Andrew W. Lo, MIT Professor in the Sloan School of Management, CSAIL PI, and Faculty Co-Director of FinTechAI@CSAIL, says, "The question is: `Do large language models have our back?’ The answer is no, not yet.” 

An algorithm flags a patient as high risk for sepsis; a risk score informs whether a woman receives additional cancer screening; a deterioration model triggers an alert that sends a care team to a bedside. 

As AI tools reshape what it means to write software, a hard question is emerging for educators: what do you teach the next generation of computer scientists in the world of AI?

For MIT Professor Armando Solar-Lezama, one of the most common misunderstandings about AI is the notion that it can be dropped into existing human roles like a plug-and-play replacement.

Confidence is persuasive. In AI systems, it is often misleading.

David Clark, Senior Research Scientist at MIT CSAIL, helped design the system that connects nearly every computer on earth. As Chief Protocol Architect of the Internet from 1981 to 1989, he was there for the beginnings of the Internet. Calling the wave of AI technology an “echo” of what happened in the 80’s, Dr. Clark is cautioning, “ maybe we need to slow things down and think a bit.”

What happens when the team behind PlayStation meets the researchers pushing the boundaries of AI? You get The Nexus of Games and AI, a 12-part MIT Independent Activities Period (IAP) course, now available to stream.

AI models are proliferating fast. There’s Claude, ChatGPT, Gemini, Copilot, DeepSeek, Grok, Mistral, Llama, and many more emerging every day. But which ones to work with? And why? We asked MIT CSAIL faculty and students which AI tools they’re reaching for right now. The responses showed a variety of preferences, a clear winner in one area, and a word of caution about what goes into any public model’s memory.