Ads as the Monetization Engine for AI Apps

Koah’s bet: ads inside AI chats

Koah, a startup that just closed a $5 million seed round, is pitching a simple idea: many consumer AI apps won’t scale on subscriptions alone, so in-chat advertising will become a mainstream way to monetize. Rather than trying to put ads inside products like ChatGPT, Koah targets the long tail—third-party apps built on large models and apps that serve users in regions where $20/month subscriptions aren’t realistic.

The company is already running ads in AI assistants and niche apps such as Luzia, Heal, Liner, and DeepAI, pairing sponsors like UpWork and Skillshare to relevant user queries. Ads are labeled as sponsored and are designed to surface at commercial moments—recommendations, service searches, or when a user expresses buying intent.

Why ads could unlock scale for long-tail apps

Co-founder Nic Baird argues ad monetization will be necessary once AI apps expand beyond wealthy early adopters. Many apps face the same inference costs regardless of where users are located, but users in emerging markets are less likely to pay subscriptions. Ads can subsidize those costs and allow quirky, vibe-driven experiences to exist without deep VC support.

Koah reports early performance gains—clickthrough rates near 7.5% and partners earning roughly $10,000 in their first 30 days on the platform—while claiming a smaller hit to engagement than traditional display ads. That suggests well-timed, contextual sponsorships can sit in the middle of the funnel between awareness and purchase.

The product challenge: capture commercial intent without breaking the chat

Ads in chat can’t be crude banner drops. They need to feel like helpful answers or suggestions that match what the user seeks. Koah’s focus is understanding where users signal buying intent inside a conversation and surfacing an appropriate sponsor or service—so the ad actually aids the user journey rather than interrupting it.

That raises technical and ethical questions: how do you detect intent reliably, how do you avoid biasing recommendations toward top bidders, and how do you remain transparent and privacy-compliant with targeted offers? Startups need measurement systems and guardrails before scaling ad placements.

Practical steps for AI startups testing ads

If you’re building an AI app and testing ads, start with a data-driven approach to placement, relevance, and user experience. Instrument chat flows to tag moments of commercial intent, run controlled A/B experiments, and prioritize labeled, contextual sponsorships over generic display creatives.

• Map commercial intents inside conversations and prioritize high-intent triggers.
• Design ad formats that read like helpful suggestions and clearly mark them as sponsored.
• Measure downstream actions—searches, clicks, conversions—and model attribution between chat interaction and final purchase.
• Run small pilots in different regions; ad economics vary widely by market and audience willingness to pay.

What this means for product teams and investors

Ads inside AI chats are not a silver bullet, but they are a pragmatic lever. For product teams they offer a path to monetize global scale without charging a high subscription price to every user. For investors, the presence of multiple viable revenue models—subscriptions, commerce referrals, and contextual ads—reduces risk for long-tail plays.

Startups must balance revenue with trust. The best outcomes come when ads actually help users find services relevant to their needs, when labeling is clear, and when targeting respects privacy. That’s where measurement, careful UX design, and thoughtful partner selection matter most.

How an analytics-first partner can help

Companies like Koah are building the plumbing for contextual ads in chat. Organizations that want to test this model need experiment design, intent tagging, regional economics modeling, and privacy-safe targeting frameworks. With those building blocks you can run fast pilots, compare ad-first vs subscription-first scenarios, and choose the approach that sustains both growth and product quality.

Monetizing AI will be a mix of models—and those who invest in measurement and user-first ad experiences will have an edge. If your AI product serves users who can’t or won’t subscribe, contextual, labeled ads could be the difference between a niche prototype and a sustainable global product.