Dear Reader,

Earlier this year, central banks bought a staggering amount of gold in a single month.

Was it $1 billion? That's roughly what the analysts thought going in.

$2 billion? $3 billion? $5 billion?

Not even close.

The real number came in north of $9 billion — and Goldman Sachs publicly admitted their model had been undercounting actual purchases by more than 500%.

So here's what two of Goldman's senior strategists found when they dug into the gap:

Central banks have been accumulating physical gold at nearly twice the pace anyone on Wall Street had estimated.

(spoiler… find out what this signal could mean for your retirement account before the rest of the crowd catches on.)

Since then, the banks have quietly revised their monthly estimates upward. The new figure: nearly $7 billion per month on average.

That's close to a 100% increase from where their models were before.

So why the gap? The answer isn't reassuring.

A significant portion of those purchases were happening completely off the books — sovereign buyers moving through channels that don't show up in standard trade data.

Goldman now expects these buyers to keep accumulating at an $8–9 billion monthly pace through the end of 2026. That's close to $100 billion in a single year.

Which is why Goldman is holding its year-end price target at $5,400 per ounce. And they're not alone:

- Bank of America: $6,000/oz

- JPMorgan: $6,000/oz

- Wells Fargo: $6,300/oz

Request your free Wealth Protection Playbook from American Alternative Assets →

When central banks quietly accelerate, the supply left for everyone else gets thinner every day. There's already an IRS-approved process to move funds from eligible 401(k)s, IRAs, or TSPs into physical gold — tax-deferred and penalty-free.

Get the free guide and see exactly how it works.

Grab it here →

Sincerely,

American Alternative Assets

P.S. The next central bank data print is days away. If Goldman is right, the numbers only get bigger from here. Get the free guide before it drops.

Quantum Earnings Could Decide Whether the Sector’s Sell-Off Has Gone Too Far

Authored by Nathan Reiff. Date Posted: 7/31/2026.

The first week of August 2026 will provide another major test for the quantum computing industry, as several of its leading companies are slated to release their second-quarter 2026 earnings. Heading into earnings season, it is understandable that investors would be skeptical. Shares of pure-play quantum computing firms have been slammed throughout much of the year, with shares of leaders such as IonQ Inc. (NYSE: IONQ) and D-Wave Quantum Inc. (NYSE: QBTS) falling approximately 25% and 35% year to date (YTD), respectively.

In recent weeks, it has appeared that the appeal of speculative, pre-profit quantum names has cooled considerably. This has occurred even as some firms in the space posted better-than-expected results for the first quarter of the year. One question investors may seek to answer heading into earnings is whether any of these firms has had a catalyst large enough to reverse the industry-wide downward trend. IonQ and D-Wave both have promising developments that could help in this respect, although neither is likely to be reflected directly in second-quarter earnings.

IonQ's SkyWater Acquisition May Provide a Boost

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With 1 in 3 Super Bowl viewers using buy-now-pay-later services and 40% of Americans carrying more credit card debt than savings, Tilson believes Elon's message reveals a major economic current - and a clear signal for where smart money should be positioned.

Watch Tilson's free presentation to see what he thinks you should do now

IonQ announced in January 2026 that it intended to acquire SkyWater Technology (NASDAQ: SKYT), a major domestic semiconductor foundry. The acquisition recently received final regulatory approval and closed on July 31. The deal should dramatically accelerate IonQ's capacity to build quantum chips while strengthening its domestic supply chain. This may be transformational, as it allows the company to complete the full scope of its production without relying as heavily on imported goods or facing applicable tariffs.

How the acquisition might be reflected in the company's upcoming earnings, however, is unclear. The deal's most significant financial impacts are likely to appear in future quarters, but IonQ management may use the earnings call to share key updates about its plans to integrate SkyWater's operations. Those updates could give investors insight into what to expect in the coming months.

Otherwise, investors will likely be watching to see whether IonQ can continue its positive revenue trend, which stood at 755% year-over-year (YOY) growth in the first quarter. The company established itself as one of the fastest-growing pure-play quantum firms in terms of sales, so it will be imperative that it maintain—or, ideally, improve—that revenue trajectory in the second quarter.

