Image created with gemini-3.1-flash-image-preview with claude-sonnet-4-5. Image prompt: Using the provided reference image, preserve the exact compositional layout with subject in left third and misty right two-thirds, the deep blue-purple cinematic color wash, atmospheric smoke bleeding rightward, and emotional gravity, but replace the central figure with an extreme macro close-up of a single microchip tilted slightly with golden contact pads and circuit traces visible, iridescent glitter particles scattered across its surface catching light, maintaining the post-party melancholy tone, with ‘chips’ in thin lowercase white Helvetica Neue Light overlaid on the right smoky area.
We’ve signed an agreement with Google and Broadcom for multiple gigawatts of next-generation TPU capacity, coming online starting in 2027, to train and serve frontier Claude models.
https://x.com/AnthropicAI/status/2041275561704931636
Anthropic expands partnership with Google and Broadcom for multiple gigawatts of next-generation compute \ Anthropic
https://www.anthropic.com/news/google-broadcom-partnership-compute
Google has the equivalent of roughly 5 million Nvidia H100 GPUs! Therefore, it’s no surprise that Anthropic’s needs are now benefiting Google. As I said yesterday, Google is exceptionally well-positioned: strong revenue streams, its own chips, and above all: distribution.
https://x.com/kimmonismus/status/2041464540446228484
Compute may be the most important input to AI. So who owns the world’s AI compute? Introducing our new AI Chip Owners explorer, showing our analysis of how leading AI chips are distributed among hyperscalers and other major players, broken down by chip type over time.
https://x.com/EpochAIResearch/status/2041241187252945071
New essay by @ansonwhho: Chinese and open model AI labs have ≈10× less compute than the frontier. But they can distill frontier models, replicate innovations fast, and have enormous talent. Is that enough to compete at the frontier? 🧵
https://x.com/EpochAIResearch/status/2041923793166491778
Who owns the world’s compute? Our new Chip Ownership hub shows that Google leads, holding around 25% of all compute sold since 2022.
https://x.com/EpochAIResearch/status/2041600102654148673
Google controls the most AI computing power, driven by its custom TPUs
https://epochai.substack.com/p/google-controls-the-most-ai-computing
Iran threatens ‘complete and utter annihilation’ of OpenAI’s $30B Stargate AI data center in Abu Dhabi — regime posts video with satellite imagery of ChatGPT-maker’s premier 1GW data center | Tom’s Hardware
https://www.tomshardware.com/tech-industry/iran-threatens-complete-and-utter-annihilation-of-openais-usd30b-stargate-ai-data-center-in-abu-dhabi-regime-posts-video-with-satellite-imagery-of-chatgpt-makers-premier-1gw-data-center
Apple Shows Its Cards, Plans To Move The Production Of Its Upcoming Baltra ASIC In-House
https://wccftech.com/apple-shows-its-cards-plans-to-move-the-production-of-its-upcoming-baltra-asic-in-house/
If Huawei hadn’t been banned from TSMC in 2019, @dylan522p thinks it would have already eclipsed Apple as TSMC’s biggest customer. And that it would have better AI chips than Nvidia. Before it was banned, Huawei was actually the first to ship a 7nm AI chip — two months before
https://x.com/dwarkesh_sp/status/2040081903013822796
MLX creator @awnihannun sharing the story of MLX. Apple management called him right after the launch: “why didn’t you tell us this was going to be so big??” 😂
https://x.com/ronaldmannak/status/2042425851455902152
I’m open-sourcing an MLX port of the ESM-2 protein model family so more people can tinker with AI & biology on Apple silicon! (1/5)
https://x.com/josephjojoe/status/2041215366177636468
If we get AGI before 2030 but can only produce ~200 GW a year of chips on advanced process nodes, can we ease the bottleneck by going back to older nodes? Some of the most singularity-pilled investors think so. Most of the FLOPS improvement between chip generations has come
https://x.com/dwarkesh_sp/status/2040111083281944978
@scaling01 I don’t think writing a kernel that is 400x faster than some baseline counts as speeding up AI research by up to 400 times.
https://x.com/RyanPGreenblatt/status/2041596782216671365
AI Networking in Data Centers – Cisco
https://www.cisco.com/site/us/en/solutions/artificial-intelligence/ai-networking-in-data-center/index.html?dtid=pdixsp001642
Cisco Data Center Networking Presents at Networking Field Day 40 – Tech Field Day
Cisco Data Center Networking Presents at Networking Field Day 40
Cisco N9100 Series Switches–AI Networking Switches – Cisco
https://www.cisco.com/site/us/en/products/networking/cloud-networking-switches/n9100-series-switches/index.html?dtid=pdixsp001642
CoreWeave Takes As Much Financial Engineering As It Does Datacenter Design
https://www.nextplatform.com/cloud/2026/04/09/coreweave-takes-as-much-financial-engineering-as-it-does-datacenter-design/5215794
Cisco secures AI infrastructure with NVIDIA BlueField DPUs – Cisco Blogs
https://blogs.cisco.com/datacenter/cisco-secures-ai-infrastructure-with-nvidia-bluefield-dpus?dtid=pdixsp001642
Intel is proud to join the Terafab project with @SpaceX, @xAI, and @Tesla to help refactor silicon fab technology. Our ability to design, fabricate, and package ultra-high-performance chips at scale will help accelerate Terafab’s aim to produce 1 TW/year of compute to power
https://x.com/intel/status/2041501301318766866?s=20
TorchTPU: Running PyTorch Natively on TPUs at Google Scale – Google Developers Blog
https://developers.googleblog.com/torchtpu-running-pytorch-natively-on-tpus-at-google-scale/
Day 93/365 of GPU Programming Studying parallelism today and stumbled upon this incredible blog post/book The Ultra-Scale Playbook: Training LLMs on GPU Clusters by Hugging Face that dives deep into data parallelism, expert parallelism, tensor parallelism, pipeline parallelism
https://x.com/levidiamode/status/2041229052804280811
Monarch: an API to your supercomputer – PyTorch
https://pytorch.org/blog/monarch-an-api-to-your-supercomputer/
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