The Economic Case for Generative AI and Foundation Models: Transforming Industries and Creating New Markets

The Economic Case for Generative AI and Foundation Models: Transforming Industries and Creating New Markets

Introduction:

Generative AI, powered by foundation models, is poised to have a significant economic impact across various industries. The potential of this technology to drive transformation and create new markets is directly linked to the median wage of each industry. However, while AI has shown impressive results, it has struggled to establish viable business models in private markets. The high costs of achieving and maintaining accuracy, coupled with the fast-following nature of competitors, have hindered the growth of AI companies. Additionally, the need for human involvement to ensure accuracy in AI-powered solutions has proven to be costly and difficult to scale.

The Cost Factor and Human Involvement:

The global average wage stands at approximately $5 an hour, with some regions having an average wage of less than a dollar a day. Interestingly, when it comes to tasks involving perception and other fundamental human capabilities, humans are often more cost-effective than AI solutions. While AI has surpassed human capabilities in certain well-defined tasks, humans still outperform AI in long-tail problems that require contextual understanding. This reliance on human involvement can become a burden on gross margins and hinder scalability.

The Rise of Startups and Emergent Consumer Needs:

Startups have historically capitalized on emerging consumer needs and behaviors to drive market shifts. Fringe secular movements often pave the way for startups to cater to unmet demands without facing competition from established incumbents. This was evident in the emergence of personal microcomputers, the Internet, smartphones, and the cloud. The same fertile ground exists for generative AI, as it addresses the creative content generation and companionship needs of users.

Unprecedented Adoption and Revenue Growth:

Generative AI solutions have witnessed unprecedented levels of adoption and revenue growth. ChatGPT, a language model, reached over 230 million monthly active users within just six months of its launch. Social platforms like Facebook took years to achieve similar user numbers. Similarly, companies like Midjourney and Character.AI experienced rapid growth in user engagement and revenue, showcasing the economic viability of generative AI.

The Potential for New Markets:

While generative AI is already being applied to existing markets, such as images, videos, music, games, and chat, it has the potential to create entirely new markets. The current markets serve as proof points for the value of generative AI but merely scratch the surface of its capabilities. The automation of natural language processing and content creation, which are tasks the human brain has not evolved for extensively, opens up numerous possibilities for generative AI. White-collar jobs that demand higher wages, such as programming, law, and therapy, can benefit from the cost-efficient and sophisticated nature of generative AI.

The Marginal Cost and Demand:

Generative AI has the potential to bring the marginal cost of creation to zero, similar to how the microchip brought down the cost of compute and the Internet reduced the cost of distribution. This drop in marginal value will drive increased demand, as history has shown with previous technological advancements. The Jevons paradox, which states that a decrease in the marginal cost of a good leads to increased demand, will likely manifest with generative AI. This will result in more jobs, economic expansion, and better goods for consumers.

"Do Things that Don't Scale": Startup Growth Strategies

Startups often succeed because founders take proactive measures to drive growth. One of the initial unscalable tasks founders must undertake is recruiting users manually. While this may seem daunting or insignificant at first, the power of compound growth should not be underestimated. By manually recruiting users, founders can kickstart their startups and gradually transition to more scalable methods as the user base expands.

The Fragility of Startups and the Importance of Growth:

Inexperienced founders and investors often misjudge the fragility of startups. The key to success lies in recognizing the potential size of a company if the founders execute the right strategies. Building a product to solve personal problems can lead to finding like-minded users, simplifying the initial market acquisition process. Founders should focus on delivering an exceptional user experience, even with an early, incomplete product. Attentiveness and engagement can compensate for any shortcomings and create a loyal user base.

Leveraging Early User Feedback:

Engaging directly with early users provides invaluable feedback for startups. Founders can gain insights and make improvements based on this direct interaction. Identifying a subset of the market where critical mass can be achieved quickly can also expedite growth. However, it is essential not to dismiss any part of the growth pattern, as overlooking certain aspects can hinder initial market acquisition.

Doing Things by Hand and the Big Launch Fallacy:

In the early stages, startups can often get away with manual processes that will later be automated. This allows for a faster launch, and the hands-on experience gained during this phase informs future automation efforts. However, founders should avoid relying on a grand launch to attract users. The expectation that a big launch will generate widespread user adoption is a fallacy rooted in laziness. Founders must be willing to put in extraordinary effort to achieve extraordinary results.

Conclusion:

The economic case for generative AI and foundation models is compelling. The transformative potential of this technology across various industries, coupled with the rise of startups catering to emerging consumer needs, underscores the viability of generative AI. By leveraging manual user acquisition strategies and focusing on delivering an exceptional user experience, startups can overcome initial fragility and pave the way for rapid growth. As generative AI reduces the marginal cost of creation, the demand for its applications will likely increase, leading to job creation, economic expansion, and improved goods for consumers.

Actionable Advice:

• 1. Embrace manual user acquisition: In the early stages, founders should actively recruit users manually to kickstart their startups and gain valuable feedback.

• 2. Prioritize user experience: Even with an incomplete product, founders should strive to provide an exceptional user experience, compensating for any shortcomings with attentiveness and engagement.

• 3. Avoid the big launch fallacy: Instead of relying on a grand launch to attract users, founders should focus on putting in extraordinary effort and making incremental progress to achieve sustainable growth.