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Autonomous Vehicles as the Next General-Purpose Technology

Blog Post | Motor Vehicles

Autonomous Vehicles as the Next General-Purpose Technology

Vehicle autonomy is likely to be the first mass application where AI becomes physical.

Summary: Autonomous vehicles are following the classic adoption path of a general-purpose technology — the same S-curve that steam, electricity, and the automobile once traced. If current growth rates hold, human driving has one to two decades left as an everyday activity. The payoff: reclaimed time, far fewer road deaths, cities rebuilt around people instead of parking, and a wave of new businesses made viable by cheaper transaction costs.


In 1939, five million people queued at New York’s World’s Fair to ride Futurama, General Motors’ model of America in 1960. Among the marvels were highways and mountain roads where cars, with no driver, whizzed their passengers across the country. Riders left wearing a pin: “I Have Seen the Future.” Sixty-six years later than General Motors expected, that driverless future is here.

The adoption of self-driving cars, also known as autonomous vehicles (AVs), is growing fast. In just two years, Waymo’s weekly paid rides are up tenfold to 500,000 across ten American cities. Using cameras, laser-based sensors (LiDAR), and machine learning trained on physical surroundings and movement, a new kind of car is outgrowing the human driver.

The industry calls today’s technology “Level 4” autonomy: self-driving with no need for human backup, but only within intensively mapped and geofenced areas. So far, no company has reached “Level 5” autonomy: a car that can self-drive wherever a human driver can. Yet, even without Level 5 autonomy, AV rides per week are ballooning. If that keeps up, the AV boom will soon rival the AI boom in scale and consequence, transforming transportation, cities, and the economy along the way.

That is what the arrival of a general-purpose technology looks like. It’s one of the main ways that capitalism keeps overhauling the human condition for the better. Lured by profit or chased by the fear of obsolescence, plenty of people and companies are working hard to bring human progress about. The rest of us will sit back and enjoy the ride.

On General-Purpose Technologies

Economists, starting with Bresnahan and Trajtenberg, call certain inventions “general-purpose technologies,” or GPTs. The name “ChatGPT” is a lucky mnemonic: while it actually stands for “Generative Pre-trained Transformer,” the pun is apt. AI is earning its place in the GPT list, alongside steam, steel, electricity, refrigeration, the internal combustion engine, telecommunications, the automobile, mass production, the transistor, the computer, the internet, and the smartphone. AVs may be next GPT.

GPTs stand out for being transformative, not merely additive. They spawn new infrastructure, chart-topping companies, redraw human geography, create and destroy whole occupations, define eras of macroeconomic history, and accelerate productivity. The key is that a GPT becomes a hub that almost every supply chain runs through. It gets built into existing supply chains and spins off new ones, ending in new downstream products. Some GPTs reach mass consumer adoption in the home too.

Today, AI is transforming office work, but its victories are in bits rather than atoms, in data rather than stuff. Vehicle autonomy is likely the first mass application where AI becomes physical.

The AV S-Curve

Extrapolation is a good way to forecast the future. Extrapolate a fast growth rate, and you get exponential acceleration. Moore’s Law, the rule of thumb that computer power roughly doubles every two years while costs drop, is the classic example of exponential acceleration: it persisted for decades, expanding computing power in a way few initially predicted. But exponential growth doesn’t last forever. For new technologies, it eventually levels off, tracing an “S-curve”: flat, then steep, then flat again, as in Figure 1:

Figure 1: The Adoption S-Curve

Source: stratechi.com/adoption-curves/

Figure 2 shows adoption patterns for specific technologies among US households:

Figure 2: Technology Adoption in US Households, Percent

In Figure 2, the steep part of the adoption curves is when a new technology transforms the human experience and the economy. AI is on the steep part now, with its computing power and the volume of data it processes growing more than fivefold a year. And AVs might achieve that growth rate soon.

Waymo, the market leader, runs about half a million paid robotaxi rides a week across ten U.S. cities — up from roughly 10,000 a week in 2023. They are targeting a million rides a week by the end of 2026, with expansions into Tokyo and London next. From spring 2024 to spring 2025, rides per week increased fivefold. From spring 2025 to spring 2026, they roughly doubled— a slowdown widely attributed to a limited supply of vehicles, not waning demand. With almost every car ride in the world still human-driven, there is lots of room for annual growth to keep doubling or even increasing fivefold.

Project that growth forward and the math becomes mind-boggling.

