Ignoring AI in Computer Science could cost your career: OpenAI executive warns students risk falling behind
In a time where artificial intelligence is reshaping the technology landscape at a dizzying pace, computer science students face a new imperative: adapt or risk falling behind. Alexander Embiricos , OpenAI ’s Codex product lead, has a pointed warning for the next generation of software engineers . On a recent podcast, as reported by Business Insider, he cautioned that students in computer science programs that restrict AI usage could find themselves at a disadvantage.
“The main place where I would be worried, if I was a student right now, is if I was studying CS and my college didn’t allow the use of any AI, because then I would just feel like I’m falling behind,” Embiricos said.
Computer science remains essential, if it evolves
Despite the caution, Embiricos is confident in the enduring value of a computer science degree. The demand for software engineers, he noted, is only set to grow. “I think there’s going to be so much more software created and therefore so much more software engineers needed,” he said. “But I also think, figure out how to be using AI constantly while you do it.”
His remarks come at a time when universities are grappling with how to teach computer science in the age of agentic AI tools such as OpenAI’s GPT-5 Codex, which can generate code and automate development tasks. Traditional models of instruction like lectures, textbooks, and assignments, are being tested against the rapid integration of AI into the curriculum.
Lessons for computer science students
For those pursuing computer science, Embiricos’ advice is more than a caution; it is a roadmap. Here are key lessons drawn from his insights and broader educational commentary:
Embrace AI as a core tool, not a crutch
Students who resist learning AI tools risk lagging behind peers who leverage them to solve complex problems. AI is no longer optional, it is becoming integral to software development, from automating mundane coding tasks to accelerating innovation.
Build, don’t just study
Embiricos emphasized that OpenAI looks for evidence of initiative and creation in new graduates. “When I look at new grad profiles, for me, the thing that I take the most signal from is if they’ve built something,” he told Business Insider. For students, this means investing in personal projects, open-source contributions, and experiments that showcase practical skills.
Develop mental plasticity
Carnegie Mellon’s Thomas Cortina told The New York Times that AI has “really shaken computer science education,” with students realizing they often don’t fully understand the code generated by AI. Embiricos advocates for a mindset shift: combining foundational learning with outcome-driven projects that integrate AI. Students should learn to adapt, rethink, and apply concepts dynamically.
Balance manual learning with AI integration
Mastery of core principles remains crucial. AI should augment, not replace, understanding. By building foundational knowledge manually and then applying AI to amplify outcomes, students can cultivate both technical depth and innovative problem-solving skills.
Stay ahead of the curve
The landscape of technology is evolving faster than curricula can. Students must proactively seek exposure to AI developments, participate in workshops, and experiment with tools beyond the classroom. Adaptability and curiosity will define career trajectories more than static grades.
The new rules of computer science
Educators are already confronting the challenge of integrating AI responsibly into learning. Embiricos’ vision underscores a fundamental shift: computer science education must move beyond rote memorization to cultivate curiosity, creativity, and adaptability. Departments should guide students in using AI effectively while ensuring they retain the conceptual understanding necessary to innovate independently.
For the students navigating this new frontier, the message is clear: AI is not an ancillary skill, it is central to the future of software engineering. Those who ignore it risk more than missing trends; they risk being left behind in a profession defined by rapid technological evolution.
“The main place where I would be worried, if I was a student right now, is if I was studying CS and my college didn’t allow the use of any AI, because then I would just feel like I’m falling behind,” Embiricos said.
Computer science remains essential, if it evolves
Despite the caution, Embiricos is confident in the enduring value of a computer science degree. The demand for software engineers, he noted, is only set to grow. “I think there’s going to be so much more software created and therefore so much more software engineers needed,” he said. “But I also think, figure out how to be using AI constantly while you do it.”
His remarks come at a time when universities are grappling with how to teach computer science in the age of agentic AI tools such as OpenAI’s GPT-5 Codex, which can generate code and automate development tasks. Traditional models of instruction like lectures, textbooks, and assignments, are being tested against the rapid integration of AI into the curriculum.
Lessons for computer science students
For those pursuing computer science, Embiricos’ advice is more than a caution; it is a roadmap. Here are key lessons drawn from his insights and broader educational commentary:
Embrace AI as a core tool, not a crutch
Students who resist learning AI tools risk lagging behind peers who leverage them to solve complex problems. AI is no longer optional, it is becoming integral to software development, from automating mundane coding tasks to accelerating innovation.
Build, don’t just study
Embiricos emphasized that OpenAI looks for evidence of initiative and creation in new graduates. “When I look at new grad profiles, for me, the thing that I take the most signal from is if they’ve built something,” he told Business Insider. For students, this means investing in personal projects, open-source contributions, and experiments that showcase practical skills.
Develop mental plasticity
Carnegie Mellon’s Thomas Cortina told The New York Times that AI has “really shaken computer science education,” with students realizing they often don’t fully understand the code generated by AI. Embiricos advocates for a mindset shift: combining foundational learning with outcome-driven projects that integrate AI. Students should learn to adapt, rethink, and apply concepts dynamically.
Balance manual learning with AI integration
Mastery of core principles remains crucial. AI should augment, not replace, understanding. By building foundational knowledge manually and then applying AI to amplify outcomes, students can cultivate both technical depth and innovative problem-solving skills.
Stay ahead of the curve
The landscape of technology is evolving faster than curricula can. Students must proactively seek exposure to AI developments, participate in workshops, and experiment with tools beyond the classroom. Adaptability and curiosity will define career trajectories more than static grades.
The new rules of computer science
Educators are already confronting the challenge of integrating AI responsibly into learning. Embiricos’ vision underscores a fundamental shift: computer science education must move beyond rote memorization to cultivate curiosity, creativity, and adaptability. Departments should guide students in using AI effectively while ensuring they retain the conceptual understanding necessary to innovate independently.
For the students navigating this new frontier, the message is clear: AI is not an ancillary skill, it is central to the future of software engineering. Those who ignore it risk more than missing trends; they risk being left behind in a profession defined by rapid technological evolution.
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