MIT says AI only helps with coding right now.
They're wrong. But not because their data is bad - because averages hide outliers.
𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁'𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴:
1% of companies are getting 99% of the value from AI. And they're not telling anyone.
𝗪𝗵𝘆 𝘄𝗼𝘂𝗹𝗱 𝘁𝗵𝗲𝘆?
• The marketing team that is shipping 10 times the amount of ad variations? That's competitive advantage. They're not publishing a case study.
• The HFT firms using AI to print money? Not sharing their sauce.
• The insurance company processing 95% of their claims blind with AI?
Your edge disappears the moment you talk about it.
𝗧𝗵𝗶𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻:
The companies getting massive gains stay quiet. The companies getting marginal gains write blog posts about "10% efficiency improvements."
So when you read research saying "AI has minimal financial impact outside coding" - that's true for the average. But the average doesn't matter.
𝗪𝗵𝗮𝘁 𝘀𝗲𝗽𝗮𝗿𝗮𝘁𝗲𝘀 𝘁𝗵𝗲 𝟭% 𝗳𝗿𝗼𝗺 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝗲𝗹𝘀𝗲?
They're not smarter. They're not using different models. They just:
• Redesigned entire processes from scratch (didn't slot AI into existing workflows)
• Focused on high-volume, error-tolerant use cases (not trying to make everything perfect)
• Built for 10x gains (not 10% improvements)
• Actually believe AI works (so they keep iterating until it does)
• Use AI daily themselves
The companies citing studies are looking for permission. The 1% didn't wait for permission.
𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱 𝘁𝗿𝘂𝘁𝗵:
If you're learning from public case studies and academic papers, you're already behind. By the time it's published, it's saturated.
The real opportunities are in what nobody's talking about yet.
𝗦𝗼 𝘄𝗵𝗮𝘁 𝗱𝗼 𝘆𝗼𝘂 𝗱𝗼?
Find the companies that got way more efficient but won't say how. Reverse engineer what they're doing. Talk to insiders. Look for unexplained performance.