"Live experiences are the new luxury good," Kevin Hartz said in 2013 when Eventbrite, the ticketing startup he cofounded, got a big new funding round.

By that point, modern factories, supply chains, and Amazon had boiled down "stuff" to a commodity. You can now buy an overwhelming variety of tennis shoes, or spatulas, or sweatpants online. This abundance has taken much of the satisfaction away from purchasing physical things. This is why experiences, which by definition are finite, became more valuable.

There are only a few opportunities to see Taylor Swift on stage, versus the availability to purchase more than 20,000 kinds of tennis shoes on Amazon. So the price of Eras tickets soar, and shoes are cheap.

The same inevitable supply-and-demand dynamic is about to wash over us again with large language models and generative AI.

The ultimate outcome will be a new limited-edition luxury item: Humans. 

AI models can now automatically generate text, software code, medical diagnoses, images, voices, music, video, and lots more. The barriers to using this technology are falling away quickly. Anyone can fire up ChatGPT, GPT-4, DALL-E and other tools to produce an almost unlimited quantity of content.

This should be a boon to society. Many tasks will be completed more efficiently, making products and services more affordable and accessible, as venture capitalist Marc Andreessen recently explained.

There will be a reaction though: In a world of machine-generated abundance, human-centered services and experiences will become increasingly rare, valuable, and therefore desirable.

"The world's information is being turned into 1s and 0s and all this is being commoditized," Hartz said when I caught up with him recently. "What can't be commoditized is finite resources like real estate, travel, seeing the sunset on Mediterranean, or surfing in Fiji. These are the luxury goods of the power elite."

The more that AI automates restaurants, the more we'll want personal chefs such as John Barone, who cooks five days a week in the home of a wealthy Silicon Valley couple.

As AI tutor bots proliferate in education, the richest will pay for more exclusive access to the best human tutors for their kids.

The more robo-advisors handle our money, the stronger the urge of the wealthy to recruit savvy human experts to manage their family offices.

Email marketing is a simple example that some technologists are already worried about.

Generative AI tools are making it much quicker and easier to write marketing copy. The end result will be a flood of new emails that will overwhelm recipients and make them even less likely to open the messages.

"And our own machines will read those AI automated sales emails," Hartz quipped.

So, either your marketing email won't reach the humans you're trying to engage, or another AI bot will open it and you'll never be quite sure who read the message. A hand-typed email from a real human will be, relatively speaking, a rare and beautiful thing (complete with typos).

AI models are beginning to revolutionize education, according to Sal Khan, the founder of Khan Academy. His organization has been working with OpenAI models to coach students in powerful new ways and help teachers develop class plans.

The gold standard throughout history has always been to have a personal tutor, and AI models can help personalize the education experience to bring some of this curated approach to more students, he explained during a No Priors podcast earlier this year.

"We don't have the resources to give everyone a tutor," he said during the podcast. "A generative AI tutor supporting students. That's going to be mainstream in 3 to 5 years," he added.

And yet, Silicon Valley's top private schools, where many tech execs send their kids, are all about getting access to human teachers in small group settings. 

Castillja in Palo Alto highlights a student to faculty ratio of 7 to 1. Nueva, a Silicon Valley school for gifted kids, promises a similar ratio. The Menlo School in Menlo Park says it has a student-teacher ratio of 10 to 1 in the upper school.

These institutions cost $58,000 to $60,000 a year and I don't see any drop-off in demand among the tech elite. They're still jostling to get their kids into these bespoke, human-centered learning environments.

One persistent, apocryphal Silicon Valley story illustrates this point. On weekends, one tech billionaire has been known to hire a personal carpenter to hand-make wooden toys for their kids build and play with.

What about when it comes to managing fortunes amassed by successful tech entrepreneurs? The wealthiest rely on talented financial advisors who are hired directly to oversee this money in family offices.

Bill Gates has his own private investment firm, Cascade, which has been run by money manager Michael Larson since 1994. Elon Musk's family office, Excession, has been run by a former Morgan Stanley banker called Jared Birchall for years.

Using AI for trading has been tough so far. AI models are trained on masses of data from the past. When new situations arise, they struggle to adapt quickly enough.

Even quantitative hedge fund firms, which use machine learning and other automated techniques, rely on humans. Two Sigma, a famous quant firm, is for the first time exploring ways to add traders who rely on their human judgment to make money, Bloomberg reported recently.

"The major challenge with using things like reinforcement learning for trading is that it's a non-stationary environment," AI researcher Noam Brown said on the No Priors podcast in April. He's worked on algorithmic trading strategies in the past and was a researcher at Meta before recently joining OpenAI.

"So you can have all this historical data but it's not a stationary system," he explained, referring to how markets respond swiftly to world events and other developments.

Part of the problem relates to what he calls sample efficiency. Humans are good at learning quickly from a small amount of data, while AI models need mountains of information to train on.

"Humans are very good at adapting to novel situations," he added. "And you run into these novel situations pretty frequently in financial markets."

AI is making social media increasingly machine-driven, too. Soon, human content creators will be vying for attention with content generated by AI models.

Last month, Meta CEO Mark Zuckerberg unveiled more than 25 new AI assistants with different personalities that use celebrities' images. Users will be able to interact with these bots on Meta's platforms in the future.

In a recent podcast, he described this new supply-and-demand situation well, saying human creators can't keep up with demand from followers.

"There are both people who out there who would benefit from being able to talk to an AI version of you," Zuckerberg explained. "You and other creators would benefit from being able to keep your community engaged."

So Meta will make an AI version of celebrities that can post constantly. Again, this will be infinite. And actually interacting with the real human celebrity will become more rare and valuable.

Meanwhile, when Zuckerberg is relaxing outside of work, he spends some of that time pursuing a very human pastime: Rolling around with other humans in martial arts contests.

AI models, such as Google DeepMind's Med-PaLM 2, are becoming incredibly good at answering medical questions and analyzing x-rays and other health data. But when wealthy parents have really sick children, they will still seek out the smartest doctors in the relevant fields of medicine.

You can see this in Silicon Valley's embrace of medical concierge services that provide special access to doctors and other human health specialists.

One Medical succeeded by offering better access to human doctors, and Amazon ended up buying it for almost $4 billion.

"We're inspired by their human-centered, technology-forward approach," an Amazon executive said when the deal was announced.

Hartz, a venture capitalist who now chairs Eventbrite's board, says successful technologists will continue to spend heavily on human experiences. But he says this depends on the activity and the motivations behind different actions.

He breaks this into "utility, value and signaling."

Many standard, common situations can be handled by software bots or even physical machines. Repetitive tasks at work and some educational functions are examples of these utility-type solutions.

In other situations, users will get more value from having machines handle the work, so humans can focus on more valuable tasks. If you're a well-paid machine-learning engineer, it will be better to have a robot clean your house so you can focus more on your job, he explained.

And then there will still many situations where humans will want to enjoy their success and signal the fruits of their achievements. And these activities will increasingly focus on finite human resources and experiences, Hartz said.

"You can't put on headset and pretend to be in Fiji," he added.