"Patience is a Super Power" - "The Money is in the waiting"
Showing posts with label OpenAi. Show all posts
Showing posts with label OpenAi. Show all posts

Saturday, May 23, 2026

As Anthropic and OpenAi begin the IPO dance, we look at some second tier plays that shoukd return more alpha

The Year of Mega IPOs 

Why Second-Tier Infrastructure Companies Could Produce the Greatest Alpha



A Retail Investment Thesis Built Around MRVL + CRDO


Executive Summary

Many retail investors will instinctively try to buy the coming AI IPOs:

  • Anthropic
  • OpenAI
  • potentially future agentic AI leaders and infrastructure platforms

That instinct may be wrong.

Historically, the largest wealth creation in platform revolutions often came not from the headline companies, but from the second-tier tollbooths enabling the ecosystem.

Think:

  • Internet → Cisco, Qualcomm, Broadcom
  • Smartphones → TSMC, Qualcomm, ASML
  • Cloud → Nvidia, Arista, Equinix
  • EVs → semiconductor and battery suppliers

The argument here is:

The largest risk-adjusted AI alpha from 2026–2029 may not come from buying Anthropic or OpenAI at trillion-dollar valuations. It may come from owning the infrastructure companies required to make them function.

That is where the MRVL + CRDO thesis becomes compelling.

Anthropic and OpenAI are both increasingly expected to pursue IPOs in 2026, amid extraordinary investor enthusiasm around frontier AI. Recent reporting suggests OpenAI and Anthropic could be among the largest IPOs in history, with valuations approaching the trillion-dollar range.


Part 1: Why 2026 Could Be “The Year of AI IPOs”

The market is entering what could become:

The public monetization phase of the AI revolution

We are moving from:

Phase 1 (2023–2025)

GPU scarcity / model training

Winner:

  • NVIDIA

Phase 2 (2025–2027)

Agentic AI deployment

Winners:

  • Anthropic
  • OpenAI
  • enterprise AI ecosystems

Phase 3 (2026–2029)

Infrastructure scaling

Likely winners:

  • networking
  • optics
  • interconnect
  • memory movement
  • AI compute orchestration

This shift matters enormously.

The market is beginning to realize:

AI does not scale linearly.

Every leap in intelligence requires:

  • exponentially more bandwidth,
  • lower latency,
  • greater memory movement,
  • more energy efficiency,
  • larger AI clusters.

Anthropic’s rapid growth and massive compute commitments illustrate the scale of infrastructure required. 

Recent reports indicate Anthropic has committed to extraordinary compute spending and is scaling aggressively to support Claude and future agentic systems.


Part 2: Why Buying Anthropic/OpenAI IPOs May Not Produce the Best Alpha

This may sound counterintuitive.

But by IPO:

OpenAI and Anthropic may already be priced for perfection.

Potential issues:

1. Massive valuations

Reports now discuss valuations:

  • OpenAI: ~$850B–$1T
  • Anthropic: hundreds of billions approaching $1T

At those levels:

future upside becomes mathematically harder.

A stock at a $900B valuation doubling to $1.8T is possible—but far harder than a $60–$100B infrastructure supplier tripling.


2. Capital intensity risk

AI model companies burn extraordinary capital.

Anthropic reportedly spends billions on compute and infrastructure to maintain frontier capability.

Retail investors may discover:

Owning the “brains” is expensive.

Sometimes:

owning the shovels is better!


3. Commoditization risk

Over time:

Claude, GPT, Gemini, xAI, and others may compete aggressively.

Margins could compress.

But:

the infrastructure still gets paid.

Whether OpenAI wins or Anthropic wins:

"Data still moves no matter who wins or how systems eventually commoditize".


Part 3: The Real Bottleneck = Moving Intelligence

This is the core thesis.

Most investors still think:

AI = chips.

That is increasingly incomplete.

The next bottleneck appears to be:

data movement

Meaning:

Compute cannot function without:

  1. Networking
  2. Interconnect
  3. Optical systems
  4. Memory fabrics
  5. Low-power transmission

This framework is becoming increasingly correct:

GPU boom → networking boom → photonics boom


Part 4: Why MRVL Matters

Marvell Technology = The “AI Infrastructure Backbone”



Marvell sits at the intersection of:

  • custom AI silicon
  • networking
  • optical interconnect
  • cloud AI scaling
  • hyperscaler architecture

Importantly:

Marvell is deeply tied to Amazon Trainium, which is highly relevant because Anthropic increasingly depends on AWS infrastructure. 

Amazon and Anthropic expanded their collaboration in 2026 around Trainium compute and large-scale cloud commitments.

Why MRVL could outperform expectations

Marvell is selling:

"The roads AI travels on"!

Whether:

  • Anthropic wins,
  • OpenAI wins,
  • xAI wins,
  • or all of them win,

Marvell still benefits.

