Sign up to save your podcasts
Or
Share Uber COO questions tokenmaxxing
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Uber COO questions tokenmaxxing
In this hot-take episode, Paul Karner and Dave Mangot analyze a massive red flag in the current tech landscape: Uber's COO recently admitted it's getting harder to justify the money spent on AI "token maxing," while the company is slowing hiring to fund these AI investments. Dave and Paul break down why high token consumption often just creates stranded "inventory" rather than revenue-generating features, and why cutting your engineering labor force before AI proves its actual ROI is a massive mistake.
Key Takeaways:
- The "Token Maxing" Illusion: Why an increase in AI token consumption does not proportionally translate into useful consumer features or revenue.
- Inventory vs. Revenue: AI helps developers write more code, but it's getting stuck in feature branches. That code is simply "inventory," and you only make money when inventory hits production.
- Protecting the Productivity Engine: Why the decision to slow headcount/labor to offset AI costs is deeply flawed if the AI isn't actually yielding the expected efficiency gains.
- The Data-Driven Playbook: Why leadership must look at actual production metrics and token ROI before disrupting the underlying labor force.
https://www.businessinsider.com/uber-coo-andrew-macdonald-ai-token-spending-harder-justify-2026-5
View all episodes
By Paul Karner and Dave MangotIn this hot-take episode, Paul Karner and Dave Mangot analyze a massive red flag in the current tech landscape: Uber's COO recently admitted it's getting harder to justify the money spent on AI "token maxing," while the company is slowing hiring to fund these AI investments. Dave and Paul break down why high token consumption often just creates stranded "inventory" rather than revenue-generating features, and why cutting your engineering labor force before AI proves its actual ROI is a massive mistake.
Key Takeaways:
- The "Token Maxing" Illusion: Why an increase in AI token consumption does not proportionally translate into useful consumer features or revenue.
- Inventory vs. Revenue: AI helps developers write more code, but it's getting stuck in feature branches. That code is simply "inventory," and you only make money when inventory hits production.
- Protecting the Productivity Engine: Why the decision to slow headcount/labor to offset AI costs is deeply flawed if the AI isn't actually yielding the expected efficiency gains.
- The Data-Driven Playbook: Why leadership must look at actual production metrics and token ROI before disrupting the underlying labor force.
https://www.businessinsider.com/uber-coo-andrew-macdonald-ai-token-spending-harder-justify-2026-5