What happens when an AI prediction does not just forecast the future, but helps create it?

In this episode of AI-Curious, we talk with philosopher and ethicist Carissa Véliz about AI ethics, AI privacy, predictive AI, and the hidden power of algorithmic decision-making. We explore how AI systems used in hiring, lending, insurance, and other high-stakes settings can become self-fulfilling prophecies, shaping outcomes rather than simply measuring them.

We also examine the growing privacy risks of large language models and AI agents, especially as they gain access to more personal data, communications, and systems. Along the way, we discuss automated decision-making, surveillance, human autonomy, and why predictions about people are far more ethically fraught than predictions about things like the weather.

This conversation also goes beyond policy and into philosophy: how narratives about AI shape public thinking, why humor can be a response to technological power, and how individuals and companies can use AI responsibly without giving up judgment, control, or resilience.

If you are interested in AI ethics, algorithmic bias, AI privacy, AI agents, responsible AI, predictive algorithms, self-fulfilling prophecy, and the future of AI, this episode offers a clear and thought-provoking framework for understanding what is at stake.

Guest
Carissa Véliz — Philosopher, Associate Professor at the Institute for Ethics in AI at the University of Oxford, and author of Prophecy, Prediction, Power, and the Fight for the Future: From Ancient Oracles to AI.

Carissa's TED talk:

https://www.ted.com/talks/carissa_veliz_beware_the_power_of_prediction

Carissa's new book: Prophecy, Prediction, Power, and the Fight for the Future: From Ancient Oracles to AI.

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What if the job you have today will soon require a completely different set of skills? 

In this episode of AI-Curious, we talk with Dan Roth, Editor in Chief of LinkedIn, about what LinkedIn’s data reveals about the future of work, the rise of AI literacy, and why deeply human skills may matter more than ever. We dig into LinkedIn’s “Skills on the Rise” research, what employers are actually looking for now, and why the shift toward skills-based hiring is changing how people get hired, promoted, and evaluated.

We also explore the surprising rise of storytelling, public speaking, conflict resolution, and stakeholder communication in an AI-driven workplace. Along the way, we discuss why traditional resumes and polished cover letters may matter less in a world where anyone can use AI to sound impressive, and why some companies are moving toward live prototyping and real-time problem solving in interviews instead.

Later, we get into AI agents, what Dan is building himself, and how leaders can create stronger AI adoption inside their companies. We also talk about what it takes to stay competitive in a job market where AI is changing the stack of work, but not necessarily replacing the worker.

Guest
Dan Roth — Editor in Chief, LinkedIn

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What happens when software stops just “chatting” and starts acting in the real world, across real workflows, with real consequences?

In this episode of AI-Curious, the Head of AI at Cognizant goes deep on AI agents and agentic workflows: what they are, why enterprises are investing heavily, and what it actually takes to make agent systems reliable and safe at scale. We unpack what separates an AI agent from a traditional chatbot, why “agency” changes the stakes, and how multi-agent systems can be designed to reduce risk instead of amplifying it.

We also explore concrete enterprise use cases, including agent hierarchies that coordinate across complex systems (like networks, utilities, and other operations), plus how “agentic process automation” builds on older automation models while adapting to unexpected edge cases. Finally, we zoom out to the future of work: which tasks get augmented first, why disruption is happening faster than most forecasts, and how trust in AI systems may shift over the next several years.

Guest

Babak Hodjat — Head of AI at Cognizant; leads AI lab work focused on scaling reliable, trustworthy agent systems; longtime AI builder with deep experience in applied natural language systems. 

Key topics we cover

• 07:00 — What an AI agent is (and how it differs from a chatbot)
• 13:03 — State of play: what’s working, what’s not, and why “agent systems must be engineered”
• 17:00 — A practical multi-agent design pattern across telecom, power, and agriculture
• 20:28 — Agentifying rigid processes (and handling unforeseen situations)
• 24:14 — Who should deploy agents, why single “do-everything” agents are risky
• 26:34 — An open-source starting point for experimenting with multi-agent systems
• 29:12 — Guardrails: reducing hallucinations, adding redundancy, and safety thresholds
• 35:29 — Why we should use LLMs for reasoning, not knowledge retrieval
• 38:15 — The future of work: tasks, jobs, and decision-making roles shifting upward
• 41:59 — AGI, limitations, and why modular multi-agent systems may matter
• 44:57 — A prediction: we’ll delegate more than we expect as systems become more trustworthy

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Dmitry Shaprio has been in the space for decades; he was a product lead at Google and the CTO of MySpace. 

Now he's the CEO of MindStudio, a company that helps companies create custom apps to integrate AI.  

And a LOT of companies are using MindStudio.  34,000 apps have now been created using MindStudio, and this ranges from large corporations to solo-entrepenuaers to government agencies.  The apps are used for sales, HR, marketing, operations -- everything.

Dmitry, essentially, is helping companies go from AI Hype to AI Substance.

We get into the nitty gritty of why these Apps matter, how they're used, and how businesses can (actually) integrate AI into their operations. 

Fun episode!

MindStudio.ai:
https://youai.ai

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At a recent hackathon, Ian Laffey was part of a team that designed, created, 3D-printed, and assembled a drone in 24 hours... and for under $500.

The drone's breakthrough: It doesn't need a signal for GPS, which means that it can be deployed off the grid, and can't be "jammed" in a war zone...like Ukraine.

Laffey's drone is now a sensation in the world of defense-tech. 

Laffey is now the CTO of Theseus, and he joins the pod to discuss how his team pulled this off, why the drone is such a breakthrough, how AI played a crucial role -- we get into the nuts and bolts, not just platitudes -- and then Laffey shares, on a day-to-day basis, what it's really like to be a 24-year-old AI engineer working in San Francisco, the world's nexus for AI innovation.

Fun convo! 

Ian Laffey on Twitter/X:
https://twitter.com/ilaffey2

Theseus:
https://theseus.so

Article from AviationWeek on the drone Laffey helped create:
https://aviationweek.com/aerospace/emerging-technologies/how-trio-engineers-developed-gps-denied-drone-under-500

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There are many AI doomsdayers.

Kevin Surace is not one of them.

Kevin has been in a pioneer in the AI space since the 1990s, went he helped develop the tech that would directly influence Siri and Alexa. He has since been named by CNBC as one of the "Innovators of the Decade" and is now a regular keynote speaker.

We have a *wide-ranging* conversation on how AI will shape the future, covering everything from robots to flying cars to job losses to whether AIs can become sentient. 

A really fun conversation.

You can find Kevin at:
https://kevinsurace.com

and 
https://twitter.com/kevinsurace?lang=en

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If you need to get a puppy, what's the best way to go about it?

Harnessing AI,  of course.

I recently decided to get a puppy. I didn't know what to get or how to do it. (I've never owned a dog.)

So I made liberal use of ChatGPT in research, analysis, and execution, and then turned to AI for help with puppy training.

I doubt you're in the market for a new puppy, but I think this is a useful extended metaphor of how AI -- and specifically ChatGPT -- can help in a very concrete, real-world analysis situation.

(This episode is sponsored by my new puppy.)

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AI might change the world. But it can it help change you? 

You could argue that AI coaching, fundamentally, might be the most important impact of the entire tech. In today’s world, professional career coaching is generally only available for the privileged. What if it was available for all? But what are the risks and concerns?

Joining us to break this down is Dr. Friederike Redlbacher, Managing Director of Symbolon AG. She’s working with the German Research Center for Artificial Intelligence and developed the world’s first AI-based coaching tool in 2023, which is largely art-based. 

In this episode, Dr. Redlbacher and I cover the pros, cons, upsides, risks, and exciting potential of injecting AI into the coaching landscape. 

Link to Symbolon self-coaching:
https://www.dfki.de/symbolon-coaching/?language=en

To claim one of the 5 free codes offered by Dr.  Redlbacher, email me at jeff@jeffwilser.com with the subject line “AI coaching - code.”

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If AI gets super intelligent and super powerful, will it be benevolent? Or will it want to crush us like ants?

Perhaps they’ll be benevolent. Maybe they’ll be “aligned” with our interests. But if not? What should we do? How can we prepare for this nightmarish scenario?

Trent McConaghy has a plan. McConaghy has been working in AI for decades, both as an entrepreneur and as a researcher, and he’s widely respected in the field. 

