MaxWitness

1. One-liner

Tells a PPC agency whether Google’s forced AI Max upgrade helped or bled each client account.

2. Trend signal — why now?

On 5 August 2026 an email landed in advertiser inboxes, signed “the Google Ads Team,” with no accompanying blog post. It said that from 1 September 2026, every Search campaign using automatically created assets or campaign-level broad match would be auto-upgraded to AI Max. No opt-in. The only escape was to go and disable those features before the date.

That is the whole shape of this business. A platform reached into hundreds of thousands of accounts and changed how money is spent, and the people who are accountable for that money — agencies — got 27 days’ notice and no measurement tool.

The independent data says the outcome is a coin flip. Smarter Ecommerce (SMEC) analysed more than 250 Search campaigns running AI Max: median revenue +13%, median CPA +16%, with a spread from 42% above baseline ROAS to 35% below. Only 22% of campaigns landed close to their original ROAS targets — the other 78% over- or under-shot significantly. AI Max is not good or bad. It is unpredictable per account, which is precisely the condition that creates demand for measurement.

Meanwhile the practitioner mood is documented: in the State of PPC 2026 survey, 53% of advertisers say Google Ads is harder to manage than two years ago and 62% say the platforms make decisions they cannot manually override. Search term matching now works on “inferred intent rather than literal query text,” so a chunk of spend is attributed to queries the advertiser is never shown. As one practitioner guide put it: “Use experiments, not vibes. A proper test tells us clearly whether AI Max is incremental or just claiming credit for conversions you’d have won anyway.”

Google’s own answer is AI Max experiments — a 50/50 in-campaign split. Useful, and it is genuinely why this is not a $50M company. But it is per-campaign, it cannot run where the campaign uses shared budgets, portfolio bidding, text customization or another active experiment, and on low-volume accounts it returns inconclusive. Google shipped an Experiment Power Score precisely because so many experiments end without a winner. An agency with 30 clients, most spending under $20K/mo, cannot run 30 conclusive experiments — and even if it could, the referee is the same company selling the upgrade.

Provenance:

• Signal 1 (demand): Google auto-upgraded Search campaigns using ACA or campaign-level broad match to AI Max on 1 Sept 2026, notified by unsigned email on 5 Aug 2026, with no opt-out other than disabling the features first — https://ppc.land/google-ads-ai-max-auto-upgrade-lands-september-1-will-ads-all-look-alike/ — 2026-08-29
• Signal 2 (economic): SMEC study of 250+ AI Max Search campaigns — median revenue +13%, median CPA +16%, only 22% near original ROAS targets — https://searchengineland.com/google-ai-max-revenue-higher-cpa-study-470928 — 2026-08-29
• Signal 3 (feasibility): Google Ads API exposes experiments, campaign drafts and search term data; incumbent tooling (Karooya from ~$300/6mo, Optmyzr from ~$250/mo, Adalysis) sells negative-keyword hygiene and optimisation, while incrementality/lift measurement sits at enterprise tier (Skai) — https://www.groas.com/post/google-ads-management-pricing-2026-agencies-ai-tools-groas-comparison — 2026-08-29 Category: Platform shift

3. The opportunity

Every incumbent in this category sells optimisation — do more, do it faster, cut waste. Nobody sells adjudication: an independent, defensible answer to “did this change help, on this account, and can I show the client?”

That gap exists because of who pays. Optmyzr, Adalysis and Karooya are bought by the person who wants to improve the account. The AI Max question is asked by someone else in the room — the client, who wants to know whether last month’s CPA jump was the agency’s fault or Google’s. Nobody is invoiced for answering that today, so nobody built it.

The structural point that makes this defensible for a while: the machine grades its own homework. Google’s AI Max experiments are run by Google, scored by Google, inside the campaign Google wants upgraded. An agency handing a client a Google-generated report saying Google’s product worked is not evidence — it is marketing. A third-party, cross-account, before/after record is.

