Today, we saw a massive market rally in response to positive news regarding Iran, China-US trade, and bond yields/inflation expectations. Our personal favorites, Nebius (+5%), AMD (+10%), and Intel (+12%), all rose upward on the optimistic sentiment shift. With a week full of catalysts, it is a must to understand Gamma exposure amongst any company you are trading and dealer positioning. So let’s break down what we see as of today (as always, GEX changes daily, so always be sure to update yourself daily)

Subscribe now

Also a note specifically for this week, due to there being a plethora of catalysts occurring over the week these GEX levels are not something to live and die by as the news that comes out over the week will trump any call or put walls; on that front we remain bullish on the outcome of said catalysts. However this information is still useful in understanding how the market is expecting stocks to move.

Nebius

Nebius currently sits at a close price of 232.80, and with the current GEX exposure, we see a concentration of call OI at the 250 strike for this Friday and a concentration of put OI at 220 for this Friday as well.

Above $213, we are in a net dealer positive gamma zone indicating less volatile swings as dealers move against the trends.

Read more

As of recent, Nebius has been experiencing a very frustrating, yet intriguing price action. What investors must know is that those violent swings are amplified by something sitting right under your noses… that thing is called Gamma.

Subscribe now

Source: BarChart

As of September 17th, the chart above shows where dealers are considered short or long gamma. Using near-term expiries, dealers are net short gamma under $222.87 ($223) and net long gamma after. Now, this is indicative of what happened today, which presents the issue with GEX analysis, as it changes every single day. Gamma exposure is entirely dependent on the underlying stock’s changes in price, fundamental news, and dealer positioning.

During market close, we flagged a few things that were unusual. Because of our findings, we believe that for tomorrow’s session, dealers will actually flip short gamma at $230. Below is an in-depth analysis providing evidence for our claim and what this means for Nebius stock.

Read more

Intro

We are currently experiencing a market environment showered with many different conflicting signals. Oil prices and the bond market are telling us we are screwed with inflation with rate hikes imminent yet company earnings are growing at massive rates quarter over quarter with demand in the entire AI trade not only holding but ACCELERATING further.

But which factors hold more weight? Although no one has a crystal ball and knows for sure what might happen in this article we will lay out all the major catalysts for this trading week and the next as well as give you dealer positioning and how the market is currently predicting the next two weeks.

Upcoming Catalysts

We have a huge two trading weeks incoming with massive catalysts that can completely shock the market in either direction. Lets start with exploring what is happening and give some insight into how it can effect the markets trajectory…

1) Anthropic Public S-1 Filing - (Expected this week of Sept 7-11)

Anthropic is reportedly expected to make public “shortly after labor day” their S-1 filing for their upcoming IPO targeted for late September/October. This will give the world clear insight into the companies financial statements including earnings, growth rates, and asset/liabilities.

We expect Anthropic to release great numbers in their S-1 Filing which will solidify demand for AI hardware into next year at the minimum. Expect names like Nvidia, Broadcom, AMD e.t.c to react positively to good news from anthropic.

Once again though this report is unconfirmed by Anthropic themselves so take it as a possibility not fact.

2) PPI/CPI (Thursday Sept 10 / Friday Sept 11)

The massive inflation data is dropping this week and will be very telling of what we could expect in the FOMC meeting the following week, if inflation comes in hotter than expected that strengthens the case for a rate hike when the market is already pricing in odds of a rate hike at 60%. This will rock the market as the entire AI trade relies on cheap debt for sustaining the hyper growth phase we are in. Expect a meaningful market drop if inflation comes in hot and a much stronger fall if rates are increased as well.

If inflation data comes in cool that would be amazing for the AI sector as it will decrease the odds of a September rate hike forcing the market to reprice the AI names that rely on cheap debt to continue growing.

If the data is right on target it just puts all the weight on the FOMC meeting to see what really will be the outcome, although we STRONGLY believe we are maintaining rates as it would be stupid in our view to hike rates due to inflation caused by a oil supply shock that may not even be a factor by the time the heightened rates actually impact the economy in 12-18 months.

3) The FOMC Decision (Wednesday Sept 16)

Now for the biggest catalyst of them all, the rate decision. We have maintained that the rates will be maintained steady as current inflation is due to the oil supply shock and not true structural inflation and that Warsh would be crazy to preemptively hike rates only to combat a shock. We also know Warsh likes to play it slow with his heavy data driven mindset and use of his “task forces” its clear he does not want to jump the gun early so to speak.

However we still see the market pricing in a 60% chance of a hike next week with bond yields rising higher and higher in anticipation. Now lets breakdown what could occur given a hike scenario vs a maintained rate scenario…

IF RATES ARE INCREASED: all of the AI names that rely on heavy debt raising will get killed, because if you take a business that already has compressed margins from debt/depreciation then you make their future debt even more costly to further compress margins and earnings wall street does not like that very much.

In fact we can see the market currently rotating money away from the names that are most debt reliant and into the names with strong cash flows NOW as a hedge against the potential rate hike. Today we saw the mag 7 fall and names like AMD, NBIS rise 5%, 8% respectively. The MAG 7 funding the AI companies do so by raising debt so they get hit the hardest from increased cost of debt. This could even at the worst turn into a cascading event where increased cost of debt slows future capex commitments therefore taking the whole of the AI hardware industry down with it. Overall a very grim outcome.

IF RATES ARE MAINTAINED: The market pricing in high odds of a rate hike give traders a great opportunity if they believe rates will remain steady. If the rates are held firm then fear around the entire AI sector will soften dramatically leading into what we believe will be the hardware rally 2.0. Everyone wins as the market will have to reprice these names now that the hike fear is off the table.

This is the outcome we believe will occur and we think the market is truly mispricing the odds of each outcome stated above, thus giving us an opportunity to capitalize on said mispricing. We are playing these two weeks very carefully and will touch on our personal strategies further below.

Market Positioning

Now that we know the catalysts occurring lets break down how the market is positioned for the next two chaotic weeks ahead.

Citadel Securities reported on Aug 31st that $9.6 trillion (about 35% of total US options exposure) expire before Sept 18. A daunting headline that has your talking heads on X, YouTube etc all claiming this will lead to a “cataclysmic event”. In reality this figure is not that daunting at all, we have seen month after month massive concentrations of options exposure falling on small timeframes exactly like we see for the window of Aug 31-Sept 18. Like in June where we had $7.7 trillion expiring in a compressed window as well. What happened? Nothing extraordinary, market was actually pretty flat because it is the details that matter and knowing dealer positioning is the most valuable takeaway from options analysis. So lets do just that and break into how the market is currently positioned…

We see on QQQ options expiring on the 18th we have a put/call ratio of 1.43. A strong ratio favoring puts but we cannot tell if this is true speculation of a market fall or just hedging by those with long positions, still important to look at and reiterating the fact that a market shock where rates are maintained can truly be the start of the next big rally as fear can erode rapidly.

As of Sept 4th we see that for the SPX (S&P 500) there exists a massive call wall at 7800, a put wall at 7500 with a zero gamma flip at 7660. With SPX closing today on Tuesday Sept 8 at 7673 what do these numbers actually tell us and how does it help us in our own trades?

We’ll start with the zero gamma flip as it is the most important of the 3 values to understand. What this is modelling is the price of SPX where dealers gamma exposure flips from net short gamma below 7660 to net long gamma above 7660. But now what the heck does that mean??

When dealers are net short gamma it means these dealers in order to hedge their positions to stay neutral directionally MUST sell shares of a stock as the stock declines and buy shares as the stock increases. This in practical senses AMPLIFIES moves both positive and negative as all of these dealers are forced to sell low and buy high which in turn pushes the stock lower or higher more aggressively.

Above 7660 this inverses where in order to stay neutral these dealers must sell shares as the stock increases and buy shares as the stock decreases. This makes big breakout moves in either direction much more difficult as the dealers are actively countering the markets movements.

Now the call and put walls. They act as natural forced support and resistance, if the price of SPX is approaching 7800 since we are in a net dealer long gamma zone as explained before it makes it harder for big rallies or big dips to occur and the closer to 7800 you get the more selling of stock occurs as delta increases faster the closer to the money. This is why the 7800 level will act as a natural resistance.

Above the call wall you actually get amplification on the upside. But not because dealers flipped short gamma. It’s because the dealers no longer need to sell shares as aggressively to stay delta neutral. So the entire “resistance force” becomes dramatically weaker and any new call buying above the call wall level creates dealer short call exposure that MUST be hedged by you guessed it, BUYING MORE SHARES!

The put wall works in a similar but not as concrete way, as it falls below 7660 moves are accelerated to the downside/upside. If the market keeps falling and falling toward the 7500 level that’s where dealer short gamma is the HIGHEST thus the moves are the most amplified. However once it passes through 7500 gamma decreases which slows down the amplification of market moves whilst simultaneously allowing dealers to “unwind their hedges” by buying back stock which is why the put wall acts as a natural support level.

Below 7500 dealer hedging becomes a non factor on market moves and price action becomes much more reliant on true supply vs demand mechanics.

IMPORTANT INFO ON CALL/PUT WALLS:

All of these values stated here not fixed in time and are constantly evolving on a daily basis due to changes in open interest and volumes at different strikes on the options chain. A call wall acts as resistance a lot stronger than a put wall acts as support just due to the put wall having the amplified moves from short dealer gamma allowing price to blow straight through that floor with ease and call walls having true forced selling of stock as it approaches the price acting as a reliable resistance level.

The best way to determine if a breakout can occur through a call wall is Call Wall Repositioning. If a call wall relocates overnight in a model to 7850 instead of 7800 the prior day that gives you a lot more confidence in price having a stronger ability to breakout past 7800 as the call wall has weakened substantially.

All this dealer positioning is never known as fact. This is all inferred data from open interest and volumes at certain strikes. Open Interest only tells you the contract exists out there, it does not tell you which side the dealer holds.

So these values are just modelled and are “best estimations” rather than being true concrete fact and anyone who tells you otherwise just has no clue what they are talking about or are extremely overconfident in their abilities.

