Sarah Drinkwater

“We tend to view open source companies at the earliest stages like social networks. Building community momentum is of the utmost importance to the success of an open source project, and that often takes precedence over revenue in the short term.

If you can show a strong community AND strong revenue growth then you will be in an incredible financing position, but often revenue is not necessary if you have the other criteria in this doc accounted for.

If your open source momentum (Bucket B) is weak then you can make up for it by having strong revenue, but there are few examples of successful open source companies that fit this example, as most tend to prioritize community building in the early stages”

One question kept coming up:

Why haven’t incumbent software providers been able to leverage their valuable data assets yet?

Both giants like Salesforce and growth-stage companies with 10+ years of collected data seem to be struggling to deliver truly differentiated AI experiences. Most of us would assume these companies are sitting on data gold mines that should give them a massive advantage. But is that actually true?

One perspective I found compelling: The historical data these companies have collected simply isn't the right kind to make AI models truly effective. The workflow data, outcomes data, or database info they've aggregated over the years might not be as useful as we'd think. What's most valuable in building agentic AI isn't just workflow data or outcomes data—it's a granular understanding of how humans actually perform tasks.