After a long pullback, OKLO is interesting again because the story has changed. A year ago, the market was mostly buying a vision: small advanced nuclear reactors, AI data centers, Sam Altman, and the need for clean baseload power. Today, the company is starting to show actual milestones across three areas: power, fuel, and isotopes.

That is the key. Oklo is not just trying to build a reactor. It is trying to build a nuclear platform.

The Near-Term Catalyst: July 4 Criticality

The biggest near-term catalyst is Groves, the isotope test reactor being built through Oklo’s Atomic Alchemy subsidiary.

Management has said Groves is targeting criticality by July 4, 2026. In plain English, “criticality” means the reactor achieves a self-sustaining nuclear chain reaction. It does not mean commercial electricity production. It does not mean Aurora is already powering data centers. But it would be a major psychological and technical milestone.

Why?

Because nuclear is viewed as slow, expensive, and endlessly delayed. See the 24% short interest on the stock as proof of this. If Oklo can show that a new nuclear asset can move from construction into startup on an accelerated timeline, investors will immediately extrapolate that progress to the broader platform.

Groves is not the main Aurora power reactor. It is an isotope reactor. But it gives Oklo a real-world proving ground: procurement, construction, regulatory sequencing, fuel delivery, installation, testing, startup procedures, and operations. Those lessons can feed directly into future deployments.

The stock will likely treat a successful Groves criticality event as proof that Oklo is no longer just a slide-deck nuclear company.

The Bigger Prize: Aurora

The real investment case is Aurora, Oklo’s fast-fission powerhouse.

Aurora is designed to deliver small-scale, reliable nuclear power close to where power is needed. That is the exact problem AI data centers are creating. The grid is strained. Interconnection queues are long. Gas turbines are backordered. Renewables are intermittent. Data centers need firm power, 24/7.

Oklo’s pitch is simple: place small nuclear powerhouses near large power users and sell energy under long-term agreements.

That is why the Meta and Switch relationships received so much attention. Investors are not buying Oklo because they think nuclear is interesting. They are buying it because AI infrastructure may require a massive new layer of power supply, and Oklo has positioned itself as one of the few public pure plays on that theme.

Recent Management Commentary

On the most recent management call, Oklo sounded like a company shifting from planning to physical execution.

Management said the company is building across three business lines: power, fuel, and isotopes.

On power, Aurora-INL has submitted its Preliminary Documented Safety Analysis for DOE review. Oklo also received NRC approval for its Principal Design Criteria topical report for Aurora. That is a regulatory building block. It establishes key safety and design criteria that can be referenced in later applications, reducing repeated review.

On fuel, management emphasized that fuel availability is one of the major gating items for advanced nuclear. This is critical. A reactor design is not enough. You need fuel. Oklo is working on multiple paths: fuel fabrication, recycling, potential plutonium-based bridge fuel, and broader fuel partnerships.

On isotopes, management said Groves has its PDSA in review, its DSA submitted, and has received a certificate of substantial completion for construction. The company said the next steps are readiness review, startup approval, final installation of reactor equipment, integrated system testing, and fuel delivery.

That is a very specific checklist. Today, I am seeing a very significant number of July $100 Calls being traded today, and I believe the market is trying to price the possibility that Oklo is close to showing a real nuclear asset coming alive in July.

Why the Pullback Creates Opportunity

OKLO is a high-risk stock, but pullbacks in story stocks can become attractive when three things happen at once:

First, expectations reset. The stock has already gone through a painful decline from its prior hype levels. That removes some of the froth.

Second, the company keeps hitting milestones during the decline. This is the part investors often miss. A falling stock price does not always mean the company is failing. Sometimes the stock is digesting valuation while the business is advancing.

Third, the next catalyst is close. July 4 is not years away. If Groves reaches criticality, OKLO will have a tangible milestone at the exact moment investors are looking for proof that advanced nuclear can actually move faster.

This creates a favorable setup: lower stock price, better balance sheet, clearer milestones, and a near-term technical catalyst.

The Technicals

Right now, the stock is not in the wheelhouse of momentum investors. It has fallen below the 200 day moving average and there is plenty of overhead resistance from the long decline. However, price is staying above the 50 day moving average and higher lows have begun to form. On top of that, price has compressed which is one of my key indicators. Check out the six day tight flag forming now. If this stock can get over near term resistance at $80 and then the 200 day moving average at $85.70, I think it could really get going, especially with a short interest of 24%.

The Balance Sheet Is a Weapon

Oklo ended Q1 with a very large cash and marketable securities position. For a development-stage nuclear company, this is essential.

Most nuclear startups fail long before the technology has a chance to prove itself because they run out of money. Oklo has given itself runway. The company is spending aggressively, but it has capital to pursue multiple workstreams at the same time: Aurora-INL, Aurora-Ohio, Aurora-Eielson, fuel fabrication, fuel recycling, Groves, and isotope commercialization.

That flexibility separates Oklo from smaller nuclear concepts that may need constant financing just to survive.

The Risk

The risk is obvious: Oklo still has to execute.

Groves criticality could slip. Aurora could take longer than expected. NRC and DOE processes can still create delays. Fuel supply remains a central issue. The company is pre-revenue in its main power business. If the stock runs hard into a July event and the event disappoints, the downside could be sharp.

This is not a sleepy energy investment. It is a venture-style public equity.

The Bottom Line

Oklo is attractive now because the company is approaching a moment where investors may get the first real evidence that its nuclear deployment model works.

The July 4 Groves target is the near-term spark. Aurora is the long-term prize. Fuel recycling and fabrication are the strategic moat. Isotopes could provide earlier commercial activity. AI data-center power demand is the macro tailwind.

The market has been waiting for proof that Oklo can move from nuclear story to nuclear execution. The next few months may provide exactly that.

For investors who can tolerate volatility, OKLO is one of the cleanest public ways to bet on the next phase of the AI power buildout: not chips, not data centers, but the energy layer underneath them.

Holdings Update

It’s been really hard to feel comfortable buying into this over extended market, but on rare occasions like this insane bull run, you have to force yourself.

Y’day I scaled into TEM equity, AVAV equity, PLTR Calls and I’m also holding NOK calls. Clearly, I’m extremely bullish on AMZN. I’ve been in and out of NVDA (NVDL) trying to catch the upturn and currently have a small NVDL position. NVDA is STILL not getting rewarded, and as I wrote in my Nvidia Market Cap Bias article, I think big money managers will ultimately be proven very wrong here. This Ai trend can go on until the mid 2030s. In the Dell call last night, the following quote was uttered and I think it’s gold:

“The majority of the installed base remains on 14th-generation or older servers, reflecting the continued refresh opportunity going forward.”

