The LeanScale Podcast · Episode 79

AdSense for the AI Era: How Ads Are Being Completely Reinvented

Mike Choi on building Koah, the monetization layer for AI apps, and why every paradigm shift reinvents advertising

Mike Choi · Co-founder, Koah · Koah Hosted by Anthony Enrico
Published Updated 00:30:16 24 min read 4,751 words
Executive Summary

The one-paragraph brief, extended

Why this conversation matters — and who should spend the hour.

Mike Choi has one of the most unusual founder origin stories the show has featured. A Korean national who followed his Air Force father to Tucson in third grade, he fell for the States over a plate of broccoli-cheddar instant rice, came back as a middle-school exchange student to a host family in Kansas, and got hooked on building consumer apps after jailbreaking a Christmas iPod. That obsession carried him to a dream job at Apple, then to Twitter under Elon Musk, and finally to co-founding Koah — the company the market is calling 'AdSense for AI,' the monetization layer for AI applications. Koah has raised over $20M from Theory Ventures, is already processing 170M+ queries, and is seeing 5%+ click-through rates, four to five times what traditional display delivers.

The thesis is simple and historically grounded: every major paradigm shift — radio, TV, web, social — has reinvented advertising without ever killing it, and AI is the next reinvention. Mike's 'marketing holy grail' is an ad tailored specifically to each person, generated on the fly, and AI can finally deliver that at scale. Koah started with dynamic text (every ad generated to match the conversation's context) and is climbing the stack toward dynamic UI — ad formats and interfaces generated per user and per brand, not a static JPEG or a 50-character line. What he is replacing is the tired banner-ad playbook: ad blindness, apps that feel cheap, developers who resent it. His guiding principle is that a publisher's surface area is 'sacred,' so the ad deserves the same craft as the app it lives in.

The middle of the conversation is a clinic on the counter-intuitive operating lessons. Koah's naive early hypothesis — user asks about shoes, show a shoe ad, they buy — turned out to be wrong, because of a consumer-psychology gap: you still research a microphone with an LLM but buy it on Shure.com to price-shop and earn credit-card points. So Koah bet early on analytics to track real down-funnel conversions, optimizes for value to the end user rather than raw CTR, and has found that high-consideration financial products — taxes, student loans, credit cards — convert best today, with strong seasonality (tax day). Meanwhile the explosion of vibe-coded apps creates a huge new monetization market: niche apps that couldn't exist before now can, and with non-zero inference costs they need a layer like Koah to sustain themselves.

Who should listen and why. Founders get a clinic on market timing — Mike shelved this 'obvious' idea for a year during the GPT-3.5 era until apps began productionizing LLM features — and on the payoff of investing in measurement before the market is ready. Marketing and growth leaders get a preview of where advertising creative is going: personalization, generative UI, and a deliberate return to 'ads as art' (Mike's north star, inspired by the Great American Ads coffee-table books). And every GTM operator gets his blunt reminder that the pre-2025 PM, engineering, and advertising playbooks are already obsolete — building for the future beats replicating what worked in the past.

Key Takeaways

12 things worth stealing

The load-bearing ideas, each with the business implication and who should care.

01

Every paradigm shift reinvents advertising — AI is next

Radio, TV, web, and social each spun up an entirely new advertising infrastructure without ads ever going away. Mike positions AI as the next such shift, which is why 'AdSense for AI' is a when-not-if bet rather than a speculative one.

Why it matters: Treat AI advertising as an inevitable new layer to build for and buy into, the way marketers eventually had to for search and social — not as a fad to wait out.

FoundersMarketing LeadersRevenue Executives
02

The marketing holy grail is a unique ad generated for every person

Today almost every ad is created once by an agency, approved for brand fit, and shown identically to the whole world. Mike's vision is an ad tailored specifically to each individual — and AI is the first technology that can do that at scale.

Why it matters: Personalization stops being a targeting tweak and becomes generation: the ad's copy, assets, and interface are produced per user, which is a categorically different capability than picking the best static creative.

Marketing LeadersFounders
03

The generative ad stack climbs from text to assets to UI

Koah began by generating ad text on the fly to match conversation context, then moved 'one level up' to the assets, and is now building dynamic UI — ad formats and interfaces generated to fit each AI app's form factor and each brand.

Why it matters: The endgame isn't a smarter banner; it's an ad experience with no fixed JPEG or 50-character limit, which means advertisers will design ads by prompting rather than by shipping fixed creative.

Marketing LeadersFounders
04

Treat the publisher's surface as sacred and optimize for the end user first

Koah's stated priority order is the end user, then the publisher, then the advertiser. Developers' apps are 'their little babies,' so the ads must carry the same craft as the app; if Joe or Sarah gets real value, engagement rises and advertisers win downstream.

Why it matters: Winning ad platforms in AI will be judged on end-user value and taste, not just fill rate — an inversion of the advertiser-first incentives that made banner ads hated.

Marketing LeadersFoundersRevenue Executives
05

CTR is the wrong north-star metric

A user can click without ever converting, so a high click-through rate on a non-converting ad is a vanity number. Koah optimizes for the outcome the advertiser actually wants; if the ads are genuinely relevant, click and conversion rates should hold rather than decay as personalization commoditizes.

Why it matters: Measure and reward down-funnel conversion and end-user value; a click rate that isn't tied to a real objective will mislead you about whether the ad is working.

