After my first post on humanoid robots received a lot of positive feedback, I went into more detail. I have subsequently added my favorites in each sector.

Extended analysis of the value chain including shovel manufacturers and potential hidden champions

New categorySecondary key sectors (sales, marketing, financing)

In additionTop 25 companies worldwide, as well as Top 10 Europe and Top 10 Asia

I have also added a video link for beginners. This will give you an idea of how far the development of humanoid robotics has already progressed.

https://www.stern.de/panorama/wissen/humanoider-roboter---wie-reagiert-er-auf-beleidigungen--35996156.html

Thank you for your attention and your support 🙏

🌐 1. value chain of humanoid robots (with hidden champions)

1. research & chip design

$ARM (-3,24 %) ARM (UK) - CPU-IP, energy-efficient processors

$SNPS (-3,14 %) Synopsys (US) - EDA software, chip design

$CDNS (-2,87 %) Cadence (US) - EDA & Simulation

$PTC PTC (US) - Engineering Software, CAD/PLM

$DSY (+1,32 %) Dassault Systèmes (FR) - 3D Design & Digital Twin

$SIE (-0,46 %) Siemens (DE) - Industrial Software & Lifecycle Mgmt

$ADBE (-3,55 %) Adobe (US) - Design, AR/UX

ANSYS (US) - multiphysical simulation - acquisition by Synopsis

Altair (US) - CAE, simulation, digital twin - acquisition by Siemens

$HXGBY (-0,75 %)
Hexagon (SE) - Metrology & Simulation

$AWE (+0 %) Alphawave IP Group (UK) - High-speed chip IP for AI/robotics

1.Synopsis, 2.Siemens and 3.Adobe are my top 3 in this sector

2. manufacturing technology & equipment

$ASML (+1,12 %) ASML (NL) - Lithography (EUV)

$AMAT (-0,43 %) Applied Materials (US) - Semiconductor equipment

$8035 (+3,76 %) Tokyo Electron (JP) - wafer fabrication

$KEYS (-1,08 %) Keysight Technologies (US) - Metrology

$6857 (-1,33 %) Advantest (JP) - Chip test systems

$TER (-3,3 %) Teradyne (US) - test systems + cobots

$6954 (-0,25 %) Fanuc (JP) - Industrial robots, CNC

$CAT (+0,48 %) Caterpillar (US) - autonomous machines

$KU2G KUKA (DE) - industrial robots

Comau (IT) - automation - not listed on the stock exchange

$ROK Rockwell Automation (US) - industrial automation

$JBL (-0,11 %) Jabil (US) - contract manufacturing (EMS/ODM)

$KIT (+0,23 %) Kitron (NO) - European EMS/ODM manufacturer

$AIXA (+0,68 %) Aixtron (DE) - deposition equipment for compound semiconductors

$LRCX (+1,38 %)
Lam Research (US) - Etch/deposition systems

$MKSI (-0,79 %)
MKS Instruments (US) - Plasma/vacuum technology

$ASM (-0,7 %)
ASM International (NL) - Deposition systems

1.ASML, 2.Keysight Technologies, 3.Fanuc are my top 3 in this sector

3. chip manufacturing (foundries)

$TSM (+0,23 %) TSMC (TW) - leading foundry

$005930 Samsung Electronics (KR) - foundry + memory

$GFS (-1,1 %) GlobalFoundries (US) - specialty chips

$INTC (-2,38 %)
Intel Foundry Services (US) - new western foundry player

$981
SMIC (CN) - largest Chinese foundry

$UMC
UMC (TW) - Power/RF/Embedded chips

1.TSMC, 2.Intel, 3.Samsung Electronics are my top 3 in this sector

4. computing & control unit ("brain")

$NVDA (+0,32 %) Nvidia (US) - GPUs, AI chips

$INTC (-2,38 %) Intel (US) - CPUs, FPGAs

$AMD (+1,66 %) AMD (US) - CPUs, GPUs

$MRVL (+0,92 %) Marvell (US) - network chips

$MU (+4,63 %) Micron (US) - Memory

$DELL (-0,26 %) Dell Technologies (US) - Edge & Infrastructure

Graphcore (UK) - AI chips (IPU) - not a listed company

Cerebras (US) - Wafer-scale engine - not a listed company

SiPearl (FR) - European HPC chip - not a listed company

1.Nvidia, 2.Marvell, 3.Micron are my top 3 in this sector

5. sensors ("senses")

$6758 (+0,65 %) Sony (JP) - image sensors

$6861 (-0,37 %) Keyence (JP) - Industrial sensors

$STM (-0,9 %) STMicroelectronics (FR/IT) - Sensors, MCUs

$TDY Teledyne (US) - optical/infrared sensors

$CGNX (-0,21 %) Cognex (US) - Machine Vision

$HON (-1,51 %) Honeywell (US) - sensor technology, security

ANYbotics (CH) - autonomous sensor fusion - not a listed company

$AMBA (-3,15 %) Ambarella (US) - video & computer vision SoCs for real-time image recognition

$OUST
Velodyne Lidar (US) - Lidar sensors - acquisition by Ouster

$AMS (-0,85 %)
-OSRAM (AT/DE) - optical sensors

1.Teledyne, 2.Keyence, 3.Ouster are my top 3 in this sector

6. actuators & power electronics ("muscles")

$IFX (-0,36 %) Infineon (DE) - Power Electronics

$ON (-1,18 %) onsemi (US) - Power & Sensors

$TXN (-0,87 %) Texas Instruments (US) - Mixed-Signal Chips

$ADI (-1,52 %) Analog Devices (US) - Signal Processing

$PH Parker-Hannifin (US) - Hydraulics/Pneumatics

$MP (+0 %) MP Materials (US) - Magnets

$APH (-0,32 %) Amphenol (US) - Connectors

$6481 (-0,42 %) THK (JP) - Linear guides & actuators

$6324 (-0,69 %)
Harmonic Drive (JP) - Precision gears & servo drives for robotics

$6594 (+4,01 %)
Nidec (JP) - Electric motors

$6506 (-0,87 %)
Yaskawa (JP) - Drives & Robotics

$SU (+1,17 %)
Schneider Electric (FR) - Energy & control solutions

$ZIL2 (-0,16 %)
ElringKlinger (DE) - Battery & fuel cell technology, lightweight construction

1.Parker-Hannifin, 2.MP Materials, 3.Infinion are my top 3 in this sector

7. communication & networking ("nerves")

$QCOM (+0,26 %) Qualcomm (US) - mobile communications, edge AI

$ANET (-8,99 %) Arista Networks (US) - Networks

$CSCO (-1,67 %) Cisco (US) - Networks, Security

$EQIX (-1,13 %) Equinix (US) - Data centers

NTT Docomo (JP) - 5G/6G carrier - not a listed company

$VZ Verizon (US) - Telecommunications

$SFTBY SoftBank (JP) - Carrier + Robotics

$ERIC B (-1,42 %)
Ericsson (SE) - 5G/IoT infrastructure

$NOKIA (-2,11 %)
Nokia (FI) - 5G/6G for industry

$HPE (-0,24 %)
Juniper Networks (US) - Network technology - acquisition by HP

1.Arista Networks, 2.SoftBank, 3.Cisco are my top 3 in this sector

8. energy supply

$3750 (+0,21 %) CATL (CN) - Batteries

$6752 (+0,91 %) Panasonic (JP) - Batteries

$373220 LG Energy (KR) - Batteries

$ALB (+1,02 %) Albemarle (US) - Lithium

$LYC (-0,15 %) Lynas (AU) - Rare earths

$UMICY (+1,26 %) Umicore (BE) - recycling

WiTricity (US) - inductive charging - not a listed company

$ABBN (+0,15 %) Charging (CH) - charging infrastructure

$SLDP
Solid Power (US) - Solid state batteries

Northvolt (SE) - European batteries - not a listed company

$PLUG
Plug Power (US) - fuel cells

$KULR (+1,05 %)
KULR Technology (US) - Thermal management & battery safety for mobile systems