And he recently gave a speech to the “existential-risk” (x-risk) group at NASA about how, to prepare humanity for this all-powerful AI…We need to become more than humans. Specifically, we need to embrace the idea of Brain-Computer Interface, or BCI. 

BCI, says McConaghy, can even help unshackle us from the constraints of being a flesh-and-blood human. In a literal mind-meld with computers, we could be 1,000 times smarter and live longer. 

This sounds like deep sci-fi, but McConaghy lucidly breaks down the vision in a way that’s step by step, easy to understand, and almost feels…plausible?

Whether you agree with the theory or not, McConaghy’s thoughts are FASCINATING and he makes for a wildly engaging conversation about the future of AI. I’m confident that anyone even remotely interested in AI — or sci-fi — will find his thought experiments intriguing. 

Among many other sweeping and futuristic topics, we cover:

Why economic incentives make AGI (or “ASI,” for Artificial Super Intelligence) is perhaps more likely than we think (13:50); Why we might soon be like an anthill compared to god-like entities of Artificial Smart Intelligence (23:00); whether these all-powerful ASIs will protect human rights (26:00); why we need a “more competitive substrate” that helps us be more than human and hyper-boost our intelligence (29:00); how creating powerful Brain-Computer-Interfaces is more grounded and less sci-fi-ish than you might think (35:00); how we will some day —maybe soon — be able to just think of videos and use telepathy to send to friends (45:00); why the human “bio-stack” won’t be enough and we’ll need a synthetic solution (53:00); the dangers of “bike shedding” the risks of ASI (59:30); how BCI could plausibly go mainstream (61:30); why when we’re 94 years old we might switch from our “bio-stack brain” and migrate to our “silicon-stack” side (65:00). 

So join us on a futuristic and fascinating journey….

McConaghy’s presentation to the existential-risk group at NASA:
https://drive.google.com/file/d/1tLK1sBCMNuJTNX8Pi99iDmLzFiIsUOk5/view

McConaghy’s Medium article that breaks down the BCI theory:
https://medium.com/@trentmc0/bci-acc-a-path-to-balance-ai-superintelligence-80bb6f32e39c

McConaghy on Twitter/X:
https://twitter.com/trentmc0

Ocean Protocol, the blockchain-powered AI ecosystem that McConaghy founded:
https://oceanprotocol.com

My article about AI and blockchain where I first interviewed McConaghy, and he shares a part of this theory (which got me fascinated in the first place):
https://www.coindesk.com/consensus-magazine/2023/09/21/is-crypto-ai-really-a-match-made-in-heaven/

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The cover of this week’s TIME Magazine has a bold headline: “The First AI War.” The subtitle: “Palantir and other tech giants are building the future of battle in Ukraine.”

The writer of this cover story is Vera Bergengruen, a senior correspondent at TIME.

And Vera joins us today at AI-Curious.

We cover: How Ukraine is using AI and what it’s accomplishing (3:00); how they’ve created a “war lab” that's a testing ground for new innovations (12:00); privacy and ethical concerns (23:00); repercussions for the future of war (37:00); and some behind-the-scenes from her reporting (40:30).

Important topic. And Vera's the perfect guest to discuss it. I thoroughly enjoyed the conversation.

Vera’s cover story in TIME Magazine, “The First AI War”:
https://time.com/6691662/ai-ukraine-war-palantir/

Vera’s author page:
https://time.com/author/vera-bergengruen/

Vera Bergengruen on Twitter/X:
https://twitter.com/VeraMBergen?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor

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Confession: I don't really care about cars. But I *love* the idea of self-driving cars.

So what's the status of this idea? There's now a fleet of self-driving cars in San Francisco.  How good are they? How safe? What are the limitations? And what are the technical challenges that still need to be overcome, and how does AI fit into all of this?

To unpack all of this, I'm joined by Noah Gedrimas, the Vice President of Strategy at GPR, a company at the forefront of the self-driving care movement.  GPR is building tools that allow for precise "localization" -- letting the car know exactly where it is on the road, a crucial piece of the self-driving puzzle.

Super fun convo! After this talk I'm even more excited about this industry's future.

GPR
https://gpr.com

Noah Gedrimas
https://www.linkedin.com/in/noah-gedrimas-14307960/


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Eric Siegel is a longtime expert on machine learning, and is the author of the new book "The AI Playbook: Mastering the Rare Art of Machine Learning Deployment."

Eric has been a professor at Columbia University, a longtime machine learning consultant, and he's the founder of the long-running "Machine Learning Week" conference series. 

Eric's take? AI is over-hyped, but machine learning still has much to offer a business...if you know how to deploy it. So in this pod, we go t through his step-by-step playbook of how to deploy machine learning, and how that's different from more squishy AI hype.

The book has been getting some buzz in the space. Scott Galloway gave it a glowing blurb, calling it a "robust primer on machine learning,” and a “must-read for anyone in the information economy.”  

Fun convo!

The AI Playbook: Mastering the Rare Art of Machine Learning Deployment
https://www.amazon.com/dp/0262048906/ 

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Can AI make you rich? Or, more to the point, can AI make investment banks, hedge funds, and financial services companies even richer?

Not so fast...

Vasagi Kothandapani is an expert in helping financial services companies train, develop, and integrate AI. She's the Senior Vice President of AI Training at RWS, a tech-enabled content and language organization.  And she stops by the pod to help separate the hype from the reality of AI in fintech... what are the exciting use cases, and what are the sober realities that need to be overcome?

You can think of this as essentially a "case study" for the challenges that all businesses face when trying to incorporate AI.  Sounds easy but the details get tricky in a blink.

So even if you're not in finance, anyone who does any kind of work can relate to much of these larger themes and tradeoffs.

AI at RWS:
https://www.rws.com/artificial-intelligence/

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Is AI quietly creating more work than it’s replacing, and are we measuring the job market the wrong way?

In this episode of AI-Curious, we talk with the CEO of Upwork, Hayden Brown, about what the platform is seeing across the global freelance economy, and why the “AI is killing jobs” narrative can miss what’s happening at the edges of the market. We also dig into how to adopt AI inside an organization without just “sprinkling fairy dust” on old workflows, and what it takes to make AI rollout a cultural shift, not just a tooling upgrade.

Guest

Hayden Brown is the CEO of Upwork, the global work marketplace connecting businesses with freelance talent across knowledge-work categories. We discuss Upwork’s vantage point on hiring trends, the rise of fractional work, and what AI-driven change looks like when companies redesign workflows end-to-end rather than retrofitting existing systems.

Key topics we cover

• 03:50 — A global background and why opportunity access shapes the mission
• 05:27 — The scale of Upwork and why freelancing is a major part of the economy
• 07:14 — How we approached AI adoption as a structured, company-wide program
• 08:47 — Early “two-year vision” ideas that reshaped marketing and product workflows
• 11:34 — Reducing fear: how we framed AI internally, including room for mistakes
• 16:03 — Building an AI agent experience (and what it changed about job posts)
• 17:14 — Why “reinventing, not retrofitting” separates AI winners from strugglers
• 22:24 — Why macroeconomics can explain more than AI in hiring slowdowns
• 23:01 — The core claim: AI creating more opportunities than it’s destroying
• 24:05 — Fractionalization: how full-time jobs get broken into AI + human slices
• 25:09 — A concrete example of humans working alongside AI in production workflows
• 26:32 — From “prompt engineer” to “AI generalist”: orchestration becomes the ask
• 28:11 — Why the AI jobs debate is too binary, and what’s getting missed
• 31:43 — Practical reskilling: embedded experts who train teams while upgrading systems
• 36:29 — AI’s impact across unexpected categories, including creative work
• 39:15 — Five-to-ten-year outlook: humans as orchestrators, premium on human skills
• 43:22 — Career advice for early-career listeners in an AI-shaped job market
• 45:40 — Real-life AI use: editing, learning, and replacing the blank page problem

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As our quick appetizer, a quick dispatch from the Sundance Film Festival, where AI was seemingly *everywhere*.

For our entrée, we speak with Hilke Schellmann, author of the new book "The Algorithm: How AI Decides Who Gets Hired, Monitored, Promoted, and Fired and Why We Need to Fight Back Now."

If you have a job, or will some day get a job, or know someone with a job, this is a topic relevant to you.

