You can find me at various other Cannes events at the end of the article.

Quo Vadis Green Shoot AdTech Interviews

Welcome back to Quo Vadis Green Shoot AdTech Interviews.

In this series, we’re stepping into the shoes of “AdTech Equalizers,” the green shoot companies changing how the advertising job gets done. We’ll delve into their worldview and unveil new strategies, tools, and thinking that make advertising better off.

Today, we have the great pleasure of speaking with Chris Pettit, CEO/co-founder of Revving. We interviewed Revving’s other co-founder, David Mandeno, in November 2025. Business is moving faster than ever across adtech, with companies shifting from revenue growth in a mature/changing market to profitability, so we thought it would be valuable to get an update from Chris Pettit.

If you’re heading to Cannes and want to reach out to Chris (or connect another time), reach out to him here: chris@revving.io

Q1: What are AdTech businesses really asking for when they talk about cash flow?

A: Chris

A lot of the time, they’re asking for control. The top-level problem is payment timing. Revenue gets earned, but the cash takes months to arrive. Everyone in the industry understands that, but the bigger issue is what that delay does to the decisions a business can make. If cash is tied up, choices get narrower. For example, you might hold back on hiring, delay media spend, stretch supplier payments, or turn down work you would otherwise want to take on. Not because the business is underperforming, but because the timing doesn’t work. That’s a very suboptimal way to run a business looking to create shareholder value.

It’s also the part I think gets missed. Cash flow isn’t just about what’s in the bank today. It’s about whether a business has the flexibility to act when it needs to. For me, the future of finance is choice: businesses being able to decide how and when they access the revenue they’ve already earned, rather than having that dictated by someone else’s payment process.

Q2: What does traditional finance still miss about how AdTech businesses actually make money?

A: Chris

A lot of traditional finance still starts with the things they know how to assess: balance sheets, fixed assets, long trading histories, invoices, personal guarantees, etc. Those things matter, but they don’t always tell you how a digital business is really performing.

When it comes to AdTech, it’s data-rich and performance-led. Revenue is being generated through commercial activity, but funding still waits for the slower paperwork that follows. That’s the important gap Revving’s Transactional-Based Funding model is built to close. Instead of starting with an invoice after the fact, our model uses verified transaction data to understand what’s already been earned and make that value available sooner. For AdTech businesses, that means earned revenue can become usable capital sooner, with far less friction in the middle.

Q3: How does Transactional-Based Funding level the playing field in AdTech?

A: Chris

It removes payment timing as a barrier to growth. Long payment terms are still treated as part of the deal in this industry. Bigger businesses can often absorb that. Smaller and scaling businesses often can’t, even when they’re doing brilliant work and generating real revenue.

That’s where I think there’s a real democratization of funding opportunities. Larger businesses typically know where to go, have the relationships, and have more ways to manage the gap. Smaller businesses often don’t. Our Transactional-Based Funding approach gives those businesses another option. By removing payment terms as the deciding factor, smaller and scaling businesses can compete properly with bigger players. Then the best business wins, not just the one with the deepest pockets.

Q4: Is this mainly an AdTech problem, or does the same funding gap show up elsewhere?

A: Chris

AdTech is one of the clearest examples because the supply chain is so layered. Brands, agencies, DSPs, SSPs, publishers, and platforms are all working with different payment terms. If one part of the chain pays in 90, 120, or 180 days, the pressure compounds and gets pushed downstream. The same problem appears across the digital economy… wherever you have platforms sitting between payors and the businesses delivering the work, payment timing matters a lot.

For example, creator platforms are a growing area for us for exactly that reason. They’re often managing campaigns for large brands while also keeping their creator ecosystem moving. If payment from the brand is delayed or on long terms, the platform still has to manage that gap. This is precisely where access to earned revenue is critical. It gives platforms more control over timing, more flexibility in how they support their ecosystem, and more choice over when and how capital is put to work.

Q5: What does the future of finance look like for the digital economy?

A: Chris

The future looks more connected to how digital businesses actually operate. Advertising is already moving towards more automated, real-time decision-making. Agentic advertising will only take that further, with buyer and seller systems coordinating directly, campaigns adjusting dynamically, and more commercial activity happening between systems rather than through manual workflows. This is why Revving is already MCP-ready for the agentic buyer-seller world. The MCP “nodes” across the ecosystem are already there and increasing every day. We don’t think it will be long before the network effects light up and rapidly accelerate from there.

In our view, as agentic environments develop, we’re looking at how funding and payment flexibility could become part of the workflow itself, rather than something managed separately afterward.

For me, that is the future and why we are building a financial layer that can evolve with an industry that has never stood still, and never will.

You can find me at various #CannesLions events.

• Sunday to Wednesday: Commerce Media: See this series of Fluent events. Sir Martin Sorrell and I will have a chat and open it up to Q&A.

• Tuesday:

  • Agency Agentic Flows: 50-person RSVP breakfast with AtomicAds, the winner of the AdEF founder pitch event in March

  • Buyer/Seller Agents: 25-person RSVP lunch with pubX

• Wednesday:

  • Signal owners, platform leaders, and operators are rebuilding the layer underneath advertising. 50-person RSVP breakfast with Brian O’Kelley and Gabriel DeWitt, hosted by Above Data.

  • The Transparency Paradox Debate on Mediaocean’s yacht. Bob Lord and Brian Wieser debate transparency vs. performance, moderated by Bill Wise and Tom Triscari

  • Criteo Lunch Panel: Measurement Models and Redefining Efficiency with Brian Wieser, Todd Parsons, and Tom Triscari

• Thursday:

  • C Wire AdTech Chill House: 1-1 chat with Sir Martin + Audience Q&A. We’ll end the week talking about what happened and why it matters.

  • PMG Beach: How to Survive in a Post-LiveRamp World at PMG Beach: Sam Bloom, Mathieu Roche from ID5, and yours truly tell it like it is.

  • Criteo Yacht Talk: Wired Different. B.R.O.s at Work

Disclaimer: This post, and any other post from Quo Vadis, should not be considered investment advice. This content is for informational purposes only. You should not construe this information, or any other material from Quo Vadis, as investment, financial, or any other form of advice.

If you would like to connect with Mathieu in Cannes, NYC, or in Europe, reach out to him here: mroche@id5.io.

What started as a discussion about a single M&A deal evolved into a broader conversation about the future economics of identity, data collaboration, and who ultimately captures value in the advertising ecosystem.

At first glance, the financial rationale for the deal appears incomplete. LiveRamp has built a meaningful business with strong market share, but it has historically generated economic returns well below those of Publicis and below its own cost of capital. Publicis, by contrast, has become one of the highest-performing agency holding companies in the world, generating exceptional returns on invested capital.

If this acquisition were simply about improving margins or extracting cost synergies, it would be difficult to justify the strategic significance of the move. Instead, the more compelling explanation is that Publicis was buying position rather than profits, vis-à-vis acquiring infrastructure, relationships, and the ability to shape how data moves through the advertising ecosystem.

Landowners, Farmers, Infrastructure: This led us to an analogy that increasingly resonates with the industry’s evolution. Historically, agencies have been farmers. They rented access to media, technology, and data owned by others, attempting to create value through expertise and execution, but the underlying assets belonged to someone else.

Over time, however, those who owned the platforms, data, and infrastructure increasingly captured a larger share of the economic value. Publicis appears to be pursuing that very same model in a hybrid form. Through acquisitions such as Epsilon, Lotame, and now LiveRamp, it is transforming itself from a renter into an infrastructure owner, to and from the land assets that can be leveraged repeatedly across thousands of clients rather than relying solely on labor-intensive services. As Mathieu observed during our conversation,

“You build it once or buy it once and then sell it multiple times. That’s how you create leverage.”

Identity Trade-Off Frontier: The discussion then turned to what we have previously described as the Identity Trade-Off Frontier. For years, marketers faced a familiar constraint:

Improving data/ID precision often meant sacrificing scale, while increasing scale came at the expense of data/ID accuracy.

Yet over the past decade, advances in identity infrastructure have steadily pushed this frontier outward. Advertisers can increasingly achieve both broader reach and greater confidence in audience recognition.

However, Mathieu highlighted an important distinction that is often overlooked.

Identity quality and audience quality are not the same thing. Identity answers the question, “Am I confident this person is who I think they are?” Audience quality asks, “Is this the right person to target?” Identity is the connective tissue that enables activation and data determines whether the resulting decision is valuable.

That distinction led to perhaps the most important insight from our conversation. As Mathieu framed it, identity is the means of exchange. It enables transactions, but it is not the transaction itself.

Identity itself is not the asset being traded. Rather, identity functions as the currency that enables trade. The assets being exchanged are media inventory, audience data, and measurement signals. Identity provides the common language that allows buyers and sellers to transact with confidence. Without it, data cannot be activated, inventory cannot be valued appropriately, and collaboration becomes difficult.

Once viewed through that lens, the network effects become easier to understand because the value of an identity framework increases with each additional participant. A single publisher adopting an identifier creates limited utility.

For instance, two participants create the possibility of a transaction, but thousands create a flywheel marketplace where publishers adopt because buyers support the identifier, and buyers adopt because publishers make inventory available, and then data providers participate because both sides have assembled. In essence, every new participant increases the value of participation for everyone else.

Importantly, the hardest participant to recruit is often the first one. Each subsequent participant lowers the friction required to attract the next.

Gravity Theory of Data Trade: This dynamic led to a discussion of what I have called the Gravity Theory of Data Trade. Borrowing from international economics, trade between nations tends to be driven by two primary variables: the size of the economies involved and their proximity to one another. A similar principle may increasingly apply to advertising, where the largest participants with the strongest connections generate the greatest volume of trading collaboration.

Viewed this way, identity infrastructure is not merely a targeting tool. It becomes the marketplace infrastructure that enables the flow of inventory, data, and measurement across the ecosystem.

The strategic question shifts from “Who owns the best identity graph?” to “Who sits at the center of the largest and most efficient network of data exchange with the ‘currency’ to fascilate trades?”

Neutrality: Of course, no discussion of the Publicis-LiveRamp transaction would be complete without addressing neutrality. Prior to the acquisition, LiveRamp operated as an independent intermediary serving a broad range of participants. Now it sits within one of the world’s largest agency holding companies.

That ownership inevitably introduces friction.

• Will competing agencies continue to rely on the platform?

• Will brands worry about becoming locked into a particular ecosystem?

• Will data partners continue to participate at the same rate?

All of those questions notwithstanding, Publicis undoubtedly modeled these risks before making its bid, hedging on the assumption that if enough of the network remains intact, the strategic advantages would still prove substantial. As Mathieu pointed out:

Publicis now controls a network that competitors may have to rebuild themselves if they choose not to participate.

Whether Publicis’ rationale for acquiring LiveRamp ultimately proves right remains to be seen. What is already clear, however, is that this acquisition has changed the conversation around identity, which is great for the ad industry.

The industry is now moving beyond debates about cookies and identifiers alone and leaning hard/fast into the next phase defined by infrastructure, collaboration, network effects, and the economics of trust.

The winners may not simply be those who own the most data. They may instead be those who build the systems that allow everyone else to put that data to work. That possibility is what makes this moment so fascinating.

Extra Education: Early this week, Mathieu posted a five-minute masterclass about data and identity on LinkedIn. Check it out here.

#CannesLions is around the corner! One thing is for sure… the Publicis-LiveRamp deal is going to make Cannes even more interesting than usual. Looking forward to seeing you at these events.

• Commerce Media: See this series of Fluent events. Sir Martin Sorrell and I will have a chat and open it up to Q&A.

• Agency Agentic Flows: 50-person RSVP breakfast with AtomicAds, the winner of the AdEF founder pitch event in March

• Signal owners, platform leaders, and operators are rebuilding the layer underneath advertising. 50-person RSVP breakfast with Brian O’Kelley and Gabriel DeWitt, hosted by Above Data.

• Criteo Lunch Panel: Measurement Models and Redefining Efficiency with Brian Wieser, Todd Parsons, and Tom Triscari

• Buyer/Seller Agents: 25-person RSVP lunch with pubX

• The Transparency Paradox Debate on Mediaocean’s yacht. Bob Lord and Brian Wieser debate transparency vs. performance, moderated by Bill Wise and yours truly

• PMG Beach: How to Survive in a Post-LiveRamp World at PMG Beach: Sam Bloom, Mathieu Roche from ID5, and yours truly tell it like it is.

• Criteo Yacht Talk: Wired Different. B.R.O.s at Work

• C Wire AdTech Chill House: 1-1 chat with Sir Martin + Audience Q&A. We’ll end the week talking about what happened and why it matters.

Disclaimer: This post, and any other post from Quo Vadis, should not be considered investment advice. This content is for informational purposes only. You should not construe this information, or any other material from Quo Vadis, as investment, financial, or any other form of advice.

• Commerce Media: See this series of Fluent events. Sir Martin Sorrell and I will have a chat and open it up to Q&A.

• Agency Agentic Flows: 50-person RSVP breakfast with AtomicAds, the winner of the AdEF founder pitch event in March

• Signal owners, platform leaders, and operators are rebuilding the layer underneath advertising. 50-person RSVP breakfast with Brian O’Kelley and Gabriel DeWitt, hosted by Above Data.

• Criteo Lunch Panel: Measurement Models and Redefining Efficiency with Brian Wieser, Todd Parsons, and Tom Triscari

• Buyer/Seller Agents: 25-person RSVP lunch with pubX

• The Transparency Paradox Debate on Mediaocean’s yacht. Bob Lord and Brian Wieser debate transparency vs. performance, moderated by Bill Wise and yours truly

• PMG Beach: How to Survive in a Post-LiveRamp World at PMG Beach: Sam Bloom, Mathieu Roche from ID5, and yours truly tell it like it is.

• Criteo Yacht Talk: Wired Different. B.R.O.s at Work

• C Wire AdTech Chill House: 1-1 chat with Sir Martin + Audience Q&A. We’ll end the week talking about what happened and why it matters.

Quo Vadis Green Shoot AdTech Interviews with Arthur Larrey, CEO/co-founder of Audion

Welcome back to Quo Vadis Green Shoot AdTech Interviews. In this series, we’re stepping into the shoes of “AdTech Equalizers” — the greenshoot companies changing how the advertising job gets done. We’ll delve into their worldview and unveil new strategies, tools, and perspectives that make advertisers more effective.

Today, we have the great pleasure of speaking with Arthur Larrey, CEO and co-founder of Audion, the leading performance audio platform. If you want to learn more about the digital audio space, reach out to Arthur directly at arthur@audion.ai or meet him at Cannes Lions.

Q1: Can audio truly deliver measurable performance?

A: Arthur

For years, audio has been viewed primarily as an awareness channel. It delivered attention, engagement, and brand impact, but marketers often struggled to connect that influence to measurable business outcomes.

That is changing. We now have better data, more sophisticated activation technologies, improved attribution models, and cross-channel measurement, making audio increasingly accountable. The question is no longer whether audio can influence consumer behavior; it is whether marketers have the infrastructure to measure and optimize that influence at scale.

In our view, performance audio should not be defined by clicks alone. Just like connected TV, audio’s evolution is about proving incremental business impact across awareness, consideration, intent, site visits, lead generation, and sales. That’s why we think of ourselves as the tvScientific of the audio space.

Q2: Is the advertising industry underestimating digital audio’s potential?

A: Arthur

In many ways, yes. Consumers spend hours every day listening to digital audio across podcasts, music streaming, radio streaming, and increasingly video-first audio experiences. Yet advertising investment has not fully caught up with consumer behavior.

We’ve seen this playbook before. Historically, audio lacked the buying, optimization, and measurement infrastructure that marketers expect from channels such as search, social, or CTV. As those capabilities mature, audio is becoming easier to activate, measure, and justify within performance-driven media plans. The opportunity is not that audio has suddenly become more valuable. It’s always been highly valued since the first radio ad.

From our vantage point, today's big audio opportunity is about how advertisers can prove the value audio has always delivered.

Q3: In what ways does dynamic creative impact performance in digital audio campaigns?

A: Arthur

Creative relevance has always been one of the strongest drivers of advertising performance, and digital audio is no exception. Marketers know that consumers respond better when messaging reflects their context, location, interests, stage in the customer journey, or even external factors such as weather, time of day, or current events. Our approach to dynamic creative allows advertisers to adapt messaging at scale rather than relying on a single generic audio asset. That’s a big difference between radio and digital audio. As audio becomes more performance-oriented, creative optimization becomes increasingly important. It’s all about delivering relevant experiences to drive performance.

Q4: How is AI changing the way digital audio campaigns are planned, optimized, and measured?