D-Wave's Major Agreement With AT&T Shows a New Path

While IonQ's first-quarter results were strong in multiple respects, D-Wave's results stood out for less positive reasons. The company saw a sharp year-over-year decline in revenue because of lumpiness in large system sales and came in well below analyst expectations for the quarter. Still, with bookings up significantly and a healthy pipeline, D-Wave may be poised for a turnaround in the second quarter.

D-Wave has set itself apart through its two-pronged approach to quantum technology. Although this strategy has been promising, it has yet to translate into distinctive revenue performance compared with other quantum firms. The recent announcement of the company's agreement with AT&T (NYSE: T), however, may signal a change. AT&T plans to use D-Wave's annealing technology to help power its agentic AI tools. The company has announced that early applications have reduced certain processing times from one hour to less than 15 seconds, suggesting significant transformative potential.

The key development, however, may be that AT&T is also evaluating D-Wave's gate-model technology for a separate set of potential applications involving quantum security and communications. This suggests a path forward for D-Wave in which it can provide two distinct sets of quantum tools with different applications that may appeal to the same customers. Such a strategy could help the firm stand out relative to others in the quantum space and drive customer retention.

Rigetti's Mounting Costs Remain a Concern

Rigetti Computing (NASDAQ: RGTI) has been hit hardest among these three companies, with shares falling nearly 40% YTD. One of the company's biggest challenges is rising costs. In the first quarter of 2026, operating expenses surged to more than $27 million, leading to a non-GAAP net loss of nearly $15 million. With costly fab upgrades and other capital expenditures (CapEx) anticipated throughout the year, expenses could remain a challenge in the company's upcoming earnings reports.

Still, Rigetti's revenue growth has been solid, and the company has already made it clear that it will include a portion of its Novera QPU revenue in its second-quarter earnings. The question will be whether revenue growth is sufficient to overcome investor concerns about costs for the remainder of the year.

The AI Chip Blockade Is Creating a Shadow Market

Authored by Jeffrey Neal Johnson. Date Posted: 8/4/2026.

Global demand for artificial intelligence (AI) appears resilient in the face of geopolitical blockades. Over the past year, United States export controls designed to limit artificial intelligence development in restricted markets have catalyzed a multibillion-dollar hardware shadow market across Southeast Asia. Sovereign entities and enterprise startups route accelerator clusters through proxy cloud architectures to bypass strict trade barriers. This systemic circumvention exposes a singular market truth: the appetite for artificial intelligence infrastructure can outweigh regulatory friction. When capital hits a wall, it routinely finds another way around.

Finding Compute in a Silicon Desert

Recent intelligence indicates that Chinese artificial intelligence startup Moonshot has secured access to a cluster of about 20,000 advanced compute chips. Instead of purchasing these units directly, Moonshot used third-party leasing structures routed through regional cloud architectures. This setup allowed the company to train foundational models without violating the letter of international trade laws. The scale of this evasion highlights how structural supply chain workarounds can operate seamlessly in plain sight.

Capital Floods the Valley: The Gray Market Premium

Elon’s big $266,000 per second purchase (Ad)

Elon Musk bought Super Bowl ad time at $266,000 per second - something he has never done before. 125 million Americans watched, but Whitney Tilson, former manager of a $200 million hedge fund, says most investors missed what it actually means.

With 1 in 3 Super Bowl viewers using buy-now-pay-later services and 40% of Americans carrying more credit card debt than savings, Tilson believes Elon's message reveals a major economic current - and a clear signal for where smart money should be positioned.

Watch Tilson's free presentation to see what he thinks you should do now

Advanced silicon consistently moves through third-party jurisdictions, with countries such as Thailand emerging as critical hubs for gray-market distribution. Buyers in restricted regions absorb significant hardware markups, paying premiums over list prices to secure the processing power they need.

In a traditional hardware cycle, a large markup would immediately compress margins, inflate the cost of goods sold and force buyers to delay technology upgrades to protect their return on invested capital. Artificial intelligence operates on a winner-take-all paradigm. The opportunity cost of falling behind in foundational model training outweighs the financial burden of gray-market premiums.

