The future of the firm

Authors’ note: This is the second essay in our series on the next economy, part of a new platform to host independent voices exploring an AGI future. It reflects our views, not those of OpenAI or our colleagues.

The two of us were twenty-four when we started managing our own teams here at OpenAI. That is less impressive once you meet our teams.

We’re supposed to be grunts. At twenty-four, you’re at the bottom of the corporate ladder. You take the meeting notes and write a few memos. After years of drudgerous work, you are (hopefully) granted a broader remit.

Our path—and the path of many young people today—is looking different. We enter the workforce already with “employees” at our behest. These deputies work day and night, weekends and holidays. They are great at the grunt work. We even have math olympiad gold medalists on staff to advise us. Our new reports still make their share of mistakes. But they are getting better, learning our needs, and we are learning how to manage them.

Neither of us climbed the corporate ladder. Management has instead come down to our rung. This essay is about our promotion, and the other ways firms might reorganize as we enter the intelligence age.

When AI enters the firm, where does it go? We present three illustrative scenarios:

Beneath us. Everyone keeps their seat, and a new basement of AI labor grows underneath. We all become managers. First we get assistants, then chiefs of staff, and eventually entire miniature firms reporting up to each of us. Every person gets a promotion.

Beside us. AI joins the org chart alongside people as a colleague of sorts, working directly for existing human managers and executives. It starts out by taking up this task and that role. In time, it performs broader functions, reporting higher up the flagpole.

Between us. The most unintuitive case: humans above and humans below, with AI in between. It coordinates and monitors work, enforcing an executive’s vision while recognizing unique contributions from enterprising workers.

None of these scenarios will play out exactly as we describe, but we think aspects of all three will come true. All three are already happening in subtle ways.

Our focus here isn’t total automation but a transitioning economy. We are interested in what chimeric firms will look like as AI improves and laces itself into existing organizations.

We believe the hybrid firm is conceptually important because productive adoption of AI isn’t instantaneous. When the models only do a subset of tasks, adoption may be easiest by ground-level employees familiar with the work. Once the models perform broad functions end-to-end, higher-ups can use them directly. In our view, more capable AI is more adoptable: firms must learn technologies, but labor learns the firm and can onboard itself.

We’ll dissect the three new classes of org chart, then examine what they mean for the future of red tape, worker agency, firm size, and pay disparity.

Our first scenario seems least disruptive. Here, people stay in the same seats with the same reporting lines, ostensibly doing the same jobs as before. The human org chart looks unchanged between today and this future.

The difference is below us. Beneath each individual contributor, a new AI labor force reports up to each of us. Our org chart gets a basement, and we all become managers.

At first, this basement is unfinished. We start with one or two agents apiece, less as seasoned employees, more as eager interns we must supervise and readily forgive. Our expectations are low: send an email here, draft a memo there, reply to Slack messages everywhere. Like junior human employees, they begin with little autonomy and trust.

But as models improve, our agents take on more tasks with less supervision. They coordinate with each other. There might be manager AIs and grunt AIs, accountants and lawyers, all working together at your behest. Far from handholding an intern, your operation eventually looks like a small firm in its own right, with its own org chart and you as its board of directors.

Jace still does the accounting at his firm, but he’s no longer clicking around the spreadsheets. His agent underlings do that. Jace’s job is more like his manager’s from a few years ago. His manager, in turn, now has an even bigger remit.

Jace’s day-to-day has completely changed, now focused on communication, coordination, and creativity. He works less and feels more challenged. His work life has more variance, and he distinguishes himself with leadership skills and a vision he couldn’t showcase before.

The accounting team hasn’t shrunk because the rest of the company has grown more productive too and demands more accounting than ever before. The agents can’t yet do everything his team does. The execs wouldn’t know the first thing about assembling financial data and need his domain expertise to use the agents effectively.

Automation without pink slips

Notice how this scenario doesn’t imply layoffs. Here we adopt AI without automating ourselves. We instead automate our past selves, and we move up in the hierarchy. AI takes on more and more of what we used to do. Our jobs remain because there is more to manage, and we gain responsibility for a greater sum of output.

Jobs aren’t static ⁠ (opens in a new window). Suppose that AI can do ninety percent of what you did before. You might conclude you’re left with only ten percent of the work. The other possibility is that you’re still doing a full job, just a different one—now focused on an objective ten times larger. That is the classic gift of technology: productivity rises, ambition rises to meet it, and wages can rise ⁠ (opens in a new window) with the ambition.

This all depends on whether we still add value ⁠ (opens in a new window) to production. This essay focuses on the coming years where automation isn’t universally capable or adopted. Even if AI could do it all, we might still value something more because a person made it, or insist that a person have the final say.