Data-driven VC #17: 10x your productivity with ChatGPT
Where venture capital and data intersect. Every week.
👋 Hi, I’m Andre and welcome to my weekly newsletter, Data-driven VC. Every Thursday I cover hands-on insights into data-driven innovation in venture capital and connect the dots between the latest research, reviews of novel tools and datasets, deep dives into various VC tech stacks, interviews with experts and the implications for all stakeholders. Follow along to understand how data-driven approaches change the game, why it matters, and what it means for you.
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Happy New Year 💥 I hope you had some restful days with your loved ones and a great start into 2023. I personally spent most time reading, reflecting and playing around with ChatGPT to make my life easier in 2023 and beyond🤓
VC x AI - A great combo
As most of you know, I spent the last decade as an operator, researcher and investor in the field of AI and continue to be incredibly excited about the (horizontal) technology itself (some related posts “Deconstructing the AI Landscape” from early 2020 or more recently “Value accrual in der modern AI stack”) but also its potential to solve specific (vertical) use cases. This is also how I got to “Data-driven VC” in the first place: Combining my interests in AI (and automation) and VC to make our industry more efficient, effective and inclusive.
Previous episodes of this newsletter were mostly problem-oriented, i.e. where is most value created and what’s wrong with the status quo. For example, 2/3 of the VC value is created in sourcing and screening, but sourcing is inefficient and screening is biased, ineffective and non-inclusive, as described in episode#1 “Why VC is broken and where to start fixing it”. To solve these specific issues, I explored a variety of software techniques/tools (such as web crawlers/scrapers in episode#4, de-duplication/entity matching in episode#5, feature engineering in episode#6 or social network analysis in episode#15, to name a few).
Said differently, until now I mostly identified problems across the VC value chain (nails) and looked for the right tools to solve them (hammer). This episode is different, it’s actually the other way around.
I got a sledgehammer. Where are the nails?
I’ve been closely following the evolution of transformers and large language models (LLMs) since the groundbreaking “attention is all you need” paper in Dec 2017. We’ve seen models like BERT applied across multiple use cases, but widespread adoption has lacked significantly behind its potential. Same with GPT-3. It attracted great (media) attention after its launch in June 2020, but widespread adoption has lacked behind too.
It was the launch of ChatGPT end of November 2022 that changed the game. OpenAI onboarded more than 1 million users in less than five days. The fastest product roll-out ever. But what was different? In my eyes, it’s the introduction of a simple and intuitive UI that in turn reduced the entry barriers for non-technical users. Everyone can use ChatGPT and, as a result, knowledge workers all across have started to play around and solve their specific use cases. So did I for my daily tasks as a VC.
1. Map your daily workflows
In early December, I started collecting different “nails”, i.e. I created a list with every single task that came up and added at least one example of the manual status quo. For better prioritization (which “nail” to start with), I added two dimensions: time per task execution and frequency of this task per week. Multiplying them, we get the total time spent per task per week, or said differently the “automation potential”.
2. Trial & error manual examples in ChatGPT
The OpenAI signup literally takes less than a minute. As a note of caution, I want to mention that whatever you insert as a prompt (=instruction for ChatGPT) will go straight to the servers of OpenAI for inferencing (=processing by a pre-trained model such as “text-davinci-003” which is the latest version of GPT-3.5), so be cautious with sensitive information.
Based on my sorted VC task list above, I started to copy & paste the manual examples into ChatGPT and iterated the prompts multiple times until the answers eventually met my expectations. Below you can find some detailed examples (prompts at the top and the response of the AI highlighted in green below) that you can easily copy and adjust to your needs.
(Website) data extraction into a pre-defined format
Industry classification based on pre-defined tags
Technology classification based on pre-defined tags
Find similar companies/competitors
Measure the similarity between companies/competitors
All of the above examples work with specific company names as long as the respective name is unique. As soon as other companies with the same or similar names exist, I suggest replacing the name with the company URL for more specificity.