AI x Marketing engineer x Performance ads on Google/Meta since 2014. Running Claude Code x Codex agents non-stop. Founder @ https://xitter.cf/t.co/5xwcG3a8LL
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Joined January 2010
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New brand. Empty ad account. Full deployment by the end of the day.
Most agencies' onboarding takes 2-3 weeks.
Ours takes one day.
This is what it looks like when every step - research, feeds, ads, landing pages, creative - runs through AI agents built specifically for the job.
By the team that embraces the speed of an AI agentic system.
The video walks through every screenshot I sent the client as the day unfolded.
Research.
Deep brand analysis. Keyword research across every product angle.
Competitors pulled on both Google and Meta - their ads, their landing pages, their keyword coverage, their gaps.
The system runs 21 research queries per market and maps them into 380-620 angle variations. Every angle scored for volume, competition, and conversion potential.
Feed strategy.
The product catalog is segmented into 13 separate feed strategies.
Not 1 generic feed. 13 - each built around a different purchase intent.
Every feed is rewritten from scratch:
- Titles (140-150 chars, front-loaded with keywords)
- Descriptions (800-1,100 chars, unique per variant)
- Product highlights
- 30+ other GMC attributes
Each feed is validated against 152 quality checkpoints before it touches Merchant Center. Anything below threshold gets auto-fixed and re-validated.
All 13 deployed to GMC through API. This used to take us a full week. The systems brought it down to hours.
Pre-sell and offer pages.
For top products, AI agents build full landing pages from scratch - unique to each angle and audience intent, not templates.
- Angle discovery through keyword research and SERP analysis
- 2,000-3,500 words of conversion-optimized copy per page
- Full HTML/CSS/JS build with scroll animations
- Lighthouse speed scores above 90
- Deployed to Shopify or Cloudflare through API with SSL, CDN, and cross-domain tracking
Offer pages follow the same pipeline - bundle strategy, pricing psychology, tier selection, and direct Shopify cart integration.
Campaign architecture - everything that went live on day one:
- Brand campaigns - product URLs matched to ads matched to keyword clusters
- Cold acquisition search campaigns for top products - 3 to 6 ad groups each
- Counter-competitor campaigns built from competitor research
- Direct response campaigns for pre-sell landing pages
- 13 Shopping campaigns with separate segments for keyword data mining
- 13 PMax feed-only campaigns for performance and scaling
- Demand Gen image campaigns - AI-generated creative across 4 aspect ratios
- Demand Gen video campaigns - brand videos uploaded to YouTube, deployed as in-stream and Shorts
The Shopping campaigns are where it compounds. Search term data feeds back into title improvements and uncovers new angles for more feeds.
The system gets smarter the longer it runs.
Account-level infrastructure - sitelinks, callouts, price extensions, structured snippets, negative keyword lists, placement exclusions - all generated and deployed through API.
What makes this possible:
47 AI agent commands.
- The feed agent rewrites and validates.
- The search agent builds ads from competitor intel.
- The presell agent writes copy, builds pages, and deploys.
- The demand gen agent generates images across 5 AI models and uploads campaigns.
Each agent runs 1,500-2,000 lines of instructions plus Python scripts deploying through API.
While they handle the build, I'm on tracking fixes and page tagging.
(The agents build faster than I can configure GTM. I've made my peace with that)
Hundreds of ads live from day one. Pre-sell pages deployed. Demand gen running. Sequential testing across every angle - same philosophy as massive creative testing on Meta, applied to search, shopping, display, and video.
Full walkthrough is in the video. Every step. Every campaign. Nothing edited out.
Most onboardings take 2-3 weeks.
Ours takes one day. Due to the systems we spent years building.
Imagine how a month of working on an ad account looks like at this speed.
You tested five headlines this month. Every one of them sent people to the same page.
An r/PPC thread this week nailed the problem:
• u/EnvironmentalClue405: "we're testing different angles audiences and headlines, but once someone clicks they're getting pretty much the same message."
• u/MagicalProbation4: "at some point you're not even testing the full message anymore."
• u/Busy_Leader4145: "It's like changing the front door five times but the inside of the house is still the same."
The usual fix makes it worse.
Twenty landing pages sounds great until, as u/Significant_List5074 put it, "the offer changes and someone has to go through every single one."
The best answer in the thread was boring, which is usually a good sign.
u/QuantumWolf99 runs "3-5 page templates by intent, then swap the headline and hero line from a URL parameter set in each ad's final URL. One page, twenty matching headlines, and edits happen in one place."
