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Dear Reader,
137.3% gains in less than 12 months.
That's the hypothetical result Wall Street legend Marc Chaikin saw when he analyzed a basket of 5 top-performing AI stocks behind his new 401(ai) vision.
And Marc immediately noticed something else incredible.
SPEED.
Achieving a 137.3% gain would take the average index fund about 9 years.
Marc's 401(ai) stock basket did it in less than one year. (See the demo here.)
Of course, these results are not typical.
But for Marc, it raised an extraordinary question: Could AI help compress the amount of TIME some investors need to build wealth?
That's especially important if you're 50... 60... or older...
And don't feel like you have another 20 or 30 years to wait.
It's why Marc is now moving full steam ahead with the project.
He calls it the 401(ai).
But don't wait – Marc is aiming his system at the fastest-moving stocks. These opportunities could explode higher at any moment.
And you can see exactly how it works — including a free demo — right here.
Sincerely,
Vic Lederman
Publisher, Chaikin Analytics
P.S. The 401(ai) approach was built with SPEED in mind. Marc is aiming his system at the fastest-moving AI stocks. So, the longer you wait, the more likely you are to miss out on what could be Marc Chaikin's most important innovation yet. See Marc's new video by clicking here.
The AI Buildout Needs Power It Cannot Get: 4 Energy Stocks Already Have It
Author: Bridget Bennett. Posted: 10/4/2026.
Key Points
- Stansberry Research analyst Gabe Marshank argues the AI data center buildout cannot earn its cost of capital once compute pricing resets, putting neocloud business models like CoreWeave, Nebius Group and IREN at risk
- Atlas Energy Solutions and Liberty Energy are building behind-the-meter power that lets data centers skip a five to seven year grid interconnect wait
- Ormat Technologies and Fervo Energy anchor an enhanced geothermal story where the open question is engineering execution, not demand
- Special Report: The Untouched Energy Source Behind a New Eight Year Tax Break
Every valuation in the AI infrastructure trade rests on one quiet assumption: compute will remain scarce enough to keep prices high. That assumption is already showing cracks, and most investors haven't repriced for it.
Gabe Marshank, lead analyst of Market Maven at Stansberry Research, is not an AI skeptic. He uses the technology daily and calls the product brilliant. His argument is narrower and harder to dismiss. The technology works. The business model behind the buildout may not. And if he's right, the capital chasing data centers is headed somewhere very different from the capital chasing power.
The Return-on-Capital Math Behind a $4.5 Trillion Buildout
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Download Crypto Revolution now.Marshank puts the data center buildout at roughly $4.5 trillion by 2030. Based on his numbers, the compute layer would need to generate nearly $1.7 trillion in annual revenue to earn an adequate return on that capital.
For scale, he pegs the existing platform economy—Alphabet (NASDAQ: GOOGL), Microsoft (NASDAQ: MSFT), Amazon.com (NASDAQ: AMZN), Meta Platforms (NASDAQ: META) and Oracle (NYSE: ORCL), excluding consumer products—at roughly $1 trillion in revenue today. That means going from a standing start to $1.7 trillion in four years just to break even on the spending.
That matters because the market is treating capital expenditure as if it were value created. Marshank has seen that movie before. He points to the biodiesel buildout, when plants were valued at multiples of the money sunk into the ground until commodity economics took over and the equity went to zero.
AI Compute Pricing Is Already Rolling Over
His core claim is that data centers produce a commodity: AI compute. Supply of that commodity is set to rise enormously, and capitalism is likely to do what it always does when supply surges.
The key detail is that compute doesn't sit idle. Operating costs run near 3% of a data center's total capital cost, so the machines run regardless. Supply meets demand, and AI compute pricing becomes the clearing mechanism—much like an airline selling the last seat at whatever price travelers are willing to pay rather than flying it empty.
Marshank tracks published rate cards. By his count, prices were about $3.50 per kilowatt in 2022, climbed to roughly $4 to $4.25 as AI demand took off and are now beginning to correct, even as demand headlines remain loud. Long-term contracts muddy the picture with de-escalators and discounts to list, but he's convinced prices are headed down.
