The Ex-Agent: Why Former WPP and VML Leaders Are Launching AI-Native Consultancies

The most significant competitive signal this month did not come from a software launch or a funding round. It came from a person. Thomas Tearle, the former CEO of VML Australia and New Zealand — a 300-person, six-location operation within WPP — launched his own AI-native consultancy, Underdog Studio, in Sydney on April 29. He did not leave to join a holding company or start a traditional boutique. He launched an AI-native studio, competing directly with the agency model he spent a decade running from the inside.

This is not a coincidence. It is a pattern.

Code and Theory, part of the Stagwell network, made a similar declaration through its leadership. CEO Michael Treff told press that the "model of simply providing services is ending," positioning his firm instead around integrated tech-creativity systems for CMOs navigating AI complexity. The language matters. Former agency leaders are not using the vocabulary of traditional consultancy. They are using the vocabulary of AI-native delivery.

The conclusion is unavoidable: people who spent their careers inside traditional agencies now believe the AI-native model is the future. Not as a side practice or an internal innovation project — as their primary bet. If you are a CMO, a strategy director, or a brand leader evaluating where to spend your next strategy dollar, this should concentrate your mind.

What Insiders Understand That Outsiders Miss

Agency executives who reach the CEO level inside large networks develop a specific understanding of how these organizations actually work. They know the utilization math. They know the pyramid structure. They know that revenue per partner is optimized by keeping junior staff billable and senior partners scarce. They know the tension between what holding companies promise in earnings calls and what their operating models actually deliver.

When a senior agency executive leaves to launch an AI-native consultancy, they are not just changing employers. They are betting that the leverage model that sustained agencies for fifty years is structurally incompatible with the speed and transparency demands of the current market. That is a meaningful signal, because it means they reached the top of the old model and concluded it was not sustainable.

Y Combinator's recent recognition of AI-native agencies validates this from a different angle. When the most selective startup accelerator in the world creates a funding category for AI-native agency models, it is not because the partners at YC have a sentimental attachment to the agency industry. They have identified a structural inefficiency — traditional agencies deliver value at a cost structure that cannot compete with AI-native delivery — and they are funding companies to exploit it.

The Difference That Separates AI-Native Consultancies From Traditional Agencies

Not all AI-native consultancies are the same, and the differences matter enormously for buyers. The recent wave of AI agency launches spans a wide spectrum, from firms that use AI to automate traditional service delivery — faster research decks, quicker competitive analyses — to firms that have rebuilt their operating model around AI-native principles where the output format, pricing structure, and accountability model are fundamentally different from a traditional agency engagement.

The first type is an efficiency play on the existing model. The second type is a different model entirely. The distinction is not always visible in marketing language, which is why procurement diligence matters.

A genuine AI-native consultancy differentiates on four structural dimensions. First, output format. Traditional agencies produce decks and documents. AI-native consultancies that have rebuilt their delivery model produce decision-ready recommendations — structured outputs that do not require a senior strategist to translate before they can be acted on. Second, delivery speed. Traditional agencies measure in weeks. AI-native consultancies measure in hours or days. Third, pricing structure. Traditional agencies price by project scope or billable hours. AI-native consultancies price by subscription or outcome, aligning their incentives with client results rather than effort maximization. Fourth, accountability model. Traditional agencies deliver recommendations and move on. AI-native consultancies built around strategic partnership maintain ongoing accountability for whether the recommendation produced the intended result.

The former agency leaders launching these firms understand the structural difference. They ran the old model. They know exactly which inefficiencies are built into the leverage structure and which are incidental. When they design a new firm, they are designing around the structural problems, not the surface-level ones.

What This Means for Strategy Buyers

If you are evaluating where to direct your strategy spend, the current landscape offers a broader set of options than existed two years ago. Traditional agencies remain viable for work that requires deep brand heritage, large creative teams, or sustained client relationships over multi-year engagements. AI-native consultancies offer a different value proposition: faster delivery, subscription pricing, and strategic clarity without the overhead of managing an agency relationship.

The former agency executives launching AI-native firms bring credibility that pure-tech founders lack. They understand how agency-client relationships work because they built and ran them. They understand what strategists actually need because they managed strategists. They understand the difference between a good strategic recommendation and one that sounds good in a presentation but falls apart in market execution.

At the same time, not all AI-native consultancies are equal. The structural differences outlined above are not cosmetic. A firm that delivers faster decks is not the same as a firm that has rebuilt its operating model around decision-ready strategic output. Buyers should ask directly about output format, pricing structure, delivery timelines, and accountability standards before signing any engagement.

The Window Before the Category Crowds

The AI-native strategy consultancy category is still forming. Former agency leaders are launching firms, Y Combinator is funding the category, and holding companies are watching with varying degrees of concern. The window for positioning before the category definition stabilizes — and with it, buyer expectations — is open but narrowing.

Autostrat was built on the same premise that motivated Underdog Studio's launch: the traditional agency model has structural constraints that AI-native delivery resolves. We have been making that case since our founding. The difference is that Autostrat has been operating, delivering strategic outcomes, and building proof assets while the market was still debating whether the category existed.

If you are a strategy leader exploring alternatives, the current moment offers more choice than any point in the past decade. The presence of former agency operators in the AI-native space validates the model. The question is no longer whether AI-native strategy delivery is viable. The question is which provider has the track record, the accountability standards, and the structural differentiation to deliver on the promise. That is the evaluation that matters now.