Current review map

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Every source in the current review, with its three provisional scores and the contribution it makes to the map. Each name links to the source itself.

Source	Outlook	Evidence basis	Reliability	Main contribution
Arvind Narayanan and Sayash Kapoor	38	78	82	Evidence-oriented education baseline, not mathematics-specific.
Brian Keating	61	58	74	Tao interview on AI as a complementary research tool requiring verification.
Conrad Wolfram	72	68	78	Mathematics-education reform for the AI age.
Curt Jaimungal	76	55	70	Yang-Hui He interview on AI-assisted mathematical discovery and its limits.
David Bessis	54	38	68	Mathematical understanding matters beyond theorem production.
Emily Riehl	56	86	90	Tests and verification for meaningful AI contribution to mathematics.
Francis Su	82	35	60	Humanistic case for mathematics and learning.
Gary Marcus	32	65	84	LLM reasoning can be brittle; plausible output is not robust abstraction.
Geordie Williamson	78	62	80	AI may contribute to mathematical discovery.
Jacob Tsimerman	88	55	76	Strongly future-facing research-mathematics forecast.
Jaron Lanier	60	36	64	Immersive mathematical visualization, paired with cautions about treating learners as data.
Jeremy Avigad	64	76	88	Formalization, proof, and responsible mathematical practice.
Jordana Cepelewicz	64	76	82	Quanta's reported synthesis of AI-assisted proof and changing mathematical practice.
Leiden Declaration	24	72	82	Governance, responsibility, and peer-review proposals.
Math and AI Declaration	63	61	74
Max Weinreich	12	48	65	Argues against AI-generated mathematics.
Sabine Hossenfelder	58	45	64	Explainer on claimed AI mathematics breakthroughs; full transcript pending public access.
Sean Carroll	55	54	76	Podcast discussion of neural-network mathematics and the limits of data-hungry models.
Stephen Wolfram	78	55	74	Computation-augmented AI: pair generative models with exact computation.
Steven Strogatz	60	70	84	Podcast interview on black-box models, uncertainty, and statistical reasoning.
Terence Tao	61	78	88	Conditional analysis of mathematical values, verification, and practice.
The Economist	28	86	72	Reports recent evidence on AI use and secondary-school learning.
Timothy Gowers	58	82	90	Separates verified mathematical progress from AI hype.
Yahoo News	30	70	55	Secondary reporting on student-learning evidence.
Yann LeCun	65	65	84	Broad-AI baseline: limits of current language models and future architectures.

Scores are provisional review judgments about a specific source’s argument, never a ranking of people. Outlook runs from anxious (0) to hopeful (100), evidence basis from speculative (0) to data-supported (100), and source reliability from lower (0) to higher (100).

Methodology and scoring rubric · Source ledger · Provisional assessments