1.Albemarle, 2.CATL, 3.Panasonic are my top 3 in this sector

9. cloud & infrastructure

$AMZN (-0,74 %) Amazon AWS (US) - Cloud, AI

$MSFT (+1,74 %) Microsoft Azure (US) - Cloud, AI

$GOOG (+0 %) Alphabet Google Cloud (US) - Cloud, ML

$VRT (-0,58 %)
Vertiv Holdings (US) - Data center infrastructure (UPS, cooling, edge)

$ORCL (-4,69 %)
Oracle Cloud (US) - ERP + Cloud

$IBM (-1,41 %)
IBM Cloud (US) - Hybrid cloud + AI

$OVH (+3,73 %)
OVHcloud (FR) - European cloud

1.Alphabet, 2.Microsoft, 3.Oracle are my top 3 in this sector

10. software & data platforms

$PLTR (+4,15 %) Palantir (US) - Data integration

$DDOG (-1,51 %) Datadog (US) - Monitoring

$SNOW (-2,18 %) Snowflake (US) - Data Cloud

$ORCL (-4,69 %) Oracle (US) - Databases, ERP

$SAP (+0,06 %) SAP (DE) - ERP systems

$SPGI S&P Global (US) - financial/market data

ROS2 Foundation - robotics middleware - not listed on the stock exchange

$NVDA (+0,32 %) NVIDIA Isaac (US) - robotics development - part of Nvidia

$INOD (+2,42 %) Innodata (US) - data annotation & AI training data

$PATH (-2,61 %)
UiPath (RO/US) - Robotic process automation

$AI (+0,5 %)
C3.ai (US) - AI platform

$ESTC (-1,86 %)
(NL/US) - Search & data analysis

1.S&P Global, 2.Palantir, 3.Datadog are my top 3 in this sector

11. end applications / robots

$ABB ABB (CH/SE) - Industrial Robots

$6954 (-0,25 %) Fanuc (JP) - Industrial robots

$TSLA (+7,29 %) Tesla Optimus (US) - humanoid robot

$9618 (-2,08 %) JD.com (CN) - logistics robot

$AAPL (+1,9 %) Apple (US) - Platform & UX

$700 (+0,52 %) Tencent (CN) - Platform & AI

$9988 (+0,34 %) Alibaba (CN) - logistics & platform

PAL Robotics (ES) - humanoid robots - not a listed company

Neura Robotics (DE) - cognitive humanoid robots - not a listed company

$TER (-3,3 %) Universal Robots (DK) - cobots - belongs to the Teradyne Corporation

Engineered Arts (UK) - humanoid robots - not a listed company

$ISRG (-2,09 %) Intuitive Surgical (US) - surgical robotics

$GMED (-1,18 %)
Globus Medical (US) - surgical robotics (ExcelsiusGPS platform)

$7012 (-2,58 %) Kawasaki Heavy Industries (JP) - industrial robots, automation

$CPNG (+0,15 %) Coupang (KR) - Logistics end user

$IRBT (-5,83 %)
iRobot (US) - consumer robotics (e.g. Roomba), non-humanoid, but navigation/sensor fusion

Boston Dynamics (US) - humanoid & mobile robots-no listed company

Hanson Robotics (HK) - humanoid robots (Sophia) - not a listed company

Agility Robotics (US) - humanoid robot "Digit" - not a listed company

1.Apple, 2.Tencent, 3.Alibaba are my top 3 in this sector

🛠 2. cross enablers (shovel manufacturers) - with hidden champions

Raw materials & battery materials

Albemarle - Lynas - Umicore

$SQM
SQM (CL) - Lithium

$ILU (+4,78 %)
Iluka Resources (AU) - Rare earths

$ARR (+1,85 %)
American Rare Earths (US/AU) - New supply chains

my number 1 in the sector is Albemarle

manufacturing technology

ASML - Applied Materials - Tokyo Electron

$LRCX (+1,38 %)
Lam Research (US) - Plasma/etching processes

$ASM (-0,7 %)
ASM International (NL) - ALD equipment

$MKSI (-0,79 %)
MKS Instruments (US) - Plasma/vacuum technology

my number 1 in the sector is ASML

Quality assurance

Keysight - Advantest - Teradyne

$EMR (-1,76 %)
National Instruments (US) - Measurement technology - from Emerson Electric adopted

$300567
ATE Test Systems (CN) - test systems

$FORM (-1,53 %)
FormFactor (US) - Wafer probing

my number 1 in the sector is Keysight

Motion & Drive

Parker-Hannifin

Festo (DE) - Pneumatics, Soft Robotics - not a listed company

Bosch Rexroth (DE) - Drives, Controls - not a listed company

$6481 (-0,42 %)
THK (JP) - Linear guides

my number 1 in the sector is Parker-Hannifin

Sensors/Imaging

$TDY Teledyne

$BSL (+2,44 %) Basler (DE) - Industrial cameras

FLIR (US) - Thermal imaging sensors - acquisition by Teledyne

ISRA Vision (DE) - Machine Vision - not a listed company

my number 1 in the sector is Teledyne

Magnets & Materials

MP Materials

$6501 (-3,32 %)
Hitachi Metals (JP) - Magnetic materials

VacuumSchmelze (DE) - Magnetic materials - not a listed company

$4063 (-1,01 %)
Shin-Etsu Chemical (JP) - Specialty materials

my number 1 in the sector is MP Materials

Chip Design & Simulation

Synopsys - Cadence - ARM

$SIE (-0,46 %)
Siemens EDA (DE/US)-Mentor Graphics-strategic business unit of Siemens AG

Imagination Tech (UK) - GPU-IP - not a listed company

$CEVA (-0,74 %)
CEVA (IL) - Signal Processor IP

my number 1 in the sector is Synopsys

Engineering & Lifecycle

PTC - Dassault - Siemens

Altair (US) - Simulation - no longer a listed company

$HXGBY (-0,75 %)
Hexagon (SE) - Metrology

$SNPS (-3,14 %)
ANSYS (US) - Simulation - takeover by Synopsys

my number 1 in the sector is Siemens

Networks & Data Centers

Arista - Cisco - Equinix

$HPE (-0,24 %)
Juniper (US) - Networks - Acquisition of HPE

$DTE (-0,15 %)
T-Systems (DE) - Industry cloud

$OVH (+3,73 %)
OVHcloud (FR) - European cloud

my number 1 in the sector is Arista

Cloud infrastructure

AWS - Azure - Google Cloud

$ORCL (-4,69 %)
Oracle Cloud (US) - ERP & databases

$IBM (-1,41 %)
IBM Cloud (US) - Hybrid Cloud

$9988 (+0,34 %)
Alibaba Cloud (CN) - Asian Cloud

$VRT (-0,58 %)
Vertiv Holdings (US) - Cloud/Infra

my number 1 in the sector is Alphabet (Google)

finance/information infra

S&P Global

$MCO
Moody's (US) - Ratings

$MSCI (-0,24 %)
MSCI (US) - Indices

$MORN
Morningstar (US) - Investment Research

my number 1 in the sector is S&P Global

Creative/Experience Infra

Adobe

$ADSK (-1,08 %)
Autodesk (US) - CAD & Design

$U
Unity (US) - 3D/AR simulation

Epic Games (US) - Unreal Engine - not a listed company

my number 1 in the sector is Adobe

Platform & Ecosystem

Apple - Tencent - Alibaba

$META (+0,41 %)
Meta (US) - AR/VR, Social Robotics

ByteDance (CN) - AI & platforms - not a listed company

$9888 (+2,67 %)
Baidu (CN) - AI & Cloud

my number 1 in the sector is Tencent

Infrastructure/Edge

Dell

$HPE (-0,24 %)
HPE (US) - Edge Computing

$SMCI
Supermicro (US) - AI servers

$6702 (-0,59 %)
Fujitsu (JP) - Edge & HPC

my number 1 in the sector is Dell

storage solutions

Micron

$000660
SK Hynix (KR) - Memory

$285A (+6,22 %)
Kioxia (JP) - NAND

$WDC
Western Digital (US) - Storage solutions

my number 1 in the sector is Micron

🏛 3. secondary key sectors with hidden champions

Financing & Capital

$GS (-0,52 %) Goldman Sachs (US) - investment bank; ECM/DCM, M&A, growth financing

