Community/NVDA
AInvest curates key KOL insights on NVDA.
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The distinction the market drew yesterday — "AI leader" vs "AI beneficiary" — sounds like a semantic difference. It's actually a valuation difference of $500B to $1T.
Here's how the market prices the two categories:
AI leaders (Nvidia, Microsoft, Salesforce with Agentforce, PANW with NGS ARR) trade at premium multiples because they have demonstrable, recurring, growing AI-specific revenue lines. Nvidia's AI revenue grew 143% this quarter. Microsoft's Azure AI revenue is accelerating. These companies can point to a dollar amount that AI is adding every quarter.
AI beneficiaries (Apple, Meta, Google pre-AI-revenue-disclosure) trade at a discount to leaders because their AI revenue is embedded in existing products. It's harder to see, harder to measure, and harder to attribute to AI specifically.
Apple's problem is that even after WWDC, the path from "Siri AI" to a measurable AI revenue line is unclear. Will Siri AI increase iPhone ASP? Will it drive services upsell? Will it create a new premium tier? The answers are probably yes — but probably next year, not this quarter.
For a stock trading at 37x earnings with $4.5T in market cap, "probably next year" isn't enough. The market wants to see the dollar amount on the slide. Apple needs to find a way to make AI revenue legible — either through a Siri Pro subscription tier, explicit Apple Intelligence services revenue disclosure, or pricing power on new iPhone models tied to AI features.
Until it does, the market will keep calling it a beneficiary. And beneficiaries don't get leader multiples.
Here's how the market prices the two categories:
AI leaders (Nvidia, Microsoft, Salesforce with Agentforce, PANW with NGS ARR) trade at premium multiples because they have demonstrable, recurring, growing AI-specific revenue lines. Nvidia's AI revenue grew 143% this quarter. Microsoft's Azure AI revenue is accelerating. These companies can point to a dollar amount that AI is adding every quarter.
AI beneficiaries (Apple, Meta, Google pre-AI-revenue-disclosure) trade at a discount to leaders because their AI revenue is embedded in existing products. It's harder to see, harder to measure, and harder to attribute to AI specifically.
Apple's problem is that even after WWDC, the path from "Siri AI" to a measurable AI revenue line is unclear. Will Siri AI increase iPhone ASP? Will it drive services upsell? Will it create a new premium tier? The answers are probably yes — but probably next year, not this quarter.
For a stock trading at 37x earnings with $4.5T in market cap, "probably next year" isn't enough. The market wants to see the dollar amount on the slide. Apple needs to find a way to make AI revenue legible — either through a Siri Pro subscription tier, explicit Apple Intelligence services revenue disclosure, or pricing power on new iPhone models tied to AI features.
Until it does, the market will keep calling it a beneficiary. And beneficiaries don't get leader multiples.
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The selloff last week revealed something important about how the market prices different types of AI exposure — and why the rotation from hardware to software could be both inevitable and violent.
Hardware AI (Nvidia, Broadcom, AMD, Micron) is a capex cycle bet. You're essentially betting that hyperscalers continue spending $50–60B per quarter on AI infrastructure indefinitely. The thesis is simple and powerful when rates are low: borrow cheap, build fast, the ROI comes later. When rates spike — as they did Friday with the 10Y hitting 4.54% — the "ROI comes later" part becomes a problem. Higher discount rates make future cash flows worth less. Hardware AI stocks get hit hardest because their earnings are largely front-loaded (the spending is happening now) while the monetization is still uncertain.
Software AI (Apple, Salesforce, ServiceNow, Microsoft) is a monetization cycle bet. You're betting that AI features drive incremental revenue — higher prices, lower churn, more seats, new services. The multiple is lower (40-60x vs 80-90x for hardware), but so is the sensitivity to rates. More importantly, software AI doesn't require the customer to keep spending on infrastructure. Once the AI feature is in the product, the revenue recurs.
The irony: the market spent six months rotating INTO hardware AI because the ROI story was clearer. Now, with rates rising and hardware stocks repricing, the rotation is reversing. Software AI — which looked boring compared to Nvidia's vertical chart — suddenly looks like the safer bet.
Apple sits at the intersection. 2.2 billion devices is the distribution advantage no hyperscaler can match. If Siri 2.0 drives even a 5% increase in services revenue, that's $5B+ in high-margin, recurring revenue that doesn't require a data center to scale.
Today's WWDC is the first real test of whether the rotation from hardware AI to software AI is real — or just a wishful narrative.
