AI-Curious with Jeff Wilser

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.”