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.
