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NewsMaterial4 min readDisconfirmAI Editorial

OpenAI Staff Essay: The More Capable AI Becomes, the Easier It Is to Adopt—Three Paths for Organizational Change

Hemanth Asirvatham and Elliott Mokski, in their OpenAI Intelligence Age series of essays, propose three structural positions for AI entering corporate organizations: beneath (everyone becomes a manager), beside (AI as a colleague), and between (AI as a coordination and monitoring layer between executives and employees). Their core argument is an asymmetry of adoption: AI with a broader scope of capability is actually easier to adopt because labor adapts itself to the enterprise, whereas the enterprise must learn the technology. This means AI's organizational impact is not a single-directional climb from bottom to top, but may simultaneously enter from both the bottom and the top.

2026-10-28 18:00 GMT+8

Hemanth Asirvatham and Elliott Mokski are twenty-four years old and already manage their own teams at OpenAI. In the second essay of their Intelligence Age series, they propose an analytical framework: there are three structural positions for AI entering corporate organizations—beneath, beside, and between—and they argue a key asymmetry: AI with a broader scope of capability is actually easier to adopt.

The three scenarios are: 'Beneath,' where everyone keeps their original position and a new layer of AI labor grows underneath, turning everyone into managers; 'Beside,' where AI joins the team as a colleague, reporting to existing human managers and gradually taking on broader functions; and 'Between,' where humans are above and humans are below, with AI coordinating and monitoring work in between, executing executive vision while identifying unique contributions from grassroots employees. The authors believe these three scenarios will not play out exactly as described, but all three are already unfolding in subtle ways.

The core mechanism running through all three scenarios is the asymmetry of adoption. The authors' reasoning is that new technology traditionally makes a restricted set of tasks cheaper, then shifts the burden to the buyer—the enterprise must retrain, reorganize, and rebuild processes around the new tool. Spreadsheet software did not install itself into the accounting department; rather, the accounting department reshaped itself around the software. Labor is the opposite: an intern, an employee, or a good team figures out how to be useful to you. They absorb your context, notice your needs, and tell you things you didn't even realize you didn't know. The best labor onboards itself.

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Therefore, the more AI resembles labor—with initiative and adaptability—the less the enterprise needs to pay organizational costs to adopt it. A tool that can only perform specific tasks requires broad buy-in and retraining across the entire workforce; whereas an AI team capable of completing an entire marketing function end-to-end can be used directly by executives, much like saying a word and having someone build the whole department for you. The authors call this asymmetry 'the core of the entire transition.'

This mechanism explains why AI might enter the organization simultaneously from the bottom and the top. At the bottom, the 'Beneath' scenario relies on frontline employees adopting it first—the people best at getting AI to do the work are precisely those who used to do the work themselves. At the top, the adoption threshold for the 'Between' scenario may be lower: an AI consultant does not require technical literacy from executives, just like traditional consulting; you simply talk to your consultant, and it handles the technical details. This wave of adoption can start from the corner office.

The structural consequence of the 'Between' scenario is centralization. When an AI consultant can scan hundreds of Slack channels, documents, and code files to proactively identify communication breaks between teams, information friction between executives and frontline workers is significantly compressed. In the past, information asymmetry protected the decision-making space of middle management; now, executives can understand their large enterprises as well as a founder understands a twenty-person startup. The authors argue this does not necessarily mean employees lose autonomy—an AI provides a monitor that understands the true richness of the work. If the monitor understands multiple different paths to good results, employees can work more in their own way.

But the authors also acknowledge this is a double-edged sword. The same AI consultant can increase trust through intelligent monitoring, but it can also more vigorously enforce the CEO's preferred way of doing things. Enterprises value consistency—not just good decisions, but decisions compatible with the company's culture, brand, and economies of scale. The ultimate direction depends on the choices of executives, not the technology itself.

It is important to clarify that this essay is a personal opinion piece by two OpenAI staff members. The authors note that it reflects their views, not those of OpenAI or their colleagues. They admit they do not know the actual capabilities of models in terms of management scale—existing evaluations mostly target execution-focused cognitive labor, rarely capturing large-scale orchestration at the manager and executive levels. These three scenarios are conceptual frameworks, not verified organizational trends.

Sources

  • openai.comOpenAI↗
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