Too much to read, too tangled to map
Marco's clients are remote teams, and each cares about different topics, twelve running in parallel. The hard part isn't the volume, it's the mix: English reports, Japanese white papers, German policy documents. Deciding whether a paper was worth reading used to mean reading it first.
“The time I spend reading isn't the real loss,” he says. “It's finishing something and realizing it was useless.”
The biggest change for me isn't reading faster. It's finally knowing what I can skip.
Ask first, read later
New literature goes straight into the notebook for its topic now, foreign languages through document translation first. Then he just asks: what's this report's core conclusion, and does it conflict with last week's numbers? Answers arrive with sources, so one glance at the original passage settles whether it deserves a close read.
Recorded lectures and conference sessions become transcripts through video transcription, ready for questioning. The English briefs he sends to teams get a polish from the AI writing tools first, and incoming attachments are all converted to Markdown for the archive. Mind maps keep the twelve topics' relationships straight, and data tables line up figures from different sources side by side. Before calls he exports the key points so the whole team argues from the same material.
The first time I laid all twelve topics out on one map, I could see what my research had actually been about for two years.
Reviews went from monthly to weekly
Literature reviews used to be a month-end marathon. Now they roll weekly, because the filtering and filing happen a little every day. No more late nights at the end of the month.
“I read just as much as before,” he says. “But before I open a paper, I already know what question it can answer.”
