I Built My AI Brain 3 Hours

The New Judgment Stack

Build Out Loud, or Get Encoded Out

There’s a version of this moment where women miss it.

Not because we’re not capable of being interested or interested in being capable. Because we hesitate half a beat longer while the ground is already moving.

And this moment does not reward hesitation.

What if hesitation isn’t weakness, but healthy discernment? Because when you actually look at the data, the story is more complicated than we’re making it.

On April 15, 2024, so likes ages and ages ago, before it was trendy, I wrote an article for Human Resource Executive: The AI gender gap: What HR can do. No one was naming the gender gap yet, let alone explaining it. If anyone did, it was being chalked up to the usual suspects:

• Confidence, mindset — women self-select out, hesitate, don’t raise their hands fast enough

• Pipeline and access — fewer women in STEM, therefore fewer women in AI (typical structural excuse, a little valid but lets culture off the hook)

• Interest — women “just aren’t as drawn to” technical fields (the one that lets everyone feel comfortable blaming nature; some of you come up to me at keynotes and explain your daughters this way)

• Time, caregiving — not enough bandwidth to experiment with new tools (valid: even McKinsey completely overlooked caregiving in their most recent Women in the Workplace report, so we’re still not getting it)

But what if, just what if…we’re not interested in harm.

In labor market analysis on AI exposure, the demographic most at risk of disruption isn’t entry-level workers. It’s older women with higher levels of expertise. The people who built careers on judgment, on synthesis, on knowing what matters and what doesn’t.

The people with the most to lose.

So in a way, “move fast and break things” was never built for us.

That culture prioritizes speed, novelty, and disruption without accounting for who absorbs the cost, and the cost is not (never is) distributed equally. It rarely falls on the people building the tools. It falls on the people whose work is being compressed, abstracted, or quietly replaced.

So this isn’t about whether women are adopting AI fast enough. The real question is: How do we engage with AI without surrendering the thinking that makes our expertise valuable?

Because speed without judgment is just a faster path to being wrong.

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Part 1: Why I Build Out Loud

I didn’t start building in public because it was a strategy.

I started because I could feel the pace of this thing accelerating faster than our collective confidence.

Especially women.

We’re still negotiating whether we’re “technical enough,” “ready enough,” “credible enough” to speak on AI, while men who skimmed three threads and a podcast are already positioning themselves as experts.

That’s not an intelligence gap. It’s just permission, and permission is a social construct women been over-taught to wait for.

Even more critical to understand, waiting serves no one. I started building out loud precisely because the process is the proof now.

And because, again, I don’t buy the narrative that slower adoption equals inferiority. I think it’s good and healthy to be slow to approach a system that:

• asks for our most sensitive data

• can be used to monitor, score, or replace us

• and is trained on histories that have not exactly worked in our favor

Adopting something slowly because you’re measuring consequences isn’t hesitation. It’s the real intelligence. So let’s apply it.

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Part 2: Tools Aren’t the Strategy

People followed the tools. That’s what honestly surprised me. I wrote about moving from ChatGPT to Claude and watched the reaction like I had announced a political position. Or like I’d given permission for waiters to try, or admit they were. (There’s a lot of honor in trying something new. I see you.)

I’m still learning a lot in the switch, but the biggest learning is that the model isn’t the strategy. Your thinking is.

What actually changed for me wasn’t the interface; it was the depth.

Tokens became design decisions.
Projects became workflows.
Context became the entire game.*

A quick note on terms: When you hear "context window," it refers to everything an AI model can perceive and work with at once — your inputs, its outputs, any documents you've shared, all of it. Think of it as working memory with a size limit. "Context" (as in context docs, or "giving Claude context") refers to the information you deliberately bring to that window so the model can do better work. The window is the container; your context is what you put in it. Managing what goes in — and how — is one of the underrated skills of working with AI well. DM me if you want to understand this better; there are no stupid questions.

The learning curve wasn’t technical, not all of it. It was cognitive. I thought I’d injected enough memory and context to get out of the gates fast, but it still wasn’t enough. I was constantly topping up Claude, overloading workflows, losing context, and prompting. Way too much prompting.

Here’s what people miss:

These systems don’t just respond to what you ask. They respond to the entire posture you bring into the interaction.

Overly impressed? You’ll get confident output.
Overly trusting? You’ll get less deliberation.
Running out of tokens? Desperate answers more likely to be wrong.

So if your guard goes up when something feels too polished, too agreeable, too certain… Good.

That instinct might be protecting your thinking.

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Part 3: The Pause — Building an AI Brain

Two weeks in, I stopped.

I wasn’t stuck; I was having an Oh Shit Moment.

I realized I was scaling output (after output) instead of truly scaling myself.

So I built what I’m calling my “AI brain.”

I wish I could say it’s magical. It’s boring as hell, so fundamental and foundational you might miss it. It’s just context, but way more than you think you need.

I studied how people like Allie Miller were actually doing this—not just what they were saying. What clicked was simple, but not easy:

• Stop prompting. Start building systems that run.

• Start with irritation. Complain. Let the system interview you.

• Turn repeatable thinking into reusable assets.

