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The United States could manufacture 300,000 to 400,000 humanoid robots annually by 2030, according to a new J.P. Morgan forecast. The projection points to rising demand as businesses explore humanoid robots for manufacturing, logistics, and warehouse operations.

According to a Reuters report published on November 12, 2026, J.P. Morgan expects commercial adoption to accelerate. The bank believes the humanoid robotics industry will gradually move beyond demonstrations and expand into real workplace applications. It also forecasts annual U.S. humanoid robot production could reach approximately 1.5 million units by 2035.

Growing interest in humanoid robots reflects businesses’ efforts to automate physical tasks across existing workplaces. Improvements in robotics technology, manufacturing capacity, and operating costs could support wider adoption across industries.

J.P. Morgan Expects Annual U.S. Humanoid Robot Production to Reach 400,000 by 2030

J.P. Morgan expects U.S. humanoid robot production to grow as manufacturers increase capacity and expand commercial operations. The bank forecasts annual production could reach 300,000 to 400,000 units by 2030.

The growth is expected to continue as humanoid robots become more widely used across different industries. J.P. Morgan projects annual production could reach approximately 1.5 million units by 2035.

This forecast reflects a shift in the humanoid robotics industry from testing to commercial deployment. Companies are moving beyond demonstrations and preparing to use humanoid robots in everyday business operations.

Manufacturing plants, warehouses, and logistics centers could become major markets for humanoid robots. Businesses are considering these machines for repetitive tasks, higher productivity, and ongoing labor shortages.

Labor Shortages and Lower Costs Could Increase Humanoid Robot Demand

Labor shortages could increase demand for humanoid robots as U.S. businesses struggle to fill physically demanding positions. Several industries continue to face challenges hiring workers for repetitive tasks and labor-intensive operations.

Humanoid robots could help companies address some of these workforce challenges. Their human-like design allows them to work in facilities built for people without major structural changes.

Lower operating costs could further support the adoption of humanoid robots across different industries. As manufacturers increase production and improve robot performance, manufacturing and operating expenses could gradually decline.

J.P. Morgan expects these factors to support demand across manufacturing, warehousing, and logistics operations. However, wider adoption will depend on whether humanoid robots can perform tasks reliably and deliver clear cost savings.

Humanoid Robot Industry Shifts Toward Commercial Use

The humanoid robotics industry is gaining attention as major technology and manufacturing companies increase their investments. These companies are developing robots that use artificial intelligence, sensors, and mechanical systems to perform physical tasks.

Manufacturers are also improving robot movement, task performance, and workplace safety around human workers. These improvements could help humanoid robots move beyond demonstrations and perform more complex jobs in real workplaces.

However, several challenges could slow the large-scale adoption of humanoid robots. Manufacturers must improve reliability, lower production costs, and ensure robots work consistently in different environments.

J.P. Morgan’s forecast points to significant growth in U.S. humanoid robot manufacturing by 2030. If production targets are achieved, these machines could become more common across industrial automation and business operations.

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U.S. Humanoid Robot Manufacturers Face Production Challenges

Despite the positive growth outlook, J.P. Morgan highlighted several challenges that could slow U.S. humanoid robot production. These include hardware development, AI training data, computing resources, and limited manufacturing capacity.

According to the bank, most U.S. humanoid robot manufacturers, excluding Tesla, target annual production between 10,000 and 30,000 units. These production plans remain below the industry’s projected annual output of 300,000 to 400,000 units by 2030.

J.P. Morgan also highlighted the possibility of government support for domestic robotics manufacturing. If U.S. authorities classify imported robotics and industrial machinery as national security risks, new tariffs or trade measures could follow. Such policies could encourage companies to expand domestic manufacturing and strengthen the local robotics supply chain.

The bank identified several major companies competing in the U.S. humanoid robot market. These include Tesla’s Optimus, Agility Robotics’ Digit, Boston Dynamics’ Atlas, Figure AI’s humanoid robots, and Apptronik’s Apollo.

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Humanoid Robot Components Market Could Reach $30 Billion by 2035

Rising humanoid robot production could create new business opportunities for companies supplying essential robotics components. These components include actuators, motors, vision systems, and other technologies needed to operate humanoid robots.

J.P. Morgan expects the humanoid robot components market to approach $30 billion by 2035. The bank believes suppliers could benefit significantly as manufacturers increase production and expand their operations.

However, J.P. Morgan expects more value to shift toward robot manufacturers and platform integrators as the industry matures. This could change how companies across the humanoid robotics supply chain generate revenue.

