Costco has become the most prominent public company to challenge President Donald Trump during his second term, filing a lawsuit that accuses the administration of exceeding its emergency authority when imposing wide tariffs. The retailer’s move surprised many in Corporate America, which has largely avoided public clashes with the White House while offering gifts, donations and high-profile CEO visits in pursuit of favorable treatment.
Costco filed its complaint on Friday, arguing it deserves a refund after paying duties the company says were imposed unlawfully. Several other firms, including Bumble Bee Foods, EssilorLuxottica, Revlon and Kawasaki Motors, have brought similar cases, but none match Costco’s scale or visibility.
Major corporations have shown far less willingness to confront Trump than they did in his first term, when executives openly criticized the president and some resigned from his business council after he downplayed neo-Nazi violence in Charlottesville, Virginia. The shift reflects several factors. Trump has often retaliated against companies that fall out of favor. Many businesses have benefited from his light regulatory approach, especially in the fast-growing AI sector, and some firms have seen him soften policies once they earned his approval.
Tariffs, however, have emerged as a clearer point of tension. Polls show Americans increasingly oppose new duties, linking them to higher prices. A recent CBS News survey found only 38 percent support additional tariffs, compared with 62 percent who oppose. Costco risks little backlash by targeting a policy that has become broadly unpopular.
In its filing, the company criticized “the pell-mell manner by which these on-again/off-again” tariffs were “threatened, modified, suspended, and re-imposed, with the markets gyrating in response.” Although Costco did not disclose its total tariff payments, it asked for a full refund. The Supreme Court appears poised to void most of Trump’s tariffs, and Justice Amy Coney Barrett recently warned the refund process could be “a mess.” Costco’s lawsuit signals an effort to move ahead of a likely surge of claims if the court rules against the administration.
Costco may also be more comfortable taking a stand than peers. The company maintained its diversity, equity and inclusion programs while many firms retreated under pressure from Trump and conservative media. Its focus on value has also strengthened customer loyalty during a period of elevated prices.
Challenging Trump’s tariffs can carry risks. Amazon faced public criticism from the White House for evaluating whether to show tariff-related price increases to shoppers. Walmart was threatened after saying it would raise prices. Apple announced plans to shift iPhone production to India after tariffs that Trump said would cost the company nearly one trillion dollars a quarter, only to see the threat dropped after CEO Tim Cook met Trump in the Oval Office and unveiled a plaque with a 24K gold base along with a pledge to invest an additional $100 billion in the United States.
With a Supreme Court decision looming and public support for tariffs fading, analysts say Costco’s suit could spur more companies to follow. Trade lawyer Timothy Brightbill expects others to join, while Ed Mills of Raymond James believes some firms may still hesitate. “My guess is the more business you have before the federal government, the less likely you are to sue,” Mills said.
In global energy markets where a single decision can move billions, the stakes depend on knowing what is happening inside the world’s physical infrastructure. A trader weighing a major Liquefied natural gas (LNG) position or an analyst tracking refinery turnarounds cannot rely on consumer sentiment or survey data. They need verified intelligence on which assets exist, which are under construction, and which are operational.
IIR Energy, an energy market intelligence firm powered by Industrial Info Resources, was created to fill that gap. While most research firms analyze how consumers behave, IIR has earned a global reputation for its rigorous tracking of pipelines, refineries, power plants, and data centers that define the world’s supply landscape. Its asset-level intelligence has become a critical foundation for traders, analysts, developers, and financial institutions that need ground-truth visibility into the factors shaping energy markets.
The Industrial Intelligence Difference
Most research platforms focus on what people think. IIR Energy focuses on what is physically happening. While traditional providers rely on sampling, surveys, and sentiment analysis, IIR Energy staffs hundreds of researchers who are in the field monitoring industrial assets across oil and gas, power generation, chemicals, manufacturing, mining, and data centers.
Each project is tracked individually—development status, capacity, investment values, equipment details, timelines, and operational updates. This supply‑side dataset gives users real‑time visibility into infrastructure across the globe, enabling market participants to react faster and model risk with greater accuracy than with conventional demand‑side tools.
How IIR Energy Delivers Market‑Moving Intelligence
IIR Energy’s intelligence begins with primary verification. Its teams source information directly from operators, EPC firms, regulatory filings, on‑the‑ground contacts, and real‑time project networks—not from third‑party aggregators. Data is continuously updated as projects advance, stall, or shift scope.
This produces a level of granularity that traders and analysts rarely have: Which units are down for maintenance? Which refineries are operating at reduced rates? Which LNG trains are experiencing construction delays? Which battery storage projects are about to come online?
For traders, these signals provide early warnings of supply shifts. For fintech platforms, they power risk engines, pricing models, and real‑time analytics. For developers and financial institutions, they validate assumptions before capital is deployed. In an industry defined by outages, expansions, and unplanned events, verified data becomes a competitive weapon.
The Ed Lewis Vision
IIR Energy’s methodology reflects the vision of founder Ed Lewis, who built Industrial Info Resources on the idea that industrial markets needed truth—not estimates. Lewis pushed the organization to confirm every data point and maintain rigorous global coverage long before “data verification” became an industry talking point.
Under his leadership, IIR grew from a small North American-focused start-up to one of the world’s most trusted sources for industrial intelligence, now covering more than 200,000 operational and planned assets. His emphasis on accuracy, source confirmation, and real‑time updates redefined expectations for energy and infrastructure data.
Today, Lewis’s framework enables traders, hedge funds, banks, energy-trading companies, and even governmental agencies to act with confidence in markets where even small misinformation can trigger costly mistakes.
