TalkBI & Visualization

In the Long Run, Everything is a Fad

2025/11/05

TL;DR: Benn Stancil (Mode co-founder) argues that after decades of "quantify everything," we're entering an era of AI-powered vibes—where LLMs that read support tickets might replace analysts who produce 457 days of disputed spreadsheets.

The Olympic Gymnastics Disaster

The 2024 Paris Olympics floor exercise final became a 457-day legal nightmare:

  1. Jordan Chiles scored 5th, but her coach noticed a scoring error
  2. Judges agreed → she moved to 3rd (bronze medal)
  3. Anna Barbosu (Romania) filed complaint: the appeal was 1 minute 4 seconds late (limit: 1 minute)
  4. Court of Arbitration ruled: Jordan's score reverted, Anna gets bronze
  5. Sabrina Voinea noticed: her out-of-bounds penalty was wrong (video shows she was in bounds)
  6. More lawsuits. Swiss Federal Supreme Court. Still unresolved.

The lesson: We built elaborate quantification systems (the "Code of Points") to replace corrupt judges—and got something even more broken.

The Generational Cycle

Research shows people believe the best music, fashion, economy, and morals were during their teenage years.

For our generation in data: The "best stuff professionally" was Moneyball, Nate Silver, rigorous quantification. We rebelled against pundit vibes.

But there's always a next generation...

The Rise of Vibes

EraApproach
Before usVibes (pundits, scouts, gut feel)
Our eraMath (Moneyball, A/B tests, dashboards)
Next eraAI Vibes (LLMs that "just know")

Evidence it's already happening:

  • Zohran Mamdani won NYC mayoral primary on TikTok vibes, not data-driven campaigns
  • "Taste is eating Silicon Valley" — products built on craft, not optimization
  • Dating apps now match via AI reading profiles, not algorithmic compatibility scores

The Fujitsu Gymnastics AI

Page 2 of the Olympic Code of Points? An ad for Fujitsu's 3D-sensing AI scoring system.

It doesn't:

  • Parse the code of points
  • Calculate D-scores and E-scores
  • Apply penalty deductions

It does:

  • Watch the routine
  • Compare to millions of examples
  • Give a vibe-based score

It's the perfect judge—not because it's more rigorous, but because it's seen every gymnastics routine ever.

The Dirty Secret of Quantification

That objective-looking gymnastics scoring system? Full of subjective decisions:

  • Why 8 elements, not 10?
  • Why these point values?
  • What is "poor rhythm"?
  • Why can't some scores be reviewed?

"The best stuff professionally isn't math. It's numeric vibes."

The Business Reality

Boss: "Is our business in good shape?"

Analyst: "Well, it depends on what you mean by 'good'..."

Boss: 😐

We give answers like "What's an active user? We have conservative and aggressive definitions..." when they want a straight answer.

But what if there's a tool that just reads all the support tickets and says: "Customers are frustrated about X, Y, Z"?

The Faith Problem

"There's a ton of faith in data work. How do we know it works? We hire more data people—they'll tell us."

This faith is fragile. If:

  • Quantification leads to 457-day lawsuits
  • Tools don't really work
  • Everything else runs on vibes (code, dating, politics)
  • And AI vibes actually work...

Then we become the old geysers complaining that things were better in our day.

Related Videos

"The MCP Sessions - Vol 2: Supply Chain Analytics" video thumbnail

2026-01-21

The MCP Sessions - Vol 2: Supply Chain Analytics

Jacob and Alex from MotherDuck query data using the MotherDuck MCP. Watch as they analyze 180,000 rows of shipment data through conversational AI, uncovering late delivery patterns, profitability insights, and operational trends with no SQL required!

Stream

AI, ML and LLMs

MotherDuck Features

SQL

BI & Visualization

Tutorial

" The MCP Sessions Vol. 1: Sports Analytics" video thumbnail

2026-01-13

The MCP Sessions Vol. 1: Sports Analytics

Watch us dive into NFL playoff odds and PGA Tour stats using using MotherDuck's MCP server with Claude. See how to analyze data, build visualizations, and iterate on insights in real-time using natural language queries and DuckDB.

AI, ML and LLMs

SQL

MotherDuck Features

Tutorial

BI & Visualization

Ecosystem

"LLMs Meet Data Warehouses: Reliable AI Agents for Business Analytics" video thumbnail

2025-11-19

LLMs Meet Data Warehouses: Reliable AI Agents for Business Analytics

LLMs excel at natural language understanding but struggle with factual accuracy when aggregating business data. Ryan Boyd explores the architectural patterns needed to make LLMs work effectively alongside analytics databases.

AI, ML and LLMs

MotherDuck Features

SQL

Talk

Python

BI & Visualization