DD
MM
YYYY

PAGES

DD
MM
YYYY

spot_img

PAGES

Home Blog Page 2

Workday Layoffs, Walmart AI Sign Ban and NYC Pied-à-Terre Tax Setback

0

The final days of September are offering a striking snapshot of how technology, corporate restructuring and government policy are colliding in the modern economy. Workday is cutting hundreds more jobs.

Walmart is pushing back against a wave of AI-generated store signage, and New York City’s rollout of a new pied-à-terre tax has been sent back for another attempt by a judge. The stories reveal how institutions are trying to adapt to rapid technological and economic change while maintaining control over the consequences.

Workday’s latest restructuring is perhaps the clearest sign that the enterprise software industry remains under pressure. The company announced another round of layoffs affecting approximately 500 employees, or about 2.5% of its workforce, with product and technology teams bearing much of the reduction.

It is the company’s second round of cuts this year, following approximately 400 layoffs in February. Workday says the latest changes are designed to align its teams with strategic growth priorities, rather than explicitly attributing the reductions to artificial intelligence.

That distinction matters. AI is reshaping expectations around software development and enterprise technology, but not every technology-sector layoff can automatically be described as an AI replacement story.

Workday has continued to say it intends to hire in strategic areas, suggesting that the restructuring is also about reallocating resources rather than simply eliminating technology jobs.

Still, the cuts illustrate how even major software companies are reassessing their cost structures as investors and customers demand greater efficiency. At Walmart, the AI story looks very different.

The retailer has reportedly reminded stores that AI-generated signs should not be displayed, reinforcing existing rules requiring store signage to come through approved corporate channels. The issue is not a rejection of AI across Walmart’s operations.

Rather, it is about controlling locally produced promotional material and maintaining consistent branding. The decision is revealing because AI-generated images have become so accessible that employees and managers can create polished-looking material within seconds.

Yet speed does not necessarily equal quality or consistency. Walmart’s policy demonstrates that large organizations may embrace AI in some areas while restricting it in others where oversight, branding and accuracy are especially important.

Meanwhile, New York City’s new pied-à-terre tax has encountered a legal setback. A Staten Island judge ordered the city to restart its rollout after finding problems with the way officials identified potentially affected properties and notified homeowners.

The ruling did not invalidate the tax itself; instead, it challenged the process used to implement it. The city has appealed, temporarily putting the order on hold. Implementation is becoming as important as ambition.

Workday can announce a strategic transformation, Walmart can embrace artificial intelligence while limiting its use in stores, and New York can pursue a new revenue measure, but each must translate policy or strategy into workable systems.

September therefore closes with a reminder that technological change and ambitious policy do not operate in a vacuum. Companies and governments still have to manage people, processes, public expectations and legal constraints.

The difficult part is no longer simply deciding what can be done. It is determining how to do it effectively, transparently and at scale.

Nest shifts $4.6 billion emerging-markets portfolio from passive investing to Wellington

0
United States Ten and Twenty Dollar notes next to Ten and Twenty UK Pound Notes

Britain’s largest workplace pension scheme, Nest, is moving its entire £3.5 billion ($4.6 billion) emerging-markets equity portfolio to US asset manager Wellington Management, ending more than a decade of passive investing as it seeks greater influence over companies and stronger control of sustainability risks.

The decision marks a notable shift for one of Britain’s largest institutional investors at a time when pension funds and other long-term asset owners are reassessing the balance between low-cost index investing and active management.

Nest, which manages £68 billion for more than 14 million workers automatically enrolled into its pension plans, had invested its emerging-markets allocation passively since 2014. The approach allowed the scheme to gain broad exposure at relatively low cost, but it also left Nest holding small positions in a very large number of companies.

That became a growing concern as Nest placed greater emphasis on shareholder engagement over issues including climate change, diversity, workers’ rights and corporate governance.

Nest moved from a conventional passive mandate with Northern Trust to the manager’s Climate Aware Emerging Markets Equity Strategy in 2021. But the portfolio still contained more than 1,000 stocks, making it difficult for the pension scheme to exert meaningful influence over individual companies.

The new Wellington mandate will concentrate the portfolio in roughly 100 to 150 stocks.

Rachel Farrell, Nest’s director of public and private markets, said the change would allow the pension scheme to devote more resources to understanding individual companies and using its position as a shareholder.

“A pure passive approach… just wasn’t engaged enough,” Farrell said. “We weren’t really spending time understanding each of the stocks that we would own in our members’ portfolios.”

“To influence as a shareholder, you need to be an important owner of that particular company,” she added.

The shift reveals a tension at the heart of passive investing for large institutional investors. Index strategies offer diversification, transparency, and low fees, but investors have limited flexibility to allocate more capital to companies where they believe engagement can produce meaningful change.

