AI Capital Allocation: What Great CTOs Stop Funding First
Strong AI strategy starts with a kill list. If a project cannot defend margin, risk, or speed, it should not survive the next budget meeting.
Law Zava
I engineer reliable infrastructure, reduce platform cost at scale, and lead technical teams to outship their peers.
Binary size, tail latency, memory predictability. When the runtime is the bottleneck, I reach for Rust, Zig, or C++ — not as a default, but when the numbers justify it.
ScyllaDB, Cassandra, and the operational reality of global state. Failure semantics, consistency trade-offs, and making distributed databases behave predictably under real load.
Small teams outperforming large ones. Clear intent, fast feedback loops, and async-first coordination over meetings and headcount.
Data residency, zero-trust architecture, and AI systems that satisfy regulators without crippling the product. Designed in from the start, not bolted on.
Reliability and cost discipline aren't at odds — they're the same engineering problem. Teams that understand their hardware, shrink their runtime dependencies, and make failure modes explicit end up with systems that are both cheaper and more reliable.
The best engineering organizations run on clear intent and fast feedback, not process overhead. I've seen five people with the right operating model outship fifty without one.
Strong AI strategy starts with a kill list. If a project cannot defend margin, risk, or speed, it should not survive the next budget meeting.
A CTO's AI strategy in mid-2026 is brutally simple: It is not about chasing models. It is about building resilient data infrastructure, setting operational boundaries, and measuring throughput.
Privacy is an architecture constraint, not a feature toggle. Teams that build sovereignty into their systems early avoid painful retrofits and close enterprise deals faster.