Why FAANG Are Betting on Causal AI
When the world's most sophisticated technology companies all invest in the same capability, it's not a coincidence. It's a signal.
2025#
Docker Executables: No More Install Guides
Have you ever spent hours trying to install a tool, only to hit version conflicts or mysterious errors? Do you need to run the same tool on different computers (e.g., Mac vs Linux laptops vs AWS dev servers vs CI servers), having problems maintaining the tools aligned, or even just building?
What's the ETA?
"What's the ETA for feature XYZ?"
"When will feature XYZ be done?"
Last time I counted, I've been asked and I've asked those questions about 14,400 times in 20 years I've spent in the coding trenches. Here is how I handle this question when talking to executives.
Causal AI is the Next Step of Predictive Analytics
Over the past decade, data analytics and big data have transformed the way organizations operate. Companies have invested heavily in collecting, organizing, and analyzing massive volumes of data. Dashboards, predictive models, and automated reports are now commonplace, helping decision-makers see trends, forecast outcomes, and optimize processes.
Why Causal AI is the Future of Automated Decision-Making
Over the past decade, organizations have poured immense effort into building systems that harness the power of big data and traditional analytics. These systems, spanning from dashboards to predictive models, have helped analyze historical data, detect patterns, and forecast future events. However, there's a fundamental problem with this approach: it stops at correlation. It can tell you what is likely to happen, but not why.