Link to The Algorithm:
https://www.amazon.com/Algorithm-Decides-Hired-Monitored-Promoted/dp/0306827344/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr=

Hilke on Twitter/X:
https://twitter.com/HilkeSchellmann

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What are the most important stories in AI right now? Today we chat with Sharon Goldman, Senior Writer at Venture Beat, who covers all things artificial intelligence.

Sharon and I get into:

Why the race for training data is so crucial in the future development of AI (2:30); why so much hinges on The New York Times vs. Open AI (12:40); the looming wars between Open AI and Anthropic and XAI and others (31:00); the potential coolness (and terror) of AI hardware (35:00); and much much more.

Sharon Goldman on Twitter/X:
https://twitter.com/sharongoldman

Sharon Goldman’s author page at Venture Beat:
https://venturebeat.com/author/sharongoldman/

Inc Magazine’s “4 Ways to Get AI Savvy in 2024,” featuring….the podcast AI-Curious!
https://www.inc.com/amanda-pressner-kreuser/4-ways-to-get-ai-savvy-in-2024.html

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We all know AI has tons of potential, but how do you *actually* use ChatGPT in your daily life?

Sam Stevens is the cofounder of Catalist AI, an AI-powered project management assistant. She’s also the writer of the “Prompt and Circumstance” newsletter, where she shares hyper-pragmatic tips and tricks for how to get the most out of ChatGPT and other AI tools.

So in this episode, we go deeeeeep into the practical, concrete, actual ways that you can use ChatGPT in your day-to-day life.  This is useful for beginners and pro-users alike, as Sam drops a ton of helpful strategies.

We cover: 

Using ChatGPT as a thought partner (3:40); ChatGPT for personal project managet and schedule optimization (4:50); the benefits of giving AI super-specific context (15:50); how and why to use AI to help analyze content (23:30); using AI to step up your LinkedIn game (28:30); how to use ChatGPT as a thought partner and for “rubber ducking” (30:00); favorite prompt engineering strategies and tips and tricks (32:40); Sam reveals her global system prompts (35:30); the goal of her new start-up Catalist AI (38:30) and much more. 

To get on the waitlist for Catalist AI:
www.catalistai.com

Prompt & Circumstance newsletter:
https://promptandcircumstance.beehiiv.com

The "too online" AI/web3 newsletter that Sam also writes:
BoysClubWorld
https://boysclub.vip/brandnew/

Sam Stevens on LinkedIn:
https://www.linkedin.com/in/samanthastevens01/

Sam Stevens on Twitter/X:
https://twitter.com/SamJStevens

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As deep fakes get easier and the truth gets murkier, how will AI impact the 2024 election?

We’re joined by election expert David Becker, the founder and Executive Director of the non-partisan and non-profit Center for Election Innovation & Research. David is also the co-author of “The Big Truth: Upholding Democracy in the Age of The Big Lie.’”

David breaks down the many aspects of AI and the 2024 election, including: The current state of play of election integrity and why they’re more secure than you might think (5:15); what most concerns him about how AI-enabled mischief could muck things up (14:30); how deep fakes could cause problems (21:00); why disinformation could be a problem *post* election (36:45); why in 2024 the LEFT might be the victim of a disinformation campaign and wrongly think that the election was “stolen” (39:30); and finally what we can do to protect and inoculate ourselves against AI-fueled misinformation (45:00).

David Becker on Twitter/X:

https://twitter.com/beckerdavidj

The Center for Election Innovation and Research

https://electioninnovation.org

Election Official Legal Defense Network:

https://eoldn.org

David’s book, “The Big Truth”:

https://thebigtruthbook.com

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Where is AI headed next year?

I curated some of my favorite AI predictions -- and give a few of my own -- from some of the year-end AI roundups and prediction articles.

We cover deep fakes, further AI integration into business, the 2024 election, AI going mobile, and much more.

AI prediction articles referenced in the pod:

The Economist:

https://www.economist.com/the-world-ahead/2023/11/13/generative-ai-will-go-mainstream-in-2024

PWC:

https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.html

Axios

https://www.axios.com/2023/12/27/ai-predictions-tech-trends-2024-openai-chatgpt

Yahoo Finance

https://finance.yahoo.com/news/2024-predictions-how-ai-will-impact-everything-in-healthcare-162535006.html

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So what did we learn about AI in 2023? What are the most important trends?

To make sense of this wild new space, I’m joined by tech strategist and AI consultant George Kamide, who’s also the co-host of the cybersecurity podcast, “Bare Knuckles and Brass Tacks.”

George and I cover:

The public’s “first contact” with generative AI, and why that seems to be plateauing (03:00); the merits of “tuning” a model, and what it will take to get that mainstream (07:00); the biggest AI trends in business from 2023 (17:30); whether AI will “take all the jobs” (20:30); why we are *already* in the age of synthetic reality and misinformation (25:20); the trends of multi-modality and integration and AI agents (39:00); and why 2024 might involve the “trough of disillusionment” (50:30).

This was a fun wide-ranging conversation - enjoy!

Bare Knuckles and Brass Tacks podcast
https://www.bareknucklespod.com

Ikea case study referenced by George:
https://www.reuters.com/technology/ikea-bets-remote-interior-design-ai-changes-sales-strategy-2023-06-13/

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You get a FaceTime from a loved one. They're in trouble. Big trouble.  Their life is in danger. They need you to Venmo you $, they have no choice.

Except...it's not them you're FaceTiming with, it's an AI-enabled deepfake that can talk to you real-time.

Welcome to the fun world of AI phishing and cyberattacks!

Christian Seifert, a veteran cyber-security expert (he used to head up cyber-security at Microsoft, and now he's Researcher in Residence at the Forta network), joins the show to unpack these kinds of risks. How worried should you be? How grim will the future get?

We cover:
How AI makes it easier for personalized scams and fakes (2:30); why AI security needs to be baked into the tech and tools we use today (8:30); why “prompt injections” pose a risk and what we can do about it (18:00 ); how AI agents could cause mischief as a type of “super virus” (20:00); whether he prefers open-sourced or closed-sourced AI development for AI safety (25:00); why Christian is cautiously *optimistic* that the future will bring an advantage to AI safety defenders (26:30); and recommendations to the listener of how they can minimize AI risk (29:30); and a surprise twist that AI is making the jobs of cyber-security... paradoxically easier? 

The Forta Network:
https://forta.org

Christian Seifert on Twitter/X:
https://twitter.com/cseifert

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Now it's time for the flip side of AI.

What could go wrong?

Daniel Colson is the Executive Director of The AI Policy Institute, which has a mission of helping policy-makers create smart regulation. They also conduct polling to better understand how Americans are thinking about AI.  Daniel, at heart, is a tech optimist, but in this episode he breaks down the reasons that AI makes him pause. 

We cover: The different categories of AI risk, including malicious use by bad actors, AI-arms race dynamics between states, Super-AIs going off the rails, and the less obvious ”organizational risks” (04:30); Insights from his polling on how Americans are thinking about AI (31:00); Regulation that Daniel hopes to see (40:00).

The AI Policy Institute:
https://theaipi.org

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Send in the clones. They're here. 

Don Bosco is the founder and CEO of BHuman, a start-up that is, well.... creating human clones. For real! Don has cloned himself and he can easily clone you. (Digitally, at least.)

So why the cloning? Don views this as a way for him to get more done in his own life -- his clone can knock out all the pesky online things he doesn't want to do. We cover a LOT of ground in this episode, including: The benefits of having a clone, and how it can help for businesses (3:00); what having a digital clone actually looks like in practice (5:40); why he is now CREATING 35 MILLION CLONES!! (24:10); the risks and ethics and downside of cloning (29:00); and how Don thinks that cloning ourselves, paradoxically, helps us be more human (55:00).

This episode was a blast to record. Hope you enjoy.

BHuman:
https://www.bhuman.ai

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When the AI hype began in earnest, "marketing and advertising" was one of the industries seen as most likely to be disrupted. 

This is already happening. Enter Deniz Ozgur, partner at AI marketing firm Evercopy, and one of Forbes' "30 Under 30" for her groundbreaking work in retail and ecommerce. 

Deniz shares how AI is *already* effective at generating both marketing content and even campaigns, and gives predictions on how it will change the industry going forward. (The theme: Personalization, personalization, personalization.)

Deniz is also the co-founder of Space Runners, an AI/fashion company, which lets users inject their own personalized creations into pre-existing brands. (Back to that theme: Personalization.) 

And before that, we'll get some thoughts on the true meaning of Thanksgiving from someone who knows all about human gratitude: ChatGPT.