Hardware AI (Nvidia, Broadcom, AMD, Micron) is a capex cycle bet. You're essentially betting that hyperscalers continue spending $50–60B per quarter on AI infrastructure indefinitely. The thesis is simple and powerful when rates are low: borrow cheap, build fast, the ROI comes later. When rates spike — as they did Friday with the 10Y hitting 4.54% — the "ROI comes later" part becomes a problem. Higher discount rates make future cash flows worth less. Hardware AI stocks get hit hardest because their earnings are largely front-loaded (the spending is happening now) while the monetization is still uncertain.
Software AI (Apple, Salesforce, ServiceNow, Microsoft) is a monetization cycle bet. You're betting that AI features drive incremental revenue — higher prices, lower churn, more seats, new services. The multiple is lower (40-60x vs 80-90x for hardware), but so is the sensitivity to rates. More importantly, software AI doesn't require the customer to keep spending on infrastructure. Once the AI feature is in the product, the revenue recurs.
The irony: the market spent six months rotating INTO hardware AI because the ROI story was clearer. Now, with rates rising and hardware stocks repricing, the rotation is reversing. Software AI — which looked boring compared to Nvidia's vertical chart — suddenly looks like the safer bet.
Apple sits at the intersection. 2.2 billion devices is the distribution advantage no hyperscaler can match. If Siri 2.0 drives even a 5% increase in services revenue, that's $5B+ in high-margin, recurring revenue that doesn't require a data center to scale.
Today's WWDC is the first real test of whether the rotation from hardware AI to software AI is real — or just a wishful narrative.
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TeslaCoin1000000:I’m trimming speculative hardware and increasing software weight. Keeping cash for dips; if rates stay sticky, recurring AI revenue feels like the safer portfolio position.
therealchengarang:Volatility like this is where I nibble. If NVDA keeps selling out, I’ll add; if MSFT/AAPL show real AI stickiness, I’ll rotate.
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Part 1: (What's the next move for Crypto and Stocks)
Friday, 5th June 2026, was one of the worst days for financial markets in the past 12 months. Nearly $2.5 trillion got wiped out in a single day. Stocks crashed hard, crypto and
$BTC
got hit, commodities dropped, $OIL weakened, $XAU sold off, and panic spread across almost every asset class. It is very rare to witness such a broad-based selloff where virtually everything gets sold together.
After digging much deeper into it, I came to understand that there were three major reasons behind this move.
1️⃣ The Mega IPO Liquidity Drain
The market has three massive upcoming IPOs on the horizon: $SpaceX, $OpenAI, and $Anthropic, carrying a combined valuation of around $4 trillion.
Now think about it for a moment. How will the funding for these offerings come from retail and institutional investors? Money doesn't magically appear. Large investors need liquidity, and liquidity is often raised by selling existing positions. Money never leaves the financial markets, it just rotates.
According to Business Insider reports, SpaceX and other IPO candidates will not be fast-tracked into the S&P 500 after all, but that doesn't change the fact that investors have been preparing for these listings. SpaceX alone is expected to raise approximately $75 billion, making it one of the largest IPO events in history.
When opportunities of this size emerge, money gets pulled from somewhere else. That "somewhere else" is often the market you're currently invested in.
2️⃣ Hot U.S. Jobs Data & Rate Hike Fears
The second major reason was the surprisingly strong U.S. employment data.
Reports suggested the U.S. economy added 172,000 jobs, while expectations were around 88,000, almost double the forecast.
This immediately changed the market's expectations. Strong employment data means the economy is still running hot, making it harder for the Federal Reserve to justify rate cuts. Instead, the possibility of further rate hikes gained momentum, with probabilities jumping from roughly 25% to 60%.
This pushed both the DXY (U.S. Dollar Index) and Treasury yields higher.
Historically, a strong DXY is one of the biggest headwinds for global financial markets. Even more importantly, whenever the U.S. 10-Year Treasury Yield remains above 4.5% while maintaining a bullish structure, financial markets tend to experience stress and liquidity pressure.
This is not a new narrative for me.
Since last year, I have repeatedly stated since last year that the market could eventually face a scenario where rates stay elevated and could hike for much longer than we expect. Persistent inflation, resilient economic data, rising energy prices, and geopolitical uncertainty have consistently supported that view.
Now, with a new Fed Chairman brought in by Trump, his key meeting scheduled within the next 10 days, elevated inflation concerns, double hot jobs data, high oil prices, and ongoing geopolitical tensions while the U.S. remains involved in global conflicts, investors simply do not know what comes next.
And when uncertainty rises, investors reduce risk.
Historically, Bitcoin and crypto are often among the first assets sold during risk-off events.
