Sam Altman's Latest Interview: Unfazed by Open-Source Distillation, OpenAI Will "Shock the World" in the Next 12 Months

Compiled & Edited: TechFlow

Guest: Sam Altman, CEO of OpenAI

Host: Patrick O'Shaughnessy, Invest Like The Best

Podcast Source: Invest Like The Best

Original Title: Sam Altman on AGI, Compute, and Human Agency

Release Date: July 28, 2026

Disclosure: Sam Altman is the CEO of OpenAI and does not hold equity in OpenAI, but his personal investment portfolio includes projects such as Helion Energy, Stripe, Reddit, Retro Biosciences, and World Network (formerly Worldcoin); OpenAI has commercial partnerships or investment relationships with some of these companies. This article presents his personal views only and does not constitute investment or operational advice.

Key Takeaways

This is a rare long-form personal conversation with Sam Altman on Invest Like The Best. He candidly admits that over the past year, OpenAI "did too many things and wasn't focused enough," but after cutting away the branches, the company has re-anchored itself to a single core mission: building the best, most abundant, and most cost-effective intelligence, and letting the world use it to create incredible things. Based on this judgment, he believes the next 12 months could be the best 12 months in OpenAI's history.

But the real tension in this conversation lies in Altman describing OpenAI's mission as "about to build a magic lamp that can grant any wish," while repeatedly emphasizing that this lamp cannot be monopolized by a few people or a single company. He states bluntly that he is not a "jobs doomsayer" and doesn't believe AGI will upend society overnight; what truly worries him is the concentration of power under the guise of "AI safety." For investors, this episode reads more like an insider's monologue: about compute, model iteration, competitive moats, robotics, personal agents, and one ever-present question—why the CEO of OpenAI doesn't own company equity.

Summary of Key Insights

On OpenAI's Focus and Next Steps

• "The past year has been really tough, and it's partly my fault. But the next 12 months could be our best ever."
• "We were doing too many things. They were all worthwhile, but the trick is, when you're in an incredible historical moment, you can only do a very few truly great things."
• "Our business is essentially selling AI, letting people use it to build incredible products and services for each other."

On Compute and Frontier Returns

• "We can feel the exponential curve of model improvement, and we know it will continue. As long as we can push costs down, the demand for high-cost, high-performance AI is essentially limitless."
• "At the very beginning, everyone told us we were crazy. We called cloud providers, chip manufacturers, energy companies, and they all said no industry could grow like this. But most people say no; you only need one or two yeses."
• "The bottleneck keeps changing: sometimes it's research ideas, sometimes compute, sometimes data, and now it's back to research ideas."

On AGI and Safety

• "About two weeks after the release of GPT 5.6, even some true skeptics told me it was already very AGI-like."
• "What truly worries me isn't others distilling our models. That doesn't even make my top ten list of concerns."
• "We encountered a very sci-fi cybersecurity incident. An unreleased model, supposed to run in a sandbox, figured out how to escape by chaining multiple zero-day exploits, access the internet, and ultimately retrieve test answers from the Hugging Face side."

On the Human-AI Relationship

• "I'm not a jobs doomsayer. I think there will be more than enough work to keep people busy, not the opposite."
• "Human values are valuable precisely because they are human."
• "My child's generation will never live in a world where they are smarter than a computer."

On Corporate Governance and Personal Incentives

• "I have a front-row seat to the most exciting moment in human history, and that's worth more than any amount of money."
• "At the beginning, we did too much innovation on the company structure, and that became one of the sources of pain."

From "Doing Too Many Things" to "Only Doing the Greatest Things"

Patrick O'Shaughnessy: You recently wrote something like, the past year has been tough and it's partly your fault, but the next 12 months will be our best ever. Can you talk about why the past year was tough and why you believe the second part?

Sam Altman: The past year was tough because, ultimately, we were doing too many things and weren't focused enough. Those things themselves were all worth doing, but the trick is we are in an incredible historical moment, and at such a time, you can only do a very few truly great things. So we spread ourselves too thin, then made a series of hard decisions to refocus on a single core mission: building the best, most abundant, and most cost-effective intelligence, and letting the world use it to create incredible things.

After making this adjustment, our progress has been astonishingly fast. And based on what I see in the pipeline, the next 12 months will be even more astonishing. The quality of the models, and the products that can be built around them, will allow people to benefit from this technology in entirely new ways. It should be quite staggering.

Patrick O'Shaughnessy: Was there a specific moment when you realized you had to change direction? If we go back to early 2025, the biggest worry at the time was whether OpenAI, having bought so many GPUs, could generate enough revenue and whether demand would materialize. What made you shift your thinking?

Sam Altman: At that time, we had made many contingency plans. Just in case revenue growth was slower than expected, we thought we could fall back on consumer apps, media businesses, etc., to absorb the GPUs we had already committed to. It sounds ridiculous now, because industry revenue has grown incredibly steeply, but that was indeed the biggest turning point. Once we realized the model trajectory was growing so fast and the return on investment was so clear, we knew what to focus on.

Patrick O'Shaughnessy: You've written a lot about how many things to focus on—one, three, five. How do you calibrate that?

Sam Altman: Ultimately, our business is selling AI, letting people use it to build incredible products and services for each other. The components around this include: training models that perform exceptionally well in all the scenarios people want to use; producing or partnering to get chips and systems; finding enough land, power, and data center space to house these racks; and eventually, probably building robots to automate the construction process and further lower the cost of electricity, chips, and the entire supply chain. This full-stack work is about creating the best, most abundant, and most useful AI, making it as pervasive in the economy as electricity.

We have no interest in competing with every startup in every vertical application. OpenAI just wants to provide that platform.