Evercopy:
https://www.evercopy.ai

Space Runners:
https://spacerunners.com

Deniz Ozgur on Forbes' 30 Under 30
https://www.forbes.com/profile/deniz-ozgur/?sh=7251c738432f

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What does “human-first AI” actually look like when you have to make decisions under pressure, hit numbers, and keep trust intact?

In AI-Curious, we talk with Kate O’Neill — “the Tech Humanist” and author of What Matters Next — about how leaders can adopt AI in ways that strengthen human outcomes instead of quietly eroding culture, morale, and customer experience. We dig into why so many AI initiatives fail for non-technical reasons, how to think beyond short-term wins, and why prompting is less “prompt engineering” and more like learning to delegate clearly.

Key topics:

Prompting as delegation: defining success conditions, constraints, and what “good” means (00:00)

Kate’s early work at Netflix and what personalization taught her about human impact (04:45)

What “human-unfriendly” tech looks like in practice, from subtle friction to scaled harm (09:28)

The Amazon Go example: how small design constraints can scale into behavior change over time (11:19)

AI in the workplace: why “cut, cut, cut” is shortsighted, and what gets lost when you optimize only for this quarter (14:14)

Trust and readiness: why reskilling fails when people don’t believe there’s a future for them (16:45)

The now–next continuum: making decisions that “age well,” not just decisions that look good immediately (17:29)

Preferred vs. probable futures: identifying the delta and acting to move outcomes toward what you actually want (19:22)

“Chatting with Einstein”: using AI to become smarter vs. outsourcing thinking (22:13)

Why most AI pilots fail: human and organizational readiness, not the tech itself (24:02)

Questions → partial answers → insights: building an organizational muscle that compounds (28:21)

Bankable foresight: why Netflix invested early in what became streaming (30:37)

Trend watch: the pivot from LLM hype to agentic AI, and why prompting still matters (38:58)

Sycophancy and “best self” prompting: getting better outputs by being explicit and structured (41:01)

Probability vs. meaning: what LLMs can do well, and what they can’t replace (44:45)

A fun real-world workflow: Kate’s Notion + AI system for hotel coffee-maker recon (46:26)

Career advice in the AI era: adaptability, “human skills,” and shifting definitions of value (49:21)

Guest
Kate O’Neill is a tech humanist, founder and CEO of KO Insights, and the author of What Matters Next: A Leader’s Guide to Making Human-Friendly Tech Decisions in a World That’s Moving Too Fast. She advises organizations on improving human experience at scale while making emerging technology commercially and operationally real.

KO Insights:

https://www.koinsights.com/about-kate/

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Some of the best use cases of AI are when we think of it not as Artificial Intelligence, but "Augmented Intelligence."

That's what Bryan Murphy, CEO of Smartling, is effectively doing: Using AI to enhance and super-charge their systems, as opposed to scrapping the old way and starting from scratch. 

Smartling is a company that uses AI to translate languages for clients like Peloton, Shopify, and Pinterest. On this episode, Bryan explains how they've embraced AI to do it faster and cheaper and at a higher quality (9:10); why global translation matters (14:35); whether AI can help us achieve a Star Trek-level "universal translator" (16:30); whether AI can effectively translate literature or poetry (21:00); and why all of us are probably better off in our careers embracing AI (23:00).

And before that, for the first time on this podcast, Jeff shares how he's personally using AI...

Smartling:
https://www.smartling.com

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The most important, wide-reaching, and profound use case for AI might just be drug discovery. Normally it takes YEARS for scientists to develop drugs; AI can slash the timeline.

Kennedy Schaal is the CEO of Rejuve Bio, a startup that’s harnessing AI for faster drug discovery. And Jasmine Smith is the CEO of Rejuve AI, its sister company, which is racing to build a data-set that’s more equitable and inclusive and reflective of all humanity, with an end game of extending our longevity. 

Kennedy and Jasmine discuss the current problems with drug discovery, how AI can help, and how AI can be especially impactful for more “niche diseases” that wouldn’t normally provide enough profit for Pharma companies to invest in.

Conversation with Rejuve Bio CEO Kennedy Schaal at (3:00).

Conversation with Rejuve AI CEO Jasmine Smith at (40:30).

Rejuve Biotech:
https://www.rejuve.bio

Rejuve AI:
https://www.rejuve.ai

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By now you've heard the question, "Can AI create art?"

Mario Klingemann had a more provocative question: "Can an AI machine be an artist?"

So Mario set out to do just that. He created an ingenious AI-powered system called "Botto," which is creating art as we speak. Each week, a group of humans votes on the best stuff, and then the winners are sold as NFTs. Sometimes these are listed at Christie's.

Mario's work is FASCINATING. And he's been working with art and machine learning for over 15 years; his work has appeared in places like The Met, the MoMa, and the British Library.

In this episode we cover what draws him to AI (3:30); the creation of Botto (8:00); whether AI can truly be creative (25:00); his advice for traditional artists looking to break into AI (32:00); and his predictions for the future of Botto and AI art (38:00).

Fun episode!

Mario's Wikipedia page:
https://en.wikipedia.org/wiki/Mario_Klingemann

Mario's personal site and gallery:
https://quasimondo.com

Botto.com:
https://www.botto.com

My interview with Mario at CoinDesk:
https://www.coindesk.com/consensus-magazine/2023/08/22/meet-botto-the-ai-artist-that-mints-its-own-nfts/ 

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Will AI kill the newsroom? Possibly. But today’s guest has a different goal. Winston Chen, the co-founder of Nano Media, is using AI to try and *save* the newsroom, or at least to save local newsrooms. 

The fact is that over the last 20 years, local newsrooms have been gutted — most are no longer economically viable. So Winston is using AI to create tools to help revitalize the nation’s network of newsrooms… and it has already started.

In this episode we cover:

How a loss of local news leads to tribalism and polarization (5:00 ); How a morning walk with his dog sparked the idea for AI experimentation (14:30); How the team began experimenting with using AI to summarize YouTube videos of local meetings (23:00); Whether AI should “write” a news story (29:00); Why the AI tool is designed to be boring (32:00); Future types of AI-empowered content for the newsroom (43:30 ); Predictions for how AI will impact the media industry (54:00 ). 

For any fellow media nerds out there, this one's for you!

NanoMedia:

https://nanomedia.org

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For many, the first "gateway drug" for generative AI is text-to-image, using tools like DALL-E and Midjourney to instantly flex your creative muscles.

Heather Cooper is a master of AI visual art and storytelling.

She has quickly become one of the most respected voices in the AI visual art/storytelling space. Her Substack, Visually AI, shares all kinds of tips and tricks on how to make better AI images. And she's also just really, really, really good at creating stunning AI visual artwork.

This episode is for anyone who either 1) has ever dabbled in visual AI and wants to get better; or 2) enjoys hearing stories about how people turn side-hustles into proper hustles, and what we can learn from their success.

We cover: Her strategies for learning and mastering visual storytelling with AI, and how  worked to make prompts more inclusive and representative (7:00); her favorite use cases for ChatGPT (20:00); advice she gives people when they're starting out with visual AI art (22:00); her favorite tools, and the sneaky power of spreadsheets (26:30); hyper-specific prompting tips and tricks (38:30); and much much more.

Heather Cooper's website (check out her gallery):
https://heatherbcooper.com

Heather's substack, Visually AI:
https://heatherbcooper.substack.com

Heather Cooper on Twitter/X:
https://twitter.com/HBCoop_

Heather Cooper on Instagram:
https://www.instagram.com/hb.coop_/

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What should be the role of AI in education?  Cheating is an obvious concern. But can AI be used to accelerate, personalize, and even empower education? 

That's the path taken by All Star Code, a non-profit education organization that teaches tech and coding skills to young men of color.  Danny Rojas is All Star Code's Executive Director.  And he has been *extremely* forward-thinking in integrating AI into both the curriculum and the non-profit's workflow.

We discuss:

How AI can be used as a "co-pilot" for students (4:00); How they're thinking about the issue of AI and cheating (12:00); How AI can help teachers with lesson plans (15:00), How AI can help high school students in surprising ways (22:00); How AI can help scale the non-profit (27:00); Practical tips for more effective prompting (35:15); What's exciting about AI trends in education in the future (43:30).

All Star Code:
https://allstarcode.org

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If AI becomes conscious, you could argue this is the most important inflection point in the history of humanity.