Actually, it wasn’t just the post. It was the conjunction of a few things that started to form a much bigger concept in my head.

First, the company I work for is implementing AI. But we are not necessarily using the newest, most powerful, most expensive frontier models. We are using a “good enough” model.

This means the model does not need to be the smartest model on planet Earth. It does not need to crush every benchmark. It does not need to write poetry, solve physics problems, code a trading platform, and pass the bar exam all at the same time.

It just needs to do the job.

On top of this, “work routing protocols” are starting to pop up. The idea is simple, but potentially very important: software can evaluate the work you are trying to accomplish and route that work to the appropriate LLM.

Not the best LLM. The appropriate LLM.

That means if the task is basic, it goes to a cheaper model. If the task is moderately complex, it goes to a mid-tier model. If the task is truly difficult, then and only then does it get routed to the most expensive frontier model.

And then I saw Deirdre Bosa’s post, which basically pointed to the same trend: companies are realizing they are spending a lot on AI, and they may increasingly shift toward cheaper models that are “good enough” to get the job done.

That stopped me in my tracks.

Because as bullish as I have been throughout this AI bull market, this concept introduces a very uncomfortable question:

What if revenue per token is going down?

And what if that is happening faster than investors realize?

The AI bull case has been built on two assumptions

The AI bull case has a few big pillars.

First, usage will explode.

Second, companies will pay a premium for intelligence.

Third, the best models will command the best economics.

Fourth, the companies sitting closest to frontier AI will capture enormous value.

I still believe the first point is true. AI usage is going to explode. In fact, cheaper AI probably makes usage explode even faster.

But the second, third, and fourth points are where I am starting to pause.

Because if companies start routing work to cheaper models, then the economics change.

The world may use more AI, but pay less per unit of AI.

That is a very different stock market setup than the one many investors are underwriting today.

Revenue per token could fall

This is the key point.

If work routing becomes normal, then the average price paid per token should fall.

Think about it.

Today, a company might send too much work to an expensive model because that is the easiest setup. You pick a model, plug it into your workflow, and use it broadly.

But that is not how cost-conscious enterprises behave over time.

Enterprises optimize.

They ask:

“Why are we using the most expensive model for a simple customer service question?”

“Why are we using frontier AI to summarize a short internal document?”

“Why are we using the smartest model available to classify emails, clean data, draft boilerplate, or answer routine employee questions?”

The answer is: they probably won’t.

At least not forever.

The next stage of AI adoption may be less about who has the most powerful model and more about who can route work to the cheapest model that gets the job done.

That is a huge shift.

And if that shift happens, then yes, revenue per token should decline.

This is bullish for AI adoption, but not necessarily bullish for AI stocks

This is the part that matters most for investors.

A decline in revenue per token does not mean AI is failing.

Actually, it may mean AI is succeeding.

Lower costs mean more use cases become economical. Small businesses can use AI. Consumer apps can use AI. Internal corporate tools can use AI. Edge devices can use AI. Call centers, legal teams, finance teams, healthcare administrators, coders, marketers, and analysts can all use more AI.

So AI adoption could keep ripping higher.

But stock prices do not only care about adoption.

Stock prices care about profits.

And if AI becomes cheaper faster than companies can monetize it, the market may have to rethink who actually captures the value.

That is why this concept bothers me.

Because “AI is everywhere” and “AI companies make huge profits” are not the same statement.

The internet became everywhere.

Consumers won.

Many businesses won.

But a lot of early internet companies did not capture the economics investors expected.

That is the risk here.

OpenAI and Anthropic may not want this discussion before IPO

This is where the timing gets interesting.

OpenAI and Anthropic are reportedly heading toward enormous future valuations. The valuation premise is that frontier AI remains scarce, powerful, and monetizable.

But what if the market is already moving in the other direction?

What if enterprise customers are increasingly saying:

“We like frontier models, but we only need them for a fraction of our work.”

What if the default architecture becomes:

• route simple tasks to cheaper models,

• route medium tasks to mid-tier models,

• reserve frontier models only for the hardest work?

That does not destroy OpenAI or Anthropic.

But it does change the valuation framework.

Instead of being the default intelligence layer for the enterprise, frontier labs could become premium specialists used only when necessary.

That is still valuable.

But is it worth the kind of valuation people are talking about?

That is the question.

And I do not think OpenAI or Anthropic want investors obsessing over “revenue per token” ahead of their IPOs.

Because if investors start asking whether revenue per token is structurally falling, the IPO conversation changes.

The question becomes less:

“How big can AI get?”

And more:

“How much pricing power can the model companies actually keep?”

The biggest problem: model intelligence may become abundant

The market has been treating intelligence like it is scarce.

But what if intelligence becomes abundant?

What if the cutting edge remains expensive, but “good enough” intelligence becomes cheap and widely available?

That would create a deflationary force inside the AI market.

And deflation is usually great for users, but dangerous for companies valued on premium pricing.

If several models can complete the same enterprise workflow, customers will not pay premium prices forever. They will run bake-offs. They will compare outputs. They will compare latency. They will compare accuracy. They will compare security. And then they will compare cost.

Eventually, procurement departments get involved.

And when procurement gets involved, pricing power usually goes down.

That is exactly what work routing accelerates.

It turns AI into a competitive market at the task level.

Not “which model is best overall?”

But:

“Which model is cheapest for this specific job while still meeting our quality threshold?”

That is a brutal question for premium model pricing.

This could hit Nvidia too

The first-order concern is OpenAI and Anthropic.

But the second-order concern is Nvidia and the broader AI infrastructure trade.

To be clear, I am not saying this kills Nvidia. That would be foolish. Nvidia is one of the most important companies in the world, and AI demand is real.

But if the market starts believing that a lot of AI workloads can be handled by smaller, cheaper, optimized models, then investors may start questioning the amount of ultra-premium compute required over time.

The bear case is not:

“AI is fake.”

The bear case is:

“AI is real, but maybe not every task requires frontier-scale compute.”

That distinction matters.

If AI demand shifts from frontier training and premium inference toward cost-optimized inference, then the market may begin to reprice parts of the AI infrastructure stack.

That could pressure:

• Nvidia,

• AMD,

• Broadcom,

• Marvell,

• Arm,

• AI networking names,

• power/cooling infrastructure names,

• and any company being valued as if AI capex grows in a straight line forever.

Again, usage can rise while revenue per token falls.

That is the uncomfortable part.

The software layer may face pressure too

There is another risk.

If model costs fall, customers may also question premium AI software pricing.

Many software companies are trying to charge more for AI features. Some of those features are valuable. Some are deeply embedded into workflows. Some deserve premium pricing.

But others may be glorified wrappers around models that are becoming cheaper by the month.

