AI Competition Landscape: New Trends and Record Compute Budgets
research#ml📝 Blog|Analyzed: Feb 19, 2026 13:17•
Published: Feb 19, 2026 12:34
•1 min read
•r/MachineLearningAnalysis
The analysis of over 350 Machine Learning competitions reveals exciting shifts in the field! We see the rise of AutoML packages and tabular foundation models alongside the continued dominance of gradient-boosted decision trees, indicating a dynamic and evolving landscape. Moreover, the increasing compute budgets signal a growing investment in AI research and development.
Key Takeaways
- •AutoML and tabular foundation models are gaining traction in tabular data competitions.
- •Gradient-boosted decision trees (GBDTs) remain highly effective, often used with neural nets.
- •Compute budgets are significantly increasing, with some teams utilizing substantial cloud resources.
Reference / Citation
View Original"Tabular data competitions are starting to show potential signs of change: after years of gradient-boosted decision trees dominating, AutoML packages (specifically AutoGluon) and tabular foundation models (TabPFN) were used in some winning solutions."