+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.
The best AI agent available today can pass about half of a realistic engineering exam. That would not get you licensed.
Google Cloud AI Research has released a benchmark called PLE: Physical-Engineering’s Last Exam. It tests whether frontier AI agents can do the work engineers actually bill for. The top performer, Anthropic’s Claude Opus 5.5, posted a mean score of 50.6%. The harder number is this one: the benchmark found its top agent met every hard engineering constraint in only 41.3% of design tasks.
Those constraints are the rules that keep bridges standing and circuits from catching fire. They matter more than the headline score.
What the PLE benchmark actually measures
The benchmark contains 85 long-horizon tasks spread across four disciplines. Mechanical engineering accounts for 30 of them, electrical for 27, civil for 18 and structural for 10.
Each task asks the agent to produce a billable deliverable. Think calculation packages and design reviews, the documents a client pays an engineering firm to deliver.
The agent works from a project corpus that averages 61 files per task. So the model has to sift through a pile of drawings, specs and data before it can start on the actual problem.
Task construction followed two tracks. Experts authored 44 of the tasks end to end. The remaining 41 came from an adversarial creator-solver pipeline, with experts reviewing the output.
The winning performance was not cheap in compute terms. Claude Opus 5.5 consumed roughly 27 million tokens per task and needed 63 minutes to finish each one. That works out to approximately $17 in inference costs per task.
Seventeen dollars for an hour of engineering output sounds like a bargain. The catch is that a deliverable violating a hard constraint is not a bargain at any price.
Why hard constraints are the real story
The gap between the 50.6% mean score and the 41.3% constraint-pass rate tells you where these systems stumble.
A mean score can reward partial credit. An agent might get the methodology mostly right, format the report nicely and still produce a design that breaks a non-negotiable requirement.
The research framing is direct on this point: meeting these stringent constraints is where the difficulty lies, and even the leading models have substantial room to improve. An agent that clears every hard requirement in fewer than half of design tasks is not ready to sign off on anything.
Background: a push toward realistic evaluation
The paper went up on arXiv around October 21-23, 2026, under the identifier arXiv:2610.30537.
Google paired the paper with a public GitHub repository. It includes 29 development tasks along with supporting tools.
The other 56 tasks stay hidden. They are being held back for a potential competition leaderboard.
Keeping most of the test private is a sensible defense against a familiar problem. Once benchmark questions are public, models can end up trained on them, and scores start measuring memory rather than skill.
What this means for engineering and AI developers
For engineering firms, the results draw a clear line. Current agents look capable of meaningful assistance on document-heavy work, such as digesting a 61-file project corpus and drafting a calculation package. They do not look capable of working unsupervised on anything where a missed constraint carries real consequences.
The public development tasks give researchers something concrete to iterate against, which the research summary suggests could encourage collaborative improvement. The hidden competition set means any claimed gains will eventually face an independent test.
The cost profile deserves attention too. If models need tens of millions of tokens and about an hour per task just to reach roughly coin-flip performance, efficiency will matter as much as accuracy.
The metric to track is the constraint-pass rate, not the mean score. At 41.3%, the conversation about AI in licensed engineering is still mostly theoretical.
Disclosure: This article was edited by Diego Almada Lopez. For more information on how we create and review content, see our Editorial Policy.
