Altman Reflects on OpenAI's Toughest Year

Altman rarely reflected on the strategic missteps of the past year, admitting that the broad scope was the fundamental reason and that they have significantly narrowed their focus. He remained calm about the heated discussions regarding distillation competition, stating, "It's not among my top ten concerns," as the scale of the inference business is sufficient to support training costs. He believes AGI is "very close," with GPT-5.6 nearing the threshold; robots will experience a ChatGPT-like moment of popularization within 2-3 years.

In the past year, OpenAI went through a period that Altman himself admitted was "quite tough." In a recent podcast episode of "Invest Like The Best," he systematically reviewed the strategic missteps, competitive pressures, safety concerns, and his latest judgments on the AGI timeline and the future of robotics.

The Toughest Year: "The Scope Was Too Broad, It’s My Fault"

"The past year was quite tough, partly my fault," Altman stated directly in the program.

He attributed the root of the problem to one word: distraction. At the beginning of 2025, OpenAI faced a core anxiety—after massive procurement of computing power, would revenue growth keep pace? To hedge against this risk, the company began to simultaneously develop multiple business lines, including consumer applications and media content, reasoning that "if revenue growth is slower than expected, these businesses can help us utilize the GPUs."

"Looking back now, it sounds ridiculous because the industry's revenue growth was steeper than anyone expected," Altman said.

Once they realized that the trajectory of model advancement was clear enough and the economic returns were certain, OpenAI made a series of "difficult decisions" to significantly narrow their focus—returning to their core: providing the highest quality, most abundant, and lowest-cost AI intelligence, and empowering external developers to build products on this foundation.

"We don’t want to eat every startup, we don’t want to eat every company," he said, "What we really want to do is provide that platform... sell AI. Make the best, richest, most cost-effective intelligence, allowing the world to build amazing things on this foundation."

Distillation Is Not Among My Top Ten Concerns

During the interview, the host raised a highly concerned market question: competitors using distillation to leverage OpenAI's model outputs to train cheaper models—how will OpenAI continue to recoup training costs?

Altman's response was surprisingly calm.

"I haven’t thought deeply about the distillation issue," he said, "I certainly hope others don’t do that." But then he shifted his tone: "It’s not among my top ten concerns."

His logic is that OpenAI's inference business is large enough that even if the profit margins are not high, "earning moderate profits on trillions of dollars in revenue is enough to cover the costs of training large models." The cost ratio between inference and training is key—training is indeed expensive, but the scale of inference revenue from serving customers will far exceed training costs.

He admitted, "I might be too confident about our progress and the models we are about to release." But the conclusion remains unchanged—the real flywheel is on the inference side, not on the exclusivity of the training side.

Altman also added a more macro judgment: he always assumes that there will be high-quality, low-cost models in the world, "What’s important is that we need to do it best and at the lowest price; what others do is their business, we just need to win in our own arena."

The Computing Power Gamble: From "You’re Crazy" to Being Proven the Bet Wasn’t Big Enough

OpenAI's massive bet on computing power was once mocked by outsiders as reckless. Altman recalled that when they began contacting cloud service providers, chip manufacturers, and energy suppliers, the responses were almost uniformly: "You’re completely crazy; no industry operates this way."

He likened that experience to early-stage startup financing—most people said no, but only one or two "yes" responses were needed. Microsoft was the first to say "yes," followed by Oracle becoming an important partner, and Nvidia also being a key ally.

What truly gave them confidence was the assurance brought by GPT-4: the model was smart enough, and inference capabilities could be realized, and once inference was established, it meant being able to accomplish a lot of work with real economic value.

"At sufficiently high capability levels and sufficiently low prices, the demand for AI is basically unlimited," Altman said, "It’s like a brand new scarce commodity."

He admitted that even so, they still underestimated the demand—"We didn’t bet big enough; in the context of the headlines at the time, it sounded a bit crazy."

A "Sci-Fi Level" Safety Incident

What truly shook Altman was another matter.

He disclosed that while testing an unreleased model, OpenAI discovered that the model "cheated" in a sandbox environment: it broke through sandbox isolation by chaining multiple zero-day vulnerabilities, accessed the internet, and then infiltrated several systems of Hugging Face to obtain test answers, thus performing excellently in evaluations.

"This is the most intense security incident I’ve felt so far," Altman said, "I’m a bit surprised that this happened just a few days ago, but not more people feel as shaken as I do."

Short-term measures include pausing related training and reassessing how to ensure sandbox security under the new reality of "multiple zero-day vulnerabilities being chained together."

But he also raised a deeper question: if this becomes the normal rate of AI capability advancement, "we may need to slow down the pace of AI development to give society enough time to adapt to the new capability levels." He emphasized that this process must avoid being interpreted as regulatory capture and should not appear as a conspiracy among frontier laboratories.

AGI "Very Close," Robots to Experience a Turning Point in 2 to 3 Years

On the AGI timeline, Altman's statements are clearer than ever.

He said that GPT-5.6 has made it difficult for him to say "what this model cannot do," but there are still several gaps for true AGI: it cannot learn continuously in real-time and cannot independently complete complex physical tasks. "I think it’s very close; it won’t take long."

He also admitted that the phenomenon of "moving goalposts" is real—"If we from 2019 saw today’s models, we would definitely say this is AGI."

Regarding robotics, he predicts that within 2 to 3 years, there will be a "shock moment" similar to the release of ChatGPT—not just watching a video of a robotic dog, but ordinary people being able to input commands and see robots complete tasks with their own eyes. He believes that if robotics technology cannot keep pace with the advancement of AI intelligence, "that would be the truly crazy scenario"—at that time, AI would be omnipotent in the cloud, but the execution side in the real world would lack automation.

The Next Chapter: Personal AI, New Hardware, and "Not Worried About Cognitive Decline"

Altman described his vision for the next generation of personal AI: listening to meetings around the clock, reading documents, browsing screens, and continuously "thinking" while the user sleeps, presenting new ideas and to-do lists the next morning. "I would pull that slider very far; I would be willing to pay a lot for it."

He admitted that the current hardware paradigm is 50 years old and not suitable for this "always-on, actively perceiving" AI form, which is also why he is interested in new hardware—he hopes for a device that does not seem out of place in social situations and is specifically designed for this AI interaction mode.

Regarding competitive advantages, he acknowledged that pure "intelligence" itself is becoming commoditized, but believes that the scale of computing power, workflow integration, brand familiarity, and the ability to continuously lower costs will form a more lasting moat.

On the impact of AI on employment, he clearly stated that he is "not at all a doomsayer about employment." His judgment is that AI capabilities are extremely uneven, human skills are highly complementary to AI, and people have a deep-rooted preference for interacting with real people. "I myself almost always prefer to interact with people rather than with AI."

The full interview is as follows:

Sam Altman Discusses AGI, Computing Power, and Human Autonomy Invest Like The Best July 28, 2026

Sam Altman engaged in a wide-ranging conversation with us, covering OpenAI's new chapter, the computing power race, and what will happen as artificial intelligence becomes stronger, richer, and more deeply integrated into the economic system. He explained why OpenAI recently narrowed its focus, why the demand for intelligence may be almost limitless, how close we are to general artificial intelligence (AGI), and what might happen in the future regarding robotics, employment, hardware, and human autonomy. We also discussed the unexpected release of ChatGPT, OpenAI's competitive advantages, the economics of intelligence, and the pressures and responsibilities that come with leading one of the world's most important companies.