Thinking On Paper

Quantum computing doesn't make computers faster. It changes what's computable.

Joe Fitzsimons, CEO of Horizon Quantum, explains why quantum progress is so hard to grasp: it's exponential in a way that breaks everyday intuition.

Here's the math that matters:

Each additional qubit doubles the difficulty of simulating the system on classical computers. Meanwhile, quantum processors are scaling faster than Moore's Law as the industry accelerates.

Put those together: exponential difficulty meets exponential growth. The result is capability that quickly surpasses what any classical computer—or human intuition—can comprehend.Why this matters:

Early computers didn't just speed up arithmetic. They unlocked tasks you could never complete by hand: weather prediction, aircraft design, nuclear simulation. Things that were mathematically possible but practically impossible.

Quantum computing does the same—except the tasks are even more fundamental:

- Drug discovery: simulating molecular interactions at quantum level- Cryptography: breaking encryption that protects the internet- Materials science: designing room-temperature superconductors- Optimization: solving logistics problems with trillions of variables- AI: training models that classical computers can't handleJoe's point: we're not making computers a bit better. We're unlocking a category of problems that were previously unsolvable—not just hard, but impossible with any amount of classical computing power.The comparison that clicks:Before computers, you could theoretically calculate pi to a million digits by hand—it would just take lifetimes. But some quantum problems aren't like that. They're not "hard with classical computers"—they're impossible, full stop. Like asking a typewriter to stream video.This short episode breaks down why quantum isn't incremental improvement. It's categorical change.

If you've been following quantum computing skeptically (wondering when it'll actually matter), this episode shows you why the inflection point is closer than you think.

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Making music used to require heartbreak, bleeding fingers, and a thousand late nights. Now, with SUNO, you can write AI songs in 30 seconds.

This changes everything about taste, credit, and what it means to be a musician.

Nicholas Ponari—guitarist, investor, COO at Overtune—explains how musicians get paid when AI generates the music.

The old model is dead. You used to need:

- A guitarist

- A bass player

- A drummer

- A producer

- A recording studio

- Years of practice

Now you need a laptop. But someone still created the guitar riffs AI learned from. Someone played the drums that trained the model. Someone wrote the chord progressions.

So who gets paid?

Overtune solved this with vector mathematics. Here's how it works:

They convert music into high-dimensional vectors. When AI generates a song, they measure the "distance" between the output and every input in the training data. The closest matches get credit. And payment.

Bass player's groove gets used? They get paid.

Drummer's pattern shows up? They get paid.

Producer's mixing style? They get paid.

It's automatic. It's fair. It's the only way AI music doesn't become theft at scale.

We also talk about:

- Why Suno and Udio's approach creates legal nightmares

- Whether AI musicians can coexist with human musicians

- Why taste matters more than ever (anyone can make music now)

- The 10,000 hours that separate making music from being a musician

- Why every Mars mission needs a guitarist (seriously—group survival research)

Nicholas's take: AI should lower the barrier to entry. If you outgrow Overtune and start hiring real producers, they've succeeded. You've graduated.

The question isn't whether AI can make music. It's whether we build tools that empower musicians—or replace them.

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Guest: Nicholas Ponari, COO, Overtune | Investor, Guitarist

Company: Overtune.com

Topics: AI music, copyright, attribution, royalties, music creation, licensing, vector math

Comparison: Suno, Udio (scraping approach) vs Overtune (licensed approach)

Please enjoy the show.

And remember: Stay curious. Be disruptive. Keep Thinking on Paper.

Cheers, Mark & Jeremy

PS: Please subscribe. It’s the best way you can help other curious minds find our channel.

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Take your Technology thinking beyond.

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Watch On YouTube:

TIMESTAMPS:

(00:00) Trailer

(00:59) Why music feels like “magic”

(04:51) Overtune’s real customer: vocalists who can’t produce

(07:51) The hard problem: attribution, not “make a song”

(08:05) Why the easy button fails

(12:49) Training on licensed music and where the ethics line sits

(16:08) Who gets paid: splits, volume, and realistic expectations

(18:32) How attribution actually works: vectors, thresholds, and cutoffs

(20:44) Can scraped music ever be fixed after the fact

(27:07) Interactive music, live coding, and the future of performance

(29:14) The Kevin Kelly question: what do we want humans to be?