At some point customers may ask:

“Why am I paying this much for an AI feature if the underlying model cost keeps falling?”

That does not mean all AI software is in trouble.

The winners will be companies with proprietary data, deep workflow integration, distribution, and measurable ROI.

But generic AI wrappers could be vulnerable.

If the model is commoditized, the moat has to come from somewhere else.

The winners may be different than people think

If this trend plays out, the winners may not be the companies with the absolute smartest model.

The winners may be the companies that make AI cheaper, easier, and more efficient.

That could include:

* model routers,

* inference optimization platforms,

* companies with custom silicon,

* open-source ecosystems,

* cloud providers with low-cost AI infrastructure,

* application companies with proprietary data,

* companies that own workflow distribution,

* and edge AI players that move inference closer to the device.

In other words, the market may shift from rewarding “frontier intelligence” to rewarding “cost-effective intelligence.”

That is a major regime change.

My concern

I have been very bullish on AI.

I still believe AI is one of the most important technology shifts of my lifetime.

But this concept is making me think harder.

Because if revenue per token is falling, investors need to be careful about what they own.

AI adoption can boom while certain AI stocks fall.

That is not a contradiction.

It is exactly what happens when a technology becomes more useful but less scarce.

The customer wins.

The economy wins.

Productivity may eventually win.

But the companies priced for permanent scarcity may not win.

The simple thesis

Here is the whole thing in one sentence:

If companies increasingly route work to cheaper “good enough” models, then revenue per token should fall, and the AI market may have to reprice the companies valued as if frontier intelligence will hold premium pricing power forever.

That does not mean the AI bull market is over.

But it does mean the bull market may change.

The next phase may not be about who has the smartest model.

It may be about who can deliver acceptable intelligence at the lowest cost per task.

And if that is where we are going, investors should be prepared.

The stock has been through the software-stock meat grinder. It traded around $279 and then got crushed all the way down to roughly $119. That kind of drawdown usually tells you the market has made a decision. The market decided Snowflake was just another expensive software stock — high multiple, slowing growth, too much hype, not enough operating leverage.

But I’m starting to wonder if that framing is wrong.

Snowflake may be getting lumped into the old software bucket at exactly the moment it should be getting looked at as part of the new breed of AI infrastructure winners.

This is not Nvidia. It is not a chip company. It does not sell GPUs. But it sits in one of the most important layers of the AI stack: enterprise data.

And AI does not work without clean, organized, accessible, governed data. That is where Snowflake lives.

The market may be misclassifying Snowflake

The mistake, in my opinion, is viewing Snowflake as a legacy software name instead of as an AI-enablement platform.

Companies do not just wake up one day and “do AI.” They have to organize their data. They have to govern it. They have to make it usable. They have to connect it to models, applications, agents, dashboards, and workflows.

That is exactly what Snowflake is trying to become.

On the last earnings report, Snowflake showed that its business is still growing at a very healthy pace. Fourth-quarter product revenue was $1.23 billion, up 30% year over year. Remaining performance obligations were $9.77 billion, up 42% year over year. Net revenue retention was still 125%, and Snowflake had 733 customers doing more than $1 million in trailing-12-month product revenue.

Those are not dead-software numbers. Those are strong platform numbers.

The company also guided fiscal Q1 product revenue to $1.262 billion to $1.267 billion, representing about 27% growth, and guided full-year fiscal 2027 product revenue to $5.66 billion, also about 27% growth.

So the question heading into earnings is pretty straightforward:

Is Snowflake still just decelerating software, or is it becoming one of the core AI data platforms enterprises need to build on?

I think the answer may be the latter.

The sell-side is starting to lean bullish

Two analyst notes caught my attention.

First, Mizuho reportedly said its channel checks ahead of earnings were favorable for Snowflake. The firm pointed to healthy data-modernization demand, continued data-warehousing demand, and rising adoption of products like Snowflake Intelligence and Cortex Code. Mizuho has an Outperform rating and a $220 price target on the stock.

Then Bank of America came out with a similarly positive view. BofA raised its Snowflake price target to $205 from $195 and said it expects a strong fiscal Q1 because demand entering the year remains robust. More importantly, BofA said it does not expect the quarter to change its view that Snowflake is gaining share in the AI business-intelligence opportunity.

One bullish analyst note is interesting. Two bullish analyst notes, both pointing to demand strength and AI-related opportunity, starts to look like a real setup.

Now, I do not buy stocks just because analysts like them. We all know how that goes. But when analyst checks line up with company commentary, product momentum, and external interest signals, I start paying attention.

Cortex Code may be more important than the market realizes

The product that really caught my eye is Cortex Code.

Snowflake describes Cortex Code as an AI-driven coding agent integrated into the Snowflake environment. It is designed to help with data engineering, analytics, machine learning, and agent-building workflows. In other words, it is not just a generic AI coding assistant. It is built inside the Snowflake ecosystem.

Snowflake is not only trying to host data. It is trying to become the place where companies build AI-enabled workflows on top of that data.

And here is the key part financially: Cortex Code is billed based on token consumption for customers with existing Snowflake accounts, and we all know how token use has been exploding recently across LLMs. Snowflake also says Cortex Code usage can involve normal Snowflake compute and storage charges depending on the workflow.

That means this product has a direct monetization path.

If usage grows, Snowflake can potentially benefit in multiple ways:

That is the kind of product the market should care about.

Google Trends is flashing an interesting signal

I ran “Cortex Code” through Google Trends, and the result is worth paying attention to. Now, Cortex Code was just released in February of 2026, so this does not prove a whole lot.

Let me be clear: Google Trends does not equal revenue. It does not prove adoption. It does not prove paying customers are using the product at scale.

But it is a signal.

And for a product like Cortex Code, which is tied directly to Snowflake’s AI narrative and potentially to token-based revenue, I think that signal matters.

At minimum, it tells me there is real awareness and curiosity around the product. In enterprise software, awareness usually comes before trials. Trials come before adoption. Adoption comes before revenue.

So no, I would not bet the farm on a Google Trends chart. But I would absolutely include it in the mosaic.

And similarly on Google Trends, the keywords “Snowflake” (specified to refer to the software company) and “Snowflake Intelligence” are both making spike highs over a 5 year period in the first couple months of 2026. These are additional signs of increasing interest.

The earnings setup: revenue beat likely, EPS is the question

Here is where I am on the actual quarter.

I am getting increasingly confident that Snowflake can beat revenue expectations.

Between the company’s last-quarter momentum, strong RPO growth, still-healthy net revenue retention, favorable analyst checks, and the visible interest around Cortex Code, the revenue setup looks good.

My bigger concern is EPS.

