SUBHANKAR MISHRA ଶୁଭଂକର ମିଶ୍ର

AI Conferences Are Turning Into Theory Contests — And Applied Research Is Paying the Price

If you’ve been following conferences like AAAI, ICLR, or NeurIPS, you may have noticed something worrying over the last couple of years: the main tracks are increasingly dominated by theory-heavy papers. Dense proofs, toy models, and formal guarantees now take up most of the space — while real-world AI experiments feel sidelined.

Why Theory Is Taking Over

Several factors feed this trend:

Easier to review: Committees are swamped with submissions. A neat proof is faster to check than a massive set of experiments.
Experiments are expensive: Running large-scale models or collecting real-world data takes weeks and costly compute. A theory paper, in contrast, can be evaluated with just a few pages.
The theory feedback loop: More theory papers attract more theory-focused reviewers. Those reviewers are more likely to accept similar work, reinforcing the cycle. Applied research is slowly squeezed out.

The Hidden Cost for Students and Practitioners

This isn’t just about trends — it has real consequences:

Time crunch: Out of 3,000 acceptances, if 2,500 are theory, only 500 spots remain for applied work. Each of those 500 papers demands deep experiments, multiple datasets, and rigorous benchmarking — tasks that can take months.
High competition: Even strong applied papers face a nearly impossible standard. Depth and scale are rewarded over novelty or practicality.
Structural pressure: Students and researchers may feel forced to chase exhaustive experiments just to compete, while theory papers often require fewer resources.

The result is clear: applied AI research is increasingly crowded out. The main conference tracks, once a space for practical innovation, now seem to reward formalism over real-world impact.

The Worrying Implication

This trend isn’t just frustrating — it could be dangerous for the field. If applied research continues to be sidelined, the gap between AI theory and real-world deployment will only grow. Students and labs may start prioritizing what gets accepted over what actually matters. Incentives are shifting — and not for the better.

A Call to Awareness

We don’t need to pretend that the path forward is easy — it isn’t. But it’s important to recognize the structural pressures shaping AI research today. Reviewers, conference organizers, and research labs all play a role in what gets visibility and credit.

Applied researchers, students, and practitioners deserve a voice in these conversations. Even small actions — questioning incentives, advocating for venues that value experiments, or highlighting real-world impact — can help prevent the field from drifting too far into theory while leaving practical AI behind.