A million rides a week sounds big, but there are around 1.4 billion cars on the world’s roads. If global travel trends mirror American habits of around 1.9 trips per vehicle a day (number of vehicle-trips divided by number of vehicles), that implies upwards of 18.6 billion personal car rides worldwide every week, maybe 20 billion once commercial trips are factored in. That means AV trips currently account for a fraction of one percent. At fivefold annual growth, a million becomes 5 million by end-2027 and 125 million by end-2029, still under one percent. AVs won’t reach mass adoption this decade.

But keep that fivefold growth going, and AVs hit the steep part of the S-curve and 3 billion rides a week by the end of 2031. A year or two later, they would account for most rides worldwide. Even at a mere doubling per year, the takeoff arrives by the late 2030s.

On present trends, driving has one to two decades left as an ordinary part of life. Most of us will live to see it sunset.

The Driverless Revolution

The coming AV revolution is exciting. Start with time. Americans spend about an hour a day behind the wheel, one of their least favorite times of day. With eyes off the road, this time becomes phone calls, reading, movies, work, or sleep. It should save lives too, as algorithms and infrastructure keep improving in ways human reflexes can’t. Road crashes, with human error being the critical reason for 90%+ of them, kill some 1.16 million people a year and are the leading cause of death for those aged 5 to 29.

Urban planners will get millions of acres of downtown parking to repurpose as AVs park themselves somewhere cheaper or move on to the next fare. Children, the elderly, the disabled, and the undocumented all become more mobile. Eventually, traffic lights will vanish as cars communicate with each other, making brake lights and turn signals obsolete.

Household budgets will become simpler as car ownership becomes optional. Expect mobility to become a service, where you summon the vehicle that fits the trip. That will breed more specialized vehicles: one-seaters for solo trips, cargo haulers, sleeper cabins that compete with short flights.

Some jobs, especially long-haul trucking and ride-hail driving, will disappear, but the same shift will ease the work of anyone who drives between appointments, from plumbers to real estate agents to music teachers. Their road time will become fun and/or productive, and access to those jobs will widen because driving will no longer be required. Expect more home health aides, babysitters, house cleaners, landscapers, and domestic services generally.

Without human driver wages, last-mile delivery will become cheap enough to overhaul traditional retail and reduce brick-and-mortar market share. Picture a store sending your order in a refrigerated pod that keeps the ice cream hard. You get a notification as the robot-on-wheels opens your garage with a one-time code and starts loading your fridge. A micro-AV takes a service elevator and goes fifty floors up.

The larger pattern, as economist Xiaokai Yang showed, is that economies grow through greater specialization and variety when transaction costs, the hassle and expense of getting buyers and sellers together and of contracting and shipping, fall. AVs are poised to become a prime technology that reduces transaction costs, enabling a boom in small-scale local trade because “come pick it up” will no longer mean an hour in traffic. Figure 3 shows how falling transaction costs will let many businesses, from restaurant takeout to device repair, charge less and sell more.

Figure 3: How AVs Will Boost Business

Even more interesting are the new businesses that are uneconomic right now, but that AVs will make viable. Figure 4 shows why:

Figure 4: How AVs Make New Businesses Possible

Most businesses can produce more cheaply as they grow, because fixed costs are spread across more sales. If what consumers are willing to pay is less than the average cost, the business folds, or never begins. By reducing transaction costs, AVs will flip some businesses from uneconomic to viable. Think of home kitchens offering takeout, new community farms, an artistic home tinkerer with a 3D printer, and things you never imagined. That’s where AVs will really earn their spurs as a GPT.

Freedom in the Robotaxi Age

For a century, freedom of movement has meant holding a license and keeping a machine in the driveway. Cheap driverless rides extend a freedom many of us take for granted to teenagers, the elderly, the disabled, and many others. But a self-coordinating traffic network could also become a system that knows where everyone went and when, so early safeguards against mass AV surveillance are important.

Steam, electricity, and the automobile gave ordinary people options their grandparents could not have bought, no matter the price. The robots taking the wheel are the next installment. Let them drive.