3️⃣ Cracks Starting to Appear in the AI Trade
The third major reason is that cracks are beginning to emerge in the AI trade.
Despite strong reports, Broadcom Inc. suffered a 15% decline, marking one of its worst performances of the year. The primary reason was simple: investors expected the company to raise its future revenue targets aggressively, and that didn't happen.
That single disappointment was enough to shake confidence across the entire AI space.
Broadcom's weakness quickly spread to other AI-related names, including NVIDIA, as investors started asking a very uncomfortable question:
"Are we paying too much simply because something has AI attached to its name?"
Friday, 5th June 2026, was one of the worst days for financial markets in the past 12 months. Nearly $2.5 trillion got wiped out in a single day. Stocks crashed hard, crypto and
$BTC
got hit, commodities dropped, $OIL weakened, $XAU sold off, and panic spread across almost every asset class. It is very rare to witness such a broad-based selloff where virtually everything gets sold together.
After digging much deeper into it, I came to understand that there were three major reasons behind this move.
1️⃣ The Mega IPO Liquidity Drain
The market has three massive upcoming IPOs on the horizon: $SpaceX, $OpenAI, and $Anthropic, carrying a combined valuation of around $4 trillion.
Now think about it for a moment. How will the funding for these offerings come from retail and institutional investors? Money doesn't magically appear. Large investors need liquidity, and liquidity is often raised by selling existing positions. Money never leaves the financial markets, it just rotates.
According to Business Insider reports, SpaceX and other IPO candidates will not be fast-tracked into the S&P 500 after all, but that doesn't change the fact that investors have been preparing for these listings. SpaceX alone is expected to raise approximately $75 billion, making it one of the largest IPO events in history.
When opportunities of this size emerge, money gets pulled from somewhere else. That "somewhere else" is often the market you're currently invested in.
2️⃣ Hot U.S. Jobs Data & Rate Hike Fears
The second major reason was the surprisingly strong U.S. employment data.
Reports suggested the U.S. economy added 172,000 jobs, while expectations were around 88,000, almost double the forecast.
This immediately changed the market's expectations. Strong employment data means the economy is still running hot, making it harder for the Federal Reserve to justify rate cuts. Instead, the possibility of further rate hikes gained momentum, with probabilities jumping from roughly 25% to 60%.
This pushed both the DXY (U.S. Dollar Index) and Treasury yields higher.
Historically, a strong DXY is one of the biggest headwinds for global financial markets. Even more importantly, whenever the U.S. 10-Year Treasury Yield remains above 4.5% while maintaining a bullish structure, financial markets tend to experience stress and liquidity pressure.
This is not a new narrative for me.
Since last year, I have repeatedly stated since last year that the market could eventually face a scenario where rates stay elevated and could hike for much longer than we expect. Persistent inflation, resilient economic data, rising energy prices, and geopolitical uncertainty have consistently supported that view.
Now, with a new Fed Chairman brought in by Trump, his key meeting scheduled within the next 10 days, elevated inflation concerns, double hot jobs data, high oil prices, and ongoing geopolitical tensions while the U.S. remains involved in global conflicts, investors simply do not know what comes next.
And when uncertainty rises, investors reduce risk.
Historically, Bitcoin and crypto are often among the first assets sold during risk-off events.
3️⃣ Cracks Starting to Appear in the AI Trade
The third major reason is that cracks are beginning to emerge in the AI trade.
Despite strong reports, Broadcom Inc. suffered a 15% decline, marking one of its worst performances of the year. The primary reason was simple: investors expected the company to raise its future revenue targets aggressively, and that didn't happen.
That single disappointment was enough to shake confidence across the entire AI space.
Broadcom's weakness quickly spread to other AI-related names, including NVIDIA, as investors started asking a very uncomfortable question:
"Are we paying too much simply because something has AI attached to its name?"
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Monqoloid:Staying on sidelines; risk-off vibes make me wait.
Tadikif:If the Fed leans hawkish again, does that mean crypto stays under pressure until cuts resume, or can we see a liquidity rotation back to equities?
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Everyone is debating whether to buy AVGO on the dip. Most of them are asking the wrong question.
The right question isn't "is AVGO cheap at $430?" It's "is the custom silicon moat still intact — and who is most likely to erode it?"
Let's be precise about the moat. Broadcom's business model is fundamentally different from Nvidia's. Nvidia sells the same H100/B200 to everyone. Broadcom co-designs a unique XPU for each hyperscaler client — Google's TPU, Meta's MTIA, ByteDance's internal silicon, and now chips for Anthropic and OpenAI. Each design takes 18–24 months of co-engineering. Once the chip is in production, the customer can't switch without losing 2 years of development time and rebuilding from scratch. That's an extraordinarily high switching cost.