Marketing LeadersRevenue Executives
06

The naive 'shoes → shoe ad → buy' hypothesis fails on consumer psychology

Koah's first thesis was that if a user asked about shoes, showing a shoe ad would convert them. It didn't. People research with an LLM but still buy elsewhere to price-shop and earn rewards — a consumer-psychology gap that technology can't shortcut, only measure and wait out.

Why it matters: Don't assume intent equals purchase inside an AI surface yet; build for the objectives AI ads can already accomplish while the behavior shift catches up.

FoundersMarketing Leaders
07

High-consideration, financial products convert best today

The more expensive or complex a purchase, the more a buyer researches before committing — so taxes, student loans, and credit cards are where AI ads convert now. Seasonality is real: tax-day clicks on tax ads were exceptional because people file at the last minute.

Why it matters: Aim AI advertising at high-consideration, research-heavy categories and exploit seasonal demand spikes rather than expecting impulse commodity purchases to convert in-chat.

Marketing LeadersRevenue Executives
08

The vibe-coding app explosion is a new monetization market — and inference costs make it mandatory

Anyone can now prompt an app into existence without an iOS engineer, so a long tail of niche apps that couldn't previously justify their build cost will ship. Unlike 2010's near-zero server costs, AI apps carry real per-query inference costs, so many need an embedded monetization layer to survive.

Why it matters: Expect an explosion of new ad surfaces and revenue channels; monetization becomes a survival requirement for AI-native apps, not an afterthought.

FoundersRevenue Executives
09

Bet on measurement before the market is ready

Even when the naive hypothesis failed, Koah invested early in tracking real down-funnel conversions. That analytics bet paid off: when consumer behavior started shifting about a month before recording, the company could actually see conversions climbing and know something had changed.

Why it matters: Instrument for the outcome you eventually want before you can prove it out; the teams that measure early can recognize an inflection the moment it starts.

FoundersRevenue Executives
10

Timing beats obviousness — the idea can be a year too early

Mike had the Koah idea about a year before starting, in the GPT-3.5 era, but shelved it because companies weren't productionizing elegant LLM features yet. He put it in the backlog and waited for the market to be able to adopt it.

Why it matters: An 'obvious' idea still fails if the market can't yet act on it; watch for the moment adoption becomes real rather than launching into a slot that's too early.

Founders
11

The pre-2025 playbooks are already obsolete — build for the future

Mike argues most patterns learned over two decades of display ads — and most PM and engineering books written before this year — are already out of date. Static UI 'is very early 2000s,' and LLMs are moving too fast to default to what worked before.

Why it matters: Design for where the paradigm is going, not for the safe, known past; replicating yesterday's tactics is a liability when the surface area changes by the hour.

FoundersMarketing LeadersRevenue Executives
12

Bring ads back to craft and art

Mike studies the Great American Ads coffee-table books — one per decade — and notes that ads used to be genuine pieces of art, from Got Milk to full-page New York Times spreads. His north star is producing artifacts that make people react with 'wow, that is a cool ad,' reversing the negative connotation online ads carry today.

Why it matters: Generative tools are an opportunity to raise the craft bar, not lower it; brand-true, beautiful ad experiences can be a differentiator rather than a tax on the user.

Marketing LeadersFounders
Frameworks Discussed

8 named models

Every framework Jimmy names, defined and time-stamped.

Every Paradigm Shift Reinvents Advertising

06:02

Each new media/computing paradigm — radio, TV, web, social — builds a wholly new advertising infrastructure around it, and advertising never disappears. AI is the next paradigm and will get its own reinvented ad stack.

Mike uses the historical pattern to frame Koah as inevitable infrastructure rather than a speculative product: the question is what AI-era advertising looks like, not whether it will exist.

The Marketing Holy Grail (An Ad Made Just for You)

06:46

Instead of one agency-created, brand-approved ad shown to everyone, every person is served an ad generated specifically for them — the long-sought 'holy grail' that AI can finally deliver at scale.

This reframes personalization from targeting the right static creative to generating a unique ad per user, matched to context so it feels like a value-add rather than an encroachment.

Static → Dynamic: The Generative Ad Stack (Text → Assets → UI)

08:23

A ladder from a fixed banner to fully generated advertising: start with dynamic text generated to match the conversation, move up to generated assets, then to dynamic UI — the ad format and interface generated per user and per brand.

The New York subway ad everyone sees becomes an ad tailored to Anthony or Mike specifically. The frontier is generative UI, so advertisers 'prompt away as a marketing engineer' instead of being confined to a JPEG or a 50-character line.

Sacred Real Estate / Build for the End User First

13:42

The publisher's surface area is treated as sacred, and the ad platform optimizes for the end user first, the publisher second, and the advertiser third — because end-user value drives engagement, which in turn makes advertisers happy.

Developers' apps are 'their little babies,' so ads must carry the same craft and answer 'is this valuable?' for the person seeing them. Get that right and CTR, engagement, and advertiser outcomes follow.

CTR Is the Wrong Metric

16:16

Click-through rate is a misleading success measure because users can click without converting; the right target is the advertiser's actual objective (conversion/value), which should hold steady rather than decay if the ads are genuinely relevant.

Mike expects generative UI to kill ad blindness, so a well-served ad's conversion rate has 'no reason to decrease.' If it does, the platform simply isn't doing its job.