Anthropic | Scientific Research

AI Finds a New Enzyme System in a Virus

“Many discoveries that have revolutionized biology and medicine started with a scientist noticing something odd in the staggering diversity of molecular machines found in nature. Restriction enzymes, proteins that cut DNA at specific short sequences, were found in bacterial immune systems, where they destroy the DNA of invading viruses. Researchers realized they could use these enzymes to cut DNA at chosen places and splice genes from one organism into another, which launched the biotechnology industry. Taq polymerase, an enzyme that copies DNA at high temperatures, was identified in a bacterium in a Yellowstone hot spring. It became the basis for PCR, the DNA-copying method used in much of modern diagnostics. CRISPR was first noticed as an unusual repeat sequence in the DNA of certain bacteria, and is now the foundation of gene editing-based medicines.

In the spring of 2026, we formed a research group to see whether general AI models can systematize and accelerate such discoveries. We believe that this acceleration will come from establishing a new way of doing biology research, in which agents collaborate with humans in every step of the process. Developing this new way of working required that we build our own lab and a single team working on everything from training Claude in biology to running experiments in the lab.

Today, we’re sharing early results from one of our first research programs, in which Claude autonomously discovered a novel enzyme system that is associated with an array of DNA repeats, a pattern reminiscent of CRISPR. Although we don’t yet know its function, the system that Claude discovered has a set of characteristics that have only ever been found together in a handful of other systems, all of which are programmable and perform operations like cutting, copying, and pasting DNA. Beyond CRISPR, which has already transformed science and medicine, several other such systems are now in development as promising tools.

The system that Claude found is based on a reverse transcriptase (RT), enzymes that copy RNA into DNA. While this underlying RT, found in a jumbo phage, had been identified in previous studies, Claude appears to be the first to notice the system’s defining features—an associated array of non-coding DNA sequences and an additional accessory protein of unknown function.”

From Anthropic.

The Keyword | Computing

Google Expands SynthID Detector for AI content

“As AI tools become better and easier to use, having clear context about the content you encounter online helps you make informed decisions about what to trust. Since launching Google SynthID in 2023, we've helped people identify AI-generated media using imperceptible watermarks across images, video, and audio. To date, we've watermarked over 180 billion images and videos, along with 240,000 years of audio content.

Last year, we introduced an early version of the SynthID Detector to help media professionals verify AI-generated content. Now, we’re expanding access to everyone – with the tool available globally in English starting today.

With the new SynthID Detector, anyone can easily check if an image, video, or audio file was made with AI from Google or our partners, including OpenAI, NVIDIA, Kakao, and soon, Apple.”

From The Keyword.

Wall Street Journal | Scientific Research

AI Offers Hundreds of Potential Math Solutions

“On Tuesday night, OpenAI published hundreds of proofs for mathematicians to wrap their minds around.

An unprecedented number of answers that had eluded mathematicians for decades arrived in a single night. The deluge included AI solutions to more than 300 problems across number theory, algebraic geometry and other fields with names that could give anyone haunted by high-school math a migraine. Among the major findings in a collection of 722 papers were advances related to three of the five remaining Millennium Prize Problems, including the Riemann hypothesis.

'If a human did this, it would be an instant Fields Medal, no questions asked,' Alex Kontorovich, a Rutgers University distinguished math professor, wrote on X about the 'quasi-Riemann hypothesis' proof.

The understated rollout of such monumental results marked a shift in OpenAI’s approach to publishing scientific discoveries, part of an effort to win back mathematicians who have become allergic to AI hype in recent months. On late Tuesday and early Wednesday, many of them reported feeling a sense of awe.

As stunned math experts scrolled through the results and began to absorb them, even a researcher inside Anthropic called the release 'obviously the most significant moment in mathematical history.'”

From Wall Street Journal.

MIT News | Computing

AI Beats Top Human Stratego Players for the First Time

“Using advances in machine-learning, researchers from MIT, Carnegie Mellon University, New York University, and Stanford University developed an AI that defeated top-ranked human players of the board wargame Stratego by a large margin — something no AI system had been able to achieve.

Stratego, a two-player game of imperfect information, in which the opponent’s piece identities remain hidden, is often used as a benchmark to test the strategic thinking abilities of powerful AI models.

To build their model, the researchers combined efficient training algorithms with new techniques tailored for calculated decision-making in hidden information settings.

The AI system achieved greater performance at Stratego than the next best models, while being far cheaper and less computationally demanding to train. The system also outperformed top human players in other strategic games with different rules and designs, demonstrating how it can be generalized for a variety of use-cases…

Past efforts, such as Google’s DeepMind, relied on sophisticated operations that were computationally demanding and costly. But even with millions of dollars in training costs, these models were still not strong enough to beat top human Stratego players.”

From MIT News.