America has a Technological inferiority complex. China makes over half the world's lithium batteries. They produce 90% of neodymium magnets. They mine 70% of rare earths and process 85%.

America makes burgers.

This is the story of how China won the Electric Stack—and whether America can catch up.

What's the Electric Stack?

Everything that moves will eventually run on batteries and electric motors. Cars, buses, ships, planes, robots, drones, tools. The Electric Stack is the supply chain that makes this possible: batteries, magnets, rare earths, processing, manufacturing.

China controls it.

This isn't just about EVs. It's about who builds the robots, who powers the drones, who controls the energy transition.

If it can go electric, it will go electric. And right now, that means it will be made in China.

Please enjoy the show.

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Timestamps

(00:00) The Electric Stack

(02:13) Beginnings: War, The Oil Crisis & Stan Whittingham

(03:46) The Song Handycam: Lateral Thinking With Withered Technology

(05:06) Tesla, Elon And Handycam Batteries In An EV

(06:46) China Buys US Battery Company A-123 At A Carboot Sale

(08:40) China, The Olympics And The Serendipity of Battery Technology

(11:37) Faraday And The Birth Of Neodymium Magnets

(14:26) The 3.5 Inch Neodymium Magnet Alpha Product

(16:46) Magnequench

(18:16) Drones, Ukraine And The Magnet War Machine

(20:16) Politics, Rare Earths And 'The Future's Too Important' T-shirts

Median US income: $68,000.

Median home price: $440,000.

The math doesn't work.

Only 13% of Americans earn a salary. Everyone else gets paid hourly or hustles in the gig economy. Yet housing policy assumes stable W-2 income, 20% down payments, and 30-year mortgages.

The system is built to extract value, not create stability.

Chris Moeller joins Mark and Jeremy to talk about an alternative: stable living.

Here's what's broken:

"Affordable housing" sounds nice. But it runs on outdated subsidies, wage assumptions from the 1970s, and ownership models designed to extract profit. Developers flip. Investors extract. Renters get priced out. First-time buyers can't enter.

Nobody wins except capital.

Stable living flips the model:

- Separate land from structures (land trusts own the land, residents own the building)

- Long-term security instead of short-term yield (no flipping, no speculation)

- Impact capital instead of extractive finance (returns that don't require displacement)

- Industrialized construction (modular, faster, cheaper)

- Better coordination technology (reduce waste, speed up builds)

The goal isn't homeownership. It's housing security.

Right now, housing is treated as an investment vehicle. Your home appreciates, you build wealth. Great—if you already own. Catastrophic if you're trying to enter the market.

We talk about:

- Why "affordable housing" programs fail (wage assumptions, subsidy gaps, developer incentives)

- How land trusts work (Vienna's model, community ownership)

- What impact capital means (patient investors, social returns)

- Why modular construction isn't "cheap"—it's efficient

- Whether stable living can scale (or if it's just theory)

Chris's point: Housing became financialized. We turned shelter—a basic human need—into an asset class. Private equity owns 800,000 single-family homes. Airbnb removed 300,000 units from rental markets. Zoning prevents new supply.

The result: You can't afford to live where you work.

Stable living isn't utopian. It's pragmatic. Separate speculation from shelter. Build for people who live there, not investors who don't.

If you're priced out, paying half your income in rent, or wondering why starter homes disappeared, this episode explains the system—and the alternative.

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Guest: Chris Moeller

Topics: Housing crisis, affordable housing, stable living, land trusts, impact capital, modular construction, real estate, financialization

Models discussed: Vienna housing, community land trusts, resident ownership

Stats: Median income $68K, median home price $440K, 13% salary workers

Please enjoy the show.

And remember: Stay curious. Be disruptive. Keep Thinking on Paper.

Cheers, Mark & Jeremy

PS: Please subscribe. It’s the best way you can help other curious minds find our channel.

