Quanta's engineers performed manual bookkeeping, a practice they called "engineers as bookkeepers." This forced immersion into the domain's deep complexities and edge cases, leading to a far more robust and effective automation product than if they had worked from a spec sheet.

Dan Loeb contrasts the star analyst of the 90s, who could dissect a complex bankruptcy filing, with today's ideal. The modern analyst needs deep, nuanced understanding of technology and industry specifics, rather than just pure financial modeling prowess.

A successful inflation trader gained his edge not through complex models, but by spending three months in Excel rebuilding the Bureau of Labor Statistics' calculation formula. This highlights how major financial institutions often neglect fundamental, bottoms-up analysis, creating opportunities for dedicated individuals.

Lacking formal training, Thomas Laffont built every investment model from scratch. This forced him to understand each component deeply, discard irrelevant industry-standard metrics, and create models that purely reflected his investment thesis rather than conforming to reporting conventions.

A PE professional was told customer revenue data was unavailable. Instead of accepting this, he asked how invoices were made and found all the data on an old computer, saving weeks of manual entry. Always question claims of missing data and trace the process back to its origin.

Manually analyzing 30 data points builds deep intuition and overcomes the tech industry's bias for big data. It's enough to distinguish a major signal (e.g., a 60% rate) from a minor one (10%) and inform immediate action without complex analysis.

When approached by large labs for licensing deals, GI's founder advises against simply selling the data. He argues the only way to accurately value a unique dataset is to model it yourself to understand its true capabilities. Without this, founders risk massively undervaluing their core asset, as its potential is unknown.

An investor can have pages of notes yet still lack clarity. The most critical step is synthesizing this raw data by writing a cohesive narrative. This act of writing forces critical thinking, connects disparate points, and elevates understanding in a way that passive consumption cannot.

To maintain objectivity in acquisitions, Bending Spoons separates assumption-setting from model output. The team rigorously debates and locks in all inputs without seeing the projected P&L or IRR. This prevents the common bias of tweaking assumptions to justify a desired outcome. The final model output is then treated as unchangeable.