Market News: |
AI spending hits $600B+ in 2026 as tech giants face "doom loop" concerns over massive capex sustainability Federal debt to reach 120% of GDP by 2036 with deficits up $1.4T; CBO expects 2.2% growth, 2.8% inflation S&P 500 targets remain bullish with JPMorgan at 7,500-8,000, Morgan Stanley at 7,800 despite volatility Energy leads sector rotation gaining 14.18% in January as market breadth expands beyond tech Key data releases this week including retail sales and employment data to test Fed policy path Before the Next AI IPO Wave Hits—See Why Thousands Are Investing Here First* (ad)
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Let's start with a number that deserves a moment of silence. |
$650 billion. |
That's what Big Tech is spending on AI infrastructure in 2026. Not over five years. Not as a vague long-term plan. This year. And it's up roughly 60% from what they spent in 2025. |
To put that in terms that actually land: it's more than the entire GDP of Switzerland. It's more than 21 of US' biggest industrial companies, railroads, defense contractors, automakers, utilities, are expected to spend combined across all of 2026. |
Every dollar of it is chasing one thing: the ability to build, run, and scale AI at a level that makes today's internet look like dial-up. |
So here's the real question. Who's actually collecting all that money? |
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Why They're Spending This Much |
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For years the story was simple: need AI chips? Call Nvidia. And Nvidia delivered, capturing roughly 90% of the AI accelerator market. That's still mostly true today. But something is quietly shifting underneath. |
Big Tech is designing its own custom chips. Amazon's is called Trainium3. Google's is the TPU v6. These aren't side projects. These are strategic bets with enormous implications. |
Here's the simple version. Nvidia GPUs are incredibly powerful, but built for a wide range of tasks. Amazon says Trainium3 could cut AI training costs by up to 50%. At their scale, that's billions of dollars a year in savings. |
And the timing matters. AI workloads are shifting from training (building the models) to inference (running them constantly to answer real queries). Inference is always-on and cost-sensitive. Custom silicon built for that specific task wins on economics. The companies with their own chips have a durable structural advantage over those still renting Nvidia's hardware. That advantage only grows over time. |
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The Energy Problem |
Here's the part that surprises most people. The single biggest constraint on AI growth in 2026 isn't chips. It's not money. It's electricity. |
Large AI models need baseload power: reliable, always-on electricity, 24 hours a day, 365 days a year. Solar and wind are genuinely valuable, but their intermittent nature makes them insufficient for data centers running flat-out around the clock. The math simply doesn't work at scale. |
That's why Microsoft signed a deal to restart Three Mile Island as a dedicated nuclear power source for its AI data centers. Why Amazon and Google are investing in small modular reactors. Why are these companies funding new substations and high-voltage transmission lines? Nuclear runs at near-100% capacity, produces zero carbon, and is entirely predictable. For an AI data center, it's not a political statement. It's a practical engineering decision. |
The gap between what renewable energy can deliver today and what AI actually needs? That's a multi-decade business opportunity for the right energy companies. |
| | | | What will be the biggest winner from AI's electricity demand? | |
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Who's Actually Winning |
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Here's the thing about a spending boom this big: the companies writing the checks aren't always the ones making money from them. |
The winners are the suppliers, the companies building what goes inside every data center, every custom chip, and every power grid upgrade. |
Nvidia $NVDA ( ▼ 2.21% ) is still the foundation. About 90% of AI accelerator spending flows through them, Q4 revenue hit $57 billion, and analysts expect $65.58 billion on February 25 when they next report. The forward P/E sits around 26x, reasonable for this growth trajectory. 59 Wall Street analysts rate it a Strong Buy. The custom chip trend is a long-term headwind, but it plays out over years, not quarters. |
Marvell $MRVL ( ▲ 0.49% ) and Broadcom $AVGO ( ▼ 1.81% ) are the ones most retail investors are sleeping on. When Amazon builds Trainium3 and Google builds a new TPU, they don't do it alone, they need design partners with deep semiconductor expertise. Marvell and Broadcom are those partners. They supply the networking silicon, chip interconnects, and co-design expertise that hyperscalers can't replicate internally. Marvell's data center revenue grew 37.8% YoY. Its analyst consensus price target is $115, roughly 46% above where it trades today at ~$78. Its stock jumped 10% in a single session on Amazon's announcement. Broadcom moved 7%. Watch March 5: Marvell's earnings could be a significant catalyst. |
Vertiv $VRT ( ▼ 0.84% ) solves a problem nobody talks about enough: heat. The H100 GPU can consume up to 700 watts. A full rack of them generates heat that standard air cooling simply can't handle. Liquid cooling is becoming a primary requirement, not an afterthought. Vertiv makes exactly that: he liquid cooling, power management, and thermal control systems keeping AI data centers from literally melting down. The stock surged 10% on Friday following the latest capex announcements. |
Constellation Energy $CEG ( ▲ 4.46% ) and Vistra $VST ( ▲ 5.14% ) are the energy gatekeepers. They own the reliable, always-on power that Big Tech desperately needs, and that gives them unusual leverage in long-term contract negotiations. These decade-long, fixed-rate power deals are becoming the hottest contracts in the AI economy. Vistra rose nearly 5% on Friday. These aren't your grandfather's utility stocks anymore. |
Arista Networks $ANET ( ▲ 4.79% ) and Lumen $LUMN ( ▲ 4.88% ) handle what happens inside and between data centers. AI clusters move enormous amounts of data between thousands of chips at near-zero latency, constantly. Network spending scales almost linearly with cluster size. Lumen jumped 30% in a single session — a sign the market is waking up to just how critical network infrastructure is to this entire buildout. |
Micron $MU ( ▼ 0.56% ) is the memory play. Every AI query requires massive amounts of high-bandwidth memory to function. Micron's HBM3E chips are purpose-built for exactly this, and its HBM4 is already in high-volume production. The stock is up over 300% in a year. And yet the forward P/E sits at just 12.3x, one of the lowest valuations in the semiconductor space. That gap is worth paying attention to. |
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What to Keep Honest About |
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This isn't a one-sided story. $650 billion in spending is also $650 billion in costs, and revenue doesn't always follow capex on schedule. Both Amazon $AMZN ( ▼ 0.41% ) and Google $GOOG ( ▼ 1.08% ) stocks dropped after their announcements. Investors are genuinely nervous about the timeline. |
Supply chain constraints are real. New semiconductor fabs take years to build. SMRs won't be operational until the late 2020s at the earliest. |
And companies like Marvell and Micron depend heavily on a small number of hyperscaler customers, if one pauses a design program, it shows up fast in the numbers. |
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Bottom Line |
The infrastructure spending is locked in. Amazon doesn't un-spend $200 billion. The question isn't whether this buildout happens, it's which companies are supplying the components that go inside it. |
Nvidia captures the GPU demand. Marvell and Broadcom run the custom chip design programs. Micron supplies the memory. Vertiv keeps the data centers cool. Constellation and Vistra own the power. Arista and Lumen move the data. |
That's the full AI infrastructure stack and every layer of it is benefiting from $650 billion in committed annual spending. |
Mark your calendar: February 25 (Nvidia earnings) and March 5 (Marvell earnings). Those two dates will tell us more about where this buildout is really headed than anything else in Q1. |
The picks and shovels of the AI era are being bought in enormous quantities. |
The smart question isn't whether to pay attention. It's which shovel maker you trust. |
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Disclaimer: This analysis is for educational purposes only and should not be considered investment advice. Always do your own research before making investment decisions. |
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