So the ad test only works once the page knows which ad sent the click.
TL;DR for your next ad test:
• Group ads by intent and give each intent one template.
• Pass the ad's angle in the final URL and print it in the hero.
• Judge the test on conversion rate after the click, since that's where the angle finally gets tested.
If tracking breaks on Black Friday, the fix in Google Ads has a short shelf life.
The tool is data exclusions. It tells the bidder to ignore conversion data from a period when your tags or imports were broken, so it doesn't learn from a fake slump.
Two limits from Google's help page:
• It's meant for issues you catch within about a week.
• It's not recommended for problems older than 14 days on Search, or 60 days on Shopping.
So the clock starts when you notice, and nobody notices anything on a holiday weekend.
Before Nov 27 I'd set up three things:
1. A daily check that conversions are still arriving, per campaign type.
2. One named person who can add an exclusion, with the steps written down.
3. A note of every theme or checkout app update scheduled for sale week, since that's where tags usually go missing.
Google's holiday help page says to set your budget at 3x what you expect to spend.
Not a typo. It's in the Smart Bidding guidance for Shopping and Performance Max, under Black Friday and Cyber Week.
The reasoning is headroom. If budget caps the campaign on the best day of the year, target ROAS never gets the chance to do its job.
And Google notes campaigns can spend up to 2x the average daily budget when demand jumps anyway.
What I'd actually do with that:
• Raise budgets only on campaigns where target ROAS is doing the limiting, and leave the cap as a safety rail.
• Check "Limited by budget" on the Wednesday before Thanksgiving, not on Friday at noon.
• Write down the ceiling you can truly afford for Nov 27-30, because "3x" assumes your margin agrees.
Google's page is very confident about your budget. Your CFO may have notes.
Yay awesome chat gents
🌶️ Some kind of magic happens when you have these guys on the call
@d_grimripper
@iamgalba
@KrsteKovaceski
@andreilunev
Every DTC playbook you've read was written by someone selling refills.
If your customer buys once (a mattress, a bike, a sofa), half that advice assumes an order that never comes.
The refill brand can lose money on order one, because order four pays for the ad.
You don't have an order four.
Here's where the borrowed playbook breaks:
• LTV bidding, when your LTV is your AOV
• "Break even now, profit on retention" math
• Remarketing budgets chasing people who already bought the thing
• Replenishment emails for a product nobody replenishes. Nobody restocks a sofa.
What works is less glamorous. The first order has to pay for itself, so contribution margin per first order becomes the target.
This buyer researches hard, so the feed and the landing page do most of the selling. Reviews and referrals are the closest thing you have to retention.
One number decides which playbook you're in: the 12-month repurchase rate. Founders who quote ROAS to two decimals often have never pulled it.
For a buy-once brand, the plan is three lines:
• Pull the 12-month repurchase rate before trusting any LTV in the plan
• Set targets on first-order contribution margin
• Move the "retention" budget into reviews and referrals
“At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now.”
As many people said already - service businesses that use AI, but effectively operate like - are the winners with every new model release.
Meanwhile - building AI wrappers is going to get eaten one way or another.
I spent 48 hours in SF hanging out with billionaires, data labs and AI founders and execs running $1B to $100B companies.
My observations:
1. Sooo MANY stories of data brokers making like $2M a WEEK selling company data to labs. I can't tell if it's the beginning or a bubble, and I kept wondering which niches are left. Digging into it actively.
2. At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now.
3. The bar people talk about now is hitting $1B in revenue in 12 months (I guess that's the new unicorn?). What I didn't hear much about is retention. AI revenue can come in fast and leave fast, and I think we're going to see a lot of stories of AI companies with huge revenue and terrible retention.
4. It's pretty clear we're living through the reinvention of the computer. The model is the CPU, the harness is the operating system, and skills and plugins are the apps. Thinking about it that way makes it easier to see where the whitespace is.
5. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now.
6. Agent harnesses feel like the new GPT wrappers, in a good way, especially vertical ones. The best teams I talked to treat the model as swappable. One founder walked me through routing different models to different jobs: one for taste and visuals, a cheaper one for the everyday stuff, another for text.
They use prompts they can move across models over fine-tuning, and A/B test the output on what users actually do with it: do they edit it, export it, share it? A vertical harness is basically four things: the connectors for that industry's data, evals written by people who know the work, rules for what the agent can and can't touch, and a clean screen for a human to review. The model is maybe 20% of it.