Snowflake is still investing aggressively. It is building AI products. It is funding product development. It is expanding its platform. That is good strategically, but it can limit near-term earnings upside.

The company guided Q1 non-GAAP operating margin to 9%, while full-year fiscal 2027 non-GAAP operating margin guidance was 12.5%. That gives Snowflake room to beat, but it also tells me the market will be watching expense discipline closely.

This is the part that gives me pause.

A revenue beat is good.

A revenue beat plus EPS beat is what makes the stock work.

A revenue beat plus margin upside plus confident full-year commentary could make the stock move hard.

Valuation is not crazy versus recent software winners

This is another part of the setup I find interesting.

Two recent earnings winners — Datadog and JFrog — have been rewarded by the market. Their price-to-sales multiples are much higher than Snowflake’s.

By the numbers I’m looking at:

Snowflake is not cheap in an absolute sense. I am not pretending it is a value stock.

But relative to other high-quality software names that are showing growth, Snowflake does not look outrageous. In fact, if Snowflake shows revenue acceleration and proves that AI products are contributing to consumption, I could see the market deciding that 12.74x sales is not expensive enough.

That is the opportunity.

The market has already punished the stock like a decelerating software name. If Snowflake can show it is actually an AI data-platform winner, the multiple can expand.

Innodata Results

Another interesting read-through is Innodata. INOD recently crushed earnings and had a huge stock reaction because the market is starting to reward companies tied directly to the AI data layer. INOD is not Snowflake. INOD helps AI builders with training data, post-training data, model evaluation, alignment, trust and safety, and human-expert workflows. Snowflake is a software platform where enterprise data lives, gets governed, and gets activated into analytics and AI applications.

But the common denominator is important: AI needs data infrastructure.

That is the part of the Snowflake story I think the market may be missing. Snowflake is not just a legacy software stock. It is one of the companies helping enterprises make their data usable for AI.

What I want to hear on the SNOW call

For me, the checklist is pretty simple.

I want to hear management talk about:

1. Product revenue acceleration

2. Cortex AI and Cortex Code usage

3. Snowflake Intelligence adoption

4. AI workloads driving consumption

5. Stable or improving net revenue retention

6. Operating-margin discipline

7. Confidence in full-year guidance, or ideally a raise

The dream quarter would be: product revenue beat, EPS beat, operating margin above guide, and management clearly saying that AI-related workloads are helping drive consumption.

That would change the narrative.

That would support the idea that Snowflake is not old software. It is AI infrastructure software.

Final thought

I am thinking about taking a position ahead of earnings, but I am not exactly sure how I will do this (options or stock). I will let subscribers know what I do in a note on the day of earnings. Earnings are on May 27th after the close of the market.

I’m pretty confident in the possibility of a revenue beat. The company’s last report was strong. Mizuho’s checks sound positive. Bank of America’s note sounds positive. And the Google Trends data is a legitimate signal that interest in Snowflake and in an important Snowflake AI product has picked up significantly.

But the EPS beat is what gives me pause.

Snowflake needs to show that it can grow and still deliver leverage. The market is not going to reward “growth at any cost” the way it did a few years ago. It wants revenue growth, but it also wants margin discipline.

Still, I think the setup is interesting.

The stock has already been crushed from its highs. The market has thrown it into the software bucket. But Snowflake may belong in a different bucket entirely: the companies helping enterprises actually implement AI, like Jfrog, Innodata and DataDog.

And if Snowflake proves that next week, there could be a very nice post-earnings move.

Too big to keep growing.
Too big to surprise.
Too big to deserve a higher multiple.
Too big because “the gains have already happened.”

That argument sounds intelligent on the surface. It sounds disciplined. It sounds like risk management.

But after the quarter Nvidia just reported, I think it is mostly lazy thinking.

Nvidia just put up one of the most extraordinary quarters I have ever seen from a company of this size. Revenue was $81.6 billion, up 85% year over year and 20% sequentially. Data Center revenue was $75.2 billion, up 92% year over year. GAAP gross margin was 74.9%. GAAP EPS was $2.39, up 214% year over year. Non-GAAP EPS was $1.87, up 140% year over year. And then Nvidia guided Q2 revenue to $91 billion, plus or minus 2%, while explicitly assuming no Data Center compute revenue from China.

That is not a peak.

That is not a tired mega-cap.

That is a monster still accelerating.

The Market Cap Bias Is Real

There is a mental block around Nvidia.

Investors look at the market cap first, then the fundamentals second. That is backwards.

The thought process goes something like this:

“Nvidia is already worth more than $5 trillion. Therefore, it cannot go up much more.”

But that is not analysis. That is anchoring.

The market should not ask, “How big is Nvidia already?” The market should ask:

How fast is Nvidia still growing?
How much of the AI stack is Nvidia now capturing?
How visible are forward estimates?
What multiple should the best growth company in the world trade at?

Because based on the numbers Nvidia just reported, the answer is not “upper-teens forward earnings.”

That multiple makes no sense.

GAAP EPS Is Accelerating

The attached earnings graphic tells the story visually better than I can write about it.

Nvidia’s GAAP EPS is no longer just growing. It is bending upward. It is starting to look, dare I say, parabolic.

The company reported $2.39 of GAAP EPS versus an estimate of $1.75 in the graphic. That is a $0.64 beat, or roughly a 37% upside surprise.

A 37% GAAP EPS beat is incredible for a small-cap stock.

For a company with a market cap above $5 trillion, it is almost absurd.

And yes, some of the GAAP EPS beat was helped by other income and equity securities gains. That needs to be acknowledged. But that does not change the bigger point. The operating machine underneath Nvidia is still exploding. Revenue grew 85% year over year. Data Center grew 92% year over year. Gross margins are sitting around 75%. Operating income grew 147% year over year on both a GAAP and non-GAAP basis.

So let’s not pretend this was some low-quality beat. This was a historic quarter.

Nvidia Is No Longer Just Selling GPUs

The biggest mistake the bears keep making is thinking of Nvidia as a GPU vendor.

That was the old model.

The new Nvidia is selling the AI factory stack.

Compute. Networking. Systems. CPUs. GPUs. Software. Inference optimization. AI cloud infrastructure. Enterprise AI. Sovereign AI. Industrial AI. Edge AI. Robotics. Autonomous systems. Security. Storage acceleration. NVLink. Spectrum-X. InfiniBand. Blackwell. Rubin. Vera.

This is why the stock still deserves a premium.

Nvidia is not just benefiting from AI infrastructure spending. Nvidia is increasingly becoming the operating system of AI infrastructure.