The comparison he keeps returning to is the bandwidth bubble of 1996 to 2001. Capital went into the ground, supply came online, pricing collapsed by roughly 90%, and Level 3, Global Crossing and WorldCom blew up on revenue that never matched the model.
Neocloud Stocks and the Missing Moat
This is where he gets blunt about neocloud stocks. He sees returns turning negative for CoreWeave (NASDAQ: CRWV), Nebius Group (NASDAQ: NBIS) and IREN Limited (NASDAQ: IREN), and he struggles to identify what differentiates them.
His framing is straightforward: the business model is spending itself. If a neocloud can commit $50 billion and Alphabet can commit far more, scale alone isn't a moat. He notes that IREN's $9.7 billion GPU cloud agreement with Microsoft, disclosed in a securities filing, implied returns he would describe as merely adequate in the hottest market anyone has seen.
The risk, in his telling, is simple. The capital was spent on the belief that rising volume would lift revenue. If volume rises while pricing falls, revenue never covers expenses.
Behind-the-Meter Power Offers a Shortcut Around the Grid
The bullish half of his case sits in energy, where the AI bottleneck is real and physical. Two names come straight out of the oil patch: Atlas Energy Solutions (NYSE: AESI) and Liberty Energy (NYSE: LBRT).
Both are building behind-the-meter power, meaning on-site generation that sidesteps the five-to-seven-year wait in the utility interconnection queue.
The U.S. grid is slow to absorb new sources because every addition has to be balanced.
Pressure pumping taught these companies how to deploy enormous horsepower on short notice. Pointing that capability at a data center is the same skill set with a better customer.
Geothermal Stocks Trade on an Engineering Question
Liberty Energy also moved into geothermal under founder Chris Wright, now the U.S. energy secretary. Marshank calls geothermal the most overlooked energy source in the world, and his two names are Ormat Technologies (NYSE: ORA) and Fervo Energy (NASDAQ: FRVO).
Ormat brings decades of legacy assets. Fervo is the pure play, applying horizontal drilling and multistage completions to create enhanced geothermal systems. Its Cape Station project in Beaver County, Utah, is permitted for up to 2 gigawatts, and Google has signed a 396-megawatt power purchase agreement there, with an option for roughly 600 megawatts more, within a broader 3-gigawatt framework running through 2033.
The stock trades below its $27 May IPO price, pressured by interconnection delays that pushed revenue recognition to the right. Marshank shrugs at that. The real question, he says, is engineering: whether a closed-loop system can retain the water it pumps underground. If it does, the upside is large. If water starts leaking from the wells, that's the sell signal.
Compare that timeline with nuclear, on which he was early and right years ago. New nuclear takes a minimum of five to seven years in the United States. Geothermal proof points could arrive within two.
What Should Actually Change Your Mind
Marshank doesn't use stop-losses, and he doesn't trade on headlines. Macro inputs are the most efficiently priced information in the market, so an edge is far easier to find in a single security than in the bond market. His horizon runs three to five years, looking for three- to five-times outcomes.
The risk is that pricing in geothermal and distributed power disappoints in the same way he expects compute pricing to disappoint. The upside is that both depend on physical scarcity rather than a commodity whose supply is multiplying.
His discipline is worth borrowing regardless of whether you buy the call: decide in advance what would make you sell, then watch that—not the tape. Because if your only exit plan is panicking after a 50% drawdown, you may as well panic now. For investors who want to run that kind of screen themselves, Marshank's team has built an AI-powered research tool for finding asymmetric setups at Stansberry Research.
Don’t Get NVIDIA’s Reflection AI Interest Wrong: Its Moat Could Deepen
Author: Thomas Hughes. Posted: 10/14/2026.
Key Points
- NVIDIA is reportedly exploring a deal to acquire or expand its stake in Reflection AI, a startup building open-weight foundation models and agentic tools.
- Such a deal would likely reinforce NVIDIA's hardware-centric moat rather than shift its strategy, though it could draw regulatory scrutiny similar to its Groq arrangement.