7. There's a lot of reverse engineering going on/cloning going on more than I've ever seen. People are decompiling software and rebuilding it with agents, and someone showed a suite of like 10 apps that are worth $5B+ each all that he cloned open source. Me and everyone are still thinking through what it means when software gets this easy to copy.
8. My guess is the value moves to data. If the software can be rebuilt quickly, what's worth paying for in a legacy SaaS company is the years of customer data and workflows inside it. Some SaaS companies are secretly data companies.
Gets me thinking about how do you truly value data for this saas companies? Like if we were buying these dying saas companies at 2x ARR before (as example), maybe there is a better metric. Idk, just thinking out loud here.
9. It feels like a good chunk of legacy software won't make it to the agentic era, especially anything built around people clicking through a lot of screens.
10. Every company I talked to wants to pipe its internal data into one place and expose it to agents through MCP. Then they look at the data and realize it's a mess: five versions of the same customer, old Slack channels, PDFs nobody can search. Cleaning that up feels like a big consulting opportunity!
11. A lot of founders are building the basic building blocks for agents: APIs, data layers and tools. Some of the companies I saw barely have a UI because the main user is an agent. I really believe the startups that build primitives to this agentic era will do extremely well. Building blocks is the best way to think about it.
12. Labs can get plenty of training data, but it's harder to get real examples of agents failing inside companies. Whoever has those logs has something valuable.
13. Someone running a big consumer company told me the next fight is over memory. The model that remembers the most about you wins, and the smartest model matters less than people think.
14. Physical AI came up a lot. Astra I think was the first model that got people into AI can actually do CAD, and I think a lot of people are thinking what does vibe manufacturing really look like. The line I keep thinking about came from a robotics founder: the hardest problems in AI are deployment problems now, not research problems. Their robots work on their own, call a human when they get stuck, and every one of those calls becomes training data.
15. Cybersecurity companies are having a moment and are actually cool companies to build now, especially the ones that clean up after a breach. Every agent with access to your systems is a new door into your company, and a lot of those doors were left open.
16. Some startups are worth more dead than alive now. A shutdown company with 5 years of Slack, email and code can get paid for licensing all of it. I wouldn't be surprised if founders start factoring that into when they shut down.
17. People are starting to talk about monetizing the agent web. It feels really early, but when the most popular user of the internet is an agent, what startups should you be building?
18. I think agent insurance is coming. Once an agent makes an expensive mistake, insurers will want audits before they cover anyone.
19. Several people said the hire they want most is someone with taste who can write evals. The models are getting good at generating ideas, so the hard part is judging which ones are good.
20. New trend I observed: startup raises VC, uses venture dollars to become a creator, startup fails, founder falls back on being a full-time creator and incubates other products bootstrapped.
21. Getting SIGNIFICANT distribution is HARD right now. Founders are sponsoring buses, buying billboards like crazy and throwing parties with famous DJs to get noticed. Kinda feels dotcom era in that sense.
Attention is what's scarce! The growth guy going viral sharing his UGC strategy from Wispr Flow is a good example. It's the sauce everyone wants, and I truly believe there are probably only 50 people on the planet who know how to scale these types of businesses efficiently in this era.
Always good to get that dose of SF. I felt sooo lucky to have such an incredible trip during such an incredible time in building businesses. Surreal in so many ways.
Happy to be home and locked in.
Until next time!
Q4 advice: one Performance Max for the whole catalog.
Then keep raising the budget while the total sits at break-even.
That total is an average. A raise buys more of whatever is inside it.
Here's what I'd open before the next step up, and why each one matters.
The search terms insight comes first. If a big share is your own brand name, part of the raise buys orders you were getting anyway.
Then the product report. Often two SKUs carry nearly every conversion, and the rest of the catalog rides on their numbers.
Merchant Center product status is next, because the raise can't reach a disapproved product or one missing an identifier. The "whole catalog" may be smaller than the campaign name suggests.
And returns. A returned jacket still counts as a conversion in Google Ads until somebody tells it otherwise.
Scaling is still fine once you know which part of the average you're buying.
TL;DR before the next budget step:
• Brand share of the search terms
• Conversions per SKU, top two against the rest
• Disapproved or limited products in Merchant Center
• Revenue after returns
Moving your store before Black Friday? Ask one question first: what happens to the item IDs?
