--- title: A Severe Misalignment of AI in Mathematics | daily.dev description: Twenty-five Fields Medallists, including Terence Tao, signed a declaration warning that AI companies' race to solve famous mathematical problems as benchmarks... canonical: https://daily.dev/posts/a-severe-misalignment-of-ai-in-mathematics-t8kt7o1wi twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: A Severe Misalignment of AI in Mathematics | daily.dev og:description: Twenty-five Fields Medallists, including Terence Tao, signed a declaration warning that AI companies' race to solve famous mathematical problems as benchmarks... og:url: https://daily.dev/posts/a-severe-misalignment-of-ai-in-mathematics-t8kt7o1wi og:image: https://api.daily.dev/og/posts/T8Kt7O1wI.png og:image:alt: A Severe Misalignment of AI in Mathematics 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. # A Severe Misalignment of AI in Mathematics **[Lobsters](https://daily.dev/sources/lobsters)** · 5 min read · 0 upvotes · 0 comments ## Summary Twenty-five Fields Medallists, including Terence Tao, signed a declaration warning that AI companies' race to solve famous mathematical problems as benchmarks is misaligned with the goals of the mathematical community. The statement argues that rushed, unattributed AI-generated proofs bypass the human processes of peer review, writeup, and transmission that traditionally turn a solved problem into shared mathematical understanding, raising concerns about plagiarism, attribution, and the erosion of conceptual insight in favor of rapid true/false output. Signatories call for urgent action from the mathematical community, AI companies, and society more broadly, while acknowledging AI's potential to accelerate genuine mathematical understanding if guided responsibly. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why are Fields Medal winners concerned about AI solving mathematical problems? Twenty-five Fields Medallists signed a declaration arguing that AI companies' push to solve famous math problems as benchmarks conflicts with mathematics as a discipline built on human understanding, mentorship, and idea transmission. Their concerns include rushed announcements without proper writeups, attribution and plagiarism issues, and the risk that mass-produced true/false statements crowd out genuine conceptual insight rather than fostering it. _Developers following AI's expanding reasoning capabilities can track this debate on daily.dev._ ## Community take How the wider developer community reacted, aggregated from 2 discussions and 31 comments across hackernews, lobsters (as of 2026-09-13). **TL;DR:** Reactions split between sympathy for the concern that AI-generated proofs strip away the human process of mathematical understanding, and skepticism that this framing is naive, gatekeeping, or simply the same disruption software already went through. **Sentiment:** 15% positive · 35% mixed · 50% skeptical **The case for** - The concern is seen as sympathetic because the 'journey' of discovery historically built understanding, not just the answer. - Some feel this crisis could set a useful precedent, establishing that 'proof factories' aren't truly valuable across knowledge work. - AI could eventually help by tailoring proof explanations to individual learners, improving math education. **The pushback** - Some argue the analogy to gatekeeping mountain summits is flawed since nothing stops anyone from still 'climbing' (doing math the traditional way). - Several see this as identical to a disruption software development already experienced, questioning whether math will be treated any differently once dust settles. - Doubts are raised about whether current AI methods actually invent new mathematics versus just applying existing methods more thoroughly. - One commenter calls the idealistic framing of proofs building deep understanding unrealistic, noting many human proofs are already inaccessible to most. - Concerns about provenance and attribution are seen as likely naive given how closed and Machiavellian AI companies are about training data. **By community** - hackernews (mixed): Discussion ranges from evocative support for the 'journey vs. summit' framing to pushback calling it gatekeeping, plus tangents on whether AI can create new math or just apply existing methods faster. - lobsters (mixed): Commenters draw parallels to software's own history of 'worse is better' and model collapse risk, while debating whether math and software will diverge in how disruptive unreadable AI output becomes. **Hottest debate:** Whether skipping the human 'journey' of proof-discovery is a genuine loss or just gatekeeping when the destination (a solved problem) is what matters. **Open questions** - Will AI-solved problems and their reasoning actually get fed back into future model training, or will that context be lost? - Will mathematics diverge from software in how tolerant it is of correct-but-unreadable AI-generated output? **Highlights** > In the Economist article Tao links, Hugo Duminil-Copin, draws a comparison: airdropping someone on the summit of Mount Everest is very different from climbing it. The fundamental issue with AI solving any perceived difficult problem is that we have lost the journey. The sight atop Mount Everest looks much different when you have climbed compared to being dropped from above. > — [metanoia\_ on hackernews · 2 comments](https://news.ycombinator.com/item?id=49663603) > Sure, but actually