Data-driven VC #18: What ChatGPT means for the future of startup funding
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.
Current subscribers: 4,643, +583 since last week
I’m incredibly excited to welcome more than a thousand new readers who joined in the last 10 days. Wow! 🤯 I was aware that productivity has gained significant importance for VCs but didn’t expect my 10x Productivity with ChatGPT post hitting such a nerve. I seemingly underestimated how many of you share the pain of inefficiency and the urge to free up time for more important levers of your work.
VCs and founders are two sides of the same coin
Following last week’s post, many of you have asked me about my thoughts on the future of startup funding and the impact of ChatGPT / Large Language Models (LLMs) on our job. I like to dedicate this post to sharing some thoughts on why and how LLMs / AI will change the game for all of us. It is important to note that for the majority of statements below, LPs & VCs as well as VCs & founders share the same processes but sit on different sides of the table.
For example, startups raise money from VCs in exchange for equity. Fundraising is a sales job, so startups need to build a “sales” funnel and convert potential VCs to invest in their company, see bottom left quadrant in the above graphic. Same process but on the other side of the table, VCs need to create superior returns by investing in the best companies. Therefore, they need to create a “search” funnel to identify the most promising startups and get the opportunity to invest in them, see bottom right quadrant in the above graphic.
In a perfect world, the initial matchmaking (overlaying both funnels) would be fully automated based on comprehensive data availability and cross-checking of each other’s criteria. As a result, both parties would receive a pre-qualified selection of high-potential partners whom they could extensively meet and interact with to validate fit on the human level, to sit together, look each other in the eyes and say with confidence “We want to do this together".
In reality, however, it’s completely different. Humans spend most of their time researching and creating a manual funnel, having numerous back-and-forth interactions (that are frustrating for both sides and provide little/no insights on each other’s personalities, values, norms, drivers etc.) to collect all necessary data and eventually find out that 95%+ of the potential partners on the other side of the table don’t match. Majority of time wasted with the wrong tasks and opportunities to then be rushed into the interactions with the remaining high-potential candidates and being forced to make a go/no-go decision with insufficient confidence. Suboptimal, I’d say..
Wouldn’t it be nice if we could automate the first step of the fundraising, i.e. the matchmaking, to then, in turn, have more time for the second part of the fundraising, i.e. the human interaction?
The solution is an “augmented VC”
With a clear answer to the above question in mind, I shared a post “The Future of VC: Augmenting Humans with AI” in December 2020 that has proven to be a good prediction of what happened to accelerate through ChatGPT / LLMs in the course of 2022 and beyond. The following extract briefly summarizes my core beliefs:
An “augmented approach” is a hybrid between humans and machines. Data-driven sourcing tools help VCs to move closer to comprehensive coverage and ML-based screening tools narrow the upper — steadily growing — part of the deal funnel to a constant number of investment opportunities.
As a result, investment professionals could save substantial time spent on less promising opportunities that could then be focused on properly evaluating a pre-selection of high-potential ones. They can use the freed-up resources to build stronger relationships with the selected entrepreneurial teams and to put themselves in a better position to secure the most competitive deals.
Instead of going broad and shallow by allocating limited resources to an ever-growing number of opportunities, the use of ML-based screening tools frees up time and allows a venture capitalist to go narrow and deep on a selected number of opportunities while still ensuring that promising deals are not overlooked. It’s a win-win for entrepreneurs and investors alike as both sides can get to know each other better.
Continuing this line of thought, ChatGPT / LLMs will be a fundamental part of the stack driving startup investing into more efficiency, effectiveness and inclusiveness. I’d like to dedicate the remainder of this post purely to ChatGPT’s / LLMs’ impact on these three dimensions.
1) Efficiency
As the title suggests, last week’s ChatGPT PRODUCTIVITY post was mostly centered around efficiency, i.e. improving input-to-output conversion or, said differently, reducing the time spent to achieve a specific outcome. I explained in detail how we achieve an efficiency improvement with the help of ChatGPT / LLMs and other tools like Zapier. As a result, we can free-up time for other, more valuable but also more difficult to automate, parts of our job, like meeting founders in person and actually spending time together.
Having integrated some of the described examples like (website) data extraction, call transcription, translation of all text into English, notes summarization, company classification across industries and technologies, similarity analysis as well as email and content drafting (btw the headline of this post got drafted by ChatGPT) into my daily workflows, I can confidently say that it frees up 90%+ of the time compared to the respective manual execution. For many tasks, I can already automate 99.9% whereas for others I take the model response as a draft and fine-tune it manually before submitting it to the next stage. So overall easily 10x productivity increase.
A great benefit of this newsletter is your feedback. After last week’s post, I received a so far unseen amount of DMs with new workflow ideas and links to other ChatGPT examples revealing the vast efficiency improvement potential for all of us.
Unfortunately, these prompts are widespread across the web from Twitter threads over blogposts to YouTube videos. This made me think if a targeted prompt database (easily accessible in Notion) for VCs and/or startup founders could be of value. What do you think?