How we are trading this chaos…

Read more

Nebius Earnings:

• Revenue $582M, +454% y/y, +46% q/q

• Adjusted EBITDA $236M, 41% margin (AI cloud alone: 50%)

• Net loss $190M ($0.68/sh); adjusted net loss $33M

• Operating cash flow $2.3B; capex $5.7B; cash $8.0B

• Prepaid customer money on the books: $6.0B, up from $1.6B at year end

• Debt $8.5B

• FY guide unchanged: revenue $3.0–3.4B, ~40% margin, capex $20–25B

Coreweave Earnings:

• Revenue $2.58B, +112% y/y, +24% q/q

• Adjusted EBITDA $1.51B, 59% margin

• Adjusted operating income $128M, 5% margin (was 1% in Q1)

• Net loss $626M; interest expense $640M

• Capex $9.4B; debt ~$35B

• Signed-not-yet-delivered contracts $104.2B, +246%

• FY guide raised: revenue $12.4–13.2B, capex $35–39B

The Neocloud Thesis Materializing:

One of the main legs of the neocloud thesis that needed to materialize was chips’ economic life appreciating. As we know, inference actually enables older GPUs to stay competitive, as inference is memory-bound, allowing H100 and even A100 to stream tokens out efficiently. On CoreWeave’s earnings call, CFO Nitin Agrawal stated that CoreWeave had signed a long-term agreement for A100’s stretching out to 2029.

Source: Tom’s Hardware

This completely reshapes the landscape for how we view the lifecycle of compute. The A100 first launched in 2020 and is now yielding demand into 2029. The main skepticism surrounding neoclouds was the depreciation of that compute. Below is the bear case drafted around four main areas.

1. NVIDIA ships new compute roughly once per year — making the competitive window relatively small

2. Current depreciation cycles are 4-6 years. Michael Burry claims that this cycle is too long, resulting in hyperscalers understating depreciation by $176 billion from 2026-2028.

3. This creates a negative lull. If this is true and depreciation is being understated, then the real “earning-life” of compute is much shorter than the “accounting-life” represents.

4. What makes this bear case worse is that the compute is actually used as collateral in a lot of debt agreements. Overstating the useful life assumes the assets are worth less than the deal implies, making refinancing a headwind.

   ---

The rates for the A100 deal were not disclosed, although CEO of CoreWeave Michael Intrator stated the deal was landed at an “attractive price” and “full-freight.” Alongside other AI clouds, CoreWeave has the ability to charge more than average neocloud per MW of capacity, due to their deep cloud integration and mastery of cluster orchestration. (still cheaper than hyperscalers)

Per SemiAnalysis’ ClusterMAX ranking, CoreWeave is the only Platinum AI infrastructure provider, with Nebius being placed in the “Gold Tier” above almost all AI clouds…

Source: Semi Analysis ClusterMAX

Aside from inference orchestration, the pricing curve of compute is following a directional uptrend for all GPUs stretching from A100’s to B200’s. The CoreWeave deal validates the useful life of GPUs, but the question was always about its economic life. A chip’s economic life is equal to its Utilization x Price. Michael Intrator stated on the earnings call that CoreWeave is largely sold out of prior generations of NVIDIA GPUs. This evidence of maximum utilization isn’t unique to CoreWeave. It’s stretching across the entire industry. Neoclouds and Hyperscalers alike are selling out of compute capacity across their entire fleet.

Many companies like Meta and SpaceX realized the economic value in legacy GPUs and decided to sell them to third parties who are in need of more compute. The increasing economic value is evident through the continuous uptrend in GPU spot rates. The chart below, pulled from Silicon Data, showcases the rise in H100, A100, H200, and B200 GPUs across Non-Hyperscaler fleets from January to August 2026.

Source: Silicon Data (Ricky Ho on X)

Inference/Agentic workloads enable this to happen across the entire industry, stretching the bottleneck across all GPU’s. Maximum utilization multiplied by increasing spot rates leaves us with an economic life representative of a longer-term product, and not of one Burry describes.

Rebuking Burry: Nebius/CoreWeave Say Otherwise

Michael Burry’s entire claim is that chips’ economic life is much shorter than the accounting life indicates. The data released this month actually point toward the opposite. They showcase how fast these assets create value and return cash. One of the most revealing artifacts is Nebius’s Q2 Payback Period. They stated their data centre deals signed in Q2, return all invested cash within 1 year and 10 months, even after they extended their server and network equipment useful life to 5 years from 4.

Source: The Globe and Mail

Why is this important, you may ask? The Globe and Mail argues that Nebius is undergoing a structural shift, and the Payback Period is evidence. Existing 2026 capacity is contracted at $12 million per MW, with the 4 deals signed in Q2 valued at $20 million per MW. At roughly $20 million in revenue, with 50% EBITDA margins against $25-$30 million in Capex per MW, it would take roughly 3 years to return all invested cash. Nebius’s own management states 22 months, meaning variables are in play, making this much shorter than the 36 months we just calibrated. (The cost per MW could be cheaper than the $30 million assumed, customer prepayments could be helping fund this buildout, margins are much higher than 50%, etc.) Even using the 36-month payback period figure, it still sits against an accounting life of 5 years at Nebius. Meaning all the cash invested in the capacity gets returned in 3 years against a 5 year book-life. In layman’s terms, the economic life is very strong and is evidence against Michael Burry’s claims…

Because of this data, I believe an argument can be made that the economic life is actually being understated relative to the accounting life. As referenced above, compute is becoming more and more ‘‘in-demand” with customers acting desperate for it. The trend for the “economic life” of compute is following a linear path. We saw the first signs in 2025 when CoreWeave CEO Michael Intrator said a batch of H100’s, after an expiring contract, were immediately re-booked at 95% of the original price. At the time, three-year-old compute was still lucrative, generating 95% of the book value, even after 3 new chip lines were released. The industry is following a trend that’s continuously getting strengthened. Older compute is still lucrative and in high demand. The economic life of these chips is getting more and more valuable, contrary to Burry’s entire bear thesis.

My Takeaways:

• In conclusion, the data is signalling a shift in the industry; legacy GPU economics are strengthening, due to increased prices and utilization rates. Nebius and CoreWeave’s earnings validate this direction; the cash needed to fund the DC buildout is being returned faster than the accounting life assumes, and those very assets are showing a longer economic life.

• Nebius especially is proving to be the strongest neocloud. For instance, their balance sheet is by far the best when compared to other public companies of their scale; they also have very strong software orchestration, inference management, proprietary token optimization technology, and a deep-knitted partnership with Nvidia.

  

Source: Hokagecapital on X

(I urge all of you to read the post above; it greatly summarizes the debt situation across comparable neoclouds, and why Nebius is in the best situation… NOT ALL MEGAWATTS ARE THE SAME!!!)

• Nebius’s partnership with Nvidia is another reason as to why I tend to favour them in the future. The main Nvidia-backed neoclouds such as CoreWeave and Nebius get early access to Vera Rubin at the same time. Nvidia strategically uses their neocloud ecosystem as a mode of “deployment” of their compute, to create pricing power and scarcity. (Artificially making Nvidia into a hyperscaler!) But what’s interesting is who acquired the LPX rack first. You’d assume all neoclouds would get the Groq rack at the same time, but one neocloud stands out as the preferred choice.

  

• You read it right. Nebius is the first AI cloud bringing the LPX rack to market. Now, this isn’t simply just a “luck of the draw moment” in which Nebius was chosen; it was a strategic deployment by Nvidia, proving my theory that Jensen prefers Nebius for inference and agentic orchestration.

• The LPX rack is designed for disaggregating the prefill/decode process, being completely optimized for token delivery and inference management. From this decision, I can infer that Nvidia views Nebius as the best neocloud for delivering world-class inference through Nvidia-based infrastructure. This is genuine proof of a relationship that is getting deeper. This deeper partnership is extremely bullish for Nebius, and improves the probability that Nebius can become a competitive “neoscaler” in the coming years.

• Overall, the neocloud thesis is materializing. Demand for MW capacity, legacy compute, and AI-optimized clouds is exploding. We are in the early innings of this thesis and are yet to see the best of what can come from these companies.

• I hope you all enjoyed reading this as much as I enjoyed making it… Subscribe for more!

DISCLAIMER:

This publication is produced by Aria Research and is for informational and educational purposes only. It does not constitute financial, investment, legal, or tax advice, and it is not personalized to your individual circumstances, financial situation, or risk tolerance. Nothing in this publication is a recommendation, offer, or solicitation to buy or sell any security or financial instrument. The author may hold positions in the securities discussed. All investments carry risk, including the possible loss of principal, and past performance is not indicative of future results. All analyses and views are the author’s own, and readers should independently verify any data before relying on it. The information here is believed to be accurate as of the date of publication, but it is not guaranteed to be complete or error-free. Always conduct your own due diligence and consult a licensed financial advisor before making any investment decision.

Debt Leverage Unwind:

A crowded trade coupled with over-leveraging is a recipe for disaster. Stocks don’t go down 50% from highs when nothing changes fundamentally. If I had to guess, more than half of the selloff was mechanical-based.

Source: JP Morgan

The graph on the left showcases the amount of money flowing into leveraged ETF’s. As we can see, the amount of leverage had peaked in late June/early July. It took roughly 6 months for the leveraged-AUM to reach $50 billion USD. Almost all of that leverage was unwound within the month of July, falling from $50 billion to roughly $15 billion. The concentration of leverage was overly invested into the AI sector. Take Korea as an example. As of this week, reports circulated that 3.4% of the adult population in Korea was margin called, with a fraction of them being fully liquidated. More than 50% of the Korean KOSPI index is composed of SK Hynix and Samsung, two of the largest suppliers of memory. Margin inherently amplifies bear markets, and the data completely justifies the “mechanical selling thesis” that occurred in July. For $35 billion worth of leverage to be unwound in 1 month means that massive margin calls occurred.

Take the news regarding Situational Awareness as an example. Reports came out that Leopold Aschenbrenner’s fund was forced into liquidation as a result of the July crash. Peaking with $45 billion worth of AUM, Situational Awareness was heavily invested in Neoclouds and power providers.

Fellow hedge funds and institutions realized the leverage issue occurring and proceeded to short the stocks that rallied the hardest. Sadly, the majority of those stocks are held by retail investors and smaller funds like Situational Awareness. As a result, Leopold’s assets were purchased by Citadel for an undisclosed amount.

The chart on the right measures the flow of money into the leveraged equities. Between July 15 and July 22, the KOSPI market had tanked significantly; during this time, JP Morgan cited roughly $8-$9 billion in new money went into those leveraged products. Only in the past two weeks did they start seeing net outflows, of about $2 billion. The sequence is as follows: The markets would dip, retail would lever up and purchase more, prices dip further, the new leverage gets destroyed, then people finally start pulling money out, amplifying the downturn. This chart showcases that the leverage issue is nearing its end. JP Morgan's latest read is that the “leveraged ETF unwind is 100% complete, hedge fund deleveraging is about 90% done, retail margin balances are declining moderately, and passive foreign selling has eased considerably.” As a result, JP Morgan states that the leverage AUM is “no longer problematic.”

