The RootCause.ai Blog
Causal AI Perspectives
Everything we have published.
Netflix Spent a Decade Building Causal Infrastructure. Bestie, You Don't Have a Decade.
Netflix proved that observational causal inference works in production - then spent a decade building bespoke pipelines, PhD teams, and custom tooling to make it happen. Here's what that actually cost, and why most enterprises are still waiting for an answer that already exists.
Ouija Boards & Causal Inference
A logistics company spent two years intervening on speeding, weather, and driver profiles - and nothing moved. Causal discovery found the real answer in three hours. Here's what correlation-based analytics gets wrong, and what changes when you find the cause before you act.
Your Model Thinks Helping People Makes Things Worse
Predictive models can't tell the difference between interventions that reduce claim costs and the severe claims that trigger them. Here's why insurance carriers are making expensive resource decisions on fundamentally broken logic - and what causal AI does differently.
Causal Inference Always Worked. We Just Couldn't Scale the Damn Thing. Until Now.
Combinatorial explosion, hidden confounders, and messy data have killed most enterprise causal AI projects before they started. Here's why those are engineering problems - and how they're finally being solved.
Why Deep-Tech Start-ups Fail
Most deep-tech startups don't fail because the technology doesn't work. They fail because markets, investors, and organisations aren't structured to absorb genuinely new ideas and founders burn out long before collective understanding catches up.