Gaetano Profile picture
Dec 4 15 tweets 7 min read Read on X
If you want to invest in AI with clarity, you need to see the entire system end to end
$POET $NVDA $GOOG $GFS $AMAT

I mapped the entire 12-Layer stack

Every bottleneck, every physics wall, every place value accrues.

Here’s the full system 🧵 Image
Layer 1 - The Blueprints (EDA / Chip Design Software)
$SNPS $CDNS

This is where every chip begins.

Before a wafer ever enters a fab, these tools simulate how electrons will behave at near-atomic scales.

They solve physics problems digitally long before billions are spent physically.

Why it matters - AI chips fail without accurate design. This layer shapes the limits of every processor built above it.

Investor angle: Recurring, unavoidable software revenue tied directly to each generation of silicon.
(Report posted on SS)Image
Layer 2A - The Atoms (Consumables & Chemicals)
$ENTG $LIN $CCMP

These are the ultra-pure chemicals, gases, slurries, cleans, and resists fabs use every hour.

Every wafer pass consumes materials that must meet near-perfect purity standards.

Why it matters - AI scaling depends just as much on chemical precision as transistor counts. Purity equals yield.

Investor angle - High recurrence, high visibility, and protected by strict “copy exact” rules.

(Report posted on SS)Image
Layer 2B - The Physics Wall (Structural & Enabling Materials)
$ASPI $GLW $COHR $ALMU $CRS

This layer contains the hard materials that keep the AI stack operating under massive heat, power, and bandwidth loads.

Isotopically pure silicon, immersion fluids, glass substrates, photonics materials, nuclear fuel, and advanced alloys all live here.

Why it matters - AI has reached physical limits. Scaling requires newer materials that manage heat, power, and mechanical stress.

Investor angle - Scarcity + irreplaceability define the economic moat.

(Report in progress, available by 12/7)Image
Layer 3 - The Foundry (Manufacturing Across All Nodes)
$TSM $SKYT $TSEM $GFS

Where atoms turn into logic.

This includes leading-edge 2nm fabs and strategically important specialty fabs producing power, RF, sensors, BCD, mixed-signal, and defense silicon.

Why it matters - AI demand pressures every node, not just the cutting edge. The entire silicon spectrum scales together.

Investor angle - Multi-year capex pipelines, national-security support, and huge barriers to entry.Image
Layer 4 - The Toll Booths (Fab Equipment + Test & Burn-In)
$ASML $AMAT $LRCX $AEHR

Every chip requires lithography, deposition, etch, metrology, packaging, and finally burn-in and reliability testing.

These companies supply the tools that make semiconductor production possible.

Why it matters - Without equipment, no fabs run. Without testing, no chip ships.

Investor angle - Oligopoly-level moats and consistent demand across every node and market cycle.Image
Layer 5 - The Compute (GPUs, Custom Silicon, HBM)
$NVDA $AMD $AVGO $MRVL $MU

This is the AI engine room: GPUs for training, custom ASICs for inference, and High Bandwidth Memory as the throughput lifeline.

Why it matters - AI performance depends on feeding chips fast data. Compute is evolving toward workload-specific architectures.

Investor angle - Explosive growth with major architectural shifts underway.Image
Layer 6 - The Nervous System (Networking, Optics, Interconnects)
$LITE $COHR $CRDO $ANET $CIEN

This moves data between chips, racks, and clusters.

Copper is strained at high speeds, pushing the industry toward photonics, optical switching, and ultra-low-latency fabrics.

Why it matters - Modern AI clusters are communication systems. Bandwidth and latency shape model performance as much as compute.

Investor angle: Massive TAM expansion driven by cluster density.Image
Layer 7 - The Disruptors (Photonics & Physics Breakthroughs)
$POET $ALMU $LITE $LWLG

This layer captures emerging paradigm-shifting architectures: wafer-scale compute, photonic I/O, optical circuit switching, and physics-driven designs that break today’s limits.

