# Pulse · October 12, 2026

> Rank trackers report Google swapping in fake SERP data, Merchant Center drops its image-quality exclusion, and Microsoft ships a sub-cent decision model.

Canonical: https://brandonlazovic.dev/pulse/2026-10-12/  
Author: Brandon Lazovic  
Published: 2026-10-12

## Rank-Tracking Vendors Report Google Swapping In Fake Search Results To Block Scraping

Status: Observed  |  Topics: measurement-analytics, crawling-indexing-rendering

Nozzle's October 10 research report says Google began blocking rank-tracking scrapers on September 13, then from September 20 started returning normal-looking pages with swapped rankings and no error pages, a pattern Nozzle calls poisoning rather than blocking. Nozzle measured 8.0 percent of tracked SERPs poisoned since September 13, up from a 0.17 percent baseline, with the single worst day (September 29) hitting 27 percent and poisoned pages keeping only 20.7 percent of normal top-10 results.

In plain terms: 'Poisoning' here means Google serves the scraping tool a fake but normal-looking results page instead of blocking it outright, so the rank-tracking software has no error to catch, just wrong numbers that look real.

Why it matters: Anyone reporting rank-tracking numbers to a client right now should flag the same window Nozzle measured, since a poisoned SERP can show a tracked site falling out of the top 10 with no actual ranking change behind it.

Our take: If Google keeps swapping results rather than blocking requests outright, a tool's anti-detection method stops being the deciding factor, since the fake page looks exactly like a real one. Watch whether tracking vendors converge on cross-checking SERPs against each other rather than trusting a single scrape.

- [Nozzle: Poisoned Google SERPs (research report)](https://nozzle.io/research/poisoned-google-serps/)
- [Sistrix: Google's impact on ranking data collection (describes a related but distinct scraping squeeze, no shared figures)](https://www.sistrix.com/support/google-ranking-data-collection/)

## Google Merchant Center: Poor Image Quality Alone No Longer Blocks Product Listings

Status: Confirmed  |  Topics: product-feeds-shopping

Google's Merchant Center help page now reads 'Your product will continue to show in ads and free listings' for poor image quality, replacing stricter wording that said a low-quality image 'may be prevented from showing in ads and free listings until the image quality improves,' per a September 4, 2025 Wayback Machine capture Search Engine Roundtable screenshotted and linked. The removed exclusion covered subjective image quality; a separate, objective 500x500-pixel minimum still applies, with warnings since July 2026 and exclusion enforcement starting January 31, 2027.

In plain terms: Merchant Center is the feed Google pulls product listings from for Shopping ads and free listings; this change means a blurry but correctly sized photo no longer gets a listing hidden, though an image literally too small in pixels still will, starting in 2027.

Why it matters: Merchants holding back weak product images out of fear of suppression can treat image quality as a conversion lever again rather than a pass/fail gate, but the real floor to watch is the January 2027 pixel-size enforcement date, a harder resolution-based cutoff than the quality language it replaced.

Our take: Google swapping a subjective quality judgment for a hard pixel-count floor is the same trade search ranking made with Core Web Vitals, a fuzzy editorial standard replaced by a number a script can check at scale. Expect the July 2026 warnings to be where most merchants first notice their image dimensions matter more than how the photo looks.

- [Google Merchant Center Help: Image requirements](https://support.google.com/merchants/answer/15068449)
- [Search Engine Roundtable: screenshot and Wayback Machine link documenting the prior wording](https://www.seroundtable.com/google-merchant-center-poor-image-inclusion-42273.html)

## Model & AI releases

### Microsoft Launches Decision-1, A Decision-Scoring Model Priced Under 5 Cents Per Million Tokens

Status: Confirmed  |  Topics: llm-models-agents

Microsoft released Decision-1 on October 9, a decision-scoring model built by post-training Qwen3.5-9B that outputs structured yes/no, multiple-choice, rating or rubric-based judgments for routing, classification, prioritization and workflow control rather than open-ended text. Microsoft reports the highest accuracy across 36 benchmarks spanning roughly 150,000 questions, latency about 35 times faster than GPT-6 Sol, and pricing of $0.042 per million input tokens with free output tokens. The Copilot team is using it internally to quality-check response outputs.

In plain terms: A 'decision-scoring model' here is a stripped-down model that returns a short structured verdict, like yes/no or a 1-5 rating, instead of writing paragraphs, which is why it can run far cheaper and faster than a general chat model.

Why it matters: For anyone routing agent decisions or classification tasks through a general-purpose LLM, a purpose-built decision model priced at roughly four cents per million input tokens is worth benchmarking against whatever it currently costs to get a yes/no or rubric score out of a larger model.

Our take: Sub-cent pricing on a narrow decision model is the kind of release that gets skipped past the general-purpose leaderboards and then quietly replaces a bigger model wherever an agent pipeline only needed a verdict, not prose. Worth testing anywhere a workflow currently burns a full LLM call on a yes/no or routing decision.

- [Microsoft: Decision-1 model announcement](https://commandline.microsoft.com/microsoft-decision-1-model-foundry/)