Google's Merchant Center help is blunt about it. Change a product's ID and "it will be treated like a new product." It goes back through approval, which Google says can take up to 3 business days, and its past performance "won't be associated with this new product."
So a migration can leave every product page looking identical and still send the catalog into the auction like it has never sold a thing.
It rarely shows up as an error. It shows up a month later in the P&L, and everyone blames the market.
The usual wounds in a migrated feed:
• New IDs, so the history sits on products that no longer exist
• GTINs lost in the field mapping
• Titles "cleaned up" by a new template, so they stop matching the searches they used to win
• Sale prices out of sync for a day, so disapprovals pile up overnight
TL;DR for a migration this quarter:
• Keep the old IDs, or map old to new before launch
• Diff GTINs and titles before and after the switch
• Read disapprovals daily for the first two weeks
• Push the move past Black Friday if you can
Not only this os funny way to market the new model. But sheeeesh it is good
$52M seed round. 15M+ users. But this is what we're most proud of yet.
Introducing Drama 3: the most emotive and controllable TTS model in the world
And yes, it's open-weight.
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Uncle Roger was impressed too.
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If you're an enterprise your first month is on us:
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We almost forgot that Google is also in play. Interesting. Let’s check that out
Wondering why Demandgen reported performance is through the roof recently?
That’s why. Watch out for this new impressions-through attribution Google automatically signed you on.
It doesn’t mean DG doesn’t work. But it will definitely report more of shallow touch point conversions.
Your Demand Gen Display conversions are about to go up.
Nothing in your business will have changed.
Google is changing what counts as a "view" for view-through conversions on Display ads inside Demand Gen.
Until now it used Active View: at least half the ad on screen for at least one second. The new rule is a rendered impression, meaning at least one pixel on screen for any amount of time.
It rolls out automatically over the coming weeks. Google hasn't published a date per account or a figure for how many more views will qualify.
Ishan Khatri's headline on it: "1 pixel. 0 attention. Still counts as a conversion."
A Japanese ads researcher, @yasuyuki_ad, got 139 likes saying roughly the same thing: if Demand Gen VTCs climb over the next few weeks, that doesn't look like the ads got better.
Here's the part most of the takes skip.
Google's August change moves image view-throughs in Demand Gen to secondary. In accounts that already have it, they sit in the View-through and All conversions columns, and the bidder doesn't train on them.
So for image ads, the inflation lands on people first. It shows up in the blended dashboard, the "All conv." screenshot, and the monthly report that says Demand Gen finally started working.
Video is a separate branch. VTC optimization is on by default for new Demand Gen campaigns and makes video view-throughs biddable, so check which views your bidder is actually learning from.
Jordan Fry's rule for his own accounts is blunt: "We judge Demand Gen on click-through conversions only."
How to read the number now:
• View-through conversions: compare them only against days after your own rollout date, and log that date the first time they jump.
• All conversions: the View-through share went up by definition, so pull it out before you compare it to last quarter.
• Conversions on new campaigns: check whether VTC optimization is on and whether the window is still Google's recommended one day.
For years, a capped budget quietly gave your Google campaigns a discount.
On August 17 Google took it back. Then a lot of advertisers made it worse trying to fix it.
What people reported since:
• Amsive, first full week across dozens of advertisers: Search CPA up 21%, conversion rate down 15%. Their retail tROAS campaigns lost roughly 29 to 35% of ROAS.
• Smarter Ecommerce: budget-limited tROAS campaigns now pay a 15.8% higher median CPC. Impression share went from 40% to 31%.
• Sam Piliero: "limited by budget dynamics are completely broken."
• An actual r/PPC thread title: "Is tROAS dead after August 17?"
Some accounts came through fine. Other r/PPC replies said "nothing burger," and one said ROAS went up. Which side you landed on mostly came down to one status label.
The mechanism is simple. When a campaign was "Limited by budget," Google bid low to make the money last and bought only the cheapest auctions. Google's own example: a $10 target CPA actually running at $5. It looked like skill. The budget was doing it.
Since August 17 those campaigns bid toward the number you typed. The $10 means $10.
Then came the self-inflicted part. The popular fix was to set the target to whatever you used to get. One r/PPC reply explained why that backfires: set it at 700% and Google "stops bidding on anything it doesn't think will clear 700." ROAS per order looks great. Total value falls off a cliff.
