+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, 10:51 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.
Most AI executives talk about what their models can do. Lila Ibrahim wants to talk about whether the rest of us are ready for it.
In an interview with CNBC, the Google DeepMind executive said she believes the benefits of artificial intelligence outweigh the risks. She paired that optimism with a less flashy point: communities and workers need to be prepared for AI’s changing role in their lives.
A new job title for a new kind of problem
Ibrahim holds a role that didn’t exist at DeepMind until she stepped into it. She is the lab’s first Chief AI Readiness Officer.
She took on the title after serving as DeepMind’s founding Chief Operating Officer. She joined the lab in April 2018 as its first COO.
Ibrahim’s pitch centers on collaboration. Her approach calls for involving communities and workers in how AI gets developed, rather than handing them a finished product and wishing them luck.
The philosophy prioritizes public engagement, safety, and equitable access.
TIME named Ibrahim to its inaugural list of the 100 Most Influential People in AI, citing her focus on making sure the technology is developed with communities instead of being imposed on them.
Teachers as an early test case
The most concrete example of this strategy so far comes from education. DeepMind has worked on educational tools, including LearnLM, aimed at helping people in the classroom.
Those tools reportedly save teachers approximately 10 hours per week.
Why the optimism comes with homework
Saying AI’s benefits outweigh its risks is an easy line for any tech executive to deliver. What makes Ibrahim’s version notable is the condition attached to it.
Her argument isn’t that the benefits arrive automatically. It’s that they depend on whether people are prepared.
Disclosure: This article was edited by Diego Almada Lopez. For more information on how we create and review content, see our Editorial Policy.
