Dropping eBPF CPU Cost by About 90% with Memoization (Not AI Gen)

TEXT START: My brother and I spent a lot of time designing our eBPF security agent to be really fast from the ground up, but recently we discovered we could make it much faster using memoization!

The Dissection

This is a narrowly competent systems-engineering report: it identifies repeated path-resolution work, caches policy decisions by mount namespace, mount, and inode, and demonstrates a large benchmark gain. The real achievement is not “making security intelligent.” It is removing redundant computation from a hot path. The agent becomes cheaper to run because prior classifications are reused.

The Core Fallacy

The dangerous implied leap is from local efficiency to durable superiority. A 90% reduction in measured kernel cycles on repeated access does not establish a general moat. It depends on workload repetition, cache hit rate, LRU capacity, filesystem behavior, policy churn, namespace topology, and edge-case frequency. Memoization compresses recurring work; it does not eliminate the underlying path-resolution problem.

Under the Discontinuity Thesis, this is also irrelevant to the central kill mechanism. It does not preserve mass employment, restore productive participation, or resist cognitive automation. It is an optimization inside infrastructure that may become more valuable precisely because AI-driven systems demand cheaper, faster verification and enforcement.

Hidden Assumptions

• Repeated file access is common enough for cache hits to dominate.
• Mount IDs, namespace IDs, and inode numbers remain sufficient identifiers for safe reuse.
• Policies and filesystem state do not change often enough to make cached decisions stale.
• Skipping hardlinked inodes sacrifices little meaningful coverage.
• The 200,000-open benchmark represents production workloads.
• LRU eviction and map overhead remain negligible at larger scale.
• The security agent’s policy model is simple enough that inode-level caching remains semantically valid.
• A dramatic kernel-cycle reduction translates into materially lower total system cost.

These assumptions are not fatal. They are simply where the “90%” headline stops being a law of nature and becomes a workload-dependent result.

Social Function

Partial truth and transition management. The article correctly documents a real performance improvement, but its social function is to turn competent maintenance work into a story of technological progress. It reassures operators that increasingly complex security machinery can be made affordable through cleverness. That reassurance is valid at the subsystem level and useless at the civilizational level.

This is not AI copium. It is infrastructure optimization: the kind of work that survives the transition because it sits close to enforcement, verification, and system control. The human author is not made sovereign by writing it. The durable asset is the deployed agent, its policy position, and whoever controls the surrounding infrastructure.

The Verdict

A legitimate optimization, not a discontinuity in the Discontinuity Thesis. The cache turns repeated path analysis into a cheap lookup and likely makes the agent more commercially viable. But it is a performance patch, not a strategic moat: reproducible by competitors, bounded by workload and correctness constraints, and subordinate to the larger shift toward automated infrastructure. Useful code. No macro-level escape hatch.