Rather than triggering demand destruction or forcing organizations to slash capital expenditures, these price hikes are treated as a standard cost of doing business. The willingness to pay aggressive premiums demonstrates the inelasticity of the artificial intelligence compute market. When a resource becomes a fundamental requirement for future economic survival, basic price-elasticity models can break down entirely.

An Unending Tide of Upgrades

The shadow infrastructure extends well beyond older hardware models. Recent investigations suggest that next-generation accelerator units, including highly sought-after architectures, are being actively acquired through subsidiaries in Southeast Asia. A continuous upgrade cycle persists in restricted markets, fueled by an underground supply chain that adapts faster than regulators can draft new policies.

The existence of a sophisticated evasion infrastructure serves as a strong indicator of demand inelasticity. When regulators attempt to restrict access to critical technology, they test the market's reliance on that asset. In the case of advanced graphics processing units, the market has provided a clear answer: compute power is non-negotiable.

The Rise of Sovereign Cloud Proxies

The real beneficiaries of this geopolitical friction are the intermediaries. Regional cloud providers now operate as centralized compute proxies. They purchase hardware through legitimate or gray-market channels and lease the processing power to startups and research institutions. This proxy model allows end users to scale parameter-intensive models without the liability of direct hardware ownership.

Consider the strategic positioning of technology conglomerates operating within these restricted zones. Many are developing dual-moat strategies to defend against margin compression caused by imported hardware premiums. By blending imported chip clusters with indigenous silicon accelerators, they create highly resilient, hybrid infrastructures.

Alibaba Group (NYSE: BABA) recently launched its Qwen 3.8-MAX model, demonstrating inference capabilities that rival those of leading Silicon Valley models. Alibaba achieves this by operationalizing compute proxy models. While Alibaba facilitates access to thousands of imported chips from developers such as NVIDIA (NASDAQ: NVDA) for external startups, the company also heavily deploys proprietary Zhenwu M890 artificial intelligence accelerators for internal workloads.

This hybrid approach creates a sanction-resistant technological moat. By activating fewer parameters during inference, Alibaba structurally reduces compute costs while relying heavily on indigenous silicon to protect its earnings before interest, taxes, depreciation and amortization margins. This operating leverage is crucial when navigating an environment in which standard computing hardware is artificially scarce. It allows sovereign entities to sustain model training while mitigating the exorbitant costs of the shadow market.

Hyperscaler Backstops: Insulating the Silicon Titans

This dynamic neutralizes some of the transient geopolitical risks facing silicon developers. Headlines about overseas legal probes or the detention of supply chain personnel may cause short-term volatility, but the underlying fundamentals remain exceptionally robust.

Semiconductor developers are heavily insulated by the substantial collective capital expenditures of cloud hyperscalers, which frequently exceed $200 billion annually. These hyperscalers guarantee baseline demand, enabling silicon developers to monetize their inventory long before it reaches the shadow market.

Unprecedented global capital expenditure cycles support top-line revenue expansion for the companies engineering these chips. The hardware inevitably finds a well-funded buyer, regardless of its destination or the convoluted path it takes to get there. This inherent business strength allows developers such as NVIDIA to pursue substantial capital returns, including its recent $80 billion share repurchase authorization and expanded quarterly dividends. These measures reinforce balance sheet strength and mitigate downside beta.

Riding the Wave of Disruption

The systemic rerouting of hardware confirms that regulatory friction redistributes capital flows rather than halting them. United States blockades have helped create a decentralized evasion infrastructure that shields the global artificial intelligence ecosystem from meaningful demand contraction. The capital commitments of major technology conglomerates and sovereign nations serve as a backstop for semiconductor valuations.

Investors might consider looking past the immediate headline risks associated with export controls and smuggling probes. The multibillion-dollar shadow market is not necessarily a sign of industry weakness; it is empirical evidence that end-market demand remains exceptionally strong.

Those evaluating long-term allocations in the semiconductor and cloud-proxy space may view temporary, regulation-induced dips as compelling entry points, provided the broader trend of inelastic compute demand holds firm. Investors could add these infrastructure and cloud-proxy stocks to their watchlists as global artificial intelligence capital expenditure cycles continue to expand.