A: Arthur

We think AI is most valuable when it removes friction. Friction in the audio world comes from a fragmented ecosystem comprising millions of content signals, audiences, formats, platforms, and consumption contexts. Historically, that complexity made planning and optimization difficult. But now, AI allows advertisers to process those signals at a scale that would be impossible to do manually.

The most important role we offer with our AI tools is to help marketers make better decisions faster, from contextual targeting and creative optimization to campaign pacing, performance analysis, and measurement. From what we can tell over the past two years of AI innovation, the future of digital audio will be defined by what AI enables to drive more relevant campaigns, more efficient activation, and better business outcomes.

Q5: What does the rise of video podcast consumption mean for the future of digital audio?

A: Arthur

The growth of video podcasts is a welcome expansion to the audio ecosystem as audio and video converge and grow in lock-step, and consumer attention becomes platform-agnostic. Our data show that consumers are increasingly moving seamlessly between listening and watching, depending on the moment, platform, and context. But the most important trend for us is not whether a podcast is audio or video. It’s way more important that creators are building audiences across both formats. That’s very exciting for our space and our company.

For advertisers and their media budgets, they are focusing less on channels and more on audience attention. And from what we can tell, the future of “audio” belongs to solutions that can activate consistently across audio and video environments while maintaining coherent targeting, creative, and measurement strategies.

Disclaimer: This post, and any other post from Quo Vadis, should not be considered investment advice. This content is for informational purposes only. You should not construe this information, or any other material from Quo Vadis, as investment, financial, or any other form of advice.

Publicis spent $2.2 billion to acquire LiveRamp. Most observers see an identity acquisition. What if that interpretation is incomplete? We covered our perspective a few weeks ago in a post titled "Publicis bought LiveRamp for a seat at the Identity Trade-Off Frontier."

In this special pre-Cannes webinar, Quo Vadis presents a framework called the Identity Trade-Off Frontier to explore a broader question: Where is value actually accumulating in advertising's identity ecosystem? And there is no one better than Mathieu Roche from ID5 to help answer that question and a few other topics that we'll cover:

• The economics of identity infrastructure

• The difference between scale and accuracy

• Why match rates may be an incomplete metric

• The emerging role of interoperability and data collaboration

• What the Publicis-LiveRamp transaction may signal about the future of advertising

The goal is to help the industry develop a more rigorous understanding of identity's next chapter.

Register Here

Disclaimer: This post, and any other post from Quo Vadis, should not be considered investment advice. This content is for informational purposes only. You should not construe this information, or any other material from Quo Vadis, as investment, financial, or any other form of advice.

Welcome back to Quo Vadis Green Shoot AdTech Interviews. In this series, we’re stepping into the shoes of “AdTech Equalizers” — the greenshoot companies changing how the advertising job gets done. We’ll delve into their worldview and unveil new strategies, tools, and perspectives that make advertisers more effective.

Today, we have the great pleasure of speaking with Joe Martin, CEO and founder of Tickle, the global infrastructure for saveable ads. Lightweight plumbing that makes any ad unit, on any channel, saveable to a consumer's Apple or Google Wallet as a persistent branded asset they choose to keep.

If you like this interview, join Joe and Quo Vadis on June 15 at 11 AM ET for a live webinar called “Bought by Algorithms. Ignored by Humans. Who’s Advertising Actually For?“

Register Here

If you want to learn more about how Tickle is making saveable ads the new standard, reach out to Joe directly at joe@tickle.com or meet him at Cannes Lions. Tickle is currently mid-way through its seed round, so if you are an investor who wants to understand the opportunity, Joe would certainly like to hear from you.

Before we kick off our interview with Joe, watch what the legendary Rory Sutherland had to say when he stumbled across Tickle in his own.

Rory also talked about Tickle as “it’s not ‘click here-buy-now’, it’s ‘save-to-wallet’ on The Bottleneck Podcast.

Q1: Your own research found that 78% of consumers have taken a screenshot of an ad on their phone to remember it later. What does that number actually tell us about how advertising has been built?

A: Joe Martin

It tells us the consumer was interested, but busy doing something else. The industry just gave them nothing useful to do about it.

78% comes from our own statistically significant research, and every time I share it in a room, people nod along because they’ve done it themselves. A screenshot of a sofa they want. A holiday offer. A pair of trainers. They took a screenshot because it was the most frictionless, least interruptive option available for saving something for later.

Digital advertising has run on a binary motion for its entire existence: click or don’t click. We think that’s been a product design failure for 30 years now.

The click says, “I’m ready now.” The no-click says nothing, but the industry has spent decades trying to infer meaning from it anyway. Every probabilistic model, every intent signal, every retargeting pixel is the industry guessing at what no-click means.

Tickle introduces a third state. Unlike everything that sits between the click and the no-click, this one is deterministic. It’s a simple swipe to save a micro action. One movement, no friction, just a signal of intent. The ad remains clickable for the consumer who is ready now. The save is for the majority who aren’t ready yet.

A saved ad is a deliberate, voluntary, human-verified motion that says: “I want this, just not right now.” Not inferred, not modeled, not probabilistic. The consumer is telling you something true, in real time, that no other interaction in the current stack can produce.

And here is the thing that gets missed in almost every conversation about advertising innovation: People don’t shop the way an impression is designed to be acted on. They browse, they discover, they return when they’re ready. That’s how considered purchasing has always worked.

At the end of the day, advertising was built for a moment while consumers live in time. Tickle closes that gap without asking anyone in the industry to change a single thing they’re currently doing.

Q2: When most people hear “saveable ad,” they picture a browser bookmark that is immediately forgotten. Why is the mobile wallet fundamentally different, and how does Tickle actually work in practice?

A: Joe Martin

Browser bookmarks require the consumer to remember that they made them. Nobody does that. On the other hand, a screenshot has replaced the bookmark for years now. Moreover, the mobile wallet is not just for storage. It’s a live communications channel. It sits where a consumer’s boarding pass, bank card, and event ticket already live, natively in iOS and Android.

Tickle unlocks that channel for brands to build a direct communication highway on our rails, delivering branded assets to the most trusted, most personal screen a consumer owns.

It doesn’t matter how the ad was created or how it arrived. Display, video, social, CTV, programmatic open web, direct buy, etc. Tickle exists dynamically alongside the existing creative. No new production, no creative lift. It’s the product of decades of experience from a team spanning the entire advertising stack, from leading AI practitioners and consumer psychologists to adtech architects, all distilled into a single tickbox solution that now sees our rails being used across four continents with zero to minimal lift on the client side.

On one hand, AI is making the creation and delivery of ads faster and more efficient. On the other hand, if you arrive at the consumer’s screen with the most perfectly optimized campaign and they still weren’t ready to act, and you still can’t attribute what happens next, you’ve solved the wrong problem.

Then there is the age-old topic of engagement and attribution, two stubborn failures at the core of this industry for 30 years now. Tickle works at, and after the moment the impression lands. That’s a different and more common-sense approach.

Another great thing about saveable ads is that they don’t compete with other formats. Instead, we complete them.

It gets even better when it comes to bots. Bots operate freely across every major platform in the programmatic stack, but they cannot save an asset to a mobile wallet. In that sense, every save is categorically different from any other engagement signal in the ecosystem.

Q3: What results have surprised you most?

A: Joe Martin

Two, from different categories, that reveal the same underlying ah-ha lightbulb.

The first came through one of our platform integrations. A campaign for a leading global CPG brand delivered a post-click action rate of 23%, 5x the benchmark for an equivalent action in CPG display. Of those, 88% chose to save the branded asset rather than click through to the brand’s site immediately.

The consumer signal is something like, “I want this, but not right now.” That signal does not exist in any current attribution model, and there is no metric that captures deferred intent when it forms. In effect, our infrastructure is built to reveal a consumer preference.

Of those savers, 54% clicked out to the brand’s site when prompted by lock screen notifications in the days and weeks that followed, and that number grows week on week as prompts align with the moment the consumer is actually ready to purchase. In other words, the branded asset kept delivering long after the impression had closed.

The second came from a leading luxury auction house. Nine of the top ten-selling lots had been saved as branded assets prior to auction day. The data identified the true buyer cohort as a geographic demographic that Meta’s algorithm had not predicted. Since the agency could only work with what the platform reported, Tickle was able to provide a layer of revealed preferences. The result was a 66% reduction in cost per acquisition against the market average from a single campaign.

We now see brands returning for second and third campaigns to re-engage their existing wallet audience and build it further with new placements. What’s really great to see as a founder is how they are now treating us as a new owned communication channel rather than a one-off media activation.

Q4: Advertising has tried persistent formats before, e.g., browser notifications, loyalty apps, and email lists. They all eventually broke down due to friction, fatigue, or the next platform iteration. What makes the mobile wallet different?

A: Joe Martin

We have found three key differences. The first is that the wallet is OS-level infrastructure, not a third-party app. It lives natively on iOS and Android (the same layer as your bank card). There is no user behavioral change required, no download, and no new relationship to form. The infrastructure already exists on effectively every smartphone on earth. Tickle offers a key to unlock it for brands.

Secondly, wallet notifications achieve read rates above 90% because of what they bypass. Three decades of spam, scams, and phishing have trained the industry to apply an automatic skepticism to anything arriving digitally. Psychologists call this “persuasion knowledge,” the brain’s pre-conscious filter that asks, “Can this be trusted? or Did I ask for it?” or “Do I have confidence in the intent?” before engagement ever happens.

AI is making that reflex more necessary by the day, but wallet notifications bypass it entirely because the consumer opts in, and the source is already trusted. There is zero skepticism to overcome.

Lastly, once saved, the friction is gone permanently because the consumer has already committed. Every subsequent notification doesn’t ask them to decide whether to engage with a brand. Instead, that decision has already been made at the moment of the save. You are no longer re-finding someone who saw an impression. You are returning to someone who raised their hand, and building a relationship doesn’t feel like a burden.

Q5: Every conversation in adtech right now ends with AI. Agents are about to buy and sell media autonomously, generate creative, and optimize without human input. Is Tickle a winner or a loser in that world?

A: Joe Martin

Not only does it make us a winner, but it also lets us win faster. As more of the bid stream becomes synthetic, agent-generated traffic with AI-optimized creative, automated buying and selling at scale, the most valuable signal left is one that cannot be simulated.

Bots will always improve at mimicking clicks, view-through rates, and even downstream attribution events, but there is no agentic solution that can save an asset to a real human being’s mobile wallet and generate a genuine lock screen response. In a world of synthetic everything, a verified human save becomes the scarcest and most credible signal in advertising.

Our north star is simple.

Saveable should be a standard tickbox on every media plan, the way viewability measurement is today. And what accumulates alongside that standard, campaign by campaign, is certainly a conversation worth having.

See Quo Vadis at Cannes Lions

#CannesLions is around the corner! Looking forward to seeing you at these events.

• Commerce Media: See this series of Fluent events. Sir Martin Sorrell and I will have a chat and open it up to Q&A.

• Agency Agentic Flows: 50-person RSVP breakfast with AtomicAds, the winner of the AdEF founder pitch event in March

• Signal owners, platform leaders, and operators are rebuilding the layer underneath advertising. 50-person RSVP breakfast with Brian O’Kelley and Gabriel DeWitt, hosted by Above Data.

• Criteo Lunch Panel: Measurement Models and Redefining Efficiency with Brian Wieser, Todd Parsons, and Tom Triscari

• Buyer/Seller Agents: 25-person RSVP lunch with pubX

• PMG Beach: How to Survive in a Post-LiveRamp World at PMG Beach: Sam Bloom, Mathieu Roche from ID5, and yours truly tell it like it is.

• Criteo Yacht Talk: Wired Different. B.R.O.s at Work

• C Wire AdTech Chill House: 1-1 chat with Sir Martin + Audience Q&A. We’ll end the week talking about what happened and why it matters.

Disclaimer: This post, and any other post from Quo Vadis, should not be considered investment advice. This content is for informational purposes only. You should not construe this information, or any other material from Quo Vadis, as investment, financial, or any other form of advice.

• Commerce Media: See this series of Fluent events. Sir Martin Sorrell and I will have a chat and open it up to Q&A.

• Agency Agentic Flows: 50-person RSVP breakfast with AtomicAds, the winner of the AdEF founder pitch event in March

• Signal owners, platform leaders, and operators are rebuilding the layer underneath advertising. 50-person RSVP breakfast with Brian O’Kelley and Gabriel DeWitt, hosted by Above Data.

• Criteo Lunch Panel: Measurement Models and Redefining Efficiency with Brian Wieser, Todd Parsons, and Tom Triscari

• Buyer/Seller Agents: 25-person RSVP lunch with pubX

• PMG Beach: How to Survive in a Post-LiveRamp World at PMG Beach: Sam Bloom, Mathieu Roche from ID5, and yours truly tell it like it is.

• Criteo Yacht Talk: Wired Different. B.R.O.s at Work

• C Wire AdTech Chill House: 1-1 chat with Sir Martin + Audience Q&A. We’ll end the week talking about what happened and why it matters.

1Q26 Portfolio Update

With 1Q 2026 earnings now reported, we can assess how our equal-dollar portfolios have performed since our 4Q25/FY25 update in early March.

As a reminder, each portfolio starts with a $100 investment in January 2022 divided equally across all constituent stocks. We rebalance on an equal-dollar basis whenever a constituent exits public life (IAS, RAMP, etc.) or when a relevant company goes public, like Mountain at this time last year and LiftOff today.

Benchmarks: Since January 2022, the NASDAQ and S&P 500 are up ↑70% and ↑57%, respectively.

BigTech2: We track two BigTech ad players, Google (GOOG) and Meta (META). Had you invested $100 on a 50/50 basis in January 2022, you’d be ↑115%. GOOG is up (+145%) and META (+84%) with nothing but blue sky ahead.

These two companies are the “moat” gold standard of the advertising world. With ~45%+ return on invested capital, they are cash flow printing presses. Why? Because they own the best land (data) and farmers (advertisers) love renting it to get outcomes.

On a gross ad spend basis, Google Advertising delivers $2.1 million in revenue per employee, and Meta delivers $3 million. Notably, The Trade Desk tops both at $3.8 million per employee. If you’re a classic “AdTech” company in the private sector, we recommend finding a way to $1 million in revenue productivity as soon as you can. It drives all value creation.

AdTech11: Our AdTech11 portfolio is down ↓45% since inception. The portfolio includes: TTD, CRTO, DSP, ZETA, MGNI, PUBM, DV, TBLA, TEAD, RAMP, MNTN.

Zeta in the Spotlight: Zeta is the far-and-away top performer across the AdTech11, up ↑169%.

Looking at our AdTech11+ portfolio, which includes the flywheel known as Applovin, that company is up ↑5x since January 2022. If you’re interested in a great story from a top manager, check out this interview with Applovin CEO, Adam Foroughi.

• Does Adam know what to do? Yes!

• Does Adam get others (customers, employees, suppliers, investors, etc.) to do what he knows? Yes!

MarTech8: Our MarTech8 portfolio remains deeply in the red, down ↓62%. The portfolio includes: SHOP, HUBS, SPT, SEMR, TWLO, BRZE, YEXT, AMPL. This sector is clearly suffering from investor concerns about the creative disruption AI poses to the SaaS space.

SocialTech4: Our SocialTech4 portfolio is ↑31%. The portfolio includes: META, SNAP, PINS, RDDT. Reddit (RDDT) was added in April 2024 following its IPO and is up ↑236%.

Agency5: Our agency portfolio is down ↓27% since inception, and consists of Publicis (PUBGY), WPP, Omnicom (OMC), Stagwell (STGW), and S4 Capital (SCPPF). IPG was removed following its acquisition by Omnicom, which closed in late 2025.

From our perspective, it’s hard to ignore the dichotomy between Publicis’s top performance and The Trade Desk. From our perspective, the profile of Publicis’s M&A strategy over the past ~5 years reflects what we wrote in our piece called The Two Landowners of Adwell Hollow. When Arthur Sadoun took over in 2017, he decided it was better to be a landowner than a farmer, and that’s always a good bet.

With Google and Meta having amassed so much Sun Tzu power over the past two decades (~45% of all ad spend), Publicis decided to get in the game and play for keeps. And it’s working… very, very well!

To that point, last we posted a piece called Publicis bought LiveRamp for a seat at the Identity Trade-Off Frontier and illustrated our view with a chart called The Gravity Theory of Data Trade. From Publicis’s perspective, the future of advertising is about owning the identity and connectivity layer that enables trusted data trade across brands, publishers, platforms, retailers, and media owners.

If we’re correct, Publicis wants to sit at the center of data gravity flows, capturing economic rent through identity resolution, measurement, activation, and collaboration. Time will tell.

