The Signal

What changed.

AI and the ad platforms keep marching toward full automation. Performance Max, Advantage+, Andromeda, GEM: the bidding, targeting, and budget work media buyers once did by hand is being automated toward zero, and Meta says that by the end of 2026 you'll just hand over a budget and a goal.

The Hypothesis

The emerging thesis we're exploring.

The machine ate the low-leverage work: the manual bids, the keyword wrangling, the endless micro-optimizations. So what’s left? The hard stuff: narrative, creative, data, measurement, behavioral science. In other words, the stuff that's always mattered most in advertising.

So no, AI didn't commoditize paid media, and it sure didn't make cracking it easier. If anything, the bar has risen enormously. The companies that bought the commodity story, that figured they could hand Meta the keys, churn out a pile of creatives, and sit back while the money printer hums, are getting crushed. The winners are taking the opposite approach: they invest in deep expertise and treat paid media like the high-stakes discipline it's always been.

The Big Picture

The structural shifts behind the hypothesis.

1. Hard and high-leverage were never the same thing. Yes, manual bidding and targeting optimizations were nonstop, tedious work. And yes, the algos largely handle those now. But being really good at those things was never a major edge to begin with. At most, a band-aid solution. For example, for big spenders, ad ops could be a real edge over equally funded but sloppier rivals. But it was a secondary edge, and it covered for weak fundamentals.
2. Shared automation is nobody's advantage. When every advertiser runs the exact same automated baseline, that baseline can't be your edge. Everyone has it. The advantage moves to what can't be copied out of the box: your strategy, your data, your judgment, your storytelling. Automation didn't kill the edge. It just freed up more time for advertisers to invest even more in the areas that have always been responsible for real advantages.
3. "All that's left is creative" undersells it too. Creative matters enormously, but it's just one pillar. The best advertisers also know how to obtain and feed the algorithms proprietary data, measure true incremental impact, and craft beautiful stories & experiences across the full customer journey. Concluding that “creative is all that matters” is honestly just a confession that someone has no clue what it actually takes to win in advertising.
4. The result is a moat, not a commodity. Automation wiped out the small edges that let weaker advertisers squeak by. The gap between the best and the rest didn't shrink, it widened, in a pretty massive way. The top operators are sitting on a moat they may not have had in years, maybe ever, precisely because the cheap ways to brute force it are gone.
5. In the wild, the mistake looks like outsourcing your growth to the lowest bidder. Convinced it's all automated now, we’re seeing so many founders treat paid media like a cost center. They’ll compare a seasoned operator at $10k/mo versus someone with two years' experience at $3k, and they pick the $3k because "how hard can it be, the AI does it." For a startup, that's not too different than delegating product-market fit to an intern. Growth is the highest-stakes thing you do, and they're trying to spend as little as possible on it. Then the ads don't work, they take to LinkedIn to declare paid media a scam, and the cycle repeats. Bad inputs, bad outputs. Tale as old as time.

What's Working

Tactics, experiments, and observations from the frontier.

• Stop trusting the ROAS your platforms report. Every platform grades its own homework, and it grades generously. Across 299 brands, Google brand-search ROAS collapsed from 19x to 5.7x once anyone bothered to measure incrementality. The sharp operators run holdouts to see what their ads truly caused: Advantage+ over-reports against a manual control, and blended efficiency, total revenue over total spend, never runs through platform attribution at all. Knowing your real numbers is the unglamorous work most teams skip.
• Data engineering is the lever, not the pixel. The algorithm is only as smart as the signal you feed it, and that signal is yours to control. Feed it closed-won revenue, not form-fills, so it chases customers who pay. Send it a lead-quality score so it bids toward your hot leads, not your tire-kickers. Feed it item-level margin so it stops happily scaling your thinnest products.
• The click is the start, not the win. Everyone fusses over the ad, but the ad's only job is to earn the click. The landing experience carries far more of the conversion, and the recent obsession with creative volume isn’t helping. Match the page to the buyer's stage, and if you're running ten ad angles, give each its own page instead of dumping them all on one generic PDP. What good is high-velocity creative testing when you're sending traffic to a generic landing page that hasn’t been updated in 12 months (and was probably built in 30 mins with AI)?
• Budget by the next dollar, not the blended average. A healthy account-wide ROAS hides which dollar is doing the work. Spend toward the next dollar's return, not the average, and move your efficiency target when you discount, because a 20% promo can put you underwater while your team is celebrating a "high ROAS."
• The leverage is the judgment. AI will hand you infinite creative; deciding what to make and having systems in place to learn why it worked is the part it can't do. Read hook rate against hold rate to know whether to fix your opener or your body. Learn which attributes drive performance, not just which ad won. And predict fatigue before your winner face-plants, not after. The render got free. The judgment got valuable.

