Introduction

AI no longer works like old software with fixed rules. Instead, it runs on probabilities, where answers are unpredictable but powerful.

What Problem Does It Solve?

The article explains why old methods of building digital products no longer fit AI. Traditional software was predictable and measurable, but AI creates uncertain, probabilistic outputs. To succeed, companies must learn new ways to build, test, and manage products in this unpredictable world.

Quick Summary

In the past, software worked like a machine: you gave it an input, and it always produced the same output. This allowed teams to measure success with funnels, ratios, and dashboards. Reliability was the goal, and everything was designed to hit near-perfect accuracy.

AI has broken this model. Its inputs are infinite, its outputs vary, and correctness isn’t guaranteed. Instead of fixed funnels, we now have infinite fields of possibilities. This unpredictability frustrates users and challenges businesses because the cost of AI is high, while the results are not always consistent.

The article argues that AI product building requires a scientific mindset, not just engineering. Leaders must accept uncertainty, define a “minimum viable intelligence” (how good is good enough), and constantly re-test with new data. Data becomes the shared language across all teams, guiding decisions in product, growth, and finance. Companies that learn to navigate this probabilistic reality will shape the future of tech.

Key Takeaways

• Old digital playbooks (funnels, conversion metrics, dashboards) no longer work for AI products.
• AI outputs are probabilistic, not deterministic - meaning results vary and correctness is not guaranteed.
• Controlling models too much weakens them; instead, teams must balance freedom with acceptable unpredictability.
• Building AI products now requires empirical testing, scientific methods, and constant data-driven learning.
• Data is the new operating system: it connects engineering, design, marketing, and finance in AI companies.
• Organizations that adapt to thinking in probabilities will lead the next era.

What To Do

• Stop treating AI products like traditional software - don’t expect 100% reliability.
• Define your product’s “minimum viable intelligence” (the lowest quality users will accept).
• Embrace constant experimentation - assume you don’t know until you test.
• Use data across the whole company, not in silos, to guide product and business decisions.
• Prepare to rebuild products from the ground up with each major model upgrade.
• Train teams to think scientifically: observe, hypothesize, test, and adapt.

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