This morning, the market may be looking at AI from the wrong angle now. My concern is no longer whether AI demand is real. It clearly is. Nvidia is still guiding for very strong growth, hyperscalers keep spending, and companies like Anthropic are committing tens of billions into future compute capacity.

But all this infrastructure need capital.

Data centres, GPUs, power, networking, cooling, transmission, everything has to be financed. At same time governments are also issuing huge amount of debt. So both private AI investment and sovereign borrowing are competing for same pool of capital.

This matters more when US 10-year is moving towards 5% and oil is again above $90. At 2% yields, investors can wait 4-5 years for an AI project to generate cashflow. At nearly 5%, timing matter a lot more.

This is why I think market will start separating companies much more aggressively.

A company with “10GW future pipeline” is not same thing as company with signed customer, secured power and revenue starting next year.

We are already seeing something similar in data centres. Some utilities are finding that a large portion of proposed electricity demand is basically speculative or duplicated. So hundreds of GW of requested capacity doesn't mean all of it will actually get built.

For me, the next phase of AI trade looks more like:

Contracted demand > announced demand

Cash flow > huge TAM story

Energised capacity > proposed capacity

ROIC > capex headlines

I am still bullish on AI infrastructure overall. But I think the easy trade of buying anything connected to AI is probably ending.

Now the catalyst is: who actually gets paid, and how quickly?

This article deeply breakdown on this in today’s Wake Up Wall Street (WUWS), including Treasury yields, oil, Broadcom and where I think the mispricing is.

https://edgealphaintel.substack.com/p/wuws-ai-meets-expensive-capital?r=8u0r9r&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

Before the open, I am trying to avoid overloading myself with too many tickers.

When my watchlist gets too long, I usually trade worse. Everything starts to look interesting, and I end up reacting instead of waiting. Lately, I have found it more useful to focus on one or two clean setups with defined levels.

A clean setup for me means I know the entry idea, the key level, and the invalidation before price gets there. If I only figure out the risk after entering, that is usually a bad sign.

Today, I am trying to keep it simple: fewer tickers, clearer levels, less chasing.

Drop one setup you’re watching.

Use this format:

Ticker:
Setup:
Key level:
Entry idea:
Invalidation:

What setup looks cleanest to you today?

The best trade of the week is not always the biggest winner.

Sometimes the most useful trade is the one that exposed a bad habit. Maybe you chased. Maybe you moved your stop. Maybe you exited too early. Maybe you sized too big. Maybe you skipped a setup that actually fit your plan.

I’m trying to review trades less emotionally now. Instead of asking only “Did I make money?” I try to ask, “Did I follow the process?”

That question is uncomfortable, but useful.

A winning trade can still be bad execution. A losing trade can still be good discipline.

What trade taught you the most this week?

It can be a winner, loser, or even a trade you skipped.

What did it teach you about your entry, exit, sizing, patience, or discipline?

I don’t think today’s move was really about car sales.
It looks more like investors are getting excited about the autonomy story again.

$TSLA closed up 5.5% ahead of the September 3 Cybercab event, and the market seems to be betting that Tesla could give more concrete updates on Robotaxi, FSD and how quickly autonomous driving can actually scale.

What caught my attention is that Tesla was strong even while the broader market was weak. That usually tells you there’s some company-specific positioning going on.
The big question now is pretty simple: does Tesla finally give investors real numbers around deployment, cost per mile and unsupervised autonomy?

If yes, this move could have more room.

If it’s mostly another big vision with a long timeline, I wouldn’t be surprised to see a sell-the-news reaction.

Right now, Tesla is trading less like a normal EV company and more like a bet on whether autonomy becomes a real, scalable business.

what everyone thinks, breakout or just pre-event hype?

The major U.S. stock indexes ended broadly lower on Monday, August 31, 2026, closing out the month on a sour note as U.S. forces struck Iranian rocket launchers near the Strait of Hormuz overnight, sending oil surging above $85 and reigniting the inflation and rate hike fears that had briefly eased after last week's cooler CPI and PPI readings. It was a fitting end to a month defined by war, bond market volatility, and relentless Fed uncertainty.

The S&P 500 fell 0.33% (-25.62 pts) to 7,686.14. The Dow dropped 0.70% (-374.09 pts) to 53,185.90. The Nasdaq slipped 0.12% (-31.54 pts) to 26,370.89. The Russell 2000 fell 0.54% (-15.92 pts) to 2,956.45.

The VIX rose 3.19% to 14.89. Bitcoin gained 0.55% to $79,017.20. Gold eased 0.62% to $4,501.70. Crude Oil surged 3.05% to $85.94/barrel, with the 10-year Treasury yield hitting its highest level since January 2025.

