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Applied AI·April 19, 2026·1 min read

'75% of resumes never reach a human': Here’s the hidden reason your application is getting rejected by AI

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Hiring is quietly becoming an NLP problem—if your org is still reading PDFs manually, you're losing talent to whoever is tuning their filters and prompts. Treat your ATS like a search engine: own the schema, keywords, and models, or accept that your best candidates never clear the machine gate.

Applied AI

Sources: Google is in talks with Marvell Technology to develop a memory processing unit that works alongside TPUs, and a new TPU for running AI models (Qianer Liu/The Information)

Google pairing TPUs with a Marvell-built memory processing unit is an admission that the bottleneck is now memory bandwidth and locality, not raw flops. If you're designing AI workloads, architecture decisions around parameter sharding, KV cache, and sequence length are about to be constrained — or unlocked — by how fast your memory fabric evolves, not your GPU count.