Who Relies on IIR Energy
IIR Energy serves a broad ecosystem of users:
Energy traders tracking outages, turnarounds, and unexpected supply shifts
Financial analysts and hedge fund managers who are integrating asset data into valuation models
Developers and strategists evaluating competitive landscapes before committing capital
ESG and transition‑focused investors monitoring renewable buildouts and emissions‑related infrastructure
Across all categories, the common thread is the need for high‑fidelity, asset‑level intelligence in markets where uncertainty can cost millions.
From Oil and Gas to Data Centers
IIR Energy covers the full industrial ecosystem: oil and gas, LNG, petrochemicals, refineries, power generation, renewables, hydrogen, carbon capture, advanced manufacturing, and the rapidly expanding data‑center sector. Its datasets span every major producing and consuming region worldwide, providing a unified view of how infrastructure developments ripple across markets.
Why Fintech is Turning to Verified Industrial Data
As fintech platforms overhaul the way traders and analysts consume information, demand for real‑time, supply‑side data has surged. IIR Energy’s intelligence is increasingly used for:
API‑driven integrations with trading terminals and market‑data dashboards • Real‑time alerts for outages, maintenance cycles, and major operational events • Data‑layer enhancements for risk and pricing models • Infrastructure‑aware forecasts improving short‑ and long‑term market outlooks
Compared to traditional financial data providers—who often rely on public disclosures, model‑driven estimates, or partial datasets—IIR Energy offers real‑world, verified asset information that directly impacts supply.
The Stakes of Verified Intelligence
In markets where capital moves in milliseconds, the difference between speculation and verified data is the difference between profit and loss. While other firms track consumer behavior or model trend lines, IIR Energy tracks what is being built, what is offline, and what is about to shift global supply.
In industries built on trillion‑dollar decisions, the advantage now belongs to those with the clearest—and most accurate—view of physical infrastructure.
In Dubai, a city known for positioning itself at the forefront of global innovation, organizations are increasingly exploring how artificial intelligence (AI) is reshaping communication practices. Across industries, the shift is clear: traditional marketing alone is no longer enough. In today’s digital world, perception and trust have become central to organizational growth.
From Marketing to Intelligence
The media landscape has evolved dramatically over the past decade. Whereas companies once competed primarily on volume and reach, today’s digital environment emphasizes precision. Algorithms, search behavior, and user intent increasingly determine which messages gain attention and which are overlooked.
Modern communication strategies now integrate data analytics and predictive tools to understand audience sentiment, engagement patterns, and emerging digital trends. Organizations are combining narrative strategy with data-driven insights to ensure that messages resonate effectively and build credibility across online platforms.
AI, Technology, and the Demand for Clarity
Companies in technology and AI sectors face unique challenges in communicating their innovations. Whether working on automation, robotics, or other emerging technologies, clear communication is essential for translating complex concepts into accessible narratives for stakeholders, including investors, customers, and policymakers.
AI-assisted systems and data analytics help organizations ensure that relevant information reaches key audiences, from technical communities to decision-makers. This blending of technical expertise and strategic communication reflects a broader trend: media and messaging are becoming more intelligence-driven.
Dubai as a Technology and Innovation Hub
Dubai’s rapid development into a global hub for AI and emerging technologies provides fertile ground for experimentation and growth. With investments in digital infrastructure and initiatives supporting innovation, the city offers organizations access to diverse audiences and international networks. Its strategic location between major Eastern and Western markets further supports engagement with global communities.
Reputation as Measurable Data
Increasingly, organizations are recognizing that digital credibility can be tracked and analyzed. By examining online sentiment, engagement, and content performance, companies can measure how audiences perceive brands, technologies, and individuals. In a digital economy, reputation and trust often influence market outcomes as much as, or more than, traditional financial metrics.
The Future of Communication
As AI continues to influence industries worldwide, the intersection of technology and storytelling becomes more important. Organizations must not only communicate the features of their products but also articulate their relevance in an AI-driven environment. The evolving media landscape is becoming more analytical, data-informed, and intelligence-focused, reflecting the growing need to understand both human and algorithmic behaviors in shaping attention.
China’s property market is showing deeper signs of stress as the downturn extends into a fifth year, with falling prices and swelling inventory putting more pressure on developers and homebuyers.
New figures from China Real Estate Information Corp showed that sales among the top 100 developers tumbled 36 percent in November from a year earlier, a slight improvement from a 42 percent drop in October but still signaling severe weakness. For the first 11 months of the year, sales were down 19 percent from the same period in the previous year.
Goldman Sachs chief China economist Hui Shan called the trend “real and concerning,” adding in a note that the probability of fresh housing stimulus had increased.
Secondary home prices are also sliding. The China Index Academy reported that resale prices across 100 major cities fell 7.95 percent in November, a deeper decline than the previous month, driven by high listing volumes and subdued buyer confidence. Morgan Stanley estimated that sales for 25 key developers dropped 42 percent year on year, with sluggishness expected to continue into next spring.
Daiwa Capital Markets analyst William Wu said Beijing’s goal to “halt the declines in housing market” now looks “increasingly unrealistic,” citing “renewed turmoil” late in the year, accelerating price drops and “resurfacing of high-profile defaults.”
Fresh concerns emerged after China Vanke asked bondholders to approve a one year delay on an onshore bond maturing on December 15. Vanke has long been viewed as one of the country’s more stable developers, supported by major shareholder Shenzhen Metro. That support came into focus in early November when Shenzhen said it would seek collateral for roughly 20 billion yuan in previously unsecured loans, sending Vanke’s bond prices to record lows.