The Naive Shoe-Ad Hypothesis & the Consumer Psychology Gap

20:45

The early assumption that 'user asks about shoes → show a shoe ad → they buy' is wrong; buyers research with an LLM but still purchase elsewhere to price-shop and earn rewards. Closing that behavior gap takes time, not just better technology.

The failed hypothesis still produced a durable asset: Koah built conversion-tracking technology to see actual down-funnel sales, giving it visibility the moment behavior started shifting.

High-Consideration Products Convert

24:24

The more expensive or complex a purchase, the more a buyer researches first — so AI ads convert best for high-consideration, financial categories (taxes, student loans, credit cards), amplified by seasonality like tax day.

This gives a concrete answer to where AI advertising works today: aim at research-heavy, financial purchases and ride predictable demand spikes rather than expecting impulse commodity buys in-chat.

Ads as Art

25:38

A north star of returning advertising to craft — the era when a Got Milk campaign or a full-page New York Times ad was a genuine piece of art — using generative tools to produce brand-true experiences people admire rather than block.

Mike draws inspiration from the Great American Ads books (one per decade) and wants every Koah artifact to earn a 'wow, that is a cool ad,' reversing the negative connotation of online advertising.

Best Quotes

15 lines worth clipping

Pulled verbatim. Copy or share any of them.

“They're already processing over 170 million queries and seeing over a 5% click-through rate, which is four to five times what you see in traditional display.”
Anthony Enrico 00:00
“There's ad blindness to those banner ads, and they're not really something consumers like. App developers don't like those banners either because it makes your app feel cheap and it doesn't respect the amount of craft and effort that went into creating those apps.”
Mike Choi 04:33
“What if everyone is served an ad that is tailored made for them specifically? That is the marketing holy grail — and AI is able to do that at scale.”
Mike Choi 07:02
“It's the moments where the ad shows up and I go, 'Yeah, I didn't know I needed that, but I need that right now.' Those are the moments we're trying to unlock.”
Mike Choi 09:15
“If you're slightly off, then it feels creepy. But if it's just right, there's this moment where it's, 'Wow, thank you for showing me that ad.'”
Mike Choi 09:15
“There will also be apps that had to exist, or just add value to the world, but couldn't have been made before because the dev costs were too high.”
Mike Choi 11:15
“We just believe that the surface area, the real estate the publishers have granted us, is sacred almost. The ads deserve the same amount of care and attention as the app.”
Mike Choi 15:05
“CTR is an interesting measurement, because the user can click and the number of clicks can be high. But if the user isn't converting at the end, then the CTR really isn't the best way to measure success.”
Mike Choi 15:53
“A lot of the patterns or know-hows learned over the last decade or two of ads will be completely irrelevant — kind of like how most PM books or engineering books written before this year are practically out of date.”
Mike Choi 17:21
“If the user is asking about shoes, show a shoe ad and then they'll buy it. That was the very simple naive hypothesis. And it turns out people don't do that.”
Mike Choi 21:16
“If I want to buy this microphone, I wouldn't go to ChatGPT to buy it. I'd go to Shure.com, or to Google, buy the cheapest one, use my favorite credit card to get points. That's the consumer psychological gap we still have to cross.”
Mike Choi 20:33
“The more expensive or more complex the product is, the more research the user wants to do before actually purchasing — taxes, student loans, anything financial, credit cards.”
Mike Choi 23:27
“You should have seen our numbers on tax day — people clicking on tax ads was incredible. Because some human behavior just doesn't change: people don't file their taxes until the last moment.”
Mike Choi 23:27
“Static UI, just showing a JPEG, is very early 2000s. LLMs are moving so quickly — let's build towards the future and what it can do instead of just doing what we did in the past.”
Mike Choi 25:07
“These ads were pieces of art in the past. Why can't we bring back the days where we go, 'Wow, that is such a beautiful ad'?”
Mike Choi 27:24
Practical Advice

What should you actually do?

The playbook, split by the seat you sit in.

Founders

  • Nail your timing: an idea can be obvious and still be a year too early. Mike shelved Koah during the GPT-3.5 era and launched only when apps started productionizing LLM features — watch for the moment the market can actually adopt.
  • Invest in measurement before the market is ready. Koah built down-funnel conversion tracking early, so when consumer behavior started shifting it could see and prove the inflection.
  • Build for a new surface area you genuinely respect. Treat the publisher's or customer's real estate as sacred and optimize for end-user value first; engagement and advertiser outcomes follow.
  • Expect inference costs to reshape your model. Unlike 2010's near-zero server costs, AI apps carry real per-query costs — plan monetization as a survival requirement, not an afterthought.

Marketing Leaders

  • Move from targeting the right static creative to generating the ad itself — dynamic copy first, then dynamic assets, then dynamic UI matched to each user and brand.
  • Stop worshipping CTR. Optimize for the conversion and value the advertiser actually wants; a high click rate on a non-converting ad is a vanity metric.
  • Match the motion to consideration level. High-consideration, financial products (taxes, loans, cards) convert best in AI ads today, and seasonality (tax day) is a real lever.
  • Bring craft back. Generative tools let you build brand-true, beautiful ad experiences — aim for 'wow, that's a cool ad' rather than another blindness-inducing banner.

Revenue Executives

  • Assume the pre-2025 playbooks are obsolete. Two decades of display-ad patterns — and most PM/engineering books written before this year — are already stale; build for where the paradigm is going.
  • Plan for a new wave of monetization surfaces. The vibe-coding app explosion plus non-zero inference costs creates a long tail of AI apps that must monetize — a new distribution channel for advertising and revenue.
  • Reward outcomes, not clicks. Instrument attribution to real down-funnel conversions so ad and channel decisions target value rather than engagement theater.
AI Takeaways

How AI actually changes GTM

LeanScale's signature read on the AI-in-GTM question this episode wrestles with.

