Everything on Research Strategy

A problem-first approach is superior to a data-first approach. Working with available data leads to bias and suboptimal solutions, whereas defining the biological problem first ensures that data generation and modeling efforts are aligned with translational goals.

Impact: Increases the likelihood of achieving real-world clinical impact by ensuring that AI solutions address actual biological challenges rather than convenient datasets.

— from Strategic Data Scaling for AI-Driven Bioscience · Latent Space: The AI Engineer Podcast· Oct 10, 2026

Semantic abstraction and structure provide a more efficient learning path than scale alone. Models that mimic human cognitive tools by processing abstract representations require orders of magnitude less data than pixel-level prediction models.

Impact: Enables faster progress with reduced compute costs and opens new avenues for efficiency in multimodal AI development.

— from Moon Lake AI: Causal World Models, Structure vs. Scale, and Embodied AI Strategy · Latent Space: The AI Engineer Podcast· Apr 02, 2026