# kaal:claim:4855607-002

**Claim.** The computational cost of improving deep learning performance scales so badly that halving the error rate is estimated to require over five hundred times more computational resources, which raises a sustainability problem for the deep learning paradigm itself.

**Type.** empirical  **Support.** evidenced

**Holds when.**

- scaling driven improvement of deep learning accuracy

**Source quote.**

> improving deep learning performance increases drastically, with estimates suggesting that halving the error rate would require over 500 times more computational resources. This raises concerns about the sustainability and efficiency of deep learning approaches.

**From.** Wulf A. Kaal, *How AI Models are Optimized Through Web3 Governance* (2024), Model Overview: Deep Learning Models, page 9

**Cite as.** Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607

**Verify.** sha256 of source PDF `eb0b3e62374b45a8fa888c6bde9725e606bcb46cf4b5e74a6e851d9f25099113` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202024%20-%20How%20AI%20Models%20are%20Optimized%20Through%20Web3%20Governance.pdf

**Failure mode.** Compute scaling wall  (family: scalability-and-throughput-limit)

**Topics.** institutional-design

**Keywords.** deep-learning, compute-cost, diminishing-returns, sustainability, scaling-limits

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