Uber’s COO says it’s getting harder to justify money spent on tokenmaxxing

Original Article ↗ Hacker News Discussion ↗

What “Tokenmaxxing” Actually Is

  • Companies incentivize or require engineers to maximize AI token usage, sometimes with leaderboards and perceived ties to performance reviews.
  • Reported Uber numbers (via linked coverage): average AI spend of ~$150–$250 per engineer/month, with “power users” at $500–$2,000.
  • Some devs say they now feed models pointless tasks just to avoid looking like “low users.”

Costs, Subsidies, and Economics

  • Many argue current token prices are heavily subsidized by VC or model vendors; once subsidies end, heavy agentic use may rival or exceed junior salaries.
  • Others counter that inference is highly optimizable, open-weight models are improving fast, and labor has a hard cost floor, so AI should eventually be cheaper than humans for many tasks.
  • Skepticism that major model providers are truly profitable; comparisons to prior bubbles and expectations that a recession will purge weak AI offerings.

Goodhart’s Law and Management Critique

  • Strong consensus that “tokens used” is a bad productivity metric, likened to lines-of-code or hours-at-desk.
  • Example behaviors: agents chatting with each other, redundant tool calls, and feature work driven by metric-chasing rather than user value.
  • Some see executives as herd-followers reacting to investor/board FOMO; others defend “burn money now” as an intentional exploration phase to find high-value workflows.

Productivity, Quality, and Engineering Culture

  • Reported gains range from “not helpful” to ~20% boost, with many warning that massive claimed multipliers are hype.
  • Concerns that junior engineers using AI for everything don’t build real understanding; rise of fragile, “vibe-coded” PoCs that become production-critical.
  • Teams struggle to measure real ROI: many tokens can produce many low-impact features; fewer tokens can yield one high-impact change.

Local Models, Security, and Architecture

  • Debate over centralized data centers vs. GPUs per dev; some believe local/open models (e.g., DeepSeek, Mistral) plus internal hosting will undercut cloud APIs.
  • Security worries about sending sensitive data to third-party APIs, especially foreign ones; local hosting seen as a mitigation.

Uber-Specific and Broader Strategy Questions

  • Some question why a 16-year-old “taxi + food delivery” firm still needs thousands of engineers; others point to enormous regulatory, tax, payment, and locale-specific complexity.
  • Many expect a shift from “use as much AI as possible” to “use the right model, minimally, for clear business value.”