Your AI Teammate Might Soon Cost Six Figures
And other hard truths about making AI actually work.
We're past the magic show phase of AI. Now we're dealing with the logistics, the bills, and the very real need for adult supervision.
Your New AI Colleague Could Have a Six-Figure Salary
A Reddit discussion reveals the silent budget killer lurking in your development workflow: token-based pricing.
What if your AI coding assistant's annual bill rivalled a junior developer's salary? A recent discussion projected AI costs spiralling to $100,000 per developer per year. This isn't about your monthly ChatGPT subscription; it's about the deep integration of frontier AI models into everyday workflows, where every single interaction costs money.
The problem is the shift to usage-based pricing. Every API call, every generated function, every bit of context consumes tokens, and those tokens add up. The real story here isn't the cost of the tool, but the operational expense of its usage, which scales unpredictably. We're treating AI like a fixed software purchase when it's really a variable utility, like electricity.
This forces a necessary, and uncomfortable, reality check. The focus must shift from simply using AI to using it efficiently. Companies now need to budget for AI consumption like they do for cloud compute, optimising for value and ruthlessly cutting waste. If they don't, the AI budget might just eat the entire R&D budget.
The AI Refinement Layer
The initial 'wow' of AI generation is over; now the real work of making the output reliable and secure begins.
VibeScan: Your AI code's personal therapist.
VibeScan sniffs out the bugs, security holes, and performance issues in AI-generated code. Generating code 5x faster is useless if it introduces 10x the technical debt; quality control is the new bottleneck.
ShellDef: A security guard for your copy-pasted scripts.
This tool scans shell scripts for threats before you run them, creating a clean version. We're finally building the guardrails for a risky developer habit that has existed for decades.
ClearPlan: An AI that actually listens when you edit.
ClearPlan lets you refine just one part of an AI-generated plan without the model rewriting the whole thing. This signals a critical shift from fighting with AI to actually collaborating with it.
Quick hits
Blur It: Your instant privacy cloak for screen sharing.
A simple Chrome extension to blur sensitive info on your screen during a live demo, so you don't have to frantically close tabs.
Jamocracy: A democratic DJ for your next party.
This web tool plugs into Spotify and lets party guests add and vote on songs, saving everyone from that one friend's questionable music taste.
Google Gemini Guided Learning: An AI tutor that wants you to actually understand things.
Gemini is moving beyond just giving answers by creating interactive quizzes and learning paths, directly challenging ChatGPT's own study tools.
My takeaway
The most valuable work is no longer generating the first draft, but intelligently refining and securing it.
We spent the last year marvelling at AI's ability to create code and text from nothing. But raw output is cheap and often flawed, creating new problems around quality, security, and cost. The next wave of value is in the tools that provide the crucial layer of verification, refinement, and control.
This shifts our primary role from pure creation to curation and critical thinking. The most effective people won't be the fastest prompters, but the best editors and system architects. They will be the ones who can thoughtfully manage the entire lifecycle of an AI-assisted project, not just the initial spark.
What's one process you have that relies on raw AI output, and how could you build a better verification step for it?
Drop me a reply. Till next time, this is Louis, and you are reading Louis.log().