⏮️September highlights on startups, VC, data and AI

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Welcome to our monthly wrap-up episode where we discuss September’s most relevant content at the intersection of startups, VC, data and AI.

We’ve read it all so you don’t have to – here we go👇

LinkedIn just published the best startups in the US and Europe based on signals across four pillars:

• Employment growth is measured as a percentage of staff increase over the time frame of the methodology, which must be a minimum of 10%

• Commitment looks at the opinions and followers of non-employees on the company’s LinkedIn page, as well as how many non-employees view employees at that startup

• Job interest counts the speed at which people view and apply for job openings at the company, including paid and unpaid positions

• Attracting top talent measures how many employees the startup has recruited from a global LinkedIn Top Company, as a percentage of the startup’s total workforce. Data is normalized for all eligible startups.

Another exciting piece data analysis by Adam Shuaib in Episode 1 Ventures! Over the past four years, they have built a model to predict the results of early-stage startups. After evaluating 50 factors, these are the top five that have the greatest influence on future success:

• Seed track: The size of the initial seed raising is crucial. Startups with larger pay increases are more likely to survive long enough to achieve product-market fit.

• Personality Traits of the Founder: Using NLP to assess traits such as neuroticism, agreeableness, and extroversion, founder traits were found to be the second most predictive of success.

• Incorporation Timeline: Startups that took more than three years between founding and their first capital raise were significantly less likely to raise Series A or Series B funding.

By Signal Rank herewhich shares why and how the venture capital asset class should be indexed

By Sebastiaan Vaessen hereIt predicts that the industry’s focus will soon shift from broad, horizontal AI applications to more specialized, vertical use cases tailored to specific business needs, requiring specialized data sets.

By Lukasz Karwacki from Sunscrapers herewhich provides a comprehensive introduction to how investors can become more data-driven and what dimensions they should consider, ranging from make versus buy to team and culture

By Tavis Lochhead from Kadoa herecomparing traditional web data extraction with LLM-based web data extraction

By David Cervinka herein which he shares the results of his research on how investment firms in Central and Eastern Europe are using data and AI in investment and operational processes, and how the roles of investment professionals may change in the future

Join myself and other speakers from Balderton, Inflection, NGP and many more for the Venture Intelligence Day conference in Prague (or virtually), packed with networking, discussion and workshops with experts and thought leaders on data and AI in VC. I will be giving a virtual keynote on September 17 at 9:40 AM on recent trends and key lessons from my own journey to become more data-driven.

Get 10% discount with code “DDVC10”

• Tech Lead @All Iron Ventures in Bilbao here

• AI/ML & Data intern @Cathey Innovation in Paris here

• Analytics engineer at Dawn Capital in London here

• Data engineer at Dawn Capital in London here

• Senior Data Engineer at Growth Equity Firm in the Bay Area here

• Analytics Engineer at Tidemark Capital in San Francisco Bay Area here

We just filled two Senior Software Engineer positions at Earlybird with the help of this newsletter and some great search firms. I’ve tested a dozen technologies with VC recruiters over the years, but only a few of them worked. Click ‘reply’ and I will be happy to put you in touch with the best in Europe for these types of positions.

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