The bank also estimates that humanoid robot costs are approaching human labor cost levels. Using robots across multiple work shifts could generate approximately $100,000 in annual savings for each customer.

Goldman Sachs expects annualized spending on artificial intelligence (AI) hardware and intellectual property to reach $1.4 trillion by December 2027. This forecast reflects the rising investment as technology companies expand data centers and develop more advanced AI systems.

According to Reuters, in a report published on November 12, 2026, US spending on AI-related hardware and intellectual property could exceed $900 billion by December 2026. Goldman Sachs expects this figure to rise to $1.4 trillion by the end of 2027, accounting for nearly 4% of US GDP.

The investment bank also expects AI spending to remain the biggest driver of US business investment growth in 2027. However, concerns about higher borrowing costs, stricter regulations, and rising scrutiny could affect future spending plans.

AI Spending Could Boost US Economic Growth

Goldman Sachs believes rising demand for AI infrastructure will drive business investment across the United States. Companies are increasing spending on semiconductors, data centers, computing equipment, and other technologies needed to build and run AI systems.

According to the bank, AI-related investments, including semiconductor spending, could contribute 5.3 percentage points to US capital expenditure growth in 2027. These investments could also increase US gross domestic product (GDP) growth by 0.6 percentage points.

However, the estimated economic impact is lower when semiconductor investments are excluded. Without this spending, AI-related investments would contribute 4.3 percentage points to capital expenditure growth and 0.2 percentage points to GDP growth.

Goldman Sachs also pointed out that conventional business investment data does not fully capture semiconductor spending. Additionally, some imported information-processing equipment is excluded from specific GDP calculations, which affects how AI investment contributes to measured economic growth.

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Goldman Sachs Forecasts 8% Growth in US Business Investment in 2027

Goldman Sachs expects US business investment to increase by 8% on a quarterly basis in 2027. This forecast is nearly double the market consensus estimate of 4.2%, highlighting the expected impact of rising artificial intelligence (AI) infrastructure spending on the US economy.

Major technology companies are continuing to invest in data centers, advanced semiconductors, and computing equipment to meet growing demand for AI services. These investments are expected to support business spending and drive further growth in the technology sector.

However, rising AI infrastructure costs are also creating concerns among investors. Several technology companies are relying on debt to fund their expansion plans, increasing their exposure to higher borrowing costs and changing market conditions.

Investors are also questioning whether companies can generate enough revenue from AI products and services to recover their growing infrastructure investments. This has raised concerns about the long-term returns from the billions of dollars being spent on AI development.

Regulatory Risks and Higher Borrowing Costs Could Slow AI Investments

Goldman Sachs’ latest forecast comes as concerns grow over the future of artificial intelligence (AI) infrastructure spending in the United States. Rising interest rates and closer investor scrutiny could increase the cost of financing new data centers, semiconductors, and computing equipment.

Political changes are also creating uncertainty for technology companies planning large AI infrastructure projects. Recent Democratic victories in the US midterm elections have raised concerns about stricter regulations on data center construction and expansion.

However, Goldman Sachs expects these political developments to have only a limited impact on AI investment growth. The bank believes strong demand for AI infrastructure will continue to support spending, despite higher borrowing costs and possible regulatory challenges.

ALSO READ: AI Infrastructure Spending Statistics

AI Infrastructure Spending Continues to Rise Worldwide

Goldman Sachs’ latest forecast highlights the growing investment needed to support the expanding artificial intelligence (AI) industry. Major technology companies are spending billions on AI chips, data centers, cloud computing, and networking equipment.

These investments are increasing as businesses adopt generative AI, large language models, and AI agents. Companies need more computing power and storage capacity to develop, train, and operate advanced AI systems.

Goldman Sachs has also pointed to the wider economic impact of rising AI infrastructure spending. In an August 2026 research report, the bank estimated that global AI investment could reach 1.3% of global GDP in 2027, up from 0.9% in 2026.

The latest $1.4 trillion forecast indicates that AI spending could remain a major source of US business investment through 2027. However, future growth will depend on whether companies can turn these investments into higher revenue and productivity gains.

German energy technology company Siemens Energy has reported around €15 billion in orders from AI data-center projects as demand for electricity infrastructure continues to grow. The company has also raised its fiscal 2027 profit margin forecast, supported by strong demand for gas turbines and power grid equipment.

The announcement came on November 11, 2026, alongside the company’s record financial results for fiscal 2026. Siemens Energy reported a total order backlog of €164 billion, with AI data-center orders accounting for around 9.1% of the total.

