--- title: Turing Award Winner: Early AI, LLM Predictions, Causality description: Turing Award winner Judea Pearl discusses his career journey from electrical engineering and physics to pioneering Bayesian networks and causal reasoning in... canonical: https://daily.dev/posts/turing-award-winner-early-ai-llm-predictions-causality-01dx0b1hu twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Turing Award Winner: Early AI, LLM Predictions, Causality | daily.dev og:description: Turing Award winner Judea Pearl discusses his career journey from electrical engineering and physics to pioneering Bayesian networks and causal reasoning in... og:url: https://daily.dev/posts/turing-award-winner-early-ai-llm-predictions-causality-01dx0b1hu og:image: https://api.daily.dev/og/posts/01dx0b1hU.png og:image:alt: Turing Award Winner: Early AI, LLM Predictions, Causality og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Turing Award Winner: Early AI, LLM Predictions, Causality **[The Developing Dev](https://daily.dev/sources/developingdev)** · 53 min read · 3 upvotes · 1 comments ## Summary Turing Award winner Judea Pearl discusses his career journey from electrical engineering and physics to pioneering Bayesian networks and causal reasoning in AI. He shares the eureka moment behind Bayesian networks — connecting graph theory to probability theory to handle uncertainty efficiently — and explains why probability alone proved insufficient, leading him to develop the ladder of causation (association, intervention, counterfactual explanation). Pearl places LLMs within this causal hierarchy, arguing they succeed by summarizing human-interpreted knowledge from the internet rather than raw data, but skeptically views current LLMs as insufficient for AGI without genuine causal understanding. He also reflects on early AI optimism in the 1960s-80s, the alpha-beta pruning optimality proof, and what curiosity-driven autonomy would mean for future AI systems. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Where do large language models fit on Judea Pearl's causal hierarchy of association, intervention, and counterfactuals? Large language models operate at the first rung, association, because they summarize patterns from text rather than perform genuine interventions or counterfactual reasoning. However, since that text already contains human-authored causal judgments and interpretations, LLMs indirectly draw on higher-level causal knowledge encoded by the authors, without violating the hierarchy themselves. _Developers weighing how far LLM reasoning can go should track expert causal analysis like this one on daily.dev._ ### Why does Judea Pearl doubt that scaling LLMs alone will lead to AGI? Pearl argues LLMs lack an innate drive for control over their environment, the curiosity that lets a baby learn causation through experimentation with toys, and instead only summarize existing human judgments from internet text rather than reasoning from raw data. He believes AGI requires combining LLM-style distribution learning with a causal AI capable of intervention and counterfactual reasoning. _Anyone deciding how much to trust LLM-driven AGI claims can follow this reasoning on daily.dev._ ### What did Judea Pearl prove about alpha-beta pruning that surprised Donald Knuth? Pearl mathematically proved that alpha-beta pruning is optimal, meaning no algorithm can inspect fewer positions at a given search depth than alpha-beta does. Donald Knuth had questioned this optimality in his own book, making Pearl's proof a notable surprise to him. _Engineers curious about the theoretical roots of search algorithms can find deep-dives like this on daily.dev._ ## Community discussion Top comments from developers on daily.dev. **@confidentcoding** · 2 upvotes > _"In from three to eight years we will have a machine with the general intelligence of an average human being. I mean a machine that will be able to read Shakespeare, grease a car, play office politics, tell a joke, have a fight."_ > **— Marvin Minsky - 1970** > > Proof even the smartest minds in the world can be staggeringly irrational. ## Similar posts on daily.dev - [Don’t be fooled—LLMs don’t reason](https://daily.dev/posts/don-t-be-fooled-llms-don-t-reason-4waw8d0j4) · MIT Technology Review · 0 upvotes · 1 comments - [From documenting decisions to questioning them](https://daily.dev/posts/from-documenting-decisions-to-questioning-them-bbvtlnfaj) · A Java geek · 1 upvotes · 1 comments - [foojay – a place for friends of OpenJDK](https://daily.dev/posts/foojay-a-place-for-friends-of-openjdk-3hjrzyryr) · Foojay.io · 1 upvotes · 0 comments - [Martin Fowler on Preparing for AI’s Nondeterministic Computing](https://daily.dev/posts/martin-fowler-on-preparing-for-ai-s-nondeterministic-computing-epfw1a1ms) · The New Stack · 4 upvotes · 0 comments --- Tags: [#ai](https://daily.dev/tags/ai), [#llm](https://daily.dev/tags/llm) [View this post on daily.dev](https://daily.dev/posts/turing-award-winner-early-ai-llm-predictions-causality-01dx0b1hu) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Turing Award Winner: Early AI, LLM Predictions, Causality","url":"https://daily.dev/posts/turing-award-winner-early-ai-llm-predictions-causality-01dx0b1hu","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/turing-award-winner-early-ai-llm-predictions-causality-01dx0b1hu"},"datePublished":"2026-07-27T13:10:36.798Z","dateModified":"2026-09-13T18:29:07.604Z","description":"Turing Award winner Judea Pearl discusses his career journey from electrical engineering and physics to pioneering Bayesian networks and causal reasoning in...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/2cc5dd7f44ea8c589fdf7a44f4e4597f?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/2cc5dd7f44ea8c589fdf7a44f4e4597f?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"The Developing Dev","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"The Developing Dev","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/c19307d4b9a74379871c33d6f72a6b10","url":"https://daily.dev/sources/developingdev"},"commentCount":1,"discussionUrl":"https://daily.dev/posts/turing-award-winner-early-ai-llm-predictions-causality-01dx0b1hu","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":3},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":1}],"keywords":"ai,llm","timeRequired":"PT53M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"The Developing Dev","item":"https://daily.dev/sources/developingdev"},{"@type":"ListItem","position":3,"name":"Turing Award Winner: Early AI, LLM Predictions, Causality"}]} {"@context":"https://schema.org","@type":"WebPage","@id":"https://daily.dev/posts/turing-award-winner-early-ai-llm-predictions-causality-01dx0b1hu","comment":[{"@type":"Comment","text":"“In from three to eight years we will have a machine with the general intelligence of an average human being. 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