Disclaimer: This post, and any other post from Quo Vadis, should not be considered investment advice. This content is for informational purposes only. You should not construe this information, or any other material from Quo Vadis, as investment, financial, or any other form of advice.

And if you’re interested in How Agencies Win in the AI Era: Automation, Intelligence & Growth join my exclusive breakfast session at Belle Plage Beach Club with John Kahn (Chief Transformation Officer, Digitas North America), Domenic Venuto (Chief Product & Data Officer, Horizon Media) and Jeet Singh (Founder, AtomicAds) exploring how AI agents and automation are reshaping agency models, media operations, and future growth. You can RSVP here.

Above Data Breakfast: If you’re interested in a beachfront breakfast with Above Data on how the data layer must evolve to support interoperable signals across advertising, then RSVP here.

Criteo Yacht Talk: On Wednesday in Cannes, check out Brian Wieser, Todd Parsons, and yours truly on Criteo’s yacht for our chat about how competing measurement models are redefining effectiveness. You can RSVP here.

C Wire Chill House: Besides my 1-to-1 chat with Sir Martin Sorrell on Sunday (see Fluent link above), we’ll continue our conversation at C Wire’s AdTech Chill House on Thursday morning. You can RSVP here.

Publicis bought LiveRamp for a seat at the Trade-Off Frontier

Let’s start with a simple question:

Did Publicis acquire LiveRamp because of its financial performance?

Highly unlikely. LiveRamp generated $813 million in revenue last year, representing 9% year-over-year growth (noting that RAMP’s fiscal year ends March 31). After years of struggling to achieve profitability, the company reached only modest operating margins two years ago (2% EBIT margin), followed by 1% last year and 10% this year. Notably, last year’s improvement was driven by a combination of moderate revenue growth with meaningful reductions in R&D, sales, and marketing expenses.

While LiveRamp has only recently achieved modest profitability, Publicis operates globally and delivers outstanding financial results. The company generated $3.1 billion in operating profit on $17 billion in net revenue (after pass-through costs), achieving an 18% operating profit margin.

Even with post-deal cost synergies (which could be material in LiveRamp’s case), the deal would not materially alter Publicis’ near-term earnings trajectory on a financial thesis alone.

Even if we assume LiveRamp was able to grow its operating margins from 10% to 25% over the next five years under Publicis, and grow revenue at a 10% CAGR, and further assume that LiveRamp’s ROIC grows from ~0% to Publicis's 45% level, the financial value to Publicis is only $3.3 billion. While that’s more than 10% of Publicis’s ~$25 billion market cap, it's more like icing on the cake than the actual true value of the meal.

As Auren Hoffman mentioned on Martetecture’s podcast with Ari Paparo and Eric Franchi, that outcome would generate a 30% internal rate of return, which is still really good from a pure cash-out/cash-in perspective. If Publicis were simply playing out a private playbook, that would be a home run deal.

In 2022, Publicis reported having 3,620 “major” clients (see registration document, page 31). Even if we further assume half of Publicis clients convert into LiveRamp subscription customers over the next five years, our valuation model indicates the company would be worth $4.1 billion in equity value to Publicis, generating a really nice 67% IRR. But we think there is more to it than just a really good financial return.

Now let’s ask:

Did Publicis acquire LiveRamp to win new agency clients?

Perhaps. Even if LiveRamp’s product is a great puzzle piece for Publicis, we don’t think new client acquisition alone justifies the acquisition. Although it could be a meaningful competitive advantage to have differentiated data assets in new client pitches, we think the real value is in using the data infrastructure as a centerpiece to achieve a 10x return (or more) and gain a competitive leverage with walled gardens.

How about another question floating around LinkedIn?

Did Publicis ignore the obvious risk that LiveRamp may lose customers once neutrality disappears under agency ownership?

Almost certainly not. If we are right about the real reason why Publicis acquired LiveRamp, customer churn risk will not matter much. Moreover, Arthur Sadoun (CEO), Adam Berkowitz (Corp. Dev.), and the deal team at Publicis are very sophisticated and would have modeled churn scenarios during deal diligence. They undoubtedly considered the possibility that some customers leave because LiveRamp no longer sits outside the ecosystem as an independent “Swiss” intermediary, but dismissed frictional churn as a non-risk.

That said, a reasonable bear case could be made that Publicis knowingly accepted near-term churn as a cost of acquiring a more valuable long-term infrastructure asset. Using rough estimates, Publicis (~3,600 clients) and LiveRamp (~860 clients) likely overlap by ~35% or ~320 enterprise customers, leaving ~540 potentially exposed to churn from competing agencies or brands concerned about neutrality under Publicis ownership. Even if 30% to 50% of those non-overlapping LiveRamp clients were eventually lost, our 10x data infrastructure strategic thesis is a far larger win once the dust settles.

Data “Infrastructure” Assets are Trade-Off Curve

So, if Publicis understood limited financial upside from LiveRamp as a stand-alone business, as well as the aforementioned risks, and moved forward anyway, we think a more interesting question emerges:

What if Publicis believes the future value of advertising belongs to whoever pushes the industry furthest along the Identity Trade-Off Frontier?

In our view, Publicis has proven itself to be the agency with the winning hot hand, and it is now buying deeper control over identity and data connectivity with an eye to expanding the density of connections across its giant client ecosystem, which in turn stimulates data flows for which clients will pay high rents. If so, we think the acquisition is best viewed as a flywheel-mediated play.

The Identity Trade-Off Frontier

For much of digital advertising history, marketers lived on a data utility curve where scale (reach) of audience data can be interpreted as a trade-off with data accuracy (e.g., Does this ID represent what I think it does? If I am targeting females, is the person behind the ID really a female?)

Curve A: Pre-2018, we lived in a third-party cookie-based industry that was mainly optimized for scale vs data accuracy. Truth be told, targeting in that world was slightly better than random. Cookies proliferated, third-party data exploded, and so did bad bots deployed by arbitrage actors to boost traffic and impersonate users. In that world from yesteryear, probabilistic identity promised precision by stitching together impressions, devices, platforms, and intermediaries. The scale was enormous, but identity precision was mostly overstated. Brands could buy enormous reach at relatively low cost, but much of the underlying connectivity was highly probabilistic and uncertain, resulting in poor outcomes (aka utility payoffs).

Curve B: Identity Infrastructure Pushes the Frontier Outward: Then the world changed. Privacy regulations accelerated, cookie deprecation began, the push for logged-in authentication expanded, first-party data became strategic, and identity graphs gained importance.

On the one hand, this new state did not eliminate uncertainty about data accuracy. On the other hand, it simply replaced lower-confidence probability with higher-confidence probability. By 2024, accuracy had improved, the data frontier had shifted outward, and a better payoff materialized with higher utility for advertisers.

As the frontier expanded, any company positioned closest to authenticated data (LiveRamp, Walled Gardens), transaction signals (Amazon, Criteo), and deterministic identifiers (Walled Gardens) possesses an economic advantage.

Curve C: Competing with Walled Garden

While the rest of digital advertising was dealing with Curve A in 2018 (and before), Google and Meta were already at Curve C in 2018 (and likely beyond), delivering rich outcomes with the highest utility.

By 2026, identity and data infrastructure players like LiveRamp, Trade Desk UID2.0, ID5, MediaWallah, Above Data, HighTouch, Optable, Anonymized, Growth Code, and others entered Porter’s 5 Forces as new entrants with innovative solutions that addressed the audience-targeting job in a new way.

Today, when industry practitioners talk about the new era of “outcome-based” advertising, Quo Vadis interprets Curve C (2026) not as proof that the industry has arrived, but as the inflection point separating the old world from the emerging one. Curve C represents the moment when outcome-based advertising becomes economically viable at scale and charts a course for continued high-speed improvement over the coming years. Whether advertisers spend media dollars on Walled Gardens or the Open Internet, they can achieve large-scale reach with more accurate identity data to increasingly get the outcomes they seek.

In essence, a seemingly small linguistic change from “media buying and optimization” to “outcomes-based” represents the largest economic transition in advertising history since the Internet’s first ad in 1994. When outcomes become the objective, all future competitive advantage for all players in the advertising business depends on hooking yourself to the next trade-off curve on the frontier.

The frontier becomes the prize

The advertising job, in its simplest form, is about doing the best you can to connect your product with an intended audience and capture attention maximization (aka salience) through media buying and creative, and then determining (measuring) whether the connection happened (outcomes).

From our perspective, outcome-based advertising is about an ecosystem getting materially better at both, which manifests in the outward shift from the current trade-off curve to the next frontier curve. The inertia driving the frontier forward is about data connectivity.

We think the trade-off curve model helps explain why Google, Meta, and Amazon have been years ahead of the Open Internet, but perhaps not for long. The walled gardens are “walled” because they are control points for identity, O&O media, commerce, logged-in users, and transactions. Whatever curve they are on today does not matter as much as the firm foot they always seem to have in the next frontier.

As for the rest of digital advertising, everyone is racing toward data and identity infrastructure ownership because that is where advertisers will pay the most rent. Agencies, retail media, identity firms, data infrastructure companies, DSPs, SSPs, PubTech, and publishers all need to get well-positioned today, as they know future value creation will occur on the next frontier curve.

The Density of Interconnected Data and Network Value

Owning data alone is not enough. The future belongs to market makers who enable and coordinate data connectivity between data trading partners.

For example, let’s say you’re in London and the forecast calls for wind and rain, so you take your umbrella to stay dry. Before you leave your house, you hesitate and take a second umbrella just in case the first one breaks in the wind. Before you jump on the tube, you stop at a kiosk and buy a cheap umbrella just in case you lose the second one. Now you’re walking around with two hands and three umbrellas. Each successive umbrella has decreasing marginal utility. If you were maniacal enough to grab a fourth umbrella, it would have zero marginal value.

Data is different. When you connect one data source or signal to another, not only do they both increase in value, but the combined “networked” value is even higher. For instance, let’s say two ID data sets, A and B, are both 50% accurate. When combined, A is 60% accurate, B is 60% accurate, but AB together is 75% accurate. That’s increasing marginal returns.

Yes, at some point, diminishing returns will kick in, with the nth data set providing minimal margin gains. For illustrative purposes only, the chart above maps our trade-off curves to the concept of density of interconnected data and shows how diminishing returns set in.

From the advertiser’s perspective (clients of agencies like Publicis), most proprietary data remains connected to only a small subset of potential datasets, even as brands seek to operate closer to the frontier. If infrastructure providers like LiveRamp can accelerate the creation of new connections across fragmented consumer, commerce, retail, and media data, the resulting increase in interoperability should raise the value of that data through improved measurement, activation, and outcomes.

From the perspective of agencies and other data infrastructure players, the more data connections they can enable as market makers, the more data will flow. The more data flows, the more rent can be charged for making it happen.

How The Least-Worst Alternative Can Become the Most Valuable Position

Since Publicis' announcement on May 17, we've found it interesting that so many people in the industry dislike Liveramp. Auren Hoffman mentioned this more than a few times on Marketecture’s pod.

Although it’s unintuitive, maybe that’s what makes LiveRamp so valuable. The product might not be great and has been totally underinvested in over the past 10 years, but it does have a strong position (~70% market share) as the least-worst alternative outside the walled gardens.

For all competitors, substitutes, and new entrants in the data or infrastructure game, the best strategy is to be the next best, least-worst alternative (if that makes sense). The more this happens, the further the industry moves outward on the frontier.

In our view, the negative sentiment murmured on the street is evidence of a strong strategic position, not weakness. And as you should expect, the data industry is not the first or last to discover that the least-worst infrastructure assets are rarely loved. Instead, they are tolerated, criticized, and relied upon until, one day, the market realizes that the least-worst alternative is an indispensable control point.

For instance, traders complain about Bloomberg terminals, yet they are indispensable. Advertisers and networks complain about Nielsen, yet it’s still the benchmark currency. Consumers complain about credit bureaus, but they need them to get loans. Businesses complain about SAP, but don’t switch to alternatives. And adtech people complain about LiveRamp, yet its integrations persist.

LiveRamp is not valuable because participants love using it. It is valuable because enough parties connect through it, albeit reluctantly in many cases. That said, whether there is reluctant connectivity or not, we assume Publicis sees that the density of interconnected data increases data flows, which in turn increases its economic importance. As Publicis economic importance grows, it will attract more data connections, more rents, and the loop compounds.

If that sounds less like software and more like railroads, payment networks, telecom infrastructure, and electric grids, then now you understand how we think Arthur Sadoun is valuing LiveRamp. Publicis did not acquire an identity company. Instead, they acquired a market-making data-connectivity control point to extract their fair share of future advertising rent in exchange for delivering superior outcomes at the edge of the Identity Frontier.

Viewed through this lens, LiveRamp looks less like an identity company and more like a transmission network or data exchange layer. Along with other Publicis assets, LiveRamp is a mechanism through which advertisers, publishers, commerce platforms, retailers, and media owners can increasingly connect information. For Publicis, it’s not about how much revenue LiveRamp can generate as a stand-alone business. The value creation is about how much future economic activity can be enabled through connected data and controlled by Publicis.

Gravity Theory of Data Trade

Economic theory offers another lens called the Gravity Theory of Trade. This concept was first proposed in a highly cited 1985 research paper based on Newton’s theory of gravity, known as E = MC^2.

The Gravity Theory of Trade holds that trade between two countries increases with their economic mass and proximity. For example, the US and Canada are large economies that share a long border, so their trade volume is enormous. Fiji and Mozambique are small economies and far apart, so trade volume is low.

In subsequent economic research, concepts like “friction” were introduced. For instance, while the US and Canada are large economies and close to each other, the current political environment has introduced competitive friction and reduced trade.

If we are correct about Publicis’s worldview, then advertising is about to enter a very exciting phase through bilateral data trades.

Our proxy formula states that data mass is achieved through users, transactions, audiences, spend, and/or signals. In the denominator, Competitive Proximity x Friction is the relationship between friendly cooperation and pure competition, with the “frenemy” concept somewhere in between.

Our intentionally busy chart proposes an emerging Gravity Theory of Data Trade across the advertising space, with Publicis right in the middle. If a $2.5 billion asset can be converted into $25 billion of value creation for Publicis shareholders, this is how we see that materializing.

The central idea illustrated in the chart is that data flows among participants (agencies, DSPs, SSPs, publishers, retailers, platforms, brands, measurement firms, etc.) much like trade flows in economics. Larger participants with more data, closer competitive proximity, and lower friction will tend to exchange more information with each other.

Each circle represents a participant or ecosystem. Circle size approximates data mass (users, transactions, signals, spend). Line thickness represents data flow intensity, while dashed or colored lines represent friction (privacy constraints, competitive incentives, technical barriers, regulation).

Smaller sub-networks illustrate how trading already occurs within groups (e.g., agencies with agencies, SSPs with SSPs, retailers with retailers) before and/or concurrently flowing outward into the broader ecosystem.

At the center sits an identity/connectivity infrastructure layer, representing the intermediary coordinating data exchange. In this example, Publicis + LiveRamp acts as the hub. Every time a byte of data moves through the market maker, Publicis’s cash register rings.

At the center sits an identity/connectivity infrastructure layer, representing the intermediary coordinating data exchange. In this example, Publicis + LiveRamp acts as the hub. Every time a byte of data moves through the market maker, Publicis’s cash register rings. There are also independent identity providers, such as ID5 and others, that have become increasingly valuable because they reduce friction and improve interoperability (connections) among otherwise fragmented participants, enabling more trade without touching the underlying media flows.

For advertisers, whoever owns pipes that help advertisers get their best seat at the data trade table is in just as good a position as Publicis is today. The red-highlighted section at the bottom of the chart suggests a new state in which agencies (or other party) assist advertisers in enhancing and owning their custom neural identity graphs, thereby increasing the density of their own connected data. This is precisely what makes companies like MediaWallah so valuable.

If our conclusions are correct, Publicis is betting that the next era of advertising belongs not to those who buy media most efficiently, but to those who own the infrastructure that helps move the frontier. If that is true, the LiveRamp acquisition will eventually be remembered as something much larger than an M&A transaction. It will be remembered as a very smart bet that Arthur Sadoun placed on the future data trades.

Who has the largest data mass? Which participants sit closest to the center of the data flow? Where is friction highest today vs. tomorrow? Which new connections would increase flow? Who captures economic rents as data trade expands? These questions and many more will be answered over the coming months and years.