- With a consensus Buy rating from 55 analysts and roughly 40% implied upside, NVIDIA heads into its Nov. 18 earnings report amid decelerating but still strong growth forecasts.
- Special Report: The Untouched Energy Source Behind a New Eight Year Tax Break
Being AI-lab agnostic has long been a pillar of NVIDIA’s (NASDAQ: NVDA) strategy. Selling to all labs creates a deep moat, making the question of which one wins largely irrelevant: NVIDIA benefits because it sells hardware, ecosystems, and services to the industry. For now, that moat is based largely on silicon, but it deepens with every advancement along the stack, entrenching the company’s position as a comprehensive, universal AI development ecosystem.
So, what happens when the company breaks out of its mold? Speculation is mounting that this may become increasingly likely. NVIDIA is reportedly in early talks to acquire Reflection AI or increase its investment in the startup. Reflection AI is a newer company founded by former Alphabet (NASDAQ: GOOGL) DeepMind researchers.
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NVIDIA’s Moat Will Deepen If It Acquires Reflection AI
Investors should note that this move would not necessarily alter NVIDIA’s strategy; it could strengthen it. Owning a developer of open-weight AI tools would help NVIDIA deepen its moat. Making AI models more widely accessible keeps the focus on its hardware and ecosystem rather than on which model developer wins.
The likely outcome is that model developers and, more importantly, inference providers will continue investing in NVIDIA technology as demand for AI-enabled enterprise and consumer services grows. It is not just about the models but also the equipment used to run them. Models are already a dime a dozen; the question for businesses and consumers is which ones to use and how to access them. This move could cement, secure, and strengthen NVIDIA’s revenue and earnings outlook as the AI industry expands.
And NVIDIA is not new to the business. NVIDIA previously made an $800 million direct investment in Reflection AI, anchoring a $2 billion funding round that valued the company at $8 billion. The partnership not only provides Reflection AI with capital but also strengthens its ties to NVIDIA’s GPU ecosystem, which it uses to train its models. Nebius Group (NASDAQ: NBIS) is another critical player, having secured a $1 billion contract from Reflection AI for cloud capacity and services.
The question of whether owning a model maker would give NVIDIA a new revenue stream or strain its relationships with AI labs remains open. NVIDIA may be more interested in Reflection AI for its position in the AI ecosystem than for its revenue. If NVIDIA wants to keep low-cost, open-weight AI models under Western control, an acquisition could be strategically valuable. The risk is that China emerges as the leader in low-cost models, leading the industry toward its hardware and ecosystem rather than NVIDIA’s.
NVIDIA Will Control Reflection AI; the Only Question Is How
The most important question is how the deal might be structured. Investors should consider the possibility of a nonstandard workaround, such as a hire-to-acquire strategy, although even that is not assured. NVIDIA may simply hire Reflection AI’s staff and license its technology, potentially avoiding a lengthy merger review without necessarily escaping regulatory scrutiny.
Regulatory oversight also poses risks, as authorities have already expressed interest in other deals. NVIDIA licensed technology from Groq and hired several of its executives, a company also started by Alphabet engineers, in a similar arrangement. That move raised questions from lawmakers about whether NVIDIA intentionally sought to avoid regulatory oversight.
Those raising concerns include Senators Elizabeth Warren and Richard Blumenthal, who argue that NVIDIA’s AI dominance may threaten competition. In this environment, the company will have to address regulatory concerns carefully.
Analysts remain bullish on NVIDIA, although the potential deal’s impact remains uncertain. The acquisition could represent an extension of NVIDIA’s strategy rather than a strategic shift. The stock carries a consensus Buy rating from the 55 analysts tracked by MarketBeat, with a consensus price target suggesting more than 40% upside from mid-October trading levels.
NVIDIA’s next visible catalyst is its upcoming earnings report. Expected around Nov. 18, the report is forecast to show a deceleration to about 90% year-over-year revenue and earnings growth. The company consistently outperforms estimates and could do so again this quarter. Global investment in AI infrastructure across industries continues to drive demand for its core products. Investors will be watching growth as well as profitability, cash flow, and capital returns, all of which have been accelerating.
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