$MS Morgan Stanley (US) - investment bank; tech banking, capital markets

$BLK (-1,22 %) BlackRock (US) - asset manager; capital allocation, ETFs/index funds

$9984 (+0,72 %) SoftBank Vision Fund (JP) - mega VC; growth equity in robotics/AI

Sequoia Capital (US) - venture capital; early/growth in AI/robotics - this is a classic venture capital fund

DARPA (US) - government R&D funding (robotics/defense) - independent research and development agency

EU Horizon (EU) - research funding/grants for DeepTech - Innovative Europe pillar

China State Funds (CN) - state industry/technology fund

Lux Capital (US) - VC for DeepTech - Uptake (US) - AI-based predictive maintenance

DCVC (US) - Robotics & AI focus - investing exclusively via VC fund investments

Speedinvest (AT) - EU VC for robotics - access to investment only via fund investments

my number 1 in the sector is Softbank

Maintenance & Service

$SIE (-0,46 %) Siemens (DE) - Industrial Service, Lifecycle & Retrofit

$ABBN (+0,15 %) ABB (CH/SE) - Robotics Service, Spare Parts, Field Support

$GEHC (-1,63 %) GE Healthcare (US) - Medtech service incl. robotic systems

Uptake (US) - AI-based predictive maintenance - not a listed company

Augury (US/IL) - condition monitoring, condition diagnostics - not a listed company

$KU2 KUKA Service (DE) - Robotics maintenance

$6954 (-0,25 %) Fanuc Service (JP) - global service network

Boston Dynamics AI Institute (US) - Robotics longevity - funded by Hyundai Motor Group

my number 1 in the sector is Siemens

Marketing & Advertising

$WPP (+0 %) WPP (UK) - global advertising group; branding/communications

$OMC Omnicom (US) - marketing/PR network

$PUB (+1,47 %) Publicis (FR) - communications/advertising group

$META (+0,41 %) Meta (US) - Digital Ads (Facebook/Instagram)

$GOOG (+0 %) Google Ads (US) - search & display advertising

TikTok / ByteDance (CN) - social ads & distribution - not a listed company

$AAPL (+1,9 %) Apple (US) - Branding/UX; Acceptance & Platform Marketing

$WPP (+0 %)
AKQA (UK/US) - Tech branding - Since 2012 majority owned by the WPP Groupbut continues to operate as an autonomous operating unit

R/GA (US) - Innovation marketing - not a listed company

Serviceplan (DE) - largest independent EU agency - not a listed company

my number 1 in the sector is Meta

Law, Regulation & Ethics

ISO (CH) - international standards, robotics standards

TÜV (DE) - certification & safety tests

UL (US) - safety/conformity testing

EU AI Act (EU) - legal framework for AI & robotics

UNESCO AI Ethics (UN) - global ethics guidelines

Fraunhofer IPA (DE) - Robotics safety standards

ANSI (US) - standards

IEC (CH) - Electrical engineering standards

Training & Talent

MIT (US) - Robotics/AI Research & Education

ETH Zurich (CH) - autonomous systems & robotics

Stanford (US) - AI/Robotics labs & spin-offs

Tsinghua University (CN) - Robotics/AI in Asia

CMU (US) - Robotics Institute

EPFL (CH) - Robotics research

TU Munich (DE) - humanoid robot "Roboy"

🌍 Top 25 companies for humanoid robotics

These companies are central to the development & production of humanoid robotsbecause without them, crucial parts of the chain would be missing:

Chips & computing power (brain of the robots)

$NVDA (+0,32 %) Nvidia (US) - AI GPUs & Isaac platform, foundation for robotic AI

$2330 TSMC (TW) - world's most important foundry, produces the AI chips

$ASML (+1,12 %) ASML (NL) - EUV lithography, indispensable for chip production

$005930 Samsung Electronics (KR) - memory, logic, foundry

$000660 SK Hynix (KR) - DRAM & NAND memory for AI

$MU (+4,63 %) Micron (US) - Memory solutions for AI workloads

my number 1 in the sector is ASML

Sensors & perception (senses of robots)

$SONY Sony (JP) - image sensors, market leader

$6861 (-0,37 %) Keyence (JP) - Industrial sensors & vision systems

$CGNX (-0,21 %) Cognex (US) - Machine Vision, precise image processing

my number 1 in the sector is Keyence

Actuators & motion (muscles of robots)

$IFX (-0,36 %) Infineon (DE) - power electronics, motor control

$6594 (+4,01 %) Nidec (JP) - World market leader for electric motors

$PH Parker-Hannifin (US) - hydraulics/pneumatics, motion technology

$6481 (-0,42 %) THK (JP) - Linear guides & actuators

my number 1 in the sector is Parker-Hannifin

Communication, cloud & infrastructure (nerves & data flow)

$QCOM (+0,26 %) Qualcomm (US) - Mobile & Edge Chips

$AMZN (-0,74 %) Amazon AWS (US) - Cloud & AI infrastructure

$MSFT (+1,74 %) Microsoft Azure (US) - Cloud, AI services

$CSCO (-1,67 %) Cisco (US) - Networks & Security

$VRT (-0,58 %) Vertiv Holdings (US) - Data Center Infrastructure

my number 1 in the sector is Microsoft

End Applications & Platforms (robots themselves)

$TSLA (+7,29 %) Tesla (US) - humanoid robot Optimus

$ABBN (+0,15 %) ABB (CH/SE) - Robotics & Automation

$6954 (-0,25 %) Fanuc (JP) - industrial robots & CNC systems

$7012 (-2,58 %) Kawasaki Heavy Industries (JP) - industrial robots

PAL Robotics (ES) - humanoid robots (TALOS, ARI, TIAGo) - not a listed company

Neura Robotics (DE) - cognitive humanoid robots - not a listed company

Universal Robots (DK) - cobots

my number 1 in the sector is Tesla

🇪🇺 Top 10 European key companies for humanoid robotics

$ASML (+1,12 %)
ASML (NL)

World market leader in EUV lithography - no modern chips for AI & robotics without ASML.

$IFX (-0,36 %) Infineon (DE)

Leading in power electronics & motor control - crucial for actuators of humanoid robots.

$STM (-0,9 %)
STMicroelectronics (FR/IT)

Sensors, microcontrollers & power chips - the basis for control & perception.

$SAP (+0,06 %)
SAP (DE)

ERP & data platforms, important for integrating humanoid robots into industrial processes.

$SIE (-0,46 %)
Siemens (DE)

Industrial software, automation, digital twin - key for engineering & lifecycle management.

$KU2 KUKA (EN)

Robotics pioneer, industrial robots & automation - know-how for humanoid motion mechanics.

PAL Robotics (ES) - not a listed company

Specialist for humanoid robots (TALOS, ARI, TIAGo), internationally used in research & service.

