Technical Diligence for AI and Infrastructure Companies

Every diligence review has a small number of claims that carry most of the risk. For an AI product, that may be model quality, proprietary data access, workflow adoption, evaluation discipline, or deployment safety. For an infrastructure company, it may be unit economics, hardware availability, topology, utilization, support burden, reliability, or whether the control plane can operate the promised product at scale.

The first pass should identify which claims, if false, change the decision. Those claims become the review spine. Everything else is context: useful for understanding the system, but not important enough to dominate the evidence request list.

Architecture diagrams are useful because they show what the team believes the system is. They are not proof. A diligence review should connect diagrams to repository structure, deployment paths, telemetry, incident history, customer workflows, cost records, validation output, and the team's ability to explain tradeoffs without hiding behind abstractions.

For AI systems, that means reviewing the path from user intent to source context, model call, retrieval, tool use, human review, output, logging, evaluation, and rollback. For infrastructure systems, it means reviewing how inventory, provisioning, networking, storage, scheduling, metering, observability, support, and failure recovery actually work together.

Infrastructure economics are easy to overstate when the review only looks at gross margin targets or cloud bills. A real model includes utilization, reserved capacity, hardware refresh, power, cooling, storage growth, network egress, support load, failure rates, customer onboarding, roadmap commitments, and the engineering work required to keep the platform reliable.

AI economics need the same discipline. Token cost, GPU cost, batch size, cache hit rate, retrieval overhead, evaluation load, human review, customer-specific customization, and quality regressions all shape whether the product can scale profitably. The question is not only whether the demo works; it is whether the operating model supports the business model.

Strong technical teams usually have a crisp answer for what breaks, how they know, who owns it, and how they recover. Weak diligence signals appear when every failure is described as an edge case, every roadmap gap is one hire away, or every operational risk is pushed onto future platform work.

The review should look for incident learning, validation habits, release discipline, customer-support feedback loops, and whether product, engineering, infrastructure, security, and finance are telling the same story. A team that can explain constraints clearly is often lower risk than a team that only explains upside.

A long list of generic questions makes diligence feel thorough while hiding the real uncertainty. Better follow-up questions are tied to a decision: what would we believe if the answer is strong, what would we doubt if the answer is weak, and what evidence would change the conclusion?

The output should leave a clear path for next steps: verify a benchmark, inspect a customer deployment, review evaluation data, test a support workflow, validate a cost assumption, examine a security boundary, or ask the team to explain how a specific failure would be detected and recovered.