AI-Curious with Jeff Wilser

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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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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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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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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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