In the late 1800s, Delhi had a problem most cities only deal with in movies.
Cobras.
Cobras slipped through courtyard cracks, coiled beneath carts, and appeared where people least expected them - kitchens, bathhouses, government offices.
In a world before antivenom, a cobra strike wasn’t a medical emergency. It was a death sentence.
So the British colonial administration did what many well-meaning organisations still do today: they created a metric and paid people to hit it.
Cash.
For every dead cobra delivered to the authorities. Rational. Simple. Measurable. And for a while, it worked. Dead cobras piled up. Officials congratulated themselves. Undoubtedly someone wrote a glowing report.
Then came the twist.
The numbers kept rising, and rising, and rising.
Delhi wasn’t running out of cobras. It was getting more of them.
Investigators soon discovered the truth: people had started breeding cobras - entire cottage industries of carefully raised, soon-to-be-“hunted” snakes.
And even worse, the moment the government discovered the scheme and cancelled the bounty, breeders simply released their now-worthless cobras into the city.
The snake population exploded.
Goodhart’s Law: The Metric that Eats Itself
Charles Goodhart wasn’t thinking about snakes at all.
As an economist at the Bank of England in the 1970s, he watched a different kind of creature misbehave: financial markets.
Policymakers had identified tidy correlations between money supply, inflation, lending, and growth and turned those correlations into hard targets. But the moment a metric became a target, banks and traders changed their behaviour to hit the number rather than reflect underlying reality.
The dynamics broke, the models collapsed, the policies failed.
Goodhart articulated the pattern:
When people optimise for the metric, the metric stops measuring what mattered in the first place.
Fifty years on, the cobra problem is now inside almost every KPI.
Think for 2 minutes and you’ll see it all around you. Tell people to reduce call times? Customers get rushed off the phone. Job done. Tell hospitals to see more patients? Consultation times shrink. More people get less care. Tell retail planners to reduce inventory? Shelves empty, sales crash.
The moment a metric becomes a target, people start gaming it, distorting it, or bending it.
Not maliciously. Just rationally.
This is not a history lesson. It’s a mirror.
The uncomfortable truth
Nearly every company today is still running on cobra-bounty incentives.
However sophisticated the dashboards, however pretty the reports, most metrics remain single-variable signals in a multi-variable world.
For 47 years, economists have said you can’t fix Goodhart’s Law - you can only mitigate it. And that’s what’s been happening. Balanced scorecards. OKRs. Governance committees. Multi-metric dashboards. Incentive redesign. Rolling forecasts. Three-way targets. Weighted KPIs.
Every few years, a new framework has promised to “align behaviour,” yet all they’ve done is slow the distortion. The incentives get more complicated, but the underlying trap remains the same: a static target in a dynamic system will always be gamed. Humans optimise for the number, not the truth it was meant to represent.
I’ve spent several months digging through retail, supply chain, logistics, healthcare, banking, call centres, education, manufacturing, policy ...
The pattern is universal.
Some systems blunt the edges. Some shift the distortion elsewhere.
But not once did I find, not a single deployed system in business today that neutralises Goodhart’s Law. Because to neutralise it, you need something no KPI tool has ever had.
RabbitHawk’s forecasting and optimisation model.
A constantly learning model that understands causality, updates as the world shifts, and redirects behaviour back toward the underlying goal in real time.
RabbitHawk - the first true antidote
RabbitHawk is the world’s first practical system that reconnects every metric to the economic and operational reality that generates it - eliminating the gap where gaming, manipulation, and unintended consequences creep in.
Not a workaround. Not a mitigation.
A true neutralisation of Goodhart’s Law and the end of cobra-bounty economics inside complex organisations.
And this isn’t a lucky accident.
We’re incredibly proud of what we’ve built and it deserves to be called out for what it is.
Months of first-principles thinking, relentless iteration, and 18-hour days by our founding team of world-leaders in this field have produced several genuine world-firsts including our hierarchical forecasting model, multimodal data fusion, optimisation partnership with forecasting and our self-learning agentic ecosystem.
Each breakthrough is powerful alone.
Together, they form something unprecedented:
A system that sees reality as it is, tracks how it’s shifting in real time, and keeps every team aligned to truth — not to a target someone learns to game.
Our forecasting and optimisation engine already gives data science teams and executives a competitive advantage that our rivals cannot match. But the ability to set goals that stay honest, stay aligned, and actually get achieved? That’s the multiplier CEOs, Boards, and Exec teams have been chasing for decades.
For the first time, you’re not hoping your organisation moves in the right direction.
You can guarantee it.
No drift. No distortion. No cobra effects. No “gaming the KPI.”
Just a data-driven AI ecosystem that ensures the best next move is yours — and an advantage your competitors won’t see coming.
