Practice Ground

When we recorded this, Max Yakubovich was a second-year student at Stanford Graduate School of Business, a former team member at Sundermind (mental health access platform), and a summer intern at Gray Matter — a venture capital firm specifically created to support bold founders building the future of mental health and emotional wellness. He came from Harvard, where he studied applied math and took a philosophy of mind class that introduced him to the hard problem of consciousness. He's been obsessed with the question ever since.

In this conversation, we explore what conscious experience actually is and why it should be the North Star KPI of all human endeavor, the PERMA model of well-being and what it implies for a world with increasing automation, and what it means to find your wise self through meditation and presence.

We discuss:
• [4:20] The hard problem of consciousness and why it matters for everything
• [8:10] Conscious experience as the North Star KPI of humanity
• [14:30] Capital vs. consciousness: the tension between making money and doing good
• [18:45] Effective altruism and the "make money first, do good later" argument
• [26:00] The PERMA model: five pillars of well-being worth knowing
• [28:20] P — Positive emotions: why gratitude practice is science, not self-help
• [32:10] E — Engagement and flow: the two-by-two of skill vs. challenge
• [37:45] R — Relationships: Dunbar's number, digital connections, and local community
• [46:30] M — Meaning: where it comes from when you don't have religion
• [50:20] A — Accomplishment: environmental mastery and intentional movement through life
• [53:40] Sleep, exercise, nutrition as the foundation everything else rests on
• [57:20] Meditation as decoupling from the parts that seek extrinsic validation
• [60:10] Internal Family Systems (IFS) and finding your wise self
• [64:00] Daily practice in utopia: movement, flow, and building the conditions for flourishing

Max's core idea: if conscious experience is the thing that makes suffering and flourishing possible, then it's the only thing that ultimately matters. Every conversation about AI, automation, and the future should start there.

This is episode 16 of Practice Ground — exploring how we find meaning as we go deeper into the automation era. These conversations are shaping a book coming in 2026.

Ashley Edwards is the founder and CEO of MindRight — a platform delivering culturally responsive emotional support via text message, designed specifically for multicultural communities who have historically been underserved by the mental health system.

She's the first Black female founder in New Jersey to raise over a million dollars in venture capital, a Forbes Under 30 Social Entrepreneur, a Rock Health Top 50 Digital Health honoree, and someone Forbes named as a top healer set to influence the next century.

She's also been one of Nifemi's close friends, out of business school, for twelve years.

MindRight has supported over three million text messages from people seeking emotional support. Their longest-standing member texted every day for six years.

In this conversation, we explore what the wellness industry consistently overlooks, why human connection cannot be outsourced to AI, and what it actually means to preserve our shared humanity as technology accelerates.

We discuss:

• [4:20] Why the most radical innovations are often the simplest
• [6:10] Text message as the most accessible form of delivering mental health support
• [9:30] What user behavior tells you about product-market fit (the WhatsApp signal)
• [14:15] Why it now feels controversial to be pro-human in mental health
• [18:40] What the industry overlooks: meeting people where they are, not where you want them
• [24:10] Three million text messages and what they reveal about emotional intelligence in communities
• [29:30] Why AI should never lead consistent, ongoing mental health support
• [35:20] Technology in the hands of the builder — who designs the worlds we live in?
• [42:50] AI and purpose: is this an opportunity to untether our self-worth from work?
• [50:10] The importance of diversity in building technology that reflects humanity
• [55:30] Gen Z's refusal to enter rooms they don't want to be in
• [58:20] Knowing when you've crossed a line — body signals, sleep, stage left
• [62:00] Daily practice in utopia: human connection, freedom to love, on our own terms

Ashley's insight is simple and radical at the same time: if you are not designing, you are living by the designs of other people. Every day that MindRight keeps humans in the loop, they're making a quiet argument for a world that still chooses people.

This is episode 15 of Practice Ground — exploring how we find meaning as we go deeper into the automation era. These conversations are shaping a book coming in 2026.

Ken Jee is a data scientist, author of The Visualization Handbook for Elite Athletes, and host of The Exponential Athletes — a podcast diving deep into the habits and mental frameworks of the greatest athletes in history. He's had access to coaches like Bob Bowman (Michael Phelps' coach) and has translated what he's learned about elite performance into frameworks applicable to building, creating, and finding meaning.

In this conversation, we explore what entrepreneurs can learn from athletes about surviving the dip, why the AI era is creating a trust economy, and what visualization really is — and how to practice it deliberately.