The Bottom is In:

The mechanical forced selling is more or less coming to an end. Leopold’s liquidation is the evidence. Think of liquidation as a queue. The traders able to withstand the longest are at the end of the queue, with the most exposed going first. Take the Koreans as an example. Roughly 1.2 million Koreans were margin called because they were investing on borrowed money; in this instance, they could not resist, and brokerages liquidated immediately. The last to go are always the public funds like Situational Awareness. Like these funds, Leopold had many bargaining chips: three prime brokers to negotiate with, collateral to post, private assets to sell, and investors to call. He tried all of it. Talks with lenders, offered portfolio assets to individual investors, and sent a July 24 letter inviting fresh capital. Millennium and Jane Street both evaluated the positions and declined. The Korean unwind started June 22, meaning his fund lasted five and a half weeks.

It is rumoured that other investment firms were purposefully shorting the assets Situational Awareness was holding. Take the following letter Leopold sent to his investors as anecdotal evidence…

“Aschenbrenner’s party blamed short sellers who targeted the firm’s positions for exacerbating the fund’s losses.”

Martin Shkreli, speaking on TBPN, said: “funds were already shorting Situational Awareness's holdings as early as Monday in anticipation of the liquidation.” Once it hits the mainstream that a fund has to unwind positions, institutions DO NOT waste any time to position accordingly. Funds began selling all positions that overlapped with Leopold’s fund, while simultaneously shorting the rest. The reason that funds do this is because of how lucrative it is. Operating at 3-4x leverage, Situational Awareness was extremely exposed to market fluctuations. Funds shorting his positions are betting on his inevitable liquidation; sending his positions lower means they make money on the downside, and eventually can purchase his shares when they hit the open market for much cheaper, closing their positions with massive profits.

Shkreli put it very bluntly. He claimed that “Wall Street would happily send a name like Micron to $5 if the ruin it triggers is where the money is.” Some of the selling wasn't even hostile/targeted. If a fund owned CoreWeave and knew that a multi-billion dollar holder was about to be pushed out of it, you sold that morning rather than after the wave, and hundreds of funds running that same calculation at once create crushing pressure with nobody intentionally coordinating anything. Leopold went to his prime brokers to try and issue a sale of his book. According to Shkreli, word hit the Street a week before his liquidation. There was a big issue… Leopold couldn’t simply sell on the open market. A $2.6 billion stake in Nebius and $401 million dollars worth of Iren would be equivalent to weeks of those stocks’ daily trading volume. He would only be able to sell a small slice per day before he pushed the price down himself, artificially losing him money. The worst part is that massive sell orders give away your distress. Once buyers notice large selling volumes, they tend to step away and wait for lower prices, essentially evaporating liquidity he very much needed. Due to his leverage exposure, every 1% he knocked off of his positions from selling would hurt his margin cushion by 4%. Leopold was inevitably forced to sell his book to Citadel through prime brokers at around 0.40 cents on the dollar, as the opportunity cost was more attractive than slowly selling on the open market.

Looking at the facts, all of his positions were the ones that got hit the hardest during the July selloff. CoreWeave, Nebius, Intel, Iren, and Sanisk were amongst the biggest losers during July, all tumbling over 40%. Hindsight being 20/20 is an understatement; it is clear that Leopold’s liquidation was one of the major catalysts that unlawfully pushed the markets down further.

Stocks To Have On Your Watchlist:

The whole argument I’m presenting is that hardware stocks have been sold off asymmetrically to their fundamentals. The following companies are the most oversold with immense upside in my view.

Firstly, Intel Corp. Intel tumbled from $140 to $85 per share during July. Their foundry roadmap and general fundamentals have gotten much stronger over the course of the selloff, with more companies showing interest in their EMIB technology and advanced process nodes.

Read more

Software vs Hardware: The CAPEX Risk

The AI trade so far has paid the companies selling infrastructure. But hardware revenue depends on the rate of change of customer spending, and that rate is forecast to roll over: combined hyperscaler capex has roughly tripled from $256 billion in 2024 toward ~$785 billion in 2026, with Moody’s projecting nearly $1 trillion for 2027 implying growth decelerates from ~70% to roughly 27%.

Analysts have flagged the timing of this slowdown as a direct risk to infrastructure valuations, and the strain is showing: capex now approaches or exceeds operating cash flow at some operators (vs. a ~40% ten-year average), funding has shifted toward debt, and Moody’s has warned of creditworthiness reassessments if AI profits lag. Power constraints and shareholder ROI demands make the deceleration a question of when, not if.

Software inverts the exposure. Subscription revenue rides on the cumulative stock of deployed compute and workflows which keeps growing even after spending decelerates.

Deployed GPUs become cheap, creating even more inference capacity: the raw input cost of AI software. Falling compute costs (Alphabet cut Gemini serving costs 78% in 2025) squeeze hardware pricing power while expanding software gross margins.

By seeing how software multiples have lagged the infrastructure trade badly, AI monetization is arguably not yet fully priced in at the multiples we see today.

This is the exact reason whenever hardware stocks like AMD, AVGO, e.t.c fall software stocks always seem to inverse and go up.

Big money is trying to squeeze every last drop of profit out of hardware before the inevitable shift into software companies, making software companies a very attractive investment to those with larger time horizons, which is the opposite of big money trading firms who need results every quarter to impress investors/shareholders leading to the cheap valuations we see across the software space.

Software Names Poised to Win + Price Targets

Now there are many names in this space that are truly great companies with very healthy balance/income/cashflow statements trading at attractive valuations.

The names everyone knows like Microsoft, Meta, ServiceNow all have great valuations and would realistically all succeed when CAPEX dwindles away. But we at Aria Research are really focused on 3 big names here. Microsoft (which we have written about before and will therefore be omitted here - see former article), Salesforce (CRM) and a more speculative but interesting prospect in the space UiPath (PATH).

Here I will give our analysis on the direct head to head of two of our 3 favorites in the space with our price targets and reasoning behind them…

Salesforce ( ) vs UiPath ( )

Both names sit at the application layer, but they express the thesis differently: Salesforce is proven agentic monetization at scale trading at a depressed multiple.

UiPath is a cheap, newly profitable platform with unpriced optionality on agent orchestration.

Read more

Muse Spark 1.1

Meta’s newest most powerful API model is Muse Spark 1.1. On their first day of public access, it outperformed some of the leading frontier models on agentic workflows, such as Anthropic’s Opus 4.8 on MAX.

Source: Meta.AI

From the analysis above, Muse Spark 1.1 is the current leader in agent benchmarks in 4/6 categories. Where it fails to beat Claude and OpenAI is in hardcore coding (SWE-Bench, DeepSWE) and Multimodal categories, which test software engineering and the ability to analyze, reason across, and generate different formats.

For Meta, this is a generational breakthrough. For years, analysts have been scorning Zuckerberg for his lacklustre AI spending, committing hundreds of billions of dollars to a buildout that simply didn’t have a clear link to revenues. Their underlying ads and social media business was more than able to fund the buildout; the question of ROI, however, was always overhanging. We believe that Muse Spark 1.1 is the first of many strides forward that will crown Meta as the leader of AI.

Meta now has a competitive frontier model and is monetizing it through token usage. This is the first time in Meta’s history that they’ve made users pay for their AI models, and it’s clear what their objective is. They are trying to offer the most efficient agentic model that can operate at the lowest cost per token. Meta will pose a serious threat to both OpenAI and Anthropic simply because of their token costs and heightened efficiency.

Source: Meta.AI

The Tokenomics:

As we’ve come to understand, the newest, most powerful GPUs are reserved specifically for training large-parameter frontier models. As newer generations of chips get released, the older ones slowly lose their value. H100 and H200 equivalents used to be the most powerful on the market and have been pushed toward less intensive use cases. Simply put, these chips no longer compare to the current best on the market, but that doesn’t mean they’ve gone entirely obsolete. These older-generation chips are still perfectly viable for inference and agentic use cases.

Source: Silicon Data

This chart from Silicon Data models the H100 term rate curves, showing a substantial rise in prices through 2026 when compared to November, 2025. This chart makes economic sense as to why Meta is interested in entering the “neocloud” business, as the assets they’ve stockpiled are appreciating. What Meta is aiming to achieve does not signify a compute oversupply, but rather a generational shift in financing compute. The vast majority of chips that are being rented out from Meta will be their old stockpile of H100 and H200’s, while maintaining Blackwell and Rubin chips for their frontier model training. Those higher-end GPU’s will be supply-constrained through 2028, as every single company on the planet requires them to be competitive. What also becomes supply-constrained will be the older GPUs that are now obsolete for frontier training. As we know, leading-edge GPU’s like Rubin and Blackwell deliver roughly 3x tokens per megawatt versus H100, with Nvidia claiming up to 15x lower cost per token on optimized workloads. In a vacuum, the leading-edge GPUs are a no-brainer choice, but not when they are completely sold out. This AI super cycle is causing the strongest compute to be utilized specifically for training frontier models. Even Jensen Huang, while announcing that Nvidia had secured supply for ‘‘very, very robust growth,’’ admitted the company remains supply constrained.

Blackwell's headline advantages, such as FP4 compute, giant NVLink domains, and 8 TB/s bandwidth, matter most for serving massive frontier models at high interactivity. But the token volume concentrated at the frontier could be rather misleading: Beneath it sits a huge layer of everyday enterprise AI. Small fine-tuned models, embeddings, batch summarization, document-lookup pipelines, and ordinary chatbots that don’t need frontier models or frontier hardware at all. For a bandwidth-bound 70B decode workload, an H100 isn’t something you should frown at; it's actually correctly sized to operate under these parameters.

The trade-off is token efficiency; on SemiAnalysis's benchmarks, Blackwell serves tokens 4–9x cheaper than Hopper on optimized workloads. But per-token efficiency only matters if you can get the hardware, and for workloads that fit comfortably in an H100's 80GB, oftentimes the cheapest option becomes the most rational choice. Market data pulled from SemiAnalysis’s InferenceMAX platform shows that the cost per 1 million tokens for an H100 and a Blackwell chip per 1 million tokens is $0.14 and $0.03, respectively. The reason Hopper chips are even viable is the spread between token prices and token costs. API tokens for large open models retail at $0.20-$1.00 per million; SemiAnalysis's benchmarks put serving costs for these models at $0.03–0.55 per 1 million tokens, depending on the chip. At spreads like that, even hardware with several times worse token economics still runs at enormous gross margins. The efficiency gap between legacy and frontier chips decides who earns more, and not who earns.