Other good reading on this topic:

• Publicis + LiveRamp: Big Positive For Publicis, Big Impact for Rest of Agency, Ad Tech and MarTech Sectors (Madison & Wall)

• Publicis and LiveRamp: Additional Considerations On Combination (Madison & Wall)

• Who Owns the Pipes Matters (Keith Petri)

Disclaimer: This post, and any other post from Quo Vadis, should not be considered investment advice. This content is for informational purposes only. You should not construe this information, or any other material from Quo Vadis, as investment, financial, or any other form of advice.

And if you’re interested in How Agencies Win in the AI Era: Automation, Intelligence & Growth join my exclusive breakfast session at Belle Plage Beach Club with John Kahn (Chief Transformation Officer, Digitas North America), Domenic Venuto (Chief Product & Data Officer, Horizon Media) and Jeet Singh (Founder, AtomicAds) exploring how AI agents and automation are reshaping agency models, media operations, and future growth. You can RSVP here.

Publicis Acquires Liveramp for $2.2B (EV)

The timing of this news could not be better for a story Quo Vadis has been toying with over the past few months. From our perspective, we think Publicis’s string of acquisitions that now include Liveramp (Influencial, Captiv8, Lotame, Fabric Social, Adge.ai, etc.) is less a story about buying an identity asset and more a story about moving further away from being a farmer on behalf of advertisers toward landownership and controllership of fertile land (data) upon which the best advertising output grows. With that in mind, here’s a story about a farming town called Adwell Hollow.

Part 1: The Landowners of Adwell Hollow

In a small farming town called Adwell Hollow, there were two neighboring landowners whose fields stretched farther than the eye could see. Long ago, both properties had nearly identical soil. The land was fertile and rich with productive possibilities.

The first landowner, Elias, believed land was a responsibility. He tested the soil, replenished nutrients, built irrigation systems, rotated crops, and studied what allowed harvests to improve year after year. To him, fertile land was not inherited; it was an asset to be maintained.

The second landowner, Rowan, believed land was simply acreage. His responsibility ended once rent was collected from farmers. Irrigation channels cracked, soil nutrients were depleted, overplanting was allowed, and weeds spread. Even though productivity for his tenant farmers kept falling, he always had farmers willing to rent his land. Not because the land was good, but because farmers always needed land to farm one way or the other.

Around Adwell Hollow lived ten farmers. Each planting season, they priced and competed to rent land. Over time, more farmers offered higher rent to work Elias’s fields. Even though it was more expensive, the fertile land produced greater harvests than Rowan’s cheap, exhausted land.

Over time, the road leading to Rowan’s inferior land became crowded with merchants selling plows, fertilizers, tools, and miracle devices promising larger harvests from weaker soil. Entire businesses emerged around helping farmers endure these increasingly difficult conditions.

Eventually, these middlemen merchants formed farming guilds, and farmers hired them for advice. The guilds studied yields, negotiated leases, planned planting schedules, and recommended tools. The poorer the soil became, the more valuable the guilds appeared. Complexity was their livelihood.

The guilds prospered for many years, but then something changed. More farmers began choosing Elias’s land despite having to pay higher rents. They discovered that healthy soil mattered more than sophisticated techniques.

The guild leaders grew uneasy. “If farmers increasingly move toward fertile land,” one elder asked, “what will become of us?”

A younger guild member answered: “For years, we have helped farmers with increasingly difficult land. Perhaps our future is not in helping them survive poor fields, but in moving closer to fertile ones.”

The room went quiet. “What if we build the irrigation systems feeding fertile land?” she continued. “Or the grain exchanges where harvests are traded. Or the storage houses. Or the maps measuring soil quality before farmers decide where to plant.”

Years passed. Every so often, the farmers put guilds into review and picked a new one, but they all seemed the same. Some guilds simply disappeared in their efforts to defend old work, while others gained enlightenment and transformed. The most valuable guilds no longer earned their living helping farmers extract one more basket from exhausted soil. Instead, they acquired and owned the roads, water systems, and marketplaces through which every successful harvest eventually flowed.

In the end, the people of Adwell Hollow learned something unexpected. When wealth concentrates around fertile land, other fortunes are built not by owning the land itself, but by controlling the paths leading toward it.

Part II: The Great Consolidation of Adwell Hollow

Years passed in Adwell Hollow, and a pattern became difficult to ignore as farmers increasingly chose fertile land. Elias’s fields commanded higher rents each season, yet demand continued to rise because the soil produced more outcomes than it consumed.

Meanwhile, Rowan’s land struggled. More merchants appeared along the roads selling improved tools for difficult farming, while new guilds offered all kinds of optimization services. The work that went into poor soil multiplied even though harvest outcomes did not.

Then something unexpected happened. The town entered an age that the people would later call The Great Consolidation.

A small irrigation company was purchased by a farming guild seeking greater influence over yields. A grain storage operator merged with a transport business to control more of the route between the field and the market. Some guilds purchased soil-testing businesses so their recommendations would become indispensable.

In hindsight, the smartest consolidators did not purchase land. Those who owned the best farming land would never sell it. So instead, they purchased control points surrounding productive land.

Then came another wave. Rowan, whose fields had continued deteriorating, began buying neighboring plots with even poorer soil. The townspeople were confused. “Why acquire worse land?” they asked. The answer was simple: “More acreage means more farmers,” Rowan said. “And more farmers mean more rent.” Whether harvests improved was a secondary consideration.

For a while, Rowan’s strategy worked. Rowan’s territory expanded, more merchants lined the roads, more tool providers sold more picks and shovels, and activity increased.

Yet beneath the surface, something strange occurred. The town became busier while overall harvest quality stagnated. How could that be?

As economic activity and economic productivity slowly separated, a new generation of guild leaders studied Elias’ principles and noticed something important. The greatest fortunes accumulating around fertile land were not necessarily those of the landowners themselves, but were increasingly held by those who controlled irrigation, logistics, financing, measurement, or storage.

A debate emerged among the guilds. One side argued, “We should continue helping farmers survive difficult land.” And another argued, “We should acquire the systems through which all successful farming flows. We need to get closer to the flow of value.”

The second group had more conviction and began buying aggressively. One purchased water rights. Another bought the town’s crop exchange. Another merged with storage operators. Another acquired transportation routes connecting farms to markets. The best assets rose to the top, with many little gems also finding their way to greener pastures.

Years later, those acquisitions transformed the town. The most valuable institutions in Adwell Hollow were no longer advisers who charged fees for their labor. As it turns out, infrastructure, the town learned, captures rent long after individual harvests are forgotten. While the old guilds believed value came from working harder around weak soil, the new guilds understood that when fertile land wins the day, survival and value creation depend on buying your way closer to the flow of value.

Student Notes

Adwell Hollow —The Advertising Industry

Elias — Google, Meta, and the Keepers of Fertile Land

Rowan — The Open Web

The Farmers — Advertisers

The Farming Guilds —Advertising Agencies

The Guild Elders —Defenders of Legacy Models

The Younger Guild Members — Those Willing to Move Closer to Value

The Merchants of the Road — AdTech Vendors

Picks & Shovels — Measurement, Identity, Verification, Optimization, Tools

The Soil —Consumer Attention

The Nutrients — Data and Connected Cata

Irrigation Systems — AI, Algorithms, Infrastructure

Healthy Harvests —Economic Returns

Harvest Failure — Weak Outcomes Despite Activity (busy work)

The Roads — Distribution

Storage Houses — Customer Data & Commerce

The Crop Exchanges — Marketplaces

The Soil Maps — Identity Resolution & Measurement

Buying Better Irrigation — Strategic M&A

Buying Worse Land —Open Web Scale Without Productivity (aka “cheap reach”)

The Great Consolidation — Advertising M&A (Past, Present & Future)

Guilds with Conviction — Publicis

The Smartest Consolidators —Those Buying Control Points

The Real Estate Closest to Fertile Land — The Future

Special Thanks to David Ricardo, Adam Smith, Warren Buffett, The Open Web, Compounding Returns, and Incentives, with a focus on maximizing economic profits.

Disclaimer: This post, and any other post from Quo Vadis, should not be considered investment advice. This content is for informational purposes only. You should not construe this information, or any other material from Quo Vadis, as investment, financial, or any other form of advice.

Quo Vadis is hosting a live, simultaneous online game where every attendee votes on media channels matchups in real time. This virtual event experiment promises to be the most unique content delivery experiment ever created and the most interactive content experience in the advertising space. There is nothing else like it. The gap between your voting instincts and the crowd’s is where the real learning happens. You walk away smarter, with a full breakdown of how the room voted, and get a results transcript in your inbox after the online event.

Join The Crowd on June 11 at 10AM ET

The end of headcount as we know it

There was a time when growth in technology followed a simple equation: More revenue required more people. The more an adtech company (or agency) grew, the more people it hired. As sales teams scaled and engineering teams expanded, the organizational chart (along with lower-cost outsourced staff) sprawled in lockstep with top-line growth. That basic equation has held for a long time.

Between fiscal year-end results in February and the latest Q1 earnings from both Google and Meta, it’s becoming clearer that the old equation no longer holds. Revenue for both companies is still growing at a double-digit pace, but headcount is flattening. We think the gap is one of the most important economic shifts in modern business (and a leading indicator for the sector), particularly for two companies that produce 45%+ return on invested capital and deliver very healthy operating profit margins (Google at 33% and Meta at 45%).

The first signal is evidenced by both companies reaching what we describe as headcount maturity.

Google is approaching peak employment with ~190,000 employees and is now growing its workforce at roughly 2% annually. Meta has ~78,000 employees and will have ~70,000 after the May layoffs are complete, aiming to reduce staff by ~10%.

As our Quo Vadis readers know, we sometimes turn to historical S-curves because they generally give a good indication of what’s true. In our view, reaching peak headcount maturity at Google and Meta should not be viewed as cyclical belt-tightening nor a response to macro conditions. Instead, we think it characterizes the natural end state of a two-decade-old S-curve that followed the typical path of new innovation (e.g., search, social), build (easy-to-buy), scale (eyeballs), optimize to zero marginal cost, and reach maturity as massive cash-flow machines.

Susan Li (Meta’s CFO) reinforced the new circumstances on the 1Q earnings call:

“We ended Q1 with over 77,900 employees, down 1% from Q4 as the impact of headcount optimization efforts in certain functions was partially offset by hiring in priority areas of monetization and infrastructure.”

And more poinently, “Compute is becoming increasingly important as it determines the quality of the services we can provide, including powering more capable models and delivering innovative new products. It is also becoming more critical to how we work at Meta, as we are entering a world where employees are managing agents to help them generate new ideas, run experiments, execute tasks, and build products.”

Revenue Productivity

In 2025, Google generated just over $2 million in revenue per employee, and around the same amount if we examine only its advertising business (search, YouTube, display, etc.), more on that subject below. Notably, Google’s advertising revenue as a percentage of total revenue has fallen from 83% to 73% since 2019, while cloud has grown from 11% to 15%.

Meta’s business remains 98% ad-based, generating just over $3 million in revenue per employee. On its current growth trajectory, assuming reasonable revenue growth (e.g., 15% CAGR) and near-flat headcount over the next few years, revenue productivity will approach ~$4 million soon and ~$5 million+ by the end of the decade.

The mechanism behind this shift should not be mysterious to our readers. For Google and Meta, AI is a labor multiplier on top of strong operating assets, with employees (including lower-cost outsourced staff) increasingly operating less as executors of discrete tasks and more as system managers of agents that generate ideas, run experiments, write code, optimize campaigns, and build new products. Work that once required coordinated teams now collapses into a totally different orchestration of workflows.

Critically, the productivity gains are happening internally just as much as externally. The same models that power consumer-facing products are completely reshaping how these two companies operate internally and make decisions.

Historically, labor was the dominant input and largely fixed with a high correlation to revenue growth for both companies. Today, that relationship is flipping. Labor is becoming less fixed, while computing is becoming the scaling variable. The more that any given output is required, the more computing resources, rather than people, will be deployed.

But unlike labor, computing ability benefits from cost deflation and simultaneous rapid performance improvements over time. The result is an asymmetrical outcome in which variable costs will likely rise in absolute terms, but unit economic costs will decline on an increasingly lower fixed-cost base (lower staff costs).

So, if you’re wondering how Google and Meta will likely capture future benefits from massive capex on AI infrastructure, we recommend considering how the new variable-to-fixed cost paradigm will allow them to materially expand operating profit margins (cash in flows), thereby justifying all the capex with incremental cash flows. Given that Google and Meta generate ~45% and ~50% return on invested capital, respectively, and deliver massive economic profit spreads, we could make a strong argument that both companies are actually underinvesting in AI capex.

How about the broader market?

Zooming out to the broader market presents a stark contrast. We took a random sample of 46 S&P 500 companies and examined both revenue and headcount from 2010 onward (see list below in the footnotes1).

The average company has grown revenue at roughly 3.2% annually since 2010, with headcount growing at about 1.4%, and revenue per employee sitting below $500,000.

Compared to leading-edge, highly entrepreneurial companies like Google and Meta, other large “laggard” enterprises still operate with human-centric workflows designed for coordination, oversight, and incremental efficiency gains.

We can imagine these companies as slow-moving cargo ships. They set a course and sail in a straight line. They are hard to turn or change direction, because cruising in a straight line is the default risk-averse strategy.

If you've worked in a large organization, you've no doubt felt the pain of human-based process flow, process design, and redesign over the last few decades. As far as we can tell, the current process flows that most companies run today are incongruent with how AI works.

In other words, we imagine a phase currently underway in which large enterprises are getting (or are going to get) completely reimagined from their old processes into a new way of thinking that fits with how AI functions. Over the next few years, we'd expect to see a meaningful increase in revenue productivity across the board, as well as a meaningful expansion of operating profit margins.

• Revenue growth grows in line with expectations (more or less)↑

• Fixed headcount costs decrease↓

• Revenue per employee↑

• Variable costs increase with AI compute costs↑ (and eventually get reported as a new GAAP line item)

• In summary, fixed costs decrease while variable costs increase, thus disproportionately benefiting the bottom line.

For Google and Meta, compute has become the central axis of capital allocation because the key question is no longer how many people to hire but how much infrastructure to deploy. We see companies like Google and Meta as leading indicators of what’s to come for all companies.

A number that will surprise many

When you compare advertising revenue productivity across the sector, The Trade Desk emerges as the frontrunner compared to Google and Meta.2 With an estimated $13 billion in gross billings through its platform and 3,843 employees, TTD’s revenue per employee exceeds that of both Google and Meta.

This is not because TTD has suddenly discovered AI leverage ahead of every other adtech company, but because it has been designed for efficiency from the outset. With so much attention on the mega players in the ad business (Google, Meta, Amazon), we suspect TTD’s revenue productivity goes mostly unnoticed.

What about agencies?

We’ve already established that the average revenue per employee across the S&P 500 is roughly $400K, yet across the very agencies those companies rely on for the advertising job done (Publicis Groupe, WPP, OMC/IPG, Stagwell, Havas, Monks), revenue productivity is closer to $160K.

Large advertisers generally operate with economies of scale and pricing power. They own the assets. They own products, audiences, and/or distribution to capture economic rents.

Agencies, by contrast, are a labor-based service layer. Agency headcounts tend to scale with sales (excluding Publicis as the best “rent collector” model across the agency scene), while their pricing is constrained by procurement, benchmarking, and CFO ROI scrutiny. In other words, agencies sit within the client’s cost base, not at the point of value capture, as they used to during the Mad Men days. This is precisely why any agency that can turn its value proposition into a driver of measurable and provable value capture (e.g., through outcome-based compensation) is in a winning position.

Up until recently, agencies have not been designed to maximize revenue per employee. They were designed to minimize client cost per loosely defined outcomes (or none at all). Their role was, and largely still is, to absorb complexity, aggregate expertise, get volume discounts, and convert what would be fixed costs for the client into semi-variable costs from the client’s perspective. If we boil it down, agencies basically operate as an outsourced execution layer to which procurement can flex up or down.

If you’re wondering why clients take this outsourced approach, I asked Brian Wieser the same question while sitting at an outdoor cafe in Cologne, Germany, during DMEXCO in 2015. His answer was brilliantly zen:

“Because agencies are the least-worst alternative.”

At face value, that sounds horrible. But in reality, it’s one of the best market positions a firm can possibly be in. Why? Because it implies switching doesn’t solve the problem. Clients rarely choose an agency because it's perfect. They’re choosing it because the trade-offs embedded in the other alternatives aren’t worth it.

Secondly, agencies don’t need to be exceptional every day. They just need to be consistently better than the next-best option (in-house, consultants, platform team, etc.). That’s simply a much easier position to defend over time than trying to be “best in class.”

