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Token-maxxing case: AI agents scale linearly with throughput, offer pure variable costs with no equity dilution, and enable 5–10x productivity gains; founders shipping enterprise products in months
Hiring case: AI failures are hard to debug and fix compared to managing people; token costs are volatile and vendor-dependent; hiring provides predictable salary modeling
False binary identified: Founders doing both token spending and hiring simultaneously; real question is which work categories should be automated versus require human accountability
Key insight from founders: Clear planning and precision matter more than raw token burn; automation success depends on understanding the work being done, not just spending more on agents
Recommendation: Stop asking "tokens or people" and start asking "what kind of work am I actually trying to get done"