The moat is real. But it has three genuine vulnerabilities.
First: insourcing risk. Amazon already moved Trainium 3 fully in-house. Microsoft is deepening Maia 2 without Broadcom involvement. If Meta or Google decides to follow AWS and build its own networking + accelerator stack entirely, Broadcom loses a customer representing 15–20% of revenue with no replacement pipeline.
Second: concentration. Five customers represent roughly 50% of Broadcom's semiconductor revenue. That's not a diversified business — it's a concentrated bet on the continued AI capex commitments of five specific companies. If any one of them hits a capex cycle pause, the impact is immediate and disproportionate.
Third: the software miss this week. Infrastructure software — the VMware/enterprise stack — came in $140M light. That's 0.6% of revenue. But the trend matters more than the number. If software is plateauing while AI chips soar, the "AI + software compounding" re-rating thesis loses one of its two legs.
The bull case survives all three of these risks — for now. Google's supply agreement runs through 2031. Meta's MTIA roadmap still relies on Broadcom networking even if compute moves in-house. Anthropic and OpenAI are new customers adding to the revenue base. And the Q3 guide of $16B at 200% YoY growth is, by any measure, extraordinary.
But at 87x earnings, "survives the risks" isn't enough. You need "dominates despite the risks." That's a different standard. And that's why today's NFP — and whether it gives AVGO a multiple-expansion tailwind from rate relief — matters as much as the fundamental story.
The right question isn't "is AVGO cheap at $430?" It's "is the custom silicon moat still intact — and who is most likely to erode it?"
Let's be precise about the moat. Broadcom's business model is fundamentally different from Nvidia's. Nvidia sells the same H100/B200 to everyone. Broadcom co-designs a unique XPU for each hyperscaler client — Google's TPU, Meta's MTIA, ByteDance's internal silicon, and now chips for Anthropic and OpenAI. Each design takes 18–24 months of co-engineering. Once the chip is in production, the customer can't switch without losing 2 years of development time and rebuilding from scratch. That's an extraordinarily high switching cost.
The moat is real. But it has three genuine vulnerabilities.
First: insourcing risk. Amazon already moved Trainium 3 fully in-house. Microsoft is deepening Maia 2 without Broadcom involvement. If Meta or Google decides to follow AWS and build its own networking + accelerator stack entirely, Broadcom loses a customer representing 15–20% of revenue with no replacement pipeline.
Second: concentration. Five customers represent roughly 50% of Broadcom's semiconductor revenue. That's not a diversified business — it's a concentrated bet on the continued AI capex commitments of five specific companies. If any one of them hits a capex cycle pause, the impact is immediate and disproportionate.
Third: the software miss this week. Infrastructure software — the VMware/enterprise stack — came in $140M light. That's 0.6% of revenue. But the trend matters more than the number. If software is plateauing while AI chips soar, the "AI + software compounding" re-rating thesis loses one of its two legs.
The bull case survives all three of these risks — for now. Google's supply agreement runs through 2031. Meta's MTIA roadmap still relies on Broadcom networking even if compute moves in-house. Anthropic and OpenAI are new customers adding to the revenue base. And the Q3 guide of $16B at 200% YoY growth is, by any measure, extraordinary.
But at 87x earnings, "survives the risks" isn't enough. You need "dominates despite the risks." That's a different standard. And that's why today's NFP — and whether it gives AVGO a multiple-expansion tailwind from rate relief — matters as much as the fundamental story.
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Empty_Somewhere_2135:Volatility like this is where I park cash and watch AVGO. If NFP softens and rates ease, I’ll nibble on dips, but I’m hedging with a small NVDA collar. The moat’s there, just not bulletproof.
car12703:The multiple here is hostage to the labor market and rates. If NFP stays soft into summer, the Fed might pause, and AVGO could get a tailwind. But if capex slows, even a 10% drop in hyperscaler spend hits margins fast.
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This week didn't break the AI bull market. It graduated it.
Phase 1 (2023–early 2026): The AI hype cycle. The market rewarded any company that mentioned AI, had AI revenue, or was adjacent to AI infrastructure. Valuations didn't matter. The narrative was everything. Nvidia going from $300B to $5.5T in 30 months is the defining data point of Phase 1.
Phase 2 (June 2026 onward): The execution cycle. Phase 1 ended not with a crash, but with a repricing. The market spent this week systematically repricing every major AI name based on one question: "Is what you delivered better than what we already assumed you would deliver?"
