TLDR:
- Some startups are using questionable metrics to raise capital in the AI space.
- VCs are seeing similarities to past failed attempts, such as WeWork.
In the increasingly crowded world of artificial intelligence startups, some companies are resorting to using questionable metrics to raise capital. Venture capitalists are still interested in investing in AI startups, but some companies are using metrics that don’t inspire confidence in their business prospects. These metrics include the number of AI PhDs on staff, the quantity of Nvidia AI chips the company has, and a vague measure of potential revenue.
This behavior is reminiscent of WeWork, the office-leasing company that famously used non-traditional metrics like “community adjusted” earnings to create the illusion of profitability. This tactic ultimately failed, and VCs are cautioning against similar practices in the AI space.
While AI startups may not be as extreme as WeWork in their use of non-traditional metrics, VCs are noticing these questionable practices in pitch after pitch. Some of the most common metrics being used include:
- Number of AI PhDs on staff
- Quantity of Nvidia AI chips used by the company
- Fuzzy measures of potential revenue
Overall, VCs are advising startups to focus on more traditional and transparent metrics to demonstrate their business viability and attract investment in the competitive AI market.
