At Stanford University, OpenAI’s chief executive Sam Altman outlined a growing concern about how artificial intelligence systems handle personal data.
Speaking about the next phase of AI development, he said security will become the field’s defining challenge. As models become more personalized, their closeness to users may create openings for misuse.
Altman explained that the focus in AI is shifting from abstract safety debates toward practical security problems. These involve protecting models from manipulation, theft, and data leaks. He pointed out that as language models grow more capable, securing them for public use will become harder. The ability to mislead or exploit them is already evolving faster than their defenses.
He described adversarial robustness as one of the toughest problems. Researchers are learning how easily an AI system can be tricked into producing unintended results. Small changes in a prompt or dataset can alter outcomes in ways users never intended. To Altman, that pattern resembles a familiar cycle in technology, where every new capability introduces an equal measure of vulnerability.
Among the security challenges he discussed, personalization stands out. Altman said people enjoy how tools like ChatGPT can adapt to their tone and remember details from earlier interactions. Those features make AI feel more natural and helpful, yet they also turn the model into a potential record of private life. When users connect these systems to outside services, such as email or shopping platforms, the data trail becomes even more complex.
Altman warned that the same personalization people value could one day expose sensitive information. A malicious actor could, in theory, find ways to extract data from a user’s customized AI system. What makes this difficult, he said, is that machines still lack the human instinct to judge what to share and when. A person might understand what information to keep private, but a model trained on personal context does not apply that kind of reasoning.
He offered a simple example. If an AI assistant helps with online purchases, it might unintentionally reveal personal medical information gathered from earlier chats. The model would not recognize the boundary between helpful context and private detail. Solving that problem with complete reliability, Altman said, is one of the hardest goals in AI security.
Altman’s message extended beyond identifying threats. He also said that AI can strengthen defenses. The same systems that create risks can help detect software vulnerabilities and prevent attacks. OpenAI and other developers already use models to test and harden code. Altman believes this dual role—AI as both the risk and the remedy, will define the next phase of technological progress.
He encouraged students to see AI security as one of the most promising fields to study. Traditional technology companies have long maintained large teams to secure their platforms. AI organizations, he said, will soon need the same level of attention and expertise. As models become embedded in daily life, protecting them will require a new generation of specialists who understand both coding and machine learning.
Altman’s conversation at Stanford also reflected his broader view of AI’s direction. He said deep learning is still at an early stage and that innovation in architecture, energy use, and model efficiency will continue. Yet he returned repeatedly to security. The future of AI, in his view, will depend not only on smarter algorithms but on the ability to keep them trustworthy.
For users, that means a paradox. The more AI learns to understand people, the more valuable (and potentially dangerous) it becomes. Systems that know how to anticipate preferences or recall personal details make digital life smoother. But they also hold knowledge that, in the wrong hands or through weak safeguards, could turn against the user.
Altman’s remarks summed up a simple but urgent truth. Artificial intelligence is learning fast, not only from data but from the people who use it. The challenge now is to make sure it does not learn too much.
Notes: This post was edited/created using GenAI tools.
Read next:
• Your AI Chats May Not Be Private: Microsoft Study Finds Conversation Topics Can Be Inferred from Network Data
• Carlson Interview Exposes Altman’s Fears on Suicide, Military Use, and AI Morality
Speaking about the next phase of AI development, he said security will become the field’s defining challenge. As models become more personalized, their closeness to users may create openings for misuse.
Altman explained that the focus in AI is shifting from abstract safety debates toward practical security problems. These involve protecting models from manipulation, theft, and data leaks. He pointed out that as language models grow more capable, securing them for public use will become harder. The ability to mislead or exploit them is already evolving faster than their defenses.
He described adversarial robustness as one of the toughest problems. Researchers are learning how easily an AI system can be tricked into producing unintended results. Small changes in a prompt or dataset can alter outcomes in ways users never intended. To Altman, that pattern resembles a familiar cycle in technology, where every new capability introduces an equal measure of vulnerability.
Among the security challenges he discussed, personalization stands out. Altman said people enjoy how tools like ChatGPT can adapt to their tone and remember details from earlier interactions. Those features make AI feel more natural and helpful, yet they also turn the model into a potential record of private life. When users connect these systems to outside services, such as email or shopping platforms, the data trail becomes even more complex.
Altman warned that the same personalization people value could one day expose sensitive information. A malicious actor could, in theory, find ways to extract data from a user’s customized AI system. What makes this difficult, he said, is that machines still lack the human instinct to judge what to share and when. A person might understand what information to keep private, but a model trained on personal context does not apply that kind of reasoning.
He offered a simple example. If an AI assistant helps with online purchases, it might unintentionally reveal personal medical information gathered from earlier chats. The model would not recognize the boundary between helpful context and private detail. Solving that problem with complete reliability, Altman said, is one of the hardest goals in AI security.
Altman’s message extended beyond identifying threats. He also said that AI can strengthen defenses. The same systems that create risks can help detect software vulnerabilities and prevent attacks. OpenAI and other developers already use models to test and harden code. Altman believes this dual role—AI as both the risk and the remedy, will define the next phase of technological progress.
He encouraged students to see AI security as one of the most promising fields to study. Traditional technology companies have long maintained large teams to secure their platforms. AI organizations, he said, will soon need the same level of attention and expertise. As models become embedded in daily life, protecting them will require a new generation of specialists who understand both coding and machine learning.
Altman’s conversation at Stanford also reflected his broader view of AI’s direction. He said deep learning is still at an early stage and that innovation in architecture, energy use, and model efficiency will continue. Yet he returned repeatedly to security. The future of AI, in his view, will depend not only on smarter algorithms but on the ability to keep them trustworthy.
For users, that means a paradox. The more AI learns to understand people, the more valuable (and potentially dangerous) it becomes. Systems that know how to anticipate preferences or recall personal details make digital life smoother. But they also hold knowledge that, in the wrong hands or through weak safeguards, could turn against the user.
Altman’s remarks summed up a simple but urgent truth. Artificial intelligence is learning fast, not only from data but from the people who use it. The challenge now is to make sure it does not learn too much.
Notes: This post was edited/created using GenAI tools.
Read next:
• Your AI Chats May Not Be Private: Microsoft Study Finds Conversation Topics Can Be Inferred from Network Data
• Carlson Interview Exposes Altman’s Fears on Suicide, Military Use, and AI Morality