That diversification matters.

Strengths

✔ Lower risk than smaller AI names
✔ Multiple hyperscaler exposure
✔ AWS/Trainium leverage
✔ AI networking leadership
✔ Strong institutional ownership

Weakness

❌ Already well discovered by Wall Street


Part 5: Why CRDO Matters

Credo Technology Group = The Hidden AI Bottleneck



This is the higher-alpha piece.

Credo focuses on:

  • high-speed connectivity
  • optical DSPs
  • Active Electrical Cables (AECs)
  • ultra-efficient interconnect

As AI clusters become larger:

bandwidth becomes everything.

Credo increasingly positions itself as a connectivity-at-scale company for hyperscaler AI environments, with major pushes into optical solutions for AI fabrics.

Recent growth has been explosive, driven by hyperscaler demand and AI networking expansion.

Why CRDO could become a multi-bagger

Because investors may still underestimate:

how much data movement Agentic AI requires.

Agentic systems are not simple chatbots.

They reason.

They call tools.

They chain models.

They coordinate across systems.

That creates:

massively larger networking demand.


Part 6: The Combined Thesis

Why MRVL + CRDO together makes sense

Building an

AI Tollbooth Portfolio

MRVL = stability + platform exposure
CRDO = asymmetric upside + networking torque

Why this pairing works

FactorMRVLCRDO
RiskLowerHigher
UpsideStrongVery High
Anthropic relevanceHighIndirect but meaningful
Agentic AI leverageHighExtremely high
Valuation riskModerateHigher
Hyperscaler exposureBroadConcentrated

The combination reduces risk while preserving upside.


Suggested Retail Allocation

For a retail investor seeking:

alpha without excessive concentration risk

I currently favor:

60% MRVL / 40% CRDO

Why?

Because:

MRVL acts as the anchor, while CRDO provides the torque.

In portfolio construction terms:

MRVL lowers the probability of catastrophic disappointment.

CRDO raises the probability of outsized returns.


Risks to the Thesis

1. AI capex slowdown

If hyperscalers pause spending:

Both stocks may correct sharply.

2. IPO disappointment

If OpenAI/Anthropic IPOs underperform:

AI sentiment could temporarily weaken.

3. Valuation compression

Especially for CRDO.

4. Networking commoditization

Competition from:

  • Broadcom
  • Nvidia
  • internal hyperscaler solutions

Bottom Line

The smartest way for a retail investor to play the Year of AI IPOs may not be buying the IPOs themselves.

Instead:

buy the companies that must win regardless of which AI lab dominates.

Among second-tier infrastructure companies:

MRVL + CRDO is one of the strongest two-stock AI infrastructure theses I currently see for 2026–2029

because it aligns directly with what I believe becomes the next great bottleneck:

"The movement of intelligence itself"!

Ed Note:

I have no current shares of either MRVL or CRDO at present, but have placed them on our watch list for now!


Tuesday, March 25, 2025

UiPath will be a big winner in the Ai race going forward, says Deep Research (ChatGPT)



UiPath (PATH)
 could be among the next three big AI winners because it's in a unique position at the intersection of AI and automation—one of the highest-growth areas in enterprise tech. Here's why UiPath stands out:


🧠 1. Combining AI + Automation (RPA)

UiPath isn’t just an automation tool anymore—it's now embedding generative AI and LLMs deeply into its platform. Think of it as the "brains + hands" of the enterprise:

  • Hands: Robotic Process Automation (RPA) – bots that handle repetitive tasks.

  • Brains: Generative AI copilots that can make decisions, understand documents, and even hold conversations.

UiPath is turning traditional workflows into autonomous systems, something most enterprises urgently want to achieve.


🌍 2. Real-World Adoption & Use Cases

UiPath is already deeply embedded in thousands of major enterprises across industries like finance, healthcare, government, and manufacturing.

  • Companies are using it for:

    • Invoice and document processing.

    • HR onboarding/offboarding.

    • Compliance workflows.

    • Customer support automation.

And now with generative AI, they’re scaling these use cases 10x faster and smarter.


🤝 3. Strategic Partnerships

UiPath is working directly with OpenAI, Microsoft, and Google Cloud to supercharge its platform with LLMs.

  • Example: Integration with Azure OpenAI lets companies add GPT to workflows without having to build anything from scratch.

  • It’s also working with AWS Bedrock and Google’s Vertex AI.


📈 4. Strong Growth Metrics

Even in a tough macro environment, UiPath is growing steadily:

  • Over $1.3B in ARR.

  • Solid gross margins (~85%+).

  • Moving toward profitability and strong free cash flow.

  • Enterprise renewal rates remain high.


🔮 5. Positioned for AI-Led Digital Transformation

As companies look to cut costs and boost productivity via AI, UiPath is one of the few platforms that:

  • Can plug into legacy systems (SAP, Oracle, mainframes).

