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Volkswagen Workers and Managers Clash Over Cost-Cutting Drive

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Volkswagen employees and managers are preparing for a series of extraordinary meetings at the German carmaker’s major sites, highlighting the growing tension surrounding an aggressive cost-cutting campaign.

The meetings come as workers confront increasing uncertainty over jobs, wages and the future direction of one of Germany’s most important industrial companies.

Volkswagen has been under mounting pressure to reduce expenses as the automotive industry undergoes a profound transformation.

The shift toward electric vehicles, intensifying competition from Chinese manufacturers, weaker demand in some markets and rising production costs have created a difficult operating environment. For management, reducing costs has become increasingly important to protect competitiveness.

For employees, the measures threaten to deepen concerns about employment security and working conditions. The confrontation is particularly significant because Volkswagen has historically maintained a close relationship between management and labor representatives.

Its German operations are deeply connected to powerful works councils and employee representatives, while the company’s workforce has traditionally enjoyed relatively strong employment protections. Any major restructuring therefore requires negotiations that can become politically and economically sensitive.

The extraordinary meetings are expected to provide a platform for employees and managers to confront the challenges facing Volkswagen directly. Workers are likely to demand greater clarity over proposed savings, production plans and the potential consequences for employment.

Management, meanwhile, is expected to argue that substantial changes are necessary if the company is to remain competitive in a rapidly changing global automobile market.

At the heart of the dispute is the question of how Volkswagen can lower costs without damaging the expertise and industrial capacity that have helped make it a global automotive leader.

Cutting jobs and reducing production may deliver immediate savings, but excessive reductions could weaken the company’s ability to develop and manufacture new vehicles.

The transition to electric mobility requires significant investment in batteries, software, digital platforms and new manufacturing technologies, making the balance between investment and cost reduction especially difficult.

Volkswagen also faces pressure from competitors that can often manufacture electric vehicles at lower costs. Chinese automakers have expanded rapidly, while other global manufacturers are restructuring their operations to adapt to changing consumer demand.

Volkswagen therefore needs to improve efficiency while simultaneously investing in technologies that will determine its position in the next generation of mobility. For employees, the concern is that the burden of this transformation could fall disproportionately on workers.

Plant closures, reduced shifts or restructuring could affect entire communities that depend heavily on Volkswagen facilities. The economic consequences could therefore extend beyond the company itself, particularly in German regions where automotive manufacturing remains a major source of employment and industrial activity.

The upcoming meetings could become an important test of Volkswagen’s ability to manage the transition without allowing labor relations to deteriorate further. A prolonged confrontation could disrupt production and undermine confidence at a time when the company needs stability.

Conversely, constructive negotiations could produce a compromise that allows Volkswagen to reduce costs while preserving essential skills and employment. Volkswagen’s challenge extends beyond a single cost-cutting program.

The company is confronting a structural transformation of the global automobile industry. How management and employees respond will help determine whether Volkswagen can emerge from the transition as a leaner and more competitive manufacturer while maintaining the industrial foundation that has defined it for generations.

Investors Turn to Financials as AI Trade Splits Hedge Funds and Mutual Funds

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Investors may be divided over how far the artificial intelligence boom can continue to drive stock markets, but hedge funds and mutual funds are increasingly finding common ground in one part of the market: financials.

Both groups increased their exposure to financial stocks in the second quarter, with Goldman Sachs data showing that their bullish positioning in the sector has reached the highest levels in the bank’s historical records.

Hedge funds increased their net tilt toward financials by more than 300 basis points during the quarter, taking their exposure to the highest level since before the global financial crisis, according to Goldman chief strategist Ben Snider.

Mutual funds also increased their overweight position in financials, reaching their highest level since at least 2012.

The shift is notable because institutional investors have taken increasingly different positions on the AI trade. Hedge funds have generally maintained significant exposure to companies benefiting from AI infrastructure spending, while mutual funds have not increased their exposure at the same pace as the broader market.

Financials, by contrast, have emerged as a rare area of agreement.

The rotation comes after a strong second-quarter earnings season for many financial companies, which benefited from resilient economic activity, healthy consumer spending and continued demand for financial services.

Goldman’s data points to four stocks that have become popular with both hedge funds and mutual funds: Capital One Financial, Corpay, Fiserv and Interactive Brokers Group.

The overlap is spectacular because it suggests the shift into financials is not being driven by a single type of investor or strategy. Capital One is particularly notable. The company appears on both Goldman’s list of stocks favored by hedge funds and its list of the largest mutual-fund overweight positions.

