Alphabet's Frozen v2 chip could deliver 10x efficiency gains by embedding Gemini into silicon

Alphabet Earnings Could Reveal A New Way To Lower AI Costs - Alphabet (NASDAQ:GOOGL), Alphabet (NASDAQ:GO

Can Google make artificial intelligence cheaper to run? * Alphabet stock is trading at elevated levels. What's the outlook for GOOG shares? That could make Alphabet's earnings call about far more than revenue and earnings per share. The AI Race Is Shifting To Efficiency Over the past two years, investors have largely measured AI leadership by one metric: spending. Frozen v2 hints at the next phase. According to the report, Google engineers believe the new chip could deliver between six and 10 times more tokens per unit of power than the company's latest Tensor Processing Units (TPUs) by embedding portions of Gemini's architecture directly into the silicon. If those projections hold, the implications extend well beyond Google's chip business. Running AI models -- known as inference -- is quickly becoming one of the largest operating expenses for hyperscalers as millions of users generate prompts every day. Improving efficiency could allow Google to serve more AI requests while consuming less power and fewer computing resources. Why Investors Should Listen Closely That makes Alphabet's earnings call an opportunity for management to discuss more than just AI investments. Investors will be listening for any commentary on custom silicon, inference workloads, TPU deployment and capital spending. Even modest updates could provide clues about how Google plans to manage AI costs as Gemini adoption expands. The report also comes as investors increasingly question whether hyperscalers can continue raising AI capital expenditures indefinitely. If Google can improve AI economics through better hardware rather than simply buying more compute, it could reshape how Wall Street evaluates future AI spending. The Bigger Story Isn't Another AI Chip Alphabet has built custom AI chips for years, making Frozen v2 less significant as a product announcement than as a strategic signal. The next competitive advantage in AI may not come from training ever-larger models. It may come from making those models dramatically cheaper to operate. That's why this week's earnings call matters. Wall Street already knows Google is spending aggressively to compete in AI. What investors don't yet know is whether the company has found a way to generate more AI output without proportionally increasing its infrastructure costs. If Frozen v2 is part of that answer, Alphabet's earnings could reveal that the next battle in AI isn't just about building smarter models -- it's about building more efficient ones. Image via Shutterstock Market News and Data brought to you by Benzinga APIs To add Benzinga News as your preferred source on Google, click here.

Why Is Alphabet Stock Gaining Monday? - Alphabet (NASDAQ:GOOGL), Alphabet (NASDAQ:GOOG)

The move also comes as investors await Alphabet's second-quarter earnings on July 22. Google Develops AI Chip To Boost Efficiency Google is developing an internally designed server chip code-named "Frozen v2," according to The Information. The chip is intended to embed parts of Gemini's architecture directly into the hardware, reducing the amount of computation and data movement required to generate AI responses. Engineers believe the chip could deliver six to 10 times more AI tokens per unit of power than Google's latest custom AI chips. The company is targeting deployment by 2028, although the design remains under development, CNBC reported Monday. The company is working on a new server chip that could help Google run Gemini models more efficiently and ease pressure from rising AI computing demand. Custom Silicon Could Help Relieve AI Capacity Constraints Frozen v2 would complement, rather than replace, Google's existing tensor processing units. The project is aimed at improving AI inference efficiency and easing growing pressure on the company's computing infrastructure. According to the reports, AI capacity constraints have created internal bottlenecks and forced Google Cloud to limit some external customer demand, making more efficient custom chips increasingly important as Gemini usage expands. Alphabet Technical Analysis From a longer-term trend view, GOOG is still in an uptrend over the past year (up 86.60%) and is trading 10.9% above its 200-day SMA ($321.52), which keeps the bigger-picture bias constructive. Nearer-term, it's more mixed: the stock is 0.9% above the 20-day SMA ($353.44) but trading 2.9% below the 50-day SMA ($367.20), and the 20-day SMA sitting below the 50-day SMA is a bearish short-term alignment. Earnings & Analyst Outlook The countdown is on: Alphabet is set to report earnings on July 22, 2026 (confirmed). * EPS Estimate: $2.88 (Up from $2.31 YoY) * Revenue Estimate: $113.63 Billion (Up from $96.43 Billion YoY) * Valuation: P/E of 26.4x (Indicates premium valuation relative to peers) Analyst Consensus & Recent Actions: The stock carries a Buy rating with an average price forecast of $429.67. Recent analyst moves include: * TD Cowen: Buy (Raises Forecast to $475.00) (June 9) * Oppenheimer: Outperform (Raises Forecast to $445.00) (May 15) * JP Morgan: Overweight (Raises Forecast to $460.00) (April 30) Alphabet Top ETF Exposure Significance: Because GOOG carries such a heavy weight in these funds, any significant inflows or outflows for these ETFs will likely force automatic buying or selling of the stock. Alphabet Price Action GOOG Stock Price Activity: Alphabet shares were up 2.92% at $356.23 at the time of publication on Monday, according to Benzinga Pro data. Image via Shutterstock Market News and Data brought to you by Benzinga APIs To add Benzinga News as your preferred source on Google, click here.

Google stocks gain as tech giant targets 10x efficiency with model-baked silicon By Investing.com

Alphabet Inc. (NASDAQ: GOOGL) shares rose 1.15% Monday morning following a report that the company is taking an audacious new approach to custom hardware: etching its flagship AI model directly onto its next-generation chips. According to The Information, Google's experimental project -- dubbed "Frozen v2" -- is designed to bypass the traditional processing overhead of general-purpose GPUs and TPUs. By burning Gemini's blueprint into the silicon itself, the chip slashes processing steps and data movement. Key Highlights * Projected to be 6 to 10 times more power-efficient per token than current TPUs. * Built to solve compute shortages that currently bottleneck internal teams and force Google Cloud to turn down external deals. * Planned for deployment as early as 2028 as a parallel branch to the TPU product line. * The architecture locks the hardware into Google's current AI design framework, though engineers can still update parameters via new model weights. In response, a Google spokesperson noted that while teams actively experiment with high-efficiency innovations, not every lab project reaches commercial production.