All these predictions are taken from Forbes/Rob Toews' 5 AI Predictions For The Year 2030.

Also, don't miss Forbes/Rob Toews' "10 AI Predictions For 2024" (all gathered under one tag.

• I will resolve to whatever Forbes/Rob Toews say in their resolution article for 2030's predictions.

I might bet in this market, as I have no power over the resolution.


The other 2030 prediction markets below:
1. Nvidia’s market capitalization will be meaningfully lower than it is today. Intel’s will be meaningfully higher than it is today.

2. We will interact with a wide range of AIs in our daily lives as naturally as we interact with other humans today.

3. Over one hundred thousand humanoid robots will be deployed in the real world. (This market!)

4. “Agents” and “AGI” will be outdated terms that are no longer widely used.
5. AI-driven job loss will be one of the most widely discussed political and social issues.

3. Over one hundred thousand humanoid robots will be deployed in the real world.

Description of this prediction from the article:

Today’s AI boom has unfolded almost entirely in the digital realm.

Generative models that can converse knowledgeably on any topic, or produce high-quality videos on demand, or write complex code represent important advances in artificial intelligence. But these advances all occur in the world of software, the world of bits.

There is a whole other domain that is waiting to be transformed by today’s cutting-edge AI: the physical world, the world of atoms.

The field of robotics has been around for decades, of course. There are millions of robots in operation around the world today that automate different types of physical activity.

But today’s robots have narrowly defined capabilities and limited intelligence. They are typically purpose-built for a particular task—say, moving boxes around a warehouse, or completing a specific step in a manufacturing process, or vacuuming a floor. They possess nowhere near the fluid adaptability and generalized understanding of large language models like ChatGPT.

This is going to change in the years ahead. Generative AI is going to conquer the world of atoms—and it will make everything that has happened to date in AI seem modest by comparison.

Dating back to the dawn of digital computing, a recurring theme in technology has been to make hardware platforms as general as possible and to preserve as much flexibility as possible for the software layer.

This principle was championed by Alan Turing himself, the intellectual godfather of computers and artificial intelligence, who immortalized it in his concept of a “Turing machine”: a machine capable of executing any possible algorithm.

The early evolution of the digital computer validated Turing’s foundational insight. In the 1940s, different physical computers were built for different tasks: one to calculate the trajectories of missiles, say, and another to decipher enemy messages. But by the 1950s, general-purpose, fully programmable computers had emerged as the dominant computing architecture. Their versatility and adaptability across use cases proved a decisive advantage: they could be continuously updated and used for any new application simply by writing new software.

In more recent history, consider how many different physical devices were collapsed into a single product, the iPhone, thanks to the genius of Steve Jobs and others: phone, camera, video recorder, tape recorder, MP3 player, GPS navigator, e-book reader, gaming device, flashlight, compass.

(An analogous pattern can even be traced out in the recent trajectory of AI models, though in this example everything is software. Narrow, function-specific models—one model for language translation, another for sentiment analysis, and so on—have over the past few years given way to general-purpose “foundation models” capable of carrying out the full range of downstream tasks.)

We will see this same shift play out in robotics over the coming years: away from specialized machines with narrowly defined use cases and toward a more general-purpose, flexible, adaptable, universal hardware platform.

What will this general-purpose hardware platform look like? What form factor will it need to have in order to flexibly act in a wide range of different physical settings?

The answer is clear: it will need to look like a human.

Our entire civilization has been designed and built by humans, for humans. Our physical infrastructure, our tools, our products, the size of our buildings, the size of our rooms, the size of our doors: all are optimized for human bodies. If we want to develop a generalist robot capable of operating in factories, and in warehouses, and in hospitals, and in stores, and in schools, and in hotels, and in our homes—that robot will need to be shaped like us. No other form factor would work nearly as well.

This is why the opportunity for humanoid robots is so vast. Bringing cutting-edge AI into the real world is the next great frontier for artificial intelligence.

Large language models will automate vast swaths of cognitive work in the years ahead. In parallel, humanoid robots will automate vast swaths of physical work.

And these robots are no longer a distant science fiction dream. Though most people don’t yet realize it, humanoids are on the verge of being deployed in the real world.

Tesla is investing heavily to develop a humanoid robot, named Optimus. The company aims to begin shipping the robots to customers in 2025.

Tesla CEO Elon Musk has stated in no uncertain terms how important he expects this technology to be for the company and the world: “I am surprised that people do not realize the magnitude of the Optimus robot program. The importance of Optimus will become apparent in the coming years. Those who are insightful or looking, listening carefully, will understand that Optimus will ultimately be worth more than Tesla’s car business, worth more than [full self-driving].”

A handful of younger startups are likewise making rapid progress here.

Just last week, Bay Area-based Figure announced a $675 million funding round from investors including Nvidia, Microsoft, OpenAI and Jeff Bezos. A couple months ago, the company released an impressive video of its humanoid robot making a cup of coffee.

Another leading humanoid startup, 1X Technologies, announced a $100 million financing in January. 1X already offers one version of its humanoid robot (with wheels) for sale, and plans to release its next generation (with two legs) soon.

Over the next few years, these companies will ramp from small-scale customer pilots to mass production. By the decade’s end, expect to see hundreds of thousands (if not millions) of humanoid robots deployed in real-world settings.