In this episode, we interviewed Jonah Goodhart - Co-Founder and CEO of Mobian, an advertising measurement company that helps brands measure and optimize how they show up to both humans and AI systems
Jonah previously co-founded and led Moat (acquired by Oracle) and was a founding investor in Right Media (acquired by Yahoo) - he's also a partner in WGI Group, with over 100 investments across technology
His core argument: every brand now has two audiences - people, who respond to stories and emotion, and AI systems, which respond to structure and credibility - and most companies are still only building for one
Key takeaways:
Contextual advertising was really just keyword matching - the industry got good at finding words and bad at understanding meaning, and meaning is what now matters
Every brand has two audiences now - humans respond to stories and emotion, AI systems respond to structure and credibility, and most companies build for only one
If AI can't explain its decision, you can't trust it - transparency becomes more important as AI takes on bigger decisions, not less
AI visibility becomes as standard as viewability - within 12 months, not knowing how AI represents your brand will start to feel negligent
🔗 Connect with Jonah
Who are you and what do you do?
I'm Jonah Goodhart, Co-Founder and CEO of Mobian. Mobian is an advertising measurement company. We help brands measure and optimize how they show up to humans and AI systems.
What problem did you see that everyone else was missing?
For twenty years, the industry said it was doing contextual advertising, but most of it was really keyword matching. We got very good at finding words and surprisingly bad at understanding meaning.
That matters because brands are not built in isolation. They are built by the environments they appear in, the content around them, the stories people tell about them. Reading that requires understanding meaning, not scanning for keywords.
What is different now is who is doing the reading. Every brand suddenly has two audiences: people and AI systems. People respond to stories, emotion, and context. AI systems respond to structure, data, and credibility. Most companies are still only building for one.
The next version of contextual is not about pages or keywords. It is about understanding ideas, for the humans and the machines now interpreting your brand.
Brands are not built in isolation. They are built by the environments they appear in, the content around them, the stories people tell about them.
Walk us through one concrete way your work changes what companies actually ship
A strategist or salesperson prepping for a client meeting used to ping multiple teams, wait on data exports, pull screenshots, and build the slides by hand, hoping the numbers were still current by the time the meeting started.
Now we connect Mobian's measurement data directly into AI workflows through MCP. Someone can ask, in plain language, how a brand is showing up across AI engines, where its contextual risks are, and what changed since last quarter, and get back real data with the reasoning and citations behind it, ready to action.
What's the most common thing senior leaders get wrong about AI?
The biggest mistake is assuming AI does not need to explain itself.
Leaders would never accept a human analyst saying, "Trust me, this is the answer," with no reasoning behind it. But they often accept AI scores, recommendations, or outputs without understanding why the system got there.
That is backwards. As AI becomes responsible for bigger decisions, transparency becomes more important, not less. If AI cannot explain the decision, you cannot trust it, learn from it, or improve it.
If AI cannot explain the decision, you cannot trust it, learn from it, or improve it.
What's in your AI stack? The one tool you rely on every week?
Claude is the one I'd have a hard time working without. I use it every day for reasoning and building; it's become a real partner. Around it I keep a small stack: Julius when I want to run heavy data science on a dataset, Gemini for generating presentation-ready images, ChatGPT for my voice and Perplexity for research and a second opinion. And Grok for its real-time integration with X.
The trick isn't finding the single best model. I'll put the same question to two or three, ask one to make the case and another to tear it apart, and the useful thinking usually shows up in the disagreement. A model that only ever agrees with you is just a faster way to be wrong.
In addition, we are increasingly moving away from prompting to agentic loops. In essence it's assigning an outcome and then ensuring the right guardrails are in place to get there, but letting our agents do what they do best.
What does your work actually look like day to day?
We're a small company, so there's no version of my day that's just strategy. I might spend an hour with a customer, then get into data analysis, looking at why YouTube channels, apps, or AI responses scored the way they did in Mobian's systems. And I'm building quite a bit. I write code and prototype product ideas myself, because the fastest way to know if something is worth building is to build a rough version and watch it work.
I never stop being close to the actual work. No job is too small if it helps us build a better product.
Where is your field in 12 months - one specific prediction?
AI visibility will become as standard as viewability.
Ten years ago, marketers could not imagine buying media without knowing whether an ad had the chance to be seen. A year from now, they will feel the same way about understanding how AI systems describe their brand, which sources those systems rely on, and whether those answers are accurate.
Not knowing how AI represents your brand will start to feel negligent.
Where should readers find you, and what's the first thing they should join or read?
Find me on LinkedIn. That's where I share what we're seeing in measurement, media quality, AI visibility, and how brands are getting interpreted in the market.
The first thing I'd do is simple: ask ChatGPT, Claude, Gemini, Grok, and Perplexity to describe your company and your category. Look at what they get right, what they miss, and which sources they lean on.
AI Central Voices is where the AI Central team sits down with the founders, executives, and builders shaping AI - going behind the scenes of how they operate, what they're betting on, and where the industry goes next.
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