Tokenmaxxing was the hottest trend in Silicon Valley earlier this year, with CEOs encouraging employees to push AI usage as far as it would go. Then the bill came due. Uber reportedly blew through its annual AI budget in a few months, some companies cut Claude licenses for parts of their org, and Meta killed its internal leaderboard. This tension between hype and ROI is exactly where NEA partner Tiffany Luck lives these days. She got her start convincing companies that e-commerce was the future, and now she is all in on AI, especially when it comes to the possibilities for magic moments in the consumer business. On this episode of TechCrunch’s Equity podcast, Luck joins Rebecca Bellan to talk about the future of personal agents, her thoughts on this year’s AI IPOs, and how startups are stepping in to help enterprises track return on AI spend.
The industry is moving from unmeasured experimentation to rigorous financial accountability, forcing startups to demonstrate tangible value rather than just capability. Enterprises are mixing and matching models instead of committing to one provider, while forward deployed engineers become a Trojan horse for AI adoption by embedding tools directly into workflows. Tiffany argues that value is being created at every layer of the AI stack, not just at the model layer, meaning infrastructure and application development are critical for success. The shift means companies must measure spend carefully, ensuring that every dollar invested translates into operational efficiency or revenue growth. Without this ROI reckoning, the current wave of investment could collapse under the weight of unproven utility, leaving only the most pragmatic solutions standing.
- Companies are shifting from tokenmaxxing to measuring actual return on investment for AI spend.
- Enterprises are adopting a multi-provider strategy rather than committing to a single AI model.
- Value creation is occurring across the entire AI stack, not solely at the model layer.
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