Research taste is the capacity to recognise what matters before you can fully articulate why
Research taste is a cultivated form of discernment applied to the decisions that shape a research career: which problems are worth pursuing, which collaborators will amplify your work, and which under-explored areas have genuine leverage. It is not raw intuition but the refined judgement built through practice, critical reflection, and honest assessment of outcomes. A researcher with good taste consistently works on consequential problems; one without it may be technically capable but directionless.
Research taste
One-sentence definition: Research taste is a cultivated form of discernment; the capacity to identify which problems, collaborators, and opportunities are worth pursuing, developed through iterative practice and critical reflection on one’s own judgements.
The concept was described by AI researcher Chris Olah as something that “seems possible to develop” across three domains:
- identifying interesting problems
- recognising good people to work with
- spotting neglected but high-leverage opportunities
What these three share is an act of recognition; discerning value before it has been fully demonstrated. That is what makes taste different from technical skill: skill gets better at doing things you have already decided to do; taste shapes the decisions themselves.
The distinction between raw intuition and refined intuition matters. Research taste is not the unexamined impulse to pursue certain problems because it includes critical reflection on those impulses, asking which are genuinely motivated by importance and which by less useful drives: status, novelty, or the safety of well-trodden ground. Developing taste means learning to interrogate your own judgements, not suppress them. It improves through making choices, observing outcomes, and revising the implicit criteria that shaped those choices, which is why it takes time, and why exposure to researchers whose taste you can observe is part of how it is acquired.
In health professions education
Research taste is probably most visible in doctoral supervision. A supervisor who has developed strong taste can help students distinguish problems that are tractable and important from problems that are merely researchable; the difference between asking “can this be done?” and “is this worth doing?” Modelling that distinction, and creating space for students to develop their own sense of it, is part of what supervision is for.
The same capacity is relevant to research mentorship more broadly: helping early-career researchers develop independent judgement about what matters, rather than simply replicating existing approaches or deferring to the field’s current preoccupations.
Research taste also has a specific dimension in AI-assisted research. How a researcher directs AI is an expression of their taste; knowing what to ask, what threads to pursue, and what to set aside. A researcher with well-developed taste will use AI to amplify consequential work; one without it risks producing volume without direction. In this sense, taste is not threatened by AI tools. It is made more visible by them.
What remains unclear
Research taste is domain-specific in part; what counts as an important problem in physiotherapy research differs from what counts as important in epidemiology. Whether the underlying capacity (interrogating one’s own motivations, recognising genuine novelty, identifying high-leverage gaps) transfers across domains is not fully settled.
There is also a structural tension. Research taste is partly cultivated by internalising a field’s value system, and those value systems are not neutral. Academic incentive structures, including publication metrics, grant criteria, and disciplinary prestige hierarchies, shape what “important problems” look like in practice. Taste developed within a field can systematically undervalue certain kinds of questions and recognising this is itself part of what good taste requires.
Sources
- Wiblin, R. (n.d.). Chris Olah on working at top AI labs without an undergrad degree. 80,000 Hours. https://80000hours.org/podcast/episodes/chris-olah-unconventional-career-path/
- Rowe, M. (2025). Taste and judgement in human-AI systems.
Notes
- The broader concept of taste as discernment — aesthetic and cultural — is covered in the taste and judgement essay; this note applies the concept specifically to research practice
- The AI-assisted research dimension connects directly to the argument in the PhD becoming post: that how a researcher uses AI is developmental evidence, and research taste is what that evidence is evidence of