Why it matters - These technologies change how entire AI systems are built and where the bottlenecks move.

Investor angle - High beta exposure to technologies that can reshape infrastructure.Image
Layer 8 - The Power Plant (Energy & Grid)
$GEV $VRT $FLNC $LEU

AI demand is colliding with grid constraints. Hyperscalers are turning to behind-the-meter power: gas turbines, microgrids, HV gear, SMRs, solar deployments, batteries, and nuclear-grade materials.

Why it matters - Compute follows power. Energy availability determines which regions can support AI growth.

Investor angle - Turbines, transformers, substations, storage, nuclear fuel, and grid upgrades enter a multi-year capex boom.Image
Layer 9 - The Sovereign Cloud (Infrastructure & Borders)
$MSFT $AMZN $GOOGL $NBIS $ORCL

Countries are building their own AI factories: sovereign regions, regulated clouds, local data centers, and national compute capacity.

Why it matters - AI is now a national-power asset. Sovereignty drives duplicated infrastructure and long-term demand.

Investor angle - Nations overbuild for control, increasing TAM far beyond pure efficiency models.Image
Layer 10 - The Digital Worker (Agentic Software)
$GOOG $MSFT $ADBE $CRM $PATH

AI shifts from tools to autonomous workers. Agents complete tasks, create output, and interact with workflows.

Pricing moves toward paying for outcomes rather than software seats.

Why it matters - This is the software layer where AI touches productivity and revenue directly.

Investor angle - Early but enormous potential to reshape enterprise economics.Image
Layer 11 - The Immune System (Security for Autonomous Systems)
$PANW $ZS $CRWD $OKTA

As agents proliferate, identity, permissions, and real-time trust become non-optional.

This layer protects autonomous systems from bad actors and bad outcomes.

Why it matters - AI expansion requires new security primitives built for machine decision-making.

Investor angle - Security budgets expand as enterprises adopt autonomous agents.Image
Layer 12 - The Physical Body (Robotics & Automation)
$SYM $ROK $TSLA $TER $ISRG

AI leaves the data center and enters the physical world: humanoids, warehouse automation, manipulators, logistics robots, and real-world VLA stacks.

Why it matters - Robotics is a major link between AI and real GDP productivity.

Investor angle - A direct play on labor shortages, automation, and real-world deployment.Image
On this page I will be diving really deep into each layer

I'll lay the ground work for the importance of each layer

Where each one fits into the overall AI trade

And uncover the Alpha in the companies that investors care about (the cashtags posted are only a very small group of the companies that I will cover in each layer)

I'll post summaries on X and full deep dives on my free SS (l!nk in bio)

You will gain massive knowledge and insight and edge if you follow along this series

Highly recommend turning on your post notification as well!

Bookmark this post!

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More from @crux_capital_

Nov 22
The speculative party is over. Time to find the winners.

This thread maps the 12 physical bottlenecks that will define capital allocation for the next decade.

Welcome to the reality of physics. $NVDA $NBIS $AMD Image
Pillar I represents the Silicon Foundation.
$ASML $TSM

This layer is defined by extreme physics and monopolistic choke points.

It is one of the most complex manufacturing challenges in history.

Value accrues to those who master the atomic precision required for the next decade of compute.Image
Chip design requires quantum physics simulation at the 2nm node. $SNPS $CDNS

This software is a permanent tax on the industry.

Below that lies the atoms.

The supply chain for noble gases and metal oxide photoresists remains terrifyingly fragile and vulnerable to geopolitical shock.Image
Read 18 tweets
Oct 9
I’m tracking two key players in the photonics space, $ALMU and $POET, both building the future of high-speed connectivity.

Today, let's do a deep dive on Aeluma, a semiconductor company creating a platform that could redefine AI, autonomous driving, and quantum computing

You won't want to miss this thread 🧵Image
Cracking the Semiconductor Code $ALMU

For years, chip makers had a fundamental problem.