Moreover, being the least-worst alternative creates a stable equilibrium.
When all players have flaws, and no clear dominant substitute exists, the market settles into a classic Nash equilibrium of acceptable satisfaction/dissatisfaction.
The reason clients don’t defect from agencies, even though they switch agencies all the time, is that no one expects to materially improve their outcome.

What does the future hold?

We believe large enterprises are highly likely to continue relying on agencies into the foreseeable future. However, while agencies are good at optimizing execution, they do not own the underlying economics. We think agencies will follow Publicis’ lead and take advantage of AI not just to increase revenue productivity, but to shift their business model from being farmers to landowners (data owners that collect recurring rents from clients in exchange for proven/believable outcomes).

Shifting from farmers to landowners is mission-critical for agencies to remain relevant in the coming years amid new entrants like AI tools and platforms with built-in AI. As we sit today, the new alternatives may look superior in isolation, but to displace the incumbent agency, they must be less flawed across the full stack (strategy, execution, scale, and integration).
That’s a much higher bar than most practitioners realize.

As new AI workflows across planning, trafficking, measurement, reporting, and optimization (in media and creative flows) become partially or fully automated, agency revenue will likely continue to grow at current rates while headcount declines naturally. Agencies have historically experienced relatively high annual attrition (~15–25%), so we expect them to leverage natural churn to gradually reduce labor intensity, thereby increasing revenue per employee toward a new, higher state.

The go-forward model is no longer “bigger is better.” It seems more likely that agencies will become thinner and serve clients as software-augmented coordination layers that also happen to play a market-maker role with trading inventory as principals.

Disclaimer: This post, and any other post from Quo Vadis, should not be considered investment advice. This content is for informational purposes only. You should not construe this information, or any other material from Quo Vadis, as investment, financial, or any other form of advice.

One of the biggest topics across the advertising industry is the potential scale of OpenAI’s advertising business. Late last week, Axios reported the scoop that the company projects $2.5 billion in ad revenue this year and $100 billion by 2030. Reaching $100 billion over the next five years would mean OpenAI is the fastest-growing company in the history of advertising. Whether reaching such an amazing achievement is plausible, possible, or probable is what we cover in this post.

In January, Quo Vadis used Google search usage history and diffusion curve modeling to estimate $80 billion in 2030 ad revenue for OpenAI. A few weeks ago, Madison and Wall used headcount comparables and estimated that:

“OpenAI has the underlying scale to support as much as $3 billion in global ad revenue this year and significantly more in 2027.”

What do we know?

Let’s start with what we know or believe to be true.

Valuation: We know that investors valued OpenAI at $852 billion in its last financing round (post-money).

Comparables: We believe the two closest historical comparables investors have in mind for understanding OpenAI's future are Google Search and Meta, the world's two biggest advertising platforms. Together, the two companies attracted 40% of global ad spending in 2025 (estimated at $1.1 trillion by Quo Vadis).

OpenAI Revenue Segments: According to the Wall Street Journal’s recent article, An Inside Look at OpenAI and Anthropic’s Finances Ahead of Their IPOs, OpenAI estimates $140 billion in 2030 “Consumer” revenue (Chart 1). Given the recent news suggesting $100 billion in 2030 “Advertising” revenue, we can infer that the company is forecasting non-advertising revenue (e.g., consumer subscription revenue) will represent 30% of consumer revenue. We believe 30% is a bit high, as only ~5% of ChatGPT’s estimated 900 million active users are paid subscribers today.

Speed of Growth: OpenAI believes ad revenue will grow from zero to $100 billion over the next five years. It took Google 20 years to reach that milestone, and it took Meta 16.5 years (Charts 2 and 3). Whereas OpenAI has the benefit of growing on top of a mature global installed base, Google and Meta grew up with the Internet (and vice versa).

CAGR Comparison: Google’s search ad revenue CAGR over its first twenty years of advertising life was 54%. Meta’s ad revenue CAGR over the first 16.5 years to $100 million was 63%. OpenAI’s CAGR will be 151% if it manages to grow from $2.5 billion in 2026 to $100 billion in 2030, which suggests its adoption curve will reach maturity in less than half the time it will take Google/Meta to reach terminal growth levels.

Terminal Growth: If we assume terminal growth rates in the advertising world are ~5% (the average expected growth rate of global ad spending), Google search will hit that level around 2032, and likely maintain the same growth rate into perpetuity as a GDP-like compounding cash flow stream well-anchored to the continued expansion of global advertising demand. Meta is younger with a different future. We expect Meta to grow at a ~14% CAGR over the next five years with $390 billion in ad revenue in 2030.

Economic Moats: We know that Google and Meta produce relatively high and consistent returns on invested capital (ROIC) of 40%+. The higher the ROIC, the faster the printing press prints cash flow. We also know that Google and Meta have generated an average operating profit margin of 30% (EBIT less adjusted cash taxes) over recent years, with little variance. We believe investors expect OpenAI to benefit from a similar economic moat.

Piecing the puzzle together

OpenAI’s investors are smart and experienced. They have access to good information and see a lot of deal flow. For them or any investor, the valuation they assign today is nothing more than the present value of expected future cash flows. If investors in OpenAI consider it as the next big thing after Google and Meta, we can use what we know to back into OpenAI’s advertising revenue. We’ll do that step by step.

Step 1: We know that the value of future cash flows is determined by the following finance formula:

For OpenAI, we know the most recent valuation is $852 billion. From what we can tell, investors are comparing future financial performance to that of Google and Meta, so we think it’s reasonable to use 40% as a plug for future ROIC at maturity.

For Cost of Capital, we think OpenAI is best compared to major ad platforms with betas around 1.2, as well as higher-beta companies in the AI space like Nvidia at 2.5. We lean more toward Nvidia and use a weighted average of 2.1.

As we mentioned above, we think a ~5% terminal rate is a reasonable enough result in the following value equation:

Step 2: Now all we have to do is rearrange it and solve for Operating Profit.

And now, with our reasonable plug values, we estimate OpenAI's Operating Profit at $88 billion at some point in the future, when it reaches maturity.

For context, Google’s operating profit in 2026 will be ~$125 billion (includes advertising, cloud, other bets, etc.) and Meta’s will be around $80 billion. If we assume 30% operating margins at maturity for OpenAI, total revenue at that future time will be $293 billion. Note that WSJ reported $280 billion in total revenue by 2030.

Step 3: Now that we have a good estimate of OpenAI’s Total Revenue at maturity, we can use the WSJ’s revenue segment input guides to arrive at a future ad revenue of ~$209 billion. The question when maturity will be reach is the billion dollar question.

If 20% of OpenAI’s future total revenue comes from Enterprise sales (Google Cloud is 17% of total revenue), and 5% comes from what they call “New Products,” that implies 75% or $220 billion will come from “Consumer Products.”

Finally, if 5% comes from subscriptions, then investors implicitly expect OpenAI to have ~$209 billion in ad revenue at maturity.

When is maturity?

If $209 billion is the maximum number in OpenAI’s future mature state, and the stated goal is to reach $100 billion by 2030, then maturity will happen in about 10 years. The the “best-fit” diffusion curve given our current data points.

If that happens, OpenAI will have grown 3x faster than Google and Meta’s journey to maturity. At first glance, achieving advertising success so quickly might seem plausible but not probable. However, when compared with the first 10 years of growth at Google and Meta, it not only seems plausible but perhaps quite probable even though it is hard to imagine.

Google search experienced a 109% CAGR over its first 10 years, and Meta was similar at 93%. If OpenAI’s forecast is right and it hits $2.5 billion in ad revenue this year, and reaches maturity in ~10 years, then its CAGR will be 62%. Looking at OpenAI’s advertising future through our lens turns what is plausible and possible into likely probabality.

Try Our OpenAI Revenue Growth Estimator Tool

Disclaimer: This post, and any other post from Quo Vadis, should not be considered investment advice. This content is for informational purposes only. You should not construe this information, or any other material from Quo Vadis, as investment, financial, or any other form of advice.

Let’s say we are examining AdTech Companies A and B. On the surface, Company A appears to be the clear winner, with faster revenue growth and a higher Rule of 40 (revenue growth + EBITDA margin).

Company B, by contrast, has different optics at first glance. Slower revenue growth, but better EBITDA margins and a slightly lower Rule of 40. Beneath the surface, Company B’s economics show a more efficient adtech machine.

The first apples-to-apples mistake observers tend to make is miscalculating EBITDA margins off the wrong “revenue” number.

• Company A reports actual revenue earned ($2.9 billion in 2025) from processing gross ad spend. EBITDA was $697 million, so its EBITDA margin is 24%.

• Company B reports revenue in terms of gross spend, $1.9 billion in 2025. If we incorrectly apply it to EBITDA, we get a 17% margin. Company B’s actual revenue (aka, ex-TAC contribution) is $1.2 billion, so its correct apples-to-apples EBITDA margin is 27%.

The second mistake is caring about EBITDA in the first place. EBITDA is only somewhat relevant if you’re interested in pricing a company (e.g., using comparable company multiples to “value” a target company). EBITDA is widely used because it is easy to calculate, but it is insufficient for properly valuing a company.

Let’s start with Company A

Reporting Guidelines: Company A reports under a GAAP “Agent” model. That means it only records the cut it keeps (its commission or take rate) as revenue, not the full amount of money flowing through its platform.

Revenue: Company A’s revenue in 2025 was $2.9 billion. YoY revenue growth was 18%. Not too shabby. Consensus estimates for 2026 revenue growth range from 13% to 15%. The company faces multiple headwinds, making the low end of the range a real possibility.

Gross Ad Spend: Company A processes or “sees” around $14.3 billion in media flows (~20% disclosed take rate). Before the 2025 reporting year, the company used to provide investors with information about the volume of gross ad spend it processes, but it seems management decided to remove past transparency. In our view, the main reasons likely reflect a combination of mix shift, reporting simplification, and/or underlying volume/take rate dynamics. In any case, we use either accounts receivable (audited number) or accounts payable (audited number) and look at historical days receivable or days payable to get a good enough idea of gross ad spend.

EBITDA and EBITDA Margin (Earnings Before Interest, Taxes, Depreciation, and Amortization): Company A generated $697 million in EBITDA in 2025. Creating EBITDA margins in adtech land north of 20% is not easy. Company A generated 24% EBITDA margins in 2025.

Capitalized R&D Expense: One of the first steps in valuing any tech company is to depart from GAAP by capitalizing all R&D expenses. This approach provides a more accurate view of operating profits. Given the state of rapid technological change, we amortize on a 3-year schedule

Here’s what NYU valuation guru, Aswath Damodaran, recommends, and we follow his advice:

“Expensing R&D leads to lower earnings and book values for firms that invest heavily in it, making them look less profitable than they truly are… by treating R&D as an expense rather than a capital investment, we systematically understate both earnings and invested capital for growth firms.”

Read Damodaran’s persuasive view in: “Research and Development Expenses: Implications for Profitability Measurement and Valuation Aswath Damodaran, Stern School of Business.

So, after we capitalize R&D expenses, the net effective is to add back $108 million in R&D expense to Company A’s EBTDA, then subtract $49 million. The net effect is $59 million in additional EBITDA gains for Company A, bringing total EBITDA to $756 million. Now, in real operating terms, Company A’s true operating EBITDA margin is 26%.

Rule of 40 is a finance rule of thumb and a quick way to gauge whether a company is balancing growth and profitability well. You simply add the revenue growth rate to EBITDA margin. If the total is 40% or higher, the company is generally considered “healthy.” As a company matures, revenue growth is high in the early years and eventually decays toward GDP growth (e.g., ~4%). EBITDA margins behave in the opposite direction, starting low or negative and growing over time. For Company A, its Rule of 40 in 2025 was 44%.

Turning to Company B

Reporting Guidelines: Company B reports GAAP revenue under both the “Principal” and “Agent” models. When a company acts as a principal, that means it serves as a middleman, buying and reselling the product. In this case, GAAP requires recording the full sale amount as revenue and payments to suppliers as costs, leaving the difference as actual revenue (aka, ex-TAC contribution).

When we determine that we act as principal, we recognize revenue and related costs incurred on a gross basis. When we act as an agent, we recognize revenue on a net basis.

Revenue: Company B’s actual operating revenue in 2025 was $1.2 billion. YoY revenue growth was 5%. Keep in mind, Company B is four years older than Company A, and revenue growth rates for every company ever created always decay over time. We’ll come back to this important value driver later on in the article.

Gross Ad Spend: Company A operates under a single business model. Company B runs a diversified business model across two revenue streams. On its principal-based stream, it processed $1.7 billion in gross ad spend in 2025, netting $915 million in operating revenue (principal-based accounting). Its agent-based revenue stream generated $260 million, so total operating revenue in 2025 was $1.2 billion.

EBITDA and EBITDA Margin: GAAP EBITDA for Company B was $313 million in 2025, resulting in a nice 27% EBITDA margin. After we capitalize R&D expense and smooth out amortization as we do for Company A, Company B’s actual EBIDA margin is 28% compared to 26% for Company A.

Rule of 40: Adding revenue growth and EBITDA margin, Company B delivered a healthy 33% in 2025 compared to Company A’s at 44%.

A better measure of true health

“Every time you see the word EBITDA, you should substitute the words “bullshit earnings.”

— Charles Munger and Warren Buffet

Warren Buffett and Charles Munger famously despise EBITDA, viewing it as a misleading metric that ignores real cash expenses like capital expenditures. They argue it allows management to inflate earnings and mask a company’s true, often decaying, financial health.

A better measure of any company's true health results from doing the grunt work to understand the historical spread between return on invested capital (ROIC) and its cost of capital. Using EBITDA to price a company misses the two most important things to properly value a company:

1. Economic Spread: A company can create value only when its ROIC exceeds its cost of capital. Both companies A and B are creating value for shareholders, although one is better than the other.

2. Fundamental Growth: This is the rate at which a company can grow its operating profits, which is solely determined by how much of its operating profits it reinvests (Investment Rate) and how efficiently that capital generates returns (ROIC). In plain terms, growth isn’t just about investing more; it’s about investing well.

Before we compare Companies A and B on a true historical health basis, we need to establish two gold-standard benchmarks across the adtech space that any investor can use to allocate capital. The two high bars everyone looks up to are Google and Meta. Both companies consistently deliver ~45% ROIC (amazing). Since Total Share Returns ultimately map to ROIC performance, you can see the results for yourself in our Quo Vadis BigTech2 portfolio.

In the adtech world, Google and Meta have proven to be far and away the best allocators of capital. That’s why they are the gold standard for investors. That’s also why they can announce ~$150B in new invested capital aimed toward AI and get a favorable reaction from Wall St. They aren’t investing in AI” as a project. They are both completely retooling their entire production function around AI. They are defending and rebuilding their respective moats while simultaneously building AI utility layers. Both companies are asking and answering the right question:

How much capital is required to preserve ROIC in a world where intelligence is computational discipline?

The answer is a lot more than in the pre-AI digital advertising era.

Company A:

• ROIC in 2025 was 29%, and its two-year average was 24%.

• Since 2019, the average ROIC for Company A is 20%

• Key Value Metric: Its economic spread relative to its 2-year average ROIC was 14% at an 11% cost of capital in 2025. Overall, the company’s average economic spread since 2019 is 8%. The economic spreads for Google and Meta (and Publicis too) are north of 30%.

• Company A’s average growth rate of new invested capital since 2019 is 23%.

• Over the same time period, its average revenue growth rate was 28%. Invest to grow, that’s the name of the game.

• In terms of capital efficiency, the company generated $1.40 in revenue per dollar of invested capital in 2025. Not only would investors prefer to see this driver above 2.0, but their expectations are also heading to the 3.0 range with AI in the mix.

• Company A’s current cost of capital for FY25 was 11.6%, averaging 12% since 2019, with minor variation across its capital structure.

• Fundamental Growth: Since 2019, Company A has reinvested 95% of its operating profit into new invested capital, with an average ROIC of 20%, resulting in a healthy fundamental growth rate of 19% (the same rate as revenue growth in 2025).

Company B:

• ROIC in 2025 was 20%, and its two-year average was 29%.

• Since 2019, the average ROIC for Company B is 18%

• Its economic spread relative to its 2-year average ROIC was 10% at an 9% cost of capital in 2025.

• Key Value Metric: Overall, the company’s average economic spread since 2019 is 10%, 2% better than Company A.

• In terms of capital efficiency, the company generated $1.60 in revenue per dollar of invested capital in 2025 compared to $1.40 for Company A.

• Company A’s current cost of capital for FY25 was 8.8%, and averages 7% since 2019.

• Fundamental Growth: Since 2019, Company A has reinvested 25% of its operating profit into new invested capital, with an average ROIC of 17%, resulting in a fundamental growth rate of 4% (the same rate as revenue growth in 2025).

