Why intent prediction needs more than an LLM
In a Stack Overflow podcast, Frank Portman, CTO of Yobi, explains why next‑token prediction used by large language models is insufficient for forecasting human intent. Yobi creates a foundation model of behavior by combining transformer architectures with graph neural networks to generate accurate, privacy‑preserving predictions for ad tech and marketing. The approach enables millions of personalization decisions per second while keeping consumer data private.
- ▪Portman argues that the inductive bias of next‑token prediction, which works well for language, does not capture the complexity of human behavior intent.
- ▪Yobi’s foundation model blends transformer‑based language understanding with graph neural networks to model interactions and predict future actions.
- ▪The system processes millions of personalization decisions per second, emphasizing scalability and real‑time performance.
- ▪Yobi prioritizes data privacy, ensuring that consumer information remains protected while delivering targeted predictions.
Opening excerpt (first ~120 words) tap to expand
June 30, 2026Why intent prediction needs more than an LLMRyan sits down with Frank Portman, CTO at Yobi, to talk about why next-token prediction, though great for language, isn’t the right inductive bias for forecasting human behavior. They discuss how Yobi builds a “foundation model of behavior” using transformers and graph neural networks instead of chat-style LLMs, and what it takes to run millions of personalization decisions per second while keeping consumer data private.Yobi is a behavioral AI company building foundation models that predict future behavior for ad tech, marketing, and more.Connect with Frank via fportman.com or at yobi.ai.Congrats to Hooked on winning a Populist badge for their answer to Removing whitespace around a saved image.TRANSCRIPTRyan Donovan (00:01.103)Hello…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Stack Overflow Blog.
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