+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 27, 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 27, 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 27, 2026, 8:48 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 Cloud wants its AI agents to stop acting like chatbots and start acting like colleagues. The company has unveiled Gemini Agents that can run long tasks, move across multiple apps, and keep their own email inbox, calendar, and Drive storage.
The announcement came on April 22, 2026, at Google Cloud Next, the company’s annual conference in Las Vegas. The agents sit inside a broader product called the Gemini Enterprise Agent Platform. It is built to help businesses build, scale, govern, and fine-tune autonomous AI agents powered mainly by Gemini models.
What Google actually shipped
Under the hood, it is an evolution of Vertex AI, Google’s existing machine learning platform. Instead of just hosting models, it now handles the full lifecycle of agents that are expected to operate on their own.
These agents can work autonomously on complex enterprise tasks for up to several days. Subsequent updates set the maximum runtime at 7 days.
A feature called Memory Bank lets agents retain context over time, so they don’t forget what they were doing halfway through a workflow.
Google also built in a stack of developer tools. The lineup includes:
Agent Runtime: the infrastructure that keeps agents running continuously.
Gemini Enterprise app: the main user-facing interface, where people discover, create, and manage their agents.
Model choice is broad. The platform offers access to over 200 models, including Gemini 3.1 and third-party options such as Anthropic’s Claude models.
Why the inbox matters
The Workspace integration connects deeply with Gmail, Calendar, and Drive, and agents come with a persistent identity of their own. Giving an agent its own email and calendar means it can operate as a distinct participant in a workflow. It can receive requests, schedule things, and store files without masquerading as a human employee.
Security is part of the pitch, too. Google says the platform uses cryptographic agent identities, which are essentially tamper-resistant digital ID badges that verify an agent is who it claims to be.
How the platform has evolved since launch
Updates in July 2026 expanded the availability of the extended runtime and memory features, strengthening the platform’s ability to handle lengthy workflows. Product documentation has continued to be updated, with versions current as of early October 2026.
What this means for the cloud wars
Google Cloud is positioning this platform squarely against Microsoft Azure and AWS in the race for enterprise AI spending.
Google’s advantage here is Workspace. Agents that natively live inside Gmail, Calendar, and Drive give Google a distribution channel that pure infrastructure providers cannot easily replicate.
Offering Anthropic’s Claude alongside Gemini makes the platform more attractive to customers who want flexibility. It also means Google is competing on tooling and governance rather than locking customers into a single model family.
Disclosure: This article was edited by Diego Almada Lopez. For more information on how we create and review content, see our Editorial Policy.