Neura Robotics (DE) - Not a listed company

Young high-tech company, develops cognitive humanoid robots with advanced AI (4NE-1).

Universal Robots (DK) - Not a listed company

Market leader for cobots - platform for safe human-robot collaboration.

Engineered Arts (UK) - not a listed company

Develops humanoid robots such as Amecaknown for realistic facial expressions & gestures - important for HRI (Human-Robot Interaction)

🌏 Top 10 Asian key companies for humanoid robotics

$2330
TSMC (Taiwan)

World's largest semiconductor foundry, produces high-end chips (e.g. Nvidia, AMD, Apple) - no AI hardware without TSMC.

$005930
Samsung Electronics (South Korea)

Foundry, memory, logic chips, image sensors - extremely broadly positioned in robotics components.

$000660
SK Hynix (KR) - Memory

$SONY
Sony (Japan)

Market leader in CMOS image sensors, essential for robotic vision & perception.

$6861 (-0,37 %)
Keyence (Japan)

Sensor technology & machine vision for industrial automation, widely used in robotics.

$6954 (-0,25 %)
Fanuc (Japan)

Industrial robots & CNC systems, one of the most important manufacturers of robotics hardware worldwide.

$6506 (-0,87 %)
Yaskawa Electric (Japan)

Drives, motion control & robot arms - relevant for humanoid motion control.

$6594 (+4,01 %)
Nidec (Japan)

World market leader for electric motors (from mini motors to high-performance drives).

$7012 (-2,58 %)
Kawasaki Heavy Industries (JP) - Industrial robots

$9618 (-2,08 %)
JD.com (China)

Driver for robotics in e-commerce & logistics, invests in humanoid robotics applications

The hype is all about humanoid robots, but the constant winners are in the background.

I have divided the analysis into two perspectives. 1. the complete value chain of humanoid robots, which shows all the players from the chip to the finished robot, and 2. the blade manufacturers in the background, who always earn money as enablers, regardless of which manufacturer wins the race.

ASML, Applied Materials and Tokyo Electron dominate in manufacturing technology. Quality assurance comes from Keysight, Advantest and Teradyne. Chip design is supported by Synopsys, Cadence and ARM. Data streams are secured by Arista Networks, Cisco and Equinix. The computing basis is created in the cloud by Amazon, Microsoft and Alphabet. Albemarle, Lynas and Umicore play a central role in raw materials and battery materials. These companies monetize their customers' investment waves, have high barriers to entry, service revenues and pricing power, but remain cyclical with risks from export rules, capex cuts and currency movements.

🌐 Value chain of humanoid robots Sector overview

1. research & chip design (IP / EDA)

$ARM (-3,24 %)
ARM Holdings (ARM, UK/USA) - CPU architectures

$SNPS (-3,14 %)
Synopsys (SNPS, USA) - Chip design software

$CDNS (-2,87 %)
Cadence Design Systems (CDNS, USA) - EDA & Simulation

2. manufacturing technology & equipment

$ASML (+1,12 %)
ASML (ASML, NL) - EUV lithography, key monopoly

$AMAT (-0,43 %)
Applied Materials (AMAT, USA) - Process equipment

$8035 (+3,76 %)
Tokyo Electron (8035.T, JP) - Wafer equipment

$KEYS (-1,08 %)
Keysight Technologies (KEYS, USA) - Test & RF measurement technology

$6857 (-1,33 %)
Advantest (6857.T, JP) - Semiconductor test systems

$TER (-3,3 %)
Teradyne (TER, USA) - Test systems + robotics (Universal Robots)

3. chip production (Foundries)

$TSM (+0,23 %)
TSMC (TSM, TW) - Largest contract manufacturer

$005930
Samsung Electronics (005930.KQ, KR) - Memory + Foundry

$GFS (-1,1 %)
GlobalFoundries (GFS, USA) - Specialized production

4. computing & control unit ("brain")

$NVDA (+0,32 %)
Nvidia (NVDA, USA) - GPUs, AI accelerators

$INTC (-2,38 %)
Intel (INTC, USA) - CPUs, FPGAs

$AMD (+1,66 %)
AMD (AMD, USA) - CPUs/GPUs

$MRVL (+0,92 %)
Marvell Technology (MRVL, USA) - Network/data center chips

5. sensors ("senses")

$6758 (+0,65 %)
Sony (6758.T, JP) - CMOS image sensors

$6861 (-0,37 %)
Keyence (6861.T, JP) - Vision systems, sensors

$STM (-0,9 %)
STMicroelectronics (STM, CH/FR) - MEMS sensors

6. actuators & power electronics ("muscles")

$IFX (-0,36 %)
Infineon (IFX, DE) - Power semiconductors, SiC

$ON (-1,18 %)
N Semiconductor (ON, USA) - SiC/Power Chips

$STM (-0,9 %)
STMicroelectronics (STM, CH/FR) - Motor control & power

$TXN (-0,87 %)
Texas Instruments (TXN, USA) - Motor control, power ICs

$ADI (-1,52 %)
Analog Devices (ADI, USA) - Energy & BMS chips

7. communication & networking ("nerves")

$QCOM (+0,26 %)
Qualcomm (QCOM, USA) - 5G/SoCs

$AVGO (+0,14 %)
Broadcom (AVGO, USA) - Network & radio chips

$SWKS (+1,03 %)
Skyworks Solutions (SWKS, USA) - RF components

8. energy supply

$300750
CATL (300750.SZ, CN) - Batteries

$6752 (+0,91 %)
Panasonic (6752.T, JP) - Batteries for automotive/robotics

$373220
LG Energy Solution (373220.KQ, KR) - Batteries

9. cloud & infrastructure

$AMZN (-0,74 %)
Amazon (AMZN, USA) - AWS

$MSFT (+1,74 %)
Microsoft (MSFT, USA) - Azure

$GOOG (+0 %)
Alphabet (GOOGL, USA) - Google Cloud

$EQIX (-1,13 %)
Equinix (EQIX, USA) - Data center operator

$ANET (-8,99 %)
Arista Networks (ANET, USA) - Network infrastructure

$CSCO (-1,67 %)
Cisco Systems (CSCO, USA) - Edge & Data Center Networks

10. software & data platforms

$PLTR (+4,15 %)
Palantir (PLTR, USA) - Data integration, decision software

$DDOG (-1,51 %)
Datadog (DDOG, USA) - Cloud monitoring / observability

$SNOW (-2,18 %)
Snowflake (SNOW, USA) - Cloud-native data platform

$ORCL (-4,69 %)
Oracle (ORCL, USA) - Databases, ERP

$SAP (+0,06 %)
SAP (SAP, DE) - ERP/cloud systems

$PATH (-2,61 %)
UiPath (PATH, USA) - Automation software (RPA)

$AI (+0,5 %)
C3.ai (AI, USA) - Enterprise AI platform

11. end applications / robots

$ABB
ABB (ABB, CH) - Industrial robots

$6954 (-0,25 %)
Fanuc (6954.T, JP) - Industrial robots, CNC

$TSLA (+7,29 %)
Tesla (TSLA, USA) - Optimus" humanoid robot

$9618 (-2,08 %)
JD.com (JD, CN) - E-commerce & automated logistics

🛠️ Shovel manufacturer for humanoid robots

🔹 Hardtech (physical "shovels")

These companies provide the material basis: manufacturing machines, raw materials, semiconductor base.

Semiconductor Equipment & Manufacturing

$ASML (+1,12 %)
ASML (ASML, NL) - EUV lithography (monopoly).

$AMAT (-0,43 %)
Applied Materials (AMAT, USA) - Wafer equipment.

