Arun's claim is that the enterprise AI fight was never at the foundation layer. That layer has already closed — really only three companies, maybe a few more, each needing five to ten billion dollars to enter and none of them meaningfully differentiated. The layer above it, domain-specific models built for manufacturing, energy, oil and gas, aerospace and large B2B financial services, is wide open, and Articul8 has been building for it since before the world had the vocabulary: its first production-scale deployment went live in November 2022, a month before ChatGPT. Domain-specific, he insists, does not mean small — a model should be as large as the data supports and as large as the task demands, and no larger. What enterprises actually buy is accuracy: consumer tools land 85–90% of the time, which is a failing grade on a plant floor where a technician's wrong call stops the line and where existing alerting already throws 95%-plus false positives. Articul8's smallest deployment is 10,000 to 20,000 documents; its first problem was 2.8 million. Around that sit the harder claims — that 70–80% of the value arrives without touching a data pipeline or an application stack, that you can delegate work but never thought, and that India's 'we don't have GPUs, so we'll build small things' modesty is a self-fulfilling prophecy in a country that aimed at Mars rather than the treetop. Fewer than 5% of enterprises have actually arrived. That, he says, is the reason for hope.
Worth your time if you are
CTOs sitting on fifty plant applications and a data swamp
Founders told the foundation-model companies will eat them
Plant engineers whose alert systems cry wolf all shift
Indian deep-tech founders arguing about GPU access
Students whose resume matches the job description exactly
