More from Sam Altman
Our mission is to ensure that AGI (Artificial General Intelligence) benefits all of humanity. Systems that start to point to AGI* are coming into view, and so we think it’s important to understand the moment we are in. AGI is a weakly defined term, but generally speaking we mean it to be a system that can tackle increasingly complex problems, at human level, in many fields. People are tool-builders with an inherent drive to understand and create, which leads to the world getting better for all of us. Each new generation builds upon the discoveries of the generations before to create even more capable tools—electricity, the transistor, the computer, the internet, and soon AGI. Over time, in fits and starts, the steady march of human innovation has brought previously unimaginable levels of prosperity and improvements to almost every aspect of people’s lives. In some sense, AGI is just another tool in this ever-taller scaffolding of human progress we are building together. In another sense, it is the beginning of something for which it’s hard not to say “this time it’s different”; the economic growth in front of us looks astonishing, and we can now imagine a world where we cure all diseases, have much more time to enjoy with our families, and can fully realize our creative potential. In a decade, perhaps everyone on earth will be capable of accomplishing more than the most impactful person can today. We continue to see rapid progress with AI development. Here are three observations about the economics of AI: 1. The intelligence of an AI model roughly equals the log of the resources used to train and run it. These resources are chiefly training compute, data, and inference compute. It appears that you can spend arbitrary amounts of money and get continuous and predictable gains; the scaling laws that predict this are accurate over many orders of magnitude. 2. The cost to use a given level of AI falls about 10x every 12 months, and lower prices lead to much more use. You can see this in the token cost from GPT-4 in early 2023 to GPT-4o in mid-2024, where the price per token dropped about 150x in that time period. Moore’s law changed the world at 2x every 18 months; this is unbelievably stronger. 3. The socioeconomic value of linearly increasing intelligence is super-exponential in nature. A consequence of this is that we see no reason for exponentially increasing investment to stop in the near future. If these three observations continue to hold true, the impacts on society will be significant. We are now starting to roll out AI agents, which will eventually feel like virtual co-workers. Let’s imagine the case of a software engineering agent, which is an agent that we expect to be particularly important. Imagine that this agent will eventually be capable of doing most things a software engineer at a top company with a few years of experience could do, for tasks up to a couple of days long. It will not have the biggest new ideas, it will require lots of human supervision and direction, and it will be great at some things but surprisingly bad at others. Still, imagine it as a real-but-relatively-junior virtual coworker. Now imagine 1,000 of them. Or 1 million of them. Now imagine such agents in every field of knowledge work. In some ways, AI may turn out to be like the transistor economically—a big scientific discovery that scales well and that seeps into almost every corner of the economy. We don’t think much about transistors, or transistor companies, and the gains are very widely distributed. But we do expect our computers, TVs, cars, toys, and more to perform miracles. The world will not change all at once; it never does. Life will go on mostly the same in the short run, and people in 2025 will mostly spend their time in the same way they did in 2024. We will still fall in love, create families, get in fights online, hike in nature, etc. But the future will be coming at us in a way that is impossible to ignore, and the long-term changes to our society and economy will be huge. We will find new things to do, new ways to be useful to each other, and new ways to compete, but they may not look very much like the jobs of today. Agency, willfulness, and determination will likely be extremely valuable. Correctly deciding what to do and figuring out how to navigate an ever-changing world will have huge value; resilience and adaptability will be helpful skills to cultivate. AGI will be the biggest lever ever on human willfulness, and enable individual people to have more impact than ever before, not less. We expect the impact of AGI to be uneven. Although some industries will change very little, scientific progress will likely be much faster than it is today; this impact of AGI may surpass everything else. The price of many goods will eventually fall dramatically (right now, the cost of intelligence and the cost of energy constrain a lot of things), and the price of luxury goods and a few inherently limited resources like land may rise even more dramatically. Technically speaking, the road in front of us looks fairly clear. But public policy and collective opinion on how we should integrate AGI into society matter a lot; one of our reasons for launching products early and often is to give society and the technology time to co-evolve. AI will seep into all areas of the economy and society; we will expect everything to be smart. Many of us expect to need to give people more control over the technology than we have historically, including open-sourcing more, and accept that there is a balance