Welcome to the 22nd edition of Work in Beta.

In this edition, we explain why keeping your AI wins to yourself is a losing strategy - not for your team, but for you.

So let's dive in!

You found a few AI workflows that actually work. You're faster. Maybe significantly faster. And you kept quiet about it, because an edge is for keeping, not sharing.

Three months later, here's what's strange: you're faster, but you can't find the win. You finish your part sooner and then you wait. Same approvals queue. Same review cycles. Same meetings that didn't need to be meetings. Your manager sees the same total output, on the same timeline. You got faster at your piece, but the work around your piece didn't move an inch.

You sped up your link. The line didn't move.

This isn't a teamwork argument. It's a mechanics problem.

Your work is a link in a chain: work flows into you, through you, and out of you. Speed up only your link and the line's total speed stays the same - work still arrives at the old pace and leaves at the old pace. The time you saved has nowhere to go. The bottleneck just slides to the next person. Think of an assembly line: speed up one station and parts pile up before the next.

So you get zero credit. Leadership measures the line - total output, start to finish - not your private minutes. Your gain is real and invisible. And you're not alone in the dark: 49% of workers use AI without employer approval (CSO/BlackFog), 52% use unapproved tools (Okta/Apprize360), while 90% of executives believe they have visibility into what their teams use. Everyone's privately optimizing. None of it surfaces where it's measured.

Worse: hidden speed carries hidden risk. Quality issues happen - 45% of workers have had to fix or redo AI-assisted work (Founder Reports 2026). When AI is in your workflow and nobody knows, every one of those issues is entirely on you. No "the tool got this wrong," no "we should adjust the review step" - just your mistake, landing on a colleague's desk as carelessness. And if it later comes out that AI was in the loop all along, trust drops retroactively: 43% of workers trust coworker output less the moment they learn AI was involved. Hidden use doesn't protect you. It means you carry the full downside with none of the cover.

And this is where it turns against you. At some point - a performance review, a one-on-one, a team retro - you'll want credit for the advantage you've built. So you tell your manager you've been doing serious work with AI. They look at the only thing they can measure: total output, start to finish. It hasn't moved. Now you've made a claim your own numbers don't support. If you'd said nothing, you'd be a steady performer - fine. But a bold AI claim against a flat result reads as inflated self-assessment. You went from uncredited to something worse: contradicted. Private speed isn't an edge. It's an edge you'll never be paid for because what stops at your desk doesn't count.

The only way to make your gain visible is to make the adjacent links faster too.

Help the person who hands you work send cleaner inputs. Help the person who receives your output consume it faster. Now the whole line speeds up and total output finally moves where leadership can see it.

And it traces back to you. Because the improvement runs through your method, you're no longer "the person who's personally fast." You're the person the whole chain got faster because of. That's the multiplier - the center of the flow. That's who organizations bank on.

We see this firsthand. A marketing leader we work with didn't hoard her edge - she built real AI workflows, shared the method, lifted the people around her. Two things followed: no vendor or colleague can BS her, because she built it herself and knows the constraints. And her department became the credible AI team - other departments come to them now. She didn't get rewarded because she was fast. She became the center of the flow, and the organization built around her.

This isn't one person's story. The shift is showing up in who gets hired and who gets promoted: 8 in 10 leaders say they'd rather hire someone comfortable with AI than someone with more experience who isn't (LinkedIn/CNBC). But individual skill alone isn't the differentiator. The organizations seeing real results from AI - nearly 3x more than the rest - got there by changing how work flows between people, not by giving individuals faster tools (McKinsey). The person they're looking for does both: uses AI well and makes the people around them better because of it.

The identity shift matters: from power user to capability builder. In Edition 11, we covered System Building - personal systems that make you faster. This is the next step: make the reusable piece portable so the chain can run it.

You've already done the hard part - you have AI workflows that work. This protocol isn't about doing more AI work on your own tasks. It's about converting that private skill into shared capability: the move from power user to capability builder. Six steps. Sequential. Each one builds on the last.

The key: step 3. You're not evangelizing AI to the department. You're sharing one method with one person on one real task. Start at your immediate handoff points - upstream and downstream - and let it grow from there.

Why this works: Each step converts private gain into shared infrastructure. The improvement becomes measurable at the line level, not just at your desk. And because you built it, documented it, and set the boundaries, you're the person the chain can't run without.

You're already doing this - quietly. AI saves you real time on your own work, and you've kept it to yourself. This week, bring one piece of it into the open.

Pick the recurring task where AI saves you the most. Write down what you actually do - inputs, prompt, where you check it, where it breaks and turn it into a one-page artifact someone else could run. Then take it to one person at your nearest handoff: whoever feeds you work, or whoever you feed it to. Not a pitch. Not a demo in the team meeting. One working method, on one real task.

This is step one out of the shadows - from the quiet operator who just got faster to the AI champion an organization builds around. In an AI-first workplace, that's where the real gains are. You don't get there in one week. But you do start this week.

The people who matter in the next phase aren't the ones who got fastest. They're the ones the whole line got faster because of.

Most professionals know AI can do more for them. The gap isn't awareness - it's knowing where to start, what to change, and how to make it stick.

That's what we work on through Work in Beta.

If any of this resonates, email us at [email protected] / [email protected] and we will figure out how to work together.