How will AI affect the field of computer science?
Wondering how AI will affect the field of computer science? Explore the impact of AI on the computer science industry, and discover the key skills you can develop to stand out.
Over the last several years, usage of artificial intelligence has skyrocketed. Workers and managers alike report upskilling in AI.
The average user can interact with large language models (LLMs) from a browser or through an app, and programmers and software developers have integrated tools like Copilot into their workflows.
How will artificial intelligence impact the computer science field? Continue reading to explore the impact of AI on computer science.
Computer science is changing, but it's unclear how
The field of computer science is already changing. With the advent of artificial intelligence tools like Copilot and widespread access to generalized large language models, more people than ever can code without months or years of learning.
However, it's not entirely clear how computer science will change in the long term. While generative AI may make it easier to create working code, that doesn't necessarily translate to job loss for computer programmers.
Computer science education may start earlier
With widespread access to LLMs, ambitious students could learn more about computer science at a younger age. While the temptation to use AI to generate or debug code haphazardly is certainly a risk of AI, some experts point out that thoughtful use of AI in coding can actually help new or experienced programmers learn fresh concepts in an interactive way.
"The process of using these tools becomes a learning experience for the programmer," Mehran Sahami, a professor of computer science at Stanford University, said in a webinar. "There is long-term learning that's happening as a result of using these tools."
Tasks will change, but job loss may be overestimated
While AI will likely change the tasks that workers need to focus on, fears of widespread job loss may be a misconception about AI's impact. According to the Bureau of Labor Statistics (BLS), the ultimate impact of artificial intelligence on the job market is difficult to predict.
That said, while the technology may result in job losses, it will also likely bolster growth in certain sectors. For example, the BLS projects 16% job growth for software developers from 2024 to 2034, much faster than the average for all occupations.
New jobs are already emerging
Artificial intelligence is creating a host of new jobs in the technology industry. In some cases, AI creates a higher demand for professionals fluent in data-related skills. Established roles like database architect, software developer, information security analyst, and other cybersecurity roles can expect growth in the coming years.
Entirely new AI-related roles are also emerging. For example, some companies list positions for "prompt engineers," individuals who specialize in prompting LLMs to generate specific outputs.
Fundamental coding knowledge will still be important
Even though artificial intelligence coding tools can assist in code generation, proficiency in foundational coding concepts will still be important for the professional programmer. While large language models can generate code in any number of programming languages, they do not possess higher-level reasoning capabilities or the critical thinking that humans bring to their tasks.
Generating code is only one aspect of programming. Computer science professionals also need to think critically and systematically about their software and the aims they are trying to achieve. Ultimately, even with the help of AI tools, the programmer needs a foundational knowledge of coding in order to design basic architecture and test generated code.
Computer scientists have skills that AI needs
Problem-solving: Computer scientists need to use problem-solving skills to leverage AI. While a language model can generate text based on a prompt, it doesn't have the situational and contextual awareness of a human.
Creativity: Creating new solutions requires out-of-the-box thinking. Computer scientists need creativity to design architecture that solves real problems. While AI can help with specific tasks, it lacks the higher reasoning capacities required for architecture design.
Ethical judgment: Computer scientists will need ethical judgment to leverage AI safely and fairly. Because an AI model doesn't have empathy or the capacity to reason ethically, it is up to the prompter to make high-level decisions.
Communication: Computer scientists need to communicate with their teams to ensure stakeholder alignment on any given project. Additionally, coders using AI need to practice clear and concise communication to prompt language models effectively.
Critical thinking: Because AI can produce inaccurate outputs, computer scientists must use critical thinking to evaluate code generated by LLMs. Failure to evaluate and test code could result in suboptimal or even dangerous products.
Contextual awareness: While AI can respond to a limited set of text prompts, it lacks robust contextual awareness. Computer programmers need to ensure that AI-generated output aligns with their individual, team-wide, and organization-wide values and aims.