So how will we know if and when that happens?

Professor Grace Lindsay, a neuroscientist, recently co-authored a report -- along with a team of philosophers and fellow neuroscientists -- that suggested a framework for how we might know if AIs are conscious.  

In this episode, Dr. Lindsay unpacks that report and helps us understand it, shedding light and clarity on a tough-to-understand topic.  We cover: The background and goals of the AI Consciousness Study (5:00); The philosophical chicken-and-egg problem of how to define consciousness (07:50); Why it's so damn tricky to determine if AIs are conscious (16:00); An overview of the study's framework (19:00); Why all of these possible criteria might be "necessary but not sufficient" (26:00); Whether any current AI projects are getting close to consciousness (42:00); What most people get wrong about AI consciousness (50:30). 

The report Dr. Lindsay co-authored, "Consciousness in Artificial Intelligence: Insights from the Science of Consciousness":
https://arxiv.org/abs/2308.08708

The New York Times - "How to Tell if Your A.I. Is Conscious":
https://www.nytimes.com/2023/09/18/science/ai-computers-consciousness.html

Professor Lindsay's book "Models of the Mind: How Physics, Engineering and Mathematics Have Shaped Our Understanding of the Brain":
https://www.amazon.com/Models-Mind-Engineering-Mathematics-Understanding/dp/1472966422/ref=sr_1_1?dchild=1&keywords=models+of+the+mind&qid=1612921824&sr=8-1

Professor Lindsay's site:
https://gracewlindsay.com

Professor Lindsay on Twitter/X:
https://twitter.com/neurograce

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If AI becomes sentient, what are the moral, ethical, and legal implications?
 
This is a tricky question with many, many, *many* follow-up questions. So here to help is philosopher Jeff Sebo -- a professor at New York University; the Director of the Mind, Ethics, and Policy Program; and the author of the book "Saving Animals, Saving Ourselves."
 
In this sprawling conversation, we cover the ethics of AI from a ton of different angles: What can we learn about consciousness from the study of animals, and how does that apply to AI? (4:00); How can we discern AI consciousness? (11:10); What are the implications of AI sentience, and do they deserve rights? (25:30); What are some legal and moral scenarios we might need to deal with if AIs become sentient? (30:00); Why it's surprisingly urgent to start tackling these questions NOW (35:00); Is it okay to be a "species-ist"? (46:30); Why AI Rights and AI welfare might become the most controversial and polarizing issue of our lifetime (52:15).

Jeff Sebo on Twitter/X:
https://twitter.com/jeffrsebo

Jeff Sebo's book "Savings Animals, Saving Ourselves"
https://www.amazon.com/Saving-Animals-Ourselves-Pandemics-Catastrophes/dp/0190861010

Jeff Sebo's Los Angeles Times Op-Ed, "What should we do if a chatbot has thoughts and feelings?"
https://www.latimes.com/opinion/story/2022-06-16/artificial-intelligence-morals-ethics-sentience-thinking

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You can now use AI to create a digital clone, or "digital twin" of yourself or loved one or anyone, really. Why would you do this? What's the benefit? And why do some digital twin enthusiasts view the tech, essentially, as a way to cheat death?
 
There's no better expert on digital twins than Francesco Rulli, founder and CEO of Querlo, an AI company that provides services to clients like Microsoft and IBM and Pepsi. 
 
Francesco has created his own digital twin. He shares how and why he did it (4:40), he opens up with candor and vulnerability about how, he hopes, he can use digital twins to communicate with his deceased parents (11:00), he predicts when this will all become a reality (17:00), he responds to the critics who say "this is all too creepy" (20:00), and provides some wider AI predictions (24:00).
 
 You can meet Francesco's digital twin at:
 https://www.francescorulli.com
 
 You can learn how to create your own digital twin through Querlo at:
 https://www.querlo.com/digitaltwin

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Do you work at a job? Then you're probably involved in some kind of business operations, and there's a 99.999% chance that your job will be impacted by AI.

So how do you leverage AI to make your job easier? If you're a business leader, how do you create an AI strategy for your company or department? Where should you even begin?

I spoke with an expert in all of this, Rachel Woods, CEO of The AI Exchange, who's a consultant and educator for how businesses can embrace AI. 

We get into it!

At a high level, Rachel shares the AI strategies and principles that businesses should think about, as opposed to just using the tools (4:40), how to think about the question of whether AI will take our jobs (11:00), some of her favorite AI tools for boosting productivity (14:00), the importance of creating what she calls "bespoke systems" (18:00), what AI is surpassingly good at... and surprisingly bad at (28:34), and advice for those in business just starting out with AI (39:40).

Bonus? At the end of the show, Rachel provides a promo-code for AI-Curious listeners to use for the educational courses at The AI Exchange.

Super practical episode, hope you enjoy.

You can find Rachel Woods at:
https://twitter.com/rachel_l_woods

The AI Exchange:
 https://theaiexchange.com

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What happens when the world’s most-used creative tools get smarter — and creators worry they’re losing the wheel?

In this episode of AI-Curious, we talk with Eric Snowden, Senior Vice President of Design at Adobe, about how Adobe is weaving AI into Photoshop, Lightroom, Acrobat, and beyond — while trying to keep the tools respectful of craft, muscle memory, and the human spark. We dig into the bigger question beneath the feature releases: as AI accelerates creation, do we get more powerful… or do we become passengers approving machine outputs?

Key topics:

Two buckets of Adobe AI: upgrading existing tools vs building net-new AI products (00:04:55)

Photoshop “harmonize,” Lightroom auto culling, and Acrobat “PDF spaces” (00:04:55)

Why PDFs are a bottleneck for knowledge work, and how Acrobat can help you “get 80% of the way there” (00:07:18)

Project Graph explained: node-based workflows that stitch together building blocks like Firefly and Photoshop (00:08:25)

A concrete Project Graph example: 2D product photo → 3D asset → generated ad → multiple animated versions, with the user still in control (00:09:42)

Time saved vs creating more: how Firefly helped Adobe teams move faster and “make more things,” including “like 40% improvement” on time-to-market (00:14:28)

A Max London demo that captures the core principle: “his hand was on the wheel” (00:17:45)

“Quiet AI” in practice: enhanced audio in Adobe Podcast that can make phone-recorded audio sound studio-ready (00:19:57)

Respecting creative muscle memory: why “subtraction is not always good,” and why Adobe adds new workflows without removing old ones (00:24:43)

Firefly’s principles: licensed content, knowing what’s in the model, and compensating creators (00:29:29)

Content authenticity as a “nutritional label for AI”: immutable metadata describing what was done to an image (00:30:15)

The self-driving car analogy: creators need to be able to “grab the wheel” and tweak under the hood (00:36:00)

Vibe coding inside Adobe: designers using Cursor and internal tooling to build prototypes that hit real APIs (00:39:18)

A leadership playbook for AI adoption: focus the OKRs, make training practical, show examples, remove roadblocks (00:44:19)

The future of AI creative tools: communicating intent beyond text prompts, and shifting from “look what I do with AI” to storytelling (00:46:36)


Guest
Eric Snowden is the Senior Vice President of Design at Adobe, overseeing design and the AI-infused creative tools used by millions of creators.

Mentioned in this conversation
Adobe Firefly

Project Graph (node-based creative workflow building)

Enhanced audio in Adobe Podcast

Content authenticity / provenance metadata (“nutritional label” concept)

Cursor and “vibe coding” for rapid prototyping inside enterprise teams

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Think of this as an "AI case study" -- a way to make the abstractness of AI concrete by going deep on a hyper-specific example.

Swupnil Sahai is a former Tesla engineer. He worked directly with Elon Musk on the AI that powers self-driving cars.

He then created "Swingvision," an AI-powered tennis app, which recently won Apple's prestigious Design Award. (Other winners include heavyhitters like Headspace and Duolingo.)

The app records your tennis match. It then lets you analyze the results, review your form, and even challenge line calls in real-time. It's now being used by 100+ division 1 college tennis programs.

All of this is powered by AI. So I spoke with Sahai about how the AI works (7:00), what he learned about AI from Tesla and Musk (11:45), how neural networks come into play (15:30), how they built their data model (19:40), what AI is good at and what it's surprisingly bad at (23:30), and his advice for AI founders and entrepreneurs (36:00).