Frontier Players

The people and teams building at the edge.

• ​Taylor Holiday: Founder and CEO of Common Thread Collective, and the guy who'll gently remind you that ROAS doesn't pay the bills, contribution margin does.
• ​Cody Plofker: CEO of Jones Road Beauty, and a rare example of a brand-side exec willing to tell you what's working instead of guarding it like a state secret.
• ​Curtis Howland: Independent operator running $30M+/yr in Meta spend who, unlike most people at that level, writes it all down. His "Christmas Tree" attribution framework (blended MER on top, MMM in the middle, multi-touch at the bottom) is worth the subscribe alone.
• ​Sarah Stemen: Google Ads expert and President of the Paid Search Association. While everyone obsesses over Meta, she's mastered the data side of search.
• ​Dara Denney: Paid social creative strategist who treats creative like a system, not a crapshoot. Also just a really great teacher.
• ​Raphael Paulin-Daigle: Founder of SplitBase, and one of the few people obsessed with the post-click, landing pages and CRO, aka the half of paid media everyone seems to forget exists.
• ​Dave Rigotti: Founder of Inflection, ex-Bizible, Marketo, and Adobe. One of the deepest pedigrees in B2B attribution, tying ad spend to real pipeline instead of vanity metrics.
• ​Michael Kaminsky: Recast co-founder and the internet's most patient explainer of why your platform dashboards are lying to you. Marketing mix modeling, minus the hand-waving.
• ​Olivia Kory: Chief Strategy Officer at Haus and lead author of its 640-experiment Meta study. If you want to know what's actually incremental, not what the dashboard claims, she's done the homework.
• ​Amanda Natividad: SparkToro's Chief Evangelist and the person who coined "zero-click marketing." Her whole thing: deeply understand your audience before you spend a dollar trying to reach them.

The Stack

The tools doing the heavy lifting on the frontier.

Measurement & Data

• ​Recast: Bayesian marketing mix modeling for people who'd rather measure weekly than wait on a quarterly report nobody reads.
• ​Haus: Geo-holdout incrementality at scale. They ran 640 Meta experiments so you don't have to guess whether your ads did anything.
• ​Prescient AI: MMM built around cross-channel halo: how spend on one channel drives sales on another.
• ​Lifesight: Unified MMM, incrementality testing, and geo experiments for omnichannel brands juggling more channels than any one model can handle.
• ​WorkMagic: Triangulated measurement, geo incrementality checked against multi-touch attribution instead of trusting either one alone.
• ​Paramark: AI-assisted measurement, forecasting, and planning for where the next dollar should go.
• ​Converge: Server-side tracking plus agentic media buying. Closes the conversion-data gap that's been making your reporting fib.
• ​Fairing: Post-purchase surveys that ask the one thing your pixel can't: how did you actually hear about us?
• ​Google Meridian: Google's open-source MMM. Free, transparent, and refreshingly not a black box you rent by the seat.

Creative, Testing & Research

• ​Motion: Ties ad-level performance to creative attributes, so you learn which hooks and angles work instead of arguing about it in a meeting.
• ​Foreplay: Save, organize, and study competitors' ads. Professionalized swiping.
• ​Atria: Find and develop ad ideas by hook, angle, format, and creative pattern.
• ​Marpipe: Multivariate creative testing, with performance broken out by individual element.
• ​Intelligems: Test pricing, offers, and landing pages on Shopify, and find out whether the offer was the real lever all along.
• ​Particl: Competitive intelligence on what your rivals are selling, pricing, and pushing this week.
• ​Clay: Research accounts, enrich leads, and build audiences from your own data. The tool every growth nerd won't stop talking about, this time with reason.