Goldman also identified six “shared favorites” among the two investor groups. The broader list includes Capital One, Mastercard and Visa, as well as non-financial companies such as SpaceX, Boeing and Thermo Fisher Scientific.

The pattern reveals a broader investment strategy emerging beneath the surface of the AI debate. Rather than abandoning technology altogether, investors appear to be looking for companies with strong earnings, durable cash flows and exposure to structural growth that is less dependent on the enormous capital spending currently flowing into AI infrastructure.

Financial companies fit that profile in several ways.

Banks and payment companies can benefit from economic growth through higher transaction volumes, lending activity, and investment demand. Payment networks such as Visa and Mastercard also have relatively asset-light business models that can generate substantial cash flow as digital payments expand.

Brokerages such as Interactive Brokers can benefit from increased participation in financial markets, while financial technology and payment-processing companies such as Fiserv and Corpay provide infrastructure for businesses and consumers. That makes financials an attractive alternative at a time when investors are debating more about whether valuations in parts of the AI complex have moved ahead of the underlying earnings.

Goldman’s Snider said the performance of hedge funds and their most popular holdings has been closely linked to movements in the AI trade in recent months.

“The returns of hedge funds and their most popular holdings have been closely correlated with swings in the AI trade during the last few months,” Snider wrote.

At the same time, mutual funds have increased their holdings of AI infrastructure stocks this year, but their exposure has not kept pace with the weighting of those stocks in major benchmarks.

That difference came with a wide gap.

It means some professional investors are still increasing their exposure to AI companies, but others appear to be reducing the degree to which their portfolios depend on the sector’s continued outperformance.

Financials offer a way to diversify that risk without moving entirely away from companies benefiting from long-term economic growth.

The preference for financials is also arriving after the sector’s strong earnings performance has provided investors with tangible evidence of profitability. That contrasts with parts of the AI market where investors are paying close attention to enormous capital expenditure programmes and questioning how quickly those investments will translate into revenue and free cash flow.

Among other reasons, the issue is relevant as technology companies spend hundreds of billions of dollars on data centers, chips and other AI infrastructure. If AI-related capital expenditure continues to accelerate, analysts see the companies supplying that infrastructure remaining among the market’s strongest performers. But if spending growth slows, investors may favor sectors whose earnings are less dependent on a single investment cycle.

That helps explain why financials have become such a strong destination for institutional capital.

Some prominent investors are already positioning accordingly.

Bill Ackman’s Pershing Square increased its positions in Mastercard and Visa during the second quarter, while also adding to holdings in Intercontinental Exchange, the operator of the New York Stock Exchange, and financial information provider S&P Global. The interest extends across different parts of the financial industry, from payments and exchanges to market data.

Still, Goldman cautions that the stocks attracting both hedge funds and mutual funds have historically offered higher returns alongside greater volatility. Since 2013, the bank’s basket of shared favorites has generated an annual return of 17%, according to Snider. That performance helps explain the appeal, but it also highlights the risk of crowding. When hedge funds and mutual funds converge on the same companies, the resulting demand can push valuations higher and leave stocks more vulnerable if expectations deteriorate.

For now, however, the financial sector appears to be benefiting from a combination of strong earnings, institutional demand and investor efforts to diversify away from the most crowded areas of the AI trade.

Analysts therefore see the emerging market split not simply as a choice between AI stocks and financial stocks, but a question of how much exposure investors want to have to the AI investment cycle and where they can find earnings growth that is supported by broader economic activity.

Financials are becoming one of the clearest beneficiaries of that search.

But the irony is that while AI has become the defining investment theme of the current market cycle, the strongest area of agreement among institutional investors is a sector that can benefit from the broader economy without having to bet entirely on the next generation of AI spending.

Scalable Capital Opens Door to AI Trading as ChatGPT and Claude Gain Access to Customer Portfolios

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German digital broker Scalable Capital is allowing customers to use artificial intelligence platforms such as ChatGPT and Claude to analyze their investment portfolios and execute trades, marking a significant step toward AI becoming an interface for real-world financial transactions.

The company said Tuesday that customers can now use the two AI platforms to interact with their Scalable accounts, alongside the broker’s existing app and website. Scalable described the offering as the first of its kind from a European bank and said it has incorporated security measures into the service.

The move marks a shift in the role of generative AI in finance. Until recently, consumers largely used AI tools to research companies, interpret financial statements or obtain general investment information. Scalable is now allowing AI systems to sit closer to the point where information becomes an actual financial decision and transaction.

That could make the development considerably more consequential for the brokerage industry. A customer could ask an AI system to examine their holdings, identify concentrations or compare investment options and then use the same interface to place a trade. The process removes several steps traditionally handled through a bank or brokerage application.