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Timestamps

(00:00) Trailer

(03:19) Challenges of Homeownership

(05:46) The Housing Market Dynamics

(08:29) Technology's Role in Housing Solutions

(10:41) Innovations in Construction

(12:29) Financing Housing for All

(15:06) Reimagining Ownership Models

(16:30) Technology's Role in Food Access and Coordination

(18:43) Adaptive Reuse in Real Estate and Community Development

(19:58) Commercial Real Estate Challenges Post-COVID

(23:15) Infrastructure Needs for Sustainable Living

(25:31) Global Community and Local Solutions

(26:45) Stable Living for Civil Servants and Community Heroes

(28:20) Creating Stability and Long-Term Impact

What is consciousness?

Federico Faggin—physicist, inventor of the microprocessor—says it's not created by brains. It's fundamental to reality. Everything is conscious: atoms, electrons, maybe even spacetime itself.

This is panpsychism. And Faggin argues quantum physics proves it.

We're reading his book, *Irreducible*, to figure out if we agree.

Quantum conscious units called "Seities"? A universe that's been conscious forever? We're not sure yet. But it's fascinating.

Here's Faggin's argument:

For a hundred years, quantum physics has shown us something strange. Matter isn't solid—it's vibratory energy. Everything is quantum information.

But we still don't have a theory that unifies general relativity and quantum mechanics. Faggin thinks consciousness is the missing piece.

His hypothesis: The universe has been conscious—and had free will—forever.

Why this matters:

If consciousness is fundamental (not emergent from complex brains), then AI will never be conscious. Computers process information. They don't experience anything.

Consciousness, Faggin argues, isn't computation. It's something else entirely. Something quantum. Something that exists at every level of reality.

We explore:

- The "hard problem" of consciousness (why materialism can't explain subjective experience)

- What quantum mechanics reveals about observation and reality

- Panpsychism: the idea that consciousness is everywhere

- Why integrated information theory falls short

- What "Seities" are (quantum conscious units—seriously)

- Whether this is physics or philosophy (both, probably)

- Why Faggin thinks free will is real (and quantum)

His background:

- Invented the first microprocessor (Intel 4004, 1971)

- Designed chips that powered early personal computers (Intel 8080, Zilog Z80)

- Spent 50 years studying quantum systems

- Now argues consciousness creates reality, not the other way around

The implications:

If he's right, everything changes. Meaning isn't something we invent—it's something we discover. Free will isn't an illusion. The universe isn't dead matter accidentally producing awareness. It's aware all the way down.

We don't know if we buy it. But we can't stop thinking about it.

If you've ever wondered why you experience anything at all—why there's something it's like to be you—this episode explores the most radical answer modern physics offers.

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Guest: Federico Faggin, Physicist, Inventor (Microprocessor)

Book: *Irreducible: Consciousness, Life, Computers, and Human Nature*

Topics: Consciousness, quantum physics, panpsychism, philosophy of mind, free will, AI limits, integrated information theory, materialism

Warning: Gets weird. Worth it.

Please enjoy. And share with a conscious friend.

Cheers, 

Mark and Jeremy.

PS: Please subscribe. It’s the best way you can help other curious minds find our channel.

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The internet decayed into AI slop. Marketing became manipulation. Trust disappeared.

How do brands build real connections when platforms feed you lies, hide your customers, and optimize for extraction?

Nick Richtsmeier—founder of CultureCraft, writer at Damns Given—says brands now live inside mirrored cages. You see what algorithms want you to see. Your customers see distorted versions of you. Nobody sees reality.

Funnels don't work. Neutrality is dead. And AI just made it worse.

Here's the problem:

Every platform is a black box. Meta, Google, LinkedIn—they show your ads to "customers" but won't tell you who those customers are. You're renting attention. Paying the Silicon Valley tax. Building on land you don't own.

Meanwhile, the platforms study your customers better than you do.

You don't have customers anymore. You have algorithmic intermediaries. And they're extracting 5-15% of your revenue.

What breaks first:

- Funnels collapse (they never existed—it was always networks)

- Mass neutrality fails (you can't please everyone in personalized filter bubbles)

- Influencers become trust middlemen (because platforms destroyed direct connection)

- Marketing hijacks curiosity (manipulated attention replaces genuine interest)

- AI layers onto broken systems (making extraction more efficient)

Nick's argument: Marketing became manipulated curiosity at industrial scale.

The core insight: Everyone exists in a mirrored cage of algorithmic distortion.

You think you're seeing reality. You're seeing what keeps you engaged. Your customers think they're choosing freely. They're being nudged by invisible systems.

