Analysis of the "tidal waves" sweeping over the VC landscape. The post examines how some of the macro trends + rapid advancements in AI are reshaping the venture & startup ecosystem, which include:
- The impact of increasing fund sizes on returns (and why a $500M fund might need to generate $17.5B in exit value to 3X).
- Novel strategies in which data science and AI can be applied in the VC investment process.
- How VC, traditionally a “cottage industry”, is becoming more high-frequency
As well as some predictions on where the industry might be headed:
- How Solo GPs and smaller/nimbler firms could harness AI to rival much larger investment platforms.
- The transformation of VC into a more traditional asset class (but with a twist!)
- The potential re-emergence of ‘calm funds’ in a world of capital-efficient, AI-native startups
- The changing role of CVCs and cloud hyperscalers in startup investing, and why massive funding rounds in foundation model startups probably won’t continue
Would love thoughts & feedback from the community!
Excited to share my thoughts on "model collapse". As the internet becomes increasingly filled with machine-generated data, I explore whether we are at “Peak Data”, how that might affect the efficacy of large language models & applications like ChatGTP going forward, as well as possible solutions that players at the foundation model & data layer might use to adapt to this new world.
In Part 2 of my Substack mini-series, I highlight why access to semiconductors is crucial to maintaining the pace of innovation in AI, and why up until the past several years, access to leading-edge onshore fabrication capabilities has been less of a strategic focus.
I contend that geopolitical tensions in Asia and our reliance on the supply of AI accelerators manufactured in Taiwan creates a dangerous reflexive loop, and how that affects the ability for companies in the space (like OpenAI) to continually innovate. I also explore some interesting areas of opportunity that entrepreneurs may look to exploit.
Hi all! I'm an early stage investor at FirstMark Capital.
In analyzing over 3,000 financings during my time working on the 2023 MAD Landscape (https://mad.firstmark.com/), I noticed some fascinating trends in how the US and China have taken very different approaches to advancing their respective ML/AI startup ecosystems.
In this two-part series, I explore how China has taken a much more “concentrated” approach to building its ML/AI ecosystem, and how this approach is designed to help China reach technology parity with the US (part 1).
I also highlight the importance (and the urgency) with which the US needs to achieve full semiconductor independence, so that the progress that we’ve enjoyed so far in the fields of ML/AI remain unimpeded (upcoming in part 2).
Assuming startups like Etched (with its recent massive funding) could shrink CapEx quite a bit (and make it not such a large revenue shortfall)