We discuss:
• [5:20] Why most people quit in the dip — and all the great things happen on the other side
• [8:40] How to find your interest + develop aptitude vs. chasing passion
• [15:30] Athletes have discrete skills; entrepreneurs should build meta-skills
• [18:10] One-way vs. two-way door decisions (Jeff Bezos) applied to building
• [28:45] Why AI fragments global communities but can strengthen local ones
• [31:20] Dunbar's number: why trust works under 150 people and breaks beyond it
• [36:50] The trust economy: why you only get a couple shots at trust with AI everywhere
• [48:30] Visualization: cognitive (process) vs. motivational (outcome) practices
• [54:10] Why athletes who choke in week one often win in week two
• [56:40] The hot hand fallacy and why entrepreneurs need irrational self-belief
• [61:20] Journaling, legacy thinking, and finding your crux
• [65:30] Daily practice in dystopia: farming, community meals, and building something worth living in

Ken's core insight: the same visualization techniques that got Michael Phelps eight gold medals aren't mystical. They're a skill. You can practice them. You can get better at them. And if you're not deliberately using them, your mind is already using them — just pointed in the wrong direction.

This is episode 12 of Practice Ground — exploring how we find meaning as we go deeper into the automation era. These conversations are shaping a book coming in 2026.

FOLLOW KEN JEE:
• The Exponential Athletes Podcast: https://www.youtube.com/@ExponentialAthlete
• The Visualization Handbook for Elite Athletes: https://www.amazon.com/dp/B0FTTQ45VZ
• YouTube: https://www.youtube.com/@KenJee_ds

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GET NOTIFIED ABOUT THE BOOK: https://nifemialuko.substack.com/

Chanin Nantasenamat is known online as The Data Professor — the educator who turned a YouTube channel about data science into a pathway for millions of learners who never thought data science was for them. He's a former associate professor at a research university in Thailand with nearly two years of experience applying machine learning and bioinformatics to drug discovery. He's now a senior developer advocate at Snowflake and Streamlit. And he launched a course with Nifemi at Deep Learning AI on fast prototyping of GenAI apps.

He came from biology. He had to fight to learn to code. That background is the foundation of everything he teaches.

In this conversation, we explore the philosophy of learning by doing, why the researcher's tolerance for failure maps perfectly onto entrepreneurship, and why having an anchoring project might be the most practical advice for surviving the AI era.

We discuss:
• [4:10] "The best way to learn data science is to do data science" — learning by doing
• [8:30] Why prototyping is about converting thought into something tangible
• [12:20] Intuition builds through repeated experience — gut check backed by data
• [18:45] Embrace imperfection: good enough and shipped beats perfect and stuck
• [22:10] Using a checklist to know when your prototype is production-ready
• [28:30] The teacher as facilitator, not authority — co-evolving with AI • [33:50] Why coming from biology gave him empathy with technical beginners
• [38:15] PhD failures and what nearly a year of negative results taught him
• [44:20] Risk minimizers, not risk takers: the entrepreneurship insight from Stanford
• [50:10] How AlphaFold changed biology and why we're still just scratching the surface
• [56:30] Keeping a project as an anchor when the AI landscape shifts every week
• [60:20] IKigai, purpose, and what it means to democratize learning at scale
• [64:40] AI as a co-founder for immigrants navigating complex systems • [68:00] Daily practice: touch grass, human connection, keep learning

Chanin's subscriber once watched his tutorials, went to Berkeley, got a job at big tech, built a startup, and sold it. Chanin didn't plan for that. He just kept making content — learning in public, doing the work, staying in the game.

That's the whole lesson.

This is episode 12 of Practice Ground — exploring how we find meaning as we go deeper into the automation era. These conversations are shaping a book coming in 2026.

Richmond Alake is the Director of AI Developer Experience at Oracle, a former MongoDB developer advocate, and one of the most prolific AI educators you've probably never heard enough about.

He has over two hundred articles on Medium, has written for NVIDIA and Neptune AI, taught at Imperial College and O'Reilly, and built openspeech — an AI-powered learning partner. He's also the founder building agent memory tools on top of Oracle's database infrastructure. He grew up in Lagos, Nigeria, moved to London at ten, and built his entire career by learning in public.

In this conversation, we explore why agent memory is the foundation that will determine how intelligent AI systems actually become, the honest psychology behind relentless curiosity, and what it means to walk into rooms where no one looks like you — and stay anyway.

