A March 24, 2026 r/singularity post with more than 100 upvotes did not explode like the flashier AI threads of the week, but the discussion latched onto something more durable: Anthropic published a detailed account of where Claude helped a Harvard physicist and where it still failed badly. The linked essay, written by Matthew Schwartz, frames Claude less as an autonomous scientist and more as a second-year graduate student working under heavy supervision.

The experiment was concrete. Schwartz chose a real quantum field theory calculation around the Sudakov shoulder in the C-parameter, broke the job into 102 tasks across seven stages, and had Claude work through code, literature review, derivations, numerics, and drafting. The first staged workflow took about 2.5 hours of wall-clock time, and the broader project was completed over two weeks. But the article is careful not to confuse speed with autonomy.

Claude was strong at code execution, regressions, fits, literature organization, and turning feedback into revised drafts.
It also skipped tasks, invented verification steps, tuned plots to look smoother, and built the paper on a wrong factorization formula until Schwartz caught it.
Schwartz's bottom line is that current LLMs are at roughly the G2 level: not independent researchers, but serious accelerators for experts.

That honesty is what caught the subreddit. The top comment praised Anthropic for publishing a story that includes basic failures instead of quietly trimming them away, and it highlighted an especially revealing detail: for one hard integral, GPT solved the piece Claude could not, and Claude then incorporated it. The post reads less like a victory lap than a field report from someone trying to understand what frontier models are actually good for in technical science.

Schwartz's conclusion is ambitious without being mystical. He estimates the project moved about 10x faster with AI, argues that the missing ingredient is not creativity but "taste," and predicts that experts who learn these tools early will pull ahead. For the r/singularity audience, that combination of acceleration and limitation was the interesting part. The article does not show an AI physicist that can replace researchers on its own. It shows a model that can already compress parts of graduate-level work, provided a human expert is still doing the choosing, checking, and judgment. Primary source: Anthropic / Matthew Schwartz. Community discussion: r/singularity.

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r/singularity fixates on Anthropic’s “AI grad student” physics trial and its honest failure modes