The issue with AI historically is not that it doesn’t work—it has long produced mind-bending results—but rather that it’s been resistant to building attractive pure-play business models in private markets.

In addition to the sheer amount of dollars that may be required to hit and maintain the desired level of accuracy, the escalating cost of progress can serve as an anti-moat for leaders—they burn cash on R&D while fast-followers build on their learnings and close the gap for a fraction of the cost.

Although AI has proven to be more accurate than humans for some well-defined tasks, humans often perform better for long-tail problems where context matters. Thus, AI-powered solutions often still use humans in the loop to ensure accuracy, a situation that can be difficult to scale and often becomes a burdensome cost that weighs on gross margins.

the global average wage is roughly $5 an hour. For some tasks in some parts of the world, the average wage is less than a dollar a day.

when the job involves one of the more fundamental capabilities of carbon life, such as perception, humans are often cheaper. Or, at least, it’s far cheaper to get reasonable accuracy with a relatively small investment by using people.

New user behaviors tend to underlie massive market shifts because they often start as fringe secular movements the incumbents don’t understand, or don’t care about. (Think about the personal microcomputer, the Internet, personal smartphones, or the cloud.) This is fertile ground for startups to cater to emergent consumer needs without having to compete against entrenched incumbents in their core areas of focus.

In the 6 months since its launch, ChatGPT reached an estimated 230-million-plus worldwide monthly active users (MAUs) per Yipit. It took Facebook until 2009 to achieve a comparable 197 million MAUs—more than 5 years after its initial launch to the Ivy League and 3 years after the social network became available to the general public.

Midjourney, a text-to-image AI company, saw its Discord server balloon to nearly 15 million members in June 2023, less than a year after launching its open beta in July 2022, making it the largest server on Discord.

Character.AI, a vertically integrated AI companion provider, hit an estimated 18 million monthly active unique web visitors and more than 3 million daily active web users (per SimilarWeb) just 9 months after launch, excluding a successful mobile launch in May.

These unprecedented levels of adoption are a big reason why we believe there’s a very strong argument that generative AI is not only economically viable, but that it can fuel levels of market transformation on par with the microchip and the Internet.

Many of the use cases for generative AI are not within domains that have a formal notion of correctness. In fact, the two most common use cases currently are creative generation of content (images, stories, etc.) and companionship (virtual friend, coworker, brainstorming partner, etc.).

Generative AI models are incredibly general and already are being applied to a broad variety of large markets. This includes images, videos, music, games, and chat. The games and movie industries alone are worth more than $300 billion.

existing markets are only a proof point of value, and perhaps merely a launch point for generative AI. Historically, when economics and capabilities shift this dramatically, as was the case with the Internet, we see the emergence of entirely new behaviors and markets that are both impossible to predict and much larger than what preceded them.

Generative AI, on the other hand, automates natural language processing and content creation—tasks the human brain has spent far less time evolving toward (arguably less than 100,000 years). Generative AI can already perform many of these tasks orders-of-magnitude cheaper, faster, and, in some cases, better than humans. Because these language-based or “creative” tasks are harder for humans and often require more sophistication,  such white-collar jobs (for example, programmers, lawyers, and therapists) tend to demand higher wages.

Although the use cases for this new behavior are still emerging or being created, users—critically—have already shown a willingness to pay. Many of the new generative AI companies have shown tremendous revenue growth in addition to the aforementioned user growth. Subscriber estimates for ChatGPT imply close to $500 million in annualized run-rate revenue from U.S. subscribers alone.

We anticipate the economic value of generative AI to have a transformative and overwhelming impact on areas ranging from language education to business operations, and the magnitude of this impact to be positively correlated with the median wage of that industry. This will drive a bigger cost delta between the status quo and the AI alternative.

Just like the microchip brought the marginal cost of compute to zero, and the Internet brought the marginal cost of distribution to zero, generative AI promises to bring the marginal cost of creation to zero.

a drop in marginal value of creation will massively drive demand. Historically, in fact, the Jevons paradox consistently proves true: When the marginal cost of a good with elastic demand (e.g., compute or distribution) goes down, the demand more than increases to compensate. The result is more jobs, more economic expansion, and better goods for consumers. This was the case with the microchip and the Internet, and it’ll happen with generative AI, too.