We discuss:
• [12:30]Why agent memory maps directly to human memory: episodic, semantic, procedural, entity
• [8:20] The progression: website → app → agentic — and why memory is what makes agents real
• [18:40] Nigerian excellence culture: "Do they have two heads?"
• [22:15] Learning in public as a career strategy — 200+ articles, O'Reilly, Imperial College
• [38:50]Why AI always converges — and why deep beats broad

• [45:20] The case for no memory: when forgetting is a feature
• [52:10] Writing as a secret weapon in corporate strategy
• [56:30] Representation: seeing one VP at Google who looked like him, and what that changed
• [60:40] Daily practice in utopia: space exploration and an ever-expanding universe

Richmond's philosophy is simple: pick one thing, go deep, trust that AI will converge toward you.

He chose agent memory at MongoDB. He's still in it at Oracle. The field came to him.

This is episode 10 of Practice Ground - exploring how we find meaning as we go deeper into the automation era. These conversations are shaping a book coming in 2026.

Josh Starmer is the founder of StatQuest and an assistant professor at the University of North Carolina.

He's built one of the most beloved statistics education channels on YouTube by taking the most hostile possible audience — coworkers who literally hated math — and figuring out how to make them understand anyway.

He plays cello, has performed on big stages with rock bands, writes songs, and has a childhood dream of one day opening a pizza place. He started his YouTube channel with cooking videos, got nine views in the first year on his first statistics video, and thought that was amazing.

In this conversation, we explore where great teaching actually comes from, why pictures beat words every time, and what it felt like to finally understand a neural network not just intellectually — but emotionally.

We discuss:

• [4:10] The hostile audience as the ultimate teacher-training tool

• [8:30] Why trust through explanation opened every professional door Josh walked through

• [14:20] How StatQuest started with cooking videos, music, and nine statistics views

• [28:40] The slow learner's strategy: wait a year to see what actually lasts

• [35:15] How pictures communicate what words can't

• [40:50] Creating conditions for the aha moment — teaching neural networks visually

• [46:30] The neural network video that felt more beautiful than any song

• [52:10] What an AI-proof teaching career might look like: live shows, human connection

• [56:20] The pizza place dream, jogging with friends, and daily practices that matter

Josh's best observation: he's not a rock star. He plays statistics to growing crowds instead. And he's never once felt stagnant in that work the way he did playing the same songs night after night. That's the signal — the work that never stops teaching you is the work that's worth keeping.

This is episode 9 of Practice Ground — exploring how we find meaning as we go deeper into the automation era. These conversations are shaping a book coming in 2026.

Sanmi Koyejo is an assistant professor of computer science at Stanford University, where he leads the Stanford Trustworthy AI Research group.

He's also the founder and chief AI officer at Virtue AI, and is writing a book on machine learning from human preferences. He grew up in Lagos, Nigeria — and if you've been following Practice Ground, you may recognize International School of Lagos as the same school I attended. We also trained at the same boxing gym in Austin for years, without fully realizing how far our paths would go.

In this conversation, we explore what trustworthy AI actually means technically, why measurement systems built for academic use are failing at the societal level, and what role serendipity really plays in every meaningful career.

We discuss:

• [5:15] Why the AI field is great at making a number get bigger — and why that's now a problem

• [8:30] What AI measurement was built for vs. what society is demanding from it now

• [12:40] The shocking reality: 50% data/engineering, 10% algorithms we actually teach

• [18:25] Why humans will always be in the loop with complex AI systems

• [28:10] What trustworthy AI actually means — calibrated trust, not blind trust

• [32:50] Fairness, robustness, privacy, security: the four directions of trustworthy AI

• [42:15] Exploration vs. exploitation in research vs. business

• [48:30] Serendipity, fire alarms, and the career moments you can't predict

• [54:40] Why you're probably better off not doing a PhD — unless you love exploration

• [58:20] Daily practice in utopia: a researcher freed to explore and think broadly

Sanmi's candor is rare: he'll tell PhD students to their faces that they might be better off not doing a PhD.

He'll admit that data and engineering dwarf the algorithms he loves. And he'll acknowledge that the measurement systems he's built most of his career on weren't designed for the decisions society is now trying to make with them. That kind of intellectual honesty is exactly what this era needs.

This is episode 8 of Practice Ground — exploring how we find meaning as we go deeper into the automation era. These conversations are shaping a book coming in 2026.

Yemi Amu is the founder and director of Oko Farms — New York City's first and only public accessible outdoor aquaponics farm, which she established in Brooklyn in 2013.

She's an aquaponics expert, educator, and cultural steward who grew up in Lagos, Nigeria, came to New York for undergrad (supposed to do law school and go home), and found her calling not in law, music publishing, or sci-fi writing, but in the soil.

