What You Already Have Is Exactly What AI Needs Most
The judgment you've built over years is more valuable right now than it's ever been — here's how to put it to work.
At 34 weeks pregnant, I got laid off.
I had over $2,000 worth of executive coaching waiting for me — sessions already in progress, fully paid for by my employer. I wasn't going to waste them. I took every note. Documented every framework, every question that gave me insight..
I asked my coach if she’d want to explore building something like this together.
She said no. She was afraid it would cannibalize her business.
I understood. Fear does that. It makes us protect what we have instead of imagining what's possible.
But I still wanted it. So I turned the idea on myself instead. What if I documented how I think?
I started helping other people who'd been laid off — I'm good at seeing what someone has to offer when they can't see it themselves anymore. One person became a dozen. A dozen became a program. That program became the Phoenix Formula.
I'm telling you this because of something I read recently that put a lot of what I've been building into new words.
What the Sequoia Thesis Actually Says
Julien Bek at Sequoia published a piece called "Services: The New Software." The argument: the next category-defining company won't sell you a tool. It will sell you the outcome.
He uses two terms. A copilot is AI that works alongside you — making you faster, more capable, but keeping you in the seat. An autopilot sells the outcome directly. You don't hire someone to draft your NDA. You subscribe to "your contracts get done." As Bek puts it:
A copilot sells the tool. An autopilot sells the work.
To understand which work autopilots take first, he maps it along two axes: intelligence and judgment.
Intelligence is rule-based. Think of it as if this, then that — at any level of complexity. Coding a medical claim means matching a clinical note to one of 70,000 standardized ICD-10 codes. Filing a standard insurance form. Checking a contract against a compliance checklist. The rules are complex, but they are rules. AI can now follow them autonomously.
Judgment is different. It's experience, taste, and insight built over years of making calls and learning from the ones that went sideways. Knowing which decision changes everything. When the data says one thing but something in you knows better. How to read a room, a client, a moment. That's not a rule. It's something you build over time, often without realizing how much you've built.
One line stopped me:
Today's judgement will become tomorrow's intelligence.
That means the judgment you've built — everything you've learned to do that doesn't feel like a rule anymore because it's become second nature — is the most valuable raw material in this moment. More valuable, not less.
This Was Already Predicted
What makes Bek's thesis land so hard isn't that it's new. It's that it confirms what was already being documented — before we had the tools to actually do it.
In Digital Hesitation, Thomas Lah and J.B. Wood argued that B2B buyers had already stopped thinking about what tools could do and started thinking about outcomes — what will actually get done, and by when. Their XaaS thesis said the entire customer relationship was moving from capability to result.
When you sell an outcome, the buyer expects it on a timeline. Think about Amazon two-day shipping — you don't wonder if your package is coming, you just know it is. Time-to-value stops being a metric and becomes the whole product. More than that: once the outcome is guaranteed, how fast you deliver it becomes the competitive edge. The professionals who've figured out which pieces of their judgment to automate will get there faster.
The catch right now: a lot of that value never gets realized. Companies are paying for Claude Enterprise, ChatGPT, Microsoft Copilot. The tools are live. The utilization isn't. The upleveling of the team isn't there. People have access and no real framework for using it — and nobody is on the hook for the outcome yet.
That gap is real. And it's also an opportunity for anyone willing to close it.
The first autopilots aren't hypothetical. They're funded, operating, and already taking the work.
The Map and the People Inside It
Bek's framework for deciding where autopilots win first is one of the most useful things in the piece: start where work is already outsourced. His map covers insurance brokerage, healthcare billing, accounting, legal transactional work, recruiting — industries where someone is already paying for a result, not a person. Replacing that vendor with an AI-native service is a vendor swap, not a reorganization. That's the wedge — the lowest-friction entry point.
One thing I'd add: if you're looking at your own operations and considering outsourcing something today, I'd stop. When you hand a process to someone outside your walls, you lose visibility into how it actually works. And you cannot put something on autopilot that you no longer understand. Instead of outsourcing, invest in AI consultants who build the capability alongside you — so when autopilots arrive for that work, you're the one deploying them. There's a real difference between a consultant who builds with you and one who builds for you. The second creates the same dependency you were trying to avoid.
The people inside those industries can feel the shift. The jobs are changing before the press releases say so.
I had strong offers coming out of VMware — that was my experience. But what I hear from the people I work with now tells a different story. Professionals who earned $180,000 interviewing for $90,000. Senior people taking roles two levels below where they were.
The reason isn't complicated. Companies are making room for AI. They need the cost savings. And given the state of the market right now, they can get highly experienced people for a fraction of what those roles used to pay.
A lot of these people already made sacrifices. They took jobs that weren't their passion — work that wasn't their dream, but they were good at it and they built a life around it. Now they're finding that even those jobs, the ones they showed up for every day and got genuinely good at, aren't worth what they used to be. And on top of that, they're being handed AI tools and told to use them — to automate the very tasks that made up their job descriptions.
That's not a meaning crisis. That's a survival crisis.
