In a recent LinkedIn post, Daniel Priestley explores a potential paradox emerging from the rapid advancements in Artificial Intelligence: the possibility that AI’s ability to code could lead to a global shortage of human software developers. Priestley frames this not as a direct displacement, but as an unforeseen consequence of increased efficiency, drawing parallels to historical economic shifts.
Priestley highlights the concept of unintended consequences and paradoxes within economies, referencing the Jevons Paradox, which suggests that increased efficiency in resource use can paradoxically lead to increased overall consumption. He applies this to the realm of software development, envisioning a future where AI drastically reduces the cost and time required to launch a software startup.
“Imagine this, AI sudddenly makes it cheaper and easier than ever for millions of people to create a software startup. What used to cost $500K and 6months, now costs $2000 and 2 weeks.”
The Demand Paradox in Software Development
According to Daniel Priestley, this newfound ease of creation could trigger an unprecedented surge in new software ventures. Each of these millions of nascent companies, he argues, will require at least one human software developer to bring their ideas to fruition. This, in turn, would create a massive, unmet demand for skilled coders.
Priestley supports this hypothesis by drawing upon historical precedents. He points to the proliferation of smartphones, which, by placing advanced camera and editing capabilities into everyone’s hands, dramatically increased the global demand for camera operators and editors. Similarly, the automation of accounting functions by tools like Excel led to a growth in demand for accountants and analysts, rather than their obsolescence.
“This isnt a new phenomenon. When smartphones put a production quality camera and editing studio into everyone’s pocket, the global demand for camera operators and editors skyrocketed.”
Lowering Barriers and Increasing Demand
Further illustrating his point, Daniel Priestley discusses the impact of platforms like YouTube. He notes how YouTube has significantly lowered the threshold for what constitutes a viable television show. Whereas in the past, a show needed substantial investment to be greenlit, now content creators with much smaller audiences can achieve profitability. This democratization of content creation, Priestley suggests, is analogous to how AI might democratize software creation.
Priestley also touches upon the counter-intuitive economic principle observed in fields like culinary arts and consulting. Chefs who share their recipes often see an increase in demand for their food, and consultants who publish their best ideas can command higher rates. This phenomenon, where generosity or transparency can paradoxically increase demand, underpins his argument that making a service (like coding) more accessible via AI could amplify the need for human expertise in related areas.
“When chefs give away their recipes demand for their food goes up despite people having the option to make it cheaply themselves.”
Broader Implications for Professional Services
Extrapolating from these observations, Daniel Priestley posits that the efficiency gains from AI in business creation could extend beyond software development. He suggests that within a few years, there could be a significant shortage of professionals in fields like law, accounting, marketing, and sales, driven by an AI-enabled boom in entrepreneurship. This, he concludes, would be another instance of the economy surprising us with counter-intuitive outcomes.
“It’s entirely possible that AI makes it so efficient to start a business that within a few years, there is a massive shortage of lawyers, accountants, developers, marketing and sales people.”
Priestley’s analysis offers a thought-provoking perspective on the future of work in the age of AI, emphasizing that technological advancements often lead to complex and unexpected economic adjustments rather than simple replacement.
📝 About This Content
This article is based on insights shared by Daniel Priestley on LinkedIn.
📅 Originally posted on February 12, 2026 | View original post on LinkedIn →