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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 12 new columns ({'company_tsr_idx', 'peer_tsr_idx', 'beat_peers', 'latest_granted_usd', 'latest_cap_usd', 'window_cap_usd', 'cap_to_granted_x', 'latest_fy', 'latest_ceo', 'flags', 'window_granted_usd', 'tsr_vs_peer'}) and 7 missing columns ({'peer_tsr', 'note', 'ceo', 'payout_vs_target', 'company_tsr', 'company', 'metric_type'}).
This happened while the csv dataset builder was generating data using
hf://datasets/NMAIResearch/ceo-pay-scorecard/scorecard_sp500.csv (at revision 0b6635682ac2ef151bf31d5bce9475c2ad234385), ['hf://datasets/NMAIResearch/ceo-pay-scorecard@0b6635682ac2ef151bf31d5bce9475c2ad234385/curated_targets.csv', 'hf://datasets/NMAIResearch/ceo-pay-scorecard@0b6635682ac2ef151bf31d5bce9475c2ad234385/scorecard_sp500.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
ticker: string
latest_fy: int64
latest_ceo: string
latest_granted_usd: int64
latest_cap_usd: int64
window_granted_usd: int64
window_cap_usd: int64
company_tsr_idx: double
peer_tsr_idx: double
tsr_vs_peer: double
beat_peers: bool
cap_to_granted_x: double
flags: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1881
to
{'ticker': Value('string'), 'company': Value('string'), 'ceo': Value('string'), 'payout_vs_target': Value('string'), 'metric_type': Value('string'), 'company_tsr': Value('float64'), 'peer_tsr': Value('float64'), 'note': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 12 new columns ({'company_tsr_idx', 'peer_tsr_idx', 'beat_peers', 'latest_granted_usd', 'latest_cap_usd', 'window_cap_usd', 'cap_to_granted_x', 'latest_fy', 'latest_ceo', 'flags', 'window_granted_usd', 'tsr_vs_peer'}) and 7 missing columns ({'peer_tsr', 'note', 'ceo', 'payout_vs_target', 'company_tsr', 'company', 'metric_type'}).
This happened while the csv dataset builder was generating data using
hf://datasets/NMAIResearch/ceo-pay-scorecard/scorecard_sp500.csv (at revision 0b6635682ac2ef151bf31d5bce9475c2ad234385), ['hf://datasets/NMAIResearch/ceo-pay-scorecard@0b6635682ac2ef151bf31d5bce9475c2ad234385/curated_targets.csv', 'hf://datasets/NMAIResearch/ceo-pay-scorecard@0b6635682ac2ef151bf31d5bce9475c2ad234385/scorecard_sp500.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)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.
ticker string | company string | ceo string | payout_vs_target string | metric_type string | company_tsr float64 | peer_tsr float64 | note string |
|---|---|---|---|---|---|---|---|
TSLA | Tesla | Musk | milestone options | equity_milestone | 891 | 209 | 2018 performance award; $0 salary/bonus; equity-only |
PLTR | Palantir | Karp | service-based equity | service_equity | 754.78 | 258.38 | time-vesting RSUs/SARs not gated on financial targets; $8.6M granted to $11.1B realized |
AVGO | Broadcom | Tan | $0 cash; price-hurdle PSU | annual_cash_bonus | 1,182.35 | 243.37 | annual cash set to $0 through FY2027; front-loaded PSU tracking at max |
GOOGL | Alphabet | Pichai | 200% (max) | LTI_PSU_metric | 360.95 | 138.27 | PSUs vested at max: 3yr relative TSR 92.86th percentile vs S&P 100 - earned |
ORCL | Oracle | Catz | $0 cash bonus | annual_cash_bonus | 231.5 | 172.38 | cash-bonus formula not triggered; LTI performance-based; thin peer-relative TSR |
NVDA | Nvidia | Huang | 150% (self-capped) | LTI_PSU_metric | 1,445.67 | 198.1 | committee self-capped CEO at 150% (others 200%); largest peer outperformance in the set |
AAPL | Apple | Cook | 200% (max) | annual_cash_bonus | 233.88 | 279.51 | annual cash paid at maximum on internal goals; 5yr TSR lagged the board's own peer group |
JPM | JPMorgan Chase | Dimon | discretionary | discretionary | 289.18 | 203.21 | committee-set on a performance assessment; record 2025 (ROTCE 20%) |
GE | GE Aerospace | Culp | 0-200% AEIP; 2023 PSU 175% | not_extracted | 586 | 189 | annual bonus 0-200%; 2023 PSUs capped at max on strong results |
NFLX | Netflix | Sarandos/Peters | 117.57% | annual_cash_bonus | 173.4 | 138.27 | co-CEOs; bonus ~117.57%; revenue exceeded target |
MSFT | Microsoft | Nadella | 103.69% | annual_cash_bonus | 255 | 276 | above-target annual cash; 5yr TSR lagged peer group (-21) despite top-tier pay |