Conversion & Lifecycle

• ​Mutiny: Personalizes landing pages by audience and account, so cold and warm traffic don't land on the same generic page.
• ​Replo: No-code Shopify landing pages, so you can ship a page per ad angle without opening a ticket with engineering.
• ​KnoCommerce: Post-purchase surveys that capture zero-party data and fold it into channel attribution.
• ​Customer.io: Behavior-triggered lifecycle messaging across email, SMS, push, and in-app.
• ​Churnkey: Helps subscription businesses claw back cancellations and failed payments, the revenue you already earned and nearly lost.

B2B Revenue Journey

• ​Dreamdata: Maps the full B2B buyer journey across ads, site, CRM, and revenue, the stuff that takes six months and nine touches to close.
• ​HockeyStack: Connects marketing touches to accounts, pipeline, revenue, and product usage.
• ​Factors.ai: Shows which accounts are circling a purchase before they ever fill out a form.
• ​Inflection: Sends product-led B2B companies the right message based on what users do in the product.

Signal vs Noise

The counter-arguments, and whether they hold up.

💨 Noise: "AI automated paid media, so it's basically a commodity now."

🎯 Signal: It automated the operational layer, the bidding and optimization grind. Real work, sure, but never the work that won or lost a channel. Calling the whole discipline a commodity because its most tedious layer got automated is like calling cooking a commodity because someone invented the dishwasher. The parts that decide the outcome (and always have) are still entirely in your hands. And those are the only things left to compete on.

💨 Noise: "All that's left is creative, so just pour everything into ads."

🎯 Signal: Creative is one lever, not the whole machine. A brand with sharp creative and broken measurement is still lighting money on fire, just looking good while they do it. The teams winning right now pair creative with proprietary data the algorithm can't get anywhere else, and measurement that tells them what's driving growth.

💨 Noise: "Ads just don't work for startups anymore."

🎯 Signal: They do. We have a whole roster of startup clients to prove it. Interestingly, at least to us, we can get prettttyyyy close to predicting whether or not a startup will succeed with paid marketing within the first 30 minutes of meeting them. And it has nothing to do with the fundamentals of the business and everything to do with the mindset of the founder. Founders coming in with the whole commodity mindset, expecting us to be able to “crack ads” within 30 days, are typically doomed from the start. The ones that understand the stakes, the relation a channel has with the rest of their company (channel/product fit, model/channel fit, etc.), and are willing to be patient and put in the work, their success rates are drastically higher.

Rabbit Hole

Go deeper.

• ​The new PPC playbook: from media buyer to profit engineer: Jeff Baum on why the platforms run the tactics now, so your value moves to data strategy and owning the P&L. The clearest single statement of this issue's whole thesis.
• ​The future of media buying: Mike Shields' zoom-out on how, as the AI giants automate buying, human value concentrates in strategy and first-party data.
• ​Brand vs. direct response: advertising's Achilles heel: Preston Rutherford (Chubbies co-founder) on why performance-only marketing reaches just the ~5% of buyers already in-market, and why brand is the lever that reversed Chubbies' rising CAC.
• ​Stop judging creatives by ROAS at first sight: Joy Badaaftu on why a single platform metric is a terrible creative judge, with a "losing" ad that quietly drove $70k+.

Wrapping Up

The smart crowd will tell you paid media has never been more competitive or expensive. They're right. But the conclusion they draw, that it's therefore not worth it, is exactly backwards. It's close to zero-sum, and as the whole game shifts to distribution, the rewards for being the best compound. Every competitor who pulls back because it's hard is just handing you more of the moat.

If reading this made you realize you've been treating the highest-stakes part of your business like a commodity, well, that's the gap we built DC to close. Let's talk.​

Thanks y'all!