Over the past two decades, software has made its way into virtually every kind of work, fundamentally changing how we operate. But the underlying assumption has remained largely the same: companies buy software for employees to use, and people are ultimately the ones doing the work.
The rise of AI agents over the past year is beginning to challenge that model. Some tasks can now be handed over entirely to AI, and this will only become more common. For businesses, AI is no longer just a tool. It is increasingly becoming a new form of labor.
Customer service is one of the clearest examples. In the past, companies bought customer service software to make their employees more productive. But once AI can take over part of the work itself, companies naturally begin to evaluate its value against what they would otherwise have spent on human labor.
We are beginning to see the same shift in professional services such as legal and accounting. This is why I believe the market opportunity for AI is so large: it is no longer limited to the software market; it is beginning to expand into the labor market as well.
This is also changing how some investors think about the value of AI companies. Instead of asking only how much businesses spend on software each year, they are increasingly asking how much work currently performed by people—and paid for through salaries—could eventually be handled by AI. That dramatically expands the potential economic value these companies can capture.
But once AI becomes a form of labor, companies will also need to think differently about how they allocate it.
When a new task comes up, should a company hire another person, assign it to AI, or have humans and AI work together? Companies already allocate work among employees based on skills and experience: senior employees handle tasks that require judgment, while more straightforward work is often assigned to junior staff.
I believe AI will evolve toward a similar division of labor.
A simple analogy is transportation. If you are only driving to a nearby store, you do not need to take a Lamborghini; a Toyota will get you there just fine. In the same way, companies do not need to use the most powerful—and most expensive—AI for every task. Some jobs require sophisticated reasoning, while others only require basic execution.
One of the most important capabilities companies will need to develop, therefore, is the ability to determine exactly how much intelligence a given task requires—and how much it makes sense to pay for it.
This shift will also affect how software companies charge for their products.
SaaS has traditionally been priced by the number of users or seats. But if a job that once required 50 people can now be done by 10 people working alongside AI, seat count becomes a poor measure of the value a product creates.
As companies increasingly pay not for a “tool,” but for the completion of a task, the unit of pricing will naturally change as well. We are likely to see more usage-based pricing tied to the amount of work completed, while services with clearly measurable outcomes may increasingly charge based on results.
At the current pace of development, AI may soon become embedded across the workplace as a new kind of “machine labor.” And that will raise questions that would have sounded unusual only a few years ago.
When a company hires an employee, for example, it pays not only a salary but also taxes and other employment-related costs. If the same work is instead performed by AI, the cost structure looks very different.
Some have even begun to suggest that AI usage could eventually be taxed, with the revenue used to reduce the tax burden on human workers. Whether such an idea would ever be practical remains an open question. But the fact that we are already debating whether AI “labor” should be taxed says something important about how its role is changing.
Another question companies will have to confront very soon is what kinds of people they should hire in the first place.
As AI takes over more foundational tasks, both headcount and job design will change—and so will the skills companies look for when they recruit.
The next challenge for companies, then, will not simply be learning how to use AI. It will be learning how to manage an entirely new kind of workforce.