In Smarter Ecommerce's data, the median tROAS on affected campaigns rose 12% the week of the change. Campaigns the change never touched went up 9% anyway, and spend fell about 10%. Panic is also a bid strategy.
The number that holds up is the one built from margin. Google is even testing a calculator for it: type a 15% margin and it returns 667%. That's break-even, the point where your ads make exactly $0. Google's screen says so.
TL;DR for a tROAS campaign after August 17:
• No "Limited by budget" label? Nothing changed. Leave it.
• Break-even ROAS = 1 ÷ margin. 40% margin → 250%.
• Profit target = 1 ÷ (margin − points you keep). 40%, keeping 10 → 333%.
• Margin is after shipping, fees, discounts and returns.
• Grade it on total conversion value and spend. ROAS alone hides the drop.
Marketing engenders. I like that.
Folks I can’t emphasize this enough - manual button pushing in ad accounts is dead - our AI agents launch hundreds of ad campaigns per day.
Endless iterations with designers/editors is dead - we print thousands of image and video ad creatives per day.
Creative strategy you would say?
Dead as it is. Try to compete with Opus 5.5 parsing thousands of voice of the customer pieces:
- reviews
- blogs
- comments
- amazon
And craft messaging exactly how the buyer speaks. With endless visual style iterations.
Swarm of AI agents executing everything through APIs is where it goes.
And it’s plain scary how 100x we can pull with just a team of 3 with @andreilunev and @alexkarpenev
Google can host your PMax page. Since Sept 15.
Barry Schwartz wrote the setting down that morning, which is why the wording is not a rumor. The line inside the campaign is "Where should people go after clicking your ads?"
You are choosing where that click lands. The two options are not the same kind of page.
• Your website takes the person to a page you own, the homepage or one specific URL. On a store, that page has to be the product.
• Google Business Profile, optimized for ads, takes them to a page Google hosts. The conversion tracking is already on it.
Natasha Kaurra caught the wider rollout. It was the first time the box had shown up in an account she runs.
A plumber can live on hours and a call button, so that hosted page can be the whole visit. A brand that ships a product cannot, because the person who clicked never meets the thing.
Google called that second page "optimized for ads." Love the confidence.
It graded a page you don't edit, then offered you the score. I don't have a clean 30-day read on the box, and no ROAS is sitting next to this.
You bought the click. The visit stayed on Google.
Google shipped Ads API agent skills for Claude Code.
Plugins now audit accounts, mine negatives, rewrite RSAs.
What the install notes still skip: treat brand-name search as a different job from prospecting.
I learned that the expensive way. A star PMax I pulled apart was 60% branded search the client was already getting, and the dashboard called it performance.
On a castor-oil account that was underwater, splitting brand vs cold vs presell plus a negative cleanup beat any bid tweak. ROAS +70% in 30 days, brand campaign +50%.
A Claude skill with live account access will polish whatever conversion goal you handed it. If brand and non-brand sit in one blended number, the skill keeps buying the costume.
Before any skill proposes a change on an account we run, I force one read:
1. Filter search terms to the brand name and total the spend behind it. That share is capture, not growth.
2. Put brand next to prospecting in a separate view. One blended ROAS hides which job the money is doing.
3. Cap brand. Give cold its own budget and its own kill rule.
Ask whoever runs your Google this week what percent of last 30 days spend was people already typing your brand name.
With smart gents @alexgoughcooperand @andreilunev we’re talking:
- AI agentic systems
- end-to-end AI creative process for printing 1000s image/video ads
- how to give AI agents real world data to write angles and verbatim
- how to build similar system yourself
- and much more
Enjoy
Here's the transcript for those interested: drive.google.com/file/d/1AUR…
And here's the recording: youtube.com/watch?v=q7fHj4Yo…
Thanks for inviting us mate.
Been awesome to chat
@alexgoughcooper is a GOAT when it comes down to agentic AI for ads printing
.@andreilunev and @iamgalba just dropped a masterclass on the one-person creative team.
I've not seen anyone in marketing push AI to the limit like these guys, and it makes me want to lock myself in a room for 3 days and talk to no one but WisprFlow.
Who wants the transcript?
Ruslan Galba retweeted
🌶️ That Was FIRE 🔥
@iamgalba @alexgoughcooper @andreilunev
we just held a full size webinar on AI ads, processes, media buying... sheesh, that was A LOT
1:40 hour talk!
thanks for having us, Alex!