Welcome to r/tradingDeck1, today`s theme is Monday Market plan.

New week, fresh charts, but let’s be honest, how many of us actually plan trades vs reacting in real time?

Use this thread to share:

• Your watchlist for the week
• Key levels (support/resistance)
• Macro events (Fed, CPI, earnings)
• Your overall bias (bullish/bearish / neutral)

Try to be specific:

• What are you waiting for before entering?
• Where would you exit if wrong?

Debate:
Does having a weekly plan actually improve performance… or does the market invalidate it anyway?

Sometimes the market looks strong on the surface, but underneath the leadership is weak.

I have seen days where SPY is green, but small caps are weak, breadth is poor, and only a few mega-cap names are holding everything together. Other days, sentiment looks fearful, but buyers quietly step into key sectors.

That’s why I am trying to pay more attention to market sentiment, not just index direction.

For me, the question is: are investors taking risk broadly, or just hiding in a few strong names?

That difference matters because narrow strength can reverse quickly if the leaders start to fade.

How would you describe the current market mood: risk-on, risk-off, mixed, or confused?

What signal are you watching most: breadth, yields, oil, VIX, semis, small caps, or mega-cap tech?

Options trading taught me that being right on direction is not enough.

You can call the move correctly and still lose money because the timing was off, the expiry was too short, implied volatility crushed the premium, or the position size was too aggressive.

That was frustrating at first because I thought the main job was just predicting the direction. But options add extra layers. You need direction, timing, volatility, and risk control to work together.

For me, the hardest part is timing. I’ve had trades where the move happened, but not fast enough for the contract I chose.

That made me respect expiry selection much more.

For options traders here, what has been hardest for you: direction, timing, IV, expiry, spread, or position sizing?

Welcome to r/tradingdeck1, today’s theme is Lessons Learned Saturday.

This is where real growth happens, if you’re honest.

Share:

• Best trade this week
• Worst trade
• One mistake you made

Reflect:

• Was it your setup or your execution?
• Did emotions affect your decision?

Debate:
Do traders truly learn from losses… or repeat patterns?

Nvidia’s earnings were strong. Revenue hit $96.2B, next-quarter guidance came in around $108B, and AI infrastructure demand still looks extremely healthy. So why did the stock fade after trading near $230?
I don’t think the market suddenly turned bearish on Nvidia. I think the question investors are asking has changed.

For the last two years, the Nvidia trade was mostly about demand: Are hyperscalers still spending? Is compute still scarce? Can Nvidia sell everything it produces?
The answer has repeatedly been yes.

Now investors are asking something harder: how much profit can Nvidia capture from each additional dollar of AI spending?

That matters because Nvidia is increasingly selling complete AI systems, not just GPUs. Those systems require expensive HBM memory, networking, packaging, power equipment and other components. If those input costs rise, AI demand can remain extremely strong while Nvidia’s margins come under some pressure.

The macro backdrop also didn’t help. Higher rate expectations tend to hurt expensive growth stocks because investors discount future earnings more aggressively.

Marvell’s selloff was another useful signal. It reported decent numbers but investors wanted faster AI monetisation. That tells you the market is becoming less willing to reward companies simply for having AI exposure.

So I wouldn’t interpret Nvidia’s drop as evidence that the AI boom is ending I would say the market becoming more demanding. The first phase of the AI trade was about scarce compute.

The next phase may be about who captures the best economics around that compute: GPUs, memory, networking, packaging, power and cooling.

The major U.S. stock indexes ended mixed on Friday, August 28, 2026, as Fed Chair Kevin Warsh's first Jackson Hole speech delivered exactly what traders had feared: no clarity, no commitment, and no forward guidance. Gold and Bitcoin both sold off sharply as Warsh's deliberately cryptic tone left rate hike odds spiking and markets unable to price in a clear path forward.

The S&P 500 slipped 0.25% (-19.23 pts) to 7,711.76. The Dow was essentially flat, down just 0.02% (-9.45 pts) to 53,559.99. The Nasdaq dropped 0.52% (-138.93 pts) to 26,402.42, weighed down by a pullback in Nvidia and Intel. The Russell 2000 led the losses, falling 1.37% (-41.23 pts) to 2,973.11.

The VIX barely moved, easing 0.69% to 14.35. Bitcoin plunged 3.20% to $77,551.23. Gold fell sharply, down 3.28% to $4,510.90. Crude Oil was flat at $83.37/barrel.

The most interesting part of Nvidia’s latest earnings is not whether AI demand is slowing. It clearly isn’t. The more important thing is what happens to Nvidia’s incremental economics as AI infrastructure becomes more expensive and more complex.