The Hardware Bonanza:

Hardware was sold off hard over the past week due to the misconception around Meta’s announcement. Many individuals took the selling of “excess” compute as an indication of an AI oversupply. We are here to tell you that this is factually incorrect. To begin, if there were truly an oversupply of compute, then why is Meta renting 1.6 GW worth of capacity from Crusoe, committing $27 billion to Nebius, $35.2 billion to CoreWeave, and just recently having Google cap their Gemini usage? Companies that are drowning in “excess” capacity do not exhibit behaviours like this. So why are they? The answer is only found between the lines… Meta selling their older generation chips while simultaneously locking up neocloud capacity is not contradictory. They are selling yesterday’s viable compute, while locking up tomorrow’s scarce capacity. This is Meta’s way of monetizing every part of their AI business, while also funding the next round of Capex. One of Meta’s largest criticisms was their lacklustre spending with no real sign of ROI. Muse Spark 1.1 and their “neocloud” proposal refute all claims of “lack of ROI.” The FCF and narrative legitimacy they can acquire from this can help Meta raise Capex guidance without being punished for it; AKA fuelling the AI trade for even longer. We believe this will cause a snowball effect of Capex guidance being revised upward. We predict that Capex from hyperscalers could see $1.0-$1.3 trillion in 2027.

Source: Aria Research

Compute & Neoclouds:

The following section will talk thoroughly about the impacts on the compute and neocloud sectors.

Read more

As you know, at Aria Research, Intel is one of our highest-conviction companies, so it is only appropriate that we provide an in-depth description of Intel’s foundry pipeline, along with our view of its stock valuation through 2030.

We are going to upload the same Excel File we used to model Intel’s valuation for Paid Subscribers. The Excel file we created allows you to “plug and play” with numbers and scenarios, so even YOU could make your own valuations. That being said, let’s embark on the “Intel model”.

Pillar 1: Assumptions

With our valuation model, there are some assumptions we have made in order to forecast EPS. We purposely left out any incremental AI accelerator revenue over the next 5 years, as they currently hold negligble market share in this space. We assumed as well that Intel will maintain 25% market share in server CPU’s, with a TAM of $170 billion. Anything related to the consumer market is priced into the base EPS of $6.24. This articles purpose is to ONLY model the Foundry pipeline through 2030…

Source: Aria Research

The Tera-Fab Deal:

The Tera-Fab project is a $55B joint venture between Tesla, SpaceX, and xAI, unveiled by Musk in late March 2026.

(The $55 billion is the phase-1 budget per SpaceX S-1, with full $119B scale occurring in the mid-2030s, and targeting 1 terawatt of compute per year)

Essentially, Intel would be licensing their fabrication IP and advanced packaging technology for Tesla automotive Chips and Optimus Chips, with the first chips in late 2027 and full-scale production in 2028. Intel signed on April 7, 2026, as the primary foundry partner, contributing the 14A process, advanced packaging, and design/fab expertise (Tesla selected Intel’s 14A for its Terafab chips, and SpaceX is also planning to use it).

This deal is structured so that the $55+ billion used to fund the project comes out of SpaceX/Tesla’s wallet. There is no debt affecting Intel from this agreement. Essentially, Intel is offering their expertise in chip-fabrication/packaging while maintaining 80% margins on their revenue share from Tera-Fab. By 2030, after initial ramping, we model this joint venture to have an ARR of around $4 billion. We believe demand for chips will keep Tera-Fab capacity constrained, allowing Intel to maintain pricing power.

The Economics Behind Tera-Fab:

Total capex spend by 2030 is $55 billion; modelling a fab the size of $55 billion at full ramp can produce up to 52,000 WPM (wafers per month) at 65% ramp capacity; we also project full Phase-1 will be fully ramped and capacity utilized by 2032, with a total projected production capacity of 80,000 WPM, or 960,000 WPY. By 2030 using the base revenue split at 16% allocated to Intel, this leaves them with $4 billion in revenue off of a $25 billion output. There have been specualtions about the revenue split being higher than 13%-16% (around 25%), but this cannot be true as Tesla/SpaceX will operate the FABs themsevles, and Intel would be liscencing their IP. Thus the reason why they can garner 80%-90% margins, but only a maximum of 16% revenue split.

Google TPU Packaging:

The Google TPU deal involves the advanced packaging of 3 million TPU’s for 2028. This will cover roughly half of Google’s TPU supply, with production beginning in 2027. (6 million) As reported, this deal is strictly for advanced packaging, although various sources have stated that it involves fabrication as well. We modelled this strictly for packaging. (If the deal includes chip-fab, the ARR can easily stretch upwards of $5 billion, on a 26% combined margin.)

Using advanced packaging and TPU pricing data, we model this to have an ARR of $2.325 billion, on a 32% operating margin. We landed on these numbers through standard packaging and TPU economics. We predict Intel will charge 70% of TSMC’s CoWoS pricing, as they’ve stated they will offer customers a price-efficiency advantage. (Intel charging roughly $775/unit for 3 million TPU’s equates to $2.325 billion in revenue.) The Google deal was the first large-scale customer Intel landed for its advanced packaging services. This not only benefits Intel by restoring external foundry credibility but also benefits Google by de-risking its reliance on TSMC’s CoWoS and diversifying its supply chain. We thoroughly believe that this will spark more tech giants to adopt Intel’s EMIB technology, given the sheer capacity constraint of the packaging market.

Use this chart below as a reference for TSMC CoWoS bottleneck…

Source: Aria Research

Nvidia Feynman:

Rumours have been circulating that Nvidia is showing interest in Intel’s EMIB technology. The rumours indicate that 75% of Feynman would be allocated to TSMC and their CoWoS technology, whereas the remaining 25% would go to Intel Foundry. Using the 25% number (and also predicting the volume for Feynman), we can allocate a volume of 1.25-1.75 million units.

Using $850/unit and the expected volume, we arrive at a revenue range of $1.04-$1.46 billion. NVIDIA’s higher-end GPU would call for higher pricing than other chips simply because it has a higher bandwidth-bound flagship with next-generation HBM.

This results in Intel using its Foveros 3D die-stacking and co-packaged optics, resulting in a physically larger package than the average chip. Packaging a next-generation chip like Feynman has more bond sites and more bridges and requires 3D hybrid bonding instead of 2.5D. This will lead to more assembly/test work and more yield risk, resulting in a higher service fee for Nvidia.

Apple Fabrication Deal:

The next reported deal is between Intel and Apple. The reporting suggests that Intel will manufacture legacy Apple chips on its 18A process node, with estimates of 15 to 20 million M-series chips a year allocated to Intel, with major ramping beginning in 2027. More recent reporting has Apple showing interest in Intel’s 18A-P node for an entry-level MacBook chip, while also evaluating the 14A process node for more advanced chips. We modelled this to have an ARR of between $1.0-1.2 billion for the entry-level chips. For Apple, this is the beginning of breaking their near “full dependence” on TSMC for fabrication.

This could approximately provide Intel with $1.1 billion on a 22% operating margin. The margin sits below the packaging deal by 10 percentage points, because this is wafer-side work, where costs allocated to process-nodes are much higher. Due to the “crown jewel” of foundry services sitting within chip fabrication, the true value of this deal provides Intel with external fabrication credibility. This validation is even more important, given how supply-constrained TSMC actually is.

For reference, TSMC’s 2nm node is already completely booked through 2028, while Apple currently holds just under 50% of the demand. In layman’s terms, there is no “extra slack” left for Apple to grow their 2nm footprint with TSMC. Currently, TSMC’s 2nm capacity has 78-104-week lead times, pushing new orders into 2028. Even at 100,000 WPM, N2 capacity still remains lower than the combined demand from customers. This can make a strong case for Apple to use Intel’s more advanced nodes in the future for chips such as the A21. CEO Tim Cook specifically stated that they are unable to ramp up supply to meet demand for their advanced chips. Which, in theory, could open up a foundry pipeline between Apple’s advanced chips and IFS. If Intel were to win the A21 chip with average volumes of 40-60 million per year, this alone could supply them with an extra $3.0-$4.9 billion in ARR.

Although this is a possibility, we are purposefully leaving this out of our model as Intel hasn’t fully solidified their advanced nodes as “achieving profitable margins” just yet. Although we believe that the 14A node will be deployed faster than projected (currently 50% yield), the reporting indicates that the A21 will be on TSMC’s N2 node, which makes a lot of sense, given that Apple is a priority customer for TSMC. Overall, at full ramp, the entry-level M-series chips would have an annual revenue run rate of $1.1 billion.

Advanced Packaging Customers:

For Intel, we predict 3 additional advanced packaging customer commitments by 2030, alongside Google and Feynman. We predict the three customers will have an ARR of $2.2 billion. For argument’s sake, we assumed that the customer volume wouldn’t be the same size as Google’s large-scale 3 million orders; we instead modelled it to be around 1.5-2.0 million units per customer.

Large-Scale 14A Customer

In our model, we forecast 3 large-scale customer commitments to Intel’s 14A process node. The revenue per customer sits at $3.6 billion, resulting in a total of $10.8 billion. We modelled this number through thorough estimations of the Wafer ASP for a 1.4nm class-node, along with potential volumes that could be reflective of a “large-scale customer.” Our thesis for Intel’s foundry doesn’t imply the takeover of TSMC on a large scale; it implies companies like Apple utilizing both foundry services as a hedge against a bottleneck within the market. In terms of wafer demand per month, we estimate each customer would command 7,500 wafers per month, roughly, valued at $40,000/wafer. TSMC expects to charge $45,000 per wafer on their A14 1.4nm node. CFO Zisner specifically stated that Intel’s 14A node will have a cost-efficiency advantage while remaining competitively priced. Overall, we believe the $35,000-$45,000 range suffices.

Source: Aria Research

We personally believe that 3 large-scale customer commitments through 2030 are on the more conservative side, and this is done so purposefully to show you the upside potential Intel truly has. Multiple companies have reportedly shown interest in Intel’s 14A process node. Intel has confirmed a partnership with Elon Musk on the Terafab project, while also hinting at several more customers. CEO Tan has also specified that they will not mention the names of any potential customers until they agree to do so. 14A design commitments are expected to start landing in the second half of 2026 and run into the first half of 2027.

The reason these deals have a structural aspect as opposed to a speculative one comes down to the capacity constraint we’ve been preaching about. TSMC’s leading-edge node is effectively sold out, with N2 fully booked. AI buyers are currently trying to outbid one another for allocation. Even Apple, TSMC’s priority customer for over a decade, has dropped below 50% of N2 capacity for the first time as Nvidia and the hyperscalers soak up the node. This forces companies, like Apple, to diversify their supply chain in order to scale supply to meet the red-hot demand. If the demand for XPU’s continue to accelerate (as the data suggests), then large-scale companies that need to increase supply have no other choice but to allocate wafers to Intel’s 14A.