The Future: DCF valuation comparison

Ultimately, what matters most is the forward view because valuation is driven by the future stream of free cash flow a business is expected to generate, not what it has already done.

Company A

• Current market cap (equity value) is $10.1 billion.

• Given multiple headwinds, we forecast 13% revenue growth in 2026 and 9% CAGR through 2030.

• Company A generates $750K in revenue per employee. We expect this key value driver to improve in 2026 with investments in labor productivity, resulting in flat employee growth.

• We expect continued improvement in operating profits, which implies ~30% EBITDA margins in 2026.

• For 2026, we expect the company's ROIC to rise from 29% to 33%, and reach 37% by 2030.

Equity Valuation: Given $1.3 billion excess cash and $437 million in debt + debt equivalents, our fair market valuation for Company A is $11.4 billion. Company A’s stock is trading at $10.2 billion today, so we think investors are slightly undervaluing the company’s future prospects.

Company B

• Current market cap (equity value) is $857 million.

• Consensus estimates imply 2% revenue growth in 2026. We think management will easily deliver 5% growth in 2026 and maintain that rate through our 2030 forecast period.

• Similar to Company A, we expect continued improvement in operating profits driven by AI labor productivity, implying 32% EBITDA margins.

• For 2026, we expect Company B’s ROIC to rise from 20% to ~23%, and reach 31% by 2030

• Rule of 40 by the end of 2026 will tighten: Company A at ~43% vs. Company B at 37%.

Equity Valuation: Given $250 million excess cash and $155 million in debt + debt equivalents, our fair market valuation for Company B is $3.4 billion. Company B’s stock is trading at $860 million today. From our vantage point, we think investors are massively undervaluing the company’s future prospects on what are fairly easy hurdles for management to achieve.

The Big Reveal

Bottom Line: Fundamentals and market narratives don’t always move in lockstep, but they eventually reconcile. Markets often misprice trajectory, but they rarely misprice destination over time.

For The Trade Desk, the gap between market value and fundamental value has historically been wide, reflecting periods of both exuberance and skepticism. But today, that gap is narrowing. As free cash flow scales and reinvestment efficiency improves, investor sentiment appears to be converging toward the business's underlying economics.

For Criteo, the opposite dynamic is emerging. While fundamental value, driven by steady free cash flow, remains stable to improving, market valuation is still lagging (for now). From what we can observe, investor sentiment is diverging from operating performance, suggesting either a lack of conviction about durability (wait-and-see mode) or a meaningful and temporary mispricing of cash-flow quality.

Disclaimer: This post, and any other post from Quo Vadis, should not be considered investment advice. This content is for informational purposes only. You should not construe this information, or any other material from Quo Vadis, as investment, financial, or any other form of advice.

We have something special in store at the Advertising Economic Forum this Thursday at The New York Times Center. We partnered with AdWeek to create a live interview studio and talk about a single important subject.

Back in 2024, when we first launched the forum, an important question arose on stage that caused a ton of debate:

Will there be websites in the future?

Flash forward to March 2026, and we can all see that websites are still around, albeit with reduced traffic and pressure to tweak the publisher business model as fast as possible in the AI era.

Throughout the morning this Thursday (March 19), we are handpicking ~25 attendees to ask them a simple question in 5-minute interviews. One question, many points of view.

“What is the future of the open web? And how must it reinvent itself to increase its relevance in the advertiser’s mind?”

A few sub-questions might be:

Why should advertisers care about the open web? What has to change for the open web to stay relevant as an adverting medium?

If you want to chime in at our AdWeek interview studio, get your ticket today.

Get Your Spot Today

History of the Open Web told through the lens of supply and demand

In the Beginning, 1994-2005: Let’s start with the initial equilibrium state of a healthy market for open web banner ads in 2005.

Website display ads started on October 27, 1994, when the first web banner ad appeared on HotWired.com (the online counterpart to Wired magazine). It was created by agency Modem Media for AT&T. The ad featured a 468x60 pixel banner with the text:

“Have you ever clicked your mouse right here? You will.”

By 2007, Advertiser demand grew, and publisher supply kept pace

By 2007, demand and supply looked normal. Total ad spend on web banners was roughly $15B (the grey rectangle). In the first era of digital advertising, most ad-supported websites were focused on the “onlining” of old-school offline newspapers and magazines. There were also new digital-first publications.

CPM prices were around $10 (P) on a weighted average, for example:

• Premium publisher display = $15 – $30 CPM

• Run-of-site banners = $5 – $15 CPM

• Ad network inventory = $1 – $5 CPM

• Remnant/performance networks = $0.50 – $2 CPM

Back then, in 2007, there were around 1.2 billion internet users. Today, there are over 6 billion. That means quantity sold (Q) was around 1.5 trillion impressions or about 3.5 ads per day per user on average. Who knows what the number is today? All we know is that users get tons of open web display ads, of which many they never see.

Programmatic is Born: ~2007 to 2016

When programmatic came along, the supply curve started to flatten and shift because it was easy to produce new inventory. Like alchemy that produces false gold, there was an increasing ability for any publisher, big or small, to create infinite audiences, new traffic, and new supply.

As a result, CPM prices for open web banner ads started to fall while the magic elixir and allure of “audience targeting” grew at a robust pace, increasing the total market size.

Supply is Flat: 2016 to 2023

When something is too good to be true, it probably is. By 2023, open web display ad sales were mostly done programmatically between DSPs and SSPs with data providers in the middle.

Even though supply was essentially flat, demand continued to grow despite warnings from various industry associations, with one “working media” research report after another. Quo Vadis wrote about this in an article called Funky Town and The Way The [AdTech] World Works. The article describes how demand was procurement-led, and suppliers simply produced what they wanted — “cheap reach.”

Present Day 2026

Just as market theory and the lemon market theory would predict, demand from advertisers has run out of patience.

Now they are pulling back and shifting attention to the GenAI world, where eyeballs are increasingly flowing.

Good news taking form in 2026 and 2027

The interesting thing about LLMs is their need for good information. Good sites provide useful information, bad sites don’t. The more users spend time in chatbot environments and use agents to complete tasks, the more LLMS will need to eat the good stuff.

We can also envision a future in which LLMs will have to account for a Cost of Goods Sold (COGS) expense called “publisher data acquisition cost.”

As of 2025, total global ad spending was ~$1.2 trillion. We think it’s safe to assume 6% annual growth over the next five years, so total global ad spend will be ~$1.6 trillion in 2030. Note that Brian Wieser from Madison & Wall is forecasting 8.1% this year.

That means $400 billion of new media money is coming online over the next five years. We don’t think it is hard to imagine GenAI ads on OpenAI and other apps reaching $100 billion by 2030. If we assume a 5% COGS going to publishers, that’s $5 billion extra on top of ad revenue.

The Case for 2028

The more publishers convert old programmatic thinking into better advertising propositions, the quicker the supplier curve will recover and return to a normal state, like it was way back in 2000.

At the same time, advertisers will start to see the open web in a new light and, hopefully, reward great outcomes with increased demand.

Lower revenue with more profit is a great outcome

Here’s a test for publisher management teams:

Would you rather have $100 in ad sales and $10 in operating profit OR $60 in ad sales and $13 in operating profit.

• The supply curve recovers to a normal state in the Recovery Stage.

• Low-quality publishers get boxed out of the market.

• Some variable costs ꜜdecrease (TAC, tech fees).

• Other variable costs ꜛincrease (data movement, AI usage).

• Add in LLM licensing revenue (COGS for LLM, revenue for publishers).

• All fixed costs decrease primarily due to increased AI-assisted labor productivity.

Result: A smaller market universe, but more profits.

If you want to chime in at our AdWeek interview studio, get your ticket today.

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Disclaimer: This post, and any other post from Quo Vadis, should not be considered investment advice. This content is for informational purposes only. You should not construe this information, or any other material from Quo Vadis, as investment, financial, or any other form of advice.

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Preview of Advertising Economic Forum Agenda with Brian Wieser

Yesterday, we sat down with Brian Wieser from Madison and Wall to get his take on the agenda/speaker sessions taking pace at the Advertising Economic Forum. Similar to part years, the agenda is like a storybook that we unfold one session at a time. In our video review, we quickly run through what’s in store for the session and offer little tidbits and observations along the way.

Enjoy! See you next week!

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Disclaimer: This post, and any other post from Quo Vadis, should not be considered investment advice. This content is for informational purposes only. You should not construe this information, or any other material from Quo Vadis, as investment, financial, or any other form of advice.

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4Q25 and FY25 AdTech, MarTech, Agency Update

With 4Q25 and full-year 2025 earnings now reported (except RAMP, fiscal year ending March 31), we can assess how our equal-weighted portfolios have performed since our Q3 update in November.

As a reminder, each portfolio starts with a $100 investment divided equally across all constituent stocks. If a company exits public life (e.g., IAS) or a relevant company goes public (e.g., MNTN), we remove or add it and rebalance on an equal-dollar basis.

Here’s how it works using our AdTech11 portfolio as an example. You start with $100, divide it equally across 11 companies, and buy shares in each based on the stock price on January 1, 2022. If things go well, you find alpha by beating the NASDAQ or S&P500.

Key Benchmarks: The NASDAQ and S&P 500 are both up ↑43% since January 2022. We also include our BigTech2 (Google and Meta) to benchmark what top “durable moat” performance looks like for companies with incredibly deep and defensible economic spreads.

What is a moat? Quo Vadis defines a “moat” as the spread between a company’s return on operating invested capital (ROIC) and its cost of capital (aka, economic spread). In other words, the criteria for success is the economic spread and the measure of success is ROIC > Cost of Capital.

For Google and Meta, that all-important spread is 30% or more, depending on the quarter. With top quartile spreads, both companies are happy to make giant investments in new invested capital to sustain their moat in the AI age. In our Portfolio Chart chart above, investors are basically saying, “Earnings talk is cheap. Get a sustainable moat or we’ll penalize you.”

AdTech11

Our AdTech11 portfolio is ↓54% since inception. Note that IAS was taken private by Novacap for $1.9 billion and removed from the portfolio in January 2026. MNTN was added in June 2025 following its IPO, currently trading at a $720 million market capitalization and $510 million enterprise value. Following its IPO in May 2025, the “CTV” company had a market capitalization of over $2 billion.

Overall, AdTech11 was trending upward toward the end of last year but has since reversed course and is now trending downward. In other words, the same $100 invested in a NASDAQ ETF would have returned 43% with less risk.

Gainers: ZETA (+96%) and DSP (+1%).

Downers: MGNI (-24%), RAMP (-46%), CRTO (-54%), TBLA (-59%), MNTN (-63%), DV (-68%), TTD (-74%), PUBM (-76%), and TEAD (-94%).

AdTech11+ (with AppLovin)

When we include AppLovin (APP) in the mix, our AdTech11+ portfolio is only ↓33%. APP alone is up ↑361%, single-handedly carrying the portfolio.

MarTech8

Our MarTech8 portfolio remains deeply in the red, ↓57%. The portfolio includes SHOP, HUBS, SPT, SEMR, TWLO, BRZE, YEXT, and AMPL. It turns out that SaaS revenue models are not superior to media percentage models for generating cash flow.

Shopify (-11%) used to dominate our MarTech portfolio and contribute to nearly all the gains. From our perspective, this shows how disruptive AI has become in investors' minds, particularly when they consider SaaS-based businesses.

BigTech2

We track two Big Tech ad players, Google (GOOG) and Meta (META). Had you invested $100 on a 50/50 basis in January 2022, you’d be ↑103% GOOG (+115%) and META (+91%), respectively.

Between the two companies, they controlled 40% of global ad spending in 2025. Given reasonable growth expectations, we estimate their combined share will increase to 46% by 2030.

SocialTech4

Our SocialTech4 portfolio is ↑18%. The portfolio includes META, SNAP, PINS, and RDDT. Reddit (RDDT) was added in April 2024 following its IPO. Reddit is the biggest winner, up 217% since entering the portfolio.

Meanwhile, Pinterest made news last quarter with its acquisition of TVScientific. The company reported a bullish outlook for the first quarter, citing the impact of expanding its connected-TV capabilities within its Performance+ AI optimization portfolio.

Agency5

Our agency portfolio consists of WPP, Publicis, Omnicom, Stagwell, and S4 Capital (SFOR.L). Note that IPG was removed following its acquisition by Omnicom, making the combined agency the largest in the world and displacing Publicis (for now).

The Agency5 portfolio is down ↓43% since inception. Publicis and Omnicom are the only agencies in the black, up 33% and 15%, respectively.

They say a picture is worth a thousand words. We don’t need a thousand words. We only need seven: creating and sustaining an economic moat matters. From our vantage point, Publicis has done the best job of converting itself from a farmer to landowner (data owner). Whomever owns the best soil (data) in advertising land gets to build the biggest and durable moat. That seems to be Publicis’ acquisition strategy in a nutshell.

A word on Betas

Looking at betas across our AdTech and MarTech portfolios, they are relatively high, ranging from ~1.3 to ~1.8. High betas materially affect the cost of equity and the overall cost of capital. In a tightening cycle with AI opportunities and threats everywhere, players with higher discount rates disproportionately affect efforts to create long-duration cash flow streams. The result is valuation pressure and multiple compression, even if operating performance remains intact.

If you want to hear what the experts have to say about topics like this, then come to the Advertising Economic Forum NYC on March 18 and 19.

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Disclaimer: This post, and any other post from Quo Vadis, should not be considered investment advice. This content is for informational purposes only. You should not construe this information, or any other material from Quo Vadis, as investment, financial, or any other form of advice.

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Advertising Economic Forum Returns to New York City: March 18–19, 2026.
Day 1 at Horizon Media features eight AI founders pitching to our esteemed investor panel across agentic commerce, creative, and emerging agency models. Day 2 at The New York Times Center convenes 400 senior leaders for closed-door sessions examining market structure, incentives, and the economic forces reshaping advertising today. Early-bird registration is still open for 3 more days; space is limited.

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Rebuilding the media value chain for an agentic AI world with Andrew Mole.

Quo Vadis had an insightful chat last week with Andrew Mole about how the media value chain is getting rebuilt for the agentic AI world. To set the scene, we began with a topic dominating the adtech/financial news cycle — Google’s giant AI capex bet.

Google told investors on its Q4/FY25 earnings call that it plans to invest $180 billion in capex (aka, operating fixed capital) over the near-term investment cycle. Brian Wieser from Madison & Wall covered this story last week (I recommend checking out his perspective).

Let’s put $180 billion of new investment in fixed assets into perspective. At the end of 2024, Google had $210 billion in total operating invested capital. $133 billion was fixed capital, and $77 billion was working capital.

With $180 billion in new investment, we interpret this to mean Google is aiming to replace its entire capital base (hardware, compute, etc.).

Case and Point: As far as we can, Google’s giant investment in new fixed capital is signaling a bet that AI tech and adoption are not only happening fast (obviously), but it’s going to happen even faster than the majority of the market thinks. In other words, it’s better to overinvest and be right than underinvest and be wrong.

4 Questions We Covered With Andrew

Check out the video recording to hear his answers and related commentary.

Q1 with Andrew: Why is AdCP (Predid Sales Agent) the most consequential shift across programmatic?

Q2 with Andrew: Prebid.org has taken over the code for the Ad Context Protocol (AdCP) to develop the Prebid Sales Agent, an open-source framework enabling publisher-side AI to autonomously manage, negotiate, and execute ad campaigns. This initiative aims to prevent vendor lock-in and standardize AI-driven advertising, providing an alternative to proprietary AI solutions while ensuring interoperability across the digital ecosystem. Good, Bad, or Indifferent?

Q3 with Andrew: Everyone is talking about “agentic AI,” but the industry is split on what actually changes: workflows, incentives, control points, and who captures value. What is structurally different in this cycle?

Q4 with Andrew: What does agentic unlock in today’s publisher stack to make curation scalable rather than manual and work in the publishers’ favor to avoid the sins of the programmatic era?

The Ideal State

We also covered the trampled topic of “transparency.” In adtech land, Quo Vadis treats the word “transparent” as synonymous with “trust.” Then we take it one step further and define trust as:

Confidence in a counterparty’s intentions.

This concept is important to prevent the agentic era from repeating the lemon-market mistakes of the RTB era. As our Quo Vadis community knows, the RTB era presented a classic “lemon market” asymmetry, where the sell-side had all the information about the quality of the impressions being offered. When marketing and procurement teams thought it was a good idea to allocate 20% or more to $1.00 “cheap reach” inventory, it incentivized the supply side to flood the market with subprime inventory. On the other side of the trade, buyers had difficulty distinguishing good from bad quality, so they priced programmatic inventory downward. As with all lemon markets, buyers eventually find equilibrium and exit the market altogether, a trend that became more noticeable in 2025 and continued into 2026.

