AI Is Changing the Minimum Viable Hire

We have a bad habit of turning whatever is happening in the labor market today into a permanent theory about the future of work. During the zero-interest-rate and lockdown era, everyone said remote work was the future and geography was dead. Then companies swung back toward the office. More recently, there was a narrative that AI was going to eliminate software engineers. Now companies are competing fiercely—and sometimes paying extraordinary amounts—for the very best technical talent.

Rather than asking whether AI will eliminate jobs, a more useful question is what AI changes about the economics of hiring. I think one answer is straightforward: AI is lowering the minimum viable hire.

For decades, companies have purchased knowledge work in surprisingly standardized units. Need an engineer, marketer, designer, or finance leader? The answer generally means buying roughly 40 hours of someone’s time every week. But any hire actually bundles together three different things: expertise, productive capacity, and availability. These are related, but they are not the same thing, and technology increasingly makes it possible to match each of them more precisely to the work that actually needs to be done. Starting more than a decade ago with the trend toward fractional and gig work, then accelerating with the adoption of AI, it is becoming possible to engage talent with more precision than ever before.

Expertise

The first problem with fractional work has always been context. Someone working eight hours a week is not very useful if they spend three of those hours figuring out what happened since last week. It does not matter how much expertise they have if they cannot efficiently apply it to the specific problems of the company.

Software engineering offered an early glimpse of how this problem could be reduced. Software teams can work asynchronously because much of their shared state exists outside people’s heads. Code lives in repositories. Changes live in commits. Work lives in tickets. Tests, logs, and documentation preserve information about what happened and why. A software engineer does not necessarily need to have been in every meeting to understand the current state of the system.

Increasingly, AI can help other forms of knowledge work function the same way. Meetings become searchable transcripts. Customer conversations can be summarized. Decisions and their rationale can persist. Research can be synthesized. Agents can maintain project state and prepare relevant context before a human enters a workflow.

Importantly, this can reduce the context tax associated with bringing expertise into a problem. If someone can understand what happened while they were gone without spending half their working time reconstructing it, companies gain more flexibility in how they use specialized expertise, and talented people can contribute meaningfully across a wider range of situations.

Productive capacity

Once expertise can move into and out of a workflow more efficiently, the next question is how much productive capacity a particular problem actually requires.

AI changes how much a capable person can accomplish in an hour. That does not necessarily make great people less valuable. It may make them more valuable. The value of a great marketer is not that they can fill 40 hours with marketing activity. It is that they know which market matters, what to say to it, what is not worth doing, and whether the finished product is any good. AI can increasingly do more of the production work underneath that judgment.

If someone with exceptional judgment can now produce in ten hours what previously required twenty, the relevant question is not whether the company should automatically buy less labor. It is whether 40 hours should remain the default unit of purchase in every case.

Some roles clearly justify full-time ownership. Others may not. Some problems require sustained attention, deep context, and constant availability. Others require concentrated expertise at particular moments. The point is not that one structure is better than another, but that companies increasingly have more ways to match capacity to the shape of the work.

That can also expand the market for expertise. A company that could never justify hiring a particular specialist full-time may still have an important problem that person is well-suited to solve. Lowering the minimum economically useful quantity of someone’s time can therefore create opportunities that did not previously exist for both the company and the expert.

Availability

The obvious wrinkle in this argument is that for most knowledge work, companies do not only pay people for output. They pay them to be there.

A company might only need ten hours of someone’s productive work in a given week but still need that person available when a customer calls, something breaks, or an important decision needs to be made. That availability has real economic value.

The useful distinction is that availability and productive capacity are not always the same thing. Different roles require different combinations of the two. Some require continuous presence. Others can work with clearly defined expectations around responsiveness, continuity, and reserved capacity.

That does not mean full-time employment is disappearing. Some jobs require enormous amounts of context. Leadership depends on relationships. Managers need sustained awareness. Some problems deserve someone’s complete attention. Plenty of companies also genuinely have 40-plus hours of high-value work for a particular person every week. In those cases, buying someone’s expertise, productive capacity, and availability together makes perfect sense.

The point is simply that these are separate economic components, even though employment has historically bundled them together. AI can reduce the cost of giving someone context, increase the amount of productive work that person can accomplish in an hour, and give companies and workers more flexibility in deciding how expertise, capacity, and availability should fit together.

In this writer’s opinion, AI is not eliminating the full-time employee. It is lowering the minimum viable hire.

Remote work began unbundling work from place. AI may now begin unbundling expertise, productive capacity, and availability from one another.