TL;DR
Teams are producing more output than ever, but learning less from it. AI accelerated execution and quietly collapsed feedback loops. The result is a “feedback recession”: fewer signals, weaker correction, and slower improvement. This isn’t a tooling problem. It’s a structural one. This edition breaks down what’s actually happening, why it matters, and how to rebuild feedback without slowing teams down.
A quick note
I’m hiring
Seeko (the company I'm building) is looking for a Senior Full-Stack Engineer to join as a founding team member. We're building AI-powered tools that help sales teams actually learn their products - not just access docs, but internalize knowledge through generated podcasts, quizzes, and roleplay.
If you're a strong TypeScript/React/Node engineer who wants to work on AI that makes people better at their jobs (not just faster), this might be for you. Remote, EST hours, real ownership.
Or if you know someone who'd be a fit - I'd appreciate the intro.
The paradox no one’s talking about
AI was supposed to make feedback easier.
Instead, many teams are experiencing the opposite:
More output
More speed
Less clarity
Fewer meaningful corrections
Feedback hasn’t disappeared — it’s just been diluted.
What changed (and why it matters)
Three shifts happened quietly and simultaneously:
1. Output volume exploded
AI turned one person into five. The problem is that review capacity didn’t scale with it.
What used to be 10 artifacts a week is now 50. Review becomes selective. Some things slip through. Standards blur.
2. Feedback got abstract
When work is generated quickly, feedback tends to get vague:
“Looks good”
“Seems fine”
“Ship it”
Specific critique takes time — and time is what teams feel they don’t have.
3. Learning became indirect
People aren’t seeing why something is good or bad. They’re seeing final outputs without the reasoning behind them.
Feedback used to be embedded in the process. Now it’s an afterthought.
The hidden cost: silent skill decay
When feedback drops, skill decays — slowly at first, then suddenly.
People stop forming sharp mental models.
They lose confidence in their own judgment.
They rely more on the system and less on understanding.
This is how teams end up:
Producing more work with less conviction
Arguing about taste instead of outcomes
Feeling busy but strangely stagnant
The danger isn’t bad output.
It’s unnoticed stagnation.
Why this is a leadership problem (not a tooling one)
Tools didn’t remove feedback — leadership did, accidentally.
When speed becomes the primary KPI, feedback becomes friction.
When efficiency is rewarded, reflection feels optional.
But learning systems need friction — the right kind.
Great teams don’t eliminate feedback.
They compress it.
The feedback compression model
High-performing teams replace long reviews with fast, frequent, lightweight calibration.
Here’s what that looks like in practice:
1. Short loops, not long reviews
Instead of one big review, do many small ones.
5–10 minutes
Focused on one decision or output
Immediate context
2. Visible standards
Not “do it better,” but:
“This is what good looks like.”
“Here’s the example we’re optimizing for.”
3. Shared language
Teams need words for quality.
Not vibes — vocabulary.
4. Feedback as data
Track how often outputs are accepted without revision.
Track where edits cluster.
Those signals tell you where learning is stuck.
A simple system you can try this week
Step 1: Pick one workflow
Something common and visible:
customer replies, internal docs, PRs, analyses.
Step 2: Create a 10-minute feedback loop
One person shares an example
One person says what they would change
One person explains why
Capture one insight.
Step 3: Write a one-line rule
“From now on, we always ___.”
That’s it.
Repeat weekly.
What this unlocks
Teams that do this consistently:
Learn faster without slowing down
Develop shared taste
Reduce rework
Onboard new hires faster
Build intuition instead of checklists
They don’t rely on heroics or perfect tools.
They rely on shared understanding.
The bigger shift
AI didn’t remove the need for feedback.
It made feedback the bottleneck.
The teams that win won’t be the ones with the best models —
They’ll be the ones who learn the fastest.
And learning doesn’t come from more output.
It comes from better feedback loops.
👉 If you found this issue useful, share it with a teammate or founder navigating AI adoption.
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Until next time,
Haroon