Google is making its proprietary TPUs (Ironwood) available to Meta, directly challenging Nvidia’s 90% dominance. The massive cost of AI compute is forcing tech giants to turn from Nvidia's biggest customers into its fiercest competitors.
This strategic shift will wipe out $150B in market value and signals the end of a near-unbreakable monopoly. Can Nvidia's software moat (CUDA) hold up against the combined might of hyperscalers?
NVIDIA just delivered the biggest CUDA overhaul in 20 years. CUDA 13.1 introduces the new Tile programming model, making GPU development more powerful, portable, and future-proof — especially for Blackwell-class AI workloads.
Is this really offering something new compared to smart glasses? Many advanced smart glasses can already record, transcribe, and assist with memory or focus. I’m curious whether this pendant brings any unique advantages—like better privacy, always-on functionality, or improved AI integration—or if it’s just another form factor for similar tech. Either way, Meta shifting more resources from the metaverse into AI wearables is an interesting move.
AI is reshaping the memory market faster than anyone expected. Hyperscalers like AWS, Google, Oracle, and Microsoft are locking in DRAM, HBM, and NAND supply all the way to 2028, creating a prolonged DRAM supply crunch that’s keeping prices elevated through 2026 (and likely beyond).
For everyone outside the cloud giants — enterprises, AI startups, OEMs — this means higher costs, longer lead times, and the need for smarter IT planning. The secondary hardware market is also becoming a critical option as budgets tighten and demand keeps climbing.
• Why AI is driving historic memory shortages
• How CSPs are securing long-term capacity
• When analysts expect relief (hint: not soon)
• What businesses can do to adapt
Q3 update: Global DRAM revenue jumped 30.9% QoQ to $41.4B, driven by higher contract prices, increased bit shipments, and growing HBM volumes. Micron led growth with 53.2% QoQ revenue increase, while Samsung and SK Hynix also saw strong gains. Inventories are tight, and TrendForce forecasts Q4 contract prices to surge 50–55%, confirming a strong upward cycle across DRAM and HBM markets.
That is terrible. Only less than half year! If all countries keep building AI centers, it will talk a long time for the price to be back to reasonable status.
Upgrading your computer’s RAM can boost performance, but compatibility and memory type matter.
Key Takeaways:
Check current specs — See how much RAM you have and what type (DDR4 or DDR5).
Match your system — Your motherboard and CPU must support the memory type and speed.
Pick the right size —
Basic use: 8–16 GB
Gaming: 16–32 GB
Content creation: 32–64 GB+
Use dual channels for better bandwidth; install memory in matched pairs.
Enable XMP/EXPO in BIOS to run RAM at rated speeds.
Don’t mix DDR4 & DDR5 — they’re not cross-compatible.
Recycle or sell old memory to save cost and reduce e-waste.
Tip: You can sell used server RAM or desktop modules through BuySellRam to recover value from old hardware.
The internet is undergoing a transformation with the rise of AI-powered "answer engines" challenging traditional search engines like Google. These new AI systems aim to provide direct answers to user queries, streamlining information retrieval but raising concerns about the impact on publishers and content creators. The development of AI agents has also led to copyright infringement issues, with companies like The New York Times filing lawsuits against tech giants. Online communities like Stack Overflow are also feeling the effects, with traffic declining due to AI-generated code. As AI-driven search continues to evolve, it presents both opportunities and challenges for users and tech companies alike. Balancing innovation with ethical considerations will be crucial in navigating this changing digital landscape.
The discussion on High-Bandwidth Memory (HBM) covered its current market trends, competitive landscape, applications in AI, automotive, AR/VR, and consumer electronics, as well as its projected future growth fueled by technological advancements and increasing demand for high-performance memory solutions.
Semiconductor giant Nvidia introduced its latest artificial intelligence chip, the H200, designed to support training and deployment across various AI models. An upgraded version of the H100 chip, the H200 boasts 141GB of memory, focusing on enhancing “inference” tasks. The chip demonstrates a notable improvement of 1.4 to 1.9 times in performance compared to its predecessor, particularly in tasks such as reasoning and generating responses to questions.
