The Open Internet's Efficiency Problem: How AI-Driven Curation Is Closing the Gap with Walled Gardens

For most of the programmatic era, the open internet has had structural disadvantages compared to the walled gardens.. Closed platforms know who their users are, what they watch, and what they buy, and they apply that knowledge at the exact instant an ad decision is made. The thousands of independent publishers that make up the rest of the web have rarely had anything comparable. The imbalance shows up in the money: walled gardens captured 78% of global digital advertising revenue in 2022, a share projected to reach 83% by 2027. The open question for the industry is whether that trajectory is fixed, or whether the open web can build the intelligence it has been missing.

Punit P. Shah, Director of Product Marketing at a global advertising technology platform, has spent over 15 years driving AI-powered products, platform strategy, and go-to-market execution across the technology and media ecosystem. Over the past year and a half, he led the strategy and worldwide rollout of an AI-powered programmatic curation platform built to give the supply side the real-time intelligence it has historically lacked. He was also a featured presenter at the 2025 Pollies Awards & Conference in Colorado Springs, where he spoke on winning with addressable media.

We spoke with Punit about why the open internet fell behind, what it takes to run decisioning inside a live auction, and why the next phase of programmatic advertising will be decided on the supply side.

The open internet has been losing ad share to closed platforms for over a decade. What do walled gardens actually have that independent publishers don't?

Signal and scale, applied at the moment of decision. That is the entire gap. A closed platform sees a logged-in user, their history, their context, and their likely response, and it uses all of that in the same instant it selects an ad. Nothing about that is magic. It is intelligence sitting next to inventory, with no distance between the data and the decision.

The open internet has always had the opposite architecture. Inventory lives with thousands of publishers, data lives with brands and data companies, and the decisioning lives inside buy-side platforms several hops away from the impression. Every hop loses signal and adds cost. So the open web ended up competing on volume and price while closed platforms competed on intelligence. That was never a fair fight, and the market share numbers reflect it.

You've spent the past year and a half rebuilding the supply side as an intelligence layer rather than a pipe. What does that look like in practice?

It starts with admitting that the traditional supply-side model was passive. We moved inventory, we ran auctions, and we left the thinking to everyone else. The platform we built to market reverses that. We unified inventory, data, identity, and algorithmic decisioning in a single system, so a curated deal is no longer a static list of approved sites. It is a live marketplace object that combines audience signals, segment qualification, contextual signals, and pricing logic, and it improves with every auction it touches.

The data partnerships were the foundation. We onboarded more than 300 data partners across audience, identity, and commerce signals, which lets buyers activate curated deals through any major demand platform without scattering their data across the ecosystem. My role covered the platform strategy, the product narrative, partner ecosystem development, and the global rollout across North America, Europe, and Asia Pacific. The hardest part was not the technology. It was getting an entire sales and partner organization to prove a supply-side platform as a place where decisions happen.

Walk me through the moment of the auction itself. Where does the decisioning actually live now?

Inside the auction, which is the whole point. We built a real-time infrastructure layer that lets partners deploy their own decisioning models directly into the auction path, so optimization happens while the bid request is live rather than hours later in a reporting loop. The supply side is the only position in the chain that sees the full picture at once: the impression, the page, the audience signal, and the price.

That position changes the math. When the decision happens next to the inventory, audience match rates run 30 to 40% higher than traditional demand-side targeting, and pricing efficiency improves by roughly 20% because buyers stop bidding blind. In campaign terms, public case studies on the platform documented outcomes like a 50% reduction in cost per acquisition. Those are not optimization rounding errors. They are the kind of gains that only appear when the intelligence moves to where the inventory is. This is only the starting point.

Real-time decisioning inside a live auction sounds unforgiving. What nearly broke?

Latency, first. An auction gives you milliseconds, and every model a partner wants to run has to fit inside that budget or the impression is gone. We had to be ruthless about what computes in real time and what gets precomputed upstream. The second problem was data quality. When you onboard several partners, you inherit hundreds of definitions of the same audience, and a curation platform that blends bad signals just industrializes waste.

And waste is exactly what we set out to eliminate. Only about 36 cents of every programmatic dollar reaches a publisher, with an estimated $26.8 billion lost to supply chain inefficiencies in 2025 alone. You do not fix that by adding another intermediary. You fix it by collapsing steps, which is what auction-level decisioning and audience qualification does. Fewer hops, fewer fees, less guessing. The discipline we forced on ourselves was that every layer of intelligence had to remove cost from the chain, not add a new toll booth to it.

You also spend time evaluating early-stage builders. What does that vantage point show you about where this field is heading?

Serving as a judge at the Builders of Tomorrow hackathon was a useful mirror. I assessed projects on whether the intelligence sat where the data actually lived, because that is the same architectural question my industry spent 15 years getting wrong. The strongest builders did not ask which platform to buy from. They asked where and how the decision should be made. That instinct is becoming the default for the next generation of engineers, and it should be.

Judging also reminded me how fast the tooling has moved. Teams stood up working decisioning prototypes in a weekend that would have taken a quarter to build a few years ago. The barrier to entry for intelligent systems has collapsed. What has not collapsed is the judgment about where to place and scale them, and that is what I look for, whether I am scoring a hackathon entry or reviewing a product roadmap.

Identifiers keep eroding and privacy regulation keeps tightening worldwide. Does that environment actually favor the supply side?

Yes, and I will say that plainly. The identifier era trained the industry to believe targeting is a buy-side job, because third-party cookies let demand platforms follow people across the web. That assumption is expiring. First-party data lives with publishers. Consent is collected by publishers. The durable signals that remain, context, attention, and commerce, sit closest to the supply side. The architecture is rebalancing toward where the data is born.

The platforms that survive the next 5 years will be the ones that embedd decisioning at the moment of the auction instead of bolting identity patches onto a model regulators are dismantling. We designed for that from the start: hashed identifiers, consent-based data, and first-party activation frameworks that reduce reliance on third-party cookies. Privacy law is not the threat to the open internet. Architecture built for a cookie world is.

What still has to happen for the open internet to genuinely close the gap?

Transparency and proof. Buyers need to see exactly what a curated deal costs, which signals power it, and what they received for the fee, because the open web cannot ask for budget on trust alone. The technology argument has largely been won. The accountability argument is still being made, and supply-side platforms are leading it rather than wait for buyers to force it.

The scale is coming either way. Programmatic is on track to account for 90% of worldwide display ad spending in 2026, which means the question is no longer just whether automation wins but whose intelligence runs it. My plan for the next 2 years is concrete: deepen the data partnerships, open the decisioning layer to more partner models, and keep publishing performance evidence in public case studies so the results can be inspected rather than asserted. As the open internet competes on intelligence instead of price, the gap with walled gardens stops being structural. It becomes a contest, and contests can be won.