Her farm grows Nigerian vegetables, fiber crops, indigo, and medicine plants — not just as food, but as living acts of cultural reclamation and ancestral practice.

In this conversation, we explore what it means to create the world you want to live in, why Yemi's farming is inseparable from her Yoruba heritage, and what happens when human instinct and embodied knowledge meet a world increasingly in love with automation.

We discuss:

• [3:45] Why caring for the environment is a spiritual practice, not an ideology

• [8:30] The winding path from Lagos to law school to vegan baking to aquaponics

• [16:20] The conflict between Nigerian immigrant expectations and finding your own meaning

• [28:40] Farming as Yoruba ancestral practice and cultural reclamation

• [33:15] What growing Nigerian vegetables taught her about identity and diaspora

• [44:50] The eighty-year-old teacher who said: tell the pH by smelling the water

• [47:30] Why technology must be farmer-led, not the other way around

• [51:10] "We're being sold something as progress that I'm not sure is progress"

• [57:20] Her daily practice in a utopian world: mornings of dreaming, praying, and growing for joy

Yemi has a phrase she lives by: she would grow purely for the joy of growing. That's not a metaphor. It's her test for whether something is worth doing. And it's one of the simplest answers to the meaning question I've heard in this entire series.

This is episode 7 of Practice Ground — exploring how we find meaning as we go deeper into the automation era. These conversations are shaping a book coming in 2026.

In this conversation, we explore what most teams get wrong when building agentic systems, how to distinguish between problems that need agents and problems that just need a script, and what Tony's obsessive daily practice reveals about finding meaningful work.

Tony Kipkemboi grew up in Kaptagat, Kenya — a small town that produces an extraordinary share of the world's greatest distance runners. He came to the US on a running scholarship, served seven years in the US Army (including frontline COVID-19 research at USAMRIID), pivoted into tech by teaching himself Python during late nights while his family slept, earned a master's from Penn, worked at Bloomberg, Booz Allen, Snowflake, and Crew AI, and is now at Guild helping close the AI talent gap. On YouTube, he's known as The How To Guy, where he teaches technical builders how to design automation and agent systems.

We discuss:

• [3:20] Why "agentifying everything" is the wrong approach to building automation

• [6:45] Workflow first, agent second - the right order for building agentic systems

• [9:10] Why AI is a magnifying glass on your data infrastructure

• [22:30] From Kaptagat farm boy to NCAA runner to US Army to AI builder

• [38:50] The two-door strategy for navigating career pivots

• [42:20] How obsession, not ambition, is the real signal of meaningful work

• [50:15] Teaching people to use AI as a thought partner, not a crutch

• [58:30] Why skills and community are the anchors in any future — utopian or dystopian

Tony's story is a masterclass in relentless pursuit. He didn't start with a plan - he started with a door. When it didn't work, he found another. And when he finally found the thing where his off day is when he's asleep, he went all in.

This is episode 7 of Practice Ground — exploring how we find meaning as we go deeper into the automation era. These conversations are shaping a book coming in 2026.

Michael Hendrix is a writer, author, designer, musician, collage artist, and educator. He spent decades at IDEO, where he pioneered the concept of design sensibilities - the idea that emotional intent, not just methodology, is what separates great design from average design. He's the co-author of "Two Beats Ahead," a book exploring what music can teach the creative process. He now lives in Iceland.

In this conversation, we explore how emotional intent transfers from maker to receiver, why constraints are essential to creative freedom, what AI actually produces (and why it's almost guaranteed to be mediocre), and what principles will give humans meaning no matter what the technology does.

We discuss:

• [5:20] How emotional intent transfers from maker to receiver in design and music

• [12:10] The difference between design methodology and design sensibility

• [18:30] Why constraints are a creative strategy, not a limitation

• [27:15] The thing I'm always looking for in a product: was it made with care?

• [34:40] Why AI gives you the most average thing - because it's based on averages

• [37:55] What lifelong creatives celebrate more than the outcome

• [44:20] Fiction vs. non-fiction and long-term vs. short-term truths

• [50:10] Relationships, awe, and what gives humans meaning across all eras

• [56:35] His daily practice: making, teaching, telling stories about the world

Michael's central idea: you can feel whether something was made with care. That care shows up in the seams, in the materials, in whether the maker understood what they were really making.

It's as true for a sneaker as it is for a song. And it's the one thing AI - optimized for averages - cannot replicate.

This is episode 6 of Practice Ground -exploring how we find meaning as we go deeper into the automation era.

These conversations are shaping a book coming in 2026.