Oracle's infrastructure may be cheaper and better-suited for AI workloads, but its heavy dependence on major client OpenAI has made many investors nervous. Amazon operates a diverse business that pioneered the cloud computing space, but its increasing capital expenditures have strained its balance sheet.
AI has already gutted white-collar tech jobs by the hundreds of thousands, but the next wave targets something far bigger: the physical economy and the wages of workers who never touched a keyboard.
Amazon (NASDAQ:AMZN | AMZN Price Prediction) has quietly become one of the most reasonably priced names in the Magnificent Seven, trading at a forward multiple that looks modest against the pace of
A company's earnings are important, but they don't provide the full financial picture. Cash flow is how companies actually pay for things, and right now, Amazon is spending more cash than it generates.
AWS sales increased 37.5% year over year in the second quarter, its best peformance in 18 quarters. The chip business has long-term commitments from companies like Anthropic and OpenAI.
David Tepper's Appaloosa Management increased its Amazon position by 15.8% last quarter, with the "Magnificent Seven" stock now making up 16% of its overall stock portfolio. The outsize wager on Amazon has continued paying off.
Amazon and Alphabet are still great deals, just not as cheap as you may think. Using 2027 earnings projections is a better way to value the stocks.
Druckenmiller sold some Sandisk stock before its third-quarter tumble. Cloud computing is a major beneficiary of increased AI workloads.
Amazon is taking a page from Alphabet's book and growing its custom AI chip offerings. Amazon has positioned itself to be a winner no matter how it fares in the AI arms race.
The artificial intelligence boom is turning corporate capital spending into a different kind of arms race.
Ask the market a question. Get a calculated answer.
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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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Multi-step research that runs the platform's tools for you — screen, pull the history, compute, compare, then write it up.
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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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