If math is more than proof, we need to better celebrate the rest of it

📝 Discussion Summary (Click to expand)

Three prevalent themes in the discussion

Goodhart’s Law eroding mathematical rigor
“Mathematics is suffering from Goodhart's Law: 'When a measure becomes a target, it ceases to be a good measure.'” – kurthr
AI surpassing human abilities in exposition and appreciation
“Jacob Tsimerman claims we might have superhuman expositors by April, so then what?” – E‑Reverance
“It's all good until we have superhuman appreciators :)” – ViscountPenguin
Human retreat to intuition/religion and worries about value & pay
“It starts to sound like medieval science - 'understanding' instead of proofs.... people... start to retreat into areas like intuition which supposedly aren't reachable by the machine.... Some go even further starting to talk about religion.” – trhway
“...there's an outstanding utility problem that remains unaddressed.... related to who's getting paid how much and for what.” – random3

🚀 Project Ideas

Generating project ideas…

A semantic analysis tool that evaluates mathematical papers on dimensions such as conceptual novelty, explanatory depth, and interconnectivity, moving beyond citation‑based metrics.
Provides a richer, multi‑faceted impact score that mitigates Goodhart’s Law by rewarding genuine intellectual contribution rather than easily gamed counts.

Key	Value
Target Audience	Mathematicians, research administrators, funding agencies
Core Feature	Language‑model‑driven analysis of paper text to output scores for novelty, clarity, explanatory value, and cross‑field connections
Tech Stack	Python, HuggingFace Transformers, spaCy, FAISS for similarity search, Flask/FastAPI API, React dashboard
Difficulty	Medium
Monetization	Revenue-ready: SaaS subscription tiered by institution size

Addresses kurthr’s concern: “Mathematics is suffering from Goodhart's Law… When a measure becomes a target, it ceases to be a good measure.” – offers a measure that resists simple targeting.
Gives funders and hiring committees a nuanced view of a paper’s worth, stimulating discussion on how to evaluate mathematical work in the age of AI.

A collaborative writing platform for mathematical exposition (lecture notes, survey articles, intuitive explanations) where AI assists drafting but every human contribution is immutable tracked and reputation‑weighted.
Enables fair recognition and potential monetization of the intuitive, insight‑driven work that AI struggles to replicate.

Key	Value
Target Audience	Educators, expository writers, researchers seeking to share intuition
Core Feature	Real‑time co‑editing with AI suggestions, granular provenance tracking per sentence, reputation system tied to human‑authored edits
Tech Stack	React, Node.js, Yjs for CRDT collaboration, OpenAI API for assistance, PostgreSQL, Redis for reputation scores
Difficulty	Medium
Monetization	Revenue-ready: Freemium – free basic collaboration, premium for advanced analytics and revenue‑share options

Appeals to trhway’s fear of retreating into “intuition which supposedly aren't reachable by the machine” by providing a venue where intuition is captured, credited, and rewarded.
Could spark discussion on preserving the role of human insight in mathematics while leveraging AI for drafting and formatting.

A decentralized provenance ledger (using IPFS + lightweight blockchain) that records every contribution—human ideas, AI‑generated steps, refinements—in a mathematical workflow.
Smart contracts automatically allocate credit and potential royalties when the resulting proof, algorithm, or explanation is used or cited downstream.

Key	Value
Target Audience	Researchers collaborating with AI tools, open‑source math projects, platforms hosting AI‑assisted proofs
Core Feature	Immutable log of contributions with hashes of inputs/outputs, rule‑based revenue split encoded in Solidity contracts
Tech Stack	IPFS for storage, Polygon (or similar low‑fee L2) for smart contracts, Rust/Go off‑chain indexer, GraphQL explorer
Difficulty	High
Monetization	Revenue-ready: Transaction fee on royalty flows (e.g., 1% of distributed rewards)

Directly tackles random3’s point about “who's getting paid how much and for what” by creating a transparent, automated payment split for AI‑human collaborative work.
Provides a concrete technical answer to the utility problem raised, likely to generate lively debate on fairness and incentive design in AI‑augmented research.