The issue is particularly relevant for Nest because emerging markets contain thousands of companies with widely varying standards of corporate governance, environmental practices and labor policies. A concentrated portfolio gives an active manager greater scope to select companies and engage directly with their management teams.

Nest’s decision followed an internal review that began in 2024. Farrell said the previous passive strategy had met its return objectives, suggesting the move was not prompted by a failure to generate investment returns. Instead, Nest is betting that active management can add value in a market where company information, governance standards and investor coverage vary substantially.

The Wellington portfolio will be benchmarked against the MSCI Emerging Markets index, with the US manager targeting an additional 100 basis points of annual outperformance.

“Emerging Markets is one of the markets that is somewhat less efficient,” Farrell said. “There is evidence that an active manager can add value.”

The mandate represents a significant win for Wellington, which manages about $1.3 trillion in assets globally, including $44 billion in emerging-market equities. It also comes as emerging-market equities have delivered a strong performance this year. The MSCI Emerging Markets index is up 22% year to date, compared with a 9% gain for the developed-market MSCI World index.

The recent performance has helped revive investor interest in emerging markets after years of weak flows. Asset manager Ashmore said in a February report that demand increased during the second half of 2025 following large outflows from emerging markets since 2021. It estimated total emerging-market equity assets at about $1.4 trillion.

Nest has about 5.2% of its assets allocated to emerging-market equities, with most of its public-equity exposure invested through a systematic developed-markets strategy.

The move also places Nest within a broader institutional shift toward more active approaches to emerging markets. People’s Pension, another major UK workplace pension provider, last year moved its index-tracking emerging-market equity allocation into a more active, quantitatively driven strategy.

The decisions suggest that some pension investors are becoming less willing to treat emerging-market equities simply as a low-cost basket of securities. Instead, they are looking at active ownership, corporate governance and company-specific risks as part of the investment process.

There is also a scale argument behind Nest’s decision. The pension scheme receives about £700 million in contributions each month and expects its assets to approach £100 billion by 2030. Its membership currently covers roughly a third of Britain’s working population and is expected to reach half by the end of the decade.

As the pool of retirement savings grows, even relatively small improvements in investment performance can have a material effect on members’ eventual pension outcomes. But active management also introduces higher costs and the risk that a manager fails to deliver its targeted excess return.

Nest did not disclose the cost of transferring the mandate or the fees Wellington will charge.

Therefore, the new arrangement places the focus on whether Wellington can justify the additional complexity of active management through sustained performance and meaningful shareholder engagement.

The move represents a broader evolution in how it manages members’ money for Nest. After more than a decade of using passive strategies to obtain inexpensive exposure to emerging markets, the pension scheme is now concentrating its holdings and giving its external manager greater discretion to identify companies where active ownership can potentially influence long-term value.

The decision does not amount to a rejection of passive investing across Nest’s portfolio. Rather, it shows how a large pension fund is applying different investment approaches to different markets as its assets grow and its responsibilities as a long-term shareholder become more significant.

Open Standard Launches OUSD as Base Introduces Cobalt Upgrade for Programmable Blockchain Transactions

0

Open Standard’s launch of OUSD and Base’s Cobalt mainnet upgrade point to two different but increasingly connected trends in crypto: the modernization of stablecoins and the expansion of blockchain infrastructure beyond simple transfers.

They show how the industry is moving toward programmable financial products designed to interact more directly with traditional payments and digital assets. OUSD is positioned as a yield-sharing stablecoin, with the Open Standard initiative bringing traditional financial infrastructure closer to onchain markets.

The backing and involvement of major payment names such as Stripe, Visa and Mastercard is significant because these companies sit at the center of global payments. Their connection to a stablecoin initiative highlights how stablecoins are increasingly being viewed not simply as crypto trading instruments.

But as potential infrastructure for payments, settlement and financial applications. The appeal of a yield-sharing stablecoin is straightforward. Traditional stablecoins generally seek to maintain a stable value against an underlying fiat currency.

While the assets supporting them can potentially generate income. OUSD’s model seeks to connect that underlying economic activity with users, creating a structure where yield can become part of the stablecoin experience.

That model also raises important questions around reserves, transparency, risk management and the distribution of returns. As stablecoins become more integrated with mainstream payment networks.

Users and institutions will increasingly demand clarity about how reserves are held, how yield is generated and what protections exist during periods of market stress.

Meanwhile, Base’s Cobalt upgrade represents another side of blockchain evolution. The mainnet upgrade introduces conditional transactions, allowing transactions to execute according to predefined conditions rather than requiring every action to be manually initiated in a simple one-step format.

