Dear Friend,

On June 11th, SpaceX went public.

Most investors were locked out.

By the closing bell, roughly 4,400 SpaceX employees were millionaires.

But here's what almost nobody realizes.

Only about $1 of every $20 was allowed to trade that day.

The rest is frozen.

On December 8th, the freeze ends.

More than $600 billion comes loose.

Dylan Jovine believes the lion's share floods into one small company.

It trades for $14.

And NASA just hired it for a job no company has ever done.

See the #1 stock to own before December 8th here >>

The Buck Stops Here,

Kelly Maguire
Behind the Markets

IonQ’s AI Breakthrough Looks Big—But Can It Drive Revenue?

Author: Nathan Reiff. Published: 9/22/2026.

IonQ Inc. (NYSE: IONQ) has already delivered an impressive long-term run in 2026, despite shares being down about 11% year to date (YTD).

Even as the broader quantum computing industry remains highly speculative, IonQ has built an advantage. The company has achieved significant sales and revenue growth, lending some support to its premium valuation. Shares are up more than 250% over the last five years.

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The company has also pulled ahead in several technological areas, including through its Capella acquisition, which demonstrates its ability to expand into defense, space and sensing.

Now, IonQ is positioning itself to benefit from another major development. In September 2026, IonQ and partners at Oak Ridge National Laboratory and NVIDIA Corp. (NASDAQ: NVDA) demonstrated promising advances in using generative AI to aid the design of quantum circuits and other hardware, significantly reducing compilation runtimes. The advancement could boost IonQ's prospects in the near term, even as other quantum firms make compelling cases of their own.

A Big Step Toward Commercial Use...But Not All the Way

The latest engineering feat achieved by this collaboration strengthens IonQ's argument that a hybrid model—utilizing both classical AI and quantum computing—could provide a viable path toward better quantum results. With generative AI helping write quantum optimization circuits, IonQ may be able to bypass the lengthy, latency-filled trial-and-error process that has governed this work for years.

One major accomplishment of the generative AI process was reducing circuit-finding runtime from roughly 11 minutes to about 28 seconds for 12 qubits. This is significant, even apart from the improvement in the quality of the generated solutions. Faster computation means lower costs for commercial clients, so IonQ's achievement could have a material impact on its customer base.

The operative word, however, is "could." As with many other advancements in the quantum computing space, whether by IonQ or its competitors, excitement surrounding a technological achievement must be matched by a clear path to increased revenue or other measurable benefits to demonstrate commercial relevance. To be sure, IonQ is already outperforming some of its competition in this regard, with a 287% year-over-year (YOY) revenue increase last quarter alone. However, it remains unclear when or exactly how the latest technological leap will directly affect customers.

NVIDIA's Role in the Transformation

The report detailing the accomplishments resulting from this collaboration noted that the results were achieved using a single NVIDIA H200 graphics processing unit (GPU) as part of an Oak Ridge supercomputer. The process also utilized NVIDIA's CUDA-Q open platform, among other tools. This may mean that, with the right computing hardware, enterprise customers could use these same technologies.

NVIDIA's products are deeply embedded across multiple stages of this process, potentially pointing to a lucrative path forward as a quantum hardware tollbooth for the mega-cap chip giant.

With NVIDIA now primarily known for its AI tools, this partial pivot could inject new momentum and diversify the company's offerings.

Where Other Quantum Firms Stand

Investors might add this latest accomplishment to the list of IonQ's strengths relative to its competitors. Those strengths also include its large commercial customer base, high-fidelity trapped-ion architecture, and growing number of partnerships with cloud companies and government agencies.

Still, many rivals, including pure-play companies like D-Wave Quantum Inc. (NASDAQ: QBTS) and larger technology firms like IBM Corp. (NYSE: IBM), are pursuing different architectures and technological approaches to these same challenges. It is anyone's guess which approach will ultimately prove most competitive, even if IonQ appears to be in the lead today.

In the meantime, while profitability remains elusive, IonQ has done a good job of managing its capital deliberately and strategically. It must continue to do so to have the opportunity to realize this development's full potential.

The firm reported an annual net loss of more than $510 million last year, underscoring how expensive it is to operate a quantum computing company that is investing heavily in advancing its technology. With no long-term debt and a current ratio of about 10.7, IonQ appears well positioned to maintain its strengths.

NVIDIA Just Named AI's Next Bottleneck—And These 3 Stocks Sit Right In It

Authored by Bridget Bennett. First Published: 9/20/2026.

AI's loudest voices spent the week telling everyone to slow down, and the market took them at their word. AI infrastructure names sold off as a month that is traditionally unkind to stocks got underway.

