Podcast episode

'Models Built For Me'

by Ken Rona ·August 6, 2026 ·AdExchanger Talks

ai-in-adtech dsp measurement programmatic ssp

TL;DR

Allison Schiff of AdExchanger interviews Ali Manning, COO and co-founder of Chalice AI, about building custom bidding models for advertisers that bypass generic DSP (demand-side platform) optimization. The episode covers Chalice's near-death experience as a startup, a concrete Bayer/One A Day case study, a live containerized bidding pilot with Index Exchange, and why Manning thinks the whole "outcomes" category label has become meaningless.

What was covered

Chalice AI's core value proposition: Rather than letting a DSP's black-box algorithm optimize for generic signals (clicks, viewability, last-touch CPA), Chalice builds brand-specific ML models trained on a brand's own data and true business goals, then deploys those models inside DSPs and walled gardens including Amazon and Meta.
Bayer / One A Day case study: Over 80% of One A Day vitamin sales happen in-store, not online, making standard e-commerce DSP optimization irrelevant. Chalice used a panel-based offline-sales measurement partner to model "new-to-brand" shoppers and pushed higher bids for those audiences into Amazon DSP, producing a measurable lift in new-to-brand purchases — a KPI a CEO or CFO would actually recognize.
Startup near-collapse: Chalice nearly ran out of money after Peloton (an early major client) pulled back post-pandemic, and a planned Series A fell apart. The company was saved by a mix of stretched personal savings, a bridge round partly funded by an employee's eight family members, and a single large customer scaling its budget roughly 10× — enough for Chalice to become profitable without venture funding.
Containerized bidding pilot with Index Exchange: Chalice and Index Exchange CEO Andrew Casale co-developed a setup where Chalice's ML model runs inside Index Exchange's cloud infrastructure, giving it access to the full bid stream (not a DSP-sampled fraction) and URL-level (page-level) prediction granularity rather than domain-level. Manning says this surfaces undervalued inventory and lowers effective CPMs.
IAB Tech Lab's Agentic Real-Time Framework (ARTF): Chalice is a founding partner. Manning sees this containerized standard as the architecture that lets brand-specific AI agents operate across supply environments without being locked into a single DSP — and notes some DSPs are already exploring equivalent functionality.
ID-free signal strategy: Manning drew an analogy to Meta's post-ATT (Apple's App Tracking Transparency, which limits cross-app tracking) pivot: losing ID-based signals forced Meta to build better contextual/behavioral models, improving prediction accuracy. Chalice argues programmatic has over-indexed on IDs while ignoring valuable "exhaust" data — contextual, URL-level, and cookieless signals — that partners sometimes offer for free.
Client selection discipline: With a 50-person team, Chalice actively turns away clients that cannot articulate a concrete success metric. Manning argues that vague goals ("we want sales and also viewability") lead to bad outcomes, wasted team cycles, and reputational damage for the category.

Notable claims & predictions

Ali Manning on Google's antitrust ruling: "Mostly for those who already were leaning away from Google" — implying the remedy will have limited practical effect on advertisers who haven't already diversified.
Ali Manning on Meta's post-ATT model: "Losing the ID actually made them a better predictive engine … essentially they become 10 percent more capable of predicting than when they were only doing the ID." Manning attributed this to a published paper by Chalice co-founder Adam (last name not given in transcript), released around the time of recording.
Ali Manning on the "outcomes" label: "Outcomes businesses are really just like arbitrage businesses guaranteeing things like CTR and CPA" — arguing the term has been captured by low-quality vendors and should be abandoned.
Ali Manning on curation: "It's a new fancy way to say ad network … curation is like the delivery mechanism." She acknowledged real innovation is possible but positioned curation primarily as a packaging layer rather than a fundamentally new approach.
Ali Manning on Google's monopolistic behavior: Described celebrating the bundling of YouTube inventory with DV360 (Google's programmatic buying platform) as a go-to-market win while at Google — and said she only later recognized it as the kind of tying behavior scrutinized in antitrust proceedings.
Ali Manning on programmatic's original promise: The containerized, URL-level, full-bid-stream architecture is "another way to talk about the promise of programmatic … what programmatic promised from the beginning but then kind of lost its way."