The thesis

AI is reinventing advertising the way every prior paradigm shift did — by making the 'holy grail' of a unique, personalized ad for every user finally deliverable at scale. The winning approach generates the ad (copy, creative, and eventually the UI) on the fly, optimizes for real conversion and end-user value over raw CTR, and rides the coming explosion of AI-native, vibe-coded apps that must monetize to survive.

Generation, not selection

The old model picks one agency-approved ad for everyone; Koah generates a unique ad per user and per brand, starting with dynamic text and climbing toward fully generated UI matched to each app's form factor.

The consumer-psychology gap is the real frontier

The naive 'ask for shoes → show shoe ad → buy' loop fails because people research with an LLM but still buy elsewhere to price-shop. Technology can't shortcut a behavior shift — but it can measure the moment it starts.

Optimize for value, and ad blindness dies

Serve genuinely relevant, useful ads and CTR/conversion holds; generative UI means the banner-blindness pattern of the last two decades stops applying.

Inference costs make monetization mandatory

Unlike 2010's near-zero server costs, AI apps carry real per-query inference costs — so the long tail of new, vibe-coded apps needs an embedded monetization layer just to exist.

Old playbooks expire

Most pre-2025 PM, engineering, and advertising know-how is already stale; build for the future the LLMs are racing toward, not the JPEG-and-banner past.

Agent & automation ideas

  • A generative ad engine that produces per-user creative and UI matched to the app's form factor and conversation context, with guardrails so it never tips from 'helpful' into 'creepy.'
  • A down-funnel conversion-tracking layer that attributes real sales (not just clicks) so ad optimization targets value rather than vanity CTR.
  • A monetization copilot for vibe-coded apps that auto-instruments an app to serve contextual ads and cover its own inference costs.
  • A 'marketing engineer' dashboard where a brand prompts its ad format — brand components in, a unique sponsored experience out — instead of shipping a static JPEG.
Metrics Mentioned

The numbers, with context

$20M+
Koah funding raised

Raised from Theory Ventures to build the monetization layer for AI applications.

170M+
Queries processed

Koah is already processing over 170 million queries across AI apps.

5%+
Click-through rate

Koah's CTR on AI-native surfaces.

4–5x
CTR vs. traditional display

Koah's click-through rate is four to five times typical display-advertising benchmarks.

~1 year shelved
Idea-to-launch timing

Mike had the idea about a year before starting, during the GPT-3.5 era, but shelved it as too early until apps began productionizing LLM features.

Rising ~1 month before recording
Down-funnel conversions

Actual sales/conversions started climbing about a month before recording, signaling consumer behavior beginning to shift toward buying via AI surfaces.

Entities

Companies, people & tools mentioned

Auto-extracted and linked into the knowledge graph.

Companies

People

Tools & software

AdMobMobile Advertising

Google's mobile-ad platform and the 'de facto solution' Mike used as a teenage app developer — a fixed banner pinned to the tab bar, the banner-ad status quo Koah is built to replace.

ChatGPTAI Assistant

Used as the example of where consumers research but don't yet buy: 'I wouldn't go to ChatGPT to buy it' — the consumer-behavior gap AI ads still face.

X (Twitter)Social Platform

Mike's go-to source for tech news; he still 'doom scrolls' X/Twitter to stay connected after having worked there.

InstagramSocial Platform

Named among the 'typical platforms' where social advertising still dominates, the baseline against which new AI ad surfaces will emerge.

Frequently Asked Questions

Straight answers

Generated from the conversation, marked up for search and AI extraction.

What is Koah?

Koah is a monetization layer for AI applications, often called 'AdSense for AI.' It serves dynamically generated, personalized ads inside AI-native apps. Co-founded by Mike Choi, it has raised over $20M from Theory Ventures and processes 170M+ queries at a 5%+ click-through rate — four to five times traditional display advertising.

Why are AI ads different from search and social ads?

Traditional ads are created once by an agency and shown identically to everyone. AI can generate a unique ad — the copy, the creative assets, and increasingly the interface — tailored to each individual user and matched to the app's context. Mike calls that the 'marketing holy grail': a personalized ad delivered at scale rather than a single static creative served to the whole world.

Why is click-through rate the wrong metric for AI advertising?

Because a user can click without ever converting, so a high CTR on a non-converting ad is a vanity number. Koah optimizes for the outcome the advertiser actually wants — conversion and end-user value. If the ads are genuinely relevant, conversion rates should hold steady rather than decay, and generative UI is expected to eliminate the ad blindness that plagued banner ads.

Which products convert best with AI ads today?

High-consideration, financial products — taxes, student loans, and credit cards — convert best, because the more expensive or complex a purchase is, the more research a buyer does before committing. Seasonality also matters: Koah saw exceptional click volume on tax ads around tax day, since people tend to file at the last moment.

Why does the explosion of vibe-coded apps matter for advertising?

Anyone can now prompt an app into existence without hiring an engineer, so a long tail of niche apps that couldn't previously justify their build cost will ship. Because AI apps carry real inference costs — unlike the near-zero server costs of 2010 — many of them need an embedded monetization layer like Koah just to sustain themselves, which creates a large new advertising market.

