I Have always found that acronyms are not the problem. They are necessary. However they become a problem when a lot of people start shoehorning them as attention targets instead of using them naturally in flow.
Mcp works because it exposes primitives to agentic Loop and makes dynamic calls possible which would otherwise require very elaborate deterministic algorithms. I like to think of every mcp tool as a co-ordinate Axis. The more you have the more complex paths your agentic loops can traverse. So while that protocol is a wrapper and can surely go extinct something better with similar abstraction will show up
The Problem: When your AI fails, "the algorithm did it" won't fly. Insurance, courts, and regulators need a human name.
The Pattern: Ships got captains. Bridges got licensed engineers. Planes got pilots. Medicine got attending physicians. Same reason: you can't punish "the team."
The Solution: System Liability Engineer (SLE) = one person who understands the system, has veto power, signs their name, and faces career consequences if it causes serious harm.
The Timeline: Insurance exclusions already at 28%. Courts asking "who was responsible?" by 2026. Mandatory by 2030. You can get ahead or get dragged.
The Litmus Test: Ask them: "If this system causes serious harm, are you prepared to explain it publicly and accept being fired?" If not "yes," they're not SLE.
Why It Works: AI can fake text, images, and code. It can't fake: years building reputation, a specific human body signing documents, finite career at stake, real legal consequences.
What To Do: Name one person SLE for your highest-stakes AI system this week. Give them veto power in writing. Have them map "who gets hurt, how badly." That's it—you're 80% there.
The Real Reason: When making truth-claims costs nothing, only institutions grounded in irreversible human cost survive. The SLE is that cost.
Thank you for the thoughtful comment. Your questions are valid given the title, which I used to make the post more accessible to a general HN audience. To clarify: the core distinction here is not kernelization vs kNN, but field evaluation vs point selection (or selection vs superposition as retrieval semantics). The kernel is just a concrete example.
FAISS implements selection (argmax ⟨q,v⟩), so vectors are discrete atoms and deletion must be structural. The weighted formulation represents a field: vectors act as sources whose influence superposes into a potential. Retrieval evaluates that field (or follows its gradient), not a point identity. In this regime, deletion is algebraic (append -v for cancellation), evaluation is sparse/local, and no index rebuild is required.
Great work guys, how about we replace the global encoder with a Mamba (state-space) vision backbone to eliminate the O(n²) attention bottleneck, enabling linear-complexity encoding of high-resolution documents. Pair this with a non-autoregressive (Non-AR) decoder—such as Mask-Predict or iterative refinement—that generates all output tokens in parallel instead of sequentially. Together, this creates a fully parallelizable vision-to-text pipeline, The combination addresses both major bottlenecks in DeepSeek-OCR.