Fact check

Manning's claim that Meta became "10 percent more capable of predicting" after losing ID signals (post-ATT): Manning herself flagged uncertainty — "I've read the exact number but it's something like essentially they become 10 percent more capable." She attributed this to a paper published by Chalice co-founder Adam around the time of recording. The specific figure is unverified from this transcript; no external paper, author, or publication is named precisely enough to check. Listeners should treat the number as illustrative rather than established. Note also the incentive misalignment: Chalice profits from the argument that ID-free prediction is viable, so the claim is being made in a context where Manning has a commercial reason to assert it.
Manning's claim that "over 80% of One A Day sales still happen in stores": Plausible for a mass-market vitamin brand with wide brick-and-mortar distribution, but this is unverified from the transcript alone — presented as a Chalice client fact rather than a sourced industry figure. It is not implausible, but listeners should note it comes from Chalice's own case study materials.
Manning's anecdote about Google telling P&G's CMO (Mark Pritchard) to "go take it" / "we're not going to customize our products for you": This is presented as a personal recollection of internal culture, not a documented statement. It is unverifiable and reflects Manning's characterization of events she witnessed; Mark Pritchard's actual views on Google's responsiveness have been publicly critical in other forums, which is consistent but does not confirm this specific exchange.
No other claims rise to the bar of provably false. The rest of the episode's factual assertions are either clearly labeled as personal experience, plausible industry observations, or product descriptions that cannot be independently verified from this transcript.

Why this matters for ad-tech operators

DSP disintermediation is getting concrete, not just theoretical. The Chalice/Index Exchange containerization pilot is a live example of brand logic running at the SSP layer, bypassing DSP pre-filtering entirely. If the IAB's ARTF gains traction, DSPs face real pressure to either support external model injection or lose budget share to architectures that route around them. Publishers and SSPs should watch this as a potential source of incremental demand.
The "outcomes" framing is collapsing under its own weight. Manning's critique — that "outcomes" now means CTR and CPA arbitrage rather than business KPIs — reflects a broader buyer-side frustration. Agencies and brands rebuilding measurement practices around incrementality and offline lift will increasingly reject vendors who can only report on click-based proxies; this accelerates demand for clean-room and panel-based offline measurement integrations.
ID-sparse inventory is underpriced and underserved. Manning

Full analysis

Ali Manning, COO and co-founder of Chalice AI, sat down with AdExchanger's Allison Schiff to argue something that ought to worry every DSP running an off-the-shelf bidder: the brand-specific model belongs inside the pipes, not on the DSP's terms. The claim underneath the whole conversation is that generic optimization, clicks, viewability, last-touch CPA, is the wrong target, and that a model trained on a brand's actual business goal can run at the SSP layer and route around the DSP entirely.

What's being decided (briefing frame): whether operators should treat containerized, custom bidding, a brand's own ML model running inside supply infrastructure with full bid-stream access, as a real architectural shift or a founder's pitch. Reversibility is Type 2 for most readers. You can pilot this without betting the company. The forcing function is the IAB Tech Lab's Agentic Real-Time Framework (ARTF), which gives the idea a standards home and a reason for DSPs to respond.

The Market Analyst. Follow the money and the layer it sits at. Andrew Casale's insight, that the full bid stream and URL-level signal live at the SSP, not the DSP, is a claim about where value accrues next. If brand logic runs inside Index Exchange's cloud, the SSP captures a function the DSP used to own. In plain terms: the middleman that decided what to bid on may get thinner. That favors SSPs (Index Exchange, PubMatic, Magnite) and pressures DSP margins. But note the tell: Manning also says "some DSPs are already exploring equivalent functionality." The incumbents don't need to lose. They need to open the box. The Trade Desk absorbing external models is the more likely equilibrium than The Trade Desk getting disintermediated.

The Skeptic. Manning profits from every sentence she said. The "Meta got 10 percent better without IDs" number is one she couldn't source, attributed to a co-founder's paper she half-remembers. The 80% in-store figure for One A Day is Chalice's own case-study material. The anti-curation line, "a new fancy way to say ad network," is convenient for a company selling the thing that isn't curation. For this to work, the containerized model has to beat the DSP's optimization by enough to justify running your own ML, and the only evidence offered is one Bayer case study with a favorable KPI. One win is a demo, not a category.

