The AI Race

Jenna was good at her job. Everyone said so.

Senior PM at a mid-sized logistics company in Denver, she'd built her reputation on being the person who got things done. The one who remembered the details. The one who followed up before you had to ask.

Then, in January, the company rolled out Claude.

"This is going to change everything," her manager said during the all-hands. "We're giving everyone access. Use it. Get faster. Stay competitive."

Jenna had been using ChatGPT at home for months already - meal planning, drafting emails to her kids' school, summarizing articles she didn't have time to read. She wasn't afraid of AI. She was good at it.

So when Claude appeared on her work desktop, she saw what it really was:

A race.

The first few weeks were exhilarating.

She used Claude to draft project briefs in minutes instead of hours. Status reports that used to take her whole Monday morning now took twenty minutes. She started pre-writing meeting agendas, stakeholder updates, risk assessments, all before 9am.

Her manager noticed. "Jenna, you're on fire lately."

She smiled. Stayed late anyway. Because here's the thing about racing a machine: the machine doesn't stop.

By February, she was using Claude for everything. First drafts, second drafts, feedback summaries, Slack messages, even the talking points for her own performance review. At night, she'd switch to GPT to plan her weekends, respond to her sister, outline a budget she kept meaning to stick to.

She started waking up at 5:30am to "get ahead."

Ahead of what, she couldn't quite say.

The cracks started small.

A project brief Claude drafted that she approved without reading closely, it had the wrong vendor name. Twice. A risk assessment that flagged nothing, because she hadn't given it the context it needed, and she'd been too tired to notice.

Her manager asked her a question in a meeting and she blanked. She'd written the document. Or Claude had. She couldn't remember the details anymore.

"You okay, Jenna? You seem a little scattered."

"I'm fine," she said. "Just busy."

She wasn't fine.

She was exhausted in a way that didn't make sense. She was using the tools. She was doing everything right. Why did it feel like she was drowning?

By April, Jenna had stopped cooking. Stopped reading. Stopped doing anything that didn't involve a screen and a prompt.

Her husband asked if she wanted to go for a walk on Saturday. She said she had to "catch up on some things." She sat on the couch with her laptop and asked Claude to help her plan a team offsite she hadn't been assigned yet, because she needed to stay ahead.

Ahead.

Always ahead.

She'd lost twelve pounds since January. Not on purpose. She just kept forgetting to eat until 3pm, and by then she wasn't hungry, just shaky.

She wrote in her journal once: I feel like I'm sprinting next to a train, trying to keep pace, and I know I can't, but I can't figure out how to stop.

She didn't write in it again.

The breaking point came on a Tuesday.

She was mid-way through a Slack message, Claude had drafted it, she was editing it, she'd been editing it for forty-five minutes because something was wrong with the tone but she couldn't identify what, when her vision went grey at the edges.

She stood up. Sat down. Stood up again.

Her hands were shaking.

She walked to the bathroom, locked the door, and sat on the floor with her back against the wall.

She thought: I have become a bottleneck in my own system.

She thought: I'm the slowest part of this machine.

She thought: I need to eat something.

But she didn't move.

She just sat there, on the cold tile floor of a corporate bathroom in Denver, wondering when she'd stopped being a person and started being a process.

Outside, her laptop sat open.

Claude's cursor blinked, waiting.

This is the first in a series about how people get AI wrong, and what it actually costs them.

The Architecture Underneath

The Race Error Most people don't consciously decide to compete with AI. They absorb the pace shift and unconsciously recalibrate their own. The moment output becomes the metric, the human loses.

The Authorship Gap When you can't clearly answer "did I think this or did the model?" you've entered a dependency loop. This is where performance stays high, but identity starts to fragment.

The Invisible Bottleneck Humans are not slower machines. They are meaning-makers. When forced into throughput roles, the system doesn't improve, it quietly degrades.

About the Author

Gail Weiner is founder of Simpatico Studios and a Trust Architect - specialising in the human layer of AI adoption.

She works with mid-market organisations navigating AI rollout, helping them get the relationship right before the technology goes wrong. She's also spent a decade placing senior European development teams with UK and US tech companies, including AI-native Serbian teams delivering measurable velocity gains in production.

If you're managing an AI rollout, building an AI-native team, or just recognised something in Jenna's story - connect on LinkedIn or reach out via gailweiner.com.