Creative Destruction and Technological Change: AI Photo Headshots and the Reinvention of Professional Photography

Creative destruction is one of the key principles that economists study. The term was coined by Joseph Schumpeter, who described how innovation creates new industries by destroying older industries. The emergence of AI photo headshots is probably one of the clearest examples of this process occurring in our society today.

Professional headshots have traditionally required time, money, and skilled labor to be created. Clients must schedule an appointment, drive to a studio, and coordinate with their photographer before the photo can be taken. The production process relies on lots of physical capital (cameras, lighting, and studio space) and human capital (knowledge of composition/editing and client interaction), which leads to the relatively high cost of producing a professional headshot (typically between $200-$500).

The way we interact with the world has been transformed through AI technology by allowing us to be more efficient, effective and economical in how we utilize resources. The use of machine learning combined with large data sets of human faces has enabled us to produce high-quality headshots digitally at a cost of $20-$50, compared to the hours of coordination, transportation of talent, studio rental fees etc., which can amount to tens of thousands of dollars before the image is even taken. Examples of this technology include the substitution of capital for labor where AI can replace human labor, thereby increasing productivity overall.

Efficient production and cost savings for all participants are the economic drivers behind this change in how we produce photography. One of the primary reasons AI reduces costs is due to the extremely low marginal costs (after the AI model is created) of producing additional images after the AI. Thus, suppliers can offer services globally without the necessity of investing in capital or facility infrastructure, and can do so much more efficiently than traditional processes where labor is used for each image and additional images require significant investments of labor, time, and resources.

Secondly, artificial intelligence will also help lower transaction costs. With AI, customers will not have to look for photographers or by match schedules and no longer be required to travel to photography studios. AI will provide a frictionless experience that will encourage more customers to seek out professional headshots than in previous years due to how difficult it was to find quality service at an affordable price. This trend also is consistent with the overall trends in the digital economy where companies that are able to reduce frictions tend to increase total market share.

Finally, AI will create new types of productivity gains for producers. A single AI solution can produce thousands of images at once, while a single photographer may produce a few dozen images in the same timeframe. This gives AI-based solutions an advantage over traditional photographic providers, making it very difficult for these types of providers to compete on price with their competitors, especially in price-sensitive markets.

There is plenty of information available from a wide range of sources about this disruption that is occurring. A few of those sources have included multiple platforms like Remini and Aragon AI which currently allow roughly 10 million users to access quick and inexpensive alternatives to traditional studio based photography. Professionals who need LinkedIn profile photos, corporate headshots or other images related to their own brands (for example: personal branding images) are turning more frequently to AI based solutions than they were even a year ago. Additionally there has been a decline in entry level photography service requests - especially for types of services that typically require less customization.

This trend of creative destruction does not only displace what exists today; it also creates new opportunities as well. While many traditional photographers may experience drop in demand, they will be searching for new ways of innovating and adding value in new, unique niche areas of the photography industry (such as creative portraiture, building brand awareness / image identity creation and experiential photography). The new areas of the photography market will typically be very difficult for Artificial Intelligence to replicate so although the photography industry is changing directions it isn't going away; instead the photography industry will increasingly shift to creating products or services which match the uniqueness of each client's individual needs.

Another key effect of this development is the evolution of both 'what is considered' to be the quality of an image and how that image is used to send signals. Traditionally, professional headshot photographs have been a way of signalling that an individual has invested time and money into creating a professional-looking image. As the use of AI to create images continues to increase, the formal photographic images may lose some or all of their ability to signal credibility and professionalism. Employers and clients will place less value on how a photograph was created, than they will on the professional quality of the final photograph.

In a historical context, this transformation is consistent with an ongoing historical pattern of innovation in industries that reduce costs and increase accessibility of products, thereby expanding the market and disrupting existing producers. The printing press, digital publishing, and streaming audio are all technologies that experienced similar patterns. AI-generated headshot photographs are the most recent example of this pattern, and demonstrate that the way in which an industry creates its products is transformed through technological change.

As adoption of these innovations accelerates, the long-term equilibrium of the photographic market will depend upon the responses of both consumers and producers to the new capabilities available to them. What is clear is that Schumpeter's insight is still very applicable: while innovation will not only improve existing systems, it will also change how they are defined.