Source: Aria Research

Above are estimations for overall capex allocated to the AI buildout through 2030. Jensen Huang successfully called for $1 trillion in capex by 2026 in the year 2023, and is now predicting 3-4 trillion alone by the end of the decade. Around 40% of all capex is invested into compute, meaning the amount of XPU’s that need to get fabricated will increase alongside Capex.

The Debt Structure:

Inside of the base EPS estimate, we have already included the debt that Intel has incurred PREVIOUSLY in order to buildout their foundry services. The 6% cost of debt comes from the marginal or new-issue rate; in other words, “what Intel would pay to borrow fresh money today at BBB, or a negative outlook.”

Share Dillution:

Before we begin, let’s address the share dillution: Our model assumes that through 2030, Intel will have added roughly 400 million shares to the market to raise capital. In the year 2025, Intel had utilized this method, and the equity structure is as follows…

• US Government Stake: 433.3M x $20.47 = $8.9 billion

• NVIDIA: 214.8M x $23.28 = $5.0 billion

• SoftBank: 87M x $23.00 = $2.0 billion

• TOTAL: 735M Shares for 15.9 Billion Raised

We believe it makes the most sense for Intel to lean heavily on share issuance, as their stock price has received an immense re-rating. In 2025, their share price was hovering around $20- $25 per share, whereas today it’s 5x that level. This means that for less overall dillution they can raise more money, making equity the far cheaper option than debt. If Intel decided to go to the bond market and rely heavily on debt, it would only add costs and harm its balance sheet.

If Intel decides to issue 400 million shares through 2030 (80 million shares per year) at around $100-$110 per share, it could raise $40-$44 billion. This amount of money alone allows them to fully fund the external foundry capacity needed to satisfy our model…

It would cost roughly $20.6 billion to fund the “Aria-foundry model.” Unfortunately, Intel can’t just build a fab that can output 22,500 WPM of leading-edge capacity; Intel must build the whole 40- 50 K WPM fab. (Fab 52 as a reference is 40,000 WPM) The total “funding job” required to build out both fab/packaging facilities at comparable wafer outputs would cost around 30-40 billion dollars. Given this figure, share issuance alone can cover the entire cost. A large caveat that we must cover is the 48D tax credit, from the Big Beautiful Bill…

48D Tax Credit:

The mechanism of the credit allows for a 35% reduction in Capex for qualifying fabs and machinery. The refundable nature of the credit is what makes it very material to Intel. Intel can elect to take the credit as a direct cash payment from Treasury, which means they collect the money even while Intel Foundry is still operating at a loss on paper. A normal tax break is worthless to a company that isn’t paying tax. This one writes a cheque regardless. Essentially, the United States government is co-funding a fab, in cash, with no shares issued, no debt added, and no profit splits. It’s the single most useful funding strategy of their lifetime and puts Intel right in the driver’s seat. The only catch for the credit is that it is only applicable to fabs that break ground before December 31st, 2026, meaning any fab starting construction in 2027 or later cannot reap the tax credit rewards.

In theory, Intel doesn’t even need to raise any debt to fund their foundry, since equity and the 48D Tax Credit are by far the much cheaper options. For our model, we actually assume no new incremental debt will be taken on by Intel through 2030.

Incremental Debt:

So why do we model zero incremental debt through 2030? Because there is nothing left that would require extra debt to fund. The 48D Tax Credit returns 35% of the buildout in cash, cheap equity covers most of the funding required for fabs, and customer pre-payments cover the rest. Debt is the most expensive capital Intel can raise, roughly 6% against an equity cost near 1.2-1.5% and a 48D credit that costs nothing, so a rational management team reaches for it last, and in our model, we believe they never have to.

This doesn’t mean that Intel stops issuing bonds. It will keep rolling over its existing maturities, but refinancing them isn’t incremental debt added to the balance sheet, and the ~$46 billion already pencilled into the base EPS won’t increase any further. If anything, the bigger question our model raises isn’t whether Intel needs debt; it’s whether it even needs all the equity we’ve pencilled in (as cash flow turns positive for the external foundry business, the funding sources start to overlap). Debt only re-enters in our downside case, where weaker cash flows and a pullback in the stock reopen the door to debt over equity. In the base case, it stays at zero, and the balance sheet quietly heals. The bull case is less share dillution, with the implication that the stock continues to surge. (A bull case could warrant 200-300 million shares as opposed to our 400 million model.)

The 1.2-1.5% number represents the earnings yield. , and then using the current Foward PE multiple of 65-85x gives us an earnings yield of 1.2-1.5%. We decided to use the earnings yield over CAPM since CAPM is built specifically for discounting future cash flows and valuing companies. What it doesn’t do well is answer the question of “should I fund this fab with stock issuance or bonds this year.” Comparing that to the established 6%, equity is the much cheaper option to build out their foundry business. Just to reiterate, we used the earnings yield because the equity-or-borrow decision hinges on earnings given up per dollar raised, not on investors required return.

How Much Higher Can Intel Stock Go?

From our homemade valuation model, we have a generous price target set for Intel.

The following images showcase an in-depth analysis of the whole foundry pipeline, along with the incremental EPS each deal provides, EPS before and after financing, and the 2030 “Intel value,” with a 12-month price target.

Read more

Nvidia is shipping. That is no longer the constraint. A finished rack such as the state-of-the-art 800V Vera Rubin or Rubin Ultra rack is just an expensive piece of parts until it is energized, and getting energized now runs through a chain of multi-year queues that the chip cycle has badly outrun. The single most quotable data point in the whole debate captures it: of the roughly 12 GW of 2026 U.S. data center capacity announced across about 140 projects, only ~5 GW is actually under construction, with the rest stuck in the “announced” stage despite normal build times of 12–18 months. Electrical equipment is under 10% of total data center cost and 100% of the bottleneck.

This report is about that bottleneck: what it actually consists of, how long each piece takes, whether it becomes a true ceiling on AI scaling, and which companies are positioned to profit from relieving it. The short answer on winners: the money is moving away from the chip and toward the grid-infrastructure layer. Transmission builders, transformer and switchgear makers, and on-site power generators, because that is where the scarcity now lives.

The Interconnection Bottleneck Deep Dive

The cleanest way to understand why this is so intractable is to see that it isn't one queue: it's four distinct queues stacked on top of each other, each with its own multi-year clock. A project has to clear all four, roughly in sequence, and the binding constraint rotates between them.

Queue 1 — The interconnection study (permission to connect)

This is the grid operator’s process for studying and approving a new connection. In PJM who is the regional operator covering the Mid-Atlantic and the epicenter of the data center surge became so clogged that PJM effectively froze its queue in 2022 to rebuild the entire process, only reopening it in April 2026 with more than 800 projects in progress. PJM has since pushed over 170,000 MW of generation requests through study and is clearing the last ~46 GW of its transition backlog by the end of 2026.

The reformed “Cycle” process promises one-to-two-year study reviews. But two facts gut the apparent improvement:

• A signed interconnection agreement is the midpoint, not the finish. After the agreement comes permitting, financing, equipment procurement, and construction. Realistic commercial-operation dates for projects entering the 2026 cycle stretch into the early 2030s.

• The demand wave keeps outrunning the throughput. PJM expects 30+ GW of new demand between 2024 and 2030, driven largely by data centers. Wood Mackenzie pegs PJM-utility large-load forecasts at ~55 GW by 2030 and ~100 GW by 2037. Some of that is “phantom load” (developers filing the same project across multiple utility queues to hedge their bets) which inflates apparent demand and clogs studies further.

The scale mismatch is structural: utilities designed interconnection processes for 50–100 MW industrial loads. In roughly 15 months, typical hyperscale project size jumped from 100–300 MW to 1 GW or more projects that rival baseload power plants. The planning cycle simply was not built for it.

Queue 2 — Transmission and grid equipment (the physical hardware)

Permission is useless without the tools to move the power. This is where lead times have blown out the most:

• Large power transformers that were able to be obtained 24–30 months before 2020 now stretch toward five years. The big 345kV–765kV transmission-class units run four to five years as well.

• Switchgear is similarly constrained.

• These are globally supply-limited, hard-to-scale factories, and data centers are competing for the same units utilities need for ordinary grid maintenance and replacement.

The transmission build itself including high-voltage lines, substations, and load centers are a specialized construction problem gated by skilled-labor scarcity (linemen and electricians) as much as by materials.

Queue 3 — On-site generation (the main escape, which is also clogging)

Because the grid is slow, developers increasingly bypass it by building dedicated power on-site. But this escape valve is filling up too:

• Gas turbines: GE Vernova’s heavy-duty turbines are effectively sold out through 2030–2031, with lead times around three years. Its combined backlog has blown past 100 GW and is heading toward ~110 GW against only ~10 GW of annual production capacity which is roughly a decade of output with about a fifth tied explicitly to data-center load. Turbine prices are tracking toward roughly triple 2019 levels (~$600/kW by end-2027).

• Reciprocating engines: Caterpillar’s backlog now extends 24 months minimum and is sold out through 2027 even with a major new facility doubling capacity in early 2026; orders placed in early 2026 won’t deliver until 2028, erasing the historical speed advantage of reciprocating engines over turbines. Cummins and INNIO face similar constraints.

The headline number from the field: at PowerGen 2026, one utility planner cited an 84-month (seven-year) lead time to power in a market like Columbus, Ohio, with even the best markets (Pittsburgh, Chicago, Houston, Dallas) requiring 36 months minimum.

Queue 4 — Policy and permitting (the one piece speeding up)

This is the only queue actively improving, under intense political pressure:

• The DOE directed FERC to issue rules accelerating large-load interconnection (FERC historically didn’t even assert jurisdiction over load connections), with action targeted for spring 2026.

• PJM is rolling out an Expedited Interconnection Track for large advanced projects (250 MW+, capped at ~10 per year), a Reliability Resource Initiative (~8 GW across 41 projects), and a “Bring Your Own Generation” fast track.

The flip side is a hardening political consensus that data centers should pay their own way. The White House and all 13 PJM-state governors jointly insisted in early 2026 that data centers bear the cost of their own load growth rather than shifting it onto ordinary ratepayers (echoed by a federal “Ratepayer Protection” framing). And “connect-and-manage” rules increasingly require large loads to accept curtailment (being cut off during grid stress and falling back to on-site backup) as the price of faster connection.

Will this create a point of complete inability to scale?

The scariest outcome would be a point of such saturation that data center growth is forced to grind to a halt as they just simply cannot bring the power to the facility. Limiting growth by so much causing a huge market freefall as most of these companies are propped up on future revenue forecasts.

