While the market seeks revenue from novel AI products, the first significant financial impact has come from using AI to enhance existing digital advertising engines. This has driven unexpected growth for companies like Meta and Google, proving AI's immediate value beyond generative applications.

X plans to delete all heuristics from its recommendation system. The feed will instead be powered by Grok, which will analyze every piece of content to match users with posts and videos. This is a move from a traditional, rule-based algorithm to a fully generative, AI-driven content discovery engine.

AI's evolution can be seen in two eras. The first, the "ImageNet era," required massive human effort for supervised labeling within a fixed ontology. The modern era unlocked exponential growth by developing algorithms that learn from the implicit structure of vast, unlabeled internet data, removing the human bottleneck.

Contrary to popular narrative, Google's AI products have likely surpassed OpenAI in monthly users. By bundling AI into its existing ecosystem (2B users for AI Overviews, 650M for the Gemini app), Google leverages its massive distribution to win consumer adoption, even if user intent is less direct than visiting ChatGPT.

While ChatGPT is still the leader with 600-700 million monthly active users, Google's Gemini has quickly scaled to 400 million. This rapid adoption signals that the AI landscape is not a monopoly and that user preference is diversifying quickly between major platforms.

The core technology behind ChatGPT was available to developers for two years via the GPT-3 API. Its explosive adoption wasn't due to a sudden technical leap but to a simple, accessible UI, proving that distribution and user experience can be as disruptive as the underlying invention.

Before generative AI, the simple algorithms optimizing newsfeeds for engagement acted as a powerful, yet misaligned, "baby AI." This narrow system, pointed at the human brain, was potent enough to create widespread anxiety, depression, and polarization by prioritizing attention over well-being.

The common belief that AI can't truly understand human wants is debunked by existing technology. Adam D'Angelo points out that recommender systems on platforms like Instagram and Quora are already far better than any individual human at predicting what a user will find engaging.

Unlike chatbots that rely solely on their training data, Google's AI acts as a live researcher. For a single user query, the model executes a 'query fanout'—running multiple, targeted background searches to gather, synthesize, and cite fresh information from across the web in real-time.