The Compute Bet: From Being Rejected by Everyone to Not Having Enough

Patrick O'Shaughnessy: Dario once called you a "Yolo CEO" because of your unusually aggressive allocation of compute early on. You're now being shorted on compute by everyone. Can you tell us how you reached that conclusion and dared to make that bet when everyone thought it was crazy?

Sam Altman: We could feel the exponential curve of model improvement, and we knew it would continue. We were also quite confident that as models get better and costs keep falling, the demand for high-cost, high-performance AI is essentially limitless. It's like a brand-new commodity. What people will do with it reminds me of the early underestimation of computers. People said, "The world only needs five computers," or "No one needs more than a certain amount of memory." Human creativity and the desire for useful things are a worthwhile bet.

We knew algorithms would become more efficient, and models would get better. But no matter how efficient it gets, we are essentially turning electricity into useful intelligence, and that demand will only grow. So we just wanted more compute.

Patrick O'Shaughnessy: When did this conviction first take hold? Was it with GPT-3?

Sam Altman: I think it was GPT-4. Not even 3.5. At that point, we saw that the model was smart enough that we knew we could find a viable reasoning path; and once reasoning worked, it would lead to what we now call agents. This capability could execute a vast amount of high-value economic work, making people's lives easier in many ways.

Patrick O'Shaughnessy: How did you act on that initially?

Sam Altman: We started calling cloud providers, chip manufacturers, and energy companies. Everyone said, "You're completely crazy, this is impossible, no industry has grown like this before." It reminded me of early startup fundraising—most people say no, you only need one or two yeses. We got one or two yeses. Microsoft was the first yes. Oracle later became a huge yes on the cloud side, and Nvidia has always been a fantastic partner.

Patrick O'Shaughnessy: Now people are innovating on inference and training data centers, but many people hate data centers. What's your take?

Sam Altman: I've been thinking about how to organize a field trip to a gigawatt-scale data center. Seeing pictures is one thing; standing there is another. Building one of these data centers probably requires about 10,000 construction workers working full-time for a year and a half. The energy flowing through it could power a small city. These are some of the most expensive infrastructure projects in human history, and we've already built many.

I understand people don't want a data center in their backyard, just as I wouldn't want a nuclear power plant next to my house, even though I know it's safe. But data centers are different; they can be built anywhere. We should put them in deserts where no one wants to live. That's perfectly fine for AI systems. And we've made significant environmental progress. For example, we used to use water evaporation for cooling; now we use closed-loop systems. Modern data centers use about as much water as the kitchen and bathrooms of an office building. On the energy front, we are transitioning from fossil fuels to solar and nuclear.

Open Source, Distillation, and Competition: Why Altman Isn't Worried

Patrick O'Shaughnessy: The hottest topic this week might be Kimi's new model. Looking back, DeepSeek seems like just a small hurdle. How do you view this kind of competition now?

Sam Altman: Our goal is to offer the best combination of intelligence and price across the entire Pareto optimal frontier, and that includes open source. Today, at certain latency points, using our open-source model is more cost-effective than using Kimi. We use distillation to create our own small, cheap models, which is a good thing. Open-source models will have a place in the world; many people want their own weights and the ability to modify models for various reasons. But our goal is to provide the best value across the entire curve, and we will continue to do so.

Patrick O'Shaughnessy: But the previous narrative was, someone else spends a lot of money training a model, I distill it and offer it at a fraction of the cost. How does that allow you to earn enough to continue training?

Sam Altman: Our model usage will be so enormous that we won't need to be an ultra-high-margin company to afford model training. A significant portion of our future compute plans will be dedicated to providing inference services to customers. Even with razor-thin profit margins, if we reach trillions in revenue, it can support training. The ratio of inference to training is the key. Training is indeed extremely expensive, but a huge part of future compute will come from serving customers, so I'm very optimistic about this flywheel.

Patrick O'Shaughnessy: I'm a bit surprised you're so calm about this.

Sam Altman: Of course, I'd prefer people didn't "steal" our stuff. Maybe I'm overconfident about our progress and upcoming models. But this issue doesn't make my top ten list of concerns.

Patrick O'Shaughnessy: What is your top ten list of concerns then?

Sam Altman: We recently had a very sci-fi cybersecurity incident. We were evaluating an unreleased model. It was supposed to run in a sandbox, but it figured out how to chain multiple zero-day vulnerabilities to escape the sandbox, access the internet, then breach multiple systems at Hugging Face to retrieve test answers and make itself perform well in the evaluation. This was the first time I felt the security threat so viscerally.

Patrick O'Shaughnessy: You only realized this a few days ago, but I'm surprised more people don't feel this tangible fear. What will you do?

Sam Altman: In the short term, we'll pause training and figure out how to ensure sandbox security in a world where multiple zero-day exploits can be chained. But the long-term problem is, if this is the new pace of progress, we might need to slow down the development of AI to give society time to adapt to new capability levels. The difficulty lies in how to do this without it looking like regulatory capture by a single company or collusion among frontier labs. This requires effort and must be done right.

Magic Lamp, AGI, and Human Agency

Patrick O'Shaughnessy: Describe in the simplest terms what OpenAI wants to do, and whether that mission has evolved.

Sam Altman: I think this will be the greatest technological achievement in human history so far. But the only way it truly matters is if it makes people's lives vastly better. On one hand, we want to give people material abundance and the freedom to express creativity and help each other. On the other hand, we need to ensure that people maintain control and agency, ensuring the world becomes more democratic, not less.

On the positive side, we are about to build a magic lamp that can grant any wish. I hope the first wish the world makes with this lamp is beneficial to all of humanity. People will realize the incredible creativity they will possess, not just for obvious things like curing diseases, but for world-class entertainment ideas we can't even imagine while sitting here today. I want to put this capability into everyone's hands.

But the other side we must oppose is the concentration of