What was Koah's naive early hypothesis, and why was it wrong?

The team assumed that if a user asked about shoes, showing a shoe ad would make them buy. Consumers don't behave that way — they research with an LLM but still buy elsewhere to price-shop and earn credit-card rewards. That consumer-psychology gap is what Koah is still crossing, though its early bet on conversion tracking let it see when the behavior finally started shifting.

What's next for Koah?

Dynamic UI. Koah started with generated ad text, then generated assets, and is now building tools that let any advertiser generate a brand-specific ad format — not a static JPEG or a 50-character line — by prompting a dashboard as a 'marketing engineer.' The broader goal is returning advertising to craft, so ads feel like art rather than noise.

Where did Mike Choi work before founding Koah?

Mike worked at Apple and then at Twitter (under Elon Musk) before co-founding Koah. His path started as a Korean exchange student in Kansas who jailbroke iPods with Cydia and fell in love with building consumer apps — an obsession that carried through his entire career into founding Koah.

Full Transcript

The whole conversation

Broken into chapters, searchable, verbatim from the audio. Speakers inferred (not diarized).

00:00Intro: from Tucson to Kansas to AdSense for AI

0:00 (logo whooshing) - Talking with Mike Choi, co-founder of Koa, they're building what everyone's calling AdSense for AI. Koa is the monetization layer for AI applications, and they just raised over $20 million from Theory Ventures. They're already processing over 170 million queries and seeing over a 5% click-through rate, which is four to five times what you see in traditional display. But here's what makes Mike's story especially interesting. He's a Korean national who came to the States as an exchange student in middle school, served in the South Korean military, then worked at Apple, then under Elon. And now he's building one of the most important

0:45 infrastructure companies of the AI era. We're going to talk about why AI ads are fundamentally different from search and social, why conversations aren't there yet, but will be soon, and what it actually looks like to build the advertising stack for a new computing paradigm. Mike, so excited to have you on the podcast today. Thank you so much for being here. You were a Korean exchange student in Kansas, jailbreaking iPods, and now you're building AdSense for AI. You have one of the most interesting stories we've ever had on the show, and I'd love for you to share with the audience. - Yeah, thanks, Anthony. Yeah, it's a very long story,

1:26 and I'll try to make it as pleasing or interesting to hear. But yeah, I was born in Korea. My dad was in the Air Force, and my mom was a stewardess for Korean Air. And I guess they both met in the skies. But my dad, part of his officer's training, had to go to the States, and this was around when I was in the third grade. And so I had the opportunity to kind of follow him along with side my mom to go to Arizona, or Tucson, Arizona, to be specific. And yeah, I just kind of fell in love with the States. I kind of, you know, any time I tell this story, I kind of mentioned the moment I had at my friend's house

01:36Mike's origin story: jailbroken iPods, Apple, and the path to Koah

2:15 when he made me like a Nor's instant rice, you know, broccoli and cheddar, extremely processed meal after school. And I can like still remember how that tastes in my brain and kind of like change the chemicals up there, maybe. That kind of made me go, wow, this is great. I would love to stay in the States. So after a year in Tucson, I went back to Korea, and you know, just things just didn't feel the same. You know, it's kind of like going back to the same Korean academic system and all those kinds of things felt a bit like I was missing something back in the States. And so I asked my parents to send me back

2:57 to the States as a foreign exchange student, essentially. And then I found my way to Kansas, where I stayed with essentially a host family where the mom was white and the dad was black. And you know, the kid that they had was in my same grade, was a mixed kid. And then me, the Asian kid, ran around as well. And so it was a very international family. And I learned tons there being surrounded by extremely smart people and, you know, parents who were very generous and nice to me, just like a kid from really nowhere in middle of Kansas. And, you know, I was gifted an iPod for Christmas and started downloading Cydia, you know, the PDF jailbreaking websites

3:44 to kind of really customize my iPod and became an Apple fanboy, essentially, right? And that basically kicked off my love for consumer apps, building these apps and software that brings joy and delight to people all the way, you know, from like designing it, building it, shipping it. That just brought me an immense amount of joy. And so that's, I would say, where I started my love for apps and that kind of turned out well for my professional career because, you know, I went to my dream job at Apple and then essentially eventually Twitter. And even to Koa, which is kind of, you know, inspired by my high school moment where I was making apps

4:33 and had a hard time monetizing it. At the time, AdMob was kind of like the de facto solution if you wanted to monetize your application. And at the time, the state of the art was having a fixed banner or an image ad on the bottom of your tab bar. And anytime a user clicks on it, you get paid for it essentially, right? But now it's 2025, 2026, and there's ad blindness to those banner ads. And they're not really something consumers like. And app developers don't like those banners as well because it makes your app feel cheap and it doesn't respect the amount of craft and effort that went into creating those apps. And so at Koa, what we're essentially doing

5:18 is let's reimagine what ads look like, especially when there is this entirely new surface area that is changing what feels like every hour of the day. And so, and yeah, we're well in the journey and we have some thesis and products that we think are the right tools for the job. - Such an incredible story and love that your background has really culminated into what you're working on now. And I think what's interesting is every major paradigm shift in, I'll call it media or information or content has come with some major re-imagination of advertising, but it never goes away. So if you think radio to TV, to web, to social,

06:02Every paradigm shift reinvents advertising

6:09 there's just whole new infrastructures built around that new paradigm. So you're calling this AdSense for AI. How is AI going to make things different and what's the answer to it? - Yeah, I would say a lot. I think when we first started this business, the 50,000 feet vision that we had was, hey, every single ad that you and I see on the internet is practically the same. It's been created by a creative agency, checked off by a person at a company that says, yeah, that does speak our brand, that does meet our guidelines and is circulated around the world. But what if everyone is served an ad that is just tailored made for them specifically, right?