BKNG | Booking Holdings | Fogel | ~162% | annual_cash_bonus | 244.36 | 138.27 | bonus ~162% of target; record bookings |
LLY | Eli Lilly | Ricks | 218% (max 250%) | annual_cash_bonus | 670.56 | 154.11 | bonus 218%; strong results; pay tracked delivery up |
AXP | American Express | Squeri | committee-set | discretionary | 326 | 203 | annual incentive committee-determined; record card-fee revenue |
AMT | American Tower | Vondran | 2023 PSU 157% | not_extracted | 90.61 | 126.71 | hand-read from rendered proxy; 5yr TSR 90.61 below $100 = absolute loss (REIT); LTI half time-vested |
INTC | Intel | Lip-Bu Tan | AIP 118.7% | annual_cash_bonus | 85.08 | 264.83 | above-target bonus in a -$0.3B loss year; 5yr TSR 85.08 below $100 = absolute loss; new-CEO $161.6M CAP |
AMD | AMD | Su | EIP 121% | annual_cash_bonus | 234 | 547 | bonus 121%; semis peer index Nvidia-inflated so -313 gap is composition; vs S&P 500 roughly in line |
NOW | ServiceNow | McDermott | LTI 119.7% | LTI_PSU_metric | 139 | 227 | PRSU target achievement 119.7%; FY2025 CAP -$83M (re-marked down) vs $51.5M granted |
C | Citigroup | Fraser | discretionary (PSU 51.2% of shares) | discretionary | 226.34 | 203.47 | committee-determined; 2022 PSUs earned 51.2% of target shares; beat peers +23 |
WFC | Wells Fargo | Scharf | discretionary (cap 150%) | discretionary | 347 | 196 | risk-adjusted discretionary; PSAs cap 150%; strongly beat peers +151 |
UNH | UnitedHealth | Hemsley/Witty | LTI 0% (below threshold) | LTI_PSU_metric | 102 | 148 | 2023-25 LTI paid 0% in the crisis year - pay tracked delivery DOWN; cleanest such case |
BNY | BNY Mellon | Vince | PSU 147.5% | LTI_PSU_metric | 315.84 | 203.47 | PSUs earned 147.5% (cap 150%); pay tracked delivery up; beat peers +112 |
TMO | Thermo Fisher | Casper | AIP 99.8% | annual_cash_bonus | 126.05 | 156.63 | near-target bonus while 5yr TSR lagged peers (-31) |
COF | Capital One | Fairbank | PSU 0-150% (% not surfaced) | not_extracted | 268.21 | 203.47 | $0 cash salary; 100% at-risk equity; beat peers +65; Discover-acquisition year |
META | Meta | Zuckerberg | none (founder) | none_founder | 243.32 | 445.33 | no equity no bonus; granted = CAP = $25.1M (mostly security); payout-vs-target undefined |
CRM | Salesforce | Benioff | held at 100% | annual_cash_bonus | 94.12 | 256.61 | committee declined an upward adjustment; CAP -$49M; 5yr TSR 94 below $100 = absolute loss |
ADBE | Adobe | Narayen | 2023 PSP 83% | LTI_PSU_metric | 67.11 | 187.96 | LTI cut by a relative-TSR miss; sharpest absolute decline (TSR 67 below $100); CAP -$17M |
IBM | IBM | Krishna | AIP 150% | annual_cash_bonus | 303 | 210 | bonus 150%; turnaround; strongly beat peers +93; pay tracked delivery up |
GS | Goldman Sachs | Solomon | $80M retention (no condition) | not_extracted | 375 | 203 | $80M RSU 5yr cliff with NO performance condition (Glass Lewis advised against); beat peers +172 |
TSLA | null | null | null | null | null | null | null |
PLTR | null | null | null | null | null | null | null |
AVGO | null | null | null | null | null | null | null |
COIN | null | null | null | null | null | null | null |
HOOD | null | null | null | null | null | null | null |
WELL | null | null | null | null | null | null | null |
PANW | null | null | null | null | null | null | null |
GOOGL | null | null | null | null | null | null | null |
GOOG | null | null | null | null | null | null | null |
ORCL | null | null | null | null | null | null | null |
NVDA | null | null | null | null | null | null | null |
AAPL | null | null | null | null | null | null | null |
WBD | null | null | null | null | null | null | null |
JPM | null | null | null | null | null | null | null |
CRWD | null | null | null | null | null | null | null |
APP | null | null | null | null | null | null | null |
AXON | null | null | null | null | null | null | null |
GE | null | null | null | null | null | null | null |
NFLX | null | null | null | null | null | null | null |
TTD | null | null | null | null | null | null | null |
MSFT | null | null | null | null | null | null | null |
GS | null | null | null | null | null | null | null |
ARES | null | null | null | null | null | null | null |
MPWR | null | null | null | null | null | null | null |
BKNG | null | null | null | null | null | null | null |
FICO | null | null | null | null | null | null | null |
LLY | null | null | null | null | null | null | null |
AXP | null | null | null | null | null | null | null |
CDNS | null | null | null | null | null | null | null |
AMAT | null | null | null | null | null | null | null |
REGN | null | null | null | null | null | null | null |
NOW | null | null | null | null | null | null | null |
MRNA | null | null | null | null | null | null | null |
AMD | null | null | null | null | null | null | null |
LEN | null | null | null | null | null | null | null |
SMCI | null | null | null | null | null | null | null |
LYV | null | null | null | null | null | null | null |
WSM | null | null | null | null | null | null | null |
AIG | null | null | null | null | null | null | null |
TDG | null | null | null | null | null | null | null |
ABBV | null | null | null | null | null | null | null |
MS | null | null | null | null | null | null | null |
WMB | null | null | null | null | null | null | null |
WFC | null | null | null | null | null | null | null |
WDAY | null | null | null | null | null | null | null |
EXPE | null | null | null | null | null | null | null |
FLEX | null | null | null | null | null | null | null |
TMUS | null | null | null | null | null | null | null |
CAT | null | null | null | null | null | null | null |
MU | null | null | null | null | null | null | null |
KLAC | null | null | null | null | null | null | null |
AMZN | null | null | null | null | null | null | null |
AON | null | null | null | null | null | null | null |
MSI | null | null | null | null | null | null | null |
APH | null | null | null | null | null | null | null |
IRM | null | null | null | null | null | null | null |
MNST | null | null | null | null | null | null | null |
VRT | null | null | null | null | null | null | null |
XOM | null | null | null | null | null | null | null |
CVNA | null | null | null | null | null | null | null |
RTX | null | null | null | null | null | null | null |
C | null | null | null | null | null | null | null |
INTU | null | null | null | null | null | null | null |
PLD | null | null | null | null | null | null | null |
HWM | null | null | null | null | null | null | null |
V | null | null | null | null | null | null | null |
COF | null | null | null | null | null | null | null |
WMT | null | null | null | null | null | null | null |
UBER | null | null | null | null | null | null | null |
MCK | null | null | null | null | null | null | null |
MRVL | null | null | null | null | null | null | null |
End of preview.
CEO Pay-vs-Delivery Scorecard
Reproducible, primary-source scrutiny of executive pay: what the S&P-100's highest-paid CEOs were granted versus what they were actually paid, set beside what they delivered. Built from SEC Pay-versus-Performance disclosures on EDGAR. A financial-disclosure audit in structure, published with the data and a script that regenerates every figure.
- Author: NM AI Research (independent analyst)
- ORCID: 0009-0003-4213-7769
- DOI: https://doi.org/10.5281/zenodo.20680109
- Interactive tool: https://nmairesearch.github.io/ceo-pay-scorecard/
- Source and code: https://github.com/NMAIResearch/ceo-pay-scorecard
Files
scorecard_sp500.csv(494 rows): the computed scorecard. Columns:ticker,latest_fy,latest_ceo,latest_granted_usd(pay granted),latest_cap_usd(compensation actually paid),window_granted_usd,window_cap_usd,company_tsr_idx,peer_tsr_idx,tsr_vs_peer,beat_peers,cap_to_granted_x(paid-to-granted multiple),flags.curated_targets.csv: the hand-curated layer for named cases. Columns:ticker,company,ceo,payout_vs_target,metric_type,company_tsr,peer_tsr,note.build.py: standard-library reproducer that reads the data and writes the front-end.LICENSE: Creative Commons Attribution 4.0 International.
Method
Granted pay is separated from compensation actually paid, each figure traced to the SEC filing, and delivery measured against peer total shareholder return. We do not judge, we present the numbers so the reader can. Drafting is AI-assisted; the judgement is not.
Citation
NM AI Research. CEO Pay-vs-Delivery Scorecard. Zenodo. https://doi.org/10.5281/zenodo.20680109 . Licensed CC BY 4.0.
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