The companies that passed Phase 2's test: Snowflake (expectations near zero, delivered 33% growth), Dell (AI server revenue literally not in anyone's model at 757%), PANW (recovering from sector trauma, clean beat-and-raise).
The companies that failed Phase 2's test: Zscaler (perfectly priced, imperfect execution), Salesforce (AI story known, monetization pace was the debate), Broadcom (extraordinary results, but priced for extraordinary+ results).
What changes in Phase 2:
Valuation discipline returns. 87x earnings requires 87x execution. No exceptions.
Expectations management becomes a competitive advantage. The companies that sandbag guidance and overdeliver will be rewarded. The ones that set aggressive guidance will be punished on any shortfall.
The buy-side bar replaces earnings beats as the primary signal. Sell-side consensus is the floor. The invisible buy-side bar is the real threshold.
Macro re-enters the equation. In Phase 1, rate uncertainty was an inconvenience. In Phase 2, with multiples still extended, rate moves have immediate and disproportionate impact on stock prices.
The AI bull market isn't over. The companies building AI infrastructure are still printing extraordinary numbers. The demand is real, accelerating, and multi-year. But the easy trade — buy anything AI, expect it to go up — is definitively over.
Phase 2 is harder, more selective, and ultimately more rewarding for investors who do the work to find where expectations are wrong.
Welcome to the real AI trade.
Phase 1 (2023–early 2026): The AI hype cycle. The market rewarded any company that mentioned AI, had AI revenue, or was adjacent to AI infrastructure. Valuations didn't matter. The narrative was everything. Nvidia going from $300B to $5.5T in 30 months is the defining data point of Phase 1.
Phase 2 (June 2026 onward): The execution cycle. Phase 1 ended not with a crash, but with a repricing. The market spent this week systematically repricing every major AI name based on one question: "Is what you delivered better than what we already assumed you would deliver?"
The companies that passed Phase 2's test: Snowflake (expectations near zero, delivered 33% growth), Dell (AI server revenue literally not in anyone's model at 757%), PANW (recovering from sector trauma, clean beat-and-raise).
The companies that failed Phase 2's test: Zscaler (perfectly priced, imperfect execution), Salesforce (AI story known, monetization pace was the debate), Broadcom (extraordinary results, but priced for extraordinary+ results).
What changes in Phase 2:
Valuation discipline returns. 87x earnings requires 87x execution. No exceptions.
Expectations management becomes a competitive advantage. The companies that sandbag guidance and overdeliver will be rewarded. The ones that set aggressive guidance will be punished on any shortfall.
The buy-side bar replaces earnings beats as the primary signal. Sell-side consensus is the floor. The invisible buy-side bar is the real threshold.
Macro re-enters the equation. In Phase 1, rate uncertainty was an inconvenience. In Phase 2, with multiples still extended, rate moves have immediate and disproportionate impact on stock prices.
The AI bull market isn't over. The companies building AI infrastructure are still printing extraordinary numbers. The demand is real, accelerating, and multi-year. But the easy trade — buy anything AI, expect it to go up — is definitively over.
Phase 2 is harder, more selective, and ultimately more rewarding for investors who do the work to find where expectations are wrong.
Welcome to the real AI trade.
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Sensitive-Fix8857:Feels like Phase 2 starts now. I’m watching SNOW and DELL for execution clarity before adding to my AI sleeve.
Wheremytendies:The math checks out: SNOW at 33% growth while expectations were near zero, DELL with 757% AI server revenue, PANW recovering. If multiples are disciplined, 87x requires consistency. I’m favoring SNOW and DELL over ZS and CRM until execution proves itself.
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Everyone is debating whether AVGO's selloff is a buying opportunity. Here's the question they should actually be asking: is the custom silicon model structurally defensible?
The answer is yes — but with one critical caveat. Custom chips like Broadcom's XPUs are co-engineered directly with the hyperscaler. Google's TPU isn't just "powered by Broadcom" — it's designed jointly, with roadmaps locked in years ahead. That creates switching costs that make AMD or even Nvidia almost irrelevant as competitors in this specific segment. The Google supply agreement reportedly runs through 2031.
The risk isn't competition. It's insourcing. Amazon already moved Trainium 3 fully in-house. Microsoft is deepening Maia 2. If Meta or ByteDance decides to follow, Broadcom loses a customer that represents 15–20% of revenue with no replacement on the shelf.
The selloff isn't telling you the thesis is broken. It's telling you the market finally started pricing that tail risk.