Cathy Lu, a credit analyst at Octus, said the shift “reflects a liquidity crisis that will likely end in a comprehensive restructuring,” although she does not expect a broad wave of similar extensions or defaults.
Rating agency S&P Global downgraded Vanke’s long term issue credit ratings to “CCC-” last week, warning of a potential “distressed restructuring” within six months. Several of the company’s yuan bonds fell more than 20 percent on Tuesday, triggering trading suspensions in Shenzhen.
Beijing has tried to stabilize the market, including a 300 billion yuan initiative last year to help state owned enterprises buy completed but unsold homes. Yet excess inventory remains a major hurdle. S&P Global estimated that unsold completed units reached about 762 million square meters by the end of August 2025, up from 753 million square meters at the end of 2024.
Analysts at the Economist Intelligence Unit said that if authorities effectively limit land supply to developers and reduce inventory, home prices could bottom out as early as the first half of 2027. The inventory turnover cycle has shortened by five months since its April 2025 peak but still needs about 18 more months to return to historically healthier levels.
Economists expect incremental policy easing ahead as officials try to prevent a deeper downturn. Falling prices and declining sales have strained developers’ cash flow, leading banks to place more foreclosed homes on the market. Goldman’s Shan warned that this creates a “negative feedback loop” that policymakers need to break.
Morgan Stanley said Beijing may consider an “interest rate subsidy” to lower mortgage costs without harming banks, which could help steady prices and “buy time for a gradual demand led recovery.” The bank estimated that reducing mortgage costs by 1 percentage point in the second quarter of 2026 could lift new home sales and ease deflationary pressures, with higher tier cities likely to see prices find a floor first.
Hong Kong police arrested 13 people on Monday on suspicion of manslaughter as investigators probe the city’s deadliest blaze in decades, a disaster that has claimed at least 151 lives and left more than 40 people unaccounted for.
Authorities continued to comb through the seven charred towers of the Wang Fuk Court housing estate, where residents were trapped in stairwells and on rooftops as they attempted to escape Wednesday’s inferno. Police official Tsang Shuk yin, visibly emotional, said, “Some of the bodies have turned into ash, therefore we might not be able to locate all missing individuals.”
Early findings have placed a spotlight on construction practices at the site. Tests on samples of green mesh wrapped around bamboo scaffolding failed to meet fire retardant standards, officials said. Chief Secretary Eric Chan accused contractors of using substandard materials in hard to reach areas to evade inspectors. Foam insulation used during renovations further intensified the flames, and fire alarms did not function properly, according to authorities.
The tragedy has prompted widespread mourning. Thousands of people, including relatives of at least nine domestic helpers from Indonesia and one from the Philippines, lined a canal near the complex to pay respects. Vigils are planned this week in Tokyo, London and Taipei.
Public frustration has grown alongside grief. Residents had warned officials last year about potential fire hazards tied to renovation works, including the flammability of the mesh. Beijing has meanwhile cautioned against any “anti China” demonstrations, signaling concern that anger over the disaster could fuel broader dissent.
One person involved in a petition calling for an independent inquiry and a review of construction oversight was detained for around two days, according to individuals familiar with the matter. Police have not commented. At a press conference, Security Chief Chris Tang said, “I’ve noticed that some people with malicious intent, aiming to harm Hong Kong and national security, have taken advantage of this painful moment for society. Therefore, we must take appropriate action, including enforcement measures.”
Search efforts have now shifted to the worst affected buildings, where recovery operations may take weeks. Images released by police show officers in protective gear navigating rooms with scorched walls, collapsed debris and ankle deep water left by firefighting crews.
More than 4,000 people lived in the apartment blocks, census data shows. Over 1,100 residents moved from evacuation centers into temporary housing, with another 680 placed in youth hostels or hotels. Authorities are providing HK$10,000 to each household for emergency needs and are helping survivors replace identity cards, passports and marriage certificates.
The blaze marks Hong Kong’s deadliest fire since 1948, when 176 people were killed in a warehouse inferno. The disaster comes days before legislative elections and has intensified scrutiny of construction oversight and government response.
On Saturday, police detained 24 year old Miles Kwan, a member of the petition group seeking an independent probe, though it remains unclear whether he was formally arrested. Two others have also been taken into custody on suspicion of seditious intent, according to the South China Morning Post. China’s national security office issued a warning over the weekend: “We sternly warn the anti China disruptors who attempt to ‘disrupt Hong Kong through disaster’. No matter what methods you use, you will certainly be held accountable and strictly punished.”
Baidu is positioning itself as a major player in China’s artificial intelligence chip market, aiming to fill a gap left by Nvidia’s restricted access and Huawei’s reduced presence. The company, best known as China’s top search engine, has shifted its focus in recent years to AI and driverless cars, including its majority-owned chip subsidiary Kunlunxin.
Analysts have recently upgraded Baidu’s stock, citing Kunlunxin’s potential to secure more domestic orders. This month, Baidu unveiled a five-year plan for its AI chips, starting with the M100 in 2026 and the M300 in 2027. The company already deploys a combination of its own chips and Nvidia products in its data centers to power ERNIE AI models.
Baidu monetizes its chips by selling to third-party data center builders and renting computing capacity via its cloud platform. Its strategy emphasizes a “full stack” AI offering, integrating chips, servers, data centers, AI models, and applications. The Kunlun chip unit has already won orders from China Mobile, a major telecom provider.