In the call summary, the most important detail is that Nvidia’s growth is broadening beyond hyperscalers. Data Center was split into Hyperscale and ACIE, with ACIE covering AI clouds, sovereign AI, enterprise, industrial AI factories, and other purpose-built AI infrastructure. According to the summary, Hyperscale revenue was about $38 billion, while ACIE was about $37 billion and growing faster sequentially. That matters because it pushes back directly against the idea that Nvidia is just a derivative of a few hyperscaler capex budgets.

This is bigger than Microsoft, Amazon, Google, Meta, and Oracle.

This is sovereign AI.

This is neocloud AI.

This is enterprise AI.

This is industrial AI.

This is every country and every major company slowly realizing they need AI infrastructure the way they needed electricity, broadband, cloud, and cybersecurity.

And Nvidia is sitting in the middle of it.

The Vera CPU Opportunity Changes the Story Again

The most underappreciated new growth vector may be Vera.

According to the call summary, Nvidia management framed Vera CPUs as a potential $200 billion TAM opportunity and suggested visibility to nearly $20 billion of standalone CPU revenue this year.

This is why the “Nvidia is just GPUs” argument is so stale.

Agentic AI does not just need GPUs. It needs CPUs for orchestration, memory management, browsing, tool use, workflow execution, and system coordination. The GPU does the heavy AI compute. The CPU helps coordinate the agentic infrastructure around it.

In other words, agentic AI may not cannibalize Nvidia’s GPU opportunity.

It may expand Nvidia’s total wallet share.

That is the exact opposite of the bear case.

The Semiconductor Comp Table Makes Nvidia Look Undervalued

Here is what really bothers me.

I pulled the top 20 semiconductors, by market cap, from FinViz and Nvidia is trading at only about 19.7x forward earnings.

The average forward P/E multiple of the profitable companies in that group is about 31.2x. Excluding Nvidia, the group average is about 31.9x.

So Nvidia — the clear AI infrastructure leader, the company growing revenue 85% year over year, the company guiding to $91 billion next quarter, the company with 75% gross margins — is trading at a discount to the average forward multiple of the semiconductor peer group.

That is insane.

The table makes the argument almost by itself.

Yes, the group’s average EPS growth looks high, but that is distorted by cyclical rebounds in names like Micron and Intel. Nvidia’s growth is not merely a rebound off a depressed base. Nvidia is growing from a gigantic revenue base while printing elite margins and expanding into new markets.

And the sales growth comparison is not even close.

Nvidia’s 66.9% five-year sales growth rate in the table crushes the group average of roughly 14.7%.

So why is Nvidia trading at 19.7x forward earnings?

Why is a company with Nvidia’s revenue growth, margin profile, AI dominance, platform control, and product-cycle visibility trading below the peer group average?

That is the Nvidia Market Cap Bias.

Wall Street sees the market cap and refuses to give the company the multiple its fundamentals deserve.

The Stock Should Be Re-Rated

I am not saying Nvidia should trade at any price. Valuation still matters.

But valuation has to be tied to reality.

And the reality is this: Nvidia is growing faster, executing better, generating higher margins, and controlling more of the future AI infrastructure stack than almost any company in the world.

A sub-20x forward P/E multiple does not reflect that.

Using the attached table’s Nvidia price of $223.47 and forward P/E of 19.74x, the implied forward EPS is approximately $11.32.

Here is what Nvidia would be worth at different forward earnings multiples:

The peer group average forward multiple is roughly 31x.

If Nvidia simply traded near that peer average, the stock would be around $350.

And frankly, I do not think “peer average” is some heroic bull case. Nvidia is not an average semiconductor company. Nvidia is the company defining the most important infrastructure buildout in the world.

AI Has Not Peaked

The bears keep wanting AI to peak.

They want the capex cycle to peak.
They want GPU demand to peak.
They want hyperscaler spending to roll over.
They want margins and earnings to crack.

But the numbers are not cooperating.

Q2 guidance says revenue is going higher. Blackwell is ramping. Rubin is coming. Vera opens a new CPU opportunity. Networking is exploding. AI cloud revenue is growing. Sovereign AI is growing. Enterprise and industrial AI factories are becoming real. Nvidia’s software and systems stack is getting deeper. And the company is now returning massive capital to shareholders on top of everything else.

The “AI has peaked” crowd has been wrong over and over again.

At some point, Wall Street needs to stop fighting the evidence.

My Bottom Line

The central mistake investors are making is simple:

They are valuing Nvidia based on how big it already is, instead of valuing Nvidia based on how powerful the forward earnings machine still looks.

That is the Nvidia Market Cap Bias.

It is the belief that a company cannot keep producing huge stock gains simply because it has already produced huge stock gains.

But Nvidia is not going up because people are blindly chasing a mega-cap. Nvidia is going up because the company keeps doing things that mega-caps are not supposed to be able to do.

An 85% revenue growth quarter.
A 92% Data Center growth quarter.
A 75% gross margin profile.
A massive EPS beat.
A $91 billion Q2 revenue guide.
A new $200 billion CPU TAM opportunity.
A full-stack AI factory platform that keeps expanding.
And a stock still trading at an upper-teens forward earnings multiple.

Wall Street needs to wake up.

Nvidia is not being valued like the most important company in AI infrastructure.

It is being penalized for being large.

That is not discipline. That is bias.

And if Nvidia keeps executing like this, that bias is going to break.

This thesis sounds obvious, but honestly, to me it feels like the market has an ongoing bias towards “the Hormuz situation ends fairly quickly”. This is just NOT realistic. The timing of this trade is also being driven by Washington. President Trump is scheduled to meet with top national-security advisers in the White House Situation Room to discuss potential military options involving Iran, according to a Reuters report citing Axios and two U.S. officials. That does not mean action is certain, but it does raise the probability that the market continues to price in a geopolitical risk premium for crude, refined products, LNG, shipping, and U.S.-based energy infrastructure.

In other words, this is not just a supply/demand story — it is also a policy-reaction story. If the White House signals a harder line, the market may keep rewarding companies that can help replace disrupted Middle East supply while penalizing companies whose own barrels or LNG volumes are trapped behind Hormuz. That is why the cleanest basket is U.S. refiners, U.S. LNG exporters, Permian producers, Canadian producers, select midstream/export names, and tankers — not the global majors with heavy Qatar/UAE/Iraq exposure.

I screened 63 oil and gas names and then reviewed charts as well. My top 3 picks are below, but first, here’s a quick look at some of the companies that lost oil production as a result of the Hormuz situation:

The key is to distinguish between companies that simply own oil and companies that have near-term earnings torque. In this environment, I would prioritize U.S. refiners, LNG exporters, Permian producers, Canadian producers, select midstream names, and tanker operators. Own the companies that benefit from higher oil, tighter refined products, and U.S. energy exports. Be careful with companies whose own barrels or LNG volumes are trapped behind Hormuz.

