Two Steps Forward, One Back

The same week AI helped me do something I'd only dreamed of, it broke on my team mid-class.

That might be the most accurate summary of where we are with this technology right now.

Here's what happened.

I was working remotely from Hawaii last week. Set up a VPN into my home computer, SSH'd into Claude Code, and had everything running exactly like I was sitting at my desk. We had a LinkedIn post go viral (600+ comments), and I was managing responses in real time from across the Pacific. Using AI to help draft replies at scale, keeping up with the conversation as it moved.

Genuinely cool. The kind of thing I would have called impractical a year ago.

Same day, Manisha was back home teaching one of our live courses. Google quietly pushed an update that killed their MCP server. No announcement. No warning. It just stopped working.

She had to find something more stable on the fly, in front of students.

Two steps forward. One step back.

That tension, the breakthrough and the breakdown living in the same 24 hours, that's just the state of AI right now. And I think being honest about it is more useful than pretending otherwise.

The tools genuinely let you do things that felt out of reach six months ago. That's real. But things you figured out and built into your workflow can break without warning, at the worst possible moment. Also real.

Both are true at the same time.

What I keep coming back to, though: the steps forward are quietly bigger than the steps back.

Think about what you don't have to do anymore. The analysis that used to take most of a morning. Reports that needed three rounds of revision. Repetitive tasks that sat at the bottom of your list because nobody wanted to touch them.

A lot of that has compressed or disappeared. The gains accumulate even when you're not tracking them.

The losses feel louder because they tend to arrive during live courses and client calls, exactly when you can least afford them. But they're not erasing the progress.

What this means practically: perfectionism doesn't fit here anymore.

There's no version of AI tooling where everything works all the time. If you're waiting for the stable, fully-figured-out moment to start building real workflows, it's not coming for a long time. The ground will keep shifting. An update you didn't ask for will arrive at the wrong time.

The answer isn't to wait. It's to understand the tools well enough to adapt when they change.

Action over perfection. That's the actual standard now for anyone building in this space.

Inside MeasureU, we're updating our Pro memberships around exactly that idea. The last 18 months have been a serious run of experiments, some worked brilliantly, some broke at inconvenient moments, and we're rolling the useful parts into the community now.

I'll be honest, we've hesitated to do this many times. When things break, there's a version of caution that says don't share systems that might change and break what we built for the community. But I think the better answer is: share it, explain the risk, teach the thinking not just the steps.

That's what we're building. Not a static library, but a living set of workflows that move with the technology.

If you want to run your own experiments, now is a genuinely good time. If you'd rather have someone doing the experimentation for you and surfacing what's actually working, that's what MeasureU is becoming.

Either way, keep moving.

To Your Measured Success!
--Jeff Sauer
CEO of MeasureU

P.S. Here's the LinkedIn post that sparked 600+ comments if you want to see what we were talking about.

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