Translation: The buy side of RTB had lost confidence in the seller’s intentions as the promise of programmatic failed to materialize (efficiency, transparency, quality, etc.).

The agentic world is a fresh start. Time will tell whether old habits and vested interests are hard to break or whether the tsunami of creative destruction ultimately delivers on the original promises. Quo Vadis is hopeful for the latter but paying attention to the former.

Disclaimer: This post, and any other post from Quo Vadis, should not be considered investment advice. This content is for informational purposes only. You should not construe this information, or any other material from Quo Vadis, as investment, financial, or any other form of advice.

Thanks to our amazing Quo Vadis community, there have been nearly 400 games played on Last Media Dollar and 30,000 individual 1-to-1 media channel matches since launching beta v1.0 as an experiment in September 2025. Thanks to user feedback, we are going from a “generic” simple experimental game to beta v2.0.

Last Media Dollar is a simple behavioral economics game that pits 22 competing media channels against one another in head-to-head matchups to reveal “hidden” preferences when compared to where actual media money flows.

Last Media Dollar “Live” is the most unique event in Ad Land

It’s based on the online game you’re reading about right now. The event is coming to NYC on February 25 at the Roxy Cinema in Tribeca. This is not a panel. It’s a live, on-stage decision-making with Chad Stoller, VP of Media at PMG, and Joe Zawadzki, partner at Aperiam Ventures. Two more speakers will be announced soon! Secure your ticket now to be in the room where the thinking happens.

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Actual Media Money Flows

In September 2025, we published our bottom-up research and estimates across 22 media channels, where nearly $1 trillion in media spend flows globally. Roughly 76% goes to eighteen digital channels, the remainder goes to four traditional channels.

Beta v.20 Game Updates

Simplified landing page: We condensed the landing page to get you into gameplay faster. Users get a quick sense of what the game is, why they should try it, and how to play.

Game Use Cases: The most important finding in our MVP is that playing Last Media Dollar requires context and a defined role. For example, are you playing a game as a brand advertiser or a performance advertiser? Are you spending media dollars as CPG, auto, finance, travel, or other verticals? Just like a real-world media plan, it’s obvious that both matter.

So, on the home page, players can now choose to play one of our 18 “House Cases” across eight different verticals. For example, you might step into the role of a CPG brand like Kraft Mac & Cheese, a luxury automaker like Genesis, or an insurance leader like Progressive. Each case is available as either a Brand or Performance case.

Building Your Own Case and Becoming a “Game Master”: Let’s say you don’t find a suitable House Case. No problem. Now you can use our GenAI helper tool to build your own case. All you have to do is answer a few prompts, and voila, you’ll get your own use case presented in the same clean “McKinsey” style as the house cases. Now you’ve become the “Game Master” of your very own use case(s).

Private Game Cases: As a game master, you can distribute your URL game link to a private group of players. For instance, let’s say you work at an agency and you just landed a new account. You have the client brief and will use it to create your LMD game case. Maybe you end up making it better than it was when you first read it. You send your game URL link to as many colleagues as you want. They play the game, and you get incredible insights that you cannot get today. It’s like asking for a new color to be invented and getting it.

Private Game Cases: Another new feature for game masters is the ability to turn a private use case into a public use case by adding it to the Case Library. For instance, perhaps you are an ad tech founder or investor. You want to know where the last marginal dollar is moving to better understand where durable demand exists. Or maybe you are a publisher revenue leader, and you want to know how buyers truly value your inventory relative to substitutes in order to reposition your offering, pricing, or go-to-market strategy. Now you have an easy way to move, knowing what you know to knowing what you don’t know, but need to, and as fast as possible.

Get Advice on Channel Matches: Stuck on a tough decision? During any match, tap the "Get AI Advice" button to receive instant, context-aware guidance from our media decision coach. The advice takes into account your specific case details, brand objectives, and the unique strengths of each channel in the current matchup. Whether you're weighing the reach of broadcast TV against the precision of digital targeting, or debating between influencer marketing and traditional print, the AI provides balanced insights to help inform your decision—without making the choice for you. It's like having a media planning expert in your pocket.

Take AI Notes for Future Reference: Capture your thinking as you play! Use the notes feature to record your rationale for each channel choice. Type your thoughts or tap the microphone to speak them. Your notes are automatically saved with each match and compiled into a complete transcript on your results page. This creates a valuable record of your decision-making process that you can reference later, share with colleagues, or use to explain your media preferences to stakeholders. It's perfect for turning a quick game session into documented strategic insights.

Get Your Seat To The Fun Today!

Last Media Dollar “Live” is based on the online game you’re reading about right now. The event is coming to NYC on February 25 at the Roxy Cinema in Tribeca. This is not a panel. It’s a live, on-stage decision-making with Chad Stoller, VP of Media at PMG, and Joe Zawadzki, partner at Aperiam Ventures. Two more speakers will be announced soon! Secure your ticket now to be in the room where the thinking happens.

Disclaimer: This post, and any other post from Quo Vadis, should not be considered investment advice. This content is for informational purposes only. You should not construe this information, or any other material from Quo Vadis, as investment, financial, or any other form of advice.

Last Media Dollar “Live” is coming to NYC: February 25, 2026, at the Roxy Cinema in Tribeca. This is a live, on-stage decision-making experience where senior practitioners and the audience debate assumptions and reveal how media choices are actually made under real constraints, in real time. Secure your ticket now if you want to be in the room where the thinking happens.

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Advertising Economic Forum Returns to New York City: March 18–19, 2026.
Day 1 at Horizon Media features eight AI founders pitching to our esteemed investor panel across agentic commerce, creative, and emerging agency models. Day 2 at The New York Times Center convenes 400 senior leaders for closed-door sessions examining market structure, incentives, and the economic forces reshaping advertising today. Early-bird registration is still open; space is limited.

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Ads are coming to ChatGPT

“OpenAI has announced plans to introduce advertising in ChatGPT in the United States. Ads will appear on the free version and the low-cost “Go” tier, but not for Pro, Business, or Enterprise subscribers. The company says ads will be clearly separated from chatbot responses and will not influence outputs.”

OpenAI listed five principles in their January 16 announcement:

1. Mission alignment: Ensure AGI benefits all of humanity. The company’s economic pursuit of advertising is intended to support that mission and make AI more accessible to more people.

Note that OpenAI has committed to approximately $1.4 trillion in AI infrastructure, including data centers, over the next eight years to support future development. The keyword is “committed,” which we translated to mean capitalized lease commitments, not actual cash outlays.

2. Answer independence: Ads will not influence the answers ChatGPT gives you. Answers will be optimized based on what’s most helpful to users. Ads will be clearly separated and clearly labeled.

3. Conversation privacy: Conversations with ChatGPT remain private from advertisers, and we never sell your data to advertisers. This is absolutely key if OpenAI wants to create a giant economic moat. In other words, their positioning (water-tight-walled garden) is the polar opposite of the “open” Internet.

4. Choice and control: Users control how their data is used. Users can turn off personalization and can clear the data used for ads at any time.

5. Long-term value: OpenAI does not aim to optimize for time spent in ChatGPT. They prefer to prioritize user trust and user experience over revenue. Again, this is key because their atomic unit of production is trust. We translate this to align with Google’s philosophy that utility trumps time spent. As long as consumers use Google’s products like toothpaste (twice per day), growth and free cash flow are guaranteed.

A quick historical look at the expressions of advertising

Despite constant innovation in media and technology over the past 5000 years, there are still only three fundamental expressions of advertising: print, audio, and motion (video). Therefore, every past and future adtech investment ultimately maps to one (or a combination) of these three.

As far as we know, print was the first expression of advertising and still dominates $1 trillion in media flows today. It appeared around 3000 BC and was found in the ruins of Thebes, Egypt. The ad was a papyrus print ad promoting a weaving workshop. Audio was the next expression of advertising in 700 BC China, where candy shop owners paid flute players to attract kids to the store.

Print is the original and still dominant expression (~54% of total ad spend). You can trace its roots from ancient written ads (e.g., early papyrus notices) through the Gutenberg press, early classifieds (Boston News-Letter, 1704), and the evolution from black-and-white to color posters via lithography.

Jumping forward to 1995, digital formats such as display, search, social (including influencer marketing), and retail media ads are mostly just continuations or branches of print. For example, search ads can be framed as a structured “directory” lineage (e.g., Yellow Pages), and retail media really just an evolution of catalog-style merchandising to meet online consumers where they are today.

Audio became the second major expression in the early 20th century, with the first radio commercial in 1922 by AT&T's WEAF station in New York City. It was a 10-minute commercial for the Queensboro Corporation, promoting apartments in Jackson Heights. The Queensboro Corporation paid $50 for the commercial.

Motion (video with audio) was the third expression to come onto the advertising scene. It started with silent-era theater ads (slides and pre-feature promotions) as early motion-era commercialization. The first U.S. TV ad in 1941 (for Bulova watches) was the beginning of modern televised advertising.

TV ad spend experienced hypergrowth in the 1970s and expanded again with cable ads in the 1980s. In 2026, we are well into the decade of CTV ad growth. It’s not a new expression, but rather a dollar-for-dollar migration within motion. Of the nearly $1 trillion in global ad spend in 2025, the majority is a print expression.

With our historical backdrop in mind, we can more clearly see how ChatGPT ads are unlikely create a new form of advertising. Far from it. The ad units will simply be an expression of print (similar to a sponsored listing), audio (when interfacing with ChatGPT via voice prompt and listening), or motion (a video ad experience inside chat results).

What’s Different: What will likely be different is how ads inside chatbot experiences represent a new division of labor (in the spirit of Adam Smith) by breaking the media and creative job-to-be-done into more atomic tasks to create new outcomes for advertisers. This atomic shift will change how advertising (media and creative) is produced, transacted, targeted, served, optimized, and measured with higher-calibrated instrumentation, but the underlying advertising expressions will remain print, audio, and motion.

ChatGPT Ads TAM Background

Back in 2000, it seemed like Google search came out of nowhere. Today, there are an estimated 6 billion internet users, and ~4 billion of them use Google search. Business models don’t get much more ubiquitous than that.

And according to various estimates, there are ~1.3 billion ChatGPT users, give or take. As of late 2025, the official ChatGPT mobile app has achieved massive adoption, with approximately 1.36 billion cumulative global downloads since its launch in May 2023.

Google is the top candidate for the most amazing business ever created. Microsoft is probably a close second. Back in the early 80s, IBM did not fully appreciate the strategy of Bill Gates, Paul Allen, and Steve Ballmer. They simply failed to see how Microsoft would create a massive and enduring economic moat that it still enjoys today.

Moral of the Story: Just because Google is so dominant today, like IBM was in 1980, doesn't mean it will be dominant forever. Why? Because there is always a 100% chance of disruption, and it is highly probable that a substitute or new entrant will eventually come along. From what we can observe, the most likely new player in town is OpenAI.

Sizing The Opportunity

If you work in advertising and use ChatGPT, you’re probably envisioning and discussing what these future ad experiences will look like. As far as we can tell, it is reasonable to think of ChatGPT ad experiences as a substitute good for search.

Assuming such a comparison makes sense, we looked at Google search growth since 1999 and built a best-fit diffusion curve model through 2025.

If you’re interested, here’s our best-fit Gompertz model:

We were unable to locate historical data on Google Search users; however, available reports suggest the average user conducted roughly four searches per day in 2025. We use 4.0 searches/day as a reasonable 2025 proxy and built a best-fit model such that 2.0 searches/day occured around 2011 (blue curve below). This is when Larry Page first publicly discussed the “toothbrush” strategy during Google’s second-quarter earnings conference call. During the call, Page outlined his goal to create services that people use “twice a day, just like a toothbrush.”

With a decent time-series growth in Google searches and another curve for searches/day, we derived the number of Google users over time and estimate there ~4 billion users today.

Mapping ChatGPT to Google’s Past Experience

Now that we have a decent baseline for imagining ChatGPT’s ad market size, our next step is to compare the first four years of Google’s search user growth (1999 to 2002) with ChatGPT's first four years of user growth (2022 to present). From there, we can construct an addressable market for ChatGPT.

Our model estimates that Google had 88 million unique users in its fourth year of life in 2002. Keep in mind, the Internet was still quite young in 2002 with a meaningful portion of people (young, old, 2nd and 3rd world countries, etc.) still not online. Back then, only 10% of the globe was online compared to 70% today.

In 2025, ChatGPT’s fourth year of life, it had over 1 billion unique users. If we update our model parameters to account for faster growth across a much bigger Internet install base compared to when Google started (the blue line), we can imagine ChatGPT scaling new users much faster than anything that has come before it. We don’t think such an acceclerated curve should be surprising to see because new technology cycles tend to compress adoption timelines, leading to faster adoption toward a maturity limit defined by the number of global internet users.

Forecasting ChatGPT DAU, Prompts per Day, and ChatGPT Revenue

With an estimate of unique users on the table, we convert it into an estimate of daily active users (DAU) using a factor of 15% (e.g., 15% of annual uniques converts to DAU).

We then asked ChatGPT: How many chatGPT queries or prompts to does the average user do per day?

Answer: On the order of ~3–5 prompts per user per day.

We use 4 promts per user per day as the mid-point starting value in 2026 and step up to 10 per day as a terminal value when adoption reaches maturital around 10 to 15 years from now (maybe sooner).

On average, advertisers pay Google search a $2.69 CPC and expect to achieve an average 3.17% CTR. That equates to an $85 eCPM. According to OpenAI, they are aiming for $60 CPM.

If we take these plugs as face value, our model suggests ChatGPT’s revenue could be $17 billion in 2026 and grow to $80 billion in 2030 based on five prompts per user per day. At maturity, our model suggests $160 billion at a $60 CPM and $185 billion at an $85 CPM.

To our forecsat into perspective, if Google search revenue grows at a 7% CAGR over the next 5 years (conservative), it will generate a whopping $310 billion. $80 billion, ChatGPT would sixth largest media channel.

Disclaimer: This post, and any other post from Quo Vadis, should not be considered investment advice. This content is for informational purposes only. You should not construe this information, or any other material from Quo Vadis, as investment, financial, or any other form of advice.

Last Media Dollar “Live” is coming to NYC: February 25, 2026. Last Media Dollar “Live” is coming to New York City on February 25 at the Roxy Cinema in Tribeca. This is not a panel, it’s a live, on-stage decision-making experience where senior practitioners and the audience debate assumptions and reveal how media choices are actually made under real constraints, in real time. Seats are very limited and moving quickly. Secure your ticket now if you want to be in the room where the thinking happens.

Get Your Seat To The Fun Today!

Advertising Economic Forum Returns to New York City: March 18–19, 2026.
This year features an expanded two-day format focused on where media capital, ad innovation, and financial strategy are heading. Day 1 at Horizon Media features eight AI founder pitches to our investor panel with live debate across agentic commerce, creative, and emerging agency models. Day 2 at The New York Times Center convenes 400 senior leaders for closed-door sessions examining market structure, incentives, and the economic forces reshaping advertising. Early bird registration is still open for now; space is limited.

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Liftoff IPO Valuation

As reported by Reuters last Tuesday:

Liftoff, a mobile app ​marketing provider backed by Blackstone, filed for an ‌IPO on the NASDAW under the symbol “LFTO”. Reuters reported last year that Blackstone was exploring a sale of Liftoff that could value the ‍business at $4 billion or more, including an IPO or a private sale. The company intends to use IPO proceeds for general corporate purposes and to repay $1.8 billion in outstanding debt.

Rather than anchor on the rumored $4B value, let’s instead triangulate it from fundamentals with a simplified discounted cash flow approach starting with revenue and margin structure, and then turn to investment capital, optimistic forecast assumptions, and what it all implies to arrive at a fair equity value. If you’re into TLDR, after we assess past performance and plug in optimistic forecast drivers, we get to $3.1B in equity value. That said, we have a feeling the IPO will likely trade higher than our estimate.

Revenue History: Liftoff’s S1 filing provided data for FY23 and FY24, along with data for the 9 months ending in September 2024 and 2025. The filing also provided eight quarters of revenue data from 4Q23 to 3Q25 (see table below).

We decomposed the 2024 quarterly data into a seasonal index and took a whack at estimating what Liftoff will report for Q425 and FY25 in its next filing. If the seasonal index is consistent from 2024 to 2025, we get $187 million in 4Q25 revenue and $678 million for the FY25. If our revenue estimate is accurate, then YoY revenue growth for Liftoff is quite healthy at 31%.

