$BLK (-1,22 %)

The dividends received in the screenshot do not quite match, as only those from the holding company are listed. The total net amount is €510.

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 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
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 (-1,16 %) 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
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 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 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

AI-driven flash crash

An AI flash crash occurs when modern trading algorithms trigger massive waves of selling in a matter of seconds.

These systems are usually programmed to react to price changes or data signals, such as stop loss limits or short-term price drops.

If a share reaches a critical price, programmed algorithms automatically trigger sales.

These orders drive the price down further, causing other algorithms with similar mechanisms to also sell ("sell side momentum").

This so-called cascade effect can cause the price to plummet within minutes.

(Example: Cascade effect of critical infrastructure during heavy rainfall)

(https://link.springer.com)

The trading speed of AI models today is so high that the smallest triggers (e.g. false signals) can result in a storm of trades in a flash.

Experts warn that many AI models are based on similar data, which can lead to "swarm thinking":

If several systems misinterpret the same signals at the same time, a small price slide can very quickly turn into a huge sell-off.

(Example: The flash crash of 6 May 2010 began with a large sell-off program being triggered for S&P 500 futures).

(https://www.advisorperspectives.com)

Although the markets recovered by the close of trading, this example shows how domino effects can be caused by automated orders.

AI can also have its own say:

Modern systems read news and social media in real time and react independently.

Bots can also incorporate completely new information (tweets or news) and generate buy or sell signals from this.

Incorrectly generated or misinterpreted messages can therefore immediately lead to sales.

1.

Possible triggers

Data error or manipulation:

Incorrect market data (prices, volumes) or cyber attacks on data can trigger false signals.

Algorithms that react blindly to data could falsely trigger sales or purchases.

The term:

"Simulation Deception"

(https://www.tencentcloud.com/techpedia/118834)

describes artificial patterns in the market that are created by manipulated data.

For example, an attacker could use fake buy/sell orders (spoofing) to artificially simulate liquidity, whereupon AI systems panic and trade in the opposite direction.

Fake news and deepfakes:

Artificial intelligence now allows deceptively real false reports (deepfake video, fake tweets, etc.).

(Example: on July 16, 2025, Congress member Anna Paulina Luna from Florida wrote on X (Twitter) that she had heard from President Trump that Fed chief Powell would be fired immediately).

(https://www.advisorperspectives.com)

(https://www.advisorperspectives.com)

AI searched all social media posts specifically for tradable news. It found what it was looking for and a violent reaction in the bond and stock markets followed, as shown above.

In previous cases, the impact might have been weaker, as the president could have reacted more quickly and dismissed the statements before many market participants were even aware of the rumor.

Even little-known posts can lead to strong market movements within minutes thanks to AI attention.

World Economic Forum analyses explicitly warn:

Machine-generated fake news can act like a flash crash trigger.

More and more bots are able to spread such false information in order to deceive trading algorithms.

AI misinterpretation:

Even if the data is correct, AI models can misinterpret it.

Trading algorithms that process complex data (news, technical indicators) run the risk of interpreting irrelevant noise as a signal.

Lawfare cites as an example that AI-supported systems already "misread" the market in 2010 and 2016. "misread" the market and launched unfounded waves of selling.

"A few algorithms in use simply misread
the market. The unwarranted sell-off initiated by those mistaken models then caused other programs to respond in kind. The $1 trillion lost in that half hour period was eventually made up thanks to human intervention. "

In the future, such misinterpretations will be even more critical as AI models analyze huge amounts of data from social media and news.

Panic signals/cascades:

In a battered market, automated risk offs (stop sales after a fixed loss limit) can trigger a race.

If, for example, a key figure ($VIXindex level) reaches a critical value, many systems switch to safety at the same time - which can cause a variety of similar assets to fall as an artificial panic impulse.

2.

Affected asset classes

An AI Flash Crash affects various asset classes:

Equities:

This is often the first impetus of a crash.

Globally listed stocks (indices such as S&P 500, DAX, Nikkei) see massive price losses in a matter of seconds.

A shutdown of a large position, for example, can cause other algorithms to panic sell.

Historically, the stock market has experienced such sell-off waves several times.

2010 Dow $DJIA

(https://en.wikipedia.org)

2014 US bonds

(https://www.ft.com)

An AI-supported flash crash would accelerate this mechanism even further. A sharp slump is usually followed by a partial recovery within a few days or weeks.

Bonds:

Bond markets can also "flash".

In the famous Treasury Flash Crash of 2014, the yield on the US 10-year Treasury Yield plummeted by 1.6% in twelve minutes, followed by a recovery - triggered by algorithmic sell orders at record levels.