between safety and individual empowerment that will require trade-offs. While we never want to be reckless and there will likely be some major decisions and limitations related to AGI safety that will be unpopular, directionally, as we get closer to achieving AGI, we believe that trending more towards individual empowerment is important; the other likely path we can see is AI being used by authoritarian governments to control their population through mass surveillance and loss of autonomy. Ensuring that the benefits of AGI are broadly distributed is critical. The historical impact of technological progress suggests that most of the metrics we care about (health outcomes, economic prosperity, etc.) get better on average and over the long-term, but increasing equality does not seem technologically determined and getting this right may require new ideas. In particular, it does seem like the balance of power between capital and labor could easily get messed up, and this may require early intervention. We are open to strange-sounding ideas like giving some “compute budget” to enable everyone on Earth to use a lot of AI, but we can also see a lot of ways where just relentlessly driving the cost of intelligence as low as possible has the desired effect. Anyone in 2035 should be able to marshall the intellectual capacity equivalent to everyone in 2025; everyone should have access to unlimited genius to direct however they can imagine. There is a great deal of talent right now without the resources to fully express itself, and if we change that, the resulting creative output of the world will lead to tremendous benefits for us all. Thanks especially to Josh Achiam, Boaz Barak and Aleksander Madry for reviewing drafts of this. *By using the term AGI here, we aim to communicate clearly, and we do not intend to alter or interpret the definitions and processes that define our relationship with Microsoft. We fully expect to be partnered with Microsoft for the long term. This footnote seems silly, but on the other hand we know some journalists will try to get clicks by writing something silly so here we are pre-empting the silliness…
There are two things from our announcement today I wanted to highlight. First, a key part of our mission is to put very capable AI tools in the hands of people for free (or at a great price). I am very proud that we’ve made the best model in the world available for free in ChatGPT, without ads or anything like that. Our initial conception when we started OpenAI was that we’d create AI and use it to create all sorts of benefits for the world. Instead, it now looks like we’ll create AI and then other people will use it to create all sorts of amazing things that we all benefit from. We are a business and will find plenty of things to charge for, and that will help us provide free, outstanding AI service to (hopefully) billions of people. Second, the new voice (and video) mode is the best computer interface I’ve ever used. It feels like AI from the movies; and it’s still a bit surprising to me that it’s real. Getting to human-level response times and expressiveness turns out to be a big change. The original ChatGPT showed a hint of what was possible with language interfaces; this new thing feels viscerally different. It is fast, smart, fun, natural, and helpful. Talking to a computer has never felt really natural for me; now it does. As we add (optional) personalization, access to your information, the ability to take actions on your behalf, and more, I can really see an exciting future where we are able to use computers to do much more than ever before. Finally, huge thanks to the team that poured so much work into making this happen!
Optimism, obsession, self-belief, raw horsepower and personal connections are how things get started. Cohesive teams, the right combination of calmness and urgency, and unreasonable commitment are how things get finished. Long-term orientation is in short supply; try not to worry about what people think in the short term, which will get easier over time. It is easier for a team to do a hard thing that really matters than to do an easy thing that doesn’t really matter; audacious ideas motivate people. Incentives are superpowers; set them carefully. Concentrate your resources on a small number of high-conviction bets; this is easy to say but evidently hard to do. You can delete more stuff than you think. Communicate clearly and concisely. Fight bullshit and bureaucracy every time you see it and get other people to fight it too. Do not let the org chart get in the way of people working productively together. Outcomes are what count; don’t let good process excuse bad results. Spend more time recruiting. Take risks on high-potential people with a fast rate of improvement. Look for evidence of getting stuff done in addition to intelligence. Superstars are even more valuable than they seem, but you have to evaluate people on their net impact on the performance of the organization. Fast iteration can make up for a lot; it’s usually ok to be wrong if you iterate quickly. Plans should be measured in decades, execution should be measured in weeks. Don’t fight the business equivalent of the laws of physics. Inspiration is perishable and life goes by fast. Inaction is a particularly insidious type of risk. Scale often has surprising emergent properties. Compounding exponentials are magic. In particular, you really want to build a business that gets a compounding advantage with scale. Get back up and keep going. Working with great people is one of the best parts of life.
