META: Harnessing Growth in Digital Advertising within the AI Booming

Backgrounds: we went through Meta’s recent fundamentals in the last blog, and I wanted to talk about my recent thoughts on how Meta is able to capture the most growth in digital advertising within this generative AI Booming

Meta stands at the intersection of two major shifts in digital advertising: the recovery and reshaping of online ad measurement post-ATT, and the incorporation of advanced AI models that promise to make targeting, creative, and conversion optimization dramatically more efficient and effective. While every major ad platform is investing in AI, Meta’s advantages are structural, cultural, and cumulative—leading to a scenario where the benefits of AI are not just incremental, but multiplicative. In other words, Meta is not simply catching up with AI; it is poised to use AI to generate a kind of abundance that its rivals cannot easily match.

A History of Scaling From the beginning, Meta’s approach to digital advertising has been predicated on massive scale, broad advertiser access, and relentless iteration. Over the last decade, the company combined user-generated content with sophisticated machine learning to match 10 million advertisers to billions of users worldwide. Even as Apple’s ATT disrupted deterministic tracking, Meta responded by applying AI to reconstruct missing signals, leaning into probabilistic models that would have seemed outlandish just a few years ago. The company’s swift adaptation shows that scale is more than just a number at Meta—it’s the foundation that lets them absorb shocks and re-tool quickly.

This scale is integral to how Meta’s AI models work. The company’s advantage in training complex targeting and measurement models is not just about having better algorithms; it’s about having more data to refine them on. Meta’s ability to observe billions of interactions across Facebook, Instagram, Messenger, WhatsApp, and now Threads provides a universe of signals to feed into its models. The result: a compounding effect, where each incremental user and advertiser enhances the accuracy of the system, leading to better performance for everyone.

AI as a Force Multiplier AI’s potential to improve targeting is well understood: better predictions about who will click or convert can justify higher bids and raise CPMs. What’s less appreciated is how AI can reshape ad creative generation, frequency management, ad relevance scoring, and even format innovation.

• Creative Optimization: In a world where generative AI can produce multiple variants of an ad, Meta’s vast data and feedback loops ensure it can quickly identify which creatives resonate best. Over time, this process becomes automated, so small advertisers can get results previously reserved for big brands with large creative teams.
• Dynamic Formats: Consider Reels. Initially challenging to monetize, it’s now at a $10B run-rate. Why? Because AI optimizes which Reels users see, ensuring engagement remains high. By applying AI to ad selection and pacing, Meta can close the initial CPM gap between Reels and feed ads. The same logic will eventually apply to Threads, messaging ads, or any new surface Meta explores.
• Integrated Ecosystem: While competitors excel in certain niches—TikTok in short-form video, Google in search, Snap in AR lenses—Meta sits across multiple paradigms. AI binds these disparate elements together, allowing learnings from one format to inform another. User interest gleaned from Instagram feed engagement might help improve ad relevance in WhatsApp’s click-to-message ads. This integration is uniquely Meta’s: a vast network of products unified by a single AI backbone, making every user interaction an input to improve the entire system.

The Uniqueness of Meta’s Opportunity It’s tempting to say AI benefits every ad platform. And it does—incrementally. However, Meta’s advantage is rooted in the combination of scale, data, and product diversity. That combination changes the slope of improvement. Other platforms have portions of Meta’s toolkit:

• Google: Owns massive data and powerful AI, but is concentrated on search and YouTube. While Google’s reach is enormous, its social engagement is limited. Its advantage in text-based ads is huge, but it must integrate generative AI into a different kind of user intent paradigm. The complexity of Google’s ecosystem—search, shopping, cloud—might slow focused iteration on social-like ads.
• TikTok: Remarkable engagement but comparatively immature monetization stack. AI helps, but without Meta’s decade-plus of ad auction refinement and cross-app data, gains may plateau sooner. TikTok’s user data is rich for entertainment but may lack the rich profile depth—interests, social graphs, and long-term behaviors—that Meta’s platforms have accumulated.
• Snap & Pinterest: Both have niche value propositions and unique ad products. AI will improve their targeting and creative too, but with smaller datasets and fewer global advertisers, the pace of improvement lags. Their revenue scale and margin profile limit how aggressively they can invest in cutting-edge AI infrastructure. Meta’s billions in CAPEX on AI data centers are amortized over a much larger revenue base, yielding better per-unit cost efficiencies.
• Amazon Ads: Great for commerce-based intent, but less effective for building brand awareness or capturing top-of-funnel interest. Amazon’s AI can refine sponsored product placements, but it doesn’t match Meta’s global audience graph or ability to integrate signals across various user contexts—personal feed, short-form video, messaging interactions.

Building a Moat of Complexity One underrated aspect of AI improvements is that they tend to accumulate complexity and sophistication over time. The more Meta invests, the better it integrates AI into every part of the ad stack—creative suggestions, audience expansion, bid optimization, frequency capping—the harder it becomes for newcomers to replicate. This complexity, when managed well, forms a moat. Other platforms can copy one feature or another, but Meta’s advantage is combinatorial. Every incremental improvement in targeting informs reel monetization, which informs click-to-message ads, which will eventually inform whatever Threads’ monetization becomes.

This complexity leads to abundance: abundant impressions, abundant ad performance improvements, abundant incremental revenue streams. Over time, Meta’s AI systems drive a virtuous cycle: better AI leads to better ad experiences, which leads to more advertiser demand and user engagement, which in turn enhances Meta’s training data, feeding the AI improvement loop.

Long-Term Cash Flow and Strategic Patience While the ultimate objective—sustained and growing FCF—has been previously outlined in detail, it’s worth re-emphasizing how AI’s second-order effects reinforce that goal. As the incremental cost of improving AI models and inference declines over time, operating leverage increases. Margins gradually expand, and a greater share of each incremental dollar of revenue turns into free cash flow.

This matters because it gives Meta optionality. With robust FCF:

• Meta can fund reality labs R&D for AR/VR without jeopardizing financial stability.
• It can continue to buy back shares or even consider dividends long-term.
• It can absorb macro shocks, regulatory changes, or potential new platform shifts like Apple’s rumored push into AR glasses.

In a scenario where all large tech companies employ AI, the incremental differences in scale, data, and product integration matter enormously. Meta’s position, despite all the scrutiny and challenges of the past two years, is growing stronger in relative terms. It’s not just that Meta recovers from ATT; it leaps ahead by fully embedding AI-driven improvements into its ecosystem, ensuring that future headwinds (like privacy changes, and competitor entries) are more manageable with the cushion of massive, AI-enhanced FCF generation.

Conclusion The world of digital advertising is being reshaped by AI, and while multiple platforms stand to gain, Meta’s structural advantages uniquely position it to reap outsized benefits. The company’s scale, multi-format ecosystem, and deep integration of AI into every facet of the advertising lifecycle create a moat that competitors will struggle to overcome.

As we move forward, it’s likely that much of this progression will feel incremental: a few percentage points improvements in ROAS here, a slight increase in Reels CPMs there. But just as the Internet and mobile smartphones eventually accumulated into massive secular changes, these “small” AI-driven enhancements compound into something transformative. Over the next few years, Meta won’t just restore its pre-ATT profitability; it may well surpass it, achieving a level of AI-fueled abundance that cements its place at the center of global digital advertising.

In the final analysis, Meta’s readiness to harness AI isn’t merely a defensive maneuver to recover lost ground. It’s an offensive strategy to shape the next decade of digital advertising, ensuring Meta, more than anyone else, emerges as the platform of choice for advertisers seeking scale, performance, and innovation—and one that, by extension, sets the standard for what AI-driven abundance really means in this industry.