Second gap: everything is per-campaign. The agency’s actual question is portfolio-shaped — “across my 30 clients, which 6 got worse after 1 September, and what do I do about those 6 on Monday?” No tool answers portfolio-shaped questions about a single platform change.

4. Target market

• Primary customer: Owner-operators of independent PPC agencies and senior freelancers — 1–10 staff, managing 15–60 Google Ads client accounts, most clients spending $2K–$20K/mo. Also in-house marketing managers at single-brand advertisers spending $10K–$100K/mo who report to a CFO.
• Why they buy: Not to optimise. To survive the client conversation. PPC is the highest-churn agency category at 49% churn, described as “easily commoditized with transparent performance metrics that enable rapid comparison shopping,” and 60–70% of client churn happens in the first six months. An unexplained CPA rise in September is a cancellation in November. The agency needs to say, with a number, “this was the platform, here is the proof, here is what I fenced off.”
• Rough TAM reasoning: Google Partner status requires only $10K spend per 90 days, so the small-agency population is large — tens of thousands globally in English-speaking markets alone. I don’t need a precise figure: at $99–$299/mo, 400 agencies is $1M ARR. That is a rounding error against the population and it is the whole business.
• Why now for them: The upgrade date has passed. Every affected account now has a clean pre/post boundary at 1 September 2026 — a natural experiment that will never be this crisp again. The September and October client reports are being written right now.

5. Product sketch (MVP)

• Connect Google Ads accounts once (MCC-level), pull 90 days pre- and post-1-September performance for every Search campaign.
• Upgrade exposure map — which campaigns were actually auto-upgraded (ACA / campaign-level broad match), which were already AI Max, which were untouched. Most agencies genuinely don’t know the split across their book.
• Per-account verdict — CPA, ROAS, conversion volume and spend, before vs after, with the untouched campaigns used as an internal control so seasonality doesn’t get blamed on Google.
• Query drift report — new search terms appearing post-upgrade that never appeared before, ranked by spend, flagged for brand cannibalisation and off-intent traffic.
• Waste shortlist — the specific terms and URL-expansion destinations bleeding budget, exportable as a negative keyword list.
• Client-ready one-pager — a PDF per client, in the agency’s logo, stating plainly what changed, what it cost or saved, and what was done about it.
• Portfolio board — all clients ranked by damage, so Monday morning has an order of operations.

6. AI angle — what’s load-bearing

Two places, and the product does not exist without either.

First, query intent classification at volume. Post-upgrade, matching is on inferred intent, not literal text. Deciding whether “cheap accounting software free trial” is off-intent for a $400/mo B2B product is a judgement call that used to require a human reading thousands of rows. An LLM does it per-account against the client’s actual offer and landing pages, which is what makes a per-client waste shortlist possible at $99/mo instead of at consultant rates.

Second, the written verdict. The output that gets paid for is a paragraph a client can read: what changed, what it cost, what was done. Generating that per client, per month, in the agency’s voice, from the numbers, is the deliverable. Strip the AI out and this is a CSV export — which is what already exists and which nobody pays for.

What is not AI: the before/after arithmetic. That is a difference of means with a control group, and I would rather it be boring and correct than clever. Being caught hallucinating a CPA figure kills this product on day one.

7. Localization angle (if any)

N/A — this is a global play. The trigger is a Google platform change that landed identically in every market on the same date, the customer works in English or in the language of a Google Ads UI that is already localised, and the buyer’s wallet is denominated in ad spend rather than local income. India and LATAM agencies are in scope on the same product at the same price; there is no local rail, statute or language quirk to exploit here, and inventing one would be a distraction.

8. Business model — path to $1M–$5M ARR

• Pricing: $99/mo up to 10 client accounts, $199/mo up to 30, $299/mo up to 60. Priced deliberately below Optmyzr’s ~$250/mo entry so it reads as a line item, not a platform decision.
• ACV: ~$2,000 blended.
• Rough math to $1M ARR: 420 agencies × ~$199/mo × 12 ≈ $1.0M. Against the small-agency population, this is a low-single-digit-percent penetration problem, not a market-creation problem.
• Rough math to $5M ARR: ~2,100 agencies, which requires the product to outlive AI Max — see the risk flags. Realistically $5M means becoming “the independent record of what the ad platforms did to your accounts,” covering Meta’s v24.0 forced upgrades and Performance Max, not just this one September.
• Expansion path: per-account pricing scales as agencies win clients (the good kind of expansion — it grows without a sales call). Then white-labelled client-facing reports as an upsell, then a per-seat tier when agencies put account managers in the tool.