Helion has been progressing even faster than I expected and is on pace in 2024 to 1) demonstrate Q > 1 fusion and 2) resolve all questions needed to design a mass-producible fusion generator. The goals of the company are quite ambitious—clean, continuous energy for 1 cent per kilowatt-hour, and the ability to manufacture enough power plants to satisfy the current electrical demand of earth in a ten year period. If both things happen, it will transform the world. Abundant, clean, and radically inexpensive energy will elevate the quality of life for all of us—think about how much the cost of energy factors into what we do and use. Also, electricity at this price will allow us to do things like efficiently capture carbon (so although we’ll still rely on gasoline for awhile, it’ll be ok). Although Helion’s scientific progress of the past 8 years is phenomenal and necessary, it is not sufficient to rapidly get to this new energy economy. Helion now needs to figure out how to engineer machines that don’t break, how to build a factory and supply chain capable of manufacturing a machine every day, how to work with power grids and governments around the world, and more. The biggest input to the degree and speed of success at the company is now the talent of the people who join the team. Here are a few of the most critical jobs, but please don’t let the lack of a perfect fit deter you from applying. Electrical Engineer, Low Voltage: https://boards.greenhouse.io/helionenergy/jobs/4044506005 Electrical Engineer, Pulsed Power: https://boards.greenhouse.io/helionenergy/jobs/4044510005 Mechanical Engineer, Generator Systems: https://boards.greenhouse.io/helionenergy/jobs/4044522005 Manager of Mechanical Engineering: https://boards.greenhouse.io/helionenergy/jobs/4044521005 (All current jobs: https://www.helionenergy.com/careers/)
More in AI
When Language Models Learn to See and Create
A new prototype is laying claim to the title of smallest, lightest untethered flying robot. At less than a centimeter in wingspan, the wirelessly powered robot is currently very limited in how far it can travel away from the magnetic fields that drive its flight. However, the scientists who developed it suggest there are ways to boost its range, which could lead to potential applications such as search and rescue operations, inspecting damaged machinery in industrial settings, and even plant pollination. One strategy to shrink flying robots involves removing their batteries and supplying them electricity using tethers. However, tethered flying robots face problems operating freely in complex environments. This has led some researchers to explore wireless methods of powering robot flight. “The dream was to make flying robots to fly anywhere and anytime without using an electrical wire for the power source,” says Liwei Lin, a professor of mechanical engineering at University of California at Berkeley. Lin and his fellow researchers detailed their findings in Science Advances. 3D-Printed Flying Robot Design Each flying robot has a 3D-printed body that consists of a propeller with four blades. This rotor is encircled by a ring that helps the robot stay balanced during flight. On top of each body are two tiny permanent magnets. All in all, the insect-scale prototypes have wingspans as small as 9.4 millimeters and weigh as little as 21 milligrams. Previously, the smallest reported flying robot, either tethered or untethered, was 28 millimeters wide. When exposed to an external alternating magnetic field, the robots spin and fly without tethers. The lowest magnetic field strength needed to maintain flight is 3.1 millitesla. (In comparison, a refrigerator magnet has a strength of about 10 mT.) When the applied magnetic field alternates with a frequency of 310 hertz, the robots can hover. At 340 Hz, they accelerate upward. The researchers could steer the robots laterally by adjusting the applied magnetic fields. The robots could also right themselves after collisions to stay airborne without complex sensing or controlling electronics, as long as the impacts were not too large. Experiments show the lift force the robots generate can exceed their weight by 14 percent, to help them carry payloads. For instance, a prototype that’s 20.5 millimeters wide and weighing 162.4 milligrams could carry an infrared sensor weighing 110 mg to scan its environment. The robots proved efficient at converting the energy given them into lift force—better than nearly all other reported flying robots, tethered or untethered, and also better than fruit flies and hummingbirds. Currently the maximum operating range of these prototypes is about 10 centimeters away from the magnetic coils. One way to extend the operating range of these robots is to increase the magnetic field strength they experience tenfold by adding more coils, optimizing the configuration of these coils, and using beamforming coils, Lin notes. Such developments could allow the robots to fly up to a meter away from the magnetic coils. The scientists could also miniaturize the robots even further. This would make them lighter, and so reduce the magnetic field strength they need for propulsion. “It could be possible to drive micro flying robots using electromagnetic waves such as those in radio or cell phone transmission signals,” Lin says. Future research could also place devices that can convert magnetic energy to electricity onboard the robots to power electronic components, the researchers add.
