Investors should consider a bullish position in AppLovin (APP), which is delivering rapid 60% annual ad spend growth and an outstanding 84% EBITDA margin powered by its advanced deep learning technology. AppLovin (APP) offers substantial upside potential as it leverages an estimated $6 billion in cash generation this year to expand from mobile gaming into the massive direct-to-consumer e-commerce advertising market. Maintain long exposure to Meta Platforms (META), whose core discovery advertising model creates net-new consumer demand that remains insulated from generative AI disruption. In contrast, consider trimming exposure to Alphabet (GOOGL), as conversational AI interfaces like ChatGPT present a long-term structural threat to traditional, query-based search advertising.

AppLovin (APP)

• AppLovin operates an ad tech platform across 100,000+ mobile games, reaching an audience of over 1 billion daily active adult users.
  • The total mobile gaming advertising ecosystem is estimated to be roughly $50 billion annually, with AppLovin's platform processing approximately $20 billion in ad spend while growing at roughly 60% year-over-year.
• The business exhibits industry-leading unit economics and profitability.
  • Expected to generate approximately $6 billion in cash this year.
  • Generates an 84% EBITDA margin, supported by automated, lean operations and proprietary deep learning technology.
• Management executed one of the most aggressive and profitable corporate share buyback programs in recent market history.
  • After IPOing in April 2021 at a $28 billion valuation and peaking at $40 billion, the company suffered a 92% drawdown in 2022 to a $3.8 billion market cap despite generating $1 billion in EBITDA (<4x EBITDA multiple).
  • The company repurchased approximately $6 billion of its own stock, retiring 20% to 25% of outstanding shares.
  • The stock rebounded from a low of $9 to a peak of $750 per share (reaching a $250 billion market cap).
• Growth is driven by a core technological transition from basic regression algorithms (ML 1.0) to advanced deep learning recommendation models (ML 2.0).
  • The upgrade dramatically improved advertiser return on ad spend (ROAS), allowing the company to expand from mobile game cross-promotions into broader e-commerce discovery advertising.
  • The company divested its owned gaming studios after utilizing them to acquire the initial training data needed to train its models, removing channel conflict with third-party game developers.
• Regulatory headwinds from Apple and European privacy initiatives have largely stabilized as recommendation algorithms have adapted to aggregated user data models.

Takeaways

• High-Margin AI Execution: AppLovin represents a pure-play commercial beneficiary of applied deep learning recommendation models, achieving an 84% EBITDA margin.
• Expansion into E-commerce: The company's expansion beyond gaming into e-commerce ads opens up a significantly larger addressable market, positioning it to compete with legacy ad tech giants for discovery-driven consumer ad spend.
• Disciplined Capital Allocation: Management has a proven track record of using free cash flow for opportunistic, countercyclical share buybacks during market dislocations.

Alphabet (GOOGL)

• Google Search was highlighted as the dominant "bottom-of-funnel" ad product, capturing transactions where consumer intent already exists.
• The search ad franchise faces long-term structural competition from Large Language Models (LLMs) and conversational AI interfaces such as ChatGPT, which can satisfy and close direct search queries without expanding net consumer demand.

Takeaways

• Query-based and search-driven advertising models face margin and disruption risks as AI-native chat interfaces substitute standard web search behavior.

Meta Platforms (META)

• Meta (via Instagram and Facebook) remains the primary standard for "discovery advertising"—creating new consumer intent rather than simply harvesting search intent.
• Advanced machine learning recommendation systems have transformed modern ad delivery into relevant, engaging content that drives direct-to-consumer e-commerce sales.

Takeaways

• Discovery-based ad models possess stronger defensibility against search-replacing AI tools because they generate new consumer transactions rather than fulfilling pre-existing query intent.