AI-Curious with Jeff Wilser

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 coverRamp’s unique vantage point: why transaction-level corporate spend data can reveal real behavior—not just surveys or anecdotesAI adoption is rising: what Ramp’s data suggests about the share of businesses paying for AI tools and APIsThe “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 expectChatbots vs. real workflow change: why “everyone has a chatbot” isn’t the same as transformative productivityWho’s winning the model wars: OpenAI’s default position, Anthropic’s growth, and how buyers behave differentlyBundled AI and hidden usage: why Copilot/Gemini adoption is hard to measure, and why employees expensing personal accounts mattersTrust, governance, and observability: the fast-growing category of tools that monitor AI outputs and reduce reputational or security risk996 culture is real: what corporate receipts suggest about weekend work patterns in San FranciscoOpen source reality check: what the data suggests about DeepSeek-style hype vs. actual enterprise adoptionLooking ahead: why we likely won’t see a reversal in AI adoption—and why it’s still unclear who the ultimate winners will beTimestamps:00:06:00 – What Ramp is, and what “Ramp Economics Lab” tracks00:08:00 – The biggest headline: adoption, spend, and contract sizes00:11:00 – Which industries are adopting fastest (including surprises)00:12:00 – Chatbots vs. productivity gains: where AI is actually moving the needle00:15:00 – Signals of ROI: contract renewals and retention trends00:16:00 – OpenAI vs. Anthropic: what spend reveals about “default” vs. multi-provider behavior00:18:00 – 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 tooling00:30:00 – 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 economy00:36:00 – Is AI spend crowding out other budgets?00:38:00 – The narratives that bother Ara most: data-poor hot takes00:42:00 – Predictions: continued growth, unclear winners00:44:00 – DeepSeek and open source: what actually happened in the spend dataIf 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.TopicsThe 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 guestAnindya 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 coverWhy healthcare is a uniquely hard (and high-stakes) environment for AI adoptionThe “tip of the iceberg” wins: reducing documentation burden, coding friction, and other admin nonsense that fuels clinician burnoutAmbient AI + transcription: what it does well, what can go wrong, and why “human + machine together” often beats either aloneWhere AI is already showing traction: operational efficiency, OR workflow measurement, and process improvements that sound boring but matterDiagnosis and pattern recognition: why radiology/dermatology are natural early battlegrounds for supervised learning modelsA 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 goldmineA 2040 vision: embodied surgical intelligence, personalized medicine, capturing “tacit knowledge,” and the possibility of hologram/remote expert augmentationDigital twins as behavior change tools—using simulation to make risk feel realThe biggest bottleneck: agency, vocabulary, and getting clinicians to the “young adult at the table” stage instead of having tech imposed on themIf 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 coverWhy “storytelling” matters in business and how it works at MicrosoftThe 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 takeawayAI 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 followWhat you’ll hear us exploreThe 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 managementHow “edge” shifts when everyone has access to powerful models: data quality, workflows, and strategy selectionWhat 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 WorldTopics: AI agents, multi-agent swarms, algorithmic trading, market data, risk management, DeFi, agentic automation