Cerebras looks attractive because the short report's two-year wafer-life thesis rests on unvalidated reliability assumptions, not representative failure data. Energy per useful token, lower warranty utilization and ordinary repair accounting also undercut its financial conclusions. OpenAI has integrated Cerebras into production; AMD and AWS have announced integrations, while Oracle plans 2027 availability for ultra-fast inference, adding commercial validation despite the report. x.com  x.com 

Oracle's year-long evaluation of Cerebras' costs, performance, customer uses and equipment life strengthens the case for its inference technology. Oracle chose to bring the equipment into its data centers; that real-world buyer decision conflicts with the short seller report's criticism. x.com 

Cerebras faces brutal competition from SpaceX, and competing in AI models or data centers requires cash at scale. SpaceX is already trashing Cerebras and CoreWeave. x.com 

Better power delivery appears to be helping Cerebras work through wafer-scale power and thermal limits more than expected. CS-4 gets higher clocks and improved I/O from WSE-3T, despite the same core count and SRAM capacity as WSE-3. Oracle, OpenAI and AWS choosing to work with Cerebras carries weight. But higher clocks do not answer Culper Research's allegation that its wafers fail after about two years; how representative those failures are of current systems remains unclear. x.com 

Cerebras is the purest bet on decode as disaggregated inference becomes the next shift in AI model serving. Prefill runs on AMD Helios, Amazon Trainium or Nvidia GPUs, while decode needs extreme on-chip bandwidth, where Cerebras' Wafer-Scale Engine claims 1,000-2,000 times the effective bandwidth of GPU HBM. Splitting the work yields more tokens per watt, lower latency and better economics at scale. It is already shipping in Cerebras' pairings with AMD and Amazon. x.com 

Cerebras' first buyable price, near a $100 billion market cap after its IPO price was raised 50% and the stock doubled at open, buys an uncertain call option on an architecture with hard trade-offs. Its wafer-scale chip gives each core only 48 KB of SRAM, 44 GB in total versus 13.5 TB of HBM3E in Nvidia's GB200 NVL72 rack, and its compiler complexity means it can never match Nvidia's software optimization. Severe memory constraints and limited I/O bandwidth are its key limits. x.com  x.com 

Nvidia can capture more AI infrastructure spending by supplying Vera CPUs and networking alongside GPUs. Oracle outlined an initial supply of 150,000 Vera CPUs, and OpenAI says it is putting them into production for agent tool execution; reducing CPU waits could improve cost per task and GPU-memory use. Benefits from independently scaling CPUs and GPUs still need validation in utilization and completion times. x.com 

Cerebras may be the only company willing to stitch together a true high-speed solution. If so, it retains upside optionality from high-speed pricing and gains a first-mover advantage. x.com  x.com 

Cerebras is not worth owning because its proprietary stack faces limited broad adoption. The attached analysis says NVIDIA-aligned cloud operators are unlikely to deploy Cerebras hardware, while Groq LPUs fit NVIDIA's software ecosystem more easily. It also flags Cerebras' scaling and deployment friction, despite solid product-market fit with marquee customers. x.com 

Nebius' software efficiency and a possible end to heavy AI financing support a re-rating. Per @DrTomsLens, its engineers got about 43% more video output from the same GPUs, cutting compute cost per second by 30%; newer hardware alone is not enough without capable software teams. Oracle expects to fund growth from free cash flow in FY2029, suggesting capital-heavy AI providers may eventually outgrow debt and dilution. Google Cloud reported 103% revenue growth amid strong AI infrastructure demand. … Show more