“Kunlunxin has emerged as a leading domestic AI chip developer, focusing on high-performance AI chips for large language model training, cloud computing, and telecom workloads,” Deutsche Bank analysts said in a note.
With Nvidia blocked from exporting top-end GPUs to China and Huawei’s chip dominance diminished, Baidu appears set to capture significant market share. JPMorgan analysts forecast domestic AI compute demand will continue to rise, predicting Kunlun chip sales could increase six-fold to 8 billion yuan ($1.1 billion) in 2026. Macquarie analysts estimate the unit could be worth around $28 billion.
Baidu’s push comes amid broader chip shortages affecting Chinese tech firms. Alibaba CEO Eddie Wu said supply bottlenecks could persist for two to three years, while Tencent noted limited chip availability is slowing its capital expenditure despite strong AI demand.
Nick Patience, AI practice lead at The Futurum Group, said Baidu’s strategy represents both a necessity and an opportunity. “If Baidu can ship competitive Kunlun generations on time, it doesn’t just solve its own supply problem — it becomes a strategic supplier to the rest of China’s AI industry,” he said.
As demand for AI continues to surge, Baidu’s Kunlun chips could become a critical foundation for China’s domestic AI ecosystem, reducing reliance on foreign technology while meeting a fast-growing market need.
When Japan’s new prime minister Sanae Takaichi took office, she pledged to focus on economic improvement. After her Taiwan comments, new missteps could prove costly to Japan, the region, even the world.
On October 21, Sanae Takaichi, the president of the ruling Liberal Democratic Party (LDP), was voted in as the 104th prime minister of Japan; the first woman selected for the nation’s highest post.
Barely a month later, in her first parliamentary address, Takaichi, 64, stated that Japan could become militarily involved in a conflict between China and Taiwan. That sparked a diplomatic crisis as Japan’s relations with China plunged to lowest in years.
Yet, this crisis has been long in coming. Takaichi needs a geopolitical spat to steer attention away from Japan’s secular economic challenges.
Political slide to hard right
Instead of a continued partnership with the centrist Komeito party, Takaichi launched her coalition with the center-right Nippon Ishin party. With the end of the 26-year coalition with Komeito, the LDP took a turn to hard right.
The effective goal of Takachi is to mainstream Nippon Kaigi and cement a deeper military partnership with the U.S.
Initially, Takaichi’s cabinet enjoyed some of the highest approval ratings (65%-85%) of any Japanese government in the last two decades, with strong support among young and middle-aged respondents. The Japanese see as the administration’s national priority in tackling inflation (84%), economic stimulus (64%), social security (53%) and security (47%). Bread and butter issues supersede military issues by far.
Only a minority of Japanese (17%) approved of Hagiuda Koichi, who had previously been involved in a slush fund scandal, being appointed as executive acting secretary general. After Abe’s assassination, ties between the LDP and Unification Church came under scrutiny and Hagiuda had intimate ties with the controversial Church.
Moreover, both Takaichi and Hagiuda are members of the Nippon Kaigi, Japan’s largest far-right and ultranationalist non-governmental organization. It seeks to change the postwar Tokyo Tribunal’s view of Japanese history, restore the divine status of Japan’s emperor and undermine gender equality. It champions official visits to Japanese war criminals’ Yasukuni Shrine and denies the forced prostitution of the “comfort women” in World War II.
Nippon Kaigi has a significant presence in the Japanese parliament and six prime ministers have been its members. The effective goal of Takachi is to mainstream Nippon Kaigi and cement a deeper military partnership with the U.S.
Structural economic woes
Last week, Japan’s cabinet approved a $135 billion stimulus package to address rising living costs and boost economic growth by strategic investments in semiconductors and artificial intelligence.
For months, Takaichi had called for “responsible proactive fiscal policy.” However, it is not clear how she plans to balance fiscal prudence with still more spending. In both absolute and relative terms, Japan holds the largest debt burden globally amounting close to $10 trillion; more than double the size of its economy.
The high debt-to-GDP ratio has not caused a collapse because much of the debt is held by domestic investors and interest rates remain low. While the ratio has been decreasing since the Covid-19 pandemic, Takaichi’s stimulus policies could reverse the trend.
Furthermore, years of fiscal stimulus, social welfare spending, an aging and shrinking population, coupled with stagnation compound the debt burden.
Japan General Government Gross Debt to GDP (%)
Source: Ministry of Finance, Japan
By increasing national debt, Takaichi’s stimulus could lead to higher interest rates and a weaker yen. That would trigger inflation, which could erode the effectiveness of the stimulus, a loss of investor confidence, even capital flight, with negative global spillover effects.
The LDP’s lingering contradiction
Early signs reflect rising unease in the Japanese markets. These worries are mirrored by rising Japanese government bond yields. Recently, the yield on benchmark 10-year JGBs hit 1.835%, the highest since summer 2008. Similarly, the yen briefly softened to 157.90 against the dollar amid fiscal fears and receding expectations for an imminent BOJ rate hike.
In the Japanese markets, these worries are mirrored by rising Japanese government bond yields. Recently, the yield on benchmark 10-year JGBs hit 1.835%, the highest since summer 2008. Similarly, the yen briefly softened to 157.90 against the dollar amid fiscal fears and receding expectations for an imminent BOJ rate hike.
U.S. dollar / Japanese yen
Assuming erosion in fiscal and monetary credibility, yen depreciation is likely to foster rising prices. In that case, the effectiveness of the stimulus package could be undermined, which would compel Takaichi cabinet to demand more stimulus – which, in turn, would further penalize medium- to long-term economic and financial market stability.