what's the issue? You can still climb. If one do it for the sake of the journey, not some side quest to fame, glory, and social prestige, the mountain is still there. Maybe the one that lament that the sacred mountain should be left alone and be kept pristine of human arrogance and pissing territory can complain > — [psychoslave on hackernews](https://news.ycombinator.com/item?id=49664624) > The situation seems analogous indeed. However, cynically, I do wonder whether the outcome will differ across the CS and math industry once the dust settles. One could argue that software production is funded in large part for the end product, not the process, whereas mathematics is far more exploratory. More concretely, it seems to me that an agent that outputs unreadable code implementing software that works perfectly from a user perspective is substantially more useful and disruptive than an agent that outputs a unreadable proof of a mathematical result that is formally verified. > — [jo3\_l on lobsters · 1 points, 1 comments](https://lobste.rs/s/xsbz3l/severe_misalignment_ai_mathematics#c_ak3mhj) > One question I have is whether/how AI "solutions" make it back into future models. I've sort of naively assumed that AI companies basically scrape the web in large swaths, do a bit curating, and train. In a case where an AI solves math problems, I conjecture, that lots of text is created documenting that the discovery happened (only because its new), but very little where mathematicians are spending the time to really document and discuss it in detail where future training runs will have lots of human produced content to ingest. Are Anthropic/Google/OpenAI specifically feeding back these refined gems into future training runs with a "this really matters, but since we took away the thunder of mathematicians online geeking out about it, you should still consider this as as significant as other great discoveries from the past"? > — [travisgriggs on lobsters · 1 points, 1 comments](https://lobste.rs/s/xsbz3l/severe_misalignment_ai_mathematics#c_anqyvn) > The quote at the top is inspiring but not realistic. Humans have long produced proofs that are difficult to follow and there are many proofs that only a few understand after long and specialized study. It seems out of place to me to complain about mathematical discoveries made by computers simply because they might be hard to understand by humans. I even thing it will go in the opposite direction: we will be able to train LLMs to break down mathematical proofs tailored to each of our indvidual levels and ways of understanding, thereby improving mathematical education. > — [kghose on lobsters · 1 points](https://lobste.rs/s/xsbz3l/severe_misalignment_ai_mathematics#c_fvfbfc) **Source threads** - [hackernews](https://news.ycombinator.com/item?id=49662116) · 144 points · 24 comments - [lobsters](https://lobste.rs/s/xsbz3l/severe_misalignment_ai_mathematics) · 40 points · 7 comments ## Similar posts on daily.dev - [Mathematicians issue Leiden Declaration against AI misuse of their work](https://daily.dev/posts/mathematicians-issue-leiden-declaration-against-ai-misuse-of-their-work-qwfknujan) · The Next Web · 1 upvotes · 1 comments --- Tags: [#ai](https://daily.dev/tags/ai), [#llm](https://daily.dev/tags/llm), [#math](https://daily.dev/tags/math), [#ethical-ai](https://daily.dev/tags/ethical-ai) [View this post on daily.dev](https://daily.dev/posts/a-severe-misalignment-of-ai-in-mathematics-t8kt7o1wi) ```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":"A Severe Misalignment of AI in Mathematics","url":"https://daily.dev/posts/a-severe-misalignment-of-ai-in-mathematics-t8kt7o1wi","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/a-severe-misalignment-of-ai-in-mathematics-t8kt7o1wi"},"datePublished":"2026-09-11T22:07:42.423Z","dateModified":"2026-09-13T01:53:34.626Z","description":"Twenty-five Fields Medallists, including Terence Tao, signed a declaration warning that AI companies' race to solve famous mathematical problems as benchmarks...","image":"https://media.daily.dev/image/upload/s--1KxV4ohY--/f_auto/v1722860400/public/Placeholder%2007","thumbnailUrl":"https://media.daily.dev/image/upload/s--1KxV4ohY--/f_auto/v1722860400/public/Placeholder%2007","isAccessibleForFree":true,"articleSection":"Lobsters","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":"Lobsters","logo":"https://media.daily.dev/image/upload/s--tl8v_Fku--/f_auto,t_logo/v1698841318/logos/lobste.jpg","url":"https://daily.dev/sources/lobsters"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/a-severe-misalignment-of-ai-in-mathematics-t8kt7o1wi","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"ai,llm,math,ethical-ai","timeRequired":"PT5M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Lobsters","item":"https://daily.dev/sources/lobsters"},{"@type":"ListItem","position":3,"name":"A Severe Misalignment of AI in Mathematics"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/a-severe-misalignment-of-ai-in-mathematics-t8kt7o1wi#faq","mainEntity":[{"@type":"Question","name":"Why are Fields Medal winners concerned about AI solving mathematical problems?","acceptedAnswer":{"@type":"Answer","text":"Twenty-five Fields Medallists signed a declaration arguing that AI companies' push to solve famous math problems as benchmarks conflicts with mathematics as a discipline built on human understanding, mentorship, and idea transmission. 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