Scalable founder and co-CEO Erik Podzuweit described the rollout as an early stage in a broader transition toward AI-assisted investing.

“A lot of people might still be hesitant to let ChatGPT look at their portfolio, manage their portfolio. So I think that it’s a first step,” Podzuweit told Reuters.

That hesitation is likely to determine how quickly AI trading develops.

There is a fundamental difference between asking an AI model to explain why a stock moved and allowing it to access an investment account. The latter introduces the possibility that an incorrect interpretation, ambiguous instruction, or erroneous recommendation could result in an actual transaction and a financial loss.

Scalable’s strategy appears designed to familiarize customers with that relationship before AI becomes embedded directly into the company’s own platform.

Podzuweit said the company expects broader adoption once AI capabilities are integrated into Scalable’s application. That could eventually make AI less of an optional interface and more of a standard component of the investment experience.

The development is part of a broader race among financial companies to determine how much of the investment process can be automated. The first generation of digital brokers simplified trading by putting financial markets on smartphones and lowering transaction costs.

AI could take the next step by simplifying the decision-making and execution process itself. Instead of searching for an exchange-traded fund, comparing its characteristics and entering an order manually, a customer could potentially describe an investment objective in ordinary language and allow an AI system to translate it into a transaction.

That creates both an opportunity and a new regulatory challenge.

AI could make investing more accessible by giving retail investors inexpensive access to tools capable of processing large quantities of financial information. It could also help customers understand portfolios, identify diversification gaps and compare investments without requiring specialist knowledge.

But access to more information does not guarantee better investment decisions.

Large language models can make errors, misinterpret information or produce persuasive answers that are not appropriate for a particular investor. Financial markets also contain uncertainty that cannot be eliminated by faster analysis.

Podzuweit said he believes AI could eventually produce better investment returns on average, although he acknowledged that this remains to be demonstrated.

The question is therefore not simply whether AI can analyze markets better than humans. It is whether AI can consistently turn that analysis into suitable decisions after accounting for an investor’s objectives, risk tolerance, time horizon and financial circumstances.

That is where AI-powered trading becomes materially different from conventional chatbots.

Once an AI system is connected to a brokerage account, issues such as authentication, transaction limits, customer consent, monitoring and liability become central. A system that can execute trades also needs to distinguish between an instruction to provide information and an instruction to move money or buy an asset.

The technology could also change the economics of brokerage.

Scalable has built its business by offering retail investors a relatively low-cost alternative to traditional banks. AI may reduce the amount of human intervention required for sophisticated services, potentially allowing brokers to offer automated portfolio analysis and other capabilities to large numbers of customers at low marginal cost.

That could put additional pressure on traditional banks and wealth managers.

At the same time, AI could expand the services that digital brokers offer. A customer who previously used a broker to buy stocks and ETFs could eventually receive automated portfolio monitoring, investment research and rebalancing through the same platform.

Scalable has more than 1 million customers and more than €60 billion in assets. It is primarily active in Germany and Austria, while also operating in Italy, Spain, France and the Netherlands. Its decision to work with ChatGPT and Claude is also remarkable because it places general-purpose AI companies closer to the financial infrastructure used by millions of consumers.

The AI platforms are now moving beyond answering questions toward performing actions through external services. Financial accounts are among the most sensitive applications of that capability because an AI agent can potentially move from analyzing information to directly affecting a customer’s assets.

That raises a strategic question for both the AI companies and financial institutions: who ultimately controls the transaction?

If a customer tells an AI assistant to make an investment, the brokerage executes the order, but the recommendation may have originated with the AI model. Establishing responsibility when something goes wrong could become complicated as these systems gain more autonomy.

For now, Scalable is taking a relatively cautious step. It is giving customers an additional way to access their accounts while keeping its existing app and website available.

The longer-term significance is much larger.

The financial industry is gradually moving toward a model in which AI does not merely explain markets but becomes the interface between consumers and financial services. Scalable’s experiment with ChatGPT and Claude provides an early glimpse of that future.

If customers accept the idea of giving AI access to their portfolios, the next phase could involve AI agents continuously monitoring investments, identifying opportunities, executing predefined strategies, and rebalancing portfolios with limited human intervention. That would transform the role of the brokerage app from a place where investors manually conduct transactions into infrastructure operating behind an AI assistant.

