Why Marginal Cost of Content Creation is Generative AI's Superpower

Virtually every observer might agree that artificial intelligence will automate laborious tasks and therefore increase process efficiency. AI should also accelerate decision making, as it enables rapid information processing. 

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AI should enable more personalization than already is possible for user interactions and experiences and as a byproduct could change the nature of work, entertainment and learning. 

Generative AI, though, might bring cost impact in different ways than did other computing innovations. Virtually all computing eras since the advent of the personal computer have led to lower marginal costs of doing things. 

PCs meant computing power itself was widely available to people. The internet attacked the cost of sharing information and communicating while cloud computing arguably reduced software distribution costs while boosting the ability to apply accumulated data and insights more widely in real time. 

The mobile era extended computing capabilities “everywhere” and untethered from desks, tables or laps. 

Era	Computing Paradigm	Marginal Cost Implications
PC	Personal Computing	- High upfront costs for hardware and software - Relatively high marginal costs for upgrades and maintenance - Limited scalability
Internet	Networked Computing	- Reduced costs for information sharing and communication - Increased accessibility, but still significant infrastructure costs - Marginal costs tied to bandwidth and server capacity
Cloud Computing	On-Demand Computing	- Significantly lower upfront costs - Pay-as-you-go model reduces marginal costs - Improved scalability and flexibility - Potential for cost optimization through resource management1
Mobile	Ubiquitous Computing	- Lower device costs compared to PCs - App-based ecosystem with low distribution costs - Increased connectivity, but data costs can be significant
Future AI	Intelligent Computing	- Potential for near-zero marginal costs in some applications - High initial investment in AI development and infrastructure - Continuous learning and improvement may reduce long-term costs2

So it is reasonable to ask what the AI impact will be, especially generative AI, which seems to be driving mass market and most business AI use cases. 

Angela Strange, Andreessen Horowitz general partner and James da Costa Andreessen Horowitz partner, specialized in enterprise and business-to-business software, including financial technology. 

They believe the AI era leads to lower marginal cost of client and customer interactions, using AI agents to reduce the cost of labor involved in many customer support operations, including those involving information retrieval (files, ledger entries, past transitions, billing and account status). 

source: Andreessen Horowitz 

As applied in many areas outside of financial technology, the value of generative AI is squarely on its impact on content creation. 

Whether we look at text, image, video or audio, GenAI seems destined to have the highest impact on any process or industry built on content creation and its distribution or consumption. GenAI will be useful in any number of customer support contexts, but might be impactful in financial terms for the production of software and code; entertainment content; education and training; business communications; many types of research; marketing and sales.