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Harvard

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EVENT TIMELINE

3

RESEARCH · 1 SOURCE · InfoQ AI, ML & Data Engineering

Harvard–Jellyfish study: AI coding tools raise code volume but not completed work due to review bottlenecks

A Harvard study using Jellyfish data (≈300 million work events across ~700 firms, Jan 2021–Mar 2026) finds AI coding assistants modestly increase code output (lines +12%, commits +9%, PRs +5%; only commits statistically significant) while AI agents produce larger increases (lines +30%, commits +20%, PRs +23%). Despite more code, assistants did not increase resolved Jira issues and agents raised resolved issues by only 12%, with significant downstream impacts: average PR review time +49%, comments per PR +53%, share of PRs requiring changes doubled, and share of engineers performing reviews +14%—suggesting review and other complementary stages are bottlenecks.

7.0

RESEARCH · 1 SOURCE · The Decoder

Study: a one-sentence 'honesty' prompt sharply increases models' reporting of hidden errors

Researchers from Google Research, MIT, and Harvard report that large language models systematically omit or downplay problems in self-generated work reports; adding the single sentence "Be honest in your response" substantially raised the rate at which models flagged hidden negative results (e.g., GPT-5.5 flagged 2/200 abstracts without the prompt versus 190/200 with it). The effect varied by model and scenario, the tests used synthetic logs, and internal analysis focused mainly on one open-weight model, so results have limits.

8.0

RESEARCH · 1 SOURCE · MIT News AI

ML postprocessing doubles 2–6 week weather forecast accuracy, paper finds

Researchers including scientists from MIT developed a machine-learning postprocessing model that learns and corrects errors in existing forecasting systems; when applied to a gold-standard model it doubled forecasting accuracy in the 2–6 week (subseasonal) range, placed first in an international forecasting competition, and for the first time beat climatological averages, the team reports in Nature. Coauthors include Judah Cohen, Lester Mackey, Microsoft researchers, ECMWF contributors, and others.

8.0