Business owners who use Meta's advertising suite will see changes in the ways they reach their customers. In Q3, Meta made improvements to its ad retrieval architecture that improved its performance, the company announced on Monday.
Meta's ad retrieval -- the process that chooses relevant ads that show up on users' social feeds -- is entirely automated with machine-learning. The company first started introducing these features to users in Q1 of 2025, and expanded coverage in Q2 as it scaled up training of its machine-learning models.
Why did everything change? The post-generative AI "Cambrian explosion of creatives," as Meta's VP of monetization infrastructure, ranking, and AI foundations Matt Steiner puts it, required an overhaul of the old way of doing things. AI ads have come to stay.
Not everyone has been happy about the change. On social media, entrepreneurs recently shared criticism about Meta's machine-learning ad systems as GEM, Meta's AI ad retriever, finished its rollout earlier this year. The subreddit r/FacebookAds has devolved into two camps -- people complaining that Meta is killing their engagement, and others essentially telling them "you're doing it wrong."
Here's how to do it right: Inc. sat down with Steiner to learn more about how Meta's machine learning suite works, and the new strategies entrepreneurs need to know to navigate the ad algorithm.
Meta's machine-learning advertising model has three distinct parts: Andromeda, GEM (or Generative Ads Recommendation Modeler) and Lattice. GEM launched on Instagram Reels in Q1 of 2025.
Andromeda is hardware, Steiner says, and uses Nvidia's chips to make ad retrieval more efficient through machine learning. GEM runs on Andromeda's hardware to choose ads, and Lattice ranks those ads based on their relevance, like a library of ad info.
These three parts of the ad retrieval process are basically just more effective tools of what already exist. Performance advertising on Meta has always been intended to track people's preferences, Steiner says, and the AI-assisted ad engines are going to be better at that.
Another innovation that Meta introduced in November 2024 is sequence learning, which theoretically enables its ad systems to understand what people are seeing before or after an ad. GEM basically understands the next step that consumers will take after buying X or Y product.
Let's say you run a ski equipment shop, and a consumer buys your skis. Rather than seeing more ski equipment ads from your competitors, the algorithm would start feeding them more relevant information, like other things to buy. For example, plane tickets to Switzerland or a reservation at a ski resort.
Steiner says the sequence learning has led to a 5 percent improvement on Instagram and 3 percent on Facebook in conversions per dollar. While the model required a lot of tech breakthroughs to be scalable, the results have paid off for Meta.
Businesses don't choose to use Andromeda, GEM, and Lattice. The system has been in place for nearly a year now, completely overhauling the ad retrieval process and changing the way ads are ranked on Meta.
The idea behind Meta's overhaul was to focus on automatically selecting the most effective ad creative out of the options business owners upload. GEM, the machine learning model, selects more relevant creative, then sends it to Lattice for a better ad rank, Steiner says. Ad rank determines visibility and placement.
The strategic advantage of the AI-assisted ad system is that machine learning models "don't get tired of looking at performance data per campaign," Steiner says. They're much better at pattern recognition of performance data. As such, they can profile potential audiences at a higher rate.
Amanda Shaftel, CMO of Austin-based pool installer Cowboy Pools said its ads on Meta since Andromeda's launch "have performed 15 percent better than us pushing product specs and price hooks." Its cost per impression has dropped by 8 percent since August, which Shaftel attributes to Andromeda's efficiency in matching their ads with relevant consumers.
Chris Howard, COO of UK-based digital marketing firm Nest Commerce, said producing the volume of creative necessary to make the most of GEM, Lattice, and Andromeda is so expensive that it "offsets the [revenue] benefits." Howard says that to adapt to the creative demand of GEM, Lattice, and Andromeda, he's started using AI-generated creative content for cost-effective ad campaigns.
"The only way to make the most of the change that Andromeda brought is by feeding it with creative," Howard says. The company's AI-assisted ad campaigns have saved it around 70 to 90 percent of traditional advertising costs, a spokesperson said.
To see the true success of an ad campaign, it's necessary to look at the right metrics.
Howard says that with Andromeda, he's seen declining cost per impression and increasing click-through numbers. Across experiments that Nest has run, it has seen a 38 percent improvement in return on ad spend (ROAS). His cost per acquisition (CPA) is lower, and average order values have increased, as "with a greater volume of creative, we've been able to better match higher value products to the individuals who are most likely to want to purchase those," Howard says.
Especially for small brands, more targeting advertising ensures greater overall payoff. The Nikes and Apples of the world have big enough markets to justify huge ad budgets. Smaller companies depend on their niche audiences.
The more targeted machine learning system that Meta rolled out helps identify those niches, Steiner says. The goal of the machine learning overhaul was to improve the ad ecosystem, to help ad messages resonate with the right audiences.
"Let the machines do the calculating, they're very good at that. Let the humans do the creativity, the humans are very good at that," Steiner says. "That was the whole through line."
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