OpenAI 员工撰文探讨 AI 进入企业组织架构的三种方式

The future of the firm

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

By Hemanth Asirvatham and Elliott Mokski

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Authors’ note: This is the second essay in our series on the next economy, part of anew platformto 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.

The Promoting

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.

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.

Why adoption might start at the bottom

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.

Entry level is no longer the lowest level

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.

The Interleaving

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.

The escalator of scope

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.

Firms learn technology; labor learns the firm

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 your needs. They tell you what you didn’t even realize you didn’t know. The best labor onboards itself.

The more AI becomes like labor in this sense, with initiative and adaptability, the less the firm must learn how to harness it and the more it figures out how to be useful to the firm. An executive probably won’t sit through a design software 101 lecture to learn a new tool. But he will happily entertain an offer that sounds like: say the word, and I’ll set up your whole marketing function.

This isn’t a lone-wolf economy

Too often, we name the holy grail of AGI as automating a job. Granted, we haven’t even reached that yet. Jobs are so extraordinarily complicated because we are so impressively capable. Even so, very little⁠(opens in a new window) of our economy’s output comes from lone-wolf workers. Productive jobs contribute to teams, teams to firms, and firms produce most everything we consume.

The “job” standard for AGI doesn’t give us humans enough credit. Labor isn’t just about the tasks, or even handling the non-routine components: our labor is also about prodigious coordination, our plasticity to do small parts of bigger outputs. Selling us as mere sacks of tasks is pennies on the dollar. Human capacity is in our organizations⁠(opens in a new window) more than the individual. An economic AGI must reach the standard of people, not person.

As a result, the “interleaving” scenario may be quite inefficient if the AI is still tool-like or even worker-like, instead of team-like or firm-like. A model wedged beside every worker means a thousand seams and handoffs⁠(opens in a new window). This piecemeal setup may slow adoption.

We see a race between earlier, less capable implementations and later, more capable models. The victor hinges on whether adoption of future models is so much faster that it overcomes the extra time it takes to reach those competencies.

The two scenarios so far

This “interleaving” scenario can coexist with the first “promoting” scenario. We may see firms shrink their human footprint as AI automates certain functions, while the remaining humans get much broader mandates aided by their agent workforces. Importantly, by itself this scenario doesn’t imply lower overall human employment. We could find ourselves with more firms, each with a lower headcount.

The Layering

Our third scenario is the most confounding. Here, AI enters the firm above people. The stack is no longer humans over machines but human, AI, human: an automation sandwich. Some people “report” to an AI.

It sounds far-fetched, especially as a starting point for firm adoption. But we think, in less radical ways, this has already begun.

Asha, the chief executive, is pinged by her AI consultancy that there is a problem with an upcoming launch. It has already investigated the issue and found that sales promised a feature engineering isn’t building. The evidence is pulled from ordinary work threads in Slack; nobody has written her a report or even realized the miscommunication between the teams.

The AI consultancy proposes she delay the launch and reassign three engineers. It also notices that Sarah, a work-from-home engineer, has already built some useful infrastructure for the project even though no one told her to. The consultancy ensures Sarah’s proactive contribution is recognized and communicated to Asha. Asha calls Sarah, thanks her for going above and beyond, and commends her by designating her the engineering lead of the new effort.

The ladder might burn from the top

Even when we think of AI as a mechanizer of cognitive labor, we tend to focus on the lower rungs. We typically say AI automates spreadsheets and slide decks, not management. Our instinct is that automation climbs the hierarchy: cognitive gruntwork first and judgment last, with the manager as the final redoubt.

But a manager’s work largely lives on a computer and in the mind: reading, meeting, weighing, holding context, making decisions. There is value to physical presence, to touring the office floors and being around the troops. Yet it is ground-level work, even when it is mostly cognitive work, that tangles with atoms and tools more than management. The intern is sent to the photocopier, not his boss.

Managerial decision-making is hard to teach to a model, because success is ambiguous and measured over long time horizons. It is for similar reasons⁠(opens in a new window) that training good human managers is hard. We don’t yet know how good the models are at management. Our evaluations abound for execution-focused cognitive labor, but few capture large-scale orchestration at the scope of managers and execs.

It is still worth considering that today’s models may actually be closer in capability to human managers than ground-level workers. Some managerial functions may automate before the work they oversee.

Consultants everywhere, all the time

Perhaps the full version of this “layering” scenario is an AI that does the thinking, then fills its capability gaps with human contractors who report to it. That world seems quite foreign to us. A lot would need to change.

But there is a more organic incarnation, and its opening act is already onstage: advising. Today, an exec can ask ChatGPT to read hundreds of Slack channels, documents, missives, and code files from across her company, and then answer questions. What does this team really do? Where is that vulnerability showing up? Every part of the org is increasingly parsable⁠(opens in a new window) to her.