06:46The marketing holy grail: an ad tailored just for you

7:02 That is marketing holy grail, right? And so AI is able to do that at scale. And we as a company are trying to figure out, hey, what does that look like? Is it as simple as just saying, hey, Mike, would you like insert brand name here's shoes? Or is it dynamic interfaces where the UI is generated and the ad is generated and matched perfectly to fit the foreign factor of the AI application, right? And so I think there are a lot of creative ways to do it. We started with the text generation where every ad was generated on the fly, right? To kind of match the context of the conversation. And now we're taking it even one step further

7:44 by saying that, why stop at text? Let's go one level up, which is the assets. And what's one level up above that? Well, it's the actual ad. How does the ad look and feel? What would be a good example of a static old world advertisement and then advertising with Koa? Where have you seen some of the biggest successes with your customers? - Yeah, great question. Yeah, I'll give a static very simple example for what a quote unquote traditional ad might look like, right?

08:23Static ad vs. Koah: what dynamic personalization actually looks like

8:24 Let's start from like the real world, right? You're in a New York subway, you see an ad. That's the same ad that everyone sees when they are writing that carriage, right? But let's say in Koa's real life physical world, it's a version of the product, every single Visa ad or every single insert brand ad here, the text, the content, the product that's being sold is tailored just for Anthony or just for Mike, right? It knows that I have preferences for certain products and it knows that I have, that I'm not really a fan of other category of products, right? And so it's able to kind of magically know and sense what the best ad would be that not,

9:15 that doesn't feel like a encroachment or anything, but more so a value ad, right? It's the moments where the ad shows up and I go, "Yeah, I didn't know I needed that, "but yeah, I need that right now," right? Those are the moments that we're trying to unlock and it's super hard to do so because if you're slightly off, then it feels creepy, right? But if it's just right, there is this like moments where it's, "Wow, yeah, thank you for showing me that ad," right? And so those are the moments that we're trying to unlock via the dynamic generation. And of course, there's some ad tech 101 stuff that we also have to nail down such as like the,

9:53 how do I match a user's intent to thousands or millions of ads that are living on a database, right? So it's a culmination of various factors that makes it feel dynamic and alive and very well contextually relevant. - Do you think this is gonna open up new distribution channels of advertising? I mean, social is still dominating the advertising world right now. Do you think we're gonna see more creativity in the chat harness over the models or is there some other way your envisioning consumers will interact with advertising? - Yeah, I think, yeah, there's two questions there, right? I guess the first bit is, you know, other than social,

10:39 other than Instagram and the typical platforms, yes, I do think there will just be an explosion of those platforms mainly because in the past, I'm sure you have that one person in your life that's like, hey, man, I got a really great idea for an app, let's build it. Especially if you're an iOS or an Android engineer who knows, you know, your friends know that you can build an app, you've shipped an app before. They wanna convince G-course you to say, let's build that recipe app, right? No one's done that before, right? But now they can actually do it. They don't need an iOS engineer in their life. They can just go to a vibe coding website,

10:41The vibe-coding app explosion and the monetization gap

11:15 you know, type up a prompt and then make an app and ship it, right? And so what that will lead to is two things, right? One is an explosion of apps, but secondly, it means that sure, there will be an explosion, but you know, there will be bad apps, right? Another person, that would be one person that's trying to make yet another grocery store or a recipe app that doesn't need to exist, right? But there will also be apps that had to exist or just adds value to the world, but just couldn't have been made before because the dev costs were too high, right? So very niche communal apps that just serve a lot of purpose

11:56 and value to very niche groups will definitely pop up. And yeah, and you know, and Koa is ideally there to kind of serve those apps monetize and kind of sustains themselves, right? Because the inference costs nowadays are, you know, it's non-zero, whereas in the past in 2010, your server costs were probably the only amount of bills that you had to pay to maintain an application. So yes, I think I'm bullish on the fact that there will be multiple platforms that will show ads to frankly, sustain themselves as a business. - Makes a ton of sense. And something we're seeing a ton, at LeanScale, we run GTM Ops for mostly B2B SaaS companies.

12:46 And the past couple of years, we've just seen an absolute dramatic increase in volume of outreach and people trying to get in front of other people. And now that you can automate so much of this, the noise has just, I'm curious, are we gonna see a similar thing with advertising where now you can just create an incredible amount of volume and how are people gonna stand out and stand out through the noise? - I think that's a good question. I think it really kind of depends on the platform or the ad platform. The advertiser is putting ads on because at the end of the day, it's really the ad platform's choice to show which ad, right?

13:34 And so I will tell you our beliefs and what we optimize for. And I don't, I'm sure Meta has their own principles in terms of which ads to show when. And that's a whole, it can't be covered in the next 20 minutes, probably. But for us at the end of the day, what we believe is that we wanna build for the end user. The user that is actually seeing and experiencing our ads. Those are the people that we are optimizing for and trying to add value for first. The reason we're doing that is because, hey, if Joe from, or Sarah from Arkansas is experiencing and is touching and is interacting with our ads and are gaining value, actually going,

13:42Will AI ads create the same noise problem as outbound?

