公司的未来

中文版正在随流水线自动整理，稍后刷新即可；可先阅读下方英文原文。

本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。

The future of the firm

How AI might enter the org chart—beneath, beside, and between us.

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.

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.

It also depends on our appetite. If, as consumers, we don’t care for ten times more output, the equilibrium will shift toward fewer workers or fewer hours worked. Historically, increases in output have not satiated us. We have a lot more(opens in a new window) but work only a little less(opens in a new window). Should our appetite continue to grow as output multiplies—and we remain additive to production—we will choose to keep trading our labor for more consumption. If instead production becomes so abundant that we are satiated, we might volunteer a smaller labor force.

There are more objections we discuss later (e.g. whether we’ll have a larger number of smaller firms). We still think this scenario is important because adoption has to start somewhere. Firms need a way to stably evolve from their current org structures. Bolstering the work of existing employees may be an easier first step than wholesale restructuring. We see this in action today: one worker after another delegating to AI, without an organizational revolution so far.

This scenario relies on the front line hiring first. Ground-level workers—not managers and executives—lead on AI adoption. That appears likely, so long as the person who best gets AI to do the work is the person who used to do it themselves.

Models can already automate many data science tasks, but a department head unfamiliar with the methods and minutiae will struggle to leverage the tools. Until AI becomes proactive enough to tell you what you don’t know (see our next scenario), it will probably work best for those already familiar with how the work is done.

The biggest change in this scenario is for entry-level workers. There would no longer be a lowest rung, at least not for humans. When we hire young people, it won’t be to place them at the drudgerous bottom of the firm. Those positions will be filled by something that doesn’t mind.

With that, we lose some traditional routes to educate young workers: the apprenticeship(opens in a new window), the learning-by-grunt-work, the earning of stripes. These are genuine costs, but we mostly see this as a privilege. The greenest hire with a good idea has resources to experiment. The oldest excuse in the building, we just don’t have the manpower, is dying. A junior employee with a great video game in her head and no path to the headcount can now simply build it.

In our view, this “promoting” scenario still has much for junior workers to learn—judgment, taste, the muscles of management and creative vision. We will be incentivized to cultivate these skills because they show up in our work product. We will still learn by doing(opens in a new window), but at a higher level of scope. Firm elders remain incentivized to help us as responsible stewards (with equity stakes in our success). The opportunity here is a more stimulating role for everyone at every rung. Any worker can showcase their talents with a bigger remit, which matters most to entry workers who had no remit at all.

AI’s second way into the firm weaves through the existing org chart. Here the agents aren’t dregs but colleagues. A traditional manager hands a slice of the team’s work to a model the way she might delegate to any employee. AI inserts itself next to humans, not just under them.

The key difference from the first scenario: here, productive AI is adopted by existing human managers, not only by current individual contributors. To do so, it must be competent enough to be a fully fledged team member.

Sheila is a VP at a furniture company. She has never run an ad campaign. She gives an AI marketing team a budget and a new sofa to sell. It divides the launch tasks among its own agents. Some interview the salespeople. Others coordinate with the factory on what they can actually deliver. One subagent launches an online survey while a few more prototype different visual styles.

The drafts and revisions pass between them without crossing her desk. Her human sales and design teams still report to her, alongside the team of marketing agents. On Monday, she is handed a campaign to approve and a question about whether the photos make the sofa look too blue. She would never have realized that sort of detail without an ad expert proactively flagging it. She authorizes the agents to coordinate with the human photographer on a reshoot.

Humans are generalists. A hammer helps with one task; a human does a whole lot more. AI’s journey from tool to labor is a widening ambit: automating the task, then the job, then a team, and finally a firm. Every step up in scope requires broader competence.

A job is a suitcase full of many tasks. For a whole team, that suitcase is crammed with all sorts of diverse work. More tasks in the remit mean more potential chokepoints. If even one essential task in the team involves physical labor, a whole new set of peripherals is required. To be a slot-in substitute for an entire team, the AI must be capable across all its varied responsibilities.

AI that can handle bigger scope is harder to build. But for firms—and this is the central asymmetry of the whole transition—bigger scope is easier to adopt.

An end-to-end automated team or broad function can be directly adopted by an executive. A tool with narrower capabilities can’t so simply be implemented from on high. The simpler tool relies on widespread buy-in and retraining across the workforce.

New technologies traditionally make it cheaper to do a confined set of tasks. Then the burden shifts to the buyer. The firm must figure out how to leverage the new tool, and must retrain, reorganize, and rebuild its processes(opens in a new window) around it. Spreadsheet software didn’t install itself into the accounting department—the accounting department reshaped itself around the software.

Labor is the opposite. An intern, an employee, and a good team figure out how to be useful to you. They interrogate you, absorb your context, notice

Auto-fetched and translated by AI Atlas. All rights belong to the original author.