$8035 (+3,76 %)
Tokyo Electron (8035.T, JP) - Process equipment.

Test systems (hardware-side)

$6857 (-1,33 %)
Advantest (6857.T, JP) - Semiconductor test.

$TER (-3,3 %)
Teradyne (TER, USA) - Test systems + industrial robots.

Materials & raw materials

$ALB (+1,02 %)
Albemarle (ALB, USA) - Lithium (batteries).

$LYC (-0,15 %)
Lynas Rare Earths (LYC.AX, AUS) - Rare earths for magnets.

$UMICY (+1,26 %)
Umicore (UMI.BR, BE) - Cathode materials, recycling.

🔹 Soft/infra (digital "shovels")

These companies supply the infrastructure & toolswithout which development, training and operation would be impossible.

Design Software & IP

$SNPS (-3,14 %)
Synopsys (SNPS, USA) - EDA software.

$CDNS (-2,87 %)
Cadence Design Systems (CDNS, USA) - Chip design & simulation.

$ARM (-3,24 %)
ARM Holdings (ARM, UK/USA) - CPU architectures (license model).

Test & Measurement (software/signal level)

$KEYS (-1,08 %)
Keysight Technologies (KEYS, USA) - Electronics & RF test systems.

Network & data center backbone

$ANET (-8,99 %)
Arista Networks (ANET, USA) - High-speed networks.

$CSCO (-1,67 %)
Cisco Systems (CSCO, USA) - Data center/edge networks.

$EQIX (-1,13 %)
Equinix (EQIX, USA) - Data centers (colocation).

Cloud infrastructure

$AMZN (-0,74 %)
Amazon (AMZN, USA) - AWS (cloud, AI training).

$MSFT (+1,74 %)
Microsoft (MSFT, USA) - Azure.

$GOOG (+0 %)
Alphabet (GOOGL, USA) - Google Cloud.

Takeaway: Investing in the infrastructure stack allows you to participate in the robotics trend regardless of the subsequent product winner and reduces the individual product risk, but you have to live with cycles. In your opinion, which stage of the chain offers the best risk/return combination and fits into a disciplined portfolio?

Source: Own analysis based on publicly available company information and IR materials of the companies mentioned.

Image material: Techa Tungateja/iStockphoto

When one of the best-known Youtubers shows in his latest video the disadvantages of Tesla's ($TSLA (+7,29 %)) "Full Self Driving" without lidar, and how superior other SDVs with LIDAR are, the effect on the stock markets is unlikely to fail to materialize.

Hesai Group SP ADS-B $HSAI (+1,26 %)

Luminar Tech $LAZR

Innoviz $INVZ

ON Semiconductor $ON (-1,18 %)

Cepton $CPTN

The Tesla $TSLA (+7,29 %) shares suffered another setback on Friday, falling by up to 4 %. This takes the losses from their record high to almost 50 %. After an initial post-election rally that electrified investors, the euphoria now seems to be fading. Respected analyst Dan Ives of Wedbush, considered by many to be the great Tesla advocate, described this phase as a 'good check moment' for fans of the company. It is a time for investors to pause and think about the future of the brand.

In his note to clients released on Thursday, Ives stated that 'the time has come' to defend the stock. He even added Tesla to his 'Best Ideas List' and reiterated his 'Outperform' rating with a price target of 550 dollars. However, this positive assessment is no coincidence. Thursday's 5.7% drop was heavily influenced by CEO Elon Musk's controversial ties to the Trump administration, which have significantly dampened sentiment around the stock. With a closing price of 479 dollars on December 17 and a current level of around 253 dollars at lunchtime, the picture has changed significantly.

Ives warns that negative sentiment and analyst concerns have often overshadowed Tesla's positive story in the past. Despite the political challenges and discussions surrounding Musk, Ives sees less than 5% of Tesla's global sales at risk. He is optimistic that Musk will better balance his time between his numerous commitments at DOGE and Tesla/SpaceX to get the company back on track.

In another exciting development, Onsemi has $ON (-1,18 %) announced a takeover bid for Allegro MicroSystems to strengthen its market position in the semiconductor industry. While the exact terms and price of the acquisition have not yet been disclosed, this move could help Onsemi expand its product lineup and intensify competition in the fast-paced semiconductor industry. So far, there are no other significant events related to this acquisition, but the industry will be watching closely to see how this situation develops.

Sources:

https://finance.yahoo.com/news/tesla-stock-has-dropped-34-in-2025-this-gut-check-moment-has-one-of-wall-streets-biggest-bulls-doubling-down-132150387.html

https://finance.yahoo.com/m/8913ae97-52b6-3044-a06c-0dea6544e12a/onsemi-discloses-offer-to.html

Takeover interest in Allegro

Allegro Microsystems $ALGM (-0,39 %) attracts takeover interest from ON Semiconductor, Bloomberg News reports. ON Semiconductor $ON (-1,18 %), with a market value of $19.83 billion, has hired advisers in recent months to push ahead with a takeover of Allegro, which is valued at $4.11 billion. There is speculation that other potential buyers may also be interested in Allegro. Allegro has also appointed Mike Doogue as its new CEO, while ON Semiconductor has announced a restructuring plan that will affect 2,400 jobs worldwide.

Trump designates cryptocurrencies for strategic reserve

In the US, President Donald Trump has designated three cryptocurrencies, including $XRP (-2,49 %) , $SOL (+1,01 %) and $ADA (-4,08 %), for a new strategic reserve. This news caused prices to rise by up to 55% on Sunday. Trump emphasized that his goal is to make the US the "crypto capital of the world". Despite a recent decline in cryptocurrency values, the industry is hoping for a clear regulatory direction under the Trump administration. It remains unclear how the new reserve will be set up in concrete terms and whether legislation is required for this.

Honor invests 10 billion in AI

Honor, the smartphone manufacturer from Shenzhen, is planning to invest USD 10 billion in the development of AI for its devices over the next five years. CEO James Li announced at the Mobile World Congress that the company plans to develop AI-powered PCs, tablets and wearable technologies in addition to smartphones. Honor recently completed a restructuring to prepare for an IPO. Despite a decline in the smartphone market, Honor aims to capitalize on the booming AI investment landscape in China and is receiving support from the local government for research and development.

Sources:

https://finance.yahoo.com/news/allegro-drawing-takeover-interest-semiconductor-154617062.html

https://finance.yahoo.com/news/trump-names-cryptocurrencies-strategic-prices-160454350.html

https://finance.yahoo.com/news/chinas-honor-announces-10-billion-163451037.html

• $ON (-1,18 %) ON Semiconductor (NASDAQ: ON) reported fourth quarter earnings per share (EPS) of $0.95, $0.02 below analysts' estimates of $0.97.

• Revenue for the quarter totaled $1.72 billion, compared to the consensus estimate of $1.76 billion.

Forecast 2025:

• ON Semiconductor expects earnings per share (EPS) for the first quarter of 2025 to be between $0.45 and $0.55, compared to the analyst estimate of $0.89.

• ON Semiconductor forecasts Q1 2025 revenue between $1.35 billion and $1.45 billion, compared to the analyst estimate of $1.69 billion.

Graphic: AI generated

The rapid development of AI has triggered a veritable boom in data centers. Companies such as OpenAI and DeepSeek are driving this revolution and the demand for high-performance servers is growing exponentially.

However, the increase in computing power is also accompanied by massive energy consumption, an issue that is leading to global discussions about infrastructure, efficiency and future investments [1].

At the same time, the question arises as to whether there is currently an overinvestment in computing power. The Chinese AI company DeepSeek, for example, has presented a model that works more efficiently than previous large language models (LLMs).

Does this mean that we will soon need less computing power?