The answer is yes — but with one critical caveat. Custom chips like Broadcom's XPUs are co-engineered directly with the hyperscaler. Google's TPU isn't just "powered by Broadcom" — it's designed jointly, with roadmaps locked in years ahead. That creates switching costs that make AMD or even Nvidia almost irrelevant as competitors in this specific segment. The Google supply agreement reportedly runs through 2031.
The risk isn't competition. It's insourcing. Amazon already moved Trainium 3 fully in-house. Microsoft is deepening Maia 2. If Meta or ByteDance decides to follow, Broadcom loses a customer that represents 15–20% of revenue with no replacement on the shelf.
The selloff isn't telling you the thesis is broken. It's telling you the market finally started pricing that tail risk.
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PatientPsy:Everyone assumes co-design equals monopoly; hyperscalers can pivot.
LonelyConely:Everyone cheering AVGO; I’m not convinced the risk is priced.
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The Trump administration just signed an executive order on AI innovation and security.
President's AI order is not just a chip headline...
These are the stocks I'm watching:
AI front-end: $NVDA $AMD $AVGO
Cloud: $MSFT $GOOGL $AMZN $ORCL
Data centers: $DELL $SMCI $VRT
Power/grid: $CEG $VST $ETN $PWR
Cybersecurity: $CRWD $PANW $ZS
Defense AI: $PLTR $BAH
The better question is what the order actually funds or removes friction from:
data center permitting
federal AI procurement
cyber/model security
power/grid buildout
AI exports
domestic semis
That is where the watchlist comes from.
President's AI order is not just a chip headline...
These are the stocks I'm watching:
AI front-end: $NVDA $AMD $AVGO
Cloud: $MSFT $GOOGL $AMZN $ORCL
Data centers: $DELL $SMCI $VRT
Power/grid: $CEG $VST $ETN $PWR
Cybersecurity: $CRWD $PANW $ZS
Defense AI: $PLTR $BAH
The better question is what the order actually funds or removes friction from:
data center permitting
federal AI procurement
cyber/model security
power/grid buildout
AI exports
domestic semis
That is where the watchlist comes from.
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skilliard7:Still holding NVDA, but wary of policy-driven hype.
Chemical_Home6387:I’m on the sidelines, honestly. The list looks broad, but I’d rather see a concrete timeline or budget line item before touching any of these names. Feels exciting, but I’m not chasing.
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Up $3.4M in 2026 swing trading and $160,000+ for today only.
I've explained why these will explode BEFORE the crowd figures it out:
$NOW — Jensen Huang said agentic AI won't kill software companies. Best week since its 2012 IPO.
$IBM — Surged on Nvidia's new PC chip reveal + quantum AI hype. Trump call out.
$ARM — TD Cowen named its AGI CPU roadmap a secular demand inflection with
$MSFT
$AMD — Broad AI infrastructure buying wave; chip sector rally rips higher.
$INTC — 202% YTD return; Citi raised target citing AI inference server demand.
$ORCL — Stargate partner with OpenAI; $523B AI cloud backlog growing 438% YoY.
$AVGO— Custom AI chips rival Nvidia's GPUs; Q2 earnings drop June 3.
$HPE — Q2 earnings crushed expectations; revenue up 42% YoY, guidance raised.
$DELL — Record $113.5B revenue, $43B AI server backlog, $140B FY27 outlook.
$BE — Up 1,200%+ in a year; AI data centers need its off-grid power.
$KEEL— Canadian AI infrastructure corp trading alongside crypto/digital infrastructure peers in the AI data center buildout wave. Leopold call out.
The AI supercycle is only in Year 3 of 15. So we are still super early still. Be patient.
I've explained why these will explode BEFORE the crowd figures it out:
$NOW — Jensen Huang said agentic AI won't kill software companies. Best week since its 2012 IPO.
$IBM — Surged on Nvidia's new PC chip reveal + quantum AI hype. Trump call out.
$ARM — TD Cowen named its AGI CPU roadmap a secular demand inflection with
$MSFT
$AMD — Broad AI infrastructure buying wave; chip sector rally rips higher.
$INTC — 202% YTD return; Citi raised target citing AI inference server demand.
$ORCL — Stargate partner with OpenAI; $523B AI cloud backlog growing 438% YoY.
$AVGO— Custom AI chips rival Nvidia's GPUs; Q2 earnings drop June 3.
$HPE — Q2 earnings crushed expectations; revenue up 42% YoY, guidance raised.
$DELL — Record $113.5B revenue, $43B AI server backlog, $140B FY27 outlook.
$BE — Up 1,200%+ in a year; AI data centers need its off-grid power.