You could have incredible performance from exotic compound semiconductor materials, or you could have the massive scale and low cost of silicon manufacturing.

You couldn't have both.

Aeluma has cracked the code, developing a proprietary way to grow high-performance compound semiconductor materials directly onto large-diameter silicon wafers.

This platform gets the elite performance of specialized materials and the cost structure of mass-market production, a true holy grail for the industry.

This breakthrough means they can leverage the multi-trillion-dollar global silicon manufacturing infrastructure to build chips that were previously too expensive for anything but niche applications.Image
Product Deep Dive: Next-Gen SWIR Sensing $ALMU

Aeluma's first major product area is next-generation sensing.

The company is scaling Short-Wave Infrared (SWIR) sensors that are a massive leap over current tech.

SWIR light is much safer for the human eye, which allows systems to use more powerful illumination sources. This translates directly to longer range and better resolution for 3D sensing applications like advanced facial recognition or gesture control.

It also performs far better in bright sunlight, a critical advantage for outdoor systems like automotive LiDAR where solar interference can blind other sensors.

This superior performance in all conditions is exactly what the automotive and robotics industries have been waiting for.Image
Read 16 tweets
Oct 7
Need new stock ideas?

Here is a list of 24 of my favorite names that I will be watching/buying during any down turn

I'll give a quick thesis on the investment and a chart with prices I'm watching

Let's dive in!

Disclaimer: None of this is financial advice or a recommendation. Do your own research

$SPY $QQQ $IWMImage
1/24 Innodata Inc. $INOD

Thesis: Innodata is a pure-play on the critical need for high-quality data in the Generative AI era. The performance and reliability of any Large Language Model are directly tied to the quality of the data it is trained on.

Innodata provides these essential data engineering services, positioning itself as a key partner for enterprises looking to build and deploy accurate, proprietary AI models. The company is a direct beneficiary of the massive, ongoing investment in the AI data pipeline.

Levels to Watch: The chart shows a stock that has just emerged from a year-long consolidation phase with a massive surge in both price and volume, indicating a significant change in character.

Buy Area: ~$70.60 (This represents the key breakout point from the prior trading range, which could now act as a new support shelf).

Support: ~$55.50 (The major resistance level from earlier in the year).

Resistance: ~$91.70 (The recent high).Image
2/24 Credo Technology Group $CRDO

Thesis: Credo is a key enabler play in the artificial intelligence and data center boom. The company provides the essential high-speed connectivity solutions such as chips, cables, and optical components that act as the nervous system for modern data infrastructure.

As hyperscalers and enterprises race to build out their AI capabilities, the demand for faster, more efficient data transfer explodes, placing Credo's technology at the heart of this secular growth trend.

Levels to Watch: The chart shows a stock in a powerful, long-term uptrend that is currently experiencing a healthy pullback.

Buy Area: ~$133.00 (This is a key area of interest where horizontal price support from early September converges with the primary ascending trendline that has been intact since May).

Support: ~$115.00 (Previous consolidation zone).

Resistance: ~$178.00 (The recent all-time high).
#CRDO #AIstocksImage
Read 25 tweets
Sep 28
Most data centers take years to build.

WhiteFiber $WYFI built one in months...
Out of a mattress factory.

That’s its edge: retrofits that are 2x faster and 40% cheaper.

Now it’s running a $90M GPU cloud and developing a 99MW NC site.

This thread unpacks the strategy, economics, comps, and risks 🧵

$CRWV $IREN $NBIS $WULF $SLNHImage
Strategy $WYFI

At its heart WhiteFiber runs a two-pronged strategy to capture value across the AI infrastructure stack.

First, its Colocation business acts as a specialized landlord, providing secure, power-dense facilities for customers like AI hardware firm Cerebras Systems to deploy their own servers.

Second, its higher-margin Cloud Services (GPUaaS) segment offers direct, on-demand access to high-performance computing on its own fleet of NVIDIA GPUs.