The Operator. Try to run this Tuesday morning. You need a brand data set worth training on, an offline-measurement partner like the panel Chalice used for One A Day, and an SSP willing to host your container. That is a lot of moving parts for a mid-size advertiser. Manning herself admits the gate: with 50 people, Chalice turns away any client that can't name a concrete success metric. That's honest, and it's also the whole game. Most brands can't. "We want sales and also viewability" is how real briefs read. This architecture works for the disciplined few and breaks for everyone else at scale.

The Customer / End User. The advertiser CMO and CFO are the point here, and Manning is right about one thing. New-to-brand purchases are a KPI a CFO recognizes. Click-through rate is not. The One A Day setup, bidding higher into Amazon DSP for modeled new-to-brand shoppers when 80% of sales are in-store, speaks the language of the person who signs the budget. That's the genuine pull. Buyers are tired of being sold click proxies dressed as business results. Whether they'll build the plumbing to fix it, or just keep complaining, is the open question.

The CFO. The economics cut two ways. Manning says the containerized setup surfaces undervalued inventory and lowers effective CPMs, which is real money if true. Against that: you're now paying for a custom-model vendor, an offline-measurement partner, and SSP-side compute, to replace optimization you already pay a DSP for. The payback only clears for advertisers spending enough that a few points of media efficiency dwarfs the added vendor stack. Chalice's own survival story tells you the scale problem: they got profitable when one customer went roughly 10x, not by signing many small ones.

The tensions.

The real disagreement is between the Market Analyst and the Skeptic on where this lands. Does brand logic at the SSP genuinely restack the value chain, or do DSPs simply add model-injection and neutralize the threat by the next renewal cycle? Both can point to the same sentence in the episode.

The second tension is the Operator against the Customer. The demand is real, CFOs want business KPIs, but the operational bar Chalice sets, refusing clients who can't name a metric, means the addressable market is narrow by design. The thing buyers want and the thing most buyers can execute are not the same thing.

Synthesis. This hinges on two beliefs. First, that full-bid-stream, URL-level prediction at the SSP beats DSP-sampled optimization by enough to matter. Plausible, but evidenced by a single case study and a vendor with every reason to say so. Second, that DSPs won't just copy the feature. On that, the episode gives the answer away: they're already exploring it. The council leans toward "real shift in where models can run, absorbed by incumbents rather than fatal to them." The winners are SSPs, who get a new reason to hold bid-stream data close, and disciplined large advertisers with clean offline measurement. The losers are pure "outcomes" arbitrage vendors guaranteeing CTR and CPA, whom Manning is right to call out.

Before committing, an operator should verify the one thing the episode can't: get a second containerized pilot's numbers, ideally your own, and compare effective CPM and a real business KPI against your current DSP setup. One Bayer result doesn't clear the bar.

Prediction: By the IAB Tech Lab's ARTF progress update at the next major industry milestone (Programmatic IO or an IAB Tech Lab release by mid-2027), at least one major DSP will publicly announce support for external or containerized model injection rather than ceding the function to SSPs.

Confidence: Medium. The episode says DSPs are already exploring it, and incumbents copy before they concede.

Why: Manning states directly that some DSPs are already building equivalent functionality, which means the capability is in motion inside the incumbents, not just at Chalice and Index Exchange. The pattern in ad tech is that platforms with distribution absorb threatening features rather than watch budget route around them, and a DSP that lets a brand run its own model keeps the seat even if it loses the optimization monopoly. The opposite outcome, DSPs standing pat while SSPs quietly capture the bidding-logic layer, is less likely because it would mean the largest buy-side platforms ignore a standards effort they can see forming and let their core function erode without a response.

Revisit by 2027-06-30: We're right if a top-tier DSP publicly supports external model injection or an ARTF-style container. We're wrong if no major DSP has announced such support and the capability remains confined to SSP-hosted pilots.

Listen to the episode →