United States | — | #1 | #1 | — |
| United Kingdom | — | #1 | #1 | — |
| SA | — | #1 | #1 | — |
| PE | — | #1 | #1 | — |
| PT | — | #2 | #2 | — |
| HU | — | #2 | #2 | — |
| PH | — | #2 | #2 | — |
| NG | — | #2 | #2 | — |
| New Zealand | — | #3 | #3 | — |
| BE | — | #3 | #3 | — |
| Finland | — | #3 | #3 | — |
| AE | — | #3 | #3 | — |
| Netherlands | — | #4 | #4 | — |
| RO | — | #4 | #4 | — |
| TH | — | #4 | #4 | — |
| VN | — | #4 | #4 | — |
| AR | — | #4 | #4 | — |
| CO | — | #4 | #4 | — |
| South Africa | — | #4 | #4 | — |
| GR | — | #5 | #5 | — |
Chart Positions
50 placements across 44 markets.
Chart Positions
50 placements across 44 markets.