Tag: Leadership
14 posts
The 70/30 Method Fixes Your Team’s Fear of AI
Senior engineers stall on AI because they are being asked to vouch for code they did not write. The 70/30 split gives them a boundary they can trust, and the resistance disappears.
What to Put in an AI Vendor Contract Before You Sign
Price caps, exit rights, data ownership, model-swap notice. The clauses that decide whether you're a customer or a hostage when your AI vendor changes the deal.
Your Engineers Aren't Resisting AI. They're Afraid of It.
Your team isn't resisting AI out of laziness. They're afraid of being blamed for code they didn't write. The 70/30 method draws the line that makes adoption feel safe.
Do You Need to Hire an AI Engineer, or Just Rent the Judgment?
The full-time ML hire everyone reaches for vs. what most early teams actually need. How to tell which problem you really have before you spend the headcount.
How to Spot an AI Expert Who's Faking It
A founder's vetting checklist for AI consultants, agencies, and hires — the questions that separate real operators from prompt-jockeys with a good deck.
What Happens When Your AI Vendor Triples the Price?
Agent software spend is exploding and pricing is unstable. Here's how to architect so a vendor's price hike or a deprecated model can't hold your product hostage.
5 Signs It's Time to Bring in a Fractional CTO
Five signals it's time for a fractional CTO — from becoming the technical bottleneck to shipping AI that won't survive production. None are about headcount.
When to Fire an Agent (and Hand the Work Back to a Human)
Everyone talks about deploying agents. Almost nobody talks about pulling one. Knowing when to take an agent off a job is a core management skill, not an admission of failure.
Who's Accountable When the Agent Is Wrong?
Every leader weighing AI eventually hits the real question: when the agent makes a costly mistake, who owns it? If the honest answer is no one, you're not ready to ship.
Giving an Agent a Performance Review
Evals sound like an engineering chore. They're really the management ritual you already run: a regular, honest look at whether the work is good enough. Here's how to run one for an agent.
What a Manager's Job Becomes When Agents Do the Work
If agents do the producing, what's left for the manager? The job doesn't shrink. It moves up: setting the standard, designing the system, and owning the judgment.
Job Descriptions for Agents: A Template
The single highest-leverage thing you can do for an AI agent is write it a real job description before you deploy it. Here's the template we use, with every field explained.
Your First AI Agent Is a New Hire. Onboard It Like One.
Your AI agent isn't failing because the model is weak. You skipped its onboarding. Give it a job description, access, context, and a feedback loop like any new hire.
The Agentic OS Maturity Model: 5 Stages
Most teams have no idea how far along they actually are with AI. Here are the five stages from one-off prompting to a real Agentic OS, and how to tell which one you're in.