Singh, Satinder Paul

Chief Technical Officer | Strategist in Semiconductor Technology domain | VLSI/SoC/ASIC/IC (Chip) Design Specialist

[Mr Mark Gockelen @Shahriar661731]

6 months ago, I knew nothing about AI.

Today, I've built 3 income streams using it.

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[Singh, Satinder Paul @PaulSatinder]

@Shahriar661731 IDEAS

[Josh Kale @JoshKale]

The guy who invented the chip architecture inside 99% of all processors says NVIDIA’s GPUs are built entirely wrong for this era of AI…

His argument: GPUs were designed for training. But inference, where all the money gets made, is a fundamentally different problem

It’s memory-bound and sequential. And no chip has ever been designed specifically for it. Every GPU and TPU used for inference today is just a scaled-down training chip.

The real bottleneck is memory. And every trend the industry is most excited about (MoE, reasoning, long context, multimodal) makes the memory problem worse.

The next $100B+ hardware cycle will be won by whoever solves the memory wall first

[George Noble @gnoble79]

STAY AWAY from semiconductor stocks.

The global semiconductor industry is expected to hit $975 billion in sales this year. A historic peak. Revenue growth north of 25%.

Every cycle peak sounds the same: this boom is STRUCTURAL, not cyclical. AI demand is permanent. The old rules don't apply.

"It's different this time," they say.

I've heard those 4 words more times than any others in 45 years on Wall Street. They're always wrong.

Here's how I think about it:

When any industry generates obscene profits, capital floods in to compete those profits away.

The higher the margins, the faster it happens.

Semiconductor margins right now are at levels that would make a drug cartel blush.

Look at Micron. The crowd says it's "cheap" because the PE looks low.
Except Micron's price-to-book ratio sits at roughly 7x. The 10-year median is 1.86x. The historical floor is 0.81x.

That's not cheap. That's the most expensive this stock has EVER been relative to its asset base - dressed up in a low PE because earnings are wildly above trend.

This is the oldest trap in cyclical investing.

You see it in shipping. You see it in commodities. Earnings spike, multiples look compressed, everyone piles in. Then the cycle rolls over and those "cheap" earnings disappear.

Now layer on the bigger picture:

New capacity is already being announced across the industry.

The hyperscalers alone - Microsoft, Amazon, Alphabet, Meta - plan to pour $600-700 BILLION into AI infrastructure this year. That's 70%+ more than 2025.

They're consuming roughly 90% of their operating cash flow on capex. Borrowing north of $400 billion to cover the rest.

Nobody can afford to stop spending because everyone else keeps spending. It's mutually assured destruction with better PR.

And historically, the companies that spend the MOST on capex deliver the WORST stock returns.

BCA Research just argued AI threatens all 3 pillars of Big Tech profitability;

1. Economies of scale
2. Network effects
3. Proprietary tech

Goldman Sachs compared software stocks to NEWSPAPERS in the early 2000s. The group that fell 95%.

Software is now underperforming the Nasdaq by the widest margin this century.

Meanwhile, the rotation I've been positioning for is already underway:

Most MAG 7 names are DOWN year to date. Emerging markets are up. Energy is up. Gold miners are up.

Last year, the EM ETF returned roughly DOUBLE the S&P. This isn't starting. It's been happening since 2024.

So my framework is simple:

Valuation doesn't matter in the short run.

But the longer you go out, the more it matters.

And money ain't free anymore.

When capital was free, pigs flew. Unprofitable companies soared. Narrative crushed fundamentals.

That era is OVER.

The 60/40 portfolio hedges against recession. But recession isn't the risk. The risk is continued money printing, persistent inflation, and higher real rates.

Bonds don't protect you from that. Gold does. Energy does. Real assets do.

You don't need to get clever here. Just avoid what's overpriced and own what's cheap.

The regime is changing. The market's scorecard already tells you that every single day.

Are you listening?

[Singh, Satinder Paul @PaulSatinder]

@BoucherNicolas CLAUDE

[Singh, Satinder Paul @PaulSatinder]

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[Singh, Satinder Paul @PaulSatinder]

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[Singh, Satinder Paul @PaulSatinder]

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[Singh, Satinder Paul @PaulSatinder]

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[Suryansh Tiwari @Suryanshti777]

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[Singh, Satinder Paul @PaulSatinder]

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[Onil Coder @Onil_coder]

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[anand iyer @ai]

This is the clearest map I've seen of how AI infrastructure constraints move in sequence. Every time one wall falls, another appears. I've watched this pattern since 2020: first it was "can we get enough GPUs," then "can we get HBM attached," now it's "can we plug it all in without melting the grid." By the time the market recognizes a bottleneck, the margin seems to have already moved.

[raz @ryanzarick]

Over the last 2.5 years, we've secretly assembled a team of some of the brightest minds across cryptography, GPU programming, and ASIC design to build an internal Jolt Pro team. We surpassed our wildest dreams of what was possible, having already created a 1.61 GHz proving cluster we call a cell. Jolt Pro scales to infinity, the number of cells you can use in parallel is only limited by the size of the datacenter.

This work culminated in our demo last night, where we verified a month of Ethereum in just 30 seconds. A special moment and a good start. But so much more to come.

[Cheng @zcbenz]

The "Layout Algebra" tutorials of CUTLASS/CuTE are excellent text for understanding matrix shape and strides. They made the conception so much easier by introducing a better abstraction.
https://t.co/W0XHeMPjD3

[SemiVision 🇹🇼 👁️👁️ @semivision_tw]

The Core Bottlenecks of AI Chips:

Memory Walls and Interconnect Limits Are Driving a Deep Communication Gap
Over the past two decades, the computational power of AI chips—measured in FLOPs—has skyrocketed by a factor of 60,000×. However, the data movement capabilities that must support these operations have lagged far behind: memory bandwidth has only improved by 100×, and I/O bandwidth by a mere 30×. This growing disparity, known as the “Communication Gap”, is rapidly becoming one of the most critical bottlenecks in AI-era system architecture.

https://t.co/pB6dzF6HWN

[Ed Sperling @Chip_Insider]

Chiplet Fundamentals For Engineers: eBook
A free 65-page in-depth research report on the next phase of device scaling
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[Singh, Satinder Paul @PaulSatinder]

@WhitmanXAI @SeekWiser_ Guide

[Tom's Hardware @tomshardware]

Bill Gates-backed silicon photonics startup develops optical transistors 10,000x smaller than current tech — optical chip can process 1,000 x 1,000 multiplication matrices https://t.co/UpoWGHo82F