This isn't conspiracy theory. It's business model. Platforms profit from distortion. Marketing agencies profit from platforms. Brands burn money hoping for results.

The uncomfortable truth: If you don't know your customers' names, you don't have customers. You have a vendor relationship with Meta.

This episode tracks the distortion gap—the space between what's real and what algorithms show us. It's widening. And most brands don't even notice.

If platforms collapsed into noise, where does trust come from?

Nick's answer: Analog. Patient. Real. Slow to build, impossible to extract.

Please enjoy the show.

And remember: Stay curious. Be disruptive. Keep Thinking on Paper.

Cheers, Mark & Jeremy

PS: Please subscribe. It’s the best way you can help other curious minds find our channel.

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Watch On YouTube

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Timestamps

(00:00) Trailer

(01:00) Disruptors & Curious Minds

(02:00) Mark Has A Trust Issue

(02:42) What Is Trust?

(07:14) How Deep-Tech Brands Build Trust?

(09:38) Steve Jobs And Selling A Feeling

(10:00) The Cult Of Silicon Valley

(10:35) Was the Internet Ever Not Shit? 

(15:05) What Is The “Distortion Gap”?

(20:11) Reducing Your Digital Marketing Spend

(21:45) Analog Marketing

(23:40) Why the Marketing Funnel Never Really Existed

(25:08) VCs, Capital And The Comfort Zone Of Risk

(27:04) Analog vs Digital: What Actually Creates Meaningful Connection

(28:40) How the TikTok Generation Uses the Internet Differently

(32:40) Your Curiosity Is Being Hi-Jacked

(35:22) What Are Load-Bearing Inefficiencies?

(40:47) The Importance Of Resilience in a World Of Entropy

(42:29) What Do We Want Humans To Be?

AI answers faster than any human. But can it help you think? Does it erode critical thinking, or augment it?

Pia Lauritzen has analyzed 30,000 questions across languages and cultures. She's a philosopher of the question. And she says we're losing the muscle for real wonder.

The problem: We ask "what" and "how." Rarely "why." ChatGPT answers instantly. We skip the struggle. The blank page—where thinking happens—disappears.

Who asked the first question in the Bible? Not Adam. Not Eve. The snake. "Did God really say...?" Questions don't just seek information. They transfer responsibility. They create power.

We talk about:

- Why we default to safe questions (what, how)

- Why "why" is radical (challenges authority)

- How questions transfer responsibility

- Why adults hide their curiosity (fear, ridicule, ego)

- The dancing metaphor (leading vs following)

- Why blank pages matter (AI fills them too fast)

Pia's argument: AI doesn't help you think. It replaces thinking.

ChatGPT gives you the feeling of thinking without the work. You type, get an answer, feel smart. But you didn't struggle. You didn't sit with uncertainty. You didn't build the muscle.

Three things AI can't do:

1. Sit with discomfort

2. Ask the question behind the question

3. Experience genuine confusion

What we're losing: The ability to not know—and be okay with it.

The real test isn't the machine. It's whether you can hold onto what makes questioning—and not-knowing—uniquely human.

If you've ever felt dumber after using ChatGPT, this episode explains why.

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Guest: Pia Lauritzen, Philosopher, TEDx Speaker

Research: 30,000+ questions analyzed

Topics: Critical thinking, AI, curiosity, questions, responsibility, wonder

Please enjoy the show. And click subscribe, it’s the best way for other curious minds like you to find our show.

And remember: Stay curious. Be disruptive. 

Keep Thinking on Paper.

Cheers, 

Mark & Jeremy

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Other ways to connect with us:

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TIMESTAMPS

(00:00) Trailer

(03:28) 30,000 Questions & the What/How Bias

(07:38) Questions That Connect vs Questions That Manipulate

(09:59) Do We Really Lose Our Curiosity?

(14:21) How to Start Better Conversations 

(18:40) Conversation as a Thinking Space

(19:46) Why We Lead with Polarising Topics 

(20:35) How School Trains Us to Have Answers, Not Questions

(22:22) Rethinking Education in the Age of AI

(25:22) AI in the Classroom: Tool, Threat or Opportunity?

(30:07) Why AI Can’t Help Us Think

(32:55) The Essence of Technology, AI Deception & the Turing Test

(38:17) What Could Humans Be in an Age of AI?

