Bola Olatunji@lightiam567

About me

I picked this idea because I’ve spent over 25 years building and operating complex systems where performance and efficiency mattered at scale—distributed platforms, control systems, networks, and cloud infrastructure. In those environments, the biggest failures rarely came from lack of compute; they came from coordination problems, bottlenecks, and wasted resources. As AI systems grew, I recognized the same pattern emerging—only magnified. Modern AI training clusters consume enormous amounts of power, yet a large portion of GPUs sit idle waiting for data to move across the system. This isn’t an algorithm problem; it’s a systems and optimization problem, which is exactly where my background is strongest. My domain expertise spans system architecture, distributed computing, control systems, and optimization—skills that directly apply to modeling AI workloads as flows across networks and improving efficiency through mathematical optimization. I’ve also worked hands-on with modern AI infrastructure stacks, which made it clear how limited existing tools are when it comes to optimizing communication and energy use. I know people need this because the pain is already visible. Datacenter operators are hitting power and cooling limits, AI teams are seeing training costs rise instead of fall, and infrastructure expansion is being blocked by energy and carbon constraints. These issues come up repeatedly in conversations with engineers, operators, and AI practitioners. LightRail AI is addressing a problem that is already limiting real deployments today, not a speculative future issue.

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