This is the basic contradiction that the Abe cabinets managed to contain: the stated effort to achieve sound economic fundamentals versus the nagging need for continuous stimulus packages to revive the stagnant economy. Worse, Takaichi cabinet’s starting point is more fragile, as evidenced by the weakening yen.
As the Takaichi cabinet has stressed the importance for policy coordination with the Bank of Japan (BOJ), the central bank may find it challenging to raise interest rates in December, even despite inflation at 3% in October. The “coordination” between the two could contribute to adverse pent-up effects in the coming months.
Rising inflation is the last thing Takaichi needs. It is the greatest concern of those who elected her.
Japan’s inflation rate
Source: Ministry of Internal Affairs & Communications, Japan; author
Takaichi’s ultraconservative profile
Born into a dual-income middle-class family, Takaichi grew of age in a very conservative home. Independent and enterprising, she studied in the university and worked in the U.S. as a congressional fellow for Democratic congresswoman Pat Schroeder.
Upon return to Japan, she created a career and visibility as a presenter for TV Asahi starting her political career in the early 1990s. Though running as a liberal, she switched to the LDP after election.
By the early 2010s, Takaichi was championed by the LDP leader Shinzo Abe. To profile her patriotism, she often visited the war criminals’ Yasukuni shrine. As a cabinet minister in 2011, she even allowed herself to be photographed with Kazunari Yamada, the leader of Japan’s small neo-Nazi party.
By the mid-decade, she was seen as a promising new LDP leader. But it was only her third leadership bid that made her Japan’s first female prime minister.
To Takaichi, American deterrence is vital to Japan’s hard right. That’s why she used her recent visit at the US Yokosuka Naval Base to vow to bring the US–Japan alliance into a “golden age.”
The stated effort to achieve sound economic fundamentals versus the nagging need for continuous stimulus packages to revive the stagnant economy.
Cognizant of Takaichi’s ultraright credentials, Chinese leader Xi Jinping did not send a congratulatory telegram on the day Takaichi assumed her post. But in the subsequent Japan-China summit, the two agreed to promote a “mutually beneficial relationship based on common strategic interests.”
But after Takaichi’s Taiwan comments, those hopes have deflated.
Three scenarios
Today, Takaichi faces three major scenarios.
Measured de-escalation. In this scenario, she will seek to ease tensions through diplomatic dialogue. Japan is not just heavily reliant on Chinese tourism, seafood exports, and rare earth minerals. Beijing is Tokyo’s largest trading partner. In 2024, China’s share of Japan’s total trade exceeded 20%, with 17.6% of Japan’s exports and 22.5% of its imports going to or coming from China. De-escalation would help mitigate the current economic pain. This would likely be supported by the U.S., which advocates regional stability. Yet, de-escalation is not motivated by Takaichi’s ideology, but by Japanese voters’ bread-and-butter priorities.
Protracted instability. The current status quo will linger, marked by underlying tensions and occasional flare-ups, without a full resolution. China would continue its economic pressure, while Takaichi would seize the opportunity to legitimize increased defense spending and closer alignment with the US thus sparking the odds for further escalation in regional confrontation. As the spat broadens, Japan’s GDP will take a prolonged hit while adverse spillover concerns surge in the markets.
Full-blown escalation. A more volatile scenario would mean a further breakdown of diplomatic ties and increased military posturing. China could engage in enhanced naval activities in disputed waters. Takaichi would take an even more decisive position on Taiwan and commit to military coordination with the U.S., thus crossing one redline after another. But as Ukraine and Gaza suggest, the Trump White House prefers to regionalize conflicts. Nonetheless, heightened risk of confrontation would cause Japan’s GDP to plunge drastically, which would undermine the fiscal stimulus, alienate her voter constituencies, penalize business and investor confidence risking capital flight.
The next weeks are critical. China’s decision to take the spat to the UN forces Takaichi on a diplomatic defense. But new missteps could accelerate both the geopolitical and economic slide.
This is an abbreviated version of the original commentary published by China-US Focus on Nov. 28, 2025.
Dr Dan Steinbock, an expert of the multipolar world, is the founder of Difference Group and has served at the India, China and America Institute (US), Shanghai Institute for International Studies (China) and the EU Center (Singapore). For more, see https://www.differencegroup.net/
A true generative AI (Gen AI) learning culture transcends mere training programs, and should weave a deep appreciation for growth and development into the organizational DNA. This means creating an environment where learning is continuous, integrated into daily operations, and viewed as essential for both individual and collective success. Such a culture fuels curiosity, innovation, and adaptability, which are crucial traits for traversing the current complicated business world, particularly in creating a learning culture around the rise of technologies like Gen AI.
5 Key Elements for Learning Culture That Fulfills Gen AI Promise
Several key elements are crucial for cultivating a robust learning culture, especially one designed to empower organizations to leverage the transformative potential of Gen AI.
1. Gen AI learning initiatives must be strategically aligned with the organization’s overarching goals.
Simply introducing Gen AI tools without a clear understanding of how they contribute to business objectives will likely result in limited adoption and minimal impact.
For instance, if a marketing team aims to personalize customer experiences, Gen AI learning programs should focus on natural language processing for content creation, sentiment analysis for understanding customer feedback, and AI-powered personalization engines.
This connection between Gen AI learning and tangible business outcomes motivates employees and demonstrates the value of their development.
This connection between Gen AI learning and tangible business outcomes motivates employees and demonstrates the value of their development.
