Introduction: The Question Is Not Whether AI Participates
As AI systems increasingly demonstrate capabilities in reading, reasoning, and experimentation, discussions about “AI replacing researchers” have become common.
I believe this framing misses the point.
The real question is not whether AI should participate in research, but rather:
How should humans participate, at what level, and with what responsibilities?
To think clearly about this shift, it helps to step back and examine what research fundamentally consists of.
Research as a Three-Stage Process
At its core, research can be decomposed into three stages:
- Literature Review — understanding and synthesizing existing knowledge
- Brainstorming — generating, reframing, and positioning new research questions
- Experimentation — validating hypotheses and iteratively refining ideas
Traditionally, human researchers have been responsible for all three.
Today, however, AI systems are increasingly capable of contributing meaningfully to each stage.
This does not eliminate the role of human researchers—but it changes where human effort is most valuable.
Reallocating Responsibilities Between Humans and AI
Rather than asking what AI can do, I find it more productive to ask:
What should humans continue to do—and what should AI take over?
What Humans Should Remain Responsible For
Human researchers play an irreplaceable role in:
- Value judgment: deciding which problems are worth studying and why
- Articulating research significance: connecting technical work to broader social, scientific, and ethical contexts
- Risk and potential assessment: identifying directions that may fail locally but succeed structurally
These tasks depend on human experience, social understanding, and ethical reasoning. They are not simply optimization problems.
What AI Is Especially Good At
AI systems, by contrast, excel at:
- Expanding the search space beyond what any individual researcher can explore
- Surfacing hidden assumptions and boundaries in existing knowledge
- Proposing non-intuitive combinations that humans may overlook
In this sense, AI functions best not as an answer generator, but as a cognitive amplifier and exploration engine.
Why I Do Not Compete With Industry on Scale
I intentionally avoid framing my research around system scale, throughput, or infrastructure complexity. Industry will always have structural advantages in these dimensions.
Academic research, in my view, carries a different responsibility:
- Identifying long-standing structural bottlenecks across systems
- Formalizing principles of successful human–AI collaboration
- Producing conceptual frameworks that generalize across platforms and models
For this reason, I prioritize interpretability, generalizability, and explanatory depth over short-term performance gains.
What I Consider Valuable Research Goals
To me, meaningful research aims to:
- Clarify what truly generalizes over time, rather than what merely performs well on a benchmark
- Treat human participation as a first-class system component, not an external afterthought
- Democratize discovery, reducing reliance on massive infrastructure or privileged access
Systems and experiments matter—but they should serve understanding, not replace it.
The Future Researcher: From Executor to Supervisor
Under this perspective, the role of the researcher shifts.
Away from:
manually reading every paper, implementing every baseline, and running every experiment by hand,
and toward:
supervising exploration, shaping direction, and making high-level judgments.
AI agents can handle large portions of exploration and validation. Humans focus on framing, interpretation, and cross-disciplinary transfer.
A Final Question
The question I care about is not:
Can AI replace researchers?
but instead:
How can AI maximally serve human researchers—amplifying judgment, creativity, and long-term insight?
If AI can free researchers from excessive low-level detail, research may once again become what it should be:
a deeply intellectual, creative, and meaningful pursuit.