First-generation AI coders let you chat your way to code. Second-generation tools are discovering that coffee-shop conversations don’t build production systems.
Eighteen months ago, Cursor and Cline pioneered AI-assisted coding. You described what you wanted, the AI generated code and if you were lucky, it mostly worked. We called it “vibe coding” - casual, conversational and utterly inadequate for anything serious.
Now we’re seeing the second wave. And it’s fundamentally different.
Six months ago, everyone said AI would replace developers. Today, most AI-generated code needs more debugging than writing from scratch.The AI coding revolution was supposed to be here by now. GitHub Copilot would write our applications. ChatGPT would eliminate junior developers. Cursor would make senior developers 10x more productive.
Instead... we’re drowning in plausible-looking code that doesn’t quite work.
AI vendors are racing to offer million-token context windows. But... they're not doing it for your benefit.
Remember when 4K context windows felt limiting? Then 8K arrived. Then 32K. Now we're being sold 128K, 200K and even million-token windows as the solution to all our AI problems. Sadly, it's all a con.
Here's what they're not telling you.
Every message you send doesn't just process your latest prompt. The AI re-reads the entire conversation history - every single token - to generate each response.
Send a 100-word question in a fresh chat? You're using roughly 100 tokens. Send that same question after an hour of back-and-forth? You might be burning through 50,000 tokens for the same answer.
The cost isn't linear - it's exponential. Each message gets more expensive as your conversation grows, and those costs compound fast. Think Cookie Monster but for tokens!
Larger context windows enable sloppy habits. Why bother being precise when you can just dump everything into the conversation and let the AI "figure it out"? Why manage your prompts when the context window is supposedly infinite?
This laziness has a price tag. A substantial one.
A well-structured conversation with clear prompts might cost pennies. That same interaction stretched across a rambling 50-message thread could cost pounds - delivering identical results while training you to be less effective.
AI platform owners understand something most users don't: confusion is profitable.
Larger context windows sound like features. They feel like generosity. In reality, they're revenue optimization strategies that shift costs to users who don't understand the pricing model.
The more context you use, the more you pay - but the pricing structure obscures this reality until your bill arrives.
Effective AI use requires discipline:
This isn't just about cost control. Focused, well-managed conversations produce better results. Shorter contexts mean the AI spends less computational effort tracking irrelevant history and more on your actual problem.
At Lanboss AI, we help organisations implement AI without falling for vendor marketing. Understanding how these tools actually work - and what they actually cost - is fundamental to safe, cost-effective AI adoption.
Larger context windows aren't inherently bad. But treating them as an invitation to lazy prompting and unmanaged conversations is expensive and counterproductive.
TL;DR Million-token context windows are a feature designed to increase platform revenue, not improve your results. Disciplined prompting and conversation management will save you money and deliver better outcomes.
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