The AI-Powered Shift From Media Buying to Media Performance

Q1: VideoAmp describes itself as a media performance platform, not just another point solution. What does that architectural decision actually mean — and why does it change what's possible for your customers?

It starts with the data. Every time content is streamed, it leaves a footprint on what was watched, when, and for how long. Second-by-second, device-level, census-scale data. That's the foundation. When you combine that with large-scale identity data, you get accurate reach and outcome measurement across publishers. Add co-viewing and out-of-home, and you have a unified picture across linear and digital. That's the infrastructure layer and until now, that hadn’t been available to advertisers and publishers.

What we did differently is build the full stack on top of that foundation. Publishers use VideoAmp to organize inventory, forecast revenue, measure campaigns, and serve ads. Agencies use us to plan, buy, optimize, and measure across publishers. All of it running against the same dataset and identity graph.

That architectural decision is what makes AI actually useful here. When everything shares the same foundation, AI can turn what used to be a fragmented, multi-step process into one continuous workflow — optimizing decisions in real time, learning from every campaign, and eventually underwriting outcome guarantees. That's not possible when you're stitching point solutions together because the data layers don't match, the identity doesn't reconcile, and the AI has nothing reliable to learn from.

For VideoAmp, the hard part is done. A lot of companies are still trying to figure out all of these layers that we’ve already put the work in over the years. Now we get to focus on what you can actually do with it.

Q2: What's fundamentally changed in the last few years that makes a unified, AI-driven workflow actually achievable now?

Two things have changed, and they work together.

The first is data. As mentioned, we now have second-by-second, device-level, census data at the scale needed to power every stage of the workflow. From planning, buying, optimization, measurement– all running against the same underlying asset. That's genuinely new, and it's what makes closing the loop between spend and outcome operationally possible rather than theoretical.

The second change is what you can do with that data now that LLMs exist. This is where things get interesting.

The media planning and buying process is complex. Most people operating in it, even experienced buyers, don't always know exactly what to ask for or how to ask for it. They know they want better performance, but the path from that goal to the right set of decisions across publishers, audiences, formats, and timing is not obvious. Historically, that complexity lived in the heads of experts, or got lost in the gap between systems.

LLMs trained on the context of this data make what should feel like magic possible. A system that understands what you're trying to accomplish even when you can't fully articulate it, and guides you through the process of getting there. Not a dashboard or a reporting tool, but an experience that reasons with you, surfacing what matters, so you can make better decisions at every step.

That's what we're building– an AI-powered platform that understands your campaign goals better than any single tool has before, and it should do better than you'd do on your own– not by replacing your judgment, but by elevating it.  LLMs let put an intelligent, agentic experience on top of the planning, optimization and measurement solutions we’ve already built. Turning infrastructure into something anyone can use, and that gets smarter with every campaign it touches.

The combination of census-scale data plus AI, that actually understands what to do with it, is what makes this moment different from every previous one.

Q3: Historically, buyers and sellers have operated with different data and different incentives. How does shared infrastructure change that dynamic and what does real alignment look like in practice?

The misalignment was never really about incentives. Everyone wants campaigns that perform. The problem was that buyers and sellers were literally working from different data. A seller reported on impressions served. A buyer measured what converted. Those two numbers came from different systems, different methodologies, different moments in time.

Shared infrastructure changes that at the root by making it so there's only one version of the data to begin with.

That's what makes our position unusual. Publishers use VideoAmp to organize their inventory, forecast revenue, measure campaigns, and serve ads. Agencies use VideoAmp to plan, buy, optimize, and measure across those same publishers. Both sides are operating on the same platform, against the same data, built on the same identity graph. The shared truth isn’t negotiated, it’s structural. It's not that we've convinced buyers and sellers to look at the same number. It's that there is only one number.

And that foundation is what makes everything else possible. When a buyer and seller are planning and measuring against the same census-scale, second-by-second data, there's no ambiguity. Guarantees become operationally possible because the line from "here's what we projected" to "here's what we delivered" is unbroken. Outcome-based deals become credible because both parties can see exactly what happened and why.

Real alignment in practice looks like this: spend that a CFO can defend, inventory that a publisher can price with conviction, and a campaign that both sides agree performed, or didn't, because they're both looking at the same truth.

Q4: You've spoken about a future where guaranteed outcomes become the standard, not a premium add-on. What needs to happen for the industry to get there?

The industry has been talking about outcome-based guarantees for years. The reason it hasn't happened at scale isn't lack of interest, it’s because the technology to actually underwrite a guarantee didn't exist.

Three things have to be true to make a guarantee possible. You need census-scale data. You need that data to be the same data driving every stage of the workflow, from planning through measurement. And you need a system intelligent enough to optimize in real time, learning from what's happening, and correcting course before a campaign goes off track.

That third piece is what LLMs make possible in a way nothing before them did. A large language model trained on the full context of this data, including the history of campaigns, the performance patterns across publishers, audiences, formats, etc., doesn't just report on what happened. It understands why it happened. It can optimize decisions in real time, learn from every campaign it touches, and get progressively better at predicting and delivering outcomes. It self-corrects. That's what makes it possible to underwrite a guarantee rather than just promise one.

But here's what I think people underestimate about where this is going. The path to guaranteed outcomes isn't just a technology argument, it's a trust argument. And trust gets built through experience. You try a system like this. It improves your return on ad spend. You try it again. It improves again. Over time, the performance compounds and so does the trust. That's how any powerful technology gets adopted at scale.

What makes our platform different is that the foundation for all of this is already built. The data, the identity graph, the full-stack workflow for both buyers and sellers. The LLMs have something real to learn from and act on. So when the system optimizes a campaign, it's working from the deepest, most comprehensive view of video advertising that exists. That's what makes the guarantee credible. 

Q5: Streaming has introduced what amounts to unlimited ad supply compared to traditional TV. How does the industry avoid a race to the bottom on pricing and where do outcomes fit into that equation?

The supply problem in streaming is real. When inventory expands without a consistent link to performance, pricing pressure is inevitable and self-reinforcing. Supply that can't prove its value drags CPMs down across the board, including content that genuinely deserves a premium. The market has no reliable mechanism to separate inventory that drives results, from inventory that simply adds volume.

A true outcome accounts for everything that went into it: the quality of the content, the relevance of the creative, the precision of the placement, the price paid for the impression. All of those variables collapse into a single signal–  did this investment deliver a result or didn't it?

That's a fundamentally different basis for pricing. CPMs are a proxy,measuring the opportunity to be seen. Outcomes measure whether something actually happened. When you transact against outcomes, you're no longer debating whether premium content deserves a premium price because the performance answers that question directly. Good content will prove its value. Inventory that can't prove its value will price accordingly.

This is why outcomes are so important for the long-term health of streaming. They create the economic conditions for premium content to be properly monetized. When publishers can demonstrate that their inventory drives real results, they can defend premium pricing with evidence rather than reputation. And when premium content is properly monetized, it supports the investment required to keep creating it. That's the virtuous cycle the industry needs.

Q6: Where does VideoAmp go from here? What does the next phase of the platform look like?

The hardest part is done. Most companies are still trying to figure out the data layer, the identity layer, the privacy infrastructure, let alone stitching all of that together. We figured that out, now we get to focus on what you can actually do with it.

The AI we're layering on top has a complete view of the entire media workflow, across both sides of the market, from a single data foundation. That's a system operating at a level of context no fragmented stack can match. It learns faster, corrects more precisely, and compounds in value with every campaign.

That's what makes outcome guarantees achievable. Not ambition, but infrastructure.