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Latest from Tom's Hardware in News-analysis

Kimi K3 rocks the AI industry as Moonshot AI undercuts closed-source American competitors on price — but the huge 2.8T open-weight model still needs serious hardware to deploy at scale Tower Semiconductor revives shuttered Panasonic-era fab in $3 billion Japan photonics expansion — METI-backed plan targets $3.6 billion revenue by 2028 Intel Intel Micron commits $500 million to GlobalWafers Anthropic says it can read Claude Elon Musk receives FTC greenlight to buy Mesh Optical as interconnects emerge as AI SiPearl South Korea Inside the history of DRAM price-fixing lawsuits — how HBM allocations could make a difference after two decades of failed cases U.S. PC shipments drop 7%, market isn Chinese Z.ai The AI tokenmaxxing party is crashing over spiraling costs — leaked consulting firm audio suggests no one is sure how to measure AI effectiveness US Secures Netherlands for Pax Silica Alliance in key win for strategic chip alliance — tension remains over MATCH Act restrictions Arm servers capture over 45% of data center market revenue — GPU clusters and high-end AI infrastructure fuel a tectonic shift away from x86 Post-silicon era gets closer as industry giants crack the 2D transistor scaling bottleneck with breakthrough tech — imec, ASML, and TSMC fab complementary 2D-material transistors at 50nm pitch on a 300mm wafer US pulls the Marvell details vision of optically-interconnected data centers spanning across thousands of kilometers — new interconnects sampling later this year would allow CSPs to pool resources based on workload Nvidia's high-speed AI data center storage servers break cover, touting 2.9 petabytes of storage and extreme PCIe 6.0 performance — Wiwynn shows off SCADA server with GPU-accelerated storage AI is set to consume up to 600 billion gallons of water by 2030 — rising energy consumption primarily to blame as… Google reportedly books Intel for packaging more than 3 million TPUs in 2028 — SK hynix is testing Intel's… Anthropic's warning over AI self-improvement has a hidden message — accelerating development requires more compute before companies ever risk losing control of frontier AI models Executives are cutting jobs for an AI future that hasn't fully arrived yet, even as productivity gains remain difficult to prove — data neither confirms nor refutes an AI unemployment apocalypse Jensen Huang says 'every edge device will become autonomous' — Nvidia maps one computing pattern from… AMD's Helios MI455X AI platform breaks cover, initial systems use UALink-over-Ethernet interconnects — AMD's Vera Rubin rival surfaces, but the downsides of Ethernet could hamstring performance Frore shows off LiquidJet Nexus coldplate for Nvidia Vera Rubin, other AI accelerators — offers up claimed 10% token generation boost over rival liquid-cooling solutions The rise of local agentic computing faces a brutal reality: rising DRAM prices —  RTX Spark, Gorgon Halo chips subject to 63% DRAM contract price hike this quarter Astera Labs showcases 320-lane PCIe 6.0 switch for vendor-agnostic scaling in data centers — up to 80 accelerators… AI costs begin to bite as agents may increase token demand by 24 times, says Goldman Sachs report — Uber and Microsoft among companies feeling the bite of tokenized billing IBM spins off America's first quantum chip foundry with $2 billion in federal and private funding — newly-minted 'Anderon' foundry to offer 300mm quantum wafer fab and manufacturing services
Talent over tokens: AI models are becoming more expensive...
Jon Martindale · 2026-05-01 · via Latest from Tom's Hardware in News-analysis

Earlier this month, it was reported that almost 80,000 workers were laid off in the first quarter of 2026, with companies pinning the blame on the rise of artificial intelligence.

Whether through improved task efficiency or cost savings through automation, deployment of AI within the workforce is supposed to be the economically smart decision — even if it didn't necessarily turn out to be true. But even that story may be hard to tell now, as Nvidia executive Bryan Catanzaro recently commented that, within his team, AI compute power is more expensive than actual workers.