21 Touches: Modern Performance Marketing

Full analysis

Anna Stepura, co-founder and CEO of Adlyse, went on the Aperiam podcast with Corey Ferengul and Joe Zawadzki to pitch a thesis: performance marketing now takes 21 cross-platform touches to convert a customer, up from the old "rule of seven," and the only sane response is an agentic operating system that sits above Google, Meta, TikTok, Microsoft, and OpenAI and optimizes across all of them. This is a founder pitch on a friendly stage. Zawadzki and Ferengul are co-hosts and Adlyse is an Aperiam investment, so nobody in the room is playing defense.

What's actually being decided for the reader isn't "should I buy Adlyse." It's whether cross-platform agentic buying becomes a real layer in the stack, who owns it, and whether the walled gardens let it live. That's a slow-moving, Type 2 question for now (easy to test with a pilot, easy to walk back), but it hardens into Type 1 if platforms start playing gatekeeper. The forcing function is the OpenAI ad channel going live and every early client asking to switch it on.

The Market Analyst. Follow the incentive Stepura names: every platform's native AI optimizes for spend on that platform, not return across all of them. That's true, and it's the whole business case for a neutral layer. For an operator, this is the same fight independent ad tech has run for fifteen years, now dressed in agent language. The catch is timing. Adlyse claims OpenAI CPMs are falling and targeting improving, but that's a few weeks of priority-access data with "only three buttons" to press. Not a market read. The durable signal isn't Adlyse's numbers. It's Zawadzki's own prediction that within two years a Meta or Google tries to extend its agent across rivals and go closed. That's the risk that decides who wins.

The Skeptic. The 21-touch figure is unsourced, and so was the seven it replaces. The rule of seven was never measured; it was a slogan. Stacking a made-up 21 on a made-up seven and drawing a trend line is talking your own book, because a higher touch count is exactly what justifies buying a cross-platform OS. Strip that number out and the pitch still stands on the incentive argument, so why lead with a stat you can't defend? The "100 competing hypotheses" and "20 proprietary reasoning models" are architecture claims no client can audit. And "40% of manual tasks optimized today, striving for 90%" means the autopilot everyone's excited about is roughly two-fifths built.

The Operator. Here's what breaks Tuesday morning. Stepura's own example is a 24-day trust curve: clients review every recommendation, then stop double-checking, then flip on autopilot with a cost-threshold guardrail. Twenty-four days of a human babysitting an agent is a real integration cost, and it repeats per client. The $50,000-a-month spend floor tells you who this is for, and it isn't the mid-market. The second-order problem shows up at 90 days: when the cross-platform agent reallocates budget off Meta, Meta's own algorithm degrades on the starved campaign, and now you're fighting the platform's learning phase every time the OS moves money. Neutral optimization and platform learning phases don't like each other.

The Customer / End User. Put yourself in the agency or in-house performance seat. You already distrust Advantage+ and Smart Bidding for exactly the reason Stepura names, so a layer that ignores platform recommendations and arbitrates across channels is something buyers actually want. That's real demand, not projection. Every Adlyse client asked to turn on OpenAI ads immediately, which tells you appetite for new channels is high when someone else handles the plumbing. But the buyer is also handing budget-allocation logic to a black box run by a startup, on top of the black boxes they already resent. You've added a layer of trust, not removed one.

The CFO. The economics only work above real scale. At $50k a month minimum, and a core target spending millions per platform, this is an enterprise tool with an enterprise sales cycle and a 24-day onboarding drag before value shows. The payback question is whether the cross-platform lift beats what you'd get letting each platform's native AI run, and nothing in this episode measures that against a control. The strategic cost is worse than the license fee: if agentic buying consolidates and one platform goes closed, everyone who standardized on a neutral OS eats a switching cost. You're paying for optionality that a walled garden can revoke.

Where they part ways. The Analyst and the Customer see genuine, durable demand for neutrality. The Operator and the CFO see a tool that fights the platforms' own learning phases and only pays back at scale. And the whole thesis hinges on one belief the Skeptic won't grant and Zawadzki himself put a two-year clock on: that the walled gardens keep their APIs open. Stepura says new platforms ship agent-ready APIs by design, so openness is the trend. Zawadzki says someone tries to go closed within two years. They can't both be the base case.

What it comes down to. Two things decide this, and neither is the 21-touch stat. First, do the platforms stay open to third-party agents, or does one of them weaponize its agent across rivals and force a choice. Second, does cross-platform reallocation actually beat single-platform native AI net of the learning-phase penalty, measured against a real control. An operator testing this should ignore the founder's benchmarks and run a clean holdout: let Adlyse manage a slice of spend against a native-AI-managed slice, same budget, same window, and read the difference. The demand is real. The measurement is missing.

Prediction: Before the end of 2026, at least one of Google or Meta will ship an agent-buying or MCP-style interface that favors its own inventory or restricts third-party cross-platform agents from acting on equal terms, confirming Zawadzki's "someone goes closed" call ahead of his two-year window.

Confidence: Medium. Platform incentive to protect spend is structural and already visible in Advantage+ and Smart Bidding.

Why: The core mechanism the episode names, that each platform's AI optimizes for its own spend, is exactly the incentive that pushes a dominant platform to privilege its own agent once agentic buying carries real budget. Google and Meta have a long record of opening APIs to build adoption, then adding terms that advantage house tools once the channel matters, which is the same arc header bidding and Smart Bidding followed. Stepura's "APIs are opening, not closing" read is true for new, subscale platforms like OpenAI that need advertisers; it does not hold for incumbents defending mature revenue, and Zawadzki, an investor in the neutral-layer thesis, still flagged the closed-move risk himself. The opposite outcome, both giants staying fully neutral to third-party agents as budgets scale, runs against every incentive in their own business model.

Revisit by 2026-12-31: We're right if Google or Meta ships agent/MCP tooling with terms or defaults that favor its own inventory or limit equal third-party cross-platform buying. We're wrong if both keep agent APIs fully open and neutral to independent optimization layers through year-end.