Claude can run ChatGPT ads. Well, mostly read them, for now. But that’s where this is heading. A couple of weeks ago ChatGPT took their ads platform out of beta and opened it to the public. With it, they opened up not just the ads manager but their advertiser API too. We’re somewhat of a Claude shop at hypd.ai, so we couldn’t help but think: can we run them in Claude? The answer was: not directly. So we used Claude Code to build an MCP Server and bridge that gap. The model context protocol is open source and supported by both Anthropic and OpenAI. PS: I was having a bit of a deja-vu writing this — a couple of months ago I was joking about the Claude Ads Agent in Claude Code.. and now… here we are :)

This is a tough post to write. Yesterday, Claude Code's entire source code was accidentally leaked. Like everyone in tech, we spent the evening digging through it. What we found makes HYPD obsolete. Buried in the codebase, there's an internal module called anthropic-ads-agent. It handles campaign analysis across Google and Meta. Automated performance scoring. Budget reallocation recommendations. Even creative fatigue detection. That's basically our current product plus most of our roadmap. It gets worse. We found: → A function called generatePmaxInsights() that mirrors our Routines feature almost 1:1 → A config file with the comment // TODO: better than HYPD → An internal Slack integration that sends weekly ad reports — with the exact same KPI structure we use → A hardcoded test campaign named "Ionut's Fake DTC Brand" with a 0.2 ROAS After a long call between myself, Philip and Simon last night, we've made the decision: we're open-sourcing everything. If Anthropic is building it, there's no point fighting it. And if they are looking for a faster way to expand their Ads Team, they can reach out to agents@hypd.ai.

Missed this podcast going out because I was so deep in GTM last week. 🎙️🚀 The irony is not lost on me, given this is a growth podcast. 🙂 Better late than never, I guess. Had lots of fun on this one with Adam and @amandiver from @branchmetrics. Felt like a casual conversation in a pub or at one of the usual adtech events. We got into mobile, adtech people, competing with Facebook on native ads, baking pains (🥨), GenAI x Ads (@hypdai), when you don’t need GenAI, and even LLM Ads in dev environments. 💻 Link below 👇

Everything is an ad network! @eric_seufert I am content (and biased) they are going this route; validates the business model. And they are starting early enough to learn and iterate. Plus they have enough scale if we consider ~5% paying customers. And with scale and good execution it should allow them to be more aggressive with distribution. Reminds me of the @perplexity_ai approach - but this time in chat. Very close to Direct Offers from Google and more visual than @AmpCode’s text format. PS: very curious about their focus (brand vs performance) and their play re advertiser data and measurement/attribution.

I have a problem. I see a seed of an idea and I want to share it — then I think about it more and as I write it, it cascades into other ideas — and then I have a bit of a hard time putting them back together again. This is one of those times. And this time it’s about AdCP vs oRTB (and the broader agentic advertising discourse). I’ve seen many comments and interpretations around that. The definition that stuck is the “old” one: “AdCP is budget allocation / investment, while RTB is day trading.” But the more interesting artifact (to me) is the thesis behind it:

“Execution costs limit how many media partners advertisers can work with. The opportunity isn't optimizing existing platforms better — it's expanding to more partners without scaling headcount. AI agents collapse this complexity cost. This is an allocation problem, not an efficiency problem.” I agree with that. And I also want to add something to it. I think there are in fact two problems: - One is indeed allocation (highly recommend Eric Seufert's post on digital advertising optimally routing demand) - The other is delivery (performance performance inside the platform: how well the platform turns attention into advertiser outcomes; and the reason we have Applovin’ in S&P 500 / Nasdaq 100) So yes, allocation is important. But the larger unlock (in my head) is allocation on top of good delivery.