Jason Bodner, co-founder of quantitative research firm MoneyFlows, thinks investors are reacting to the wrong headline. The one that matters came out of NVIDIA's (NASDAQ: NVDA) August earnings call, where CEO Jensen Huang guided to roughly 70% revenue growth next fiscal year and then told analysts that demand is running well above that level.

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That gap is the whole story. AI's constraint right now is not demand; it is the physical hardware that moves data around inside a data center. NVIDIA named the two pinch points: memory and silicon photonics. Bodner's three picks sit at the center of both.

Slowing the Models Does Not Slow the Buildout

Anthropic CEO Dario Amodei argued in a Sept. 12 essay that AI companies should deliberately slow the pace at which they improve model capabilities. OpenAI's Sam Altman endorsed the idea hours later, and Elon Musk backed it too. Meanwhile, a researcher who had worked at both Anthropic and OpenAI resigned days earlier with a public warning about existential risk.

Bodner reads the corporate response very differently from the way the market did.

Regulation, in his view, gives incumbents licensing regimes, capital minimums and a moat that smaller competitors cannot clear. It also makes AI acceptable for health care and government procurement. His analogy is the airline industry, which lobbied for a federal regulator and ultimately got mass adoption rather than grounded planes.

The macro backdrop gave sellers cover. The Federal Reserve raised rates on Sept. 16 rather than cutting them, oil is sitting near $100 a barrel amid U.S.-Iran escalation, and September has a long history of chewing up gains.

What is not deteriorating is profit growth. Second-quarter beat rates across the S&P 500 ran well above their five-year average, while earnings growth reached its strongest level in years even after excluding a couple of outsized outliers. That is a sentiment problem, not an earnings problem.

Micron Owns the Memory Shortage NVIDIA Keeps Naming

Micron Technology (NASDAQ: MU) is the fastest-growing producer of high-bandwidth memory (HBM), the stacked chips that sit beside a graphics processor and feed it data. Bodner's framing is simple: A GPU is the world's fastest chef, and HBM keeps the ingredients on the counter instead of scattered across town.

Management has said industry demand continues to significantly exceed supply, and that tightness could persist well beyond next year. Micron's HBM output has been selling out ahead of production, and analysts have responded with steadily higher forward estimates. Bodner's point is that a stock can be up several hundred percent and still look cheap if its forward numbers are moving faster than its price.

NVIDIA is effectively underwriting the shortage. Its supply commitments more than doubled in a single quarter, primarily because of memory procurement. Fabs do not go up overnight, capacity is spoken for, and pricing power follows.

Coherent Sits Where Copper Runs Out

Copper wiring is fine at the chip level, but it becomes problematic across a data center. Resistance, heat and distance all pile up, so the AI industry is shifting from copper to optical connections. That transition is silicon photonics, which Huang has called the bottleneck of the coming decade.

Coherent Corp. (NYSE: COHR) supplies the lasers, transceivers and optical materials that make the transition possible. NVIDIA is investing $2 billion in Coherent alongside a multibillion-dollar purchase commitment and future access to its capacity. Lumentum Holdings (NASDAQ: LITE) received an identical commitment the same day, suggesting that NVIDIA is buying insurance on a supply chain it expects to strain.

The stock has taken a hard pullback along with the rest of the AI complex. Bodner's read is that the group has already absorbed most of its selling, while energy, health care and gold have rallied.

Tower Semiconductor Makes the Wafers Photonics Runs On

Tower Semiconductor (NASDAQ: TSEM) is the pick-and-shovel layer. It is a specialty foundry that manufactures wafers designed by other companies, which means investors do not have to guess which transceiver vendor will win.

Its silicon photonics business has gone from a rounding error to one of the company's growth engines, and management raised its long-term targets on the back of it. Tower has locked in more than a billion dollars in 2027 photonics contracts, with customer prepayments already in hand. That is demand booked before the capacity exists.

What Could Break the Setup

The doomsday case is that regulation kills AI demand. Bodner does not buy it, and adoption data is on his side.

The real risk is capital intensity. Tower is committing billions of its own money to a Japan expansion, and heavy capital expenditures can outrun actual demand if photonics adoption slows. Memory has broken investors' hearts before. These three stocks trade on the assumption that tightness persists, and the same forces that lifted the group could unwind it together.

Watch supply commitments and customer prepayments rather than safety headlines. That is what is actually moving these three stocks.

Readers can get Jason Bodner's MoneyFlows institutional money flow research at its discounted annual rate and track where the big money is moving next.