Stablecoin Card Spending Seen Quadrupling to $50bn by 2028 as Payments Adoption Accelerates

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Global spending through stablecoin-linked cards is expected to quadruple to about $50 billion annually by 2028 as digital currencies pegged to fiat currencies gain traction in everyday payments, cross-border transactions and treasury operations, according to stablecoin payments company RedotPay.

The Hong Kong-based company said stablecoin card spending surpassed $1 billion in July, marking a record monthly level, based on data from crypto payments analytics firm Paymentscan.

At that pace, the emerging stablecoin card market would represent a significant expansion from its current scale, although the projected $50 billion would still be small compared with the global payments industry.

Stablecoins are cryptocurrencies designed to maintain a relatively stable value by being pegged to an underlying asset, most commonly a fiat currency such as the U.S. dollar. Unlike bitcoin and other volatile cryptocurrencies, their primary attraction in payments is the ability to move digital dollars or other fiat-linked value without exposing users to the same degree of price volatility.

Their use has expanded beyond crypto trading. Stablecoins are increasingly being used for cross-border payments, corporate treasury management, settlement, and as a means of preserving value in economies where local currencies are volatile or access to foreign currency is restricted.

Jonathan Chan, RedotPay’s co-founder and head of partnerships, said Latin America currently has the strongest adoption and growth potential, followed by Africa.

“Latin America has the highest adoption and greatest potential for growth at the moment, followed by Africa,” Chan said.

The regional pattern suggests that stablecoin adoption is being driven less by enthusiasm for cryptocurrency itself and more by practical shortcomings in existing financial systems.

“The fastest markets aren’t necessarily those with the highest crypto penetration,” Chan said. “The growth is driven by the confluence of several factors: real payment pain, easy stablecoin access, strong fiat off-ramps, and regulatory clarity.”

That is considered necessary for the future of stablecoin payments. In markets where traditional cross-border transfers are expensive or slow, stablecoins can provide a faster way of moving dollar-denominated value. Users can then convert the digital assets into local currency through exchanges or other payment providers.

The attraction can be similar for businesses. Stablecoins can potentially reduce the friction associated with international payments and settlement, particularly for companies dealing with suppliers, customers, or workers across multiple jurisdictions.

RedotPay said it now has more than 8 million users globally. Its total annualized payment volume, which includes both card spending and account top-ups, has exceeded $14 billion.

The company’s figures provide an indication of how quickly stablecoin-linked payment products are moving from crypto-native applications toward mainstream financial services. Rather than requiring users to understand blockchain transactions, card products can allow stablecoin balances to be spent through conventional payment networks.

That could be one of the more important developments for stablecoin adoption. Consumers do not necessarily need to treat a stablecoin as a cryptocurrency investment if it functions in practice as a digital dollar that can be used to make purchases.

The growth also comes as governments and financial regulators around the world develop clearer frameworks for stablecoins. Greater regulatory certainty has been advocated to encourage banks, payment companies and merchants to build infrastructure around them, potentially accelerating adoption.

But there are still significant barriers. Stablecoin payments depend on reliable conversion between digital assets and local currencies, adequate liquidity, compliant payment infrastructure, and regulatory approval. Users also need confidence that the issuer has sufficient reserves to maintain the stablecoin’s peg.

Competition is also growing as traditional financial institutions and major technology companies explore digital-dollar payment systems. That could increase pressure on crypto-focused payment companies to demonstrate that their products offer meaningful advantages over conventional cards and bank transfers.

Still, RedotPay’s projection highlights the potential scale of the market if stablecoins continue moving from crypto exchanges into everyday financial activity.

The projected increase from roughly $1 billion in monthly stablecoin card spending in July to an annual market of $50 billion by 2028 would mark a major change in how digital assets are used. The growth would be driven not simply by more people owning cryptocurrencies, but by stablecoins becoming part of the infrastructure for ordinary payments, international money transfers and corporate finance.

For emerging markets in Latin America and Africa, where currency volatility, cross-border payment costs and access to dollar liquidity can create significant friction, those use cases could provide some of the strongest incentives for adoption.

SEC Subpoenas Major Wall Street Banks Over AI Hedge Fund’s Near-Collapse

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The U.S. Securities and Exchange Commission has subpoenaed several major Wall Street banks as it examines their dealings with Situational Awareness, the AI-focused hedge fund that suffered a dramatic collapse in assets last month after a technology-stock sell-off triggered a series of margin calls.

The subpoenas seek information about the fund’s trading activity, leverage and communications with its investment banks, including Goldman Sachs, JPMorgan, Citigroup and Bank of America, according to Reuters, citing a person familiar with the matter.

