Latticework and Venture Capital, Chapter 1

Note: This is the second post in a series on Robert Hagstrom’s Latticework and VC. Chapter summaries are in no way designed to be Cliff Notes on the chapters of the book, quite the contrary, please read the book for yourself!

Summary:

  • History of the University of Pennsylvania and the introduction of practical education into Academia.
  • The obsolescence of Classics and Clergy.
  • Thorndike theory of learning: connectionism. Learning to identify commonalities and recognize patterns, classic component of VC analytical toolbox.
  • A more complex and dynamic understanding of pattern recognition.
  • The importance of metaphors.
  • Thinking of each prospective investment through each lens of knowledge and proceeding if a lens provides illumination.
  • Munger, paraphrased: “Using this approach will make you better at investing, at law, and at life. It makes you better to serve others, better to serve yourself and makes life more fun”

VC Analogues:

The importance of accurate pattern recognition is identified and underscored in this chapter. It can be easy to under-emphasize the depth of analysis required to perform pattern recognition; oftentimes superficialities and correlations, not causes, can stand in and present patterns that may not necessarily exist. As well, oftentimes the phrase ‘pattern recognition’ can be misinterpreted and can convey a justification for the homogeneity of the tech industry. When properly understood, as illustrated by Hagstrom in this chapter, pattern recognition becomes the thoughtful, algorithmic analysis that can grant a glimpse into unlocked value.

Additionally one can see a parallel between the history of the development of the modern education system, specifically the obsolescence of the Classics and Clergy in favor of more practical, applicable knowledge, and the diminishing opacity of the venture capital industry. Many of the high-priests of Sand Hill and beyond have found value in communicating more regularly and openly with founders, other investors, and the tech community at large. The easing of the regulatory environment through the JOBS act has also opened up the private markets to the ‘crowd’, further shining a light on what was more or less previously understood as a black box.

Takeaways:

Ironically, this overview might be better applied to dealings with portfolio companies post-investment than during screening or diligence. When faced with those first critical choices as an early stage startup, it’s important to conceptually walk through each choice, it’s potential aftermath, and the opportunities won and lost by pursuing the option. Don’t inflexibly use prevailing conventional wisdom or evaluate from a single perspective when faced with critical early stage strategic choices, evaluate and advise with a spectrum of analytical models accounting for the doors that may close when undertaking a specific move not necessarily the doors that pursuing said strategy might open.

Another point I recognized from this chapter is that there can be great value in finding someone within your network who can provide an alternative perspective (maybe an investor with a different sector specialty or a founder in a different vertical) about the target company or strategic decision at hand.

The Charlie quote is absolute gold, and one of the hidden gems of inordinate magnitude in the book. Making life more fun is absolutely essential to staying engaged, remaining humble and gaining knowledge. It’s a damn important thing to remember.

Applying each lens of knowledge to a prospective investment in hopes to find clarity is another worthwhile nugget. This can work well as a preliminary screening tool, especially at the pre-seed level: Does the product or platform appear with more clarity when analyzed through one of its non-native lenses? If so it might be worth a deeper look.

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