Machine learning for software engineers 3-28-25
Your weekly selection of awesome robot videos Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion. RoboSoft 2025: 23–26 April 2025, LAUSANNE, SWITZERLAND ICUAS 2025: 14–17 May 2025, CHARLOTTE, NC ICRA 2025: 19–23 May 2025, ATLANTA, GA London Humanoids Summit: 29–30 May 2025, LONDON IEEE RCAR 2025: 1–6 June 2025, TOYAMA, JAPAN 2025 Energy Drone & Robotics Summit: 16–18 June 2025, HOUSTON, TX RSS 2025: 21–25 June 2025, LOS ANGELES ETH Robotics Summer School: 21–27 June 2025, GENEVA IAS 2025: 30 June–4 July 2025, GENOA, ITALY ICRES 2025: 3–4 July 2025, PORTO, PORTUGAL IEEE World Haptics: 8–11 July 2025, SUWON, KOREA IFAC Symposium on Robotics: 15–18 July 2025, PARIS RoboCup 2025: 15–21 July 2025, BAHIA, BRAZIL RO-MAN 2025: 25–29 August 2025, EINDHOVEN, NETHERLANDS Enjoy today’s videos! This robot can walk, without electronics, and only with the addition of a cartridge of compressed gas, right off the 3D-printer. It can also be printed in one go, from one material. Researchers from the University of California San Diego and BASF, describe how they developed the robot in an advanced online publication in the journal Advanced Intelligent Systems. They used the simplest technology available: a desktop 3D-printer and an off-the-shelf printing material. This design approach is not only robust, it is also cheap—each robot costs about $20 to manufacture. And details! [ Paper ] via [ University of California San Diego ] Why do you want a humanoid robot to walk like a human? So that it doesn’t look weird, I guess, but it’s hard to imagine that a system that doesn’t have the same arrangement of joints and muscles that we do will move optimally by just trying to mimic us. [ Figure ] I don’t know how it manages it, but this little soft robotic worm somehow moves with an incredible amount of personality. Soft actuators are critical for enabling soft robots, medical devices, and haptic systems. Many soft actuators, however, require power to hold a configuration and rely on hard circuitry for control, limiting their potential applications. In this work, the first soft electromagnetic system is demonstrated for externally-controlled bistable actuation or self-regulated astable oscillation. [ Paper ] via [ Georgia Tech ] Thanks, Ellen! A 180-degree pelvis rotation would put the “break” in “breakdancing” if this were a human doing it. [ Boston Dynamics ] My colleagues were impressed by this cooking robot, but that may be because journalists are always impressed by free food. [ Posha ] This is our latest work about a hybrid aerial-terrestrial quadruped robot called SPIDAR, which shows unique and complex locomotion styles in both aerial and terrestrial domains including thrust-assisted crawling motion. This work has been presented in the International Symposium of Robotics Research (ISRR) 2024. [ Paper ] via [ Dragon Lab ] Thanks, Moju! This fresh, newly captured video from Unitree’s testing grounds showcases the breakneck speed of humanoid intelligence advancement. Every day brings something thrilling! [ Unitree ] There should be more robots that you can ride around on. [ AgileX Robotics ] There should be more robots that wear hats at work. [ Ugo ] iRobot, who pioneered giant docks for robot vacuums, is now moving away from giant docks for robot vacuums. [ iRobot ] There’s a famous experiment where if you put a dead fish in current, it starts swimming, just because of its biomechanical design. Somehow, you can do the same thing with an unactuated quadruped robot on a treadmill. [ Delft University of Technology ] Mush! Narrowly! [ Hybrid Robotics ] It’s freaking me out a little bit that this couple is apparently wandering around a huge mall that is populated only by robots and zero other humans. [ MagicLab ] I’m trying, I really am, but the yellow is just not working for me. [ Kepler ] By having Stretch take on the physically demanding task of unloading trailers stacked floor to ceiling with boxes, Gap Inc has reduced injuries, lowered turnover, and watched employees get excited about automation intended to keep them safe. [ Boston Dynamics ] Since arriving at Mars in 2012, NASA’s Curiosity rover has been ingesting samples of Martian rock, soil, and air to better understand the past and present habitability of the Red Planet. Of particular interest to its search are organic molecules: the building blocks of life. Now, Curiosity’s onboard chemistry lab has detected long-chain hydrocarbons in a mudstone called “Cumberland,” the largest organics yet discovered on Mars. [ NASA ] This University of Toronto Robotics Institute Seminar is from Sergey Levine at UC Berkeley, on Robotics Foundation Models. General-purpose pretrained models have transformed natural language processing, computer vision, and other fields. In principle, such approaches should be ideal in robotics: since gathering large amounts of data for any given robotic platform and application is likely to be difficult, general pretrained models that provide broad capabilities present an ideal recipe to enable robotic learning at scale for real-world applications. From the perspective of general AI research, such approaches also offer a promising and intriguing approach to some of the grandest AI challenges: if large-scale training on embodied experience can provide diverse physical capabilities, this would shed light not only on the practical questions around designing broadly capable robots, but the foundations of situated problem-solving, physical understanding, and decision making. However, realizing this potential requires handling a number of challenging obstacles. What data shall we use to train robotic foundation models? What will be the training objective? How should alignment or post-training be done? In this talk, I will discuss how we can approach some of these challenges. [ University of Toronto ]