When employees see a clear link between mastering Gen AI and the organization’s strategic objectives — such as increased efficiency, innovative product development, or improved customer satisfaction — they are more likely to engage fully in the learning process. This alignment creates a sense of purpose and direction, making Gen AI learning more meaningful and motivating, while managing risks.
2. Leadership must champion Gen AI learning from the top down.
Active participation in Gen AI workshops, public support for Gen AI initiatives, and open discussions about the potential and ethical considerations of Gen AI send a powerful message that continuous improvement in this critical area is a priority at all levels.
When leaders actively participate in Gen AI learning initiatives, perhaps by exploring prompt engineering techniques or discussing the implications of large language models, it demonstrates that learning is not just an expectation for employees but a shared value.
This visible commitment from leadership encourages employees to follow suit, as they see that Gen AI learning is valued at all levels of the organization.
3. Gen AI learning should be a shared and collaborative experience.
Creating opportunities for employees to learn from each other through peer-led Gen AI workshops, cross-departmental Gen AI project teams, and internal Gen AI hackathons supports a sense of community and shared purpose.
This collaborative approach enhances learning by allowing employees to share Gen AI knowledge, discuss challenges related to implementation, and learn from diverse perspectives. Learning about Gen AI should not be a solitary activity but rather a shared journey where employees can support and learn from each other.
4. Providing easy access to relevant Gen AI resources is essential.
This includes online courses on prompt engineering, fine-tuning, and model deployment, as well as access to documentation, research papers, and industry best practices related to Gen AI.
Internal wikis or knowledge bases dedicated to Gen AI can serve as central repositories for information, code snippets, and reusable prompts. Regular knowledge-sharing sessions, perhaps focused on specific Gen AI tools or applications, empower employees to learn at their own pace and find the information they need.
These resources provide a platform for employees to ask questions, share insights, and access information relevant to their roles and development within the context of Gen AI.
5. Acknowledging and celebrating Gen AI learning achievements reinforces the value of continuous development in this critical area.
This can range from simple shout-outs for innovative Gen AI use cases to formal awards ceremonies recognizing teams that have successfully implemented Gen AI solutions. Recognizing and celebrating successes and milestones in Gen AI learning is an effective way to reinforce the value of continuous education.
These celebrations do more than just reward those who have made significant strides in their Gen AI learning; they also highlight the organization’s commitment to growth and development in this vital technological area.
Case Study: Fulfilling Gen AI Promise at a Healthcare Provider
As a consultant specializing in organizational development and Gen AI implementation, I recently partnered with a regional healthcare provider facing the challenge of integrating Gen AI into their operations. Their existing training programs were fragmented and lacked strategic alignment with the potential of Gen AI. My role was to guide them in building a sustainable Gen AI learning culture.
My approach began with a thorough needs assessment, interviewing employees at all levels to identify skill gaps and learning preferences related to Gen AI. Based on the assessment, we developed tailored Gen AI learning paths for different roles.
Medical practitioners received training on using Gen AI for diagnostic support and personalized treatment plans, while administrative staff focused on using Gen AI to automate administrative tasks and improve patient communication.
We integrated Gen AI learning into daily routines through on-demand training modules accessible via the company intranet and dedicated “Gen AI exploration days.”
I worked closely with senior leaders to ensure their active participation in Gen AI workshops and strategic discussions. The CEO and other executives participated in prompt engineering workshops and discussed the ethical implications of using Gen AI in healthcare, demonstrating their commitment. We established Gen AI learning circles and an online forum to facilitate knowledge sharing and collaboration on Gen AI-driven projects.
Finally, we implemented a system for recognizing Gen AI learning achievements through digital badges and quarterly awards ceremonies showcasing innovative Gen AI applications.
Over 12 months, the healthcare provider experienced significant improvements. Employees across roles became more proficient in using Gen AI tools, leading to more efficient operations and higher-quality patient care.
Staff time devoted to admin tasks shrank by 33%, with more focus on higher-level projects. Diagnostic accuracy improved by 18%, and diagnostic efficiency by 24% due to enhanced understanding of AI-driven diagnostic tools.
Building a sustainable Gen AI learning culture is not a one-time initiative but an ongoing process.
Leadership’s visible commitment and the alignment of Gen AI learning with strategic goals fostered a culture of continuous improvement in this crucial area. The collaborative environment facilitated by Gen AI learning circles and online forums led to the development of innovative patient care solutions powered by Gen AI.
Key Takeaways Regarding Promise of Gen AI Learning
Building a sustainable Gen AI learning culture is not a one-time initiative but an ongoing process. Leaders must prioritize Gen AI learning by making it a strategic priority and allocating resources accordingly.
They must also lead by example by actively participating in Gen AI learning activities and championing development programs related to this technology. Fostering collaboration by creating opportunities for employees to learn from each other about Gen AI is also crucial.
Finally, leaders must measure the impact of Gen AI learning by tracking the effectiveness of initiatives and making adjustments as needed.
By embracing these principles, organizations can create a culture where Gen AI learning is not just valued but also drives innovation, adaptability, and long-term success in the age of intelligent machines.
At a recent tech event we same a similar pattern across all booths—from mobile apps to ecommerce applications, partner integrations and microservices. All of them are at some or the other level dependant on APIs. As systems scale, the number of endpoints, versions, and environments explodes.
Manual checks and adhoc scripts can’t keep pace without risking quality, speed, or both. That’s why the market for automated API testing solutions has grown from a nice to have to essential engineering infrastructure asset.
