What is Computing Science today, and what should it be?
(first published in LinkedIn on 15 December 2025)
Computing Science is commonly seen as a fundamental scientific discipline focused on the theory and practice of computing, defined around programming, computational systems, and communication, and is grounded in mathematical and engineering principles. As a field, it spans both core and applied areas, combining theoretical foundations with practical design and implementation,with strong emphasis on rigor, abstraction, and technical excellence.
In this traditional, broad disciplinary perspective, articulated in many academic departments’ descriptions and reflected in ACM, Association for Computing Machinery’s positioning, algorithms and models, computing fundamentals, software and systems design, and scientific inquiry form the core of what defines computing science. While ethical, human-centered, and socio-technical concerns are increasingly present, they tend to appear as extensions of the field, rather than as defining elements of its core identity.
These foundations still matter, but are they still the core of the discipline or have they become its infrastructure?
As I see it, there is an increasing sense of a shift: computing science today is less about how to compute and more about how we model the world through computation. Our systems now represent and define social realities, institutions, behaviors, and values. They encode assumptions, simplify complexity, and shape decisions. That is, computational models are not neutral, they are claims about the world, with real consequences.
This becomes especially clear in areas such as AI, data-driven systems, and policy-oriented work. The central questions are no longer only about correctness, efficiency, or performance, but about impact: Who is affected? What behaviors are incentivized? What forms of power, bias, or exclusion are reproduced or challenged? How do these systems interact with governance, regulation, and societal norms?
From this perspective, computing science increasingly looks like a socio-technical discipline. Technical design is inseparable from empirical understanding, normative judgment, and responsibility for outcomes. Algorithms and programming remain necessary, but they are not sufficient for addressing the challenges computing now creates. And even more, what it is asked to solve.
So I would like to open this up for reflection and discussion:
How do you understand computing science today?
What should be central to the discipline in a world shaped by AI and large-scale computational systems?
How should education, research, and departmental structures evolve in response?
What do we risk losing, or gaining, by moving from an algorithm-centered view to a modeling- and impact-centered one?
I think the post is spot-on on many things. I believe computing science (like any other sciences) is more useful when framed with an honest lens of applicability, utility and consequences.
ReplyDeleteWhat I find concerning is the focus on utility; some of us are genuinely curious about the underlying principles. While meaning is good, and I'd argue it's necessary, many discoveries pivotal to making utility practical were only possible because somebody wasted their time on something they found interesting.
I see people realizing academia doesn't always equate knowledge. With fields like AI moving faster than curricula can change, people are choosing cost- /time-efficient ways to learn, over rigid courses that serve as cash-cows and lead students to debt. The workplace is already competitive, and skills are more valued than degrees. Yet, there are departments that are pitching doctorates in obsolete things at absurd prices. I just hope more people realize this.