Possible Winners

Top Stock Picks

Valero (VLO)

I like the technical setup here. A base has been building since late March. A breakout in the 255-257 zone is possible. I will likely be positioning for it. VLO is the best pure refiner. Valero plans to run refineries at 92–95% utilization in Q2, and management expects high margins to persist because product inventories may take months to normalize.

EOG Resources (EOG)

EOG is a high-quality oil E&P with Delaware Basin. EOG generated $1.5B of FCF in Q1 and returned nearly $950M to holders. EOG sports another really nice technical setup with a similar triangle base formed since late March. The MACD crossover denotes a possible well-timed entry.

Venture Global (VG)

Finally, VG knocked it out of the park in its last earnings release on May 12th, 2026. VG probably has the best direct LNG torque. VG reported Q1 revenue +59%, record LNG cargoes, and raised 2026 EBITDA guidance; it has near-term cargo flexibility if global gas/LNG trade remains dislocated.

I have some experience trading this one in the past few months. It is a tricky trader, however, I do like the fact it just got back over the 50 day moving average and looks set to possibly attack the $17.50 highs.

One note about oil stocks. The don’t generally trade like Ai or Tech stocks. More patience is required and they generally don’t trend as strongly. I will be looking to purchase on intraday or intra-week weakness.

Good luck!

Overall, the goals are wide-ranging but I would say the largest contingent of CEOs comes from the financial sector. These CEOs are hoping for more access to China’s financial market. Blackrock BLK, Mastercard MA and Visa V, and Goldman Sachs GS want investment management access, capital markets access, and access to payments. These companies could rally on any deals.

Jensen Huang of Nvidia was a last minute addition to these CEOs and is likely pushing to sell H200s into the Chinese market. NVDA is already rallying in pre-market based on the possibilities here.

Elon Musk TSLA and Tim Cook AAPL are likely looking to keep supply chains intact, and Elon also wants to get more sales traction for Tesla EVs.

Ortberg of Boeing BA definitely wants a big deal here for Boeing 737 Max, and possibly some upside 787 Dreamliners. The rumor now is a deal for 500 737 Max aircraft could be on the table. CEO Lawrence Culp is also looking for opportunity in the aerospace space.

It is also very interesting Illumina ILMN CEO Jacob Thaysen is attending, likely pushing for some sort of gene-sequencing partnership.

There is a LOT at stake for these companies. If Total Addressable Market can be expanded into a massive Chinese market, I have doubt some of these stocks will pop.

That may now be changing.

Rackspace is trying to reposition itself as a company that helps large enterprises safely deploy and manage AI in production. This is not the same as being a pure AI model company, and it is not the same as being a hyperscaler like Amazon, Microsoft, or Google. Rackspace is attempting to become something more specific: the managed operator of governed AI infrastructure.

In plain English, that means Rackspace wants to help banks, hospitals, manufacturers, governments, and other large enterprises run AI inside secure, compliant, private, hybrid, or multi-cloud environments. These customers may not want to simply upload sensitive data into a public AI tool. They need data controls, audit trails, security, compliance, uptime, and someone accountable for making the system work.

That is where Rackspace wants to sit.

The company’s new pitch is built around partnerships with names like AMD, Palantir, VMware/Broadcom, Dell, Rubrik, Uniphore, AWS, Azure, and Google Cloud. AMD potentially gives Rackspace a silicon and compute story. Palantir gives it an enterprise AI workflow story. VMware and private cloud give it a legacy-enterprise installed base. Rackspace’s role is to stitch these pieces together, operate the infrastructure, and manage the AI environment for customers.

The simple framing is:

AMD provides the compute. Palantir provides the AI workflow layer. Rackspace provides the governed operating environment.

That is the bull case.

Rackspace is not just acting like a consultant. It is closer to a hybrid of consultant, systems integrator, managed-service provider, and infrastructure operator. The company can help design the AI architecture, connect it to enterprise data and software, run the private or hybrid cloud environment, monitor it, secure it, and support it over time. The key for investors is whether that consulting and integration work turns into recurring managed infrastructure revenue.

The revenue story is still early, but there are signs of stabilization. Management has guided 2026 revenue to roughly $2.6–2.7 billion and adjusted EBITDA to about $305–315 million. The important piece is the mix. Public Cloud is lower-margin and more competitive, while Private Cloud is higher-margin and strategically more relevant to the AI-governance story. If Private Cloud returns to growth and AI-related deals begin contributing real revenue, investors may begin valuing Rackspace as more than a distressed managed-services company.

That is where Apollo comes in.

Apollo controls a majority of Rackspace’s voting power and owns roughly 129.6 million shares. At a recent close of $6.30, that stake is worth more than $800 million. Every $1 increase in RXT adds roughly $130 million of mark-to-market value to Apollo’s position.

Apollo’s likely goal is not simply to squeeze shorts for fun. The real objective is probably more strategic: change the market narrative, improve the equity value, strengthen the balance sheet, and create better refinancing options before the company’s 2028 debt maturities. A higher stock price gives Rackspace more flexibility. It could potentially issue equity at a better valuation, repurchase debt, support an exchange transaction, or use the improved market perception to negotiate with creditors.

In other words, Apollo and management are likely trying to transform RXT from:

“levered old cloud services company”

into:

“governed AI infrastructure operator for regulated enterprises.”

That is a much better story to sell to investors if an equity offering eventually comes.

The valuation is highly scenario-dependent. In a bear case, if the AI story fails to convert into contracts and the refinancing wall becomes the dominant issue, the equity could still be worth very little. In a base case, using a sum-of-the-parts approach, Rackspace might be worth around $3–4 per share. In a bull case, if AMD becomes a real commercial partnership, Palantir-related work expands, Private Cloud grows, and debt concerns fade, the stock could justify something closer to $8–10+ per share.

The squeeze setup is also real. The following table outlines total shares outstanding, share ownership by Apollo, and percent of shares sold short.

With nearly one-quarter of the float sold short, and Apollo holding a large block that is effectively locked up, the tradable float is tight. If buyers chase the AI narrative or shorts are forced to cover, the stock can move violently.

That appears to be what is happening now.

The conclusion: RXT is no longer just a distressed cloud stock. It has become a high-beta AI infrastructure/refinancing story with a major short squeeze overlay. The opportunity is real, but so is the risk. The stock’s future depends on whether Rackspace can turn the AMD, Palantir, and governed AI narrative into real revenue, real EBITDA growth, and a credible solution to its debt maturity wall.

That raises the key investor question:

Who benefits most from all of these upcoming IPOs?

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Many Winners

The obvious winners will be the companies themselves.

But there is a clean second-order play hiding in plain sight: the firms that bring these businesses public.

