In a recent episode of the IntoTexas AI podcast, host Shannon Belew unpacked a provocative statement from Nvidia CEO Jensen Huang: when it comes to artificial intelligence, companies should stop obsessing over return on investment and start experimenting instead.
The advice comes at a critical time for business leaders, especially in Texas, where over 50 Fortune 500 companies and hundreds of publicly traded firms are grappling with pressure to show quarterly profits while pouring money into AI. This tension has created what Belew calls the "AI paradox" — companies are simultaneously cutting costs through layoffs and making massive investments in AI infrastructure, talent, and experimentation.
Traditional ROI models, designed for predictable projects, often fail to capture the long-term value of AI. Huang argues that focusing too narrowly on immediate returns can cause companies to fall behind competitors who embrace a longer horizon. The podcast outlines a new playbook: treat AI not as a one-off project but as a core strategy. This means shifting from a mindset of efficiency — using AI to cut costs — to one of experimentation, where the goal is learning and discovery.
For executives and board members navigating this landscape, the key takeaway is that patience and a willingness to tolerate uncertainty may be the biggest competitive advantages in the AI race.