  • Offers low-code/no-code automation tools.

  • Gives companies a tangible ROI within months.


TL;DR:

UiPath isn’t trying to compete with OpenAI—it’s helping enterprises actually use OpenAI-like tools in their everyday systems. That’s a huge opportunity, and why it's quietly becoming a giant in AI-driven transformation.





Wednesday, November 13, 2024

A massive buildout of Ai "Hyperscale" data centers is underway to support the massive shift to an Ai economy! Suppliers will be winners!


The Rise of Hyperscale AI Data Centers in the United States

Date: November 13, 2024


Executive Summary

The rapid advancement of artificial intelligence (AI) and machine learning technologies is driving significant growth in hyperscale data centers across the United States. This expansion presents substantial opportunities for technology suppliers, energy providers, real estate developers, and investors. This report explores the key technology suppliers, strategic locations of new data centers, energy supply strategies, types of energy being utilized, and the companies poised to benefit most from this infrastructure buildout.


1. Introduction

Hyperscale AI data centers are large-scale facilities designed to support robust, scalable applications and storage portfolios. They are characterized by their ability to scale computing tasks efficiently and are essential for handling the vast computational demands of AI workloads. The surge in data generation, coupled with the growing adoption of AI across industries, is fueling the need for these massive data centers.


2. Key Technology Suppliers

2.1. Semiconductor and Hardware Providers

  • NVIDIA Corporation

    • Role: Leading supplier of GPUs and AI accelerators critical for training complex AI models.
    • Impact: High demand for NVIDIA's GPUs, such as the A100 and H100 series, due to their performance in AI workloads.
  • Advanced Micro Devices (AMD)

    • Role: Provides high-performance CPUs (EPYC processors) and GPUs for data centers.
    • Impact: Gaining market share with competitive offerings in both CPU and GPU markets, appealing to data center operators.
  • Intel Corporation

    • Role: Supplies CPUs (Xeon series), AI accelerators, and networking components.
    • Impact: Integral to server processing and specialized AI tasks, maintaining a significant presence in data centers.

2.2. Memory and Storage Suppliers

  • Samsung Electronics

    • Role: Major supplier of high-speed DRAM and SSDs.
    • Impact: Crucial for handling large datasets and ensuring rapid data retrieval in AI applications.
  • Micron Technology

    • Role: Specializes in advanced memory and storage solutions.
    • Impact: Supports the need for scalable and efficient memory systems in data centers.

2.3. Networking Equipment Providers

  • Cisco Systems

    • Role: Offers networking equipment like routers and switches.
    • Impact: Ensures reliable, high-speed connectivity within data centers.
  • Arista Networks

    • Role: Provides high-performance networking solutions tailored for large-scale cloud environments.
    • Impact: Facilitates low-latency, high-throughput network infrastructures.

2.4. Server and Infrastructure Companies

  • Dell Technologies

    • Role: Supplies servers, storage systems, and networking equipment.
    • Impact: Offers integrated solutions for data center scalability and efficiency.
  • Hewlett Packard Enterprise (HPE)

    • Role: Provides servers and storage solutions optimized for AI workloads.
    • Impact: Enhances computational performance and energy efficiency.

Meta Texas facility

3. Strategic Locations of Hyperscale AI Data Centers in the U.S.

The selection of data center locations is influenced by factors such as energy availability, climate conditions, real estate costs, and proximity to network infrastructure.

3.1. Northern Virginia (Data Center Alley)

  • Description: Hosts the largest concentration of data centers globally, especially in Loudoun County.
  • Advantages: Proximity to major internet exchange points, favorable business climate, and robust fiber-optic infrastructure.

3.2. Dallas-Fort Worth, Texas

  • Description: Rapidly growing data center market with significant investments.
  • Advantages: Central location, tax incentives, and a strong energy grid.

3.3. Phoenix, Arizona

  • Description: Emerging as a data center hub due to its low risk of natural disasters.
  • Advantages: Competitive energy rates, dry climate aiding in cooling efficiencies.

3.4. Silicon Valley, California

  • Description: Established tech ecosystem with existing infrastructure.
  • Advantages: Access to technological talent and innovation, despite higher costs.

3.5. Pacific Northwest (Oregon and Washington)

  • Description: Attracts data centers due to abundant renewable energy.
  • Advantages: Access to hydroelectric power, cooler climate reducing cooling costs.


4. Energy Supply Strategies

The energy demands of hyperscale AI data centers are immense, necessitating innovative and sustainable energy solutions.