(https://www.researchgate.net)

(Theoretically, AI can act against this:

In a stock panic, investors often flee into bonds (price rises, yield falls).

But AI-controlled bond funds could simultaneously and automatically reach certain thresholds and trigger the sale of bonds or bond futures.

This could lead to sharp interest rate swings in the short term, even if the fundamentals do not justify this).

Commodities:

When uncertainty is high, commodity prices often tip.

Typically, oil ($IOIL00 (+0,92 %) ), gas ($NGS ) and industrial metal prices ($COPA (+0,43 %) , $ALUM (-2,43 %) , $ZINC (+2,11 %) ) in a crash phase due to expected weaker demand.

AI programs on the commodities market (e.g. in oil or gold futures trading) could intensify this crash or even trigger a "mini flash crash" in individual commodities.

(Example: the slump in silver futures in July 2017:

Price plunge of over 11% during Asia close when thin trading was blamed on algorithm shifts).

(https://finance.yahoo.com)

AI in commodity markets can therefore both trigger selling spikes and initiate a rapid countermovement through post-buy programs.

Cryptocurrencies:

These are considered particularly volatile.

AI trading bots are everywhere, so cryptocurrencies are in free fall when many bots recognize "fear" signals at the same time.

(Example: In May 2021 $BTC (+0,01 %)

plummeted by around 30% within hours, partly because many algorithms sold en masse after signals about China's Bitcoin ban).

(https://www.brokerdeal.de)

$ETH (-0,08 %) experienced a flash crash on one platform in 2017 because a huge sell order triggered many automated trades.

(https://www.ubergizmo.com)

Crypto markets run 24/7, are unregulated and therefore more susceptible to algorithmic chain reactions.

3.

Risk matrix by region

The probability of occurrence and the extent of damage caused by a crash differ from region to region:

USA:

• Very high trading volume and dominant use of AI algorithms in New York and Chicago.
• Large index futures can act as initiators.
• Probability of a crash is considered moderate to high as there is a lot of automated trading here.
• Damage would be extremely highas the US markets are of global systemic importance.
• Trading halts mitigate the impact on the trading day, but the crash effect on global investor sentiment would be enormous.

Europe:

• Heavy reliance on passive funds and ETFs (e.g. from $BLK (-1,22 %) iShares).
• Algorithms are widespread, but somewhat less so than in the USA.
• Probability rather mediumdamage high.
• ETF crashes show that sudden panic can also lead to chain reactions in equities.
• European banking crisis could arise if credit markets are burdened by US shocks.

Asia:

• Regulation and trading times differ.
• Flash crashes can have a rapid impact on Asia (Nikkei, SSE), especially if they start at night when trading is thin.
• 
  Medium probability and medium damage - because Asian markets close faster and usually react later.
• Crashes in Asia could affect yen or euro performance, for example.

Crypto:

• Market open around the clock, little regulation, high leverage.
• The probability of a major crash in crypto is very highas price falls are more frequent and driven by AI bots.
• Damage is often limited to crypto investors, but can also affect traditional markets via linked financial assets (Bitcoin ETFs, leveraged crypto products).

The matrix overview could therefore show

• Short term (minutes to days):

A sudden flash crash would last seconds to minutes.

Prices plummet, many stop loss orders are triggered.

Stock exchanges switch on automatic trading pauses to stop algorithm spirals.

Investors lose billions in a very short space of time, many markets are temporarily illiquid.

Confidence collapses, many investors panic and are uninformed.

• Medium-term (weeks to months):

Markets should stabilize again in the following days to weeks as counter-cyclical AI and manual orders intervene.

In the medium term, economic data could be affected if a crash impacts financing conditions.

Media and public will question confidence in digital markets for months.

Investors report consequences such as increased demand for safe assets (gold, government bonds).

• Long term (years):

Regulation and market mechanisms would adapt.

We could expect a regulatory boost:

• Stricter rules for AI in trading
• Transparency obligations for algorithm models
• Supervision of financial AI by regulators (SEC, BaFin, ESMA etc.).

Already in the past, the 2010 flash crash led to new trading interruptions and considerations regarding trading system requirements.

An AI crash would likely have a disciplining effect:

Providers need to develop more robust AI models, and contingency plans (kill switches) could become mandatory.

In the long term, confidence could be slow to recover:

Institutional investors would only have limited confidence in AI systems, and many private investors might temporarily hold back or prefer alternative strategies.

4.

Specific players and technologies

BlackRock Aladdin:

BlackRock's Aladdin AI system currently manages more than 30,000 portfolios and permanently rebalances enormous amounts of capital.

If Aladdin is routinely programmed to sell too much for ETFs or funds, this can trigger billions of orders.