A blockbuster IPO does not just create one payday. It can set off a multiyear chain of underwriting fees, secondary offerings, debt issuance, employee liquidity events, block trades, research coverage, market-making, wealth management flows, and M&A advice.

When a firm wins the lead role on a marquee IPO, it often wins much more than the initial fee pool.

If these next-generation category leaders keep scaling after they list, the same banks can stay attached to the story for years.

The two clearest ways to play it

The underwriting table (below) makes the pattern hard to miss: Morgan Stanley and Goldman Sachs keep surfacing around the highest-profile IPO candidates.

If this backlog converts into actual listings, both firms should benefit not only from IPO fees, but from:

• Lead roles on marquee IPOs

• Follow-on offerings after the IPO

• Debt issuance

• Block trades

• Employee liquidity events

• Institutional trading flows

• Research coverage

• Wealth management relationships

• Long-tail advisory work

This is the IPO tollbooth trade.

The companies going public may get the headlines.

But the banks collecting fees on the way in — and potentially for years afterward — may be the cleaner public-market way to play the theme.

Morgan Stanley: the repeat player

Morgan Stanley (MS) appears again and again in the biggest IPO conversations.

It is reportedly involved in the SpaceX IPO process. It is listed on Cerebras. It shows up around Applied Aerospace & Defense. It is also one of the banks that would logically be in the mix for future mega private-company listings.

Morgan Stanley is no longer just a pure trading and investment-banking story. The company has a massive wealth management franchise, a strong institutional securities business, and a long history of winning marquee technology relationships.

If a new IPO cycle is beginning, Morgan Stanley could be one of the biggest beneficiaries.

Technical setup: MS is showing a high tight flag — a powerful run followed by a tight, orderly pause near the highs. That can sometimes signal institutional accumulation and potential continuation if the broader trend resumes.

Goldman Sachs: the capital markets lever

Goldman Sachs (GS) is the other obvious name.

Goldman continues to show up across the elite IPO pipeline: SpaceX, Lime, Discord, Anthropic discussions, Shein, and other major private-company situations.

Unlike Morgan Stanley, Goldman is more directly levered to capital markets activity. That can be a problem during weak IPO markets, but it becomes an advantage when the window reopens.

A strong IPO cycle could help Goldman through:

• Underwriting fees

• Advisory fees

• Trading activity

• Private-to-public transition work

• Follow-on offerings

• Institutional client engagement

Goldman is one of the classic Wall Street toll collectors when animal spirits return.

Technical setup: GS appears to be shaping a potential inverse head and shoulders pattern — a classic bullish structure that can hint at accumulation and a possible continuation higher if the neckline is reclaimed with conviction.

The IPO pipeline points to the same handful of banks

The table below shows the recurring pattern.

Bottom line

If the next generation of category leaders keeps choosing the same handful of banks, Morgan Stanley and Goldman Sachs could remain the toll collectors on one of the best capital-markets themes in the market.

The companies may be the stars.

But the underwriters may be the easier trade.

My feeling of gratitude is particularly strong this time because the period of December 2025 to February 2026 was pretty tough on me. I experienced one of the largest drawdowns I’ve had in the last 10 years. It taught me some things and reinforced some things I already knew.

Normally, I go to cash during tough market periods. This does two things: it allows for a time of rest, and I avoid bad trading conditions. But this period was different. The market (SPY) didn’t just reverse trend and quickly break the 50 and 200 day moving averages. Instead, it GRINDED sideways, up and down for months, frequently oscillating above and below the 50 day moving average. It was grueling. My rule is: below the 50 day and I get cautious (size down). Below the 200 day, I stop trading. In mid-March it became obvious the market was breaking down under the 50 day and then a few days later, it was under the 200 day. But, the damage had been done: a -16% drawdown in December, and -10.8% drawdown in February. So, what did I learn here? A sideways grinding market is my kryptonite. I will be VERY aware of this going forward.

My comeback since April 1 has reinforced something I already knew: I can make a LOT of money very quickly in good or, in this case, great market conditions. Early in my trading journey, drawdowns would upset me. Until this past drawdown, they hadn’t bothered me much BECAUSE I have repeatedly come back from drawdowns over and over, a little more confident each time. New traders should understand this concept. Each time you triumph over adversity in trading, your confidence will grow. My job is to keep the money I make in trading. The number one and two methods to do this are: manage risk on every single trade always, AND avoid bad trading environments.

Performance

Quick note on my options account. I only trade options during strong market periods, when SPY is trading over its 20 day moving average. I consider it “wild money”. I always start with a $1K deposit and I try to run it up. In this case, over the past month, I ran it up to $17.5K and have withdrawn almost all of it. I will start the process again. I’ve been able to do this 5 or 6 times over the past few years. For anyone struggling with trading, I recommend the following: 1) don’t trade options until you can put up consistent returns (for years) trading just equity 2) only trade options during STRONG market periods. This applies to directional options trading only. A few people I follow on FinTwit have found ways to make a lot of money selling premium. I would like to be able to master this at some point as well.

My Stats

Stats are so important to me. Why? They tell me when my trading is HOT, in other words, when to push the envelope by sizing up. As a trader, your methods will sometimes align perfectly with market conditions. THOSE ARE THE TIMES YOU WANT TO SIZE UP. If your short-term stats are bad, it is very likely your strategies are not “flowing” with the market. I’ve always kept a spreadsheet of all my trades and basic stats on the last 10 and last 100 trades. Now, with ChatGPT and OpenClaw, I’ve been able to do so much more. Here are my stats for last 10, 25, 50, 100 and All trades. Obviously, we’ve been in a phenomenal trading environment since April 1, so as I would expect, my profit factor has been getting better and better. And, over this April 1 to May 8th period, as I saw my profit factor ramping, I definitely ramped up size.

Tracking your top tickers is useful as well. Given what SOXL has done in the last month, I find it pretty hilarious this is my biggest loser. In all honesty, I believe that was due to one oversized trade I had on when SOXL gapped down. In any case, tracking these stats allows you to remove tickers that you are not trading well, and focus on tickers making you money. Also, you can drill down into the characteristics of tickers where you made the most money. What was common between them? How do the charts look similar? I would bet you will find one common thread between the big winners: extreme strength.

Have a great weekend!

Added TXN today, sold ¾ of my HNGE position at $54.60 and $53.99.

Also added Nvidia today other form of NVDL. Could see NVDA ripping to new highs, especially after the candle today.

I’m staying mostly small and nimble and still have plenty of cash. Have a small DDOG call position ahead of earnings tomorrow too. Finally, I picked up a few GLW July $185 Call today. After a day or two of rest, I think GLW can vault to $200.

Have a great evening.