The rapid expansion of AI data centers is increasing demand for electricity generation and transmission equipment. These facilities need large amounts of electricity to run AI servers, train large language models, and handle other computing tasks.

Siemens Energy expects rising AI investments to create more demand for gas-fired power plants and electricity grid infrastructure. North America is expected to remain a major market for this growth.

Siemens Energy Reports €15 Billion in Orders From AI Data Centers

Siemens Energy has received around €15 billion in orders linked to AI data centers. CEO Christian Bruch said these orders include gas turbines and power grid equipment needed to supply electricity to large data centers.

The orders account for approximately 9.1% of the company’s record €164 billion backlog. This backlog covers orders from all its business divisions and gives the company a clearer view of future revenue.

Bruch said demand for data-center projects remains strong, with no major changes in customer activity. However, he pointed out that some earlier forecasts for data-center expansion may have been too optimistic.

Despite this, rising demand for AI computing continues to create opportunities for Siemens Energy. As technology companies expand their AI infrastructure, they need more electricity generation capacity and reliable power grids.

Siemens Energy expects data-center construction to support further investment in power generation and grid equipment. North America is likely to remain a key market as demand for AI infrastructure continues to grow.

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Siemens Energy Increases 2027 Revenue Growth and Profit Forecast

Siemens Energy has increased its financial outlook for fiscal 2027 as demand for its power generation and grid equipment continues to grow.

The company now expects its profit margin before special items to reach 14% to 16% in fiscal 2027. This is higher than the 12.4% margin reported for fiscal 2026.

Siemens Energy also forecasts comparable revenue growth of 14% to 16% for fiscal 2027. It expects net income to reach approximately €5 billion, while pre-tax free cash flow could rise to around €10 billion.

The company has also set new financial targets for fiscal 2030. It aims to achieve annual comparable revenue growth of 12% to 14% and a profit margin before special items of 20% to 22%.

These targets reflect Siemens Energy’s expectations of continued growth in electricity demand. Rising investments in power generation, electricity grids, and AI data-center infrastructure are expected to support its business expansion.

Siemens Energy Reports Record €164 Billion Order Backlog in 2026

Siemens Energy delivered strong financial results for fiscal 2026, which ended on September 30, 2026. The company reported higher revenue, rising orders, and a record order backlog of €164 billion.

Its comparable revenue grew 15.5% year over year to €44.2 billion. Total orders also increased by 18.8% to €68.1 billion. This growth pushed the company’s order backlog to a record €164 billion.

Profit before special items more than doubled to approximately €5.49 billion. Net income also rose to €4.13 billion, compared with €1.69 billion in fiscal 2025. Pre-tax free cash flow nearly doubled as well. It reached €8.8 billion, up from €4.66 billion in the previous fiscal year.

Siemens Energy said it met or exceeded all its previously revised financial targets. Its wind energy subsidiary, Siemens Gamesa, also reached break-even in profit before special items. This marks an improvement after several years of financial losses and restructuring efforts.

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Siemens Energy Announces €9 Billion Share Buyback Through 2030

Siemens Energy has announced plans to launch an additional share buyback programme worth up to €9 billion. The programme is expected to continue through fiscal 2030. The company has also proposed a dividend of €2.30 per share for fiscal 2026. This is more than three times the €0.70 per share paid to shareholders in the previous fiscal year.

These announcements reflect Siemens Energy’s stronger financial position and its expectations for continued growth in the energy infrastructure market.

The company’s latest results also show how rising AI investments are creating new demand for energy equipment. As technology companies expand their AI data centers, they need more gas turbines, transformers, and electricity grid equipment to support their operations.

This growing demand could create new business opportunities for Siemens Energy and other energy equipment suppliers as global investment in AI infrastructure increases.

Foreign investors pulled nearly $50 billion from Asian stock markets in October 2026 as concerns over artificial intelligence spending increased. Rising cash burn at major technology companies and possible regulatory restrictions also weakened investor confidence across the region.

The sell-off hit technology-heavy markets, particularly Taiwan and South Korea, where investors reduced their holdings in semiconductor and AI-related stocks. Data from the London Stock Exchange Group (LSEG) showed net foreign equity outflows of $49.77 billion across seven Asian markets in October. 

This marked the region’s largest monthly foreign investment withdrawal since June 2026, highlighting growing concerns about AI infrastructure spending. Investors are increasingly questioning whether major technology companies can generate enough revenue to justify their massive AI infrastructure investments.

Foreign selling has also continued in November, with another $22.6 billion leaving these Asian equity markets so far. A Reuters report published on November 11, 2026, shows the continued withdrawals amid growing concerns over AI spending and investment returns.

