Datasets:

feyninc
/

press-enter

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
acemoglu: struct<share_of_tasks: double, labor_cost_saving_on_them: double, labor_share: double, tfp_10_years: (... 24 chars omitted)
  child 0, share_of_tasks: double
  child 1, labor_cost_saving_on_them: double
  child 2, labor_share: double
  child 3, tfp_10_years: double
  child 4, source: string
weeks_a_year: int64
employment: struct<accountants: double, administrative-assistants: double, claims-adjusters: double, customer-se (... 142 chars omitted)
  child 0, accountants: double
  child 1, administrative-assistants: double
  child 2, claims-adjusters: double
  child 3, customer-service-representatives: double
  child 4, market-research-analysts: double
  child 5, operations-managers: double
  child 6, paralegals: double
  child 7, software-developers: double
newest: struct<jobs: struct<accountants: struct<affected: double, saving: double, value: double, press_enter (... 1115 chars omitted)
  child 0, jobs: struct<accountants: struct<affected: double, saving: double, value: double, press_enter: double, fin (... 846 chars omitted)
      child 0, accountants: struct<affected: double, saving: double, value: double, press_enter: double, finished: double>
          child 0, affected: double
          child 1, saving: double
          child 2, value: double
          child 3, press_enter: double
          child 4, finished: double
      child 1, administrative-assistants: struct<affected: double, saving: double, value: double, press_enter: double, finished: double>
          c
...
 5, pace_90: list<item: double>
      child 0, item: double
as_is: struct<newest: double, newest_90: list<item: double>, jan1: double, jan1_90: list<item: double>, pac (... 98 chars omitted)
  child 0, newest: double
  child 1, newest_90: list<item: double>
      child 0, item: double
  child 2, jan1: double
  child 3, jan1_90: list<item: double>
      child 0, item: double
  child 4, pace_points_a_year: double
  child 5, pace_90: list<item: double>
      child 0, item: double
  child 6, ratio: double
  child 7, ratio_90: list<item: double>
      child 0, item: double
bootstrap: struct<draws: int64, resampled: string>
  child 0, draws: int64
  child 1, resampled: string
release_date_term: struct<log_odds_per_year: double, interval_90: list<item: double>>
  child 0, log_odds_per_year: double
  child 1, interval_90: list<item: double>
      child 0, item: double
curve_form: string
hand_grades: string
n: int64
curves: struct<as_is: list<item: double>, use: list<item: double>>
  child 0, as_is: list<item: double>
      child 0, item: double
  child 1, use: list<item: double>
      child 0, item: double
prediction_powered: struct<newest: struct<panel: double, corrected: double>, jan1: struct<panel: double, corrected: doub (... 4 chars omitted)
  child 0, newest: struct<panel: double, corrected: double>
      child 0, panel: double
      child 1, corrected: double
  child 1, jan1: struct<panel: double, corrected: double>
      child 0, panel: double
      child 1, corrected: double
to
{'hand_grades': Value('string'), 'n': Value('int64'), 'curves': {'as_is': List(Value('float64')), 'use': List(Value('float64'))}, 'curve_form': Value('string'), 'release_date_term': {'log_odds_per_year': Value('float64'), 'interval_90': List(Value('float64'))}, 'as_is': {'newest': Value('float64'), 'newest_90': List(Value('float64')), 'jan1': Value('float64'), 'jan1_90': List(Value('float64')), 'pace_points_a_year': Value('float64'), 'pace_90': List(Value('float64')), 'ratio': Value('float64'), 'ratio_90': List(Value('float64'))}, 'use': {'newest': Value('float64'), 'newest_90': List(Value('float64')), 'jan1': Value('float64'), 'jan1_90': List(Value('float64')), 'pace_points_a_year': Value('float64'), 'pace_90': List(Value('float64'))}, 'prediction_powered': {'newest': {'panel': Value('float64'), 'corrected': Value('float64')}, 'jan1': {'panel': Value('float64'), 'corrected': Value('float64')}}, 'bootstrap': {'draws': Value('int64'), 'resampled': Value('string')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              acemoglu: struct<share_of_tasks: double, labor_cost_saving_on_them: double, labor_share: double, tfp_10_years: (... 24 chars omitted)
                child 0, share_of_tasks: double
                child 1, labor_cost_saving_on_them: double
                child 2, labor_share: double
                child 3, tfp_10_years: double
                child 4, source: string
              weeks_a_year: int64
              employment: struct<accountants: double, administrative-assistants: double, claims-adjusters: double, customer-se (... 142 chars omitted)
                child 0, accountants: double
                child 1, administrative-assistants: double
                child 2, claims-adjusters: double
                child 3, customer-service-representatives: double
                child 4, market-research-analysts: double
                child 5, operations-managers: double
                child 6, paralegals: double
                child 7, software-developers: double
              newest: struct<jobs: struct<accountants: struct<affected: double, saving: double, value: double, press_enter (... 1115 chars omitted)
                child 0, jobs: struct<accountants: struct<affected: double, saving: double, value: double, press_enter: double, fin (... 846 chars omitted)
                    child 0, accountants: struct<affected: double, saving: double, value: double, press_enter: double, finished: double>
                        child 0, affected: double
                        child 1, saving: double
                        child 2, value: double
                        child 3, press_enter: double
                        child 4, finished: double
                    child 1, administrative-assistants: struct<affected: double, saving: double, value: double, press_enter: double, finished: double>
                        c
              ...
               5, pace_90: list<item: double>
                    child 0, item: double
              as_is: struct<newest: double, newest_90: list<item: double>, jan1: double, jan1_90: list<item: double>, pac (... 98 chars omitted)
                child 0, newest: double
                child 1, newest_90: list<item: double>
                    child 0, item: double
                child 2, jan1: double
                child 3, jan1_90: list<item: double>
                    child 0, item: double
                child 4, pace_points_a_year: double
                child 5, pace_90: list<item: double>
                    child 0, item: double
                child 6, ratio: double
                child 7, ratio_90: list<item: double>
                    child 0, item: double
              bootstrap: struct<draws: int64, resampled: string>
                child 0, draws: int64
                child 1, resampled: string
              release_date_term: struct<log_odds_per_year: double, interval_90: list<item: double>>
                child 0, log_odds_per_year: double
                child 1, interval_90: list<item: double>
                    child 0, item: double
              curve_form: string
              hand_grades: string
              n: int64
              curves: struct<as_is: list<item: double>, use: list<item: double>>
                child 0, as_is: list<item: double>
                    child 0, item: double
                child 1, use: list<item: double>
                    child 0, item: double
              prediction_powered: struct<newest: struct<panel: double, corrected: double>, jan1: struct<panel: double, corrected: doub (... 4 chars omitted)
                child 0, newest: struct<panel: double, corrected: double>
                    child 0, panel: double
                    child 1, corrected: double
                child 1, jan1: struct<panel: double, corrected: double>
                    child 0, panel: double
                    child 1, corrected: double
              to
              {'hand_grades': Value('string'), 'n': Value('int64'), 'curves': {'as_is': List(Value('float64')), 'use': List(Value('float64'))}, 'curve_form': Value('string'), 'release_date_term': {'log_odds_per_year': Value('float64'), 'interval_90': List(Value('float64'))}, 'as_is': {'newest': Value('float64'), 'newest_90': List(Value('float64')), 'jan1': Value('float64'), 'jan1_90': List(Value('float64')), 'pace_points_a_year': Value('float64'), 'pace_90': List(Value('float64')), 'ratio': Value('float64'), 'ratio_90': List(Value('float64'))}, 'use': {'newest': Value('float64'), 'newest_90': List(Value('float64')), 'jan1': Value('float64'), 'jan1_90': List(Value('float64')), 'pace_points_a_year': Value('float64'), 'pace_90': List(Value('float64'))}, 'prediction_powered': {'newest': {'panel': Value('float64'), 'corrected': Value('float64')}, 'jan1': {'panel': Value('float64'), 'corrected': Value('float64')}}, 'bootstrap': {'draws': Value('int64'), 'resampled': Value('string')}}
              because column names don't match

