Jack Lau

Summary: The article discusses tech mogul Vinod Khosla and his recent success as a prominent investor. Khosla's main focus is on AI and its potential impact on various industries. He also addresses concerns regarding AI regulation and competition with Chinese AI. Key takeaways: Khosla's recent $50 million investment in OpenAI has garnered attention and success. He is passionate about the potential for AI to disrupt and improve various industries. Khosla's focus on AI regulation and concerns about Chinese AI competition. Counter arguments: Some may argue that Khosla's intense focus on AI may overshadow other important areas of technology. Critics may question the sustainability of AI in certain industries and its potential negative effects on job displacement. #ai #artificialintelligence #tech #venturecapital #vc #startups

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Early Stage Venture Capital optimal portfolio construction. Excellent paper and fund simulator tool (in the comments) by the good people at Moonfire. "Some Take-homes: 1. Risk and portfolio size are inversely correlated. The larger the portfolio, the lower the risk of losing money. 2. Larger portfolio sizes increase the probability of returning 2-5x the invested capital. 3. In general, one should aim for the lowest amount possible allocated to follow-ons. 4. One should follow-on only when doing so is the best use of the available capital – meaning that the return one expects from the follow-on investment is higher than the return one expects from any other initial investment they could make until the end of the fund deployment period. In conclusion, the 5 main factors affect portfolio performance: 1. Decision quality 2. Portfolio size 3. Ticket size 4. Follow-on policy 5. The upper bound on ROI on a single investment. Of these factors, the upper bound on returns and decision quality appear to have the greatest impact on fund performance." Full paper for download below 👇 #vc #fundmanagement #portfolioconstruction #earlystagestartups

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Some interesting stats on Data Driven VCs Top 10 Takeaways: 1. 35% of DDVCs claim that their data-driven tools are already responsible for half of the deals sourced today. 2. The Median DDVC has $ 450m AUM and 40 employees, out of which 10 are investors and 2 are engineers. 3. Top 20 DDVCs are almost equally split across US (11) and Europe (9). 4. We saw annual growth in DDVC firms from 151 to 190 and in engineers across the VC industry from 767 to 865, reducing the average number of engineers per DDVC from 5.1 to 4.6 and indicating that many new firms hired their first one or two engineers within the past year. 5. 2/3 of DDVCs already use LLMs across Sourcing, Screening & Due Diligence, Communication & Emails, Investment Memo & Document Drafting, Reporting, and more. 6. 80% of DDVCs prefer an internally developed core stack complemented by best-of-breed external solutions. 7. 73% of DDVCs plan to increase their tech stack budget. 8. 1/3 of DDVCs believes that AI allows them to cut headcount in their investment teams. 9. 3/4 of DDVCs believe that Augmented VC will be the winning model of the future. 10. The biggest pain points when leveraging data & AI in VC is cutting through the noise and finding the right tools (50% of respondents) and data sources (46% of respondents). datadrivevc.io --------------- Follow me ( Zamir Shukho, MBA ) for more and subscribe to ‘Siliconnector’ Telegram channel for insights and news from Tech world and Silicon Valley: https://t.me/siliconnector

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