SwingVision:
https://swing.tennis

SwingVision wins Apple Design Award:
https://techcrunch.com/2023/06/06/apple-reveals-its-2023-apple-design-award-winners/#:~:text=Image%20Credits%3A%20SwingVision,of%20strategy%20to%20the%20game.

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Welcome to AI-Curious,  a podcast that explores the good, the bad, and the creepy of artificial intelligence.  

For the debut episode, I'll introduce the conceit of the show, and then we'll speak to some Hollywood producers who are pioneers of using AI:  the cofounders of Toonstar Studios, CEO John Attanasio and COO Luisa Huang.

John and Luisa bring a somewhat unique perspective of AI in Hollywood. For years, they've used AI *not* to replace writers and actors... but instead to speed up production and build communities. They share first how AI helped lower the barriers to entry for animation (5:30), then how their shows and franchises (The Gimmicks, Space Junk, Fortun3) have embraced AI (11:10), how they think about using AI ethically and responsibly (30:00), their thoughts on the writers and actors strike and a possible way forward (37:30), and finally some ideas of what AI in Hollywood could look like in the future (44:30).

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What happens when AI can “read the whole internet” but the internet stops volunteering its best work?

In this episode of AI-Curious, we talk with Tony Stubblebine, CEO of Medium, about what he calls AI’s “broken social contract” with the web, and why the next era may be less about a “dead internet” and more about a dead public internet. We unpack the incentives that made the open web thrive, how AI search summaries change the traffic bargain, and what a realistic path forward could look like for publishers, platforms, and writers.

Key topics we cover:

-Why generative AI broke the web’s old value exchange, and what “social contract” means in practical terms (00:03:24)

-Tony’s “three Cs” framework for a healthier AI ecosystem: consent, credit, compensation (00:05:13)

-The publisher response spectrum: blocking crawlers, fighting spam/slop, and what happens if collaboration fails (00:04:25)

-The shift from public publishing to private communities (Discords, group chats, newsletters) and what drives that retreat (00:07:06)

-How AI search summaries can cut the incentive to publish publicly by reducing click-through and traffic (00:08:21)

-Why AI systems still depend on human source material, and what happens when the best content moves behind “closed doors” (00:09:27)

-Cloudflare’s role in the escalating crawler arms race, including large-scale blocking and other countermeasures (00:16:48)

-A proposed solution: an internet-wide licensing standard instead of one-off deals, including the Really Simple Licensing (RSL) approach (00:18:07)

-What “paying creators” could look like in practice, including opt-in/opt-out controls and better transparency for writers (00:19:33)

-“Dead internet theory” vs. the more plausible outcome: a dead public internet, and why Tony is cautiously optimistic about a new equilibrium (00:23:06)

-The “second wave” of AI: moving from replacement to augmentation, and how Medium is thinking about AI tools that support flow state rather than write for you (00:26:03)

-Why AI detectors don’t solve the problem, and why Medium focuses on quality and reader value as the enforceable standard (00:34:04)

-Advice for writers: the difference between the creator economy and the “expert economy,” and what’s likely to be more sustainable (00:38:43)

-Tony’s prediction: “trust but verify” becomes the balance point, and the web finds an equilibrium because AI can’t function without public sources (00:43:27)

Guest
Tony Stubblebine is the CEO of Medium and a leading voice on the evolving relationship between generative AI and the open web.
Mentioned in this conversation
Medium’s framework: Consent, Credit, Compensation

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Is AI making creators more powerful… or more replaceable? And if you start with a blank page for a living, there’s an even sharper question underneath it: should AI write for you… or write with you?

In this episode of AI-Curious, we sit down with Victor Varnado—a New Yorker cartoonist, comedian, actor, and creative technologist—to explore a grounded, practical philosophy for using AI without becoming a passenger.

Victor draws a sharp line between generative AI (press a button, get “a masterpiece”) and what he’s more interested in: transformative AI—tools that take messy raw material (notes, transcripts, half-ideas) and turn it into something structured enough to revise. We also talk about how taste becomes a real moat in an AI-saturated world, why “vibe coding” can go sideways fast when you don’t understand the domain, and how Victor’s accessibility-first mindset shapes everything he builds.

Along the way, Victor breaks down his tools—including Magic Bookifier and the Writing Coach—designed to get writers from zero to first draft faster through guided questions and structured interviews. He frames the goal with a concept he calls cognitive discourse: using AI like a thinking partner that makes you sharper, not a crutch that makes you lazier. His metaphor is perfect: do you talk with Einstein and get smarter… or do you just hand Einstein your homework?

We wrap by looking at Victor’s newest effort, BrightWrite, which aims to bring structured, supportive AI into education—especially for students facing cognitive or creative barriers. Victor also shares discount/freebie codes for listeners who want to try his tools, and we’ll include the specifics in the show notes and links.

Topics we cover:

• Victor’s multi-hyphenate path: comedy, New Yorker cartoons, production, and tech
• Why “transformative AI” is more useful than one-click generative output
• The Writing Coach approach: structured interviews that turn your ideas into drafts
• “Cognitive discourse” vs. “cognitive offload” (and the Einstein metaphor)
• Why taste may be the creative moat in an AI-heavy world
• The risks of “vibe coding” outside your expertise
• BrightWrite and the promise (and limits) of accessibility-first AI in education
• Practical ways to use AI for writing, revision, and everyday communication

Guest: Victor Varnado

Tools mentioned: Magic Bookifier, Writing Coach, BrightWrite

What’s actually happening with AI adoption inside U.S. businesses—and how much of the public discourse is just vibes?

In this episode of AI-Curious, we dig into the hard numbers behind AI spend and adoption with Ara Kharazian, an economist at Ramp and the leader of Ramp Economics Lab. Using anonymized, real-time corporate spend data across tens of thousands of businesses, Ara shares what the “receipts” reveal about who’s buying AI, how fast budgets are shifting, and where the hype diverges from reality.

What we cover

• Ramp’s unique vantage point: why transaction-level corporate spend data can reveal real behavior—not just surveys or anecdotes
• AI adoption is rising: what Ramp’s data suggests about the share of businesses paying for AI tools and APIs
• The “ROI” question: how we can infer whether AI is working (hint: contract sizes and renewals)
• Where spend is concentrating: tech and finance lead—but healthcare and manufacturing are climbing faster than many expect
• Chatbots vs. real workflow change: why “everyone has a chatbot” isn’t the same as transformative productivity
• Who’s winning the model wars: OpenAI’s default position, Anthropic’s growth, and how buyers behave differently
• Bundled AI and hidden usage: why Copilot/Gemini adoption is hard to measure, and why employees expensing personal accounts matters
• Trust, governance, and observability: the fast-growing category of tools that monitor AI outputs and reduce reputational or security risk
• 996 culture is real: what corporate receipts suggest about weekend work patterns in San Francisco
• Open source reality check: what the data suggests about DeepSeek-style hype vs. actual enterprise adoption
• Looking ahead: why we likely won’t see a reversal in AI adoption—and why it’s still unclear who the ultimate winners will be

Timestamps:

• 00:06:00 – What Ramp is, and what “Ramp Economics Lab” tracks
• 00:08:00 – The biggest headline: adoption, spend, and contract sizes
• 00:11:00 – Which industries are adopting fastest (including surprises)
• 00:12:00 – Chatbots vs. productivity gains: where AI is actually moving the needle
• 00:15:00 – Signals of ROI: contract renewals and retention trends
• 00:16:00 – OpenAI vs. Anthropic: what spend reveals about “default” vs. multi-provider behavior
• 00:18:00 – Why Copilot/Gemini are tricky to track (bundled AI)
• 00:21:00 – The real blocker: trust in outputs (and how companies respond)
• 00:26:00 – The rise of AI observability / governance tooling
• 00:30:00 – What spend data can reveal about how work is changing (996 / SF)
• 00:33:00 – How rare it is to see a trend that truly moves an economy
• 00:36:00 – Is AI spend crowding out other budgets?
• 00:38:00 – The narratives that bother Ara most: data-poor hot takes
• 00:42:00 – Predictions: continued growth, unclear winners
• 00:44:00 – DeepSeek and open source: what actually happened in the spend data

If you want to understand AI adoption the way a CFO would—through budgets, renewals, and real purchasing behavior—this conversation will give you a sharper, more grounded lens.

Guest: Ara Kharazian, Economist at Ramp; Lead, Ramp Economics Lab

What does an AI-driven economy actually look like when you zoom out far enough—and what does that mean for jobs, power, and policy?

