Breaking Math Podcast

In this episode with award-winning journalist and author Maia Szalavitz challenges the idea that addiction is simply about pleasure or willpower. Instead, she explains addiction as compulsive behavior that continues despite negative consequences — and shows why withdrawal, dependence, and addiction are not the same thing.
The conversation explores “wanting” versus “liking,” why dopamine is misunderstood, how social media and AI can exploit reward systems, and why punishment often fails. Ultimately, Szalavitz argues that recovery depends less on tough love and more on connection, purpose, safety, and care.

Chapters
00:00 Understanding Addiction: Definitions and Mechanisms
10:43 The Role of Dopamine in Addiction
14:18 Addiction as a Learning Disorder
16:22 Substance vs. Experience: The Nature of Addiction
20:13 Evidence-Based Methods for Overcoming Addiction
25:20 Finding Meaning and Purpose Beyond Addiction
33:30 The Pursuit of Meaningful Experiences
34:15 Understanding Dopamine and Pleasure
39:10 The Complexity of Addiction
43:00 Social Media and Addiction Dynamics
50:42 Generational Perspectives on Technology and Addiction
57:53 Lessons Learned in Addiction Science
01:02:03 Rethinking Addiction: A New Perspective
01:03:54 The Compulsive Nature of Addiction
01:04:14 Understanding Addiction Beyond Pleasure
01:05:27 The Importance of Connection and Compassion

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In this episode of Breaking Math, Autumn and Noah speak with Ron Wasserstein, Executive Director of the American Statistical Association, about what statistics means in a world increasingly shaped by AI, misinformation, and fragile public trust. Wasserstein argues that statistics is not merely a “bag of tools,” but a way of thinking: asking where data comes from, what it leaves out, how uncertainty should be communicated, and when numbers are being used to illuminate rather than manipulate.

Chapters
00:00 The Golden Age of Statistics
02:36 AI's Impact on Statistics
08:16 Data as Fuel for AI
10:55 Bias in AI and Statistics
14:01 Preparing Future Statisticians
16:58 Bridging the Gap: Academia and Industry
22:58 The Misconception of Statistics
23:08 The Role of Statistics in Public Discourse
26:20 The American Statistical Association's Mission
32:18 Statistics and Politics: A Historical Perspective
36:02 Addressing Misinformation and Misuse of Data
39:51 The Importance of Statistical Literacy
44:01 Misconceptions About Statistics and Expertise
46:57 The Essence of Statistics
47:22 Statistics as a Way of Thinking

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What if ransomware did not begin with criminals, but with curiosity? In this episode of Breaking Math, Autumn and Noah talk with Anja Shortland, professor of political economy at King’s College London and author of Dark Screens.
This conversation explores how playful hacking evolved into professionalized cybercrime, why ransomware gangs operate like morally questionable internet startups, how cryptocurrency made ransomware scalable, and why hospitals, governments, universities, and critical infrastructure remain especially vulnerable. We also dig into the mathematics behind encryption, asymmetric cryptography, game theory, negotiation, cyber insurance, and the uncomfortable trade-offs between freedom, privacy, and regulation.
Chapters
00:00 The origins of ransomware and early hacker culture
02:13 The evolution of ransomware attacks since 2013
03:14 The paradox of cybercriminals as entrepreneurs
06:19 Early hackers: Steve Jobs and Wozniak as pioneers
12:34 The moral and legal landscape of hacking and cybercrime
13:39 The importance of cybersecurity awareness for individuals
15:03 The arms race: attackers vs defenders and the role of math
16:02 The technological innovations behind ransomware
19:21 Asymmetric encryption and cryptocurrency in ransomware
20:53 Bitcoin and the dark web: enabling cybercrime
22:45 The impact of AI on future cyber threats and defenses
34:07 The future of ransomware and cybersecurity challenges

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What does it actually mean for a number to be “big”? In this episode of Breaking Math, Autumn chats with mathematician Richard Elwes to explore how huge numbers reveal the limits of human intuition, language, and even mathematics itself. The discussion moves from exponential growth in pandemics and finance to numbers larger than the universe itself, emerging in games like chess and abstract possibility spaces. Finally, it reaches one of the most profound ideas in modern mathematics: that there are true statements about numbers that can never be proven. This episode challenges how we think about scale, complexity, and the systems we rely on to make sense of reality.

Key Topics
Limits of ancient numeral systems like Roman numerals
Mathematical logic and the concept of huge numbers
Evolution of number notation from Roman to Hindu-Arabic systems
The significance of place value in expressing large numbers
The Mayan long count and its implications for understanding time scales

Chapters
00:00 Introduction and Inspiration for the Book
01:39 Redefining Big Numbers
01:55 Limits of Numerical Systems
05:33 Evolution of Number Sense
10:02 Language and Numerical Understanding
11:53 Cultural Influences on Numerical Systems
14:18 Hacks in Ancient Number Systems
16:55 Archimedes and the Concept of Infinity
22:01 The Importance of Place Value
25:45 Mayan Cosmology and Time Scales
31:55 Exponential Growth and Its Dangers
32:20 Understanding Exponential Growth
36:14 The Dangers of Exponential Growth
37:23 Limits of Exponential Growth in the Physical World
39:42 Exploring Possibility Space
45:38 Goodstein's Theorem and Mathematical Logic

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In this episode we sit down with Sheamus McGovern, founder of the Open Data Science Conference (ODSC AI), to unpack what AI actually looks like. Sheamus shares what’s really happening behind the scenes of the AI boom and why the biggest shift isn’t job loss, but a complete transformation of skills. From explaining why AI is reshaping—not replacing—jobs, to breaking down the gap between hype and real-world applications, this conversation explores how early algorithmic trading foreshadowed today’s AI revolution, why open-source tools like TensorFlow and PyTorch changed everything, what the “AI Skill Flip” means for your career, and why even data scientists are questioning their future. Along the way, the biggest mistake people make when trying to learn AI, and why the smartest approach isn’t to learn everything—but to start intentionally and build from there.

Timestamps
00:00 – The biggest misconception about AI
02:00 – Algorithmic trading and the origins of AI in finance
05:00 – The birth of ODSC AI and the data science movement
09:30 – Breakthrough moments in AI
16:30 – Democratization of AI and open-source tools
19:00 –The AI Skill Flip
24:00 – The truth about AI replacing jobs
27:00 – Real-world AI success stories
32:30 – How to actually start learning AI today

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