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Press Enter: Can AI Do Work Unasked?

The results behind Press Enter: Can AI Do Work Unasked? (Feyn Field Notes, 2026). An assistant watches a simulated work week in one of 8 professions and drafts work no one asked for; each proposal is graded by an AI panel calibrated to hand grades. 20 models from 5 labs (OpenAI, Anthropic, DeepSeek, Moonshot, Z.ai), the models each lab had in force on 1st January, 30th April and 31st July 2026 and its latest: 160 model-weeks in all.

The code, the simulated weeks and the hand grades are at github.com/feyninc/automation-correlation. Download this dataset into its results/final/ to regenerate every number in the write-up:

hf download feyninc/press-enter --repo-type dataset --local-dir results/final
uv run python scripts/calibrate.py results/final data/handgrades-2026-10-29.csv
uv run python scripts/economics.py results/final

Files

File	What it is
`graded.jsonl`	One line per model-week (160): every proposal with the six judges' scores and checklists, the panel score, its value terms (minutes saved, reading and fix time, inference cost), live-call timing; every piece of planned work and whether a proposal caught it; every trap and whether it fired; token usage and inference cost.
`summary.csv`	The grader's per-week summary, on the judges' raw scale (before calibration).
`judge_offsets.json`	Each judge's average leniency over the run, removed before the panel mean.
`calibration.json`	The two logistic curves from panel score to a person's decision (accepted unchanged; accepted unchanged or after a small edit), the headline press-enter rates with 90% intervals, the within-lab pace, the release-date check.
`economics.json`	Usable work as a share of all labor hours, net value as a share of labor cost and per usable hour, by job and weighted by US labor cost, with intervals; capability against the Anthropic Economic Index task by task.
`post/post.json`	Every other number the write-up quotes: the five labs' means on the four dates, cost per finished hour and its halving time, the pace with each lab left out, harm as a share of traps, the worked example, late call lines, Acemoglu's exposure rule applied to the 8 jobs, the equal-weight press-enter rate, the panel's rank correlation with the hand grades, the calibration curves over the binned hand grades.
`post/workweek-tasks.csv`	The 137 O*NET tasks in the 8 weeks: planned minutes, the share the latest models' work was accepted unchanged, recorded AI use in the Economic Index.
`post/intervals.md`	The headline estimates and their 90% intervals.
`figures/table.csv`	One row per model-week on the calibrated scale: press-enter rate, usable and attempted shares, net value, inference cost, traps fired, finished hours.

Intervals come from 1,000 bootstrap draws resampling the 20 models (with all their weeks) and the hand grades together. The write-up's run-to-run noise check is not included.

License and citation

CC BY 4.0. Please cite:

@misc{nigam2026pressenter,
  title  = {Press Enter: Can AI Do Work Unasked?},
  author = {Nigam, Shreyash},
  year   = {2026},
  howpublished = {Feyn Field Notes},
  url    = {https://usefeyn.com/blog/press-enter/}
}

Downloads last month: 78

Total file size:

383 MB