Every valuation in the AI infrastructure trade rests on one quiet assumption: that compute stays scarce enough to keep prices high. That assumption is already showing cracks, and most investors haven't repriced for it.
Jim Cramer said it live on CNBC's Squawk on the Street on September 28, 2026, after seeing a demonstration of a new AI shopping capability from Meta Platforms (NASDAQ:META | META Price Prediction): “I'm an Amazon guy, but maybe I
A $44.92 Billion Quarter Alphabet (NASDAQ:GOOGL | GOOGL Price Prediction) spent $44.92 billion on capital expenditures in the second quarter of 2026, up 100.1% from a year earlier. The company reported the figure on July 22, 2026.
Amazon (NASDAQ:AMZN | AMZN Price Prediction) is in talks to move $8 billion of NVIDIA (NASDAQ:NVDA) Grace Blackwell chips into a special purpose vehicle, the Financial Times reported.
Headline Number: $1 Billion for the Neighbors On October 2, 2026, Amazon (NASDAQ:AMZN | AMZN Price Prediction) attached a dollar figure to something no chip plant can make: permission to build.
Amazon Web Services has made a $1 billion bet on FDEs. Finding the right workers isn't easy, an Amazon exec told Business Insider.
Amazon blocked Meta's Muse from crawling its website, while Shopify offers direct access to its sites. Amazon has a business that relies on customers using its website directly.
Amazon Web Services CEO Matt Garman said the company has stopped using nondisclosure agreements (NDAs) in its dealings with government agencies as it seeks approval to build new data centers.
Amazon, Meta Platforms, and Alphabet make a rock-solid growth stock lineup to outperform an index. All three posted revenue growth of 20% or more in the second quarter of 2026.
On top of technical performance metrics, there seems to be a new criteria to judge AI agents by: cuteness.
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The AI is not a chatbot bolted onto a document store. It calls the same analytics engine that powers every screen on this platform — so what comes back is a number it computed from raw history, with the command that produced it.
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A real analytics engine
Screening, backtesting, technicals, options analytics, correlations, seasonality and factor models — computed on demand from raw prices, never a stale cache.
It shows its working
Answers arrive with the charts, tables and tool calls behind them, so you can check the number instead of trusting a paraphrase.
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Upload filings, decks and research. Ask across them and the answer cites the page it came from.
Agents and workflows
Multi-step research that runs the platform's tools for you — screen, pull the history, compute, compare, then write it up.
MCP, CLI and API
The same command catalogue from Claude, your own agent, a shell or your pipeline. The answer on screen is the answer your job gets at 4am.
You ask
“How does NVDA usually trade through earnings?”
It calls
→ ka.options_expected_move(NVDA)
It answers
NVDA has averaged a 9.2% absolute move on the day after earnings and closed higher 67% of the time. Two in three reactions land between −4.2% and +16.3% — the distribution is skewed right, not symmetric.
Every figure computed live from our own history — not scraped, not summarised.
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