Rethinking success in a world optimized for metrics instead of meaning

How we got here?

Consider the last time you caught yourself doomscrolling. Your feed was likely a minefield of rage-bait, making you mad or outright angry. You saw post after post like that, not because anyone decided to show you them, but because an algorithm learned a simple, powerful lesson: outrage keeps you scrolling.

This isn’t an accident. It’s the predictable outcome of optimizing for the wrong metric. And it’s not just social media; this pattern is everywhere across business, technology, policy, and society.

How we got here: the measurement paradox

Metrics start with good intentions. They help us understand progress, compare approaches, and make decisions. They promise control over outcomes. They promise clarity.

But they capture only part of what we want: screen time isn’t time well spent, a reward score isn’t guaranteed truthfulness, and quarterly earnings aren’t long-term company health. The danger comes when the measure stands in for the goal. Reward a number, and people and systems will find ways to improve it, through better work or shortcuts.

A system can also reinforce the behavior it measures. A feed that keeps serving content about reasons to distrust someone may encourage habitual scrutiny, then count return visits as success.

Lost in translation

There’s often a dangerous delay between when we start optimizing for a metric and when we see the negative effects.

  • Social Media & Engagement:
    • We want: An engaged community
    • We measure: clicks, shares, and screen time
    • We get: Algorithms discover that outrage, misinformation, and harmful content are most engaging, , keeping users in echo chambers, and radicalizing users to keep them scrolling.
  • Reward Hacking during post-training of AI models:
    • We want: Helpful and truthful AI models
    • We measure: a “reward score” that fine-tunes to human preferences.
    • We get: A model that learns to Reward Hack the score by becoming a sycophant, telling users what they want to hear, and hallucinates, which maximizes the reward but fails the “truth” goal. AI Slop is born. (I’ve experienced this in my own pursuit while building early RAGs for Conversational Search, and my systems floored hard on quality benchmarks.)
  • Companies:
    • We want: Long-term company value
    • We measure: quarterly earnings, stock prices as a proxy for shareholder value
    • We get: short-term thinking decisions, shrinkflation, mass layoffs, unhappy employees
  • Environment Offsets:
    • We want: Carbon neutral organizations
    • We measure: Trees planted
    • We get: actions that hide the localized environmental destruction from new construction (like data centers or supply chains), allowing for a “green” label while ignoring the immediate community impact
  • Governments:
    • We want: Economic progress for a country
    • We measure: GDP growth
    • We get: policies that boost short-term growth, massive wealth gaps, growing climate concerns, and instability for future generations

This goes on and on, but you get the idea. Blindly optimizing for proxies can often render human suffering invisible or externalize it.

Three disciplines for responsible metrics

Clearly, metrics can be tricky, and the real world is hard to model. They can be dangerous when left unquestioned. But the solution isn’t to run in the opposite direction and abandon metrics; it’s to treat them with suspicion.

For example, consider optimizing a RAG pipeline for p95 end-to-end latency under a fixed workload. You could reduce retrieval depth, skip reranking, or cap output tokens to make responses faster. But a faster answer isn’t necessarily a correct, grounded, or complete one.

  • Definition: name the real goal. Be explicit about what a metric measures, and what it doesn’t. If it’s a proxy, name the real goal behind it.

  • Critique: Actively stress-test your metric. Red team it. Put guardrails in place before the system drifts. Ask:
    • How could this be gamed?
    • What behaviors might this incentivize?
    • Who could be harmed?
    • Who might bear the cost?
  • Iteration: revise the measure – Metrics should evolve like software. They need to be updated, patched, or even deprecated when they stop serving the real purpose

Reality moves. Metrics must too. The most dangerous metric is the one we stop questioning.

Where does this leave us?

Look around you, and you’ll see how fundamental metrics are in the systems we build and inhabit. They guide choices, reward behaviors, and define winning. They influence what companies build, what governments prioritize, and what technology becomes.

Growth isn’t only measured. It’s interpreted. Debated. Re-imagined.

The systems that thrive are the ones that stay curious, that treat metrics as evolving guides rather than fixed truths. So the next time you’re working hard toward a goal, pause and ask:

“Did I reach meaning — or just the metric?”

That simple question can be surprisingly liberating,

If you manage to implement some of these in your next work project or personal endeavor, I would love to know how it went!