---
id: "prereq-llm-training-mechanisms-d1"
type: "prereq"
source_timestamps: ["§ Value Creation Is Not Value Capture"]
tags: ["machine-learning", "technical-literacy"]
related: ["concept-ai-first-mover-disadvantage", "claim-early-movers-train-competitors"]
reason: "Required to understand the mechanism behind the 'first-mover disadvantage' in Gen AI."
sources: ["spine"]
sourceVaultSlug: "hbr-seg-spine"
originDay: 1
articleStem: "hbr-cl-96-ai-no-sustainable-advantage"
sourceUrl: "https://hbr.org/2024/09/ai-wont-give-you-a-new-sustainable-advantage"
sourceTitle: "AI Won’t Give You a New Sustainable Advantage"
---
# LLM Data Ingestion and Training

**Prerequisite knowledge.** The premise that early movers train the models for late movers assumes a basic grasp of how Generative AI models **continuously scrape public data, user inputs, and market outcomes** to update their weights and pattern-recognition capabilities.

**Why it's required:** This ingestion loop is the physical mechanism behind [[concept-ai-first-mover-disadvantage]] and [[claim-early-movers-train-competitors]] — a first mover's public results become tomorrow's training signal.

**Enrichment caveat:** The mechanism is real under *shared/public/provider-level* training, but enterprise deployments increasingly segregate customer data and use private/isolated fine-tuning, which can break the spillover. Knowing *which training regime* applies is essential to judging whether the first-mover-disadvantage story holds in a given case.