---
title: "How Much Accepted Work Are We Producing per Unit of Human Attention?"
description: "5 source-backed AI claims from How Much Accepted Work Are We Producing per Unit of Human Attention?, with key statistics, context, and the original AI…"
url: "https://kbanc.com/claims-library/how-much-accepted-work-are-we-producing-per-unit-of-human-attention"
source: "https://aiadopters.club/p/how-much-accepted-work-are-we-producing"
date: "2026-09-22"
topics: ["measurement", "implementation", "business"]
generated: "2026-09-23"
---

# How Much Accepted Work Are We Producing per Unit of Human Attention?

By Kamil Banc | September 22, 2026

## Claims

1. **Measure Accepted Work per Hour** (Kamil's interpretation): Measure AI value as accepted jobs divided by total human hours spent on all attempted jobs.
2. **40% Received Workslop** (source summary): A 2025 survey of 1,150 U.S. desk workers found 40% received workslop in the previous month.
3. **Two Hours per Workslop Incident** (source summary): Workslop recipients estimated about two hours to resolve each incident, based on self-reported experiences.
4. **BCG Consultants GPT-4 Experiment** (source summary): A randomized experiment with 758 BCG consultants found GPT-4 improved performance within its capabilities.
5. **Keep Rejected Work Counted** (Kamil's interpretation): Keep time spent on rejected work in the denominator so dashboards do not hide failures.

## Evidence

### Quote
> "How much accepted work are we producing per unit of human attention?" - Kamil Banc

### Key Statistics
- **40%**: Share of 1,150 full-time U.S. desk workers in a September 2025 BetterUp Labs and Stanford Social Media Lab survey who reported receiving workslop in the previous month.
- **~2 hours**: Time recipients estimated to resolve each workslop incident; self-reported, not measured company-wide productivity losses.
- **758 consultants**: Number of BCG consultants in a randomized GPT-4 experiment first reported in 2023, which found better performance within the model's capabilities and worse correctness outside them.
- **0.8 vs 1.0 accepted proposals per hour**: Illustrative comparison: eight accepted proposals in ten human hours versus nine accepted in nine hours, with scope and quality standard held constant.

## Context
The article proposes a practitioner-oriented productivity metric: accepted jobs divided by total human hours across all attempted jobs, including preparation, checking, correction, and the recipient's review time. The author grounds the approach in external evidence, citing the BetterUp/Stanford workslop survey and the BCG GPT-4 experiment to show why first-draft speed alone misleads. Six operational controls translate the metric into practice, including agreeing acceptance checks with recipients upfront, matching review intensity to consequence, and logging reasons work gets returned. The recommended rollout compares ten consecutive jobs before and after the new rules, tracking first-pass acceptance, returns, and post-acceptance errors. The author explicitly frames these as practical proposals consistent with NIST's voluntary AI risk guidance, not a proven productivity intervention.

## Source
- Original: [How Much Accepted Work Are We Producing per Unit of Human Attention?](https://aiadopters.club/p/how-much-accepted-work-are-we-producing)
- Cite: kbanc.com/claims-library/how-much-accepted-work-are-we-producing-per-unit-of-human-attention

## Primary Evidence
- [BetterUp Labs and Stanford Social Media Lab survey](https://www.betterup.com/workslop) (betterup.com; supports claims 2, 3)
- [randomized experiment with 758 BCG consultants using GPT-4](https://www.hbs.edu/ris/Publication%20Files/dell-acqua-et-al-2026-navigating-the-jagged-technological-frontier_5c589c8c-fbb5-458f-b285-c944746cd717.pdf) (hbs.edu; supports claim 4)