Executives have long laced the org chart with mercenary middlemen—they called it consulting. A management consultancy layers you, briefly and expensively. Going forward, the AI consultants could be vanishingly cheap and ever-present.

Models are just beginning to reliably parse firm documents and communications. Their taste and proactivity must mature. For now, ChatGPT helps a motivated exec understand her org in greater detail. We see it eventually becoming a full-service consultancy. It won’t be a true “manager” of lower-level employees, but rather an in-house correspondent and advisor to executives. It will leverage business expertise to scan everything, proactively surfacing pain points and helping triage.

Whether this is beneficial or detrimental will depend on the executive. The structural effect is centralization. We see this shortening the distance between C-suite and the company proper. It vests more power at the top, where information frictions used to favor more decentralized decision-making.

We find this scenario profound not just for how it shifts authority, but for how easily adoptable AI-as-a-consultancy can be. Just like with traditional consulting, the AI consultancy doesn’t require technical acumen from the executive. This is not the computer, adopted from below by whoever could type and terminal. You just talk to your consultant, and it handles the technical know-how. This adoption wave can start from the corner office. The consultancy, in turn, could accelerate AI adoption throughout the rest of the organization.

The evolving employment contract

One reason looms large for why firms distrust remote work and contracting: enforceability. There has never been a cheap way for firms to observe the doing of work in its deserving richness. So they measure pantomimes⁠(opens in a new window) of it, clicks counted, mouse wiggles, green status dots, signals so divorced from whether good work is happening.

AI offers a monitor that understands. It can look at what a person produces and recognize real progress towards bigger objectives. As we explore in the next section, any new surveillance mustn’t be implemented lightly⁠(opens in a new window). But, under the right circumstances, it can increase trust and encourage firms to give their employees more freedom.

If the monitor comprehends the many disparate paths that can lead to good work product, employees can work more the way they want. Progress toward good results will matter more, rather than one prescribed method easy for bosses to track. An intelligent monitor affords flexibility and punishes the performative productivity we all loathe.

The value proposition here resembles that of the insurers who reduce your rates if you install a tracker in your car. If you were trying to game the system, this hurts you; if you were simply doing your job, the monitor can vindicate you, and management trusts you more.

That trust is generative. We might see new willingness⁠(opens in a new window) from firms that were previously reluctant to hybrid work, to unorthodox minds, to contractors, or to hiring brilliant people in countries far away.

There are many problems that ground-floor employees have with the org chart today. It is immensely dispiriting to have no way to surface a problem up the ladder that we see firsthand. It’s a raw deal to be stuck under an ineffectual manager, or to do great work that goes unrecognized.

We see the previous wave of business hyperoptimization as being built on limited data, rough guidelines, and dumb algorithms. If all you can process is gear ratios, then everyone looks like a cog. Firms have long had a measurement problem of their workforce. An intelligent monitor could recognize valuable contributions and unique approaches that today’s crude metrics miss.

Four big questions

Drawing from across the three stylized scenarios, we now explore some of the determinative questions on the future of the firm.

Will firms have more or less red tape?

Building is generally quite expensive. An app, a film, a deck, a feature, an analysis: these are all costly, slow, and agonizing to redo. When you effectively get one shot, you invest in aim. There are planning cycles and roadmap ceremonies and sign-off committees all to ensure that when the firm finally says go, it goes at something worthwhile. Many checks in our hierarchies exist because execution was precious.

Across many domains, AI is collapsing the cost of building—at least to the level of prototyping, if not to final completion. We believe the firm’s approach should shift with it: aim less, fire more. A firm can build a prototype, observe, learn fast, reorient itself, and build it again. Easy prototyping⁠(opens in a new window) should drift firms away from the institutions which underpin planning-intensive production.

The simplest version of this is a team that iterates more quickly. In a more radical interpretation, the team itself is called into question. If collaborative planning matters less and uncorrelated diverse ideas matter more, is it better to have a single team that iterates quickly, or many individuals each putting forward their own working prototypes? If a real prototype transmits a vision better than any scoping document ever could, will our norms for communication change: sharing more, earlier, and sharing artifacts rather than memos?

Cheap building may tilt the firm toward individuals. This would mean fewer layers of middle management and less asking for permission, because asking used to be cheaper than doing and now it is not.

All that said, as we discuss next, the same technology which empowers individuals to prototype with less risk can also enforce larger spools of red tape.

Will workers have more or less agency?

A major reason firms enforce top-down management is the belief that executives make better business-relevant decisions⁠(opens in a new window) than junior workers. Firms pay more for highly competent veterans at the top, and managers are often trained in business strategy while individual contributors are specialized in their expertise. If AI offers excellent advice to all workers, the gap in decision-making quality shrinks. There would be less reason for managers to meddle. This would increase worker autonomy, and potentially reduce layers of management.

