Your AI Agent Isn’t Growing — And Web3 Might Be the Fix
This title could be clearer and more informative.Try out Clickbait Shieldfor free (5 uses left this month).
AI agents don't truly grow — their apparent improvement comes from external databases, not model parameter changes. Genuine personalization requires fine-tuning (LoRA), but faces barriers of cost, data quality, and catastrophic forgetting. Web3's Account Abstraction (AA) transaction logs offer a structurally different data source: instead of behavioral proxies like clicks, AA records actual signed decisions, delegations, and module activations — capturing intent directly. This structured, verifiable data could feed inductive AI personalization research, enabling empirically-derived LoRA training granularity. The author also proposes 'lanekey,' an Ethereum Magicians proposal that adds contextual agreement structure to SmartAccount transactions, potentially serving as a pipeline for value-labeled training data. The realistic near-term architecture combines context windows, vector DBs, periodic fine-tuning, and LoRA delta updates, with AA logs as a future origin point for value data.
Table of contents
The Fundamental Limit: “Updating Through Forgetting”Three Layers of PersonalizationThree Barriers to Individual Fine-Tuning1. Cost2. Data quality and volume3. Catastrophic ForgettingGet Shota Moue ’s stories in your inboxThe Web3 Connection: Toward Inductive DesignWhy AA transaction logs are differentThe self-reinforcing loopA Concrete Connection Point: lanekeyOn PrivacyWhere Things Stand TodaySort: