NEA’s Tiffany Luck says enterprises are still figuring out their AI ROI
TL;DR
TechCrunchs Equity podcast talks with NEA partner Tiffany Luck about the point where AI enthusiasm meets budget discipline: companies are using tools more widely, but many still cannot measure return clearly. The hook is the cooling of the tokenmaxxing craze: Uber reportedly burned through its annual AI budget within months, some firms cut Claude licenses, and Meta shut down an internal usage leaderboard.
Nauti's Take
Tokenmaxxing was useful as a culture signal, but weak as a management model. More tokens do not automatically mean better decisions, lower headcount pressure, or higher revenue.
The harder question is which AI usage changes a process enough to show up in the business result. That bar will hurt many internal AI initiatives, but it also makes the market more mature.
Briefingshow
The next phase of enterprise AI will not be decided by who writes the most prompts, but by who can connect cost, quality, and measurable impact. If AI spending grows faster than internal finance and operations teams can track it, a market opens for tools that translate usage into defensible business metrics. That is where investors and startups are now trying to position themselves.
Sources