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.
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| Original publisher | Stack Overflow Blog |
| Canonical URL | https://stackoverflow.blog/2026/06/30/why-intent-prediction-needs-more-than-an-llm/ |
| Publication time | Tue, 30 Jun 2026 07:40:00 GMT |
| Retrieval time | 2026-06-30T07:24:23.532Z |
| Last seen | 2026-06-30T07:24:23.532Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | numolkrmG_Um |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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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