As pointed out by Brian Wieser at Madison & Wall:

“Liftoff is a smaller peer when compared with AppLovin. But it is pursuing the same strategy: building a single platform that controls both ad buying and app monetization and using its data to improve performance over time.”

On a trailing 12-month basis from 3Q25 reporting, AppLovin’s revenue is $6.3 billion, or over 9x larger than Liftoff. According to ChatGPT and app tracking service 42matters, Apple’s App Store and Google Play publish ~1,700 new apps every day. We assume a decent portion seek the kinds of services provided by companies like AppLovin and Liftoff.

Operating Profit: Turning to operating profit margins (EBIT), Liftoff generated a material jump from 4.9% in FY23 to 18% in FY24, and has sustained that level through 2025. As we’ve pointed out in previous posts, achieving EBIT margins greater than 20% across all NASDAQ-listed companies is a low-probability event.

Notably, AppLovin is in a different class with 40% EBIT margins in 2024 and trending higher (~55%) on a TTM basis as of 3Q25 reporting.

Flywheel Business Model

Those are insane operating margins. So, before moving on, let’s take a step back to examine the basics of AppLovin/Liftoff’s flywheel business model:

1. App developers want revenue, so they plug tools into their apps (ad mediation + in-app bidding) to maximize ad yield across many ad buyers.

2. App advertisers want efficient user acquisition (UA), so they buy ads through AppLovin/Liftoff’s demand-side products to acquire users at the lowest cost possible.

3. More spend + more inventory = more data. As more ads run across more apps, AppLovin/Liftoff gains more signal about what works, e.g., which creatives, audiences, placements, and times drive installs and downstream value.

4. Better targeting/optimization improves results. Their optimization engines use that data to improve performance, e.g., lower CPI, better ROAS for app advertisers.

5. Better performance attracts more advertisers and spending. If app advertisers hit their ROI goals more consistently, they write a blank check.

6. More advertiser demand increases publisher revenue as more demand increases competition for impressions, which generally increases eCPMs and monetization for developers.

7. Higher publisher revenue attracts more apps. More developers integrate, adding more inventory, and the loop repeats.

Net Operating Profit After Cash Taxes (NOPAT)

Looking at Liftoff’s income statement, we make a few adjustments to get a pure view of operating vs. non-operating performance. The first adjustment we make is the difference between reported taxes and cash taxes on an all-equity basis.

Although minor, we first add operating lease interest expense based on the reported operating lease liability sitting on the balance sheet. We apply a BBB interest rate (AppLovin is rated BBB according to S&P Global). We also add back impairment expense (a non-operating expense) to get the purest sense of operating profit possible (Adjusted EBIT).

As mentioned above, Liftoff has $1.8 billion of long-term debt, resulting in $98 million in FY24 interest expense and $91 million through 3Q25. Since these are flows to debt holders, we add back the tax shield effects of interest expense based on a 27% marginal cash tax rate (federal, state, and local tax rate assumption).

The last adjustment to make before arriving at our cash tax estimate is to account for the YoY change in deferred tax liabilities.

We estimate actual cash taxes of $36M in FY24 and $21M for the first 9 months of 2025. Unsurprisingly, the difference in cash taxes (what you cannot avoid paying at some point) and reported taxes on the income statement (GAAP accounting, timing differences) is material to equity holders.

After accounting for cash taxes, Liftoff generated $70M in FY24 in pure operating profit (13.4% margins) and $72M through the first 9 months of 2025.

Invested Capital

Now we turn to a few balance sheet adjustments to identify operating assets and liabilities vs non-operating items to get a decent view into invested capital. The first adjustment is in “Other Assets,” where we reclassify interest rate swaps as non-operating and keep the remaining portion as operating assets.

The second adjustment is on the liabilities side, where we reclassify “Contingent Consideration” (earn-out payments to former stockholders of Liftoff Mobile) as non-operating and keep the remaining portion as operating liabilities.

The last material adjustment we make is to remove goodwill from invested capital as a non-operating asset. FWIW, we strip goodwill out of invested capital because it’s a purchase-accounting plug that reflects what the acquirer paid, not incremental operating assets deployed.

Retained Earnings/Losses

The first thing our eyes go to on Liftoff’s balance is –$1 billion in retained losses. Interestingly, AppLovin went public in April 2021 and also had –$1 billion in retained losses in FY21. However, post-IPO AppLovin’s fortunes changed dramatically with retained earnings standing at +$1B as of 3Q25 reporting. That’s a $2 billion swing.

Invested Capital

Liftoff produced respectable Return on Invested Capital (ROIC) in FY24 at 19.2%. We estimate FY25 will be around 24% and rise over the coming years. FWIW, AppLovin’s ROIC in FY24 was a whopping 73% and might go higher in FY25.

One thing to keep an eye on with Liftoff is the downward trend in invested capital. Between FY23 and FY24, invested capital decreased by 20% and by another 3% as of September 2025. This non-investment trend seems to be sector-specific because AppLovin is also not investing back into its business. We find that a bit odd because with such a high ROIC and a large spread of its cost of capital (large economic moat), you’d think a company would be investing as much as possible to grow and protect it.

When companies don’t invest in new invested capital, investors should be wary of future growth prospects. For instance, if you look at Google and Meta, you’ll find that both companies invest 20%+ per year in new invested capital during normal years. With the AI arms race in play over the past year, investment in new invested capital for Google and Meta is 30%+.

In any case, we think rational investors should take note and expect the same bold thinking from any advertising platform that is serious about winning, building an economic moat, AND sustaining it well into the future.

Valuation

When it comes to valuation in general, we prefer taking an optimistic approach (heroic assumptions) with forecast assumptions to arrive at what the highest valuation could be and get a directional idea of where Liftoff’s IPO valuation might land.

• Let’s start with revenue growth at 31% in FY25, and gradually move downward to a 20% target value in 2030. If that happens, Liftoff’s average annual revenue growth would be 25%. We think that would be phenominal growth. That said, with the potential for so many new-AI apps coming online, our growth 25% rate could also be on the light side. Time will tell.

• AppLovin’s NOPAT margins were 33% in FY24, Liftoff’s were 13%. We estimate that AppLovin’s margins will surpass 40% when they report FY25. That’s an insanely productive business model and an incredibly rare operating margin. With that in mind, we set Liftoff’s NOPAT margins to gradually grow to 40% by 2030.

• Liftoff’s current ROIC sits somewhere around 24%. Applovin’s ROIC is much higher at 73% (note that Google and Meta’s ROIC are ~45% and ~40%, respectively). Let’s keep our forecast picture positive and assume Liftoff’s ROIC will reach 50% by 2030.

With just these few value drivers in mind, we derive a 5-year forecast of invested capital, net investment, and end up with a sense for operating free cash flow creation.

Capital Structure and Cost of Capital

AppLovin’s capital structure is 95% equity and 5% debt. What’s more interesting is how it trades at a very high 2.5 beta, resulting in a high cost of equity of around 14%.

We noted above that Blackstone reported valued Liftoffat $4B. If we assume their valuation is enterprise value with $1.8 billion in debt, then the equity portion in 2025 would be 55% and the debt at 45%.

As noted above, post-IPO funds will be used to pay down the debt, so we assume 2026 equity/debt portions will be more like 90/10 and tend toward a normalized equity portion over time. We also assume the beta for the sector will move down to 1.3 over time, which is highly favorable to our equity valuation for Liftoff.

Valuation Stack: Given everything presented so far, we end with the following valuation stack for Liftoff.

• $5.3 billion in operating value

• Plus $14 million in excess cash today and $93 million in net operating loss (NOL) carryforwards gets us to an enterprise value of $5.4 billion.

• Less current debt and debt equivalents, resulting in $3.1 billion in equity value or $29/share based on 106 million common shares.

As we mentioned above, “We have a feeling the IPO will likely trade higher than our estimate”. Here’s why…

If investors simply apply AppLovin’s EBITDA or revenue multiple to Liftoff’s FY25 revenue, you could see an IPO equity value around $5.4 billion.

Disclaimer: This post, and any other post from Quo Vadis, should not be considered investment advice. This content is for informational purposes only. You should not construe this information, or any other material from Quo Vadis, as investment, financial, or any other form of advice.

Last Media Dollar “Live” is coming to NYC: February 25, 2026. Following a wildly successful London debut, Last Media Dollar “Live” comes to New York City on February 25 at the Roxy Cinema in Tribeca. This is not a panel, it’s a live, on-stage decision-making experience where senior practitioners and the audience debate assumptions and reveal how media choices are actually made under real constraints, in real time. Seats are very limited and moving quickly. Secure your ticket now if you want to be in the room where the thinking happens.

Get Your Seat To The Fun Today!

Advertising Economic Forum Returns to New York City: March 18–19, 2026.
This year features an expanded two-day format focused on where media capital, ad innovation, and financial strategy are heading. Day 1 at Horizon Media features eight AI founder pitches to our investor panel with live debate across agentic commerce, creative, and emerging agency models. Day 2 at The New York Times Center convenes 400 senior leaders for closed-door sessions examining market structure, incentives, and the economic forces reshaping advertising. Early bird registration is still open for now; space is limited.

Get Your Spot Today

WEBINAR RECORDING: Inside C Wire’s AI Stack, How In-House Tools Are Creating Superhuman Productivity

On one hand, adtech companies can layer AI on top of legacy workflows and get incremental gains. On the other hand, “AI-native” companies redesign processes end-to-end on their own, resulting in leaner and faster organization.

Last week, Quo Vadis had a detailed discussion on how Rui de Freitas (CEO/co-founder of C Wire) has replaced fragmented SaaS tooling (external email, CRM, marketing apps, etc.) with purpose-built, lightweight, and internally-built systems that the company fully owns and tweaks regularly.

Our conversation tied performance culture and outcomes. For example, as AI absorbs “busy work,” employees and teams are increasingly evaluated on demonstrable impact rather than activity. The best example of this is how Andy Jassy is handling it at Amazon.

“To kick start 2026, Andy has asked his corporate workforce a very simple question: What did you do last year?”

In essence, Amazon is asking its workers to list three to five accomplishments that show the best things they achieved at the company in 2025, where accomplishments are defined as specific projects, goals, initiatives, or process improvements that show the impact of their work. If you’re just listing “busy work” that AI can do, that’s not enough. Newer companies like C Wire are thinking the same way and acting on it very early in their existence.

We discussed two concrete examples anchor our discussion.

1. We covered C Wire’s internal events/meeting setup tool used to operationalize conference presence (tables, availability, duties, booth workflows), including a persona-style interactive experience built quickly due to falling development costs.

2. We also dug into C Wire’s internal email/CRM layer designed around its specific customer and supplier world, which categorizes inbound messages, proposes tasks, standardizes follow-ups, and creates a foundation for richer automation with LLM-driven agents.

It’s All About Net Revenue per Employee

When we stack up our AdTech10 by age and filter out net revenue for each company on an ex-TAC basis (TTD, CRTO, DSP, MGNI, PUBM, DV, IAS, TBLA, TEAD, and RAMP), we can visualize how net revenue per employee grows over time.

By the 12th year of life, an average pre-AI adtech company should be $363K/employee. MarTech is a bit lower at $241K. In the future (e.g., now), investors expect a meaningful AI-based expansion in revenue productivity, a much higher hurdle rate on revenue productivity, and much better financial performance relative to other investment alternatives.

How Investors Think About AdTech

Although investors might not verbalize it, this is how they think about the intersection of adtech and investing criteria.

Consider a 5-year old adtech company that processes gross ad spend, collects a take rate, and earns $10 million in net revenue.

• In the old paradigm, the cost of revenue expectation would be around 12%. In the new AI paradigm, let’s say it’s 5% because AI computes more efficiently than before.

• Let’s also say investor expectations on revenue per employee have doubled from $150K to $300K, cutting required headcount in half.

• Let’s increase all-in salary/benefits by 50%. The humans working in the new paradigm are top-notch, creme de la creme talent. On average, they earn more than before.

• In the old adtech financial structure, 80% of fixed costs are people costs. In the new AI workplace, it drops to 65%. On a nominal basis, non-people fixed increases because the cost of AI is not, and will not be, free. But the benefits far outweigh the costs.

• The difference in expected operating profit (EBIT) is material, increasing from 6% (C-) to 18% to (A-).

• In addition, investor expectations on capital efficiency (net revenue ÷ invested capital) will likely increase with AI in play everywhere. That means new adtech companies will generate more net revenue for the same $1.00 in invested capital. Put another way, the new paradigm adtech company needs less invested capital to generate more revenue and faster.

Bottom Line: Whether investors verbalize it or not, or perhaps prefer to simplify the world by talking about EBITDA or adjusted EBITDA, what always matters most is return on invested capital. The grand majority of older adtech companies have had difficulty breaking above 10% ROIC. Notably, the average cost of capital in adtech land is greater than 11%, which is value-destructive if ROIC is less than that hurdle rate.

For new AI adtech companies (or re-invented companies from the last adtech vintage), investor expectations on ROIC are more in line with what they get from Google, Meta, Applovin, etc. If you can’t deliver it, then why should they invest in you instead of them?

Disclaimer: This post, and any other post from Quo Vadis, should not be considered investment advice. This content is for informational purposes only. You should not construe this information, or any other material from Quo Vadis, as investment, financial, or any other form of advice.

Advertising Economic Forum Returns to New York City: March 18–19, 2026.
This year features an expanded two-day format focused on where media capital, ad innovation, and financial strategy are heading. Day 1 at Horizon Media features eight AI founder pitches to our investor panel with live debate across agentic commerce, creative, and emerging agency models. Day 2 at The New York Times Center convenes 400 senior leaders for closed-door sessions examining market structure, incentives, and the economic forces reshaping advertising. Early bird registration is still open for now; space is limited.

Get Your Spot Today

Last Media Dollar “Live” is coming to NYC: February 25, 2026. Following a wildly successful London debut, Last Media Dollar “Live” comes to New York City on February 25 at the Roxy Cinema in Tribeca. This is not a panel, it’s a live, on-stage decision-making experience where senior practitioners and the audience debate assumptions and reveal how media choices are actually made under real constraints, in real time. Seats are very limited and moving quickly. Secure your ticket now if you want to be in the room where the thinking happens.

Get Your Seat To The Fun Today!

London PubCast Interview with Revving

In a candid pub-side conversation, Revving co-founders (Chris and David) unpack a structural problem at the heart of adtech:

Long payment terms force every intermediary in the supply chain to act like a bank. Brands pay late, platforms get paid early, and agencies, DSPs, SSPs, and publishers are left financing growth with their own balance sheets.

Drawing on their backgrounds in corporate finance and private equity, the two founders explain how Revving emerged from an insight first observed in mobile gaming and later across the broader digital economy.

Real-time transaction data exists everywhere, yet funding decisions remain anchored to slow, invoice-based models.

Revving’s solution is what they call transaction-based funding. A modern evolution of invoice factoring designed specifically for digital marketplaces. Instead of waiting for invoices and reconciliations, Revving integrates directly into platforms like DSPs and SSPs, ingesting live transaction data via APIs. This approach allows Revving to underwrite credit based on actual delivery and payer quality (e.g., Publicis, Google, The Trade Desk, Magnite, etc.), not the balance sheet of a fast-growing intermediary.

Funding can be advanced daily or weekly, giving finance teams precise control over working capital without giving up equity or posting blanket security over the business. Customers can dial funding up or down as needed, use Revving alongside existing bank facilities, and keep Revving invisible to their commercial partners.

Our discussion emphasized why this model is fundamentally different from traditional receivables factoring or bank debt:

• No setup / legal fees

• No minimum utilization

• No intrusive debtor notifications

• No asset-wide liens.

Beyond capital, the founders highlighted RevHub, Revving’s unified reporting and reconciliation layer, which consolidates receivables, transactions, and payment performance across dozens of counterparties, which dramatically reduces the finance team’s overhead.

Over time, Revving’s growing dataset also improves risk models, enabling more accurate pricing and lower costs for customers.

The conversation closed on scale and strategy: Revving has raised £107m (£7m equity, £100m funding capacity) backed by global asset managers, and is expanding aggressively in the U.S.

Revving is also embedding AI and agentic workflows to keep headcount low while scaling volume. The ambition is not just to fund adtech more efficiently, but to become the agentic credit infrastructure layer the industry has been missing.

If you’re interested in learning more, reach out to David and Chris

• david@revving.io

• chris@revving.io

Disclaimer: This post, and any other post from Quo Vadis, should not be considered investment advice. This content is for informational purposes only. You should not construe this information, or any other material from Quo Vadis, as investment, financial, or any other form of advice.