AI Weekly Malaysia

A Futurum CEO, quoted by Tom's Hardware, claims AI agents consume about 5x more tokens than human users and that the figure will eventually reach 10x, largely because agents keep re-reading context they have already seen. The article frames this as a KV cache demand problem that compounds existing RAM shortages. The excerpt carries no methodology, benchmark, or per-model breakdown — only the multiplier claims and the cache/RAM framing.

Why: If the 5x-to-10x token multiplier holds for agentic workloads, your per-seat agent pricing, free-tier limits, and API cost forecasts built on human-chat token volumes are understated by roughly an order of magnitude, and the re-reading pattern means prefix/prompt caching — not just cheaper models — is where the savings sit. The linked KV cache and RAM shortage angle is a second-order decision: self-hosted or reserved GPU memory for agent workloads is likely to get more expensive before it gets cheaper. No Malaysian or Southeast Asian detail appears in the text, so treat this as a general cost and infrastructure planning signal, not a local policy or funding item.

A blog post on insufferable.dev argues the competitive dynamic between Western and Chinese AI labs has flipped: instead of Western labs accusing Chinese labs of distilling their models, Western labs are now quietly adopting Chinese inference optimizations. It cites DeepSeek's KV cache work — MLA at roughly 15x compression, then Compressed Sparse Attention and Heavily Compressed Attention, and DeepSeek-V4.1-Flash with CSA2, cross-layer cache reuse and FP4 caching bringing the global KV cache to 890 bytes per token, roughly 437x below DeepSeek-V1 — and claims Claude Opus 5.5 and GPT-6.1 Sol shipped with these techniques, with Opus 5.5 cutting cache-read pricing 60% versus Opus 5. The excerpt is truncated mid-sentence, and the pricing claims and model-release details are asserted by the author without cited primary sources.

Why: If the cache-read price cuts described here are real, the cost of running long-context coding and agent sessions shifts from output tokens toward a much cheaper cache-read line item, which changes how you'd budget and architect retrieval-heavy agents. But the article gives no links to DeepSeek's papers or to Anthropic/OpenAI pricing pages, so before repricing anything, verify the 890 bytes-per-token figure and the claimed 60% Opus cache-read reduction against the vendors' own docs — the HN thread (349 points, 368 comments) is a better starting point than the post itself.