Tip
Carilu Dietrich talks about picking companies: “I spent the next five years trying to figure out, how do you pick your next Atlassian?” She keeps a Post-it note with her 10-point framework: Rule of 40 (profitability + growth), quality of investors, stage/funding rounds, Net Promoter Score, net dollar retention (Snowflake was 180% - almost doubling revenue from existing customers alone), growth rate last year, burn rate, market position (#1 in Forrester/Gartner), and Glassdoor ratings.
Turns out evaluating AI companies works the same way.
You’re looking at three AI startup opportunities. All have impressive demos, smart founders, big TAMs. One has mediocre investors, 105% net dollar retention, burning through runway fast. Another has top-tier VCs, 140% net retention, Rule of 40 hitting 55. Third has great early investors but recent rounds were down-rounds.
Younger candidates get dazzled by the demo and the founder’s vision. They don’t know which signals predict success versus which predict struggle. You’ve watched enough companies over decades to know: net dollar retention above 120% means customers are expanding usage organically. Top-tier investors in recent rounds means smart money still believes. Rule of 40 above 40 means healthy unit economics.
This pattern recognition—knowing which metrics actually predict success—comes from watching enough companies succeed and fail to recognize the signals early. You’ve seen startups with amazing products fail because they couldn’t scale efficiently. You’ve seen mediocre products succeed because they had incredible net retention and investor backing.
That judgment lets you evaluate opportunities faster and more accurately than someone who hasn’t watched as many company trajectories play out. You know the difference between “this looks exciting” and “this has the fundamentals that predict success.”
Context
Carilu Dietrich was CMO at Atlassian through IPO and advises hypergrowth B2B companies like Miro, Segment, 1Password, Bill.com. Her 10-point evaluation framework came from spending five years after leaving Atlassian trying to identify the next high-momentum company.
For experienced executives evaluating opportunities, this isn’t theoretical—you’ve watched enough companies over decades to know which early indicators predict later success. That pattern recognition only comes from seeing the full arc repeatedly.