Or will the Jevons paradox occur instead, i.e. the effect that more efficient technologies actually increase overall consumption in the long term? [2, 3]

In this article, I will focus on the key developments in the data center sector, the growing demand for energy, regional characteristics, current challenges and potential investment opportunities.

As always, the article is intended to shed light on the background to current events, provide food for thought and give impetus. The stocks mentioned do not, of course, constitute investment advice.

🤖 Data centers: the foundation of the AI revolution

The growing global demand for AI-supported software and digital applications requires powerful data centers. Goldman Sachs analysts forecast that the global demand for power from data centers will increase by 50% by 2027 and by up to 165 % could increase [1].

This chart below forecasts the energy consumption of data centers (in terawatt hours) by 2030, distinguishing between AI- and non-AI-based applications in the US and the rest of the world. Total consumption is expected to rise to over 1,000 TWh by 2030 [4].

Our analysts expect data center power consumption to increase by more than 160% by 2030

Source: [4], primary: Masanet et al. (2020), Cisco, IEA, Goldman Sachs Research

This data shows how AI applications will massively increase energy consumption. The rapid increase in the area of "US AI" and "Rest of world AI" is particularly striking.

The three main reasons for this increase are

• Larger AI models:

New models such as GPT-5 or DeepSeek AI require more and more computing power. Training and operating these models requires trillions of calculations [1].

• Real-time AI applications:

Companies are integrating AI into numerous applications: from search engines to personalized financial and healthcare services.

• Cloud computing & data storage:

As digitalization progresses, the global demand for data storage and cloud services is increasing [1].

Which companies dominate the market?

On the demand side for data centers, large hyperscale cloud providers and other companies are building large language models (LLMs) that are capable of processing and understanding natural language. These models need to be trained on huge amounts of information using power-intensive processors [4].

On the supply side, hyperscale cloud companies, data center operators and asset managers are deploying large amounts of capital to build new high-capacity data centers.

These include, among others:

• Microsoft $MSFT (+1,74 %) : Operator of Azure Cloud and partner of OpenAI
• Alphabet $GOOGL (+0,02 %) : With Google Cloud and DeepMind
• Amazon $AMZN (-0,74 %) : AWS, the world's leading cloud provider
• Meta $META (+0,41 %) : Develops its own AI chips and continues to expand its infrastructure

In addition, specialized data center providers such as Equinix $EQIX (-1,13 %) and Digital Realty $DLR (-0,6 %) as they supply physical infrastructure to the hyperscalers [6].

According to Goldman Sachs Research, demand for data center infrastructure will increasingly outstrip supply in the coming years.

The utilization rate of existing data centers is expected to rise from around 85% in 2023 to more than 95% by the end of 2026. However, the situation is expected to ease from 2027 onwards as new data centers are commissioned and demand growth driven by AI slows down (see chart below) [1].

Goldman Sachs currently estimates that the global power consumption of the data center market is around 55 gigawatts (GW). This is made up of cloud computing workloads (54%), traditional workloads such as email or data storage (32%) and AI (14%) [1].

For the future, analysts predict that electricity demand will increase to 84 GW by 2027. The share of AI is expected to grow to 27%, while the cloud share will fall to 50% and traditional workloads to 23% [1].

By the end of 2030, around 122 gigawatts (GW) of data center capacity will be online.

At this point, I asked myself as a layman how the units mentioned so far are to be understood, in my first graphic I speak of 1,000 TWh of energy consumption of all data centers by 2030 and now here is talk of 122 GW of data center capacity? In order not to go completely beyond the scope of the article, I have added a section at the very end in case some of you also feel like a layman and want to put the "units" into perspective.

... and now on with the article...

One central problem remains:
Where does all the energy come from?

⚡️Energieversorgung: Can the grid keep up?

According to estimates by Goldman Sachs, more than 720 billion US dollars will have to be invested in expanding the power grid worldwide by 2030 in order to supply the new data centers with sufficient energy [1].

Europe in particular, where electricity consumption was expected to decline for many years, is experiencing a veritable "demand shock" [1].

Which energy sources supply data centers?

• Natural gas & battery storage:

Natural gas is seen as a realistic short-term solution to meet continuous demand. It serves as a bridging technology until renewable energy and storage solutions are further developed, as renewable energy is not available around the clock [4].

• Renewable energies:

Wind and solar energy could cover around 80 % of demand in the long term, provided that sufficient storage solutions are integrated [4].

In practice, solar plants run on average only about 6 hours per day, while wind power plants run on average 9 hours per day. There is also a daily volatility in the capacity of these sources, depending on the radiation of the sun and the strength of the wind [4].

The graph shows the fluctuations in capacity factors for wind and solar energy in the USA in 2023. The capacity factor indicates how efficiently an energy source utilizes its maximum output throughout the year.

• Wind energy (light blue line): The highest capacity factors occur in the winter months (Jan-March) and drop significantly in the summer months (Jun-Aug).

• Solar energy (dark blue line): Efficiency rises in the spring (Mar-May) and reaches its maximum in the summer months (Jun-Aug) before falling in the winter (Nov-Dec).

The graph illustrates that wind and solar energy can complement each other seasonally: While wind is more efficient in winter, solar energy provides the highest yields in summer. This shows how important a balanced energy mix is to ensure security of supply.

In addition to finding environmentally friendly energy sources to power data centers, technology providers can reduce emissions intensity through efficiency gains.

The following chart shows the development of the workload and energy consumption of data centers between 2015 and 2023. Although the workload almost tripled, energy consumption remained almost constant until 2019 thanks to efficiency gains. The efficiency gains then slowed down from 2020 onwards.

Source: [4], primary: Masanet et al. (2020), IEA, Cisco, Goldman Sachs Research

This chart supports the discussion on the Jevons paradox (see below). Efficiency gains could be offset or even exceeded in the long term by higher workloads and AI demand. This highlights the need to make data center energy sources more sustainable.

• Nuclear energy:

Meanwhile, governments are also becoming more supportive of nuclear energy on the whole. Switzerland is reconsidering the use of nuclear generators for its electricity supply, while nuclear energy enjoys bipartisan support in the US and the Australian opposition party has put forward plans to introduce nuclear reactors [4].

Participants at the COP28 conference at the end of 2023, an annual summit convened by the United Nations to combat climate change, agreed to triple global nuclear capacity by 2050 [4].

Nuclear energy is considered the ideal option for basic power supply as it provides a reliable and constant supply of energy.

As a result, more and more large tech companies such as Alphabet, Amazon and Microsoft are turning to small modular nuclear power plants (SMRs).

📊 Increasing efficiency & the Jevons paradox

With new technologies such as DeepSeek, AI could work more efficiently in the future. But does greater efficiency automatically mean that less computing power is required?

The Jevons paradox: More efficiency = more consumption?

The Jevons paradox describes the fact that increases in efficiency often do not lead to lower consumption, but to higher consumption overall.

-Example:

In the 19th century, more efficient steam engines did not lead to lower coal consumption; on the contrary, as the machines became cheaper and more versatile, coal consumption actually increased.

With cars: more fuel-efficient engines did not lead to less gasoline consumption, but to people driving more cars.

-Applied to AI:

As AI models become more efficient, the cost per computation decreases. This makes AI applications attractive in even more areas, which in turn leads to a higher overall demand for computing power.

🌎 Regional distribution and global expansion of data center infrastructure

Current distribution: Where are the data centers located today?

Today, most data centers are located in the Asia-Pacific region and North America. Well-known locations are:

North America:

- Northern Virginia

- San Francisco Bay Area

Asia: Beijing

- Beijing

- Shanghai

These regions are characterized by high computing power, intensive data traffic and strong demand from corporate campuses [1].