This can create new possibilities for automated payments, trading strategies, subscriptions and applications that need transactions to respond to specific events.

The addition of B20 asset functions also expands the kinds of assets and financial logic that can operate within the Base ecosystem. Rather than treating blockchain merely as a ledger for transferring tokens, these capabilities move the network closer to functioning as programmable financial infrastructure.

The significance of conditional transactions becomes clearer when considered alongside stablecoins. A programmable dollar could theoretically be used in transactions that execute only after certain conditions are satisfied.

That could support automated settlements, machine-to-machine payments, escrow arrangements or financial applications where timing and conditions are embedded directly into the transaction logic.

This convergence between stablecoins and programmable blockchains could become one of the defining themes of the next phase of digital finance. Payment companies bring distribution, regulatory relationships and existing user networks, while blockchain networks provide programmability, transparency and composability.

Yet adoption will depend on more than technical capability. Stablecoins must maintain credible reserve structures and user confidence, while blockchain upgrades must demonstrate reliability, security and practical utility.

The industry has repeatedly shown that technological innovation can move faster than consumer adoption. OUSD and Cobalt therefore represent more than two isolated product developments.

They illustrate a broader shift toward financial systems where money, assets and transaction rules can exist within programmable digital infrastructure. If that architecture continues to mature, the boundary between traditional payments and decentralized networks could become increasingly difficult to define.

Tencent Reportedly Strikes $7 Billion Oracle Deal for 100,000 AI Chips in Southeast Asia

0

Tencent has reportedly signed its largest overseas cloud leasing agreement with Oracle, securing access to about 100,000 advanced artificial intelligence chips through data centers in Southeast Asia as Chinese technology companies seek additional computing capacity outside the country.

The five-year agreement, reported by the Financial Times on Wednesday, citing people familiar with the matter, is estimated to be worth about $7 billion and would involve multiple Oracle data centers across Southeast Asia. Tencent is also expected to make an upfront payment of about 30%, according to the report.

If confirmed, the scale of the arrangement would highlight the extraordinary computing requirements emerging from China’s AI race, as Tencent and other major technology companies invest heavily in training and deploying more capable models.

More importantly, the reported structure illustrates how Chinese technology companies are seeking access to advanced AI computing capacity through infrastructure outside mainland China as U.S. restrictions limit the availability of leading AI processors inside China.

But Tencent’s reported decision to lease computing capacity in Southeast Asia rather than simply expand its domestic infrastructure points to a broader challenge facing China’s AI industry.

The United States has imposed export controls restricting China’s access to some of the most advanced AI chips, particularly processors from Nvidia and other U.S. suppliers. Beijing has responded by accelerating development of domestic alternatives, while Chinese companies have simultaneously sought other ways to obtain computing capacity.

A five-year Oracle agreement worth roughly $7 billion would mark a substantial commitment to overseas AI infrastructure. The reported 100,000 chips would give Tencent access to a large pool of advanced computing resources that are unavailable to it domestically.

The arrangement would also shift part of Tencent’s AI infrastructure footprint outside China.

This is considered a huge shift because, among other things, training large language models requires enormous amounts of computing power, while running AI products at scale also requires sustained access to inference capacity. As models become more capable and AI applications gain users, the computational burden increasingly extends beyond the initial training phase.

Tencent has been expanding its AI ambitions across consumer and enterprise products, increasing the need for both model-training infrastructure and computing capacity for commercial deployment.

The company recently released a preview version of a new AI image-generation model aimed at professional creators. The model supports text-to-image and image-to-image generation, adding to Tencent’s growing portfolio of AI applications.

The Economics of The Chip Squeeze

The reported deal also illustrates how export restrictions can change the economics of AI development.

For Chinese companies, access to cutting-edge processors is not simply a question of purchasing chips. Computing capacity can be obtained through cloud providers that operate data centers in jurisdictions where certain processors can legally be deployed.

Tencent would reportedly be leasing computing capacity from Oracle rather than importing the processors directly into China. The structure could allow the company to use advanced chips without those processors being physically deployed inside mainland China.

The reported arrangement thus underpins the growing importance of cloud infrastructure as an intermediary between semiconductor restrictions and AI development. It also reveals why Washington has increasingly focused not only on direct chip exports but on the possibility that restricted Chinese companies could obtain access to advanced computing remotely through overseas data centers.

For cloud providers, meanwhile, the surge in AI demand creates an enormous infrastructure opportunity. Oracle has been expanding its cloud capacity to serve AI developers and has signed large computing agreements with technology companies seeking access to scarce advanced processors.

Tencent’s reported commitment is expected to add another major customer to that trend.