In this episode of AI-Curious, we talk with Anindya Ghose (NYU Stern; author of Thrive) about the “AI economy blueprint”: how the modern economy starts to resemble a vertically layered tech stack—from energy and chips all the way up to consumer-facing apps—and why that stack is quietly reshaping everything from corporate strategy to the future of work.

We cover what’s changing fastest, where leaders are getting tripped up, and what skills matter most if you want to stay valuable in a world of copilots and agents.

Topics

• The AI economy as a tech stack: energy → semiconductors → data centers/cloud → LLMs → applications, and why the consumer “app layer” is just the visible tip.
• Why every company is becoming an AI company (even airlines, banks, retailers)—and how the real dependency sits beneath the apps in infrastructure and model providers.
• Consolidation and vertical integration: how a handful of companies can span multiple layers (chips, cloud, models), and what that could mean for pricing power and competition.
• Jobs and labor markets: why disruption is outpacing creation in the near term, and a provocative forecast for how “portfolio careers” could become the norm.
• Reskilling at scale: from self-learning to certificates to formal programs—and why government-led approaches may be required.
• A concrete framework from Singapore: a “Marshall Plan”-style push to fund AI upskilling and retooling.
• Agentic AI reality check: why many agent projects fail in practice—and the unglamorous workflow work companies often skip.
• Regulation, in three arenas: competition/antitrust dynamics across the stack, copyright/fair use lawsuits, and whether consumers should be told when content is AI-generated.
• Geopolitics of models: the global trade-offs between Western model ecosystems and lower-cost open-source alternatives abroad.
• The underrated career edge: not just knowing what GenAI can do—but knowing when it fails and why, and how that becomes a durable source of leverage.

About the guest

Anindya Ghose is a professor at NYU Stern and leads NYU’s MS in Business Analytics & AI program. His work focuses on AI, digital transformation, and the modern data-driven economy. He’s also the co-author of Thrive.

If you want to pressure-test your own AI strategy for 2026, this episode is a good place to start: think “stack,” not “tool.”

Could AI actually make healthcare more human—less paperwork, less burnout, fewer errors—or is it mostly hype layered on top of a legacy system?

In this episode of AI-Curious, we talk with Dr. Michael Karch, an orthopedic surgeon (hip + knee replacement) with ~30 years of clinical experience who also made a serious pivot into data, machine learning, and AI strategy for healthcare. We dig into what hospitals are actually doing with AI today, where the real friction points are, and what a smarter, safer AI-enabled hospital might look like over the next decade-plus.

What we cover

• Why healthcare is a uniquely hard (and high-stakes) environment for AI adoption
• The “tip of the iceberg” wins: reducing documentation burden, coding friction, and other admin nonsense that fuels clinician burnout
• Ambient AI + transcription: what it does well, what can go wrong, and why “human + machine together” often beats either alone
• Where AI is already showing traction: operational efficiency, OR workflow measurement, and process improvements that sound boring but matter
• Diagnosis and pattern recognition: why radiology/dermatology are natural early battlegrounds for supervised learning models
• A provocative analogy: why surgery shares surprising similarities with autonomous driving (stochastic, partially observable, high consequence)
• The “data flywheel” and why healthcare’s massive unstructured data may be the real goldmine
• A 2040 vision: embodied surgical intelligence, personalized medicine, capturing “tacit knowledge,” and the possibility of hologram/remote expert augmentation
• Digital twins as behavior change tools—using simulation to make risk feel real
• The biggest bottleneck: agency, vocabulary, and getting clinicians to the “young adult at the table” stage instead of having tech imposed on them

If you care about AI but you’re tired of hype—and you want concrete examples, realistic risks, and a forward-looking view that still stays grounded—this one’s for you.

Could AI help you lead—not just do—especially if you’re thinking about building something entrepreneurial?

In this episode of AI-Curious, we talk with Miri Rodriguez, formerly a “storyteller” at Microsoft, now the CEO of Empressa.AI, about what it means to go from Doer to Leader in an AI era—and how an AI-first operating style can give a small team outsized leverage.

Miri shares how storytelling functioned as a practical tool inside Microsoft (not fluffy marketing), why she decided to leave Corporate America, what she's focused on at Empressa.AI, and what she’s learned building an AI-first company—especially around agent-like workflows, research automation, and the discipline of separating real value from AI hype.

What we cover

• Why “storytelling” matters in business and how it works at Microsoft
• The origin-story lens: how companies reinvent themselves (and why transformation stories matter)
• Miri’s path from Microsoft into entrepreneurship—and the “gaps” she saw as an early adopter of Copilot-era tools 
• Why she believes AI can either widen or narrow workplace gaps—and why adoption, not just access, is the real issue ([00:06:40]–[00:09:30])
• What “skilling up” actually means now: moving from execution to strategy + orchestration as AI takes on more of the doing ([00:11:15]–[00:14:30])
• Where agentic workflows are showing up first—and the looming mismatch between automation and employee upskilling ([00:14:30]–[00:16:45])
• A concrete, real-world example of an “agent-style” workflow for communications + marketing (and why research becomes a superpower) ([00:17:00]–[00:23:10])
• The simplest anti-hype test: if you can’t explain the value without saying “AI,” you may be building a trend, not a solution 
• Advice for would-be entrepreneurs: why mission and clarity matter more than “AI-first” branding 
• How Miri uses AI personally and creatively—especially translation, voice, and writing experiments 

Key takeaway

AI isn’t just a productivity boost—it’s a forcing function for how we lead: setting direction, designing workflows, making judgment calls, and supervising a growing layer of digital labor.

Please enjoy our conversation with Miri Rodriguez.

Empressa.AI

What does it actually look like to use AI tools in the real world, beyond the usual chatbot prompts and hype?

In this episode of AI-Curious, Jeff Wilser shares five AI tools and workflows that are shaping how he works right now, from Claude Code and personalized news briefings to NotebookLM, multi-model prompting, and using AI to write more closely in your own voice. The goal is not to offer a comprehensive list of every AI product on the market, but to show how these tools can be used in practical ways that expand capability, streamline research, and create new workflows.

We explore how vibe coding and AI agents can help non-coders build useful internal tools, why personalized AI news feeds may become increasingly common, and how NotebookLM can synthesize large amounts of information across transcripts, documents, and YouTube videos. We also look at the benefits of using multiple AI models together instead of relying on just one, and why feeding AI much richer context can dramatically improve writing outputs.

Throughout the episode, we return to a core idea: using AI to empower, not eliminate. Rather than treating AI only as a cost-cutting tool, we examine how it can help individuals and businesses do more, think more creatively, and build smarter systems around the work that matters most.

Key topics we cover

• 3:15 — Claude Code, vibe coding, and why non-coders should be paying attention
• 6:01 — Building a custom AI-powered conference outreach and research tool
• 11:05 — “AI to empower, not eliminate” as a guiding philosophy
• 16:16 — Personalized AI news briefings and the future of customized information
• 21:58 — How NotebookLM helps synthesize transcripts, documents, and YouTube content
• 27:04 — Why a “polymodel” approach can be better than relying on one chatbot
• 31:15 — Using AI to write more closely in your own voice through deeper context

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Could AI agents become better traders than humans—and what happens when “decision-making” gets outsourced to software that can act at machine speed?

In this conversation, we go deep with Saad Naja, founder of PIP World, on the rise of AI agent auto-traders: multi-agent “swarms” that resemble a miniature trading desk—specialist analysts feeding into an AI “portfolio manager” that can decide whether to buy, sell, or hold. Even if you’ve never day traded, finance may be one of the clearest real-world testbeds for autonomous agents—because markets keep score in real time.

