- UC Berkeley sees a 48% jump in first-year applications to study computer science.
- Despite generative AI advances, students are eager to pursue computer science careers.
- Human developers remain essential for creating something new.
One of the most persistent concerns around generative AI is whether the technology will put workers out of a job. This idea has particularly caught on in the context of software coding.
Github Copilot can write a lot of code these days, so is it even worth studying computer science now? That's been a question on the minds of math-minded high schoolers since ChatGPT burst on the scene in 2022.
There's a new data point that helps answer at least part of this question: Students are still lining up in droves to take computer science in college.
An eye-popping data point
Let's take The University of California Berkeley as an example, as this college at or near the top for computer science.
First-year applications to UC Berkeley's College of Computing, Data Science, and Society CDSS increased 48% this year. There were 14,302 (non-transfer) applications for these CDSS majors in the Fall 2024 incoming class, versus 9,649 the previous year.
For context, the number of first-year applications to UC Berkeley as a whole didn't change much from a year earlier.
This was announced last week by Professor Jennifer Chayes, the dean of Berkeley's College of CDSS. She popped these eye-popping stats during a fireside chat with Governor Gavin Newsom and Stanford Professor Fei-Fei Li at the at the Joint California Summit on Generative AI in San Francisco.
There's a role for human software developers
Afterwards, I got in touch with John DeNero, Computer Science Teaching Professor at UC Berkeley, to talk about this some more.
He's also chief scientist at Lilt, a generative AI startup, and he was previously a researcher at Google working on Google Translate, one of the first successful AI-powered consumer apps.
"Students express some concern that generative AI will affect the software engineering job market, especially for entry-level positions, but they are still excited about careers in computing," he wrote in an email to Business Insider. "I tell them that I think many of the challenging aspects of software development can't be performed reliably by generative AI at this point, and that I expect there will still be a central role for human software developers long into the future."
AI can't do new things very well
Generative AI is currently very good at replicating parts of software programs that have been written many times before, DeNero explained.
That includes computer science homework assignments! See BI's coverage on how much ChatGPT is used to cheat on homework.
What if you want to create something new? This is where smart human coders will still be needed. (This makes logical sense as AI models are trained on data. If that information doesn't exist yet or it's not part of the training dataset, the models often get in trouble).
Generative AI "requires a lot of thoughtful human intervention to produce something new, and all consequential software development projects involve quite a bit of novelty," DeNero said. "That's the hard and interesting part of computing that currently requires clever and well-trained people."
"Generative AI can speed up the more mundane parts of software development, and software developers tend to adopt efficiency tools quickly," he added.
What happens at Lilt?
This applies to what's happening at Lilt, which is building an AI platform for translators.
Google Translate first came out 18 years ago. And still, human linguists have jobs and are relied upon when translations are really important. For instance, you can use Google Translate to read a Japanese train timetable maybe, but would you use the app to translate your business's most important contract without having a human expert check it? Probably not.
"To reliably produce publication-quality translations, human expert linguists are still at the center of the process, but by using Lilt's task-specific generative AI models, those experts are much faster, more accurate, and more consistent," DeNero said. "As a result, more text gets translated at higher quality into more languages."
He expects this same pattern to play out in software development: A small team of highly trained human developers will have an even greater capacity to build useful high-quality software.
"And so, future Berkeley graduates will have plenty of opportunities to use their computing skills to improve the world," DeNero said. "Hopefully some more of them will come work for Lilt."