14:25 oh yeah, that was a really cool product or that was a really cool experience. Then their engagement with the app that the publisher built will increase. And from engaging with those ads, the advertisers will also be happy because they're there to optimize or to accomplish a task that that format is designed to help them accomplish, right? And so, and finally, I always, one more thing is, we just believe that the surface area, the real estate, the publishers have granted us is sacred almost, right? It's the apps that these developers are building are kind of their little babies right there. They're putting a lot of craft,

15:05 they're putting a lot of care into those apps. And we think the ads deserve the same amount of care and attention. And so kind of putting the emphasis not only on the UI, but also asking the question of, is this valuable? Is this adding value to the end user? And of course, looking at the metrics to make sure ad ID X is doing that is our newest star. - Well, I think in order for anyone to keep up the hyper personalization component is going to be key. It is showing in your rates right now, 5% click through conversion rate, which is incredible three to five X typical benchmarks. Do you see that continuing to stay at that level?

15:53 Do you think it'll commoditize at a certain point and come back down when more people are personalizing their ads or where do you see the trend going? - Yeah, I think if I were to tell you exactly where it goes, I would be a magician and exactly know how the business will Japan out for the next five, 10 years. But I can tell you what I think, right? I think CTR is an interesting measurement because sure the user can click and the number of clicks with the click through rate can be high. But if the user isn't converting at the end, then the CTR really isn't the best way to measure success. Now that depends on what the objective of the advertiser is, right?

16:16Will the 5% CTR hold — or commoditize?

16:39 But we actually think at the bottom of it is, if we are doing our job correctly of serving the most relevant ads or ads that will add user value, keep saying that again and again, the CTR engagement rate or conversion rate or whatever the advertiser is optimizing for should stay the same, right? There's no reason for it to decrease. If it does decrease, we're not doing a good job, right? If it increases, well, we're knocking out of the park, right, and so, and along with some of the products that we're building right now, it's ad blindness is not going to be a thing because of the generative nature of the UI, right?

17:21 So we think a lot of the patterns or know-hows that were learned over the last couple, last decade or two of ads will be completely irrelevant, kind of like how most PM books or most engineering books that were written before this year are practically just out of date. They're just no longer irrelevant, right? And so we think the same will happen to ads, UX and, well, yeah, behavior. - Yeah, and it does feel like things are changing at such a fast rate. It's pretty tough to keep up. I'm curious, maybe going to the origin of COVID, what was the hardest thing about getting it off the ground and bringing this to market? - There were a couple,

18:10 but I would say the main thing that was hard was we, from the get-go, we kind of felt like the idea was obvious. It was an obvious one. We had the idea, I would say even like a year before when we actually started the business. And at the time it was around when GPT 3.5 was out. And it felt too much like a slot where people weren't productionizing elegant features yet because they felt like it was too early, right? And so we kind of shelved the idea, put it into our backlog, hoping that the right time will come. And after a year, we thought the timing was right. And so initially when the idea first came to be, the problem was, are there even apps out there

18:47The hardest part of getting Koah off the ground

19:00 that are using LLMs as like the main feature, right? Sure, there could be a chatbot here, chatbot there, but are there enough of those apps out there that justifies a billion dollar or a trillion dollar company to exist, right? So that was big question number one. And number two is I would say, well, that the first problem I just mentioned still exists today, I would say. But the second question is, what is user behavior in AI like? And what is the perfect ad in AI? We still don't know, frankly, right? And I'm not sure if we'll ever know, and that might be a controversial statement, but we are the ones that are trying to kind of define the standard in AI ads

19:52 to be able to say, yeah, this is what, this is what state of the art looks like. This is what working looks like, right? And some of it I think is stuff that we can control via our technology, improving our engineering, improving our design. But then there's also a different bit where it's just pure consumer behavior, right? Shift in consumer behavior. And that just takes time. And I'll give you an example, right? If I want to buy this microphone, right? I wouldn't go to Chachipiti to buy it, right? I would simply go to, well, sure.com, or I would go to Google and type sure. And then I would browse through all the platforms

20:33 and providers that are selling this microphone, buy the cheapest one, use my favorite credit card to get points, and then go from there, right? And so there are, that's the kind of consumer psychological gap that we still have to cross. But meanwhile, there's still tons of other objectives and goals that AI ads can help advertisers accomplish. And so we're looking at those opportunities and winning in those areas. - Is there a certain inflection point or signal that you had that gave you the confidence that it's moving in that direction enough? - Yeah. - To make you jump in? - Yeah, well, I think, you know, when we started,

20:45The consumer psychology gap nobody's solved yet

21:16 we had the thesis of, yeah, just, you know, if the user is asking about shoes, show a shoe ad and then they'll buy it, right? That was the very simple naive hypothesis. And it turns out people don't do that, right? Because of various reasons, which one of them being like the psychological gap that they have sought to cross. But the good thing, the side effects of that was that we developed technology to be able to track and know when the user does convert at the end of the pipe, right? And so we always had kind of visibility into the actual sale or the actual conversion that was happening down funnel, right?

