How AI Enables One-Person Billion-Dollar Companies
Leverage Technology to Achieve More With Less
👋 Hi, I’m Andre and welcome to my weekly newsletter, Data-driven VC. Every Tuesday, I publish “Insights” to digest the most relevant startup research & reports, and every Thursday, I publish “Essays” that cover hands-on insights about data-driven innovation & AI in VC. Follow along to understand how startup investing becomes more data-driven, why it matters, and what it means for you.
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Data Engineering is no longer just a backstage player but is taking center stage in the VC space. It is about constructing and maintaining the architectures (think databases, large-scale processing systems) that allow for data availability—a cornerstone for insightful analysis and well-informed decisions.
Dive deeper into this video, where we break down the impact of data engineering on the VC industry. Turning raw data into usable outputs and insights has great use cases for deal sourcing, due diligence, or portfolio monitoring.
The Big Bang of GenAI
Yesterday, I gave a talk at the Chatbot Summit 2024 in Berlin about how AI changes the future of work. In light of the great conversations and feedback that followed my talk, I decided to turn extracts of my presentation into this episode.
In order to understand the impact of AI on the future of work, we first need to understand why we are witnessing the big bang of gen AI today, and not already years before. Keeping things simple, I split the Cambrian explosion of GenAI into three ingredients: Algorithms, Compute, and Data - “The Magic Triangle of AI”.
First AI algorithms evolved in the 1950s. Moore’s Law described the evolution of computing power in the 1960s, but it took until the 1990s to reach first breakthroughs with CPUs. Data became a thing in the early 2000s, with the “big data” and “data is the new oil” hype following thereafter.
About ten years later, about a decade ago today, we moved from sequential to parallel computing and GPUs, a significant acceleration in computing power. While the “Attention is all you need paper” introduced transformers as the breakthrough algorithms, it seemed the magic triangle had been completed, yet AI didn’t take off.
Why? Because awareness of its power had been limited to a group of privileged nerds in the bubble. The majority of our society had never heard about transformers, GPTs, and alike. Only the introduction of ChatGPT, an interface on top of GPTs, changed this end of 2022.
This interface has allowed humans to suddenly interact with LLMs through natural language instead of programming code. A milestone that will most likely be remembered as the inception of AI, even though in reality it took 70 years and advancements across all three components of the magic triangle to get there. What lasts long will eventually be good.
Cost of Experimentation Framework
During my PhD days at the Technical University of Munich, one of my colleagues researched the impact of different technologies on the “cost of experimentation”, a term to describe the resources required to start a business. A well-cited study in this domain is the “Cost of experimentation and the evolution of venture capital” paper by Ewens, Nanda, and Rhodes-Kropf (2018).
The introduction of cloud computing services by Amazon is seen by many practitioners as a defining moment that dramatically lowered the initial cost of starting Internet and web-based startups. (..) We show that subsequent to the shock, startups founded in sectors benefiting most from the introduction of AWS raised significantly smaller amounts in their first round of VC financing. (Ewens, Nanda, and Rhodes-Kropf, 2018)
I first took this framework and applied it to AI about a year ago here, predicting that similar to the introduction of the cloud, AI is about to increase productivity and thus significantly decrease the cost of experimentation. As a result, I assumed that AI would be a monumental driver for companies to achieve more with fewer resources.