A pixel can reveal a pattern—but not the history or sentiment behind it. As MBZUAI President and University Professor Eric Xing (@ericxing) noted at the Embedded Vision Summit, world models are video models that capture context across generated visual sequences. That context turns observation into understanding—and helps AI reason about what comes next. Learn more:

Great to be back in the UAE meeting with partners and spending time with students at @MBZUAI. It’s exciting to see the energy and momentum around AI and advanced computing across the country.

Hector Liu, IFM's Head of Technology and US Lab Director, walked MBZUAI through how K2 Horizon was built. A rare, candid close-up of LLM training. From the room: - Two new attention designs: softplus gated attention is a more expressive form of gated attention; Mixture-of-Value Attention adds capacity at no extra KV cache cost. - Nearly half the pre-training data was synthetic. An interesting generation method: Socratic dialogues, a teacher model questioning a student model toward the answer. - A scaling law guided learning-rate choices across model sizes. Four models trained on the same data learned at the same pace, with normalized loss curves lining up almost perfectly. More in the upcoming paper.

JUST IN: CEREBRAS $CBRS CLOSED ITS NASDAQ DEBUT UP 68% AT $311.07. MARKET CAP: ~$95 BILLION. The $5.55B IPO is the largest U.S. tech offering since Uber in 2019. The full Day 1 breakdown: Today's trading: - IPO price: $185 - Opened: $350 - Intraday high: $386 - Closed: $311.07 (+68%) - Drifted lower into the close after the open pop - 30M shares offered, $5.55B raised (could reach $6.38B if underwriters exercise the full option on 4.5M additional shares) The financial backdrop: - 2024 revenue: $510M (+76% YoY) - 2024 net income: $88M (swung from a $481.6M loss the prior year) - Cerebras is now profitable The G42 concentration story has evolved: - 2023: G42 was 85% of revenue - 2024: G42 fell to 24% of revenue - BUT Mohamed bin Zayed University of AI (MBZUAI) accounted for 62% of 2024 revenue - Combined UAE entities are still ~86% of revenue - "There's some whales out there, there's some really big customers," CEO Andrew Feldman told CNBC The cap table: - CEO Andrew Feldman: ~5% voting power, ~$2B stake at the IPO price - Fidelity: ~11% - Benchmark: ~9% - OpenAI and Amazon both hold warrants to purchase Cerebras stock The competitive setup: - NVIDIA $NVDA paid $20B for assets from Groq in December (Cerebras' closest architectural rival) - Cerebras has shifted from selling hardware systems toward cloud services, putting it in direct competition with Google $GOOGL, Microsoft $MSFT, Oracle $ORCL, and CoreWeave $CRWV - OpenAI cloud deal worth $20B+ runs through 2028 - AWS deployment via Amazon Bedrock launched March 2026

CEREBRAS $CBRS IS ABOUT TO GO PUBLIC ... HERE IS A DEEP DIVE ON WHAT THEY ACTUALLY DO Most people have no idea what this company actually does. Here is the plain-English version: THE BIG IDEA Every AI model you have ever used (ChatGPT, Claude, Gemini, Llama) is trained on chips. The dominant company is NVIDIA $NVDA, which sells lots of small chips that get wired together into clusters by the thousands. Cerebras went the opposite direction. They build ONE giant chip the size of a dinner plate. Not exaggerating. Their flagship "Wafer-Scale Engine" (WSE-3) is 46,225 square millimeters of silicon. A normal AI chip is smaller than a postage stamp. THE NUMBERS On that one giant chip: - 4 trillion transistors (a top-end NVIDIA GPU has roughly 80 billion) - 900,000 AI cores - 44 GB of on-chip memory - 125 petaflops of compute power - Built on TSMC's 5nm process WHY THAT MATTERS When you wire thousands of small chips together, the wires become the bottleneck. Data has to travel between chips constantly. That eats time, power, and money. Cerebras keeps everything on one piece of silicon. No cables between chips. No slow networking. Just one giant brain. The pitch: faster training, faster inference, fewer engineers needed to manage cluster bottlenecks. WHAT THEY SELL Three ways to use Cerebras: - Buy the system outright (the "CS-3" is the box that holds the chip) - Rent compute via Cerebras Cloud - Dedicated capacity contracts for big customers They have 6 new AI inference data centers coming online across North America and Europe. WHO ACTUALLY USES IT The customer list is the validation: - OpenAI: $20B+ committed over three years - Meta $META: powers the Llama API for developers - Perplexity: runs its Sonar search model on Cerebras - Mistral: the French AI lab runs Le Chat on Cerebras - Mayo Clinic: trains genomic AI models on Cerebras infrastructure - GSK $GSK: trains biological language models - Argonne National Lab: has used Cerebras hardware since 2019 - AWS: hosts Cerebras chips inside Amazon data centers, accessed through Bedrock - US Department of Energy: signed an MOU for the Genesis Mission THE TRADE-OFF Cerebras is small compared to NVIDIA. 2025 revenue: $510M. 2025 operating loss: $146M. Concentration is the risk most coverage will not flag: - G42 (the UAE conglomerate) was 85% of 2024 revenue per Reuters - G42 plus MBZUAI (the Abu Dhabi AI university) were 86% of 2025 revenue per FT - The OpenAI deal is the big bet to diversify away from that concentration THE STORY IN ONE LINE NVIDIA bet that the future of AI is millions of small chips working together. Cerebras bet on one giant chip doing the work in one place. The market just decided their bet is worth nearly twice what they priced it at.