9. Go-to-market wedge — first 100 customers

• The free exposure scan, aimed at r/PPC and PPC Twitter. One-click read-only connect that answers “how many of your campaigns got auto-upgraded on 1 September?” — a question every agency has and none can answer quickly. Free, no paywall, ends with the per-account verdict behind a card. This is the wedge; the scan is cheap to run and the answer is inherently shareable.
• Publish the aggregate. After ~200 scans I hold something nobody else has: cross-agency data on what the forced upgrade actually did to small accounts. Publish it as the counterweight to Google’s own numbers. Search Engine Land, PPC Land and Search Engine Journal all covered the SMEC study — this is the same story with a bigger, scrappier sample, and it makes the tool the citation.
• Target the loud ones directly. The r/PPC and PPC Land comment threads about the 5 August email are a named list of people who are angry about this specific thing this specific month. Cold DM with their own account’s scan result attached, not a demo request.
• Agency mastermind communities and PPC newsletters (PPC Mastery, Paid Media Pros audiences) — one sponsored slot each, offering the scan rather than the subscription.
• The September/October reporting cycle is the forcing function. Every agency writes a client report in the first week of the month. Being in the inbox on 1 October with “here’s your client one-pager” converts far better than the same email in December.

10. Build complexity — justification

Low. Everything is off-the-shelf: Google Ads API for campaign, experiment and search-term data, an LLM API for query classification and verdict prose, standard web stack, PDF generation. There is no data science to invent — the statistics are a before/after comparison with a control group, deliberately kept simple enough to be auditable. The real work is OAuth/MCP-level account connection, tolerating the API’s version churn (v21 sunsets August 2026, v22 in October — a monthly release cadence with ~6-month sunsets), and making the report genuinely presentable. A competent solo builder ships v1 in 6–8 weeks.

11. Gating checklist

Gate	Pass?	Note
Legal in target market	✅	Read-only use of the official Google Ads API under the customer’s own OAuth consent. No scraping, no ToS grey area.
Ethical — no harm / dark patterns	✅	The product’s entire function is giving advertisers honest visibility into their own spend.
Market exists (evidence above)	✅	Incumbents charging $250–$300+; 250-campaign study showing 78% miss ROAS targets; documented practitioner demand for incrementality testing.
1–5 person team can build this	✅	Solo builder, 6–8 weeks.
Launchable with <$50K / ₹40L	✅	API costs and a laptop. Well under $10K to first revenue.

12. Feasibility score

Axis	Weight	Score	Notes
Problem intensity	20	16/20	Real money, felt monthly, tied to client retention in a 49%-churn category. Not a 19 — the pain is acute in Sept–Dec 2026 and then normalises as agencies adapt.
Demand evidence	15	12/15	Strong indirect evidence: incumbent pricing, the SMEC study, survey data on override frustration. Docked because I found no one asking for this exact product by name — the demand is inferred from the gap, not quoted.
Build feasibility	15	13/15	Off-the-shelf throughout; the only real friction is Google Ads API version churn.
Distribution clarity	15	12/15	Named channel, named list, a free scan with a naturally viral question. Docked because r/PPC is hostile to vendor self-promotion and can bury a launch.
Revenue mechanics	15	11/15	$1M is credible at plausible penetration. $5M requires expanding beyond this single event, which is unproven.
Time to first revenue	10	8/10	The reporting cycle gives a monthly close trigger; expect paying customers in 4–8 weeks.
Defensibility	10	4/10	Honest score. This is copyable in a quarter. The only durable assets are the cross-agency benchmark dataset and the position of not being Google.
Total	100	76/100

13. Qualitative modifiers

Founder-fit tags

technical-heavy · content-heavy — API work plus the credibility to publish data that PPC practitioners will cite. A builder with no standing in the PPC community will struggle to get the aggregate report taken seriously, and that report is the distribution.