$KEEL— Canadian AI infrastructure corp trading alongside crypto/digital infrastructure peers in the AI data center buildout wave. Leopold call out.
The AI supercycle is only in Year 3 of 15. So we are still super early still. Be patient.
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🚨 MICHAEL BURRY: S&P500 IS A BUBBLE
This guy solo predicted 2008 crash,
and made $800M for him and his investors.
Here's what he says now:
In 2024, Nvidia alone drove 33% of S&P 500 entire gain.
1 company. Out of 500.
And that's the revenue that made Nvidia worth $3+ trillion?
Michael Burry just called it "Fugazi."
SO THIS IS WHAT HAPPENED, AND I AM SHOCKED:
> Nvidia sold $5.4B worth of GPUs to a company called Valor
> Valor is a shell company with no operations, no products, no employees
Its only purpose: hold legal title to those chips
> Nvidia also invested $1.9B of its own money INTO Valor
> So Nvidia sold chips to a buyer it partially funded itself
> Then booked $5.4B as completed revenue
The chips are physically inside Elon Musk's xAI data center, running Grok.
But on paper? They belong to nobody real.
Now where did Valor get the money?
Apollo raised $3.5B in debt, packaged it into securities, sold it to Athene.
Athene sells retirement annuities to ordinary Americans.
Your grandparents' savings are funding Elon's AI cluster.
And they have no idea.
Athene's numbers:
> $103B in Level 3 assets (no observable market price)
> 16x leverage Based in Bermuda, outside US insurance regulation
Every step is technically legal. That's exactly what they said about mortgage-backed securities in 2006.
One company driving 1/3 of S&P 500 gains,
while people believe we are in the bull market.
This guy solo predicted 2008 crash,
and made $800M for him and his investors.
Here's what he says now:
In 2024, Nvidia alone drove 33% of S&P 500 entire gain.
1 company. Out of 500.
And that's the revenue that made Nvidia worth $3+ trillion?
Michael Burry just called it "Fugazi."
SO THIS IS WHAT HAPPENED, AND I AM SHOCKED:
> Nvidia sold $5.4B worth of GPUs to a company called Valor
> Valor is a shell company with no operations, no products, no employees
Its only purpose: hold legal title to those chips
> Nvidia also invested $1.9B of its own money INTO Valor
> So Nvidia sold chips to a buyer it partially funded itself
> Then booked $5.4B as completed revenue
The chips are physically inside Elon Musk's xAI data center, running Grok.
But on paper? They belong to nobody real.
Now where did Valor get the money?
Apollo raised $3.5B in debt, packaged it into securities, sold it to Athene.
Athene sells retirement annuities to ordinary Americans.
Your grandparents' savings are funding Elon's AI cluster.
And they have no idea.
Athene's numbers:
> $103B in Level 3 assets (no observable market price)
> 16x leverage Based in Bermuda, outside US insurance regulation
Every step is technically legal. That's exactly what they said about mortgage-backed securities in 2006.
One company driving 1/3 of S&P 500 gains,
while people believe we are in the bull market.
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ConstructionOk6948:Funny how we celebrate innovation while letting banks and insurers off-ramp risk. If these structures fail, who bears the pain?
Easy-Reference-8189:I’m new here—what exactly is a shell company, and how does it affect the stock price? If Nvidia sold chips to a shell, does that mean the revenue isn’t real? How would that impact my portfolio?
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Bear case on Micron
The current memory upcycle could shift to a downcycle
I've been watching the AI supply chain for months and something feels off. Everyone's bullish on NVIDIA, TSMC, and memory suppliers like SK Hynix and Samsung, citing the "huge demand for compute." But the bear case isn't about AI being useful—it's about a flaw in the software side that's about to crash hardware demand.
Enterprises are using their API credits 10x faster than planned. Some tech firms burned through their entire 2026 AI budgets in just a few months. Companies can't keep paying high API costs at this rate.
The consensus thinks OpenAI or Anthropic will just raise prices. They can't. Why? Open-source platforms like OpenRouter, Venice, and Baseten have weakened their pricing power. It's like a food delivery app—developers can switch between models in one line of code, choosing the cheapest option at the moment.
Chinese labs are flooding the market with open-source models like DeepSeek V4, selling them at 1/30th or 1/100th the price of closed models. These labs get the models for free, and inference providers just charge for server power.
Closed labs are losing money (OpenAI reportedly near -122%). They have no organic cash flow. They rely on massive funding rounds and upcoming IPOs to keep buying GPUs and subsidizing usage.
