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.”