Taiwan and South Korea Lead Asia’s Foreign Stock Market Outflows

Taiwan and South Korea recorded Asia’s largest foreign equity outflows in October as investors reduced their stock market holdings. Foreign investors withdrew a net $30.68 billion from Taiwanese stocks during October, marking a record monthly outflow.

This was Taiwan’s largest foreign equity withdrawal since January 2008, highlighting growing concerns among overseas investors. South Korea also experienced heavy selling, with foreign investors pulling approximately $14.59 billion from its equity market.

The withdrawals reflect growing investor concerns about technology stocks, particularly companies linked to semiconductors and artificial intelligence infrastructure. Foreign selling has continued into November, adding further pressure on both Asian equity markets.

Taiwan has recorded another $19.76 billion in foreign equity outflows so far this month, while South Korea lost approximately $8.32 billion. Both countries have strong links to the global semiconductor supply chain and manufacture hardware used in AI data centres.

However, uncertainty surrounding future AI infrastructure spending has raised concerns about technology valuations and expected corporate earnings.

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Investors Question Returns as Big Tech Increases AI Infrastructure Spending

Growing doubts about the profitability of artificial intelligence investments have emerged as a major reason behind the recent stock market selloff. Major technology companies, especially large cloud providers, are spending billions of dollars to expand their AI infrastructure.

Their investments cover advanced processors, servers, data centres, electricity systems, and networking equipment needed to support AI services. However, investors are questioning whether future revenue from AI services will cover these rising infrastructure costs.

Increasing capital expenditure and cash burn have also raised concerns about the financial risks of expanding AI operations. Wael Makarem, lead financial market strategist at brokerage firm Exness, linked the withdrawals to growing concerns about AI infrastructure spending and profitability.

He said investors were reducing their exposure to AI-related investments as doubts about future returns continued to increase. These concerns have also affected Asian stock markets, where major semiconductor and technology companies supply hardware for AI infrastructure.

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US AI Regulations Raise Concerns Over Data Centre Expansion

Possible changes to US AI regulations have created additional uncertainty for investors concerned about future technology investments. Ahead of the US midterm elections, investors worried that stricter environmental reviews could delay new data centre projects.

Additional AI regulations could also slow data centre construction and reduce demand for advanced semiconductors and related equipment. Recent Democratic victories in several states have kept these concerns alive, particularly where opposition to new data centres continues growing.

Investors are now assessing whether tighter environmental rules and regulatory oversight could affect major technology companies’ expansion plans. These concerns have added pressure on Asian technology stocks, especially markets heavily dependent on semiconductor exports and AI infrastructure demand.

Asian Stock Markets Underperform Global Equities Amid Foreign Investor Sell-Off

Foreign investor withdrawals came as Asian stock markets recorded steeper losses than global equities during October 2026. The MSCI Asia Pacific ex-Japan index dropped 6.91% in October, significantly underperforming broader international stock market benchmarks.

By comparison, the MSCI All Country World Index declined just 1.69% during the same period. The wider gap indicates that investors reduced their exposure to Asian equities more sharply than global stocks.

Scarlett Liu, Asia-Pacific equity derivatives strategist at BNP Paribas, said international investors may have favoured US stocks over Asian markets. The relative strength of US equities may have encouraged investors to move their money away from Asian stock markets.

Rising US Treasury yields may have also influenced investment decisions, making government bonds more attractive to investors. The yield on 10-year US Treasury securities reached 5.3% in early October, increasing the appeal of these investments.

Foreign Investors Return to Indian Stocks With $5.68 Billion in November Inflows

India experienced foreign investor withdrawals for the second consecutive month in October, adding pressure to its equity market. Foreign investors withdrew a net $3.6 billion from Indian stocks during October, raising concerns about overseas investment trends.

However, foreign investment flows have reversed in November, with Indian equities attracting approximately $5.68 billion in net inflows. HSBC has also upgraded its outlook for Indian equities to overweight, citing stronger corporate earnings and more reasonable valuations.

Herald van der Linde, HSBC’s head of equity strategy for Asia Pacific, said Indian stocks offer better investment opportunities. He pointed to the country’s earnings outlook and stock valuations as key reasons behind the improved market assessment.

Meanwhile, several other Asian markets recorded comparatively smaller changes in foreign investment flows during October.

Source: Reuters

AI Investment Concerns Put Asian Technology Stocks Under Pressure

Nearly $50 billion in foreign equity outflows highlight growing concerns about the rising costs of global artificial intelligence investments. Although demand for AI chips, computing systems, and data centre capacity remains strong, investors are questioning future financial returns.

