Why perfect strategy is perfectly stupid
How optimization addiction makes you predictable and weak
When I started teaching my kids poker recently (perfect way to teach kids the game theory that actually matters in life IMHO), I was reminded of one of the most counter-intuitive lessons in decision-making: sometimes, you need to deliberately make "wrong" choices. In poker, if you always play your hands the same way – strong hands aggressively, weak hands passively – observant opponents will quickly decode your patterns and extract maximum value from you. The only defense is deliberate unpredictability: occasionally playing terrible hands like they're gold, just to keep opponents guessing.
First thing that will come to your mind in relation to this will be MVPs and prototyping. But that’s exactly wrong. Prototypes and explorations are tools that help you find the optimal choice, help you to decide for the best option. Which is just the opposite of what I’m talking about - about intentionally selecting options you know aren't optimal. And while that might sound crazy, there's profound wisdom in this approach that extends far beyond the poker table.
If you only have 5 minutes: here are the key points
Optimization logic dominates most decision-making, but can make us rigid and predictable.
Strategic unpredictability — deliberately making suboptimal choices — can be a superior approach in adversarial, fast-changing environments.
Netflix and AlphaGo are powerful examples of injecting randomness to achieve resilience and breakthroughs.
Game theory supports mixed strategies over deterministic ones in competitive settings.
Controlled chaos builds adaptability and prevents brittleness — a lesson traditional optimization often ignores.
The disease we all share
Here's the uncomfortable truth: We're all optimization zombies. Every decision-maker I know, myself included, is infected by the same disease. We've been trained by Kepner-Tregoe decision analysis, McKinsey matrices, and MBA programs to systematically evaluate options, assign weighted scores, and always – ALWAYS – choose the path with the highest expected value (yes I do have way too much business training, too).
This methodology has become so ingrained that we don't even realize we're doing it. We build our spreadsheets, conduct our analyses, and pick the "winner" with religious devotion. Even when we think we're being innovative or disruptive, we're just optimizing within our comfortable parameters.
The Kepner-Tregoe method – the gold standard taught in every business school – demands we:
List all alternatives
Score them against weighted criteria
Calculate risk adjustments
Choose the highest-scoring option
Never, ever pick the "wrong" answer
Sounds logical, right? It's also why Blockbuster optimized their late fees while Netflix mailed DVDs. Why Kodak perfected film while digital destroyed them. Why every taxi company optimized dispatch systems while Uber built an app.
The hidden power of strategic chaos
The antidote to our optimization addiction has a name: Strategic Chaos. It's what happens when smart organizations deliberately inject randomness into their decision-making.
Take Netflix's Chaos Engineering. While every other tech company was optimizing for uptime, Netflix did something insane – they programmed their systems to randomly break things in production. The optimization zombies called them crazy. "Why would you deliberately cause failures?"
The result? Netflix built the most resilient streaming platform on Earth. By randomly breaking working systems, they discovered weaknesses no optimization model could predict.
"Netflix didn't just break things randomly - they built an entire philosophy around it. Their 'Simian Army' of chaos tools has names like Chaos Monkey, Chaos Gorilla, and Latency Monkey. While their competitors optimized for 99.9% uptime, Netflix deliberately drove their uptime down to 99.8% - and used the extra failures to build systems that could handle anything. When AWS had massive outages in 2017 that crippled half the internet, Netflix barely hiccupped. Their deliberate 'wrong' choices had made them antifragile.
Or consider DeepMind's AlphaGo. The program that beat the world champion was designed to make random "bad" moves 10% of the time during training. Human Go experts watching these moves called them mistakes. The optimization crowd said it was inefficient.
Then AlphaGo destroyed the best human player using strategies no one had seen in 2,500 years of Go history.
The strategic value of being unpredictable
Here’s a lesson from game theory that most business leaders feel instinctively — but rarely name outright: in some competitive environments, the winning strategy is randomness.
Take product launches. Suppose your company releases updates like clockwork — say, every Q1. It feels efficient, even disciplined. But once competitors notice the pattern, they start working around you: launching distractions, releasing counter-features, or simply stealing your spotlight. You’re not setting the pace anymore — you’re giving them a head start.
This is exactly the kind of setup game theory was built to explain. In adversarial, fast-moving scenarios where players move without reacting to each other — think launch timing, pricing battles, strategic bids — there’s often no pure winning move. Instead, the equilibrium — the place where no one can outplay you — is to mix your strategy.
It’s the same logic behind the game rock-paper-scissors. If you always lead with rock, you’re toast. The only winning strategy? Randomize your choices. Keep your opponent guessing.
That’s what sophisticated businesses do, too. They don’t just act — they randomize when the stakes demand it. Sometimes they delay a release. Sometimes they drop it early. Sometimes they skip the fanfare altogether. It’s not chaos — it’s controlled unpredictability. It’s playing the game.
Because once your playbook becomes obvious, you’re not playing strategy — you’re getting played.
The optimization trap
When we always choose the "optimal" path, we:
Become predictable - Competitor/the world/circumstance can anticipate and counter our moves
Miss breakthrough opportunities - The best ideas often look terrible in spreadsheets
Atrophy our adaptation muscles - We never learn to maximize suboptimal situations
Create brittle systems - Optimization assumes stable conditions that rarely exist
Amazon's Bezos understood this. His "Day 1" philosophy explicitly rejects optimization in favor of deliberate inefficiency. He makes "two-way door" decisions quickly, knowing many will be wrong, because the cost of being slow is higher than the cost of being wrong.
The next time your team presents you with three options and recommends the 'obvious' choice, try this: pick the second-best option and execute it flawlessly. Watch what happens. Your competitors are counting on your predictability. Stop giving them the advantage.