Annyce Davis

Davis Technology Consulting

Tokens Rule Everything Around Me

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If you grew up listening to 90s hip hop, you probably already know where the title came from. “Get the money. Dolla, dolla bill, y’all!” Proud 80s baby here. That aside, here’s what led me to this.

A few days ago, I found out I was going to have to start paying a lot more attention to my AI token usage at work. My first reaction was not some profound engineering insight. It was basically: ugh.

I use GitHub Copilot a lot. Like…a lot a lot. 😅 Over the past year, AI has slowly worked its way into almost every part of how I build: planning something new, debugging, refactoring, exploring an unfamiliar repo, writing tests, or sometimes just asking, “What am I missing here?”

So adding more limits initially just felt like friction. Then I started looking at how I was actually using AI.

And, well…

Maybe I needed the friction.

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Annual Planning and Budgeting Need to Die

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It’s August. It’s also budget and roadmap season. I’ve already been planning for next year for a couple of months. You can’t make this up.

Like most leaders in large organizations, I’ve spent plenty of time in planning conversations. We build roadmaps, forecast budgets, negotiate headcount, debate priorities, and try to make reasonable decisions about what we’re going to need six, twelve, sometimes eighteen months from now.

There is nothing inherently wrong with doing that. Companies need a strategy. Finance needs some idea of where the money is going. Teams need enough direction to make decisions without renegotiating priorities every week.

The problem is what happens after the plan is approved.

Because between now and next August, a lot is going to change.

Customer expectations will shift. New technology will become viable. Some projects will turn out to be harder than expected. Others will turn out to be unnecessary. New opportunities will emerge that we haven’t even considered yet.

And somehow, despite all of that new information, we’ll still be talking about whether we delivered what we said we would deliver back when we were sitting here in August.

I’ve started to wonder whether annual planning and budgeting, at least the way most large organizations practice them, need to die.

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Invisible Signals: Why Smart Engineers Still Get Overlooked

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A few months ago, a friend asked me to review her résumé.

She had submitted more than 300 job applications and was barely getting any responses.

I knew her work. She’s thoughtful, highly capable, and exactly the kind of engineer I’d jump at the chance to hire. So when she told me she wasn’t getting traction, I assumed the market was just rough.

Then I opened her résumé.

About halfway through, I had a very different reaction.

No wonder nobody is calling.

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AI-Augmented Leadership Is Not About Using AI

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I thought AI was going to make me more productive. Maybe even give me some of my time back. That was the plan 😅 It didn’t quite work out that way.

Instead, it made it a lot harder to ignore where things were actually broken.

If I’m being honest, the signs were already there. Over the past year of building with AI, I kept seeing the same pattern: the tools weren’t the bottleneck. The system was.

I wrote about some of that in A Year of Building, Learning, and Laughing My Way Through AI at Ford—but I don’t think I fully appreciated what it meant yet.

The model I started with

I was thinking about AI the same way most leaders are right now: As a tool. Something teams could use to move faster. Write code quicker. Summarize more. Do more in less time. And to be fair—it can do that.

But that framing assumes something important: That your system already works.

That your workflows are clean.
That your systems of record are reliable.
That your teams are operating with enough discipline for speed to matter.

If that’s true, AI helps.

If it’s not…

AI doesn’t fix it.
It exposes it.

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