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LLM EconomicsGemini 3.6 Flash Pricing: What $1.50/$7.50 Per Million Tokens Means for Agent Margins
Google's new Gemini 3.6 Flash costs $1.50 per 1M input tokens and $7.50 per 1M output tokens, undercutting 3.5 Flash on price while using roughly 17% fewer output tokens per task, a combination that compounds into a bigger margin gain than the sticker price alone suggests.
Pricing StrategyCutting Your Price 5x Won't Kill Your Margin. Your Architecture Will
Aggressive usage-based pricing does not fail because the price is too low; it fails when founders cut the price without first cutting their cost of goods.
LLM EconomicsThe Hidden Token Tax on AI Agents: Tool Bloat
Loading an agent's full tool catalog into context every turn inflates input tokens on every call; retrieving only the two or three tools that matter per turn cut input tokens by up to 85% in one benchmark.
LLM EconomicsHow to Read the True Cost of Your AI Coding Agent
Reconstruct a per-session cost floor from your own agent transcripts, then re-price the same tokens on a cheaper model to see what you could save.
AI Pricing Needs to Fall 90%? Your Margin Floor Decides Who Survives
Palo Alto Networks CEO Nikesh Arora argues enterprise AI pricing must drop roughly 90% as token costs balloon; whether or not the number is exactly right, the only companies that survive a pricing war are the ones that knew their margin floor before it started.
AI Token Budgets Are Coming: What Meta's Per-Engineer Caps Mean for Founders
Instagram head Adam Mosseri says that within a year or two a strong engineer's AI token burn could match their salary, which means token spend is about to be managed like payroll, and founders should apply the same discipline to per-customer burn.
Founder GuidesHow to Manage AI Spend in the Agentic Era: A Founder's Playbook
OpenAI's new enterprise guidance says to stop staring at token prices and start measuring useful work per dollar; for a founder, that shift is the difference between guessing your margins and knowing them.
LLM EconomicsRenting vs Owning AI: When Do Open Models Beat Frontier APIs on Cost?
Companies typically start on frontier APIs and shift work to open models as usage scales, so rent versus own is a break-even calculation driven by volume and task mix, not an ideological choice.
LLM EconomicsToken Pricing Economics: Will LLM Providers Keep Their Pricing Power?
Benedict Evans argues that today's token prices reflect a temporary supply crunch and that every visible market dynamic points toward frontier models becoming commodity infrastructure, which means founders should plan for falling LLM costs rather than assume today's rate cards.
LLM EconomicsPaying Twice for AI: What Nadella's Warning Means for Your LLM Costs
Satya Nadella argues that companies buying proprietary AI pay twice, once in cash for tokens and again in the proprietary knowledge their usage teaches the model, and that second payment should change how you count AI costs.
Founder GuidesThe AI Cost Crisis Is Self-Inflicted: How to Control LLM Spend Without Killing Adoption
Most runaway AI bills come from panic-driven defaults rather than workload needs, and a five-step governance framework can pull spend back without slowing adoption.
LLM EconomicsTokenizer Inflation: Why $/1M Token Prices Are Not Comparable Across LLMs
The same file can become up to 73% more tokens on one frontier model than another, so a $/1M token price is only comparable after you adjust for each model's tokenizer.
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