In good news, all these queues are rate limiting factors, not complete walls to progress. The US has enough aggregate capacity for approved projects it is just the physical hardware (transmission, transformers, switchgear, turbines and the throughput to connect and deliver said power)

None of these are going to be permanently scarce, its just that each of these in itself is a 3-7 year project. So relief along the supply chain is going to arrive slowly but it is expanding as all these manufacturers are pouring billions to maximize capacity as soon as possible.

The realistic outcome is a plateau, not a full stop. Expect a sustained, multi-year period of roughly 2026 through ~2029-2030 where announced AI capacity dramatically exceeds energized capacity, timelines will slip routinely, power is the explicit gating line item in every site decision, and secured firm power becomes a genuine competitive moat. Buildout continues, but at the pace the slowest link allows. This is why when looking at certain company revenue estimates you can see through 2028 most analysts expect lower growth annually as this bottleneck is well understood and accounted for. (For the most part)

Where a true ceiling can occur. Specific regions like PJM's Mid-Atlantic above all, Northern Virginia, parts of Texas can hit effective saturation where no meaningful new large load can be energized for years. That redirects buildout geographically (toward grid headroom, stranded gas, hydro, or wherever firm power exists) rather than stopping it nationally. The constraint reshapes the map of the AI buildout more than it caps the total.

Which Companies are Positioned to Win From Said Bottleneck?

Read more

Micron ($MU) just printed a massive earnings beat across the board, soaring past every single estimate…

• Revenue: $41.46 billion versus $23.86 billion for the prior quarter and $9.30 billion for the same period last year; roughly +346% YoY and +74% sequentially

• Non-GAAP EPS: $25.11; GAAP EPS $24.67 (GAAP net income of $28.24 billion, non-GAAP net income $28.86B)

• vs. consensus of roughly EPS of $20.39 and revenue of $35.1 billion; so a ~$6B revenue beat and a $4.50+ EPS beat

• Board of Directors declared a quarterly dividend of $0.15 per share; ended the quarter with cash, marketable investments, and restricted cash of $30.2 billion

On top of those numbers, Micron increased their margin from 81% - 86% QoQ. This is a company who is printing cash, and those insane margins prove demand is insatiable, and that they have all the pricing power in the world. This earnings call wasn’t just pivotal for Micron as a business, it was a necessity for the AI boom to keep going strong. You see, Micron is one of the global leaders in HBM-manufacturing. HBM is one of the most important bottlenecks in the entire AI stack, and as agentic AI makes its way to the enterprise/consumer level, the need for HBM will only increase. If Micron showed a slowdown in HBM demand, or the beginning telltale signs of an “oversupply,’’ Micron would’ve crashed, and brought the entire market down with it. Micron actually printed the opposite. CEO Sanjay stated that they foresee the HBM bottleneck persisting well past 2027, as well as their pricing power. If you read our previous 2 memos, you would’ve been caught up to speed with this idea. One of our lead AI/Semi analysts, on YouTube and X, has been calling for the HBM bottleneck to persist into 2028 since early April. Once again, this earnings call reiterates this thesis…

Seoul, South Korea:

One of the most random drops we saw in the markets occurred on June 23rd, caused by Korean Debt/leverage. We made a memo regarding the severity of the “leverage issue” sneaking up on Korean investors; primarily retail investors, who have been taking out record amounts of debt in order to invest into tech-heavy companies. Most of these investments being Sk Hynix and Samsung, who make up 42% of the KOSPI index.

Due to the very weak Korean currency (Won), The bank of Korea is planning on raising interest rates toward 3% by September, in order to strengthen it. Seoul, South Korea has already “launched its first joint FX-bank inspection in 14 years, tripled its FX-stabilization bond ceiling, and put the pension fund into $54B of dollar-forward hedging. Tighter Korean policy is contractionary, so the weak won converts straight into the same "rate hike" pressure that's causing de-risking in the leveraged AI trade.”

The MSCI, once again, had excluded Korea from its developed market watchlist, removing the passive-inflow catalyst foreign investors had bought in anticipation of. (About $2.5 billion of foreign capital fled in a single session)

When you have a vast amount of equities being funded by debt, downturns can turn into full on crashes. When and investor borrows money to pay for an investment, the collateral put up is the shares themselves; and when the collateral is losing value, often times, investors cut their losses so they can maintain solvency and pay off their debt. Coupling this with the MSCI exclusion, rate-hikes, and over-leveraging, you have a recipe for a potential market crash. And that’s exactly what happened… for the most part. Leveraged positions like the 2x Sk Hynix and Samsung ETFs both were forced to continuously sell into the close to maintain their multiplier rates. This amplified the sell-off, leading to more and more debt unwinding.

Why this Can Lead to a Relief Rally:

Read more

From claims that Microns earnings will absolutely destroy the entire AI/tech super cycle and lead us into a grave recession to Micron having the best report ever and causing a pump in the market never before seen. Traders are taking very extreme stances on this earnings report.

What we are going to do is cut through the noise and tell you everything you need to know regarding Microns earnings report tomorrow.

Market Expectations

Wall Street is expecting Micron to report revenues of $35.85 billion compared to the $23.86 billion Micron raked in last quarter representing a 50.25% quarterly increase and an EPS estimate of 20.83 vs the reported 12.20 last quarter representing a 70.74% increase from last quarter.

Its clear Wall Street is expecting a huge increase in Microns revenues and EPS and at first glance it can seem like it’s an extreme ask. But once you deep dive into Microns true earning potential it’s clear they will very likely report numbers close to or above these projections.

Now that we know what the market is expecting let’s dive into what our research is telling us and our personal opinions on what the outcome of tomorrows earnings call could be based on our research…

Our Deep Insights into Microns Earnings and Future

We have made it to a point in the AI super cycle where it is clear that memory has become a huge bottleneck. With massive demand and very limited supply that is the core reason behind all of the big memory companies massively increased revenues and EPS.

This bottleneck has given these memory companies all of the negotiating power as they are the ones selling the core piece behind all of the AI hyperscalers ambitions and growth potential. Either they agree to pay whatever memory vendors are charging or they risk falling behind in growth to other companies that will pay that price.

At current estimations we can see this massive pricing power lasting into mid 2027 at the earliest and potentially late 2027 to early 2028 at the latest. However even if supply does increase at that point the prices of DRAM and HBM could and likely will remain elevated from todays prices as demand is unlikely to slow and most likely grow as time goes on.

This will allow all the memory vendors to continue to reach record revenues and earnings for an extended period of time implying a very bullish long term future.

In terms of how much DRAM and HBM are expected to rise; traditional DRAM is estimated to rise ~45% in Q3 with DRAM and HBM combined estimated to rise ~38%. Through Q4 and Q1 2027 DRAM is modelled to rise ~37% total with HBM prices modelled at ~80% increase at the end of 2027 compared to the end of 2026.

All of this is telling you memory is going nowhere soon and will only become a more premium and expensive material in the AI supercycle with the memory vendors continuing to rake in the profits at huge margins.

Earnings Predictions

Read more

If you wanted some red light therapy, you probably decided not to go to your local spa; instead, you looked at CNBC. At the time of writing this article, the Nasdaq 100 is down 2.88%, and the S&P 500 is down 1.30%. Leveraged ETFs like the Direxion 3X Bull Semiconductor ETF are down 21.98%. Overall, not a great day for investing.

You and I alike probably woke up today asking ourselves, “Why are all my positions getting killed?” No need to look any further, because Aria has the answer.

The KOSPI Sell-off

The KOSPI fell 9.99% on June 23rd in an accelerated liquidation. Led by SK Hynix and Samsung (which make up roughly 42% of the index), each dropped around 12% intraday. After a 164% runup during the year, this was the most violent single-session loss of the year. This huge market sell-off was sparked by a few main catalysts that added fuel to the fire.

1. Investors Are Over-Leveraged

   The KOSPI index is the worlds most concentrated expression of the global AI/memory trade, with almost half of the index being SK Hynix and Samsung, the two largest HBM suppliers in the entire world. This in itself isn’t an issue, it only becomes an issue when you take into account how this trade is being funded. Koreans are taking on large amounts of debt to fund their invesments. Korean margin debt hit a record high in June 2026, with specific “leveraged products” being tied directly to the AI/Chip sector. A 3% drop in the overall market could represent a 10% drop in a “debt leveraged” trade; coupling that with high beta AI/Chip companies prone to vast fluctuations, can lead to margin calls at a rapid scale. Margin balances in SK Hynix and Samsung rose from 2.53T won by the end of 2025 to 9.10T won by June 17 2026; roughly 3.6x in 6 months. The collateral put up in these trades are the shares themsevles, so when price falls, the collateral can fall short, leading to margin calls and forced selling of the shares. This already happened recently, with forced sales topping 100B won for five straight sessions (june 5-9), with 169.8B won liquidated on June 9th alone.

2. The Weak Korean Currency

   Coupling over-leveraged margin calls with a depreaciating won, and you have a recipe for an accelerated sell-off. The won currently sits a 1,533 per US dollar; its weakest since the 2009 financial crisis. In order to combat this, the Bank of Korea is planning on raising interest rates toward 3% by September. Seoul has already launched its first joint FX-bank inspection in 14 years, tripled its FX-stabilization bond ceiling, and put the pension fund into $54B of dollar-forward hedging. Tighter Korean policy is contractionary, so the weak won converts straight into the same "rate hike" pressure that's causing de-risking in the leveraged AI trade. When rates rise, the cost of debt does as well, so when this happens, the last thing you need as a leveraged investor is your collateral losing its value. If this does occur, you will be forced by the lendor to liquidate your position.

3. The Japanese Carry Trade

   On top of everything we have discussed, the loaded spring of the Japanese carry trade could release at any given moment. Like Korea, the Bank of Japan is seeing the weakest yen in over 4 decades. Because of this, inflation is running rampid in Japan. This forces the hand of the BOJ to raise interest rates to protect against the inflation issue and strengthen their currency. As we know, the Japanese carry trade has been funding Mega-Cap AI companies in the United States for many years. Insitutions would use this as an “arbitrage technique.” They would borrow yen at an extremely low interest rate, convert it into USD, and purchase treasuries and or stocks that yeild higher returns than the interest they owe.

Looking at the chart above, there is roughly $500b in outstanding yen-funded carry positions worldwide, the majority of which are US equity-related. When rates are rising in Japan, this forces those institutions to adjust their risk. Often times by liquidating the high-beta tech names in order to prevent themselves from being “margin-called.”

What Does This Mean For Your Stocks?