Key assumptions to validate (3–5)

1. Assumption: Agencies cannot already answer “which of my campaigns got auto-upgraded and what did it cost?” in under an hour. How to test: Ask 20 agency owners directly, in r/PPC DMs and PPC Slack communities, to answer it for one client. Time them.
2. Assumption: The buyer is the agency owner protecting a client relationship, not a performance marketer seeking optimisation. How to test: Run two landing pages — “prove AI Max helped or hurt your clients” vs “cut AI Max wasted spend” — and compare signup intent. The messaging determines the whole product.
3. Assumption: Willingness to pay $199/mo for measurement rather than optimisation. How to test: Put the price on the scan result page from day one and count card entries, not email signups.
4. Assumption: Google’s native AI Max experiments don’t satisfy the need. How to test: Ask scan users whether they ran one, and if not, why not. If most say “the native experiment was fine,” the idea is dead and I want to know in week three.

Risk flags

1. Platform dependency — severe. This is a Google Ads API business about a Google product. Google can change the API, restrict data, or ship a portfolio-level before/after view into the MCC UI for free. The mitigation is speed and the fact that Google will never publish an independent verdict on its own upgrade.
2. Market timing — this is an event, not a market. The 1 September boundary is the sharpest it will ever be, and it decays. By mid-2027 “before AI Max” is ancient history and the product must have become a general platform-change witness (Meta v24.0, Performance Max, whatever ships next) or it dies with the news cycle. This is the single biggest reason it’s a 76 and not an 85.
3. Defensibility is thin. Optmyzr or Adalysis can ship a competing view as a feature in a quarter, and they already have the accounts connected. The bet is that they won’t, because it cannibalises their optimisation positioning and their customers aren’t asking them to referee Google.
4. Verdict risk. If the honest answer for most accounts is “AI Max was roughly neutral,” the product tells customers they didn’t need it. Real possibility given the median figures. Mitigated by the 78%-miss-target spread — being told “you’re in the fine 22%” is still worth knowing, but it’s a weaker sale.

14. Structured verdict

Score:                  76/100
Verdict:                GO
Confidence:             Medium
Best-fit builder:       Technical solo founder with existing standing in the PPC community
Time to revenue:        4–8 weeks
Capital to launch:      $5–8K (₹4–7 lakh)
Top 3 assumptions to validate first:
  1. Agencies can't already answer the upgrade-exposure question — time 20 owners doing it manually
  2. The buyer wants adjudication, not optimisation — A/B the two landing pages before writing code
  3. Google's native AI Max experiments don't close the gap — ask the first 50 scan users
Kill criteria:
  - Abandon if <15% of free-scan users click through to the paid verdict
  - Abandon if the aggregate data shows AI Max was roughly neutral for >70% of scanned accounts
  - Abandon if Google ships a free portfolio-level before/after comparison in the MCC UI
  - Abandon if Optmyzr or Adalysis ships an AI Max impact report before v1 launches

15. Next step — 1-week validation sprint

• Day 1–2: Build only the exposure scan — read-only OAuth, count campaigns that had ACA or campaign-level broad match before 1 September. No verdict, no report, no billing. Ship it behind a one-page site.
• Day 3–4: Take it to 30 agency owners across r/PPC, PPC Slack/Discord communities and the comment threads on the 5 August upgrade coverage. Ask each for one thing: connect and tell me if the number surprised you. Record whether they already knew it.
• Day 5: Put a $199/mo “full verdict” button on the results page for everyone who scanned, and count card entries.

Falsifiable outcome: ≥30 agencies complete a scan, ≥15% of them enter a card for a product that does not exist yet, and a majority report the exposure number surprised them. Fewer than 15% carding, or most saying “I already knew” — the measurement need isn’t real and I walk away in week one rather than month four.