Nathaniel Whittemore (NLW)

Nathaniel Whittemore, known as NLW, is the founder and chief executive of Superintelligent and host of The AI Daily Brief. His work centers on enterprise AI adoption: finding worthwhile applications, preparing employees, deploying agents, and determining whether the results justify the investment.

Whittemore studied history at Northwestern University, graduating in 2006, and cofounded its Center for Global Engagement with Jonathan Marino. He later became founding editor of Change.org’s social-entrepreneurship publication and a principal at the education-focused investment firm Learn Capital. His subsequent work in technology media included hosting The Breakdown, a daily cryptocurrency and macroeconomics podcast distributed through CoinDesk. In 2023, he expanded the show into the independent Breakdown Network and began broadening his focus to artificial intelligence.

That shift produced The AI Daily Brief and Superintelligent, separate businesses addressing AI from complementary perspectives. Founded in 2023, Superintelligent initially offered practical AI education, then added ways for teams to share effective use cases. It has since become an enterprise AI planning platform, using tools such as employee-interviewing voice agents to identify workflow bottlenecks and prioritize opportunities across organizations.

• AI adoption depends on organizational readiness. Whittemore treats employee enablement, managerial coordination, and workplace culture as prerequisites for meaningful deployment. He argues that organizations need systematic, cross-departmental adoption instead of disconnected pilots.

• AI ROI benchmarking must measure more than productivity. His audience-sourced enterprise study organizes reported outcomes into eight categories: time savings, increased output, improved quality, new capabilities, better decision-making, cost savings, revenue growth, and risk reduction. Because participants volunteered from an AI-focused audience, he explicitly treats the findings as directional, not representative of all businesses.

• Rare use cases can have outsized consequences. Although time savings dominated reported applications, risk-reduction projects were unusually likely to be rated transformational. Coding, automation, and agent-related workflows also produced strong self-reported outcomes, while results varied by company size, employee role, and industry.

• AI agents should change how work gets done. Whittemore questions whether faithfully reproducing existing human workflows is the right long-term design goal. He instead emphasizes hybrid human-and-agent workforces, new organizational capabilities, and better ways to accomplish the underlying task.