China’s AI Race is Becoming An Infrastructure Race

The deal comes as Chinese technology companies compete to develop more capable AI models while Beijing encourages greater reliance on domestic technology.

China’s strategy is producing two parallel efforts.

One is the development of domestic processors and software ecosystems that can reduce reliance on Nvidia and other foreign suppliers. The other is securing access to advanced computing resources wherever they remain available.

Tencent is one of China’s largest technology companies and has substantial financial and engineering resources to devote to AI. Its willingness to reportedly commit billions of dollars to overseas cloud capacity underscores how valuable advanced computing has become.

The scale of the reported transaction also puts the economics of the AI boom into perspective. A $7 billion commitment over five years would amount to roughly $1.4 billion a year, before accounting for other infrastructure, energy, networking, personnel and model-development expenses.

That spending underlines why AI is becoming an increasingly capital-intensive business even for companies that already possess extensive cloud and data-center infrastructure.

Nevertheless, the investment is expected to provide Tencent with the computing capacity needed to accelerate model development and support large-scale deployment. For Oracle, a deal of this size would reinforce the importance of cloud infrastructure providers in the global AI supply chain.

But the reported agreement also underscores the limits of China’s current domestic chip ecosystem. If Tencent needs to secure tens of thousands of advanced processors through overseas infrastructure, it suggests that domestic alternatives have not yet fully eliminated the computing gap created by U.S. restrictions.

China’s technology industry is therefore pursuing two tracks simultaneously: developing its own AI hardware and software while finding ways to access global computing capacity. The Tencent-Oracle agreement, if confirmed, would be one of the clearest examples yet of how those two pressures are reshaping the geography of AI infrastructure.

AI Investment Gap Widens as Top 1% Companies Now Spend More on The Technology Than The Top 10%

0

Enterprise spending on artificial intelligence is showing a widening gap between the companies investing most aggressively in the technology and the broader group of adopters.

According to data highlighted by Andreessen Horowitz (a16z) in its State of Markets II report, which tracked enterprise AI-vendor spending through July 2026, the median AI-vendor spending of companies in the top 1% has risen to roughly eight times that of companies in the top 10%.

By July, median spending among the top 1% had climbed to approximately $800,000, while spending among the broader top 10% remained below $100,000.

The divergence became particularly visible from late 2025, suggesting that AI adoption is not progressing evenly across enterprises.

Instead, a relatively small group of companies appears to be moving from experimentation into much larger-scale deployment, committing significantly more capital to AI vendors, cloud infrastructure, models, and related tools.

The spending gap is significant because it points to a two-speed AI adoption cycle. On one side are companies that remain in the early stages of evaluating and deploying AI tools.

On the other are a small number of large adopters that are spending at a substantially higher level as they integrate AI into core business processes.

For the leading adopters, AI spending increasingly extends beyond individual productivity tools. Companies are using AI across software development, customer service, data analysis, automation, and other enterprise workflows, creating demand for more computing power and model usage.

That dynamic also has implications for the companies supplying the AI infrastructure. As enterprise deployments become larger, demand can flow through the broader AI stack, including cloud platforms, computing infrastructure, model providers and specialized software vendors.

The concentration also helps explain why the AI market continues to generate substantial infrastructure demand even while many businesses remain cautious about large-scale deployment.

A relatively small number of high-spending companies can account for a disproportionate share of overall AI consumption, creating substantial demand for compute and model capacity before adoption becomes widespread.

At the same time, the data does not necessarily mean that most companies are rejecting AI. Rather, it indicates that enterprise adoption remains uneven.

The widening spending gap could represent a transition period in which leading companies are moving first into production-scale AI while other businesses are still testing use cases, determining returns and establishing the infrastructure needed for broader deployment.

For the AI industry, the trend therefore highlights an important characteristic of the current market: AI adoption is expanding, but the intensity of spending is concentrated among a relatively small group of early enterprise leaders.

Perhaps one of the most important findings in the report is that nearly 30% of S&P 500 companies report some quantifiable impact from AI, but only around 2% report having a tracked metric for that impact.

The distinction suggests that many companies are already experiencing or identifying benefits from AI, but relatively few have established mature systems for measuring those benefits.

The same pattern appears with AI agents. Although agentic AI has become a major focus of enterprise technology development, only a small proportion of users are deploying agents at meaningful scale

If adoption broadens over time, the current concentration could eventually give way to a much wider distribution of AI spending across businesses.

Outlook

Looking ahead, the concentration of enterprise AI spending is likely to remain a defining feature of the market as companies move at different speeds from experimentation to production-scale deployment.

The leading adopters are expected to continue increasing spending as AI becomes embedded in software development, customer service, analytics, automation, and other core business functions.