Uber recently announcing they have already blown their entire 2026 Claude Code budget, probably due to “token-maxxing”.
If someone told you last century a factory was "electricity-maxxing", leaving every light on, running every machine, and saying their power bill proved the plant was productive, you would call them insane.
Why do we treat AI usage differently?
I've been looking into the internal operations of a few law firms recently as part of my research, and I see the exact same reflex every time a partner decides they need to "figure out AI."
They are completely lost on how to actually use it, so they assume they need to buy or build some massive, perfect agentic system on day one.
You don't.
If you want to actually incorporate AI into your practice here is how I'd recommend to get started:
Start with using the native "Interview" tool. The best is Claude's AskUserInterview tool, Gemini's is okay, and I would avoid using ChatGPT's for this critical first step. You can use a skill like this to help by typing Use /interview to interview me for ways to implement AI at my law firm.
# /interview
Turn a vague idea into an implementable spec by asking the questions the user hasn't thought to answer yet.
## Input: $ARGUMENTS
## Phase 0: Build an Internal Question Map
Before asking anything, write every question you might want to ask to `/tmp/interview-questions.md`. Organize by category: technical, UX, data, edge cases, security, operations. Aim for
30+ questions across 6+ areas.
This map is internal — never show it to the user. Use it to ensure you don't skip categories. Mark questions resolved as answers come in. When an answer reveals new complexity, add
follow-up questions.
## Phase 1: Understand the Input
- File path: read it, summarize your understanding, identify gaps
- Description: acknowledge what you know, note what's missing
- Empty: ask what to interview about
## Phase 2: Conduct the Interview
Batch up to 4 questions per round. Cover at minimum:
-
**Core:**
What user pain does this solve? Who uses it first vs. most? What does success look like?
-
**Technical:**
What existing code does this touch? Simplest version? External dependencies?
-
**Data:**
Where does data live? What happens offline? Conflict handling?
-
**UX:**
Entry point? Happy path? Frustrated path? Existing patterns to follow?
-
**Tradeoffs:**
What are we explicitly NOT building? What could break?
-
**Operations:**
How is this monitored? Debugging? Who owns it long-term?
When presenting options,
**recommend one and say why**
— don't make the user evaluate from scratch.
Keep going until the question map is exhausted. Judge completeness yourself.
## Phase 3: Confirm
Summarize your understanding. Flag remaining assumptions. Ask user to correct anything before writing.
## Phase 4: Write the Spec
Ask where to save it, then write:
-
**Feature**
→ user stories + acceptance criteria
-
**Initiative**
→ PRD (problem, solution, scope, success metrics)
-
**Technical**
→ architecture, implementation steps, considerations
-
**Bug/enhancement**
→ problem, proposed fix, testing approach
The goal is to let the AI build context on you. You want it to understand how your firm operates, how you deal with clients, your daily bottlenecks, and the challenges you've had with AI in a legal setting in the past. If you think it didn't cover something, be sure to ask it about it.
Once it understands your actual baseline, have it generate a prioritized list of small, low-risk use cases.
Work through that list slowly over time.
Your goal is just to put down a solid foundation. Yes, people will brag online about their fully automated, zero-touch AI firm setups. They don't actually have those setups anywhere except in their dreams.
What matters is that you try it, find one or two things that actually work, and build from there.
If you run into roadblocks, bring your questions back here, or just ask the AI system directly to explain why it failed.
Happy to answer any questions below.