The Impact of AI on Organizations
Jan 5, 2026
Engineering
What if everything we believe about AI's impact on organizational structure is backward? The widespread assumption is that artificial intelligence will inevitably lead to smaller, leaner teams. This is characterised by the pithy comment 'Do more with less'. However that assumption may be fundamentally wrong.
Here, I outline why the organizations that thrive in the AI era won't be those that downsize, but those that leverage AI to build and manage larger, more impactful teams than were previously possible.
The Economics of Team Size
To understand how AI will impact optimal team size, we first need to understand what determines team size in the first place. In any organization, there are two key functions that determine the optimal number of employees: the cost function and the value function.
The Cost Function
Adding an employee incurs two types of costs:
Fixed costs: These are straightforward - salary, benefits, equipment, office space, etc.
Variable costs: These are subtler but often more significant. They stem from organizational complexity and are based on principles like Dunbar's number and the exponential growth of communication paths as teams expand.
When you plot the marginal cost of hiring employee N, you get a hockey stick curve. Early on, fixed costs dominate, resulting in a relatively flat line. But as the team grows, variable costs take over, causing the curve to bend sharply upward. Anyone who has managed a rapidly growing team will recognize this pattern intuitively - it gets exponentially harder to add people past a certain point.
The Value Function
On the value side, let's consider a simplified model of diminishing returns. While real organizations exhibit much more complex patterns, including potential network effects and complementarities between employees, a basic diminishing returns model can still offer useful insights. In this simplified view, your first hire delivers significant value, and each subsequent hire adds value, though typically at a decreasing rate. This creates a generally downward-sloping line when we plot the marginal value of employee N. While this is a significant simplification of organizational reality, it serves to illustrate our key points about AI's impact.
The Intersection
The optimal team size occurs where these two curves intersect - the point where the marginal cost of adding another employee equals their marginal value contribution. Past this point, each new hire costs more than the value they bring.
Enter AI
Now, here's where it gets interesting. AI doesn't just shift these curves - it fundamentally reshapes them. Let's examine how:
Value Function Changes:
The baseline value per employee increases as AI amplifies individual productivity
The diminishing returns effect may be moderated as AI tools support productivity at scale
AI can facilitate more effective employee specialization
This causes the value curve to move upwards indicating more value per employee
Cost Function Changes:
The fixed cost component might increase slightly due to AI tool costs
The variable cost curve may become more manageable as AI helps with certain aspects of organizational complexity
Some communication and coordination costs can be better handled with AI-powered tools
These attributes lead to lower costs per employee causing the cost curve to move downwards or to the right
When you plot these new curves, the intersection point tends to move to the right. This suggests that the optimal team size for an AI-enabled organization could be larger than for a traditional one.
Why This Matters
This insight has profound implications for how we think about AI's impact on organizations:
Instead of viewing AI as a replacement for human workers, we should see it as a catalyst for organizational scaling
The focus should be on how AI can help manage complexity and maintain productivity at scale
Companies that understand this will gain competitive advantages by building larger, more capable teams that deliver more value
What's Next
Here we've covered an economic model for describing how AI will affect organisations. In the next post we will explore how these theoretical insights translate into practice by examining the impact on a software engineering team.
Aidan McGinley, Engineering
June 30, 2025
Super Thinking