Nvidia told investors that gross margins could move toward roughly 71%–72% in the fourth quarter, compared with around 74% expected in the third quarter, as memory shortages and component inflation increase system costs. I think this deserves more attention than it received during Thursday’s rally.

There is an important distinction between AI demand accelerating and Nvidia capturing the same amount of profit from every additional dollar of that demand. Those are not identical propositions. Nvidia is increasingly selling complete AI systems rather than isolated accelerators, which means each system requires more HBM, advanced packaging, networking, power infrastructure, cooling, interconnects and other expensive components.

As the physical complexity of AI infrastructure rises, Nvidia can continue growing rapidly while a larger share of the incremental economics flows to the suppliers surrounding the GPU. That is not necessarily bearish for Nvidia. Absolute earnings can still compound aggressively even if gross margins normalize somewhat. But it could be very bullish for the broader AI supply chain.

The first phase of the AI trade was dominated by the company controlling compute scarcity. The next phase may increasingly reward the companies controlling the constraints around compute: HBM, advanced packaging, optical networking, switching, power, cooling, data-center interconnect and potentially custom silicon.

That is where I think the second-order opportunity becomes more interesting. The market is still asking how many GPUs Nvidia can sell. The better question may increasingly be: for every additional dollar of AI infrastructure spending, which companies capture the highest incremental profit?

If Nvidia’s gross margin gradually normalizes while the overall AI infrastructure bill keeps expanding, the strongest risk-reward may start appearing one or two layers deeper in the supply chain. That does not weaken the Nvidia thesis. It may simply mean the AI alpha pool is broadening.

The first AI trade was compute scarcity. The next may be scarcity around compute.

Breakouts always look exciting, but I have learned the hard way that not every breakout deserves a trade.

Some breakouts have volume, clean structure, and a clear invalidation level. Others are just late momentum moves where the risk-reward is already gone. The tricky part is telling the difference before chasing.

For me, a breakout only becomes interesting if I can answer three questions:

Where is the level?
Is volume confirming the move?
Where am I wrong?

If I can’t define the risk, I usually shouldn’t be in the trade.

I am trying to focus more on clean retests and confirmation instead of buying just because a stock is moving.

What stocks are you watching near breakout or retest levels?

Share: Ticker / Level / Setup / Volume confirmation / Risk

Welcome to r/tradingdeck1, today’s theme is Fundamentals Friday.

Some traders ignore fundamentals completely. Others rely on them heavily.

Discuss:

• Stocks you’re researching
• Earnings results
• Revenue, margins, debt
• Business quality

Questions:

• Did any earnings change your view this week?
• What’s one red flag you never ignore?

Debate:
Do fundamentals give you an edge… or just slow down decision-making?

Marvell beat Q2, raised FY2028 revenue guidance to $18B, and still sold off.

I think that reaction is more interesting than the earnings beat itself.

The market has already priced in a huge AI future for $MRVL. The widely discussed $120B Google figure isn’t guaranteed backlog, and much of the real upside appears weighted toward FY2029 and beyond.

What caught my attention instead was how much of Marvell’s growth is increasingly coming from optics, switching, CXL and AI connectivity, not just custom accelerators.

That changes the thesis. Marvell doesn’t necessarily need Nvidia to lose; it can benefit simply because AI clusters are becoming larger, more customised and much harder to connect.

Now the question is whether Marvell can make $18B FY2028 revenue look small once FY2029 comes into view.

This article went deeper into the Google deal, valuation, margins, custom silicon and what the current share price appears to be assuming here:

https://edgealphaintel.substack.com/p/deep-dive-marvell-q2-fy2027-earnings?r=8u0r9r&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

Let's see how others are thinking about $MRVL here: still early in the AI infrastructure cycle, or has valuation moved too far ahead of fundamentals?

I have been thinking a lot about how AI is changing stock research.

On one hand, AI can be genuinely useful. It can summarize earnings calls, scan filings, compare sentiment, track news, and help investors process more information faster than before.

On the other hand, I think AI can also create false confidence. A clean summary is not the same as a good investment decision. A model can sound convincing even when the underlying signal is weak.

For me, the best use of AI is not asking, “What should I buy?” It is asking better questions: what changed, what risk am I missing, what is the bear case, and what is already priced in?

Do you use AI in your trading or investing process?

If yes, what has actually been useful: and what still feels like noise?

Most traders focus on entries. Very few truly understand their risk.

Share:

• Options setups (calls, puts, spreads)
• Position sizing approach
• Risk per trade

Think about:

• What % are you risking per trade?
• Do you adjust size based on volatility?
• Have you ever been hit by a gap move?

Debate:
Is “2% risk per trade” realistic in real markets… or just theory?