# kaal:claim:5095633-013

**Claim.** Latency, throughput limits, and the absence of fully automated continuous training and validation together defeat the timeliness advantage that real-time data is supposed to deliver.

**Type.** failure  **Support.** argued

**Holds when.**

- AI pipelines ingesting live, high-velocity data streams

**Source quote.**

> Latency, data throughput constraints, and the absence of fully automated mechanisms for continuous training and validation can collectively undermine the timeliness of insights derived from AI models.

**From.** Wulf A. Kaal, *Artificial Intelligence The Final Frontier* (2025), 2.1.4. Real Time Data Challenges, page 4

**Cite as.** Wulf A. Kaal, Artificial Intelligence The Final Frontier (2025). SSRN: https://ssrn.com/abstract=5095633

**Verify.** sha256 of source PDF `cbb484711f89bcefc9fc6a5730a1ed0a3f764d7999ad9b6f7d8ea05634c26c63` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202025%20-%20Artificial%20Intelligence%20The%20Final%20Frontier.pdf

**Failure mode.** real-time pipeline latency failure  (family: scalability-and-throughput-limit)

**Topics.** education-and-practice

**Keywords.** real-time-data, latency, continuous-training, infrastructure

**Canonical form.** This markdown file is the canonical hashed representation of the claim. Its sha256 is the content hash used for attestation.