According to @CNBC, Samsung joined a $230M round for Euclyd, signaling rising demand for Nvidia GPU alternatives and specialized AI accelerators.

According to @CNBC, Macquarie says this Google-aligned AI chip supplier can gain share as accelerator demand rises and hyperscaler spending stays strong.

According to @CNBC, Nvidia’s outlook highlights sustained AI chip demand, while Meta’s $16.7B deal may reshape data practices for ad targeting.

According to @CNBC, Nvidia guides 70% 2028 sales growth, signaling sustained AI accelerator demand and data center spend, as reported by CNBC.

According to @CNBC, Nvidia is shifting its AI moat from chips to capital, using long term financing and cloud credits to lock in enterprise demand.

According to @CNBC, Nvidia is tapping Wall Street for $500B to scale AI chips, with Jensen Huang calling accelerators an investable asset, per CNBC.

According to @CNBC, Jim Cramer urged buying Astera Labs, citing AI data center demand and accelerator interconnect growth, as reported by CNBC.

According to @CNBC, UBS expects further upside for an AI chipmaker that doubled in 3 months, citing demand for data center accelerators and strong orders.

According to @CNBC, Meta will start production of its AI chip in September to double compute capacity, enabling more efficient LLM training, per Reuters.

According to @CNBC, Baidu’s chip unit Kunlunxin targets a $50B Hong Kong IPO, lifting Baidu shares 7% and signaling rising AI accelerator demand.

According to @CNBC, Qualcomm launched an AI data center CPU and signed Meta as its first major customer, signaling new competition in server silicon.

According to Sawyer Merritt on X, Tesla has begun hiring AI Hardware Engineers in India for the first time, with roles focused on custom silicon and optimized architectures to power its autonomous driving and energy products; this move suggests localized talent scaling for AI chips and systems design (as reported by Sawyer Merritt). According to the job description excerpt cited by Sawyer Merritt, the team’s mandate is to build custom silicon and architectures to keep Tesla leading in AI-driven automotive and energy solutions, indicating potential growth of in-house accelerators and hardware-software co-design for Full Self-Driving and Dojo-class compute. As reported by Sawyer Merritt, establishing AI hardware roles in India could lower R&D costs, expand 24x7 engineering coverage, and tap India’s semiconductor design talent pool, creating supplier and hiring opportunities for EDA tools, verification, and physical design services in the region.