Nvidia & AI chips:

$NVDA (+0,32 %) Supplies the hardware for many AI models and is itself a market star.

High expectations for AI have fueled Nvidia's share price for years.

Algorithms are strongly fixated on such shares.

If, for example, Nvidia's share price falls abruptly, many strategies trigger sell programs.

Such a domino effect

$NVDA (+0,32 %) -> $SEMI (+0,56 %) -> $CSNDX (+0,32 %)

could fuel a crash.

In practice, it has been shown that Nvidia reacts very volatile to macroeconomic and geopolitical news, so the next AI turbulence could drag down the entire tech sector.

AI bots on Binance (Crypto):

On crypto exchanges like Binance, many users trade with automated bots.

A large part of the crypto trading volume comes from AI-supported systems.

These bots can generate simultaneous sell or buy waves.

AI-driven ETF rebalancing:

Large index ETFs and passive funds (BlackRock iShares, Vanguard etc.) use automated systems to implement index changes.

If indices rise or fall quickly, many ETFs start rebalancing at the same time.

If the AI signal is negative, all AI-based funds could sell at the same time.

This creates massive sell orders in a short space of time.

Because the volumes involved are in the billions, rebalancing alone can drive a crash further.

Other players:

News agencies, index operators (eg. $MSCI (-0,24 %) ), hedge funds with AI strategies and social trading platforms also contribute.

Any sudden outage (e.g. power failure at NYSE) or hacker attack on stock exchange systems could further irritate the AI systems on the stock market.

"When algorithms collide and markets tremble in fractions of a second, the new power of AI is revealed: speed without mercy, precision without emotion. One spark is enough - and the domino effect races through indices, derivatives and crypto-spheres. The AI-driven flash crash is no longer a distant shadow, but the echo of a future in which machines set the pace of the financial world."

Feel free to write your feedback on this post in the comments and tell me if you're interested in something like this.

My plan this morning was actually just to write a short post about this topic, but it turned out to be a bit longer. It's so easy to sit and write all day.

@Kundenservice Please increase the maximum number of pictures for a post, unfortunately I didn't get all the pictures in that I had picked out.

Sources:

• https://www.ig.com/en/trading-strategies/flash-crashes-explained-190503#:~:text=speeds%20based%20on%20pre,as%20the%20prices%20go%20down
• https://www.advisorperspectives.com/articles/2025/07/28/ai-transforming-markets#:~:text=I%20started%20this%20article%20by,a%20flash%20crash%20or%20surge
• https://www.lawfaremedia.org/article/selling-spirals--avoiding-an-ai-flash-crash#:~:text=an%20otherwise%20normal%20trading%20day,up%20thanks%20to%20human%20intervention
• https://www.ig.com/en/trading-strategies/flash-crashes-explained-190503#:~:text=2010%20flash%20crash%3A%20Dow%20Jones
• https://www.advisorperspectives.com/articles/2025/07/28/ai-transforming-markets#:~:text=For%20example%2C%20on%20July%2016%2C,last%20week%20was%20lightning%20fast
• https://www.binance.com/en/square/post/22230680857314
• https://www.tencentcloud.com/techpedia/118834
• https://www.weforum.org/stories/2023/04/technology-vulnerabilities-financial-system/#:~:text=However%2C%20IoT%20botnets%2C%20which%20tamper,grid%20and%20influence%20market%20prices
• https://www.lawfaremedia.org/article/selling-spirals--avoiding-an-ai-flash-crash#:~:text=But%20this%20was%20not%20a,speed%20selling%20spirals.”
• https://corporatefinanceinstitute.com/resources/career-map/sell-side/capital-markets/flash-crashes/#:~:text=Using%20algorithms%20to%20trade%20has,plunge%20in%20the%20market%20occurs
• https://www.ig.com/en/trading-strategies/flash-crashes-explained-190503#:~:text=The%20flash%20crash%20of%20the,impact%20these%20events%20can%20have
• https://www.tastyfx.com/news/flash-crashes-explained-190503/#:~:text=The%20DJIA%20suffered%20yet%20another,NYSE
• https://www.occ.gov/news-issuances/speeches/2024/pub-speech-2024-61.pdf#:~:text=flash%20crashes%2C%20which%20have%20been,4
• https://www.zerodaylaw.com/blog/ai-compliance-safeguarding-financial-markets#:~:text=The%20reliance%20on%20AI%20for,reaching%20consequences
• https://www.nasdaq.com
• https://medium.com
• https://corporatefinanceinstitute.com
• https://www.tastyfx.com
• https://www.binance.com/en
• https://www.lawfaremedia.org
• https://www.weforum.org
• https://www.ig.com/de
• https://www.tencentcloud.com
• https://www.occ.gov
• https://www.ssrn.com/index.cfm/en
• https://www.advisorperspectives.com
• https://www.zerodaylaw.com
• https://www.curiousmonky.com

