A Top AI Researcher Is Now Teaching Stanford Students About AI Safety

Amanda Askell, a researcher at Anthropic—an AI company focused on safety—has joined Stanford University's CS 153 course as a guest lecturer. She teaches on Wednesday afternoons, bringing her real-world experience with AI systems directly into the classroom alongside Stanford's regular computer science program.

At Anthropic, Askell works on AI safety and alignment research. In plain terms, that means she studies how to make AI systems like chatbots more trustworthy and predictable. She has helped develop Claude, Anthropic's conversational AI assistant, paying special attention to making it helpful, honest, and safe to use.

Why Industry Experts Teach at Universities

Stanford regularly invites tech professionals to teach alongside full-time faculty. These guest lecturers give students a glimpse of real problems that exist outside the textbook. Companies benefit too: universities can help them think through hard problems, and they gain access to talented graduates.

Askell studied philosophy at New York University, earning a PhD in decision theory and philosophy of mind. That background may seem distant from AI, but as AI systems become more powerful, the field increasingly needs people who can think carefully about ethics, human values, and how to align machines with what we actually want them to do.

What Makes Anthropic Different

Anthropic builds AI systems using what it calls "constitutional AI." Think of it like giving an AI system a set of rules or principles to follow, rather than just letting humans correct it case by case. The goal is to create AI that behaves in predictable, controllable ways—particularly important as these systems get more powerful.

Askell's research feeds directly into Claude, the AI assistant Anthropic has released to the public. The company uses a combination of training methods to make Claude safer and more aligned with human intentions.

The Bigger Picture

Worth flagging: Anthropic's focus on safety research reflects growing concern across society about how AI systems are being deployed and what risks they might pose.

This kind of partnership—bringing industry experts into universities—is not new. In the 1990s, companies like Netscape and Google sent engineers to teach about the emerging web. Those collaborations worked well: students learned cutting-edge skills, and companies found talented people.

AI collaboration feels different, though. Web technologies mainly changed how we access information and do business. AI systems might affect decisions in healthcare, transportation, finance, and many other domains where mistakes could matter a lot more.

What Students Are Learning

Universities used to teach AI mainly as an engineering problem: how to build and optimize machine learning models. Now they are also asking how to build these systems responsibly. That shift reflects the field's recognition that technical skill and safety considerations need to develop together.

Askell is well-positioned to teach both. She understands the technical details of how Claude works, and she has the philosophical background to discuss harder questions: How should AI systems make decisions when values conflict. What does it mean to make an AI honest. How do we deploy these systems safely in the real world.

Analysis: The appointment suggests that AI safety—once a niche academic topic—is now being taken seriously by universities as a core part of training the next generation of AI engineers.

For students who graduate from Stanford and go on to build or oversee AI systems, understanding these safety concepts early matters. It shapes how they think about responsibility from the start of their careers.

Looking Forward

Askell's role at Stanford represents a growing trend: the back-and-forth between AI research happening in labs and the classrooms where tomorrow's engineers learn their craft. Companies like Anthropic and OpenAI now employ dedicated teams focused on safety—work that used to be considered fringe but now influences how products are actually built.

These kinds of partnerships show how the AI field is maturing. It is not just about making systems smarter anymore; it is about making them trustworthy.