The chart also shows the historical development of data center capacity by region (North America, APAC, etc.) from 2017 to 2024. The figures illustrate how fast the infrastructure for the AI revolution is growing and underlines why the energy requirements of data centers are increasing so rapidly.

The increase in capacity from around 20 GW in 2017 to almost 60 GW in 2024 shows an enormous growth trend. This correlates directly with the increasing demand for AI applications and cloud computing.

How is supply growing?

Goldman Sachs Research estimates that global data center capacity will increase to around 122 GW by the end of 2030, as mentioned above. The share of hyperscalers and specialized operators will increase from the current 60% to around 70% [1].

• Asia-Pacific:

The largest expansion of data centers has been recorded here in the past ten years.

• North America:

The largest expansion of new data centers is planned in North America over the next five years.

📈 Investment opportunities: Some winners of the AI and data center revolution

US shares e.g.:

• Carrier Global $CARR (-0,85 %) : Precise cooling technology and air conditioning for data centers
• Vertiv Holdings $VRT (-0,58 %) : Specialist in cooling and power solutions specifically for data centers
• Brookfield Renewable Partners $BEP.UN : Leading provider of renewable energy (hydropower, solar, wind) - supply contracts (PPAs) with data centers
• ON Semiconductor $ON (-1,18 %) : Leader in chips for energy efficiency and thermal management. Solutions reduce power consumption in data centers and support AI integration
• Texas Instrumentes $TXN (-0,87 %) : Energy-saving semiconductor products used in data center servers
• Equinix $EQIX (-1,13 %) : Specialized in data center infrastructure
• Digital Realty $DLR (-0,6 %) : Provider of physical infrastructure for data centers
• IBM $IBM (-1,41 %) : Quantum computing technologies that potentially consume less energy and development of energy-efficient AI solutions
• Arista Networks $ANET (-8,99 %) : Specialist in high-speed networking products for data centers
• Nvidia $NVDA (+0,32 %) : Leader in AI GPUs, Leader in AI training market. Best choice for large AI models and data center training
• AMD $AMD (+1,66 %) Competing with Nvidia with its own AI chips, but better positioned in the AI interference market where energy efficiency and cost-effectiveness are key. The interference market will be the next most important market, perhaps even the more important one.
• Broadcom $AVGO (+0,14 %) Profits from network solutions for data centers
• Microsoft $MSFT (+1,74 %) Google $GOOGL (+0,02 %) Amazon $AMZN (-0,74 %) : The big hyperscalers investing heavily in AI and cloud

European stocks e.g.:

• Siemens Energy $ENR (-0,94 %) Important role in modernizing power grids, integrating renewable energies and improving storage solutions for data center reliability
• Schneider Electric $SU (+1,17 %) : Leader in the development of energy management and cooling technology for data centers - specialty in the automation of both systems.
• ASML $ASML (+1,12 %) : Indispensable for modern chip production
• Infineon $IFX (-0,36 %) and STMicroelectronics $STM (-0,9 %) : Leading semiconductor companies with a focus on AI applications
• RWE $RWE (+0,31 %) and Enel $ENEL (+0,67 %) Utilities that are increasingly focusing on renewable energies for data centers

Japanese stocks e.g:

• Daikin Industries $6367 (-3,33 %) World market leader in air conditioning and cooling, offers specialized cooling systems for data centers and AI-supported plant management systems to further increase efficiency
• Tokyo Electron $8035 (+3,76 %) : Important supplier for semiconductor manufacturing
• Mitsubishi Heavy Industries $7011 (-1,54 %) : Works on the development of new nuclear power plants to secure the energy supply

🧠 Conclusion: AI, data centers & energy as the trend of the century?

Although some analysts warn of possible overinvestment, the figures indicate that the demand for computing power and energy for AI data centers will continue to rise sharply.

• Efficiency gains from models such as DeepSeek or new chip technologies could reduce energy consumption per computer, but the Jevons paradox means that overall demand will increase because more efficient systems will be used more often.

The biggest winners are therefore:

• Semiconductor companies: They supply the AI chips needed.
• Data center operators: They build the necessary infrastructure.
• Energy suppliers: They ensure the energy supply for the AI revolution.

In the long term, these companies could be among the biggest beneficiaries of the coming decades.

👨🏽‍💻 How do I position myself?

Personally, I think I am well positioned with the NASDAQ 100 $CSNDX (+0,32 %) (portfolio share of 23%), as the focus is on US technology and growth stocks. The ETF complements my All-World with a stronger weighting in innovative sectors such as AI and cloud computing.

In the near future, I will also take a closer look at Daikin Industrie $6367 (-3,33 %) share in order to increase the exposure to Japan and the share price offers an entry point at first glance.

In addition, AMD $AMD (+1,66 %) has also caught my attention, the reason being its positioning in the aforementioned interference market. Most of the capital is currently flowing into the expansion of new AI models. However, as soon as these become a "commodity" and everyone uses them, most of the capital will probably flow into the interference market (market for the application of AI models).

Furthermore, I have Siemens AG $SIE (-0,46 %) with approx. 2.3% portfolio share (still growing to approx. 4%), which I also see as well positioned for the future for the following reasons (in the context of the article):

Network stability

• Develops technologies for intelligent power grids ("smart grids"), essential for integrating renewable energies into the supply of data centers.

Data center control

• Provides automation and monitoring systems that optimize the energy consumption and efficiency of data centers

Efficient building structure

• The "Smart Infrastructure" division supports data centers with energy-efficient solutions for lighting, air conditioning and building monitoring

Not directly cooling systems, but:

• offers technologies that increase the energy efficiency of cooling systems by optimizing energy flows and data analysis

What is your opinion❓

• Which companies do you have on your radar?
• Is there a threat of overinvestment or are we just at the beginning of a century revolution?

Thanks for reading! 🤝

...Said digression follows after the sources...

__________

Sources:

[1] https://www.goldmansachs.com/insights/articles/ai-to-drive-165-increase-in-data-center-power-demand-by-2030

[2] "The Coal Question"
http://digamo.free.fr/peart96.pdf

[3] https://de.m.wikipedia.org/wiki/Jevons-Paradoxon

[4] https://www.goldmansachs.com/insights/articles/is-nuclear-energy-the-answer-to-ai-data-centers-power-consumption

[5] https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

[6] https://www.cbre.com/insights/reports/global-data-center-trends-2024

__________

🧭 Digression: on gigawatts and terawatt hours

In order to understand the relationship between the two figures, 122 GW (gigawatts) and 1,000 TWh (terawatt hours), it is important to clarify the units and their meaning:

• 122 GW (gigawatts):

Refers to the current average power capacity that data centers worldwide require to function. Power (measured in GW) describes the amount of energy consumed per second. This is therefore a snapshot of energy requirements.

• 1,000 TWh (terawatt hours):

This is an indication of energy consumption over a certain period of time, in this case one year. It describes how much energy is required in total in 12 months.

The forecast of 1,000 TWh is slightly below the value resulting from the calculation. The graph shows values slightly above 1,000 TWh; according to the calculation based on 122 GW of power capacity, energy consumption should be around 1,069 TWh.

Nevertheless, general reasons for deviations may be as follows:

• Efficiency improvements: Data centers could operate more efficiently through improved cooling, optimized hardware and software and thus consume less energy.
• Peak vs. average consumption: The figure of 122 GW could reflect peak demand, while the actual average annual demand is somewhat lower.
• Adjustments to the model: It is possible that the forecast of 1,000 TWh is conservative and does not take into account all additional loads or regional differences.

This shows how much the demand for data centers and energy will increase due to AI and digitalization by 2030

__________

Why I am currently bearish on the market, a report.