Technology companies face increasing pressure to prove that their AI investments can generate enough revenue to cover rising costs. Possible changes to US AI regulations have added uncertainty, particularly for Asian economies reliant on semiconductor production and technology exports.

Another $22.6 billion has left Asian equity markets in November so far, adding pressure on regional stock markets. Investors will closely watch AI infrastructure spending, corporate earnings, US regulations, and broader market conditions before making investment decisions.

The recent foreign investor withdrawals do not necessarily signal weaker long-term demand for AI technologies and infrastructure. However, the sell-off suggests investors are becoming more cautious about companies exposed to the global AI boom.

Alibaba has launched a preview of Qwen4-Max, its next-generation flagship AI model, with a context window of up to 1 million tokens. The model can process text, images, and videos. It is designed to handle complex coding tasks, long documents, research, and AI agent workflows.

Announced on November 11, 2026, Qwen4-Max is the first preview release in Alibaba’s upcoming Qwen4 model family. The company is offering early access through its Token Plan subscription and several AI platforms, including the Qwen app and Qoder.

The launch adds a new flagship model to Alibaba’s AI lineup as competition grows among companies developing advanced AI models. Major areas of competition include longer context windows, better reasoning, and stronger coding performance.

Alibaba Qwen4-Max Supports 1 Million-Token Context Window

Alibaba’s Qwen4-Max AI model supports a context window of up to 1 million tokens, allowing it to process large volumes of information in a single conversation. This makes the model suitable for tasks that require analyzing lengthy documents, large codebases, and multiple sources of information.

The larger context window could help developers review entire software projects, summarize long reports, and complete complex tasks without splitting information into smaller sections. Qwen4-Max also supports image and video inputs, allowing users to analyze visual content alongside text. This expands its capabilities beyond text-based AI interactions.

According to Alibaba Cloud’s documentation, Qwen4-Max includes deep reasoning, function calling, web search, and structured output capabilities. The model also supports OpenAI’s Chat Completions and Responses APIs, along with Anthropic’s Messages API. This allows developers to integrate Qwen4-Max into existing applications with fewer compatibility changes.

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Alibaba Qwen4-Max Preview Availability, Access, and API Details

Alibaba has released Qwen4-Max Preview through its Token Plan subscription. The preview is available from November 11 to November 25, 2026, giving users a limited period to test the new AI model.

Developers can access Qwen4-Max through the Qwen app, Qoder, QwenWork, and QwenStudio. However, API access is restricted to Token Plan subscribers during the preview. Alibaba has not enabled pay-as-you-go API access for this release.

The model is available through the API under the identifier qwen4-max-preview. It supports a maximum output of 393,216 tokens, covering both reasoning tokens and the final response.

Alibaba has confirmed that the preview model will remain unchanged during the testing period. The company plans to discontinue access to this version after November 25, 2026.

Qwen4-Max Brings New Capabilities for Coding and AI Agent Workflows

Alibaba is positioning Qwen4-Max as an AI model for coding, software development, and multi-step tasks. The model is aimed at professional users who need advanced reasoning and the ability to handle complex instructions. According to Alibaba, Qwen4-Max can support several tasks, including:

• Software development: Writing code and handling complex programming tasks.
• Frontend development: Creating websites and designing user interfaces.
• Office productivity: Processing documents and completing workplace tasks.
• AI agent workflows: Using external tools to perform tasks across different applications.
• Research and data analysis: Reviewing large volumes of information and handling long-context tasks.

These features could make Qwen4-Max useful for developers building AI agents and software applications. Its 1 million-token context window could also help users work with large codebases, lengthy documents, and complex datasets.

However, Alibaba has not yet published independent benchmark results for Qwen4-Max. Its claims about coding, reasoning, and AI agent performance have not been independently verified.

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Qwen4-Max Preview Signals the Next Phase of Qwen AI Models

The launch of Qwen4-Max Preview marks an important step in Alibaba’s next generation of Qwen AI models. Its 1 million-token context window and support for image and video inputs put it in competition with other advanced AI models focused on coding, research, and AI agent development.

The preview release also gives developers a chance to test Alibaba’s latest AI model and explore its capabilities before a wider launch.

However, Alibaba has not announced a general release date or final API pricing for Qwen4-Max. The company has also not confirmed whether it plans to release the model’s open weights. A complete technical report and independent benchmark results are also unavailable.

For now, developers will need to test Qwen4-Max to assess its coding, reasoning, and AI agent capabilities. These results will help determine how it compares with competing AI models from other companies.