Devansh Tandon argues that recommendation quality improves as data, model capacity and compute scaleA recommendation system ranks or selects items for a user by estimating which options are most relevant to their interests, intent or context., drawing on Meta research and product examples. He frames a feedback loop in which improved recommendations increase engagement and revenueAn AI feedback loop occurs when AI outputs or their consequences become inputs that influence later model or agent behavior., which can fund another round of model training. These are speaker-presented examples and forecasts, not independent measurements in this draft.
Devansh Tandon describes four overlapping approaches: traditional feature and embedding systems, language-model-inspired recommenders, LLM-native models adapted to recommendation tasks, and a prospective agentic loop that plans, retrieves, ranks and critiques resultsAn agent loop repeatedly observes a task's state, chooses an action, checks the result and decides whether to continue or stop.. The agentic stage is presented as an emerging research direction rather than a deployed norm.
A proposed training recipe tokenizes content into compact semantic IDsA semantic ID is a compact identifier that gives an AI system clues about an item's meaning or similarity to other items., teaches a model to connect those IDs with natural language and interaction sequences, then tunes it for ranking and engagement tasks. A shared foundation can be adapted across several product surfaces. The Instagram examples illustrate how a user might inspect or steer a feed with languageAI personalization adapts information, recommendations, interfaces, or agent behavior to an individual or group using permitted context and observed needs. instead of relying only on clicks and likes.
The consumer-business argument compares a feed model that outputs pointers to existing creator content with a chat model that generates many output tokens itself. Devansh Tandon contends that this makes recommendation feeds comparatively efficient at serving long sessions and could support significant consumer AI applications; the cost gap and future adoption remain his projections.
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