There's something else worth naming. If your judgment trains a model — the patterns you've built over decades, the hundreds of thousands of dollars you've put into education and professional development — who gets compensated for that?
Tim Rayner has been thinking about this out loud. He calls it cognitive sovereignty — the right to control, protect, and license your professional expertise. Right now, teams are being handed AI mandates with no additional pay, no equity in what gets built from their expertise. When your knowledge becomes someone else's infrastructure, that's not deployment. That's extraction. We don't have an answer yet. But it's the right question to be asking.
What to Delegate and What to Keep
Some of your judgment can be automated — and doing it right makes you more powerful, not less.
There are two kinds of judgment-adjacent work.
The first is thinking that's become routine. The patterns you run through every time. The diagnosis you've made so often you don't think about it anymore. Your wisdom made systematic. That part can be documented, handed to AI, and scaled.
The second is the real-time read. What's actually happening with this specific person, in this specific conversation, right now. What they're not saying. The thing you catch before they do. That's the judgment you keep — and when you automate the first kind, you have far more space for the second.
There's a third kind that's harder to see and worth protecting: the experience that lets you look around corners. You've been in enough rooms, made enough calls, seen enough cycles play out — that you can look at a situation and have a read on what's coming. Not because you have data, but because you've seen this before. When a product team has lived through five launches, they know which customer complaints are noise and which ones are the signal of something bigger. When a sales leader has seen a market turn, they can feel the next one coming before the numbers show it. That's not intelligence. That's accumulated pattern recognition — and it's yours.
Paul Daugherty calls the space where this plays out the "missing middle" — where humans and AI extend each other's capabilities rather than one replacing the other. The way to find your missing middle is to decompose your role the way you'd build a team. If I had unlimited budget and could bring in anyone, who would I want for each piece of this work? That question separates your intelligence tasks — the complex but rule-based work AI can take — from your judgment tasks. The judgment tasks are the work you actually want to do. When you automate the intelligence layer, you free up capacity for far more of them.
The professionals staying ahead aren't protecting their judgment from AI. They're figuring out which pieces of it to delegate — and operating from a permanently higher floor.
How I Built the Phoenix Formula
I tested this in the domain that everyone assumes is permanently human.
I'd spent 15 years investing in my own career development — executive coaching, leadership programs, all of it. And I'd worked with hundreds of professionals through career transitions. When I started my own job search after VMware, the gaps in how people were being supported were impossible to miss. The tools existed. The frameworks didn't.
I wanted to see if I could automate the pieces of my judgment that were consuming my time, so coaching could be the highest and best use of the hours we actually had together. I built a main AI coaching model that clients could interact with between our sessions — it knew their situation, their progress, what they'd been working through. By the time we got on a call, I already had AI-surfaced insights about where they were stuck. What would have taken the first thirty minutes to uncover, I could get to in five. Ten hours of coaching work, compressed into one.
But before I could build any of it, I had to solve a measurement problem. Feelings aren't proof. Career coaching produces a feeling — people feel better, more confident, more clear. I needed to know whether the inputs that actually lead to getting hired were moving. That became the foundation.
Here's what I did.
I voice-memoed my root cause analysis — a real account of how I think about transformation, not a framework. Then I fed that to AI, asked it to act as a measurement expert using Douglas Hubbard's framework from How to Measure Anything, and had it design my assessment questions from my own thinking. Then I ran my call transcripts through AI to surface the patterns in how I was actually making decisions — the judgment showing up in real time, not just what I said I believed.
Then I built specialized agents — one for each role in the job search process. Ten thousand hours makes you a master in one thing. A great resume writer is not a great interviewer. A great LinkedIn strategist is not a great mindset coach. If you hired the best possible person for every single aspect of a job search, you'd have a team no one person could be. The Phoenix Formula gives every participant access to that team.
What I found: I didn't know the full architecture of my own thinking until AI surfaced it. The judgment was already there — I just couldn't see all of it from inside my own head. AI gave it language, structure, and the ability to reach people I never could have reached one-on-one.
The pre/post results, measured before a single participant had gotten a job, showed a 300% improvement in LinkedIn proficiency, 150% improvement in articulating unique value, 75% reduction in burnout, 80% reduction in sleep disruption. I measured inputs, not outputs. If the inputs move, the outcome is a matter of time.
As AI takes on more intelligence work, judgment gets pushed to a higher level. For anyone who's built real expertise, that's good news.
The work that becomes ours is more creative, more consequential, more human. Design thinking lives here — not optimizing what already exists, but looking around corners. Understanding what people actually need before they can say it. Designing what comes next rather than reacting to what already happened.
The ones who stay valuable are the ones who keep upleveling — using AI to compress the intelligence work so they have more space for the judgment above it.
Document your judgment. Voice-memo your thinking before you open a prompt. Ask AI for insights based on how you think — to surface what you already know, not just to generate output.
What you've built over your career is the most valuable thing in this equation. The question is whether you've made it visible enough to use it.
More capable. Not more dependent.
Sources
Julien Bek, "Services: The New Software" (Sequoia Capital)
Thomas Lah and J.B. Wood, Digital Hesitation