Similarly, the “AI consultancy” ensures that nothing too consequential happens without an executive being alerted. If a worker is about to publish a blog post with serious repercussions for the company, the consultancy will proactively flag it to the exec. This begets trust: management need not worry big mishaps will happen without their awareness, so they needn’t keep such a tight leash⁠(opens in a new window) on how a worker practices their job.

But the AI consultancy is a double-edged sword⁠(opens in a new window). It can instill more trust in workers as an intelligent monitor; it can also more vigorously enforce a CEO’s preferred way of doing things throughout the organization. Firms value coherence⁠(opens in a new window): not just a good decision, but a decision compatible with the company’s culture, brand, and economies of scale. A restaurant chain may prefer a common menu across its franchises, so that ingredients can be bulk-ordered and customers can expect a consistent product.

Relatedly, the incentives for firm bosses don’t always align with workers. Workers optimize for their domain’s success⁠(opens in a new window) while execs care about the firm overall. Equity owners have different preferences than wage workers, because they reap the rewards⁠(opens in a new window) of harder work. The consultancy could enforce stricter adherence to specific regimens prescribed from the top.

Our view is, on net, workers will gain agency. Our optimism rests on firms using AI to flag consequential exceptions while leaving more decisions with workers.

We believe that AI will act as a scaffold—even if executives wish to maintain a coherent brand, this will merely be a constraint to operate within. Workers are made better aware of the business constraints at play and are better insured for creativity outside those restrictions. With their bases covered, execs can let employees try potentially lucrative new approaches. Aided by AI helpers beneath us, workers will also be responsible for more substantial outputs. We see more opportunities for creativity when commanding more complex work.

Will firms shrink or widen?

The same logic on worker autonomy also applies to firm size. Ronald Coase⁠(opens in a new window) asked why we organize production inside firms rather than through a web of contracts. Even an airplane could be constructed purely through peer-to-peer contracting, but it would be very inefficient. Firms muster economies of scale, align incentives, organize large projects, and absolve the need for constant bartering.

By easing communication, AI could justify larger or smaller firms. The classical firm is infrastructure for passing information up and instructions down, full of frictions and telephone-game. A larger firm (with more human employees) is less unwieldy if AI acts as a coordination layer. With the “AI consultancy,” an executive can know her firm the way a founder knows a twenty-person startup. She can watch an initiative’s implementation rather than await its quarterly retelling, and be pinged if anything happens. Her vision can infuse more broadly. Everyone is more aware of what everyone else is doing, and of the organization’s overall priorities.

It could instead work the other way. If communication is frictionless and compliance more monitorable, a firm needn’t internalize so much of its supply chain. The firm can better trust outside suppliers and contractors.

A major motivation for vertical integration is to align incentives. There’s the idea of incomplete contracts⁠(opens in a new window)—an aerospace company could contract with a jet engine company, but for everything not explicitly required in the contract, the jet engine company is incentivized to cheap out and look after its own bottom line. To avoid this, the aerospace company buys the jet engine company. Just like with workers and bosses: if contracts are more monitorable and arbitrated intelligently rather than by checking off boxes, firms may be more comfortable relying on other firms. There would be less cause to do everything in-house.

We expect to see many smaller firms, but also sprawling AI “holding companies” where a huge number of different projects are conducted within one enterprise. The conglomerate is economical where there is some common asset leveraged by all the different projects. This common asset could be compute, though compute can easily be provisioned in small ad hoc quantities. A common factory or robot fleet is a better motivation.

The large “holding company” can also be justified by aligned incentives. If a large organization of AIs and capital is entirely devoted to a common mission—say, curing cancer—there is less need for separate smaller initiatives. An AI firm could avoid power struggles, share all information, and, where needed, instantiate creative diversity among its agents so that different paths are explored.

Will pay disparity rise or fall?

Today’s system is supposed to reward standout workers. They rise through the ranks and are better compensated. But in a world of superb model advisors, how will workers distinguish themselves?

If AI gives the same great idea to everyone, we have fewer opportunities to differentiate. Pay scales should compress.

What stands against this compression is judgment, taste, and scope. If we each lead AI firms beneath us, the residual human value is our decision-making. If each person is responsible for a larger effort under them, the difference between good judgment and bad rings louder⁠(opens in a new window).

We have long applied this logic to the top of the org chart. We justify CEO pay⁠(opens in a new window) with the idea that marginally better decisions up top can have huge effects on the bottom line. Pay inequality could increase if some workers better manage the AI firms beneath them to deploy scarce resources.

The more at stake, the more your small edge over someone else shows up in your paycheck. On the other hand, AI will give good advice to everyone. Less differentiation squishes the pay distribution.

Here and elsewhere, the prevailing force will perhaps hinge on whether we are mostly providing judgment or following it.

• 2026
• The Next Economy

Authors

Hemanth Asirvatham, Elliott Mokski