What makes neutron stars so fascinating that they once fooled astronomers into thinking they were aliens?

1967: PhD student Jocelyn Bell Burnell discovers repeating radio pulses from space using a homemade array of wooden poles and copper wire. Regular. Precise. Unnatural.

They called it LGM-1. Little Green Men.

It wasn't aliens. It was something stranger: neutron stars. The densest objects in the universe. A teaspoon weighs a billion tons.

Katia Moskvitch—science journalist and author—joins us to explore pulsars, cosmic mysteries, and why Bell Burnell's supervisor got the Nobel Prize instead of her.

We talk about:

- Why neutron stars were only theoretical for decades

- Who first imagined their existence

- How Bell Burnell built the radio telescope that changed astronomy

- Why the discovery was almost dismissed as interference

- What pulsars are (neutron stars spinning hundreds of times per second)

- How they're used as cosmic lighthouses for navigation

- The Nobel Prize controversy (her work, his award)

- Whether she was robbed—or if the system worked as designed

Neutron stars are stellar corpses. When massive stars explode, their cores collapse into objects 20 kilometers wide but heavier than the sun. They spin so fast they bend spacetime. Their magnetic fields are quadrillion times stronger than Earth's.

Bell Burnell discovered them. But the 1974 Nobel Prize went to her male supervisor and another male colleague. She's never publicly complained. Others have.

The question: Is this science's greatest injustice? Or does the Nobel Prize honor theory over observation—mentors over students—by design?

This episode is about discovery, recognition, and what we choose to honor.

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Guest: Katia Moskvitch, Science Journalist, Author

Topics: Neutron stars, pulsars, astronomy, Nobel Prize, Jocelyn Bell Burnell, scientific discovery, recognition

Cheers,

Mark & Jeremy

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Everyone thinks they're a great driver. They're wrong.

Most drivers think they can judge a safe overtake. They can't. And that's why we crash.

Barry Lunn breaks down the sensor technology that sees eight cars ahead, detects velocity before brake lights appear, and intervenes when you're about to make a mistake.

The tech: Radar. Not cameras. Not lidar. Millimeter-wave signals that bounce around traffic and see what you can't.

More than half of global crashes are rear-end collisions. All preventable with earlier detection.

We talk about:

- Why radar beats cameras and lidar for safety

- How sensors detect danger before humans register it

- Why machines see eight cars ahead while you see two

- How velocity changes are detected before brake lights

- Why rear-end collisions dominate crash statistics

- The trust paradox (people resist automation but quickly rely on it)

- Why hands-off driving feels wrong even when it's safer

The problem isn't technology. It's human ego. We think we're good drivers. We're not. We're slow, distracted, overconfident.

The machine doesn't get tired. Doesn't check its phone. Doesn't misjudge closing speed. It just prevents the accident you didn't see coming.

The question: Why do we resist the system that saves us from ourselves?

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Guest: Barry Lunn

Topics: Self-driving cars, autonomous vehicles, radar technology, driver assistance, crash prevention, automation, trust

Format: Short episode

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What if someone handed you the recipe for a quantum computer?

Coleman Collins of IonQ breaks down DiVincenzo's criteria—the five capabilities any system needs to be a quantum computer.

Physicist David DiVincenzo created the checklist. Every major quantum architecture (superconducting circuits, trapped ions) follows it.

The five requirements:

1. A well-defined qubit (your basic unit of quantum information)

2. Initialization (set every qubit to a known starting state reliably)

3. Long coherence times (qubits stay stable long enough to compute without losing quantum state)

4. Measurement (read each qubit's state at the end—ideally individually)

5. Universal gate set (single-qubit control + entanglement = any computation you want)

Mix them together. You have a quantum computer.

We talk about:

- Why these five criteria matter (the foundation of every quantum system)

- What coherence means (how long quantum states survive)

- Why measurement is harder than it sounds

- How entanglement enables universal computation

- Which quantum architectures excel at which criteria

- Why trapped ions vs superconducting qubits make different tradeoffs

This is the foundation. Every major quantum company—IBM, Google, IonQ, Rigetti—is solving these five problems in different ways.

Now you know what they're building toward.

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Email: [email protected]