Burn Your Focus Groups: Predicting the Future Have Fundamentally Changed

Discover how "vibe coding" and AI simulations reveal the hidden human chaos your spreadsheets miss. Unlock a God’s-eye view to predict market shifts before they hit.

Every quarter, smart teams sit around a table and project their growth using historical data.

They look at past acquisition costs, run a linear regression on churn, and nod at a chart going up and to the right.

It’s a comfortable ritual. It feels safe.

It’s also completely delusional.

The most costly assumption in business today is the belief that past data cleanly dictates future outcomes.

We treat user acquisition and market sentiment like physics equations. But growth isn’t a math problem; it’s a sociological one.

Let me tell you a story about how fast the rules of leverage are changing.

Recently, a 20-year-old undergrad named Guo Hangjiang built a product from scratch in exactly 10 days.

He didn’t hire a massive engineering team. He used “vibe coding”, relying heavily on AI coding assistants to do the grunt work while he directed the architecture.

He sent a rough demo to Chen Tianqiao, the billionaire founder of Shanda Group. Within 24 hours, Chen wrote a $4.1 million check to incubate it.

The investor didn’t just back a smart kid. He backed the “super-individual” theory: the reality that in the age of AI, a single highly-leveraged builder with the right mental models can match the output of an entire legacy tech company.

But it’s what this super-individual built that is making traditional forecasting look like a relic from the 1990s.

He built MiroFish. He didn’t build a better mathematical algorithm. He built a “digital sand table”.

It is a swarm intelligence engine that ingests real-world data, spawns thousands of autonomous AI agents, gives them unique backstories, biases, and memories, and unleashes them on simulated Twitter and Reddit platforms to watch them argue.

MiroFish In Plain English

Think of MiroFish as The Sims, but for high-stakes business strategy and market prediction.

Usually, when you want to predict the future, like how customers will react to a new pricing model, or how the market will respond to a PR crisis, you look at past data and build a spreadsheet. You assume people will act rationally and that history will repeat itself in a straight line.

MiroFish throws the spreadsheet in the trash.

Instead of doing math, it builds a massive, fake digital society to test your ideas.

Here is how it works, minus the technical jargon:

1. You build the crowd

You feed the system some basic information, and it spawns thousands of AI agents. But these aren’t generic bots. Each one gets a distinct personality, a backstory, biases, and a memory. You get simulated CEOs, angry Redditors, nervous retail investors, and skeptical journalists.

2. You drop the bomb

You introduce a new variable into this digital world. Maybe it’s a rumor about your company. Maybe a competitor launches a smear campaign. Maybe you change your app’s core feature overnight.

3. You watch them fight

The system unleashes these agents onto simulated versions of social media platforms. They read your news. They post. They argue. They form echo chambers. They panic. You get to sit back with a “God’s-Eye View” and watch the social contagion unfold in real-time.

4. You get the cheat codes

The system analyzes all this simulated chaos and hands you a report detailing what the actual, human, second-order effects are likely to be. You literally get to “interview” the fake users to ask them why they canceled their subscription or why they joined the mob.

It’s a sandbox for human irrationality.

Instead of guessing how the market will react based on what happened three years ago, you simulate the market today, inject your strategy, and watch if it survives.

It lets you make your most expensive mistakes in a fake world so you never have to make them in the real one.

He isn’t looking backward at spreadsheets. He’s simulating human chaos to see what happens next.

Humans form echo chambers. They panic. They are influenced by a single viral tweet or a sudden macroeconomic shock.

Math can’t predict a bank run or a viral breakout, because math doesn’t feel FOMO.

In fact, early research into these massive AI simulations shows that agent swarms are actually more susceptible to herd behavior than humans.

When you rely entirely on historical forecasting, you’re driving by looking in the rearview mirror. It works fine on a straight, empty highway. It gets you killed the second there’s traffic.

Here is what the shift from historical math to behavioral simulation actually looks like in practice:

1. The SaaS Pricing Trap (The Reddit Mob Test)

You want to raise your app subscription price by 20%. Your historical spreadsheet says your MRR will increase. But when you run the simulation, you see your “power user” agents feel betrayed. They form a vocal mob on a simulated Reddit, influencing the casual users to cancel en masse. You catch the 40% churn spike and the reputation hit before you ever change the price tag.

2. The “God’s-Eye” Market Shock

The Fed unexpectedly hikes interest rates, or Apple changes its App Store privacy policies overnight. Historical data is instantly useless. Instead of waiting 30 days for your analytics dashboard to show the bleeding, you use the simulation’s “God’s-Eye View.” You pause the digital world, inject the new reality (the shock), and press play. You watch how retail investors, buyers, and casual users recalibrate their spending habits in real-time, allowing you to pivot your ad spend weeks before your competitors even realize what happened.

3. The 90-Day PR Crisis Fallout

A competitor launches a smear campaign, or your platform goes down during a critical event. Instead of issuing a generic apology and hoping for the best, you inject the crisis into the simulation. You track the rumor spread. Simulations prove that misinformation travels faster and wider than official news. You don’t just react; you map the second-order effects of the outrage, identify the simulated “hub” influencers, and surgically address the core narrative before it reaches escape velocity.

AI is leverage, not magic. Using AI to write better Excel formulas is a waste of that leverage.

Using it to simulate how real people will react to your product before you spend a single dime on distribution? That’s how you build an unfair advantage.

Historical data tells you what happened in a vacuum. Simulating human irrationality tells you what will actually survive the real world.

Your spreadsheet predicts what happens if nothing changes. Your job is to predict what happens when everything does.