Tonight, based on the earnings report, I took a position in HNGE after hours at $51.60.

Why I bought it

This was a strong quarter. Revenue came in at $182.3 million, up 47% year over year, and the company also raised guidance. What stood out to me is that this is not just a “story stock” with AI buzzwords attached. Hinge is showing real revenue growth, strong margins, and expanding adoption.

For me, this checks several important boxes:

• Real AI use case

• Rapid revenue growth

• Expanding customer footprint

• Raised guidance

• Still relatively under-followed by the broader market

The revenue story is getting hard to ignore

Hinge’s quarterly sales trend has been moving steadily higher, and tonight’s quarter pushed that trend even further.

A few key numbers:

• Q1 2026 revenue: $182.3M

• Year-over-year growth: +47%

• Q4 2025 revenue: $170.7M

• Revenue has now climbed from roughly $82.7M in Q1 2024 to $182.3M in Q1 2026

That’s a strong growth curve, especially for a company many investors still don’t know well.

What Hinge actually does

Hinge Health helps employers and health plans manage musculoskeletal conditions — things like back pain, neck pain, joint pain, and physical therapy / rehab needs.

This matters because musculoskeletal issues are one of the biggest healthcare cost buckets for employers and insurers. Traditional physical therapy can be expensive, inconvenient, and hard to scale.

Hinge’s solution is to use:

• AI / computer vision

• Remote exercise therapy

• Wearable / movement tracking tools

• Clinician support

• Software-driven care plans

Its flagship AI system, TrueMotion, helps guide patients through physical therapy exercises remotely while collecting movement data and feeding that back into the care process.

That’s why I think this is more than just a digital health app. It is trying to become an AI-enabled care platform.

And now management is taking that playbook beyond PT. The newly launched Migraine Care Program suggests Hinge is aiming to expand into additional pain-related and chronic-care markets over time.

Customer growth is real

This is another important part of the story: the business is not just growing revenue — it is growing reach.

Recent company metrics show:

• Clients: 2,849, up 23% year over year

• LTM billings: $769.9M, up 52% year over year

• Contracted lives: roughly 25 million

• Health plan / PBM / ecosystem partners: 60+

That tells me the company is continuing to deepen its footprint with employers, payers, and healthcare partners.

In simple terms: more customers are signing up, more lives are under contract, and the company’s distribution network continues to widen.

An under-the-radar AI stock

One reason I like this setup is that Hinge still feels underappreciated relative to the size of the opportunity.

A lot of investors are focused on the obvious AI names — chips, hyperscalers, software giants. But there may be a second layer of AI winners: companies that use AI to reshape large, inefficient industries.

Healthcare is one of the biggest and most inefficient markets in the world.

Hinge is using AI to attack a real problem:

• physical therapy is costly,

• access is limited,

• patient adherence is inconsistent,

• and employers / insurers desperately want lower costs.

That’s why I think HNGE has the potential to become a sleeper AI stock if execution continues.

And awareness does seem to be building.

The Google Trends chart shows that search interest in Hinge Health has been climbing over the last five years. That doesn’t prove the stock goes higher, of course — but it does suggest the name is starting to get onto more investors’ radar.

This is a pretty hefty spike in Google’s “Interest Over Time” measurement. I believe it’s possible Shark Ninja has hit on a series of viral products. Anecdotally, my wife bought the Shark FlexStyle and she loves it. When I chart the individual products, “Ninja Creami” also stands out. Also, the general term “hair wand” sees a pretty massive spike. I think Dyson and Shark Ninja are the two main makers of hair wand or hair wand like products.

The chart begins spiking in February 2026 and peaks on April 18th. This means the spike in interest runs through the quarter reported on tomorrow morning.

Anyway, I’m taking a small position ahead of earnings based on this research. This is not a recommendation. Just sharing research here.

Well, this morning, Amazon currently at a $3 trillion market cap, made the announcement they are offering their supply chain for use by other companies.

FedEx and UPS stocks are NOT liking this and both are currently down -8%.

It is remarkable to me that a company of this size can just add an entirely new and massive business line. But it also feels 100% natural. Amazon is going to blow the doors off UPS and FedEx. I mean, they can get your order to you in under 4 hours in many cases. Here is a look at the companies already signed up for this business.

The market, once again, is undervaluing this announcement. All the infrastructure is ALREADY THERE. There’s only the matter of signing up customers.

Great move by Amazon!

In the spirit of 100% transparency, here are some screenshots of 3 accounts I’ve been tracking on a monthly basis since January 2024. In 2023 I placed 20th in the United States Investment Championship with a 67.6% return, and, 13th in 2024 with a 133.2% return. I did not compete in 2025 and 2026 because it was a lot of extra pressure on top of, well, my life. All of these accounts are retirement accounts, so I am always risk conscious, and never wanted to YOLO 50%-100% of an account into one position like some of the USIC winners have done. I’m not taking anything away from the winners, by the way. There is no doubt, in order to win that competition, you have to trade high-flying stocks with SIZE. There are some unbelievable traders in the USIC, so if you feel like you are becoming an elite trader, I highly recommend competing with the best. I will say, those two years taught me a TON about myself and my trading.

In this Substack, I plan to do the following:
1) Teach ALL of my methods through a series of articles. The series will range from trader mindset, to risk management, to the (simple) math of becoming a profitable trader, to candlestick reading, to using your performance data as a market signal, my favorite setups, when to size up and when to size down, AND everything else. I have a ton of videos created as well that can add detail to my articles

2) Periodically show my holdings

3)Periodically highlighting specific stocks I like

4)Reviewing the overall market health including market sentiment

Please join me! I want to help as many people reach an early retirement as possible. During my 15 year journey to profitability, there were many times I thought early retirement wasn’t going to be possible. But it is! Don’t let anyone tell you EXACTLY how to trade. There are many ways to make money in this market. The key is, to match your personality and style with a system that works for your passion level, time commitment, and risk tolerance.

Below, are screenshots from one of my original accounts.

Traders are re-thinking the “software bad” narrative as the new month rolls in. I’m in the software biz, and it hasn’t made a whole lot of sense to me. Some parts of software will be taken out by Ai. But, products provided by TEAM (Jira and ticketing), DDOG (logging), and CRM (sales pipelines and customer relationship) have some of the brightest software minds in the biz. They will all be using these AI tools to development. This means these companies will be releasing builds faster and faster with more features. Their moats will increase. At my company right now, we are hiring more software devs to apply Ai tools, and to rework legacy codebases at a faster pace. There are literally millions of codebases out there, just like ours, needing the same rework and re-write.

The Wall Street analysts think they know, but they don’t know. I would also like to add a caveat that “price is king”. No matter what I feel, I always defer to price.