This is the first episode in our data-driven VC series. Amir is a Senior Vice President of Fund & Investment Analytics at Techstars and has ton of knowledge. Hope you enjoy our conversation. Let us know what are your thought in comments.

Perspectives shared are Amir's and not those of Techstars

Show notes and topics we are discussing:

* Role of the data person in the company and also in context of portfolio

* Signal to noise ration across investment scene

* Being a data detective. Everyone has the same data but different conclusions

* Data roles in data organisation and team work

* State of Data-driven investing

* About getting the right data from companies

* Products which generate unique data not available anywhere else and ARK Kapital

* SVB case - signal vs noise.

* Jesse Livermore on fear and greed

* Market shocks and change of culture

* Court of Versailles Vs. The Wild West

* Risk reward trade-off in VC industry

* Astrology of VC industry

* Data-driven vs. So what?

* Large language models

* Embeddings as a way to represent information

* Agents and agent based models

* Hallucinating and improving prompts

* Blind reliance on tools and FOMO

* Potential biases in the data and how that affects results

* Language models from startup side of the table

* BabyAgi

* Dramatron

* Cereal box pitch

* SVB Risk assessment by ChatGPT

* Information arbitrage and obligation to invest

* Money Ball vs Babe Ruth

* Valuation distribution and risk reward dynamics

* Becoming managers of agents

* Exponential curve of innovation - The AI Revolution: The Road to Superintelligence

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