Read more

War Escalation/Recap:

• We saw a slight escalation with the war in Iran, as the Iranian delegation stalled talks with the USA, after renewed threats from the president

• This didn’t sit well with markets, as futures opened down 0.50% across the QQQ and SPY

• After futures opened, reports from various news sources like BRICS News stated that the US and Iran are going to continue talks throughout the week, ultimately entering into a final agreement soon.

• Reports from Iran’s delegation and foreign minister Aragachi claim that Pakistan’s mediation efforts have led to “significant progress toward ending the war in Lebanon,” that war being one of the most material issues for Iran and the progression of peace talks.

What to Expect Today: (with charts)

Read more

So Kevin Warsh just held his first meeting with the FOMC, equity markets sold off heavily into the close interpreting his updated projections as hawkish. However after truly deep diving into the meeting and running our own projections we believe there may be more than meets the eye to the fed meeting and that the market may have grossly overreacted to Kevin Warsh and the FOMCs projections. So lets dive into it to better understand what occurred at the meeting…

The Policy Decision

The FOMC held the federal funds rate target at 3.5-3.75%, the fourth straight meeting with a change. The market did not react well to the updated dot plot (the chart that records where rates are expected to go) these projections turned moderately hawkish with the median year end estimate rising to ~3.8% from 3.4% in March with more than half of the committee predicting a rate hike rather than a rate cut.

At first glance this does sound unfavorable for a rapidly growing AI sector that will rely heavily on low interest rates to speed up growth, but the most important detail was the one that markets overlooked; the long run “terminal rate” which is theoretical equilibrium rate where economic growth is neither stimulated nor inhibited did not change at all; remaining at 3.1%.

Simply put, the committee raised their expectations for the next 18 months but left the endpoint unchanged. The most reasonable read of this info is that the Fed regard the current inflation pressure as a shock we must ride out rather than a permanent shift.

Market vs Fed Expectations

Now diving into how the market vs the Fed is pricing in the future federal funds rate is where it gets really interesting, a popular claim online that the bond market is pricing in three to four hikes is not supported by the rate curve at all. In reality, the market and the fed actually agree on the short term: both of which are implying at least one rate hike toward that 3.8% figure later this year.

However the market and the fed split midway to the end of 2027, where we see the market pricing in additional rate hikes that the feds official projections do not. (see chart below)

So the base case here is that we get one rate hike later this year with divergence through 2027. However this is not set in stone at all as the Fed could potentially reference an alternative inflation measure that could change the projections entirely…

Read more

Its clear everyone’s eyes are on this stock so we’ll break down what is going on in the SpaceX options chain but also examine some of our favorite names in the broader market and we’ll give you exactly what we are eyeing ourselves.

SpaceX -

So first up the big name on the market this week. Options trading just began for SpaceX today and we can already see some pretty incredible things going on so let me break it down for you. First off what is the overall sentiment around this company according to the options market? Short answer: VERY BULLISH!

We saw a call-to-put ratio of 1.4 on this first day which implies a strong bullish sentiment behind the company as the market is very clearly expecting a upward trend in SpaceX. The majority of the volume of calls/puts traded expire July 17 (1 Month till Expiration) with these investors hoping to capitalize on this very volatile stock.

The most popular call strike price is $260 with a volume of 38,677 contracts traded and the most popular put strike price is $135 with a volume of 13,158. So as you can tell the market is expecting HUGE moves in the stock given how wide these strike prices are.

Read more

• We saw huge de-escalation with the war in the Middle East.

• The United States randomly stopped strikes, after it was reported that Iranian Officials offered a framework for a peace-deal, in which the United States had accepted

• The reason it hasn’t been formalized is because of the long transfer times of messages going back and forth, sometimes stretching days.

TODAY:

• After this de-escalation, the need for rate-hikes is almost certainly off the table. Albeit, a formalized agreement materializes

• Watch for a breakout to $725 on the

• If we can sustain $725 in the first 15 we should be good for vast breakout to the upside, post SpaceX IPO

Read more

Nebius -

Nebius was just added to the Nasdaq after hours June 11th, strengthening an already strong stock we’ve been eyeing for months. In particular it will be very important to watch how it moves at open as it is currently up to $236 after hours from its close at $222. Nebius as of June 11th has IV’s for its June 12 contracts at 130-140% so the market is pricing in huge moves of the stock and by June 19th sitting around 120% so if you are a call/put buyer on Nebius you would have to hope it makes a massive move to be profitable.

Overall Rating: Very High IV - High Risk - If you are going to invest in Nebius look to purchase only long dated calls if price is around 230-235 to avoid huge IV crush and theta decay - selling spreads can capitalize on high premiums

Invesco QQQ Trust -

Read more

Introduction

Quantum computing for decades was just a thought experiment; in the 18 months preceding this article, it evolved into something tangible; an engineering program with verified results, peer-reviewed studies, and 10s of billions of public and private capital flowing into this new scientific breakthrough.

However, there is a high amount of confusion regarding what quantum computing actually is and also what industries it can benefit in the future. So in this thesis, we will break down what Quantum Computing actually is, the current trajectory of the sector, and how it all ties in with the current investment landscape surrounding it.

Pillar 1: Science of Quantum Computing

The science of Quantum Computing is extremely complex, and this would turn into a 1000-page textbook if I explained every detail, so I will provide a simplified explanation of the basic principles of Quantum Computing that will become relevant in later parts of the thesis.

Classical Computers store information in bits, which can hold a state of either 0 or 1. Every program ever made runs on this framework. Quantum Computing replaces bits with Qubits, these Qubits can occupy the state of 0 and 1 simultaneously, which are described by two numbers called amplitudes and this state is called superposition. This is the foundation on which all the power of Quantum Computing lies, as it simply allows for an exponentially higher level of computing compared to classical computers for certain specialized tasks.

However, since these qubits exist at the microscopic level, they are incredibly sensitive to the environment around them. Things like temperature changes, vibration, or electromagnetic radiation can scramble their state and result in errors far more often than any typical transistor. The solution to this is Quantum error correction (QEC), combining Logical qubits and Physical qubits to counteract errors from destroying the original encoded data. But how does it do that?

Logical qubits are the software-defined unit of data, and Physical qubits are the physical hardware pieces (like trapped ions or photons) that exist in the processor but are also highly sensitive, as stated. So the single logical qubit encodes its information to many different physical qubits so that if one goes out of whack, the system can recognize and correct the one qubit’s error without having to destroy the original data. A good analogy takes us back to the medieval times, when there was no email, so all messages had to be delivered face to face, but entrusting just one person to travel great distances, kingdom to kingdom, delivering a message was risky because if something happened to them, then the entire message chain collapsed. What they would do is send 10 different people off to deliver the same message to lower the risk of a complete failure. Quantum computing leverages the same principle, encoding the data amongst many different physical qubits so that if one strays, it is easy to correct that error without risking the entire piece of data being lost.

In Quantum computers, each logical qubit requires hundreds to thousands of physical qubits to ensure widespread errors are not a concern. Although this protection mechanism is also the #1 reason behind Quantum Computing’s limitations, the scale needed to minimize errors is huge!

Pillar 2: The Quantum Computing Frontrunners

Now that we have the basic understanding of Quantum computing under our belt, we can get into the fun parts, the companies that are on the front lines of this new emerging technology.

They are separated into 2 distinct categories: The Pure Plays (companies whose entire company is quantum) and the Tech Giants (companies that are cash-rich with great underlying business who are also pursuing Quantum). Let’s start with the Pure Plays.

Pure Players: IonQ, Rigetti, D-Wave, Quantinuum, PsiQuantum

IonQ (NYSE: IONQ) is the largest pure-play by revenue and the first to go public. Its trapped-ion systems reported a record 99.99 percent two-qubit gate fidelity in 2025, and the company has assembled an aggressive roadmap, targeting 10,000 physical qubits by 2027 (following its purchase of Oxford Ionics) and two million physical qubits by 2030. IonQ’s 2025 acquisitions (Oxford Ionics, Lightsynq, Capella Space, Vector Atomic, a majority stake in ID Quantique, and a pending SkyWater foundry deal) extend it into quantum networking, sensing, and security as well as computing.

Rigetti Computing (Nasdaq: RGTI) pursues a vertically integrated, modular superconducting strategy, assembling larger processors from 9-qubit chiplets in its own Fab-1 facility. Its 108-qubit Cepheus-1 system reached general availability in early 2026 at roughly 99 percent median two-qubit fidelity, and its roadmap targets 150-plus qubits in 2026 and 1,000-plus qubits by the end of 2027 at progressively higher fidelity. Rigetti also supports NVIDIA’s NVQLink platform for tying quantum hardware to AI supercomputers, something we will touch on again later.

D-Wave Quantum (NYSE: QBTS) is the commercial veteran of quantum annealing, a specialized approach aimed at optimization rather than universal computing. Its Advantage2 system, delivered through the Leap cloud, underpins real customer deployments, and its 2025 revenue grew 179 percent to roughly $24.6 million at an unusually high gross margin. In 2026 D-Wave also unveiled a gate-model roadmap targeting 100 logical qubits by 2032, signalling a move toward universal computing.

Quantinuum — formed from Honeywell Quantum Solutions and Cambridge Quantum — is the most valuable specialist, reaching roughly a $10 billion valuation in 2025 and filing to go public, with a Nasdaq listing expected in 2026. Its trapped-ion systems are widely regarded as best-in-class on fidelity.

PsiQuantum, still private at a roughly $7 billion valuation after a 2025 round backed by BlackRock, Temasek, Baillie Gifford, and NVIDIA’s venture arm, is making the boldest single bet: skipping the NISQ era to build a fault-tolerant, million-qubit photonic machine using silicon-photonic fabrication with GlobalFoundries, targeted before the end of the decade.

The Tech Giants: IBM, GOOGLE, Microsoft, Amazon

IBM: IBM has published the field’s most detailed and most consistently executed roadmap. At its November 2025 Quantum Developer Conference, it introduced Nighthawk, a 120-qubit processor with 218 next-generation tunable couplers that allows circuits with about 30 percent more complexity than its prior Heron chip and supports up to 5,000 two-qubit gates. IBM projects Nighthawk-class systems reaching 7,500 gates by the end of 2026, 10,000 in 2027, and as many as 15,000 two-qubit gates across 1,000-plus connected qubits by 2028. Alongside it, the experimental Loon processor demonstrated, for the first time in IBM’s line, all the hardware components it considers necessary for fault-tolerant error correction, and the company reported a tenfold speed-up in QEC decoding, a year ahead of schedule, and a shift to 300-millimetre wafer fabrication to double the development pace.

