+15.1% (FY ended 2025-12-31 vs FY ended 2024-12-31) 10-K
Annual net income
132,170,000,000 USD (FY ended 2025-12-31, attributable to the company) 10-K
Annual operating margin
32.0% (FY ended 2025-12-31)
Forward P/E
Unavailable: no dated forecast source
Gloria daily close for session Oct 28, 2026. Market cap and trailing P/E use this close with the SEC share total and trailing diluted earnings, each dated next to its figure. Annual revenue, revenue growth, net income and margin show their own fiscal period and link the 10-K. Current context may postdate this article.
Alphabet Inc. and its peers
Company
Price
Market cap
P/E (trailing)
Forward P/E
Annual revenue (USD)
Annual revenue growth
Annual net income (USD)
Operating margin
Alphabet Inc.
GOOGL · Price dated Oct 28, 2026 · tokenized GOOGLx
3,333,751,000FY ended 2025-12-31, attributable to the company · 10-K
75.8%FY ended 2025-12-31
Missing figures stay unavailable. Forward P/E requires a dated source. Annual revenue, revenue growth, net income and margin come from each company's own 10-K, in US dollars, and each row shows that fiscal year. Revenue growth compares the fiscal year with the prior year stated in the same 10-K. Net income is the full year amount attributable to the company, or available to common stockholders where the filing states only that, or consolidated net income. Each row names its basis. Trailing P/E reads "Not meaningful" when the dated trailing diluted EPS is a loss or zero. An operating margin appears only when the filing states both the operating income and the revenue for the same year.
Sources: SEC filings, Gloria daily closes and CoinGecko. Oldest record refresh: Oct 28, 2026, 4:51 PM EDT. Refresh is not a price or filing date. Each row shows its price date and its annual fiscal period. Peers use industry code and company size, or editorial selection. Market cap multiplies that row price by the company share total the latest SEC filing states for every share class together. Trailing P/E divides it by trailing diluted earnings from SEC filings. Rolled earnings per share are approximate because share counts can change between periods. Both appear only when the SEC data carries its date, so they are estimates at one price date, not synchronized filing valuations. Not investment advice.
Token vs underlying stock
Current comparison for GOOGL. Historical observations may postdate this article.
Split-adjusted regular-session stock closes from the existing Gloria shared history, paired only with an observed token price within 45 minutes of 16:00 New York time. Up to 60 matched sessions. No interpolation or missing-date fill. Weekends and known early closes are omitted. Token units are not adjusted for issuer multipliers or dividend treatment. This compares quoted prices, not fair value, executable premiums or arbitrage returns. Solid line: token. Dashed line: stock. Exact quote times and values appear below. Historical observations. Retrieval refers to the token source. Token quote times use UTC. The fixed cohort shows its oldest member fetch.
Alphabet token vs Alphabet stock: exact source values
Quoted token and share units can have different rights, multipliers and dividend treatment. This is not an executable premium or arbitrage signal.
Google DeepMind just built something that could reshape how AI models pick up new tricks. SkillSmith, detailed in a paper submitted to arXiv on July 29, 2026, generates new model capabilities at inference time by blending stored text knowledge with parametric weight information. No retraining necessary.
The system uses prefix-tuning to treat stored model weights as a native modality alongside traditional text input. SkillSmith bridges the gap between text-based knowledge composition and parametric skill libraries, which have traditionally been processed separately. It synthesizes rich textual metadata and prefix weights together, then generates new prefix weights tailored for specific skills through what the researchers call an “instruction-steered paradigm.”
Lead author Lucio M. Dery and six co-authors, all affiliated with Google, demonstrated that SkillSmith significantly outperforms both text-only and weight-space-only baseline models in instruction-steered tasks.
Prefix-tuning itself isn’t new. It’s been a known technique for adapting models without full fine-tuning. What SkillSmith adds is the ability to treat those prefix weights as composable building blocks that can be mixed and matched based on instructions.
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Discussion on social platforms like X and LinkedIn indicates noteworthy industry interest in the paper. Google’s internal collaboration on this paper, with all seven authors under one roof, highlights the coordination advantage that large labs still hold over fragmented open-source or decentralized efforts.
Disclosure: This article was edited by Editorial Team. For more information on how we create and review content, see our Editorial Policy.