The linchpin is its "retrofit" model. Instead of building data centers from the ground up, $WYFI acquires and rapidly upgrades existing industrial sites.

Management claims this approach is "two times faster and 40% cheaper," a critical edge in a market where speed to deployment is paramount.

This strategy allows them to target buildout costs of $7-$9 million per megawatt, a significant potential saving over traditional greenfield projects.Image
Self-Funding Engine $WYFI

The power of the White Fiber model is its self-funding engine.

The colocation business provides a bedrock of financial stability through long-term, predictable contracts with creditworthy tenants, such as the anticipated seven-year term for the flagship NC-1 facility.

This recurring revenue stream is highly attractive to lenders, lowering the company's cost of capital and de-risking the overall enterprise.

This stable financial foundation then empowers the company to strategically channel capital, including its recent IPO proceeds, into the higher-growth, higher-return Cloud Services segment.

The company's own projections show a compelling ~30% unlevered IRR for its GPU cloud investments.

In essence, the steady, utility-like revenue from colocation funds the aggressive expansion of its GPU fleet, creating a virtuous cycle of growth without heavy reliance on dilutive financing.Image
Read 19 tweets
Sep 26
Why can one company make $7 Million from a megawatt of power, while another makes just $2 Million?

It's the most important question for valuing AI infrastructure stocks like $WYFI, $IREN, and $RIOT. They are not the same business.

This thread breaks down the one metric that matters: revenue per MW. Here's how to tell the landlord from the manufacturer. 🧵

#WYFI #IREN #RIOT #AI #Investing #DataCenterImage
The Foundational Model - Pure-Play Mining $RIOT

Riot Platforms represents the large-scale, pure-play Bitcoin mining model.

Their business is a direct power-to-digital-asset conversion. They secure power infrastructure for the primary purpose of running their own mining fleet to earn Bitcoin.

Revenue Model: An industrial-scale digital asset production and energy arbitrage play.

Annual Revenue per MW: ~$1.5M - $2.5M.

This figure is directly calculable from the Bitcoin network's hash rate economics (hash price) and a company's reported power capacity and fleet efficiency.

The revenue is almost entirely dependent on the price of Bitcoin and their operational uptime.
#RIOT #Bitcoin #Mining #EnergyImage
The Transitional Model - Infrastructure Arbitrage $IREN

Iris Energy is executing a strategic pivot. They are leveraging their core competency. Building and operating efficient, low-cost data centers to capture the higher-value AI market. This is a move up the value chain.

Revenue Model: An asset transition, capitalizing on existing infrastructure for a more lucrative purpose.

Annual Revenue per MW: A blended target of ~$3M - $5M.

AI colocation commands a significant market premium over crypto hosting due to more complex power, cooling, and security requirements.

This range reflects the higher lease rates for this specialized infrastructure, a fact supported by their public statements on seeking higher revenue per megawatt.

#IREN #AI #HPC #DataCenterImage
Read 5 tweets
Sep 14
What if you could invest in the AI revolution but with a built-in safety net?

Smith-Midland $SMID is a key builder of vaults for AI data centers, but its less glamorous businesses, like making highway barriers, provide a powerful foundation.

This is a story high growth potential with a security blanket of diversification. 🧵

$NVDA $AMD $NBIS $CRWVImage
Think of $SMID as a three-legged stool.

Leg 1: AI Data Centers. They build the essential precast utility vaults that power the AI boom.

Leg 2: Public Infrastructure. They make the J-J Hooks barriers and Soundwalls lining America's highways.

Leg 3: Architectural. Their innovative SlenderWall panels are used on modern high-rises.

Each leg supports the company, providing a powerful and balanced business model.Image
The AI data center business is their high-growth engine.

In the AI gold rush, $SMID sells the "picks and shovels."

Their precast concrete vaults and buildings are critical for the rapid construction of new AI facilities, especially in their home turf of Virginia's "Data Center Alley."

This is their primary driver for explosive growth and where much of the upside lies.Image
Read 11 tweets

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