IBM’s stated targets are community-verified quantum advantage by the end of 2026 and fault-tolerant quantum computing by 2029, supported by its Qiskit software stack and a hybrid model that couples classical high-performance computing (CPUs and GPUs) with quantum processors (QPUs). Among incumbents, IBM also has the deepest enterprise-sales motion, which matters more than raw qubit counts for near-term revenue.

Google: Google Quantum AI’s 105-qubit Willow processor produced the field’s defining 2025 result: the first verifiable quantum advantage, using the Quantum Echoes algorithm built on out-of-time-order correlators, published in Nature and estimated at roughly 13,000 times faster than the best classical method for the task. The word verifiable is doing important work: unlike Google’s contested 2019 “quantum supremacy” claim, the 2025 result is reproducible and connects to a real scientific task, measuring molecular structure via nuclear magnetic resonance, which is why it drew broad, if cautious, validation. Google is careful to note this is a specialized tool, not a general-purpose quantum computer.

Microsoft: Microsoft is pursuing the highest-risk, highest-reward path. Its February 2025 Majorana 1 chip is built on topological qubits, which aim to protect quantum information in the hardware itself and, Microsoft argues, could eventually scale toward a million qubits. The science, however, is contested: independent physicists have questioned whether Microsoft’s claims are actually scientifically possible, and rival executives have publicly doubted the result. If Microsoft is right, it could leapfrog the field; if wrong, it has spent years on an unproven foundation. A reader should treat topological qubits as a genuine option, not an established one.

Amazon: Amazon, through AWS, took a more incremental route with its February 2025 Ocelot chip, which combines “cat qubit” technology with conventional error correction in a design that AWS says could substantially reduce the resources needed for error correction and shorten the path to a practical machine by up to five years. Amazon’s commercial center of gravity, though, is its Braket cloud service, which resells access to many hardware partners, a strategy that earns revenue regardless of which modality ultimately wins.

Source: Aria Research

Pillar 3: Use cases, AI + Data Center infrastructure, Quantum Realities

Quantum computing offers an exceptional advantage for certain specialized tasks and no advantage for others. What tasks will Quantum computing actually be useful for? Let’s dive in…

Read more

• Overnight, we saw escalations with the Iran/US war, which led to a final hour of pain in the broader markets

• A United States fighter jet was struck down over the Strait of Hormuz, although Iran denied responsibility

• Trump pledged strikes, causing markets to spook. After trading hours, it was confirmed by CentCom on X that the United States carried out strikes targeting specific locations.

• This sent broader indexes down heavily. The SPY was down an additional $40 after hours, or 0.55%.

• Trump then stated that the strikes were called off after it was reported that Iranian Officials called him pleading to end the strikes. Trump gave them a 1 day extension to sign the deal or be “bombed.”

• This has now lifted markets after hours, leading the QQQ up 0.7%, and the SPY up 0.41%

Jobless Claims & PPI Scheduled for Today:

• The jobless claims are scheduled for 8:30 am today, and the projections are 220,000, with the prior coming in at 225,000

• A blowout beat to the downside will most likely spook markets the same way the labour report did last week. Anything in line or slightly higher will probably help markets recover, giving more fuel to a relief rally on Friday

• In our opinion at Aria, we believe the only catalysts to worry about from the calendar above are the Jobless Claims. Unless PPI is extremely higher than estimates, this shouldn’t move the needle the way the Jobless Claims will.

Key Levels and Stocks to Watch: (AS LONG AS THERE ISN’T AN IRAN ESCALATION)

• Make sure you pay attention to the QQQ’s

• After the dust has settled from the Jobs Report, a recovery of $700 will help push the rally farther. Pay attention to the first 15 mins for the regain of this level. This is crucial for an intraday tech rally.

Read more

We at Aria Research have compiled a group of key catalysts this week. Citing what actually happened, the key hidden details, and what this means for your money. So without further ado, let’s begin…

Nvidia:

NVIDIA kicked off this action-packed week with their keynote speech at Computex 2026. Jensen Huang gave some key insights into the AI trade, boosting specific companies throughout the speech. Take NBIS, for instance. He referred to them as a “world-class AI cloud company,” noting that they are growing extremely fast. Because of this comment, Nebius proceeded to pop 14% on Tuesday, while being up 176% YTD. Marvell also sustained some intense upward pressure after Jensen called them the next “1 trillion dollar company.” Marvell moved 30%+ after the presentation.

Source: CNBC

During the keynote, Jensen kept mentioning that “Compute is revenue.” Now, if you are familiar with our stance here at Aria Research, we are a firm believer in the “capacity constraint moat.” Jensen essentially reiterated what we have been stating for a while. The demand for capacity will be eating up any new energy that comes online for the coming years, with interconnection queues taking 54 months on average. The most qualified and knowledgeable man in the AI space stated that the most scarce product on the market equates to revenue for the masses. Essentially, the companies that can offer quick “time-to-compute” and “compute capacity” will be the winners in this space. If you haven’t yet, please go read the research paper we wrote regarding Iren and Nebius. We explain in great detail the strategic positioning these companies have to gain exponentially from the capacity-constrained environment.

Source: NVIDIA.com

NVIDIA also revealed their RTX Spark Superchip, partnered with Microsoft on ARM architecture. This is a direct shot at AMD, INTC, and Qualcomm, attempting to take market share within the consumer computer sector. One thing Aria flagged that we find interesting is that NVIDIA is dipping their toes in every layer of the AI stack. NVIDIA pushing ARM-based SoCs is a direct shot at x86. Thus, the reason why the three companies mentioned above traded downward after the keynote.

Broadcom:

Broadcom, after hours on Thursday, released their Q2 2026 earnings report.

Source: Broadom Investor Relations

Q2 revenue $22.2B (+48% YoY); AI semi revenue $10.8B (+143% YoY); AI bookings topped $30B. Guided Q3 AI revenue to $16.0B, growth above 200% YoY. The stock printed a monster quarter, but future guidance failed to dazzle Wall Street, even though Q3 revenue guided 29.4B, above 28.5B. Also, fears arose surrounding Marvell and Media-Tek potentially taking market share of their TPU business. Thursday, Macquarie’s downgrade came from their modelling of Broadcom's share of Alphabet's TPU revenue falling from ~95% in 2026 to 80% in 2027 to 65% in 2028, as MediaTek and Marvell gain a role and Google develops in-house capability.

CEO Hock Tan also gave warnings surrounding their margins. Tan's point regarding margins was that segment margins remain very stable and very solid; it's the mix between software and non-AI versus the very rapidly growing AI semiconductor that's diluting gross margin. In other words, the weighted average falls purely because the lowest-margin bucket is becoming the biggest and fastest-growing slice of the pie.

This dilution is mechanical, not a demand signal. Margins aren't compressing because customers are paying less or pulling back; they're compressing because AI demand is so strong it's outgrowing every higher-margin line Broadcom has. If anything, the falling blended margin confirms the boom rather than warning of its end. What actually sold the stock off was a different set of fears: a reaffirmed-but-not-raised full-year AI target into a stock already priced for a raise, and a Google supply chain that's no longer Broadcom's alone. At Aria Research, we’ve modelled demand to keep booming until at least 2029 as a base case, with the bull cases exceeding 2030 onwards. The data backs up demand only accelerating, not softening, which is why we called this a textbook “buy the dip” opportunity on Thursday.

Intel:

Intel kicked off their action-packed week with their Keynote speech at Computex 2026. Firstly, Intel confirmed their next-gen Xeon 7 chip, “Diamond Rapids,” to be manufactured on their 18A-P node in 2027, which is extremely positive. Many analysts have written about Intel having their yields at 50%, raising heavy issues regarding the future of its foundry. CEO Tan has consistently reiterated that they are increasing yields at 6-8% per month, and their Diamond Rapids chip got rid of any worries regarding yields and future node capabilities, in our opinion.

Source: Intel.com

Intel also leaned heavily on the “AI-everywhere story,” reiterating x86’s deep legacy as the architecture leader, as well as the CPU revival story. Intel presented a live demo of inference requests on a program, showing that the GPU/CPU ratio could reach 1:1, as agentic AI scales and becomes more prominent. Intel had left out any key information regarding their foundry, which is what investors care about most. At Aria Research, we believe more deals are inevitable for Intel’s foundry, as it is extremely bottlenecked. Due to the lack of “foundry talk,” Intel stock saw a pullback.

The Great Market Crash of June 5th 2026:

If you wanted to do some red light therapy, looking at the stock market on Friday last week was the best way to do it. The QQQ was down -4%, the S&P 500 dropped around -2.6%, resulting in 1.5 trillion dollars being wiped from the stock market. This was the first losing week in 10, and the biggest drop since October 10th. The trigger? The labour market, and SpaceX ipo.

Employers added 172,000 jobs in May, more than double the estimate of 80,000. The reason this killed markets is that it got rid of any potential for a rate cut. When the labour market is running really hot, it leads to more money circulating in the economy, resulting in more consumer spending (demand), increasing prices… or, in layman’s terms, inflation. Investors hate inflation, and the only way to combat it is to raise rates. The expectations for a rate hike rose to 66% on Polymarket. This is so pivotal for the AI trade because companies are borrowing a large amount of money for expansion, and when rates increase, it artificially cools growth and the economy. This spooked investors and made them sell out of stocks at an alarming rate. Great companies that we’ve flagged dropped 10%+, such as AMD, INTC, NBIS, IREN, and MRVL.

An important part of this sell-off was the SpaceX ipo. We saw a lot of money flow out of the stock market and into T-bills and sidelined cash. This is extremely important as no money flowed into precious metals like gold and silver. When money flows into gold and silver, this means institutions believe that a recession or large-scale crash is coming. Since this didn’t happen, we can rest assured this was more of a “one-off-flash crash.” The largest IPO ever is occurring on Friday, June 12th, offering at $135 per share, and a 1.77 trillion market cap.

A “once-in-a-decade” offering can make any investor pull sidelined cash and invest in it, and SpaceX is exactly that. When money moves into cash/cash equivalents, it means people are most likely preparing to unleash it. And the timing makes perfect sense. The “dry powder” is unambiguously there, the largest ever, and the SpaceX raise is trivially small against it, so a relief rally is well within the realm of what this cash could fund. What the data does not show is that the cash is already turning over. What this means is that a solid SpaceX IPO could be the trigger for a relief rally in hardware stocks in particular.

Below is a chart showing the amount of sidelined cash and comparing SpaceX's funding against the total cash value. (Split roughly 3 trillion in retail, and the rest institutional)

How to Strategically Play the Upcoming Weeks:

Encompassing everything that occurred, we have learned a few things that need to be addressed, so that we can make informed investment decisions…

Read more