For teams exploring design stage and contract first testing, we’ll talk about how top functional testing tools enable virtualization and AI assisted automation without turning this into a product pitch.
Why should you automate your API tests (with data)
Complexity and scale are up: More services and dependencies drive more interfaces to validate. The API testing market is projected to surpass $3B by 2030 on sustained double digit growth—reflecting long term adoption of automation across engineering teams.
Late defects are expensive: The cost to fix a bug rises steeply later in the SDLC. Research teams have documented orders of magnitude it increases from design → dev → staging → prod, driven by diagnosis time, rework, hotfixes, and customer impact as quoted by NIST overview.
AI is helping, but needs a platform: Capgemini’s World Quality Report 2023–24 states that organizations using AI in testing report material reductions in test creation and maintenance effort—when those capabilities are pushed in platforms and processes, not used as one off scripts.
What to look for in API automation testing tools (buyer’s checklist)
Protocol breadth: REST is table stakes; enterprise teams often need SOAP, GraphQL, and WebSockets. gRPC is increasingly common in microservices.
Contract alignment: Ability to Import OpenAPI/Swagger. Validate request/response against schema. Detect contract drift early.
Auth and security: Built in support for API keys, OAuth 2.0, JWT; negative tests for unauthorized/forbidden access; basic security assertions.
Data handling: Parameterization from CSV/JSON/DB; synthetic data generation for edge cases.
CI/CD and governance
Pipeline fit: Ability to integrate with GitHub Actions, GitLab, Jenkins, Azure DevOps; merge gates on test/policy failures; artifacts you can archive.
Reporting and analytics: Step level diagnostics, historical trends, flake tracking, SLA/SLO views for latency and error rate.
Scale: Parallel/distributed execution for large suites; headless runners; containerized agents.
Enterprise controls: RBAC, audit logs, secrets management, versioning of tests/specs.
Ease of use and collaboration
Nocode/‑lowcode‑: To save time teams need the ability to skip code and directly click to‑ ‑test so QA, BAs, and PMs can contribute—without waiting on an SDET.
Environment management: Teams should have the ability to make easy switches across Dev/Staging/Prod without copying tests.
Reusability: Rewriting test cases takes up a lot of time so if a tool can offer the ability to copy components, libraries, and test templates it will make things a lot easier.
Intelligence and resilience
AI test generation: From specs and traffic—cover happy paths, negative inputs, and data driven variants.
Self healing suggestions: When a field name or schema changes, propose a fix (or auto apply with review).
Impact analysis: After code changes, point to the smallest test subset that matters (speed without blind spots).
Tool landscape: Strengths at a glance
The market for API testing automation tools is rich. Here’s a neutral review to help you position your shortlist.
Postman
Strengths: API exploration, collaboration, collections/environments; ubiquitous across dev teams.
Trade-offs: Automation at enterprise scale can become script heavy; governance/testing depth may need addons.
ReadyAPI/SoapUI
Strengths: Deep SOAP, contract/security testing; mature for legacy/enterprise estates.
Trade-offs: Heavier UX/licensing; steeper learning curve for new teams.
Katalon
Strengths: Combined UI + API automation with low-code; good for mixed skill teams.
Trade‑offs: Broad suite can feel heavy if you only need API.
BlazeMeter
Strengths: Performance testing heritage; functional API testing + monitoring; solid for SLO/SLA focus.
Trade-offs: Functional authoring features improving; best fit when reliability under load is primary goal.
Strengths: The automated testing platform pushes contract first + virtualization to enable earlier testing; AI automates end-to-end flows and test generation; functional + performance in one place.
Tradeoffs: Best leveraged by teams adopting contract-driven workflows and CI discipline. The only tool to offer end-to-end API testing.
When it comes to qAPIs Automap feature it’s one of the fastest and simplest when it’s compared to older tools in the market. From moving API validation and test creation earlier—around the contract (OpenAPI) and running tests the desired outcome can be achieved in a matter of minutes.
How to Identify the Right Tool Stack for your API Collection
Generally speaking, the tech stack you have with your space will have some setbacks. Try to identify the real paint points and fetch tools that can replace those problems and save your time at the same time.
Lets say I’m using qAPI, make sure that the collection is accessible or download it so it can be tested. We recommend qAPI because it’s an easy-to-use, quick to load automated functional testing tool. Start by:
1. Importing your API collection to validate your spec
Load OpenAPI/Swagger into your tool. Confirm required fields, data types, enums, and auth scheme.
2. Generate tests (qAPI generates test cases automatically for you)
Auto‑create tests for:
Happy path per endpoint
Missing required fields; wrong types; out of range values
401/403 for missing/invalid tokens
405/415 method/content‑type errors
3. Chain a simple workflow
Build Login → Create User → Get User.
4. Parameterize
Add a CSV with roles, regions, and invalid inputs (e.g., non‑ASCII names; negative quantities; very long strings).
5. Run in CI/CD
Example (GitHub Actions) pseudo YAML:
on: pull_request
Steps:
checkout
set up runner
run: api-tests –env=staging –report= junit.xml
if: failures > 0 → fail the PR
6. Nightly performance tests
Run 100–500 virtual users against your critical workflow (auth → cart → promo → checkout) to track regressions in p95 latency and error rate.
Pro tip: If your tool supports virtualization, spin up a mock API from the spec on day one. That unblocks frontend/QA and lets you author tests before the backend exists.
Best practices that consistently pay off
Make the contract a product: Treat OpenAPI as the single source of truth; require contract reviews before implementation.
