About this article
This article was generated using an automated workflow powered by generative AI. It reviews 'The eternal complement' published by OpenAI and outlines the perspective that AI's value lies not only in genius ideation, but also in its ability to support execution, coordination, and iteration.Verification status: 📘 OpenAI essay verified – Future predictions separated from facts
Official release date: October 5, 2026
Information verification baseline date: October 6, 2026
On October 5, 2026, OpenAI published 'The eternal complement'.
The core thesis of this text is that the value of AI is not solely in generating new ideas, but rather insupporting the massive volume of execution tasks required to turn those ideas into reality—that is the perspective presented.
The article explicitly states that these are the author's views and do not necessarily represent the official views of OpenAI or its colleagues.
What is it saying?
Research and technological advancement cannot progress on good ideas alone.
Experimentation
Implementation
Coordination
Documentation
Approval
Logistics
Operations
and other large-scale tasks are required.
flowchart LR
A["アイデア"] --> B["実装"]
B --> C["検証"]
C --> D["修正"]
D --> E["運用"]
E --> F["成果"]
G["AI"] --> B
G --> C
G --> D
G --> E
The author discusses how AI can accelerate the conversion of ideas into results by scaling such mundane yet necessary execution capabilities.
In enterprise AI, the easily understandable
When implementing generative AI in an enterprise, looking only at which model is the smartest will not measure its effectiveness.
For example,
generating to-do lists from meeting notes
extracting differences between regulations
identifying missing data in Excel
categorizing logs
updating procedural manuals
In such repetitive tasks, rather than model intelligence,the total time including human reviewis what matters.
The KPI should be total task time rather than response speed
For example, measure the following.
| Item | Metric |
|---|---|
| AI processing time | How many seconds until a response is generated |
| Manual review time | How many minutes spent on source text verification |
| Correction time | Time spent fixing AI outputs |
| Omissions | Number of issues found during manual re-verification |
| Reusability | Whether the same procedure can be used for other projects |
AI回答時間 + 人の確認時間 + 修正時間 = 実務上の総時間
Only when this total time decreases can it be properly evaluated as an improvement in operational efficiency.
Even if the AI is fast, other bottlenecks remain
Even if AI can generate 100 proposals in seconds,
experimental facilities
legal review
approval
manufacturing
customer confirmation
if these take time, the entire process gets backed up there.
In other words, when implementing AI,it is important not to just speed up the AI portion and call it done.
Try it yourself
Select one task that you repeat every week.
Example:
対象: 会議メモからToDo一覧を作る 従来: 15分 AI利用: 1. ToDo候補抽出 2. 担当・期限を表にする 3. 不明は「不明」 4. 人が確認
Measure it about three times, and compare the total time and the amount of correction.
Separate facts from opinions
Verified facts
Published on the OpenAI website on October 5, 2026
An essay from the "The Next Economy" series
Authored by Hemanth Asirvatham and Elliott Mokski
Explicitly stated not to be an official OpenAI view
Author's arguments
AI has the potential to increase not only ideation but also execution capabilities
In the future, the execution side may become a new bottleneck
What is still unknown
Which tasks AI will actually replace and to what extent
The margin of productivity improvement for each enterprise
Long-term allocation of work across society as a whole
