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

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.