1. macroeconomic environment

US exceptionalism: dominance and risks

• US market at all-time highs:
• S&P 500 and other indices are at a 75 year high compared to the rest of the world. This is based on strong macroeconomic fundamentals, the attractiveness of the US market and a high risk appetite among investors.
• Problematic strengthSuch a level could indicate overvaluation. Any weakness in the US growth data could trigger an abrupt correction.

• Record high US dollar:
• The US dollar is at a 55 year high in the real effective exchange rate (REER). (The REER measures the competitiveness of a currency against the currencies of its major trading partners, taking into account inflation and trade volume) This increases the pressure on emerging markets and burdens the competitiveness of US exporters.
• John Connally's paradox ("The dollar is our currency, but your problem"): While a strong dollar is a sign of global confidence in the short term, it creates tensions, especially for countries with high dollar debts. (Argentina, Brazil, Chile, Turkey, India, etc.)

Interest rates and inflation

• Rising interest rates and risks in the credit market:
• Should yields on 3Y US government bonds rise to 5 % (and the 2-year to 4.5 %), financing conditions could deteriorate sharply. This would particularly affect credit-dependent sectors such as small capsemerging markets and corporate bonds.
• Financials (KBE)Banks may initially benefit from higher interest rates, but could collapse if financing conditions deteriorate. financing conditions become excessively restrictive. KBE stands for Credit-Bank ExposuresThis refers to the loans and financing that banks grant to companies or other institutions.

• US financial conditions are tightening:
• With expectations of interest rate cuts by the FED canceled and long-term yields rising (even without political measures), the first "cracks" in liquidity are appearing:
• Gold: Outflows of -1.6 bn $.
• EM Equities: Strong sell-off.
• Risk parity strategies: Vulnerable to volatility

Trump 2.0 and his radical policies

• The return of a "Trump-like" approach could move markets sharply:
• Positive in the short term: Tax cuts, infrastructure programs and protectionist measures could boost growth and investment in the US in the short term.
• Negative in the long termTrade wars, higher tariffs and global decoupling could weigh on growth worldwide.
• You can find out more on my account

2. market movements and financing conditions

US equity markets

• Massive capital inflowsUS equities are seeing the largest inflows since March 2024 (+$55.8bn), with records in large caps (+$44.1bn) and a focus on small caps (+$6.7bn).

• Sector-specific trends:
• Materials (+$5.6bn)Investors expect infrastructure programs and investments in the real economy.
• Financials (+$2.6 billion)Banks benefit from higher interest rates, but could suffer from rising defaults or recession risks.

Gold and commodities

• GoldOutflows of -1.6 bn $ show a clear move away from "safe havens". This is typical for a phase of risk appetite.

• Commodities and semiconductors:
• Commodity producers benefit from short-term demand due to inventory build-up.
• Semiconductors (SOX back to 200-day line): In particular "blue collar" semiconductors ($STM (-0,9 %) , $ON (-1,18 %) , $MCHP (-0,42 %) ) in particular are suffering from weak global PMIs, which shows their dependence on EM production.

3. global trends and emerging markets

Capital outflows and global disparities

• EM markets: Largest outflows since 2015 (-$7.5bn in one week).
• China under pressure:
• Record outflows (-$21.1bn over 5 weeks) point to a confidence deficit.
• Macroeconomic challengesWeak growth, real estate crisis, capital controls.
• Geopolitical tensionsTrade barriers and US sanctions act as an additional burden.

Risk of a global recession

• EAFE (Europe, Australasia, and Far East) (-8 %) and India (-10 %) under pressure:
• Weaker economic data and capital outflows signal growing global fears of recession.

• Emerging markets in crisis:
• Weaker currencies and rising dollar costs are making it difficult for countries such as Brazil, Argentina and Turkey to refinance their debt.

4. capital flows and investment strategies

Fund Flow Highlights:

• Cryptoinflows of $6 billion reflect the willingness to invest in alternative assets.
• Bank Loansinflows of $2.1bn show an increase in riskier credit strategies.
• EM: Outflows from equities (-$7.5bn) and bonds show a clear reduction in risk.

Hennington Research Approach:

• US positioning:
• Favored large caps and materials short term as long as the US economy is supported.
• Reconsider exposure to small caps as they are vulnerable to interest rate changes.
• Avoid emerging markets:
• Capital outflows and dollar weakness continue to weigh on these markets.
• Monitor yield risks:
• A breach of 5% in 30-year Treasuries could mark a turning point. Watch bond markets at all times.

Earnings next week 🚀

$AMZN (-0,74 %)

$AMD (+1,66 %)

$SOFI (+2,08 %)

$MSFT (+1,74 %)

$AAPL (+1,9 %)

$META (+0,41 %)

$GOOGL (+0,02 %)

$INTC (-2,38 %)

$PYPL (-0,42 %)

$LLY (-0,45 %)

$F (-0,4 %)

$SNAP (+0,48 %)

$CMG (-0,75 %)

$COIN (+0,07 %)

$UBER (+1,08 %)

$BP. (-1,26 %)

$MCD (-0,63 %)

$HOOD (-1,9 %)

$PFE (-3,59 %)

$V (-1,22 %)

$NOVA

$ETSY (-1,08 %)

$ON (-1,18 %)

$FUBO

$PTON (-1,22 %)

$TMDX (-1,85 %)

$FSLR (-0,7 %)

$RIOT (+0,67 %)

$ROKU (-2,12 %)

$RCL

$CVNA (+1,83 %)

$CVX (-0,6 %)

$CAT (+0,48 %)

$HUM (-0,78 %)

$CDNS (-2,87 %)

$SBUX (-0,51 %)

$WM (-1,07 %)

$NXT

$THC

$JBLU (-2,49 %)

$CROX (-4,76 %)

$XOM (+0,01 %)

$MRK (-2,48 %)

$RDDT (-1,2 %)

$PHIA (-1,09 %)

$VFC (-4,16 %)

$RIG (+0,72 %)

$COP (-1,14 %)

$EPD

$RMBS (-2,58 %)

Semiconductor shares since their 52-week highs

-61% Intel $INTC (-2,38 %)

-54% Aixtron $AIXA (+0,68 %)

-45% STMicroelectronics $STMPA (-0,65 %)

-44% Micron Technology $MU (+4,63 %)

-37% Globalfoundries $GFS (-1,1 %)

-36% BE Semiconductor $OXVE

-33% Lam Research $LRCX

-29% AMD $AMD (+1,66 %)

-29% Qualcomm $QCOM (+0,26 %)

-28% ON Semiconductor $ON (-1,18 %)

-27% ASML $ASML (+1,12 %)

-26% Applied Materials $AMAT (-0,43 %)

-26% Elmos Semiconductor $ELG (-1,41 %)

-24% Microchip Techn. $MCHP (-0,42 %)

-23% ARM $ARM (-3,24 %)

-23% Synopsys $SNPS (-3,14 %)

-23% Infineon $IFX (-0,36 %)

-22% NXP Semiconductors $NXPI (-1,58 %)

-20% Cadence Design $CDNS (-2,87 %)

-17% Nvidia $NVDA (+0,32 %)

-17% VanEck Semicon. ETF $IE00BMC38736 (+0,32 %)

-17% Nordic Semi $NRS

-17% KLA Corporation $KLAC (+1,31 %)

-13% Marvell Technology $MRVL (+0,92 %)

-12% TSMC $2330

-11% Broadcom $AVGO (+0,14 %)

-7% Texas Instruments $TXN (-0,87 %)

-6% Monolithic Power Syst. $MPWR (-0,74 %)

>> Which stocks are you invested in and how are they performing? Which of these stocks do you have on your watchlist? #semiconductor
#halbleiter