Recently, a familiar question has been echoing through the Facebook advertising community: Is Meta Ads getting harder to run?

A seasoned media buyer recently shared a frustrating experience: his campaigns, which historically maintained a stable 3.0 ROAS, suddenly generated zero conversions for five consecutive days. Strangely, his CPM and CPC metrics remained normal, and there were no changes to the website, checkout flow, or offers. Even duplicating his best-performing promotional campaigns yielded no results. To make matters worse, he encountered frequent Ads Manager glitches, such as broken filters and campaigns displaying incorrect ad sets—issues affectionately known in the community as "Facebug."

Some advertisers suspect an influx of bot traffic, while others have noticed that campaigns strictly set up for cold audiences are suddenly spending significant budget on retargeting warm users. While UI bugs and data discrepancies are common complaints, the real shift lies deeper within Meta's evolving ad delivery logic.

The Death of Manual Targeting: Enter Andromeda and GEM

In the past, media buyers spent hours tweaking interest groups, Lookalike audiences, age demographics, and complex ad set structures. Today, the heavy lifting of audience targeting has been completely taken over by Meta's machine learning models.

At the core of this shift are two major algorithmic systems:

  • Andromeda: Meta's ad retrieval system. Out of tens of millions of candidate ads, Andromeda filters and identifies the top few thousand ads most likely to match a user's intent, passing them to the ranking system.
  • GEM: Meta's foundational recommendation model, which now powers ad delivery across both Facebook and Instagram.

This algorithmic evolution explains why traditional tactics—like duplicating campaigns, swapping interest groups, or splitting ad sets—are yielding increasingly volatile results. The system no longer relies on manual guardrails; it relies on creative signals and machine learning.

A Modern Troubleshooting Checklist for Meta Ads

To diagnose performance drops in this new AI-driven ecosystem, media buyers must look beyond basic metrics. Here is a structured approach to troubleshooting your Meta Ads account:

1. Analyze Traffic and Delivery Metrics

Monitor foundational metrics to see if your traffic source has shifted. Check for sudden fluctuations in CPM, CTR, CPC, placements, geographic distribution, and device types.

2. Evaluate Post-Click User Behavior

If your on-platform metrics look normal but conversions have dropped, analyze post-click behavior. Compare active sessions, average session duration, View Content rates, Add to Cart (ATC) actions, and Checkout Initiations to identify where users are dropping off.

3. Audit Your Data Signals

AI models rely entirely on data feedback. Ensure your Meta Pixel and Conversions API (CAPI) are functioning correctly. Check for event deduplication errors and match quality issues, as poor signal health will directly degrade the algorithm's learning capability.

4. Focus on True Creative Diversity

In the era of algorithmic targeting, creative is your targeting. True "creative diversity" does not mean running the same image with five different headlines. It requires testing entirely different hooks, visual styles, user personas, unique selling propositions (USPs), and content formats.

The Paradox of Meta's Growth

While many media buyers complain that Meta Ads are becoming unprofitable, Meta's financial reports tell a different story. The company reported Q2 ad revenue of $59.36 billion, representing a 27% year-over-year growth. This massive growth indicates that while the old playbook—finding a single interest group and a winning creative to run for six months—is dead, advertisers who adapt to the new AI-driven framework are finding unprecedented scale.

The hardest part of running Meta Ads today is not learning how to set up a campaign; it is unlearning the legacy strategies that once brought guaranteed success.