• Anchor everything in structured context.

This isn’t about better prompts.

It’s about externalizing your thinking so it can be used, reused, and challenged.

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The Analyst Agent: What This Actually Looks Like

I partly work as an industry analyst and advisor to technology providers—product strategy, roadmap, messaging, market fit.

This is work that lives in context, so perfect use case.

Years of briefings.
Private conversations.
Pattern recognition across vendors and markets.

You don’t prompt your way into that. (Pretty vanilla and shallow if you try; I wouldn’t pay me for that.)

My first AI brain use case was to build a project for each vendor I work with.

Each one is pre-loaded with my real-life analyst brain:

• the frameworks I use to evaluate product–market fit

• how I diagnose messaging

• the patterns I’ve seen in who scales and who stalls

• the red flags you only recognize after watching things break

Then I fed it everything I know about that vendor—public and private, current and historical.

Before a briefing or analyst day, it generates my pre-read and sharpens my questions.

After, I feed it what I learned.

It reconciles that against what it already knows, and I ask it to push back.

That part matters. The result isn’t a chatbot that helps me write faster. It’s a contextual intelligence layer that thinks like me because it’s been taught to. (Richer than you’d imagine; I would pay me for that.)

What this unlocks:

• Deliverables that used to take days now take hours—because I’m directing, not drafting

• A second analyst that argues against my own POV

• A system that gets smarter with every engagement

The real leverage isn’t speed.

It’s replicability without dilution.

My expertise becomes portable.

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The Judgment Stack

Most people think they need better prompts. They don’t. They need a system that captures how they think.

What they’re actually missing is this: A Judgment Stack.

A structured system that reflects:

• how you decide

• how you evaluate

• what you trust

• what you reject

In an AI world, execution is suddenly abundant.

Judgment is not.

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The Judgment Stack has four layers:

Identity — who you are
(values, story, perspective)

Judgment — how you decide
(frameworks, patterns, red flags)

Execution — how you operate
(workflows, voice, outputs)

Live Context — what matters right now
(priorities, clients, evolving signals)

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Most people stop at Identity.

That’s why their AI sounds like everyone else’s.

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The Part People Miss

If you don’t build your Judgment Stack, you don’t lose speed (commodity). You lose signal (differentiation).

Because the model fills every gap you leave, filling it with:

• the internet

• the average

• the loudest voices

Ew, gross. Which means:

If you don’t define your thinking,
you will unknowingly outsource it.

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Part 4: This Doesn’t Level the Playing Field

We keep saying AI will level the playing field.

I don’t think that’s true.

I think it strengthens existing advantages and compounds existing disadvantages, unless you intervene.

AI is trained on the internet, and the internet is not neutral. It’s a compressed archive of who had voice, access, and authority. So when we scale intelligence on top of that, we don’t get equality. We get amplification,

at scale.

Which is why I get wary when Reese Witherspoon pops into the conversation telling women to “get into AI.”

She’s not wrong, she’s just late, annnnd she’s selling AI courses. So it feels a bit infantilistic when she could just say she has something to sell us.

And Reese, this moment doesn’t need more gender marketing.

It needs depth.

Women don’t need an invitation, we need infrastructure. And we need to build it in a way that reflects how we think (brilliant, bold), not just what already exists (boring, biased).

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Also, Let’s Not Pretend This Is Neutral

Leaders like Alex Karp have been explicit about how AI disruption may disproportionately impact certain groups, including women.

And Palantir Technologies builds systems used in government, defense, and intelligence.

He says a lot of other things, too, including that “the appearance of software working is not software working,” which weirdly lends itself to this piece.

So when we talk about trust, we should be clear-eyed.

Suspicion here isn’t backward, it’s informed.

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Part 5: The Open Question

If your brain becomes an operating system, what happens next?

If I can externalize how I think—my judgment, my patterns, my decision-making—what exactly is the unit of work?

Is it me?

Or is it the system that represents me?

Could someone deploy 50 versions of my thinking?

Run my judgment at scale?

Without me in the room?

And if they can—

What happens to ownership?
To labor?
To value?

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We are asking small questions in a big moment.

Not “will AI replace jobs.”

That’s lazy, and so is “people who use AI will take your job.”

The real question is:

What happens when intelligence becomes infrastructure—
trained on a past that wasn’t built for all of us—
and deployed into a future that will affect all of us anyway?

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We’re not late to AI.
We’re early to questioning it.

Day 33 of building Now to Next in public using [almost nothing but] AI.

This is where we are.

Yes, we’ll hire humans. They’re more important than the AI infrastructure we’re building to support them (note the order in which I said that), but they’ll be wasted if we don’t get the foundation right. Practicing what we preach is hard; holding ourselves accountable is everything. We wrote a job description (egads, I just said that) for our 5th hire and caught ourselves making a lot of mistakes, including writing a job description. I said no old answers, so I’m ripping it up. I’ll let you know what it becomes.

Godspeed,

Jess Von Bank

Co-Founder, Now to Next

I write at the intersection of work, technology, and humanity. You’ll find essays here that challenge orthodoxy and make space for possibility. All are written with the future—and the people who will live in it—in mind.