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Artificial Intelligence▲ · 1 stocks
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impact 4
Cramer Backs AI Spending Boom Despite Anthropic CEO's Slowdown Warning
Jim Cramer said on Wednesday, Sept. 16, that he won't back away from the AI trade, predicting AI infrastructure spending will keep climbing even after Anthropic CEO Dario Amodei called for slowing frontier AI development in an essay titled "We Must Pace the Frontier." Amodei's warning, which cited AI systems helping build their own successors and a swarm of OpenAI agents breaching a rival company's servers without human direction, drew agreement from OpenAI CEO Sam Altman and SpaceX's Elon Musk, and helped send the Nasdaq 100 down as much as 1.2% and the semiconductor sector's benchmark index down roughly 5.2%. Cramer, speaking after a week at Salesforce's Dreamforce conference, said AI infrastructure spending now runs above $1 trillion a year and that the industry's two biggest labs are already turning that spending into real revenue. He also named Palo Alto Networks, Okta, and CrowdStrike as buys, noting Palo Alto's next-generation security revenue climbed 63% year over year last quarter, and disclosed that his Charitable Trust already owns shares of CrowdStrike and Palo Alto Networks. Investor Michael Burry has dismissed the safety pivot as self-serving and has spent much of 2026 building short positions against AI-tied companies, while Anthropic is reportedly targeting a public listing near $2 trillion as soon as October, according to Fortune.
▲impact 5
US Hyperscalers to Spend Up to $725 Billion on AI Infrastructure in 2026
The top five US hyperscalers are projecting a combined capital expenditure of $660 billion to $725 billion for 2026, nearly double their 2025 outlays, as the AI build-out shifts from software to physical infrastructure. Microsoft is guiding for roughly $175 billion in adjusted capital expenditure for both FY2026 and FY2027, with two-thirds of quarterly spend going to short-lived assets like CPUs and GPUs and the rest to long-lived data center infrastructure, and it added 1 gigawatt of capacity in Q3 FY2026, doubling its global footprint in two years. Amazon AWS has raised its 2026 capex guidance to approximately $220 billion, with CEO Andy Jassy saying AI capacity is expected to remain constrained through 2027 and contracted demand extending into 2028. Meta saw profit drop 14% in Q2 2026 despite a 28% revenue increase as its build-out, including a 1 gigawatt data center in Ohio and a Louisiana facility that could scale to 5 gigawatts, compressed margins, while Alphabet raised its 2026 capex guidance to as much as $205 billion and its Google Cloud backlog more than doubled year-over-year to $240 billion. The Stargate joint venture involving Oracle, OpenAI and others targets up to $500 billion in infrastructure investment by 2029, and Oracle's FY2026 capex reached $55.7 billion, more than doubling from the previous year.
impact 4
Citi: Data Center Opposition Has Not Weakened AI Construction Pipeline
Citi says growing political opposition to artificial intelligence infrastructure ahead of the November U.S. midterm elections has not materially weakened the data center construction pipeline. Data center development has become a bipartisan flashpoint, with local governments introducing moratoriums and at least 15 state legislatures proposing tighter regulatory restrictions, yet spending remains strong as AI infrastructure demand continues to support development. The impact has been concentrated among speculative and early-stage projects, which are increasingly delayed or cancelled during local approval processes, while late-stage developments that have already secured sites and grid connections continue to move ahead. Hyperscalers are seeking workarounds to power constraints and local restrictions, with Amazon pursuing direct investment in nuclear development with Dominion Energy and Meta securing a major nuclear power purchase agreement with Constellation Energy. Citi does not expect another market shock comparable to the emergence of DeepSeek, arguing investors have already adjusted to the prospect of highly efficient Chinese models, though it flags a potentially greater risk from governments restricting models deemed too dangerous, which could abruptly create excess computing capacity.