The OG market map of Data/AI returns for its TENTH year (2,011 logos in total vs 139 back in 2012)
+ 24 themes we're thinking about in 2024, including:
* Is the Modern Data Stack dead?
* Consolidation in data infra
* Checking in on Databricks vs Snowflake
* The Rise of the Modern AI Stack
* Where are we in the AI hype cycle?
* Experiments vs reality: was 2023 a headfake?
* LLM companies: maybe not so commoditized after all?
* What's interesting: AI agents, edge AI
* Is Traditional AI dead?
* Is Generative AI heading towards a plateau?
* Is NVIDIA overvalued?
* Open source AI: too much of a good thing?
* Is AI going to kill SaaS?
* Is AI going to kill venture capital?
Just one important nuance -- VCs like me who come in early and stay with portfolio companies for 5-10 years are both on the "buy" and "sell" side of the market.
So yes, a slower VC funding environment does impact valuations favorably for VCs, for net new investments (the "buy" side), so I'm talking my book to some extent.
BUT net new investments is only a part of the job (<50% for sure) for a VC like me. Most of my time is spent working with my existing portfolio (sitting on boards, etc). And a harsher environment is bad news for my existing portfolio (and/or anyone's portfolio), and me (and/or investors like me) as a result. Like a lot of VCs, I've looked like a "genius" for the last couple of years as many of my investments became unicorns, with huge paper markups. Now it's likely that the pace of markups is going to slow down significantly. Perhaps we're entering a world of flat valuations - or even downrounds? No markups is not a good look for VCs - makes it harder to raise the next funds, etc.
So yes, in the long term, it's good for VCs if we're able to invest in (the right) companies in the 2022-2023 cohort at lower valuations. But that will take 8-10 years to manifest into concrete results, by the time those companies become very big. In the meantime, VCs won't have a lot of fun navigating a flat round/downround environment (if it does indeed materialize) with their existing portfolio.
Great points! Thanks for hopping in and adding extra color.
Navigating a whole portfolio through a flat/down environment sounds incredibly stressful.
If the driver of these changing environments is predominantly investor perception, public early warnings seem like they would accelerate or exacerbate them.
For example, if you publicly announce you’re expecting flat or down rounds (and similarly lowering your offers on new deals), it’s almost like applying downwards price fixing pressure. The next investor in your businesses feels they can also offer less without losing the deal. In this way, the warning manifests the crisis.
To protect the portfolio valuations, it would then seem strategic to prepare and react in private. But public warnings seem more strategic towards lowering valuations of new deals.
That’s why I’m naturally skeptical about the motivation for signaling.
The ML, AI, and Data stacks are all in early days compared to the broader software stack. We were curious about how the small but growing number of public companies in these stacks have performed compared to the broader market, so we created a new public index. Introducing the MAD Public Company Index...