4.1. How They Will Be Supplied with Energy

  • Partnerships with Energy Providers

    • Data center operators are forming strategic partnerships with energy companies to secure reliable power supplies.
    • Power Purchase Agreements (PPAs): Long-term contracts to purchase electricity directly from renewable energy projects.
  • On-site Renewable Energy Generation

    • Installation of solar panels and wind turbines to supplement energy needs.
    • Utilization of fuel cells and battery storage systems for energy resilience.
  • Investment in Energy Infrastructure

    • Collaborations with utilities to upgrade transmission lines and substations.
    • Development of dedicated energy facilities to meet specific data center requirements.

4.2. Types of Energy Being Utilized

  • Renewable Energy Sources

    • Wind and Solar Power: Increasingly preferred due to declining costs and sustainability goals.
    • Hydroelectric Power: Particularly in regions like the Pacific Northwest.
  • Natural Gas

    • Used for backup power generation due to its reliability and lower emissions compared to coal.
  • Nuclear Energy

    • Offers a consistent, low-carbon energy supply; some data centers are exploring nuclear options in regions where it's feasible.
  • Emerging Technologies

    • Hydrogen Fuel Cells: Potential for clean energy generation, with ongoing investments in research and infrastructure.
    • Advanced Nuclear Reactors: Small modular reactors (SMRs) are being considered for future deployment.

5. Companies Poised to Benefit Most from the Buildout

5.1. Energy Companies

  • NextEra Energy

    • Strengths: Leading producer of wind and solar energy in the U.S.
    • Opportunities: Supplying renewable energy to data centers through PPAs and expanding its customer base.
  • Exelon Corporation

    • Strengths: Major nuclear energy provider with a focus on low-carbon electricity.
    • Opportunities: Meeting the energy demands of data centers seeking sustainable power sources.
  • Duke Energy

    • Strengths: Diverse energy portfolio including nuclear, natural gas, and renewables.
    • Opportunities: Leveraging its infrastructure to provide reliable power to data centers in key markets.


5.2. Technology Suppliers

  • NVIDIA Corporation and AMD

    • Impact: Expected to see increased demand for their AI-optimized hardware.
    • Opportunities: Expansion of product lines and services tailored to data center needs.
  • Cisco Systems and Arista Networks

    • Impact: Growth in networking equipment sales due to the need for high-speed connectivity.
    • Opportunities: Development of innovative networking solutions to handle increased data traffic.

5.3. Real Estate and Infrastructure Companies

  • Digital Realty Trust

    • Role: Provides data center, colocation, and interconnection solutions.
    • Impact: Positioned to benefit from increased demand for data center space.
  • Equinix, Inc.

    • Role: Global data center REIT offering colocation and interconnection services.
    • Impact: Expanding facilities to accommodate hyperscale clients and leveraging global presence.

5.4. Construction and Engineering Firms

  • AECOM and Fluor Corporation
    • Role: Offer engineering, procurement, and construction services for data center projects.
    • Impact: Potential for significant contracts in the design and construction of new facilities.

6. Investment Considerations

6.1. Growth Drivers

  • AI and Machine Learning Adoption

    • Widespread integration of AI in sectors like healthcare, finance, and manufacturing is driving demand for data processing capabilities.
  • Cloud Computing Expansion

    • Growth of services from Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform.
  • Data Generation and Storage Needs

    • The Internet of Things (IoT) and big data analytics are contributing to exponential data growth.

6.2. Risks and Challenges

  • Energy Consumption and Sustainability

    • Data centers are energy-intensive; regulatory pressures and sustainability commitments may impact operations.
  • Technological Obsolescence

    • Rapid advancements may render current technologies outdated, necessitating continuous investment.
  • Supply Chain Constraints

    • Global semiconductor shortages and supply chain disruptions can affect hardware availability.
  • Regulatory Environment

    • Changes in data protection laws and energy regulations can impact data center operations and costs.

7. Conclusion

The expansion of hyperscale AI data centers in the United States represents a significant opportunity for various sectors. Technology suppliers, energy companies, real estate firms, and construction companies are all poised to benefit from this growth. Investors should consider the potential for substantial returns while also being mindful of the associated risks, such as technological changes and sustainability challenges.


8. Recommendations for Investors

  • Diversify Across Sectors

    • Invest in a mix of technology, energy, and infrastructure companies to mitigate sector-specific risks.
  • Focus on Sustainability Leaders

    • Companies with strong commitments to renewable energy and sustainable practices may have a competitive advantage.
  • Monitor Technological Trends

    • Stay informed about advancements in AI hardware and data center technologies to identify emerging opportunities.
  • Assess Geographic Strategies

    • Consider companies investing in strategic locations with favorable conditions for data center operations.

Disclaimer: This report is for informational purposes only and does not constitute financial advice. Investors should conduct their own research or consult with a financial advisor before making investment decisions.

Editor Note:

We own shares in several of the companies mentioned in this report!


Related Articles:

Hyperscale Ai Data Centers have many suppliers, such as this vital smallcap that supplies Semiconductor Inspection Equipment & Metrology Tools