Key moments

• [00:02:00] How AI has quietly shaped trading for decades—long before ChatGPT
• [00:05:00] Why retail traders lose so consistently: data disadvantage + execution problems
• [00:10:00] What’s changed with generative AI: analysis that used to take teams can now happen fast
• [00:12:00] Why “AI swarms” differ from old-school trading bots (context, coordination, and specialization)
• [00:17:00] The “trading desk in software” model: specialist agents + a chief decision-maker
• [00:21:00] How PIP World trained and tested models—and why win-rate isn’t the whole story
• [00:26:00] Why they launched in simulation first—and what it reveals about performance
• [00:30:00] How agents trade differently than humans (patience, confirmation, discipline)
• [00:37:00] Hallucinations, guardrails, and why specialization reduces “AI going rogue” risk
• [00:40:00] The endgame: “agent vs. agent” markets, shrinking edges, and the data arms race
• [00:45:00] A 5-year prediction: how much trading could become fully agentic
• [00:47:00] Why crypto/DeFi is a natural early proving ground—and how TradFi could follow

What you’ll hear us explore

• The difference between traditional algo trading (single-strategy rule sets) and agentic systems (multiple specialized “analysts” + a coordinating decision layer)
• Why most retail traders aren’t necessarily wrong on ideas—but lose on execution and risk management
• How “edge” shifts when everyone has access to powerful models: data quality, workflows, and strategy selection
• What finance teaches us about the broader economy as agents move from “assistants” to “actors”

If you’re curious about autonomous agents—whether you trade or not—this is a concrete, high-stakes preview of what “agentic work” could look like when the scoreboard is real.

Guest: Saad Naja, Founder, PIP World

Topics: AI agents, multi-agent swarms, algorithmic trading, market data, risk management, DeFi, agentic automation

Can AI actually help eradicate poverty for real people, right now—not in some vague future?

We talk with two leaders from Opportunity International who are trying to do exactly that, using AI to support smallholder farmers and low-cost private schools across Africa and beyond.

In this episode of AI-Curious, we sit down with Ama Akuamoah and Paul Essene from Opportunity International’s Digital Innovation Group. We explore how they’re deploying AI chatbots over WhatsApp to help farmers diagnose crop diseases, optimize planting decisions, and access localized agricultural advice, and how they’re building classroom tools that give overstretched teachers better lesson plans and more time for their students.

We hear the origin story of their farmer chatbot—from a mud-brick home in Malawi to pilots now running in five countries—and the 80-year-old farmer who saved her okra crop by using an AI tool through a trusted “farmer support agent.” We also dig into how they use retrieval-augmented generation (RAG) grounded in local government content, why “human in the loop” is non-negotiable, and what it really takes to make AI work in communities with limited electricity, spotty connectivity, and low digital literacy.

Along the way, we talk about ethics and trust: data consent, privacy for highly vulnerable populations, and the risk of leaving people behind in this new wave of AI. And we zoom out to the bigger picture—why conversational AI in local languages could be a genuine game-changer for economic development if infrastructure, funding, and partnerships keep pace.

What we cover

• [01:00] Opportunity International’s mission and why they focus on farmers, teachers, and micro-entrepreneurs
• [08:00] The Malawi farm-floor moment that sparked their AI journey
• [09:00] How a WhatsApp-based chatbot helps thousands of farmers, and how “farmer support agents” multiply its impact
• [13:40] Using RAG and local government content to keep answers accurate and context-aware
• [15:30] Bringing AI into crowded, low-resource classrooms and supporting teachers with lesson plans and copilots
• [20:15] The hard parts: infrastructure gaps, low-cost devices, digital literacy, and why this work is heavy lifting
• [24:30] Human-centered design in action: co-creating with communities, iterating in the field, and learning from pilots
• [37:50] Guardrails, consent, and building trust around AI in vulnerable communities
• [41:00] What’s needed for real scale: infrastructure, funding, language support, and the right partners
• [43:00] Their hopeful vision for AI as a lever for economic development—if no one gets left behind

If you’re interested in AI for social impact, global development, or what it really takes to deploy AI outside Silicon Valley, this conversation is a grounded, hopeful look at what’s already working—and what still needs to change.

Is AI making us smarter or dumber—and how do we make sure we’re on the right side of that divide?

In this episode of AI-Curious, we talk with Professor Vasant Dhar, author of the new book Thinking With Machines: The Brave New World of AI. Vasant isn’t just a historian of AI; he’s part of the story. In the 1990s, he helped bring machine learning to Wall Street, founded one of the world’s first ML-based hedge funds, and became the first professor to teach AI at NYU Stern, where he’s now the Robert A. Miller Professor of Business. He also hosts the podcast Brave New World.

We explore how AI evolved from early efforts around “thinking, planning, and reasoning” to the long era of pure prediction and machine learning, and then to today’s general-purpose models that blur the line between expertise and common sense. Vasant explains why the autocomplete problem turned out to be a gateway to something like “general intelligence,” and why that matters for how we define knowledge, understanding, and reasoning.

We then dive into finance and the search for “edge.” Vasant shares war stories from his days at Morgan Stanley, where machine learning systems quietly reshaped trading strategies and risk-taking. We unpack his work on “the DaBot,” an AI built on the writings and valuation framework of Aswath Damodaran, and what happens when every analyst and firm can tap this kind of supercharged valuation machine. Does AI erase the edge—or simply raise the bar for everyone?

Finally, we zoom out to careers, education, and everyday life. Vasant argues that AI is likely to bifurcate humanity into those who become “superhuman” by thinking with machines, and those who outsource their thinking and fall behind. We discuss how classrooms will change, why many teachers (and professors) may be more automatable than they realize, and how each of us can periodically test whether AI is making us smarter or dumber.

If you’re curious about how to work with AI rather than be replaced or outpaced by it, this conversation offers a grounded, big-picture way to think about your edge in the age of intelligent machines.

Can a city use AI to cut red tape, fill potholes faster, and shave minutes off commutes—without sliding into surveillance? We sit down with San José’s mayor, Matt Mahan, to unpack how a highly regulated public institution can adopt AI pragmatically and responsibly. In this episode, we dig into the playbook: pilots that become policy, guardrails that build trust, and workforce upskilling that actually moves the needle.

We cover how bus routes now hit fewer red lights, why real-time translation boosts civic inclusion, what “privacy by design” looks like for license-plate readers, and how a 10-week AI curriculum is turning city staff into hands-on builders. We also press on the risks—bias, privacy, and transparency—and explore where city AI is headed next: transit, permitting, and procurement.

Highlights

• From pilots to scale: Bus route optimization with Light AI cut red-light hits by 50%+ and reduced travel time by 20%+, now rolling out citywide.
• Inclusion by default: Real-time multilingual access (e.g., Wordly) and improved translations informed by San José’s deep Vietnamese-language data.
• Eyes on the street, not faces: No facial recognition, strict retention, no third-party data sharing, and tightly controlled access to ALPR data.
• Upskilling at scale: A 10-week AI curriculum (plus a data track) with San José State; staff build custom GPTs (including a budget-analysis GPT) to speed analysis.
• Culture that ships: A “coalition of the willing,” clear problem statements, and a Mayor’s Office of Technology & Innovation to operationalize change.
• Road ahead: Smarter mass transit, faster permitting, and streamlined procurement—practical abundance without new tax dollars.

If you’re new here, we’d love your support—subscribe on Apple, Spotify, or YouTube, and consider leaving a quick rating or sharing this episode with a colleague who’s wrestling with real-world AI adoption.

Does AI make us more creative—or quietly replace us?

In this episode of AI-Curious, we sit down with Dr. Maya Ackerman—author of Creative Machines: AI, Art, and Us—to probe where human creativity ends and machine creativity begins, and how incentives in Big Tech and venture capital shape the tools we all use. 

We explore why today’s dominant systems skew “convergent” (safe, samey, oracle-like) instead of “divergent” (surprising, generative), what that means for artists, and how to design AI that actually elevates human imagination rather than displacing it.

Why listen

We wrestle with uncomfortable truths: bias mirrored back at us, investor pressure to “replace” vs. “augment,” and the risk of a cultural sea of slop. We also map a constructive path forward—collaborative systems, richer human–AI interfaces, and a 10-year horizon where AI expands human creative range.

Guest

Dr. Maya Ackerman — AI researcher, entrepreneur, and author of Creative Machines: AI, Art, and Us. 

Takeaways

• AI reflects us. Bias in → bias out; representation fixes are not enough without cultural understanding.
• Incentives matter. Many well-funded tools are architected to replace creators; augmentation tools are underfunded.
• Creativity ≠ autocomplete. Today’s LLMs are optimized for correctness and convergence, not genuine divergence.
• Better interfaces beat bigger models. Beyond “text-to-X,” human-centred, interactive tools can coach, not usurp.
• A hopeful arc. With the right design, collaborative AI can measurably raise human creative ability—and stick.

Dr. Ackerman's new book: Creative Machines

https://www.amazon.com/Creative-Machines-Future-Human-Creativity/dp/1394316267