These shares reached a new 52-week high today:

Meta $META (+0,41 %)

Broadcom $AVGO (+0,14 %)

AMD $AMD (+1,66 %)

Blackrock $BLK (-1,22 %)

Astera Labs $ALAB

Arista Networks $ANET (-8,99 %)

Reddit $RDDT (-0,16 %)

Lam Research $LRCX (+1,38 %)

KLA Corp $KLAC (+1,31 %)

S&P Global $SPGI (-1,16 %)

Celsius $CELH

United Rentals $URI (-2,1 %)

Do you hold one of the shares? If yes, congratulations!

I received a dividend from BlackRock at Scalable today! Strange because I already got my June dividend from BlackRock! Is it possibly a mistake 🤔 Does anyone have similar experiences/problems?

Best regards $BLK (-1,22 %)

BlackRock achieves record assets despite decline in turnover

BlackRock $BLK (-1,22 %) achieved an impressive record of 12.5 trillion dollars in assets under management in the second quarter of 2023. But behind this glittering figure lies a dark cloud: at 5.42 billion dollars, turnover fell short of analysts' expectations, who had expected 5.44 billion dollars. The reason for this revenue shortfall? An institutional client withdrew a whopping 52 billion dollars from the index funds, which significantly reduced net inflows. In the final hours of trading, the markets reacted sensitively: the shares fell by almost 6% after previously reaching an all-time high. Despite these setbacks, BlackRock has still been able to record an increase of around 3% in equities since the beginning of the year.

In addition to the revenue woes, BlackRock announced that the acquisition of HPS Investment Partners for $12 billion was completed as early as July 1, 2023. This acquisition brings with it not only 165 billion dollars in client assets under management, but also 118 billion dollars in fee-based assets. The question remains as to whether this strategic decision can strengthen the company in the long term or whether the current challenges will overshadow the positive effects.

René Benko faces serious charges

In the world of real estate, René Benko, who was celebrated as the star of the industry, is now facing serious legal difficulties. Prosecutors have brought charges against him based on various allegations, the details of which have not yet been disclosed. According to reports, the investigation includes extensive evidence pointing to a possible legal dismantling of his business. Benko was known for his aggressive acquisition strategies, which brought him both fame and criticism. His current situation not only raises questions about his personal future, but could also have far-reaching implications for his legacy in the real estate world. The industry is watching with interest to see how the situation will develop.

Sources:

https://finance.yahoo.com/news/blackrock-stock-tumbles-revenue-misses-160133670.html

https://www.manager-magazin.de/unternehmen/rene-benko-was-1000-seiten-ermittlungsakten-vom-system-benko-verraten-a-86f5c933-3b07-408c-a519-ad2394e92083

As the earnings season starts again, here is a summary of the most important figures next week.

$JPM (+0,32 %)
$C (+0,76 %)
$WFC (+0 %)
$BLK (-1,22 %)
$JNJ (-0,64 %)
$GS (-0,52 %)
$BAC (-0,32 %)
$AA (+2,94 %)
$ASML (+1,12 %)
$PLD (-0,1 %)
$UAL (-2,68 %)
$KMI (+0 %)
$PEP (-0,6 %)
$ABT (+1,42 %)
$Netflix
$TSM (+0,23 %)
$USB (-0,07 %)
$IBKR (+0,54 %)
$AXP (-1,1 %)
$MMM (-0,71 %)

Does anyone know what's going on with $BLK (-1,22 %) they are literally going through the roof 🚀🚀 Luckily I got in on time 🙂

Partly due to the following article by @Epi (many thanks for that! 👍🏻) I am considering reducing my US share in my portfolio.

Specifically, I am considering, for example, selling my shares in $BLK (-1,22 %) to sell:

These are close to the all-time high in dollars (which I generally don't think is a bad time to sell), but roughly about 10% lower in EUR. So selling would currently mean giving up about 10% of the return.

The share in my portfolio is 3.5%, I would currently realize a profit of 13% in EUR.

My investment horizon is 15 years+, my US share in the portfolio is approx. 80-85%.

How are you dealing with the current weakness of the dollar? Does it even make sense to react by buying/selling?

Are you currently selling positions in dollars because of the weakness of the dollar (and accepting the loss of return in EUR)?

Or is that precisely why you are not selling, even though you actually want to sell?

Or are you perhaps even building up positions in dollars in order to benefit from an appreciation of the dollar from 2027?