Here's the key point for hardware investors: When these labs hit their peak funding or IPOs this year, that's the peak of hardware demand. The market will see these labs as a temporary fix with no path to profit. When funding stops, the massive capex cycle reverses instantly.
Even worse for memory and chip companies: Open-source inference doesn't need giant clusters. It's small, efficient, and distributed. Models are being simplified to run on smaller, cheaper setups. As demand shifts from massive training clusters to cheap inference from open-source players, they won't buy top-tier HBM. Instead, they'll build custom ASICs or NPU setups with cheaper, high-density DDR5 or LPDDR to cut costs.
AI query volume might soar, but the dollar value of memory/silicon per server (P × Q) will drop because open-source models push everyone to build cheaply.
The hardware cycle is peaking now due to artificial, venture-subsidized demand. The problem is on the demand side, not supply. When funding ends and inference becomes commoditized, the hardware unwind will be tough. Also check out the tweet where I developed this idea:
Credit to:
The current memory upcycle could shift to a downcycle
I've been watching the AI supply chain for months and something feels off. Everyone's bullish on NVIDIA, TSMC, and memory suppliers like SK Hynix and Samsung, citing the "huge demand for compute." But the bear case isn't about AI being useful—it's about a flaw in the software side that's about to crash hardware demand.
Enterprises are using their API credits 10x faster than planned. Some tech firms burned through their entire 2026 AI budgets in just a few months. Companies can't keep paying high API costs at this rate.
The consensus thinks OpenAI or Anthropic will just raise prices. They can't. Why? Open-source platforms like OpenRouter, Venice, and Baseten have weakened their pricing power. It's like a food delivery app—developers can switch between models in one line of code, choosing the cheapest option at the moment.
Chinese labs are flooding the market with open-source models like DeepSeek V4, selling them at 1/30th or 1/100th the price of closed models. These labs get the models for free, and inference providers just charge for server power.
Closed labs are losing money (OpenAI reportedly near -122%). They have no organic cash flow. They rely on massive funding rounds and upcoming IPOs to keep buying GPUs and subsidizing usage.
Here's the key point for hardware investors: When these labs hit their peak funding or IPOs this year, that's the peak of hardware demand. The market will see these labs as a temporary fix with no path to profit. When funding stops, the massive capex cycle reverses instantly.
Even worse for memory and chip companies: Open-source inference doesn't need giant clusters. It's small, efficient, and distributed. Models are being simplified to run on smaller, cheaper setups. As demand shifts from massive training clusters to cheap inference from open-source players, they won't buy top-tier HBM. Instead, they'll build custom ASICs or NPU setups with cheaper, high-density DDR5 or LPDDR to cut costs.
AI query volume might soar, but the dollar value of memory/silicon per server (P × Q) will drop because open-source models push everyone to build cheaply.
The hardware cycle is peaking now due to artificial, venture-subsidized demand. The problem is on the demand side, not supply. When funding ends and inference becomes commoditized, the hardware unwind will be tough. Also check out the tweet where I developed this idea:
Credit to:
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BrockDiggles:As a long-term holder, I’m not convinced this bear case holds. If open-source models drive efficiency, that could actually extend memory demand over time. But the risk is capex reversals; I’d need clearer inflection points before adding.
MrJSSmyth:Feels like the AI party’s getting a little too loud.
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$AMD Lisa Su needs to be more social. Nvidia and Intel are getting all the attention
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$NVDA We've made some decent money with this, but should have gone with DRAM. NVDA is too affected by options and treated like the step-sibling. Even though the company is top-tier, the stock is seen as just average. The next big move, we'll probably take profits and just trade small swings. Disappointed that the best is treated like crap.
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$NVDA print tomorrow
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BetterPlayerTopDecks:Holding NVDA; fundamentals still far outweigh tomorrow’s noise.
GoRiLLa_Skillz:Reading this as a classic pre-earnings squeeze: if NVDA hits the 50% target, it’s a relief rally; if not, volatility spikes. I’m leaning bullish but not chasing.
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$NVDA $SPY $QQQ $AMZN Check this chart out. If we're really at the bottom, then we're already facing the worst economic situation we've ever seen. No one will admit we're in trouble. The whole idea is to make people tough, but millennials still act like kids when they're stressed. You can't point out problems or they'll start blaming themselves and get upset. That makes things worse. But eventually, things will bounce back. There's a massive amount of money to be made as everything turns around.
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Patience rewards, especially with $NVDA. The chart has been building, the fundamentals are strong, and AI demand keeps growing. Big gains aren't sudden—they're earned. Hold on, let the momentum build, and the gains could be huge.
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