The regulatory inquiry follows one of the most severe recent episodes of forced deleveraging tied to the AI investment boom. Situational Awareness, led by former OpenAI researcher Leopold Aschenbrenner, saw its assets plunge from about $45 billion to roughly $10 billion in late July after losses on concentrated technology positions triggered margin calls from several prime brokers.

The fund was subsequently forced to unwind a large portion of its publicly traded portfolio, including positions in semiconductor maker SK Hynix and AI data-center company CoreWeave.

The episode has brought increased attention to the role leverage has played in the rapid expansion of AI-related investments, especially as hedge funds and other institutional investors have built large positions in companies benefiting from surging demand for AI infrastructure.

Situational Awareness reportedly used leverage of as much as 400%, meaning relatively small changes in the value of its underlying positions could produce disproportionately large gains or losses.

When technology shares declined sharply in July, that leverage became a major vulnerability.

The resulting margin calls forced the fund to sell assets into a falling market, accelerating the unwinding of its positions and contributing to the sharp contraction in its portfolio.

Citadel, the multistrategy hedge fund founded by Ken Griffin, stepped in to purchase the positions at a discount understood to be around 10%. Griffin said in a letter to investors Friday that Citadel had since reduced about 80% of the risk associated with the portfolio.

The shares that were at the center of the forced liquidation have subsequently recovered, with both SK Hynix and CoreWeave rallying.

The development exposes the difficulty of unwinding large leveraged positions when multiple financial institutions are simultaneously exposed to the same trades. Prime brokers provide hedge funds with financing, securities lending, trading infrastructure and other services that allow them to take positions substantially larger than the amount of capital they have invested. Those relationships can amplify returns during periods of rising markets but can also force rapid asset sales when collateral values fall.

The SEC’s subpoenas appear aimed at understanding how those relationships operated in Situational Awareness’ case, including how much leverage the fund used, the scale of its positions and how its prime brokers communicated with the firm as losses mounted.

The inquiry does not mean that the banks or Situational Awareness have been accused of wrongdoing.

Regulatory information requests can ultimately conclude without enforcement action, and the SEC’s investigation may simply be an effort to understand how the losses developed and whether risks were adequately managed.

“It is to be expected that regulators would closely examine any funds that are high profile, produce significant returns, or have particularly dramatic drawdowns,” Situational Awareness said in a statement.

“We are a highly-regulated business and will cooperate to the fullest extent with any regulatory request.”

The inquiry nevertheless puts a spotlight on a broader issue confronting financial markets: how much leverage has accumulated around the AI trade.

AI has become one of the dominant investment themes in global markets, attracting enormous amounts of capital into semiconductor manufacturers, cloud companies, data-center operators and other infrastructure providers. The scale of the opportunity has encouraged investors to take increasingly large positions in companies expected to benefit from AI spending.

That concentration can become dangerous when positions are financed with borrowed money.

A leveraged investor does not need the underlying investment thesis to be wrong for losses to become destabilizing. A sharp enough decline can trigger margin requirements, forcing the investor to sell assets regardless of its longer-term view.

That selling can then put additional pressure on the same securities held by other leveraged investors.

Situational Awareness provides a particularly visible example because of the size of its positions and the reported scale of its leverage. The fund’s rapid contraction also occurred during a period when investors were already questioning the enormous valuations attached to some AI-related companies and the ability of the industry to generate returns sufficient to justify its capital spending.

The SEC’s interest in the banks could therefore extend beyond the individual fund’s losses. Regulators may also be examining how prime brokers assess and manage counterparty exposure when multiple clients are pursuing similar AI-related strategies.

The issue is particularly relevant for stocks such as SK Hynix and CoreWeave, where strong institutional demand and concentrated positioning can contribute to rapid price movements in both directions.

A forced liquidation by one major investor can create losses for others even when the underlying companies’ business fundamentals have not materially changed. The recovery in SK Hynix and CoreWeave since the liquidation reveals that. The forced selling reflected the fund’s financing constraints rather than necessarily a fundamental reassessment of either company’s long-term prospects.

A fund generating strong gains can appear successful while taking on substantial hidden risks through borrowing, derivatives, and concentrated positions. Those risks become visible when markets move sharply against the strategy.

The SEC’s subpoenas could provide regulators with a clearer picture of how those risks accumulated around Situational Awareness and whether the fund’s prime brokers had sufficient visibility into its exposures.

The immediate investigation remains fact-finding rather than an enforcement proceeding. But the episode has already become a case study in the financial risks accompanying the AI boom, showing how a sharp reversal in a concentrated technology trade can rapidly turn into a liquidity and leverage problem when investors are heavily dependent on borrowed capital.