21:56 And so I would say like starting a couple of weeks ago, actually, oh, like a month ago, that number started to go up, right? And so we're like, oh, something's happening, right? We sold X, we sold Y and the numbers are growing up. There must be something happening, right? So either we did something with our ads or the serving technology or the advertisers that were on the platform or people are starting to feel more comfortable or they're starting to feel like, yeah, let's try it. And then kind of at the end of the day convert, right? And so I think the decision to kind of invest in that analytics component of the business early on has been very fruitful.

22:14The inflection point that signaled it was time

22:39 - That's fantastic. I'm curious, you used the sure example. I definitely had to go to an LOM to figure out what are the best mics? What should I actually get in it? - Yeah, yeah, yeah. - But I didn't buy through there, so I got to consumer behavior. I've heard anecdotes that the price point dictates how much research they may do and the likelihood of maybe going to an LOM to do some of that research. Do you have any data on which products are most successful and get the best ROI from leverage ads? - Yeah, yeah. I think that's 100% right. The more expensive or more complex the product is, the more research the user wants to do

23:27 before actually purchasing that item, right? So what we've seen from the product category perspective that kind of fits into that are taxes, student loans. Anything financial related or credit cards, right? Has been very, much so in that category. And what we've seen recently because of seasonality that works is taxes, right? You should see our numbers on tax day of people clicking on tax ads was incredible, right? Because some human behavior just doesn't change. And it's very consistent. People don't file their taxes until the last moment, right? And so I would say that's one of the things we've seen

24:16 that really works for high consideration products, yeah. - What's next for you, the team at Koa? What does the next frontier look like for you and the team? - Yeah, I was always asking that question, but right now we've thought about this for a bit and we're on a path to really build the dynamic UI component of our business. And that is, like I said earlier, we started with dynamic text, but we're allowing or we're building the technology for any market or any advertiser to build an ad format that really uniquely represents their brand, right? And so you're not confined to a single JPEG. You're not confined to a 50 character limit.

24:24Why taxes, student loans, and credit cards convert best

25:07 You can come to Koa, use our dashboard and prompt away as a marketing engineer, right? And say, I would like this, this, and this in the ad format or, you know, and here are some components of our brand that uniquely speaks our brand and craft a unique sponsored experience just for your brand, right? That is something that will be delivered to the end user and they can engage with it. They can click out, all those kinds of things. But I mean, the cool thing that we really want to enable is yeah, static UI, just showing a JPEG is very early 2000s, right? LLMs are moving so quickly. Let's build towards the future and what it can do instead of going,

25:38What's next: dynamic UI and ads as art

25:57 oh yeah, what do we do in the past? Like, let's just do that first 'cause we know it works, right? Because I think building for the future is more exciting and I think there's tons more creative things that marketers, really talented designers could do within our platform that we might not even be able to imagine ourselves, right? So we are building the tools, the Lego bricks that then they can use on their own to kind of craft these beautiful experiences. - Such an exciting time. I'm curious. I have a lot of people, especially at LeanScale, we're heavy on building AI workflows and automations for our customers. So we're definitely like deep in the space.

26:40 How do you stay connected? Where do you go for your information? Where do you go for inspiration of what you need to be building next? - Yeah, well, for tech related news, since having worked at Twitter, I still have X or Twitter on my phone. And yeah, my coworker was like, where do you get all this? Like, how do you stay so connected? I was like, oh, I doom scroll from time to time, right? Should probably stop doing that here. But for the products and those kinds of things, I've recently been reading and purchased the Great American Ads. It's like a coffee table book and there's one book per decade, right? So the Great American Ads of the '40s

27:24 looks very different from the Great American Ads of the 2000s, right? And so there's the Got Milk campaign as one of the examples in the 2000s book, whereas in the '50s, there's like a cigarette ad or an ad that probably wouldn't fly today, right? And so kind of looking at those and being like, wow, these are so beautiful. These ads were pieces of art in the past, right? And now it has certain negative connotations, especially online. Why can't we bring back the days where we go and wow, that is such a beautiful ad, you know, there's a full pager ad that a person might take out on the New York Times and go, let's rebuild that.

27:51Where Mike goes for inspiration (and the coffee table book he loves)

28:04 Let's make sure that whatever artifact we produce at Koa kind of makes people go, wow, that is a cool ad. That is also one of our north stars. - I love that. And somebody's got to do the doom scrolling and fill everybody in, so I appreciate that for us. - Pleasure to be a service, yeah. - Mike, this is a really, really exciting conversation, especially timely for what a lot of our customers, people who listen to the podcast are trying to work towards. And I think in order to maintain your level of competitiveness, you're gonna have to personalize, build meaningful messaging, imagery, copy, that speak to the person who are looking at an advertisement.

28:51 And I really appreciate the work that you're doing. And it's reflected so much in your personal story, all of your experiences, kind of culminating into jumping into a life of entrepreneurship, birthing something to life, and really being at the forefront of is one of the most exciting times in human history. So I just appreciate everything you've shared, what you're building. And for anyone who's listening, what's a good next step? Where should they go take a look at? Where would you guide them? - Yeah, if you want to talk about what it's like to start a company, or any questions about what is a startup like,

29:36 I'm available on Twitter, the handle is guard_if, and some of the Swift programmers out there will kind of understand what that means. And if you're curious about what ads and AI looks like, and what the frontier feels like, go visit our website, coallabs.com. And we have a new brand, and a visual guideline dropping soon, too. And it's gonna be really fresh, and I think the folks will like it. And so stay tuned for that as well. - Mike, thank you so much, and can't wait to see what you all build next. - Thank you so much, Anthony, I appreciate you.