Test real journeys, not just endpoints: Cross endpoint flows (auth → action → readback) expose integration bugs unit tests miss.
Shift right, too: Pair early functional automation with small, frequent performance smokes (e.g., 100–500 VUs) and production monitors.
Track the right KPIs: Flake rate (<3–5% target), mean time to diagnose (MTTD), defect escape rate, p95 latency vs SLOs, coverage across critical endpoints.
Automate maintenance: Use impact analysis and self healing suggestions to keep suites healthy as contracts change.
How platforms like qAPI support contract first and shift left (brief, neutral)
If you want to start testing at development stage qAPI helps with:
API virtualization: Create a live, interactive mock from a schema so frontend/QA can build/test in parallel on day one.
AI Automap: Automatically chain endpoints into end-to-end flows (auth tokens and IDs passed forward; assertions scaffolded).
Test generation: Positive, negative, and data driven cases from specs and validated journeys.
One place for functional + performance: Build once, reuse for load tests against the same journeys.
Autofill of API data points: Faster setup; realistic inputs.
Impact based regression: Run the smallest set of tests needed per change.
FAQs
Q1: What’s the difference between API functional and performance testing?
Functional testing checks correctness—status codes, schema, business rules—across endpoints and workflows. Performance testing validates behavior under load (latency, throughput, error rates) so you know if SLAs/SLOs hold at realistic traffic.
Q2: How do I prevent contract drift across teams?
Center everything on OpenAPI/Swagger. Require review + approval for changes, regenerate tests on contract updates, and gate merges on contract violations in CI.
Q3: Do I need both OpenAPI and Postman collections?
You can work with either, but OpenAPI tends to be better for contract validation and auto generation. Collections are great for exploration and examples. Many teams use both.
Q4: Where should API tests run in CI?
A common pattern: smoke tests on PRs; full functional suite on merge; nightly performance smoke and weekly heavier load tests. Alert on SLO breaches.
Q5: How do I pick latency/error SLOs?
Start with user visible thresholds (e.g., p95 < 300 ms for critical reads; p95 < 800 ms for write workflows) and historical data. Iterate based on real usage and business impact.
Offer a non gated PDF/Google Sheet the host can provide:
Journey coverage: auth → action → readback per critical flow
Performance targets: p95 latency, acceptable error rates per endpoint/workflow
CI gates: PR smoke, merge checks, SLO alerts
Data privacy: masking/anonymization guidance for test data
Conclusion
Automated functional testing tool is now a prerequisite for building reliable software at scale. The biggest gains come when you move testing earlier—around the contract—and give teams a virtual API to build and test against from day one. That’s how you cut idle time, prevent drift, and find issues when they’re cheap to fix.
Use the buyer’s checklist to evaluate API automation testing tools objectively, adopt a contract first workflow, and measure what matters. If your roadmap includes design stage testing, look at platforms that combine virtualization, AI assisted test generation, and functional + performance in one place—whether that’s your current stack or a tool like qAPI.
A new pedagogical methodology is emerging in business education, forcing a fundamental question: Can you teach someone how to be creative, and how would you even grade that?
For decades, the path to business leadership was guided by a principle of analytical certainty, where comprehension of spreadsheets, financial models, and data-driven protocols defined the essential competencies for aspiring executives. However, the rise of AI is systematically automating analytical tasks, thereby displacing the relevance of this traditional foundation. In response, business schools are redirecting their curricula to prioritize the human competencies expected to define future leadership skills such as, creativity, empathy, ethical reasoning, and resilience. The challenge, however, is how do you design a syllabus for creativity? How do you issue a grade for empathy? This new pedagogical push is forcing institutions to confront a contradiction at the core of modern education, in an attempt to preserve what makes us human, are they trying to turn it into an algorithm?
The New Curriculum: From Case Studies to Improv Classes
Imagine walking into a classroom at a business school. You expect to find students in suits, hooked over laptops, debating the numbers in quarterly earnings report. Instead, you find a circle of future executives on their feet. One student, her face expressed with theatrical frustration, slams an imaginary fist onto a table. “This is unacceptable!” she declares. Facing her, a classmate embodying the ‘manager’ takes a breath, his mind racing as he searches for a response. His only instruction: “Yes, and…”. In the classroom environment there are no spreadsheets, no PowerPoint slide decks, just the raw, volatile energy of human interaction. The scene unfolding before you is far from a simple acting exercise. While it may look like a drama club rehearsal, this is, in fact, a deliberate pedagogical experiment in leadership development at the University of Pennsylvania’s Wharton School. The skills being developed here, spontaneous thinking, deep listening, and seamless collaboration, are becoming the new foundation of management. In a world reshaped by AI, the ability to navigate the unscripted moments of human interaction is now deemed as crucial as constructing a flawless financial model.
This new focus includes a creation of courses with titles like “Leading with Empathy,” “Design Thinking for Complex Problems,” and “The Art and Science of Creativity.” At Stanford’s Graduate School of Business, programs send students to museums like the San Francisco Museum of Modern Art to practice observation and interpretation, arguing that understanding abstract art builds the cognitive flexibility needed to see business challenges from new angles.
Instead of multiple-choice exams, students write reflective essays about leadership challenges, build “innovation portfolios,” and get peer feedback on teamwork. This allows for grading students on the robustness and originality of their creative process instead of the success of their ideas. This shift towards qualitative assessment is a top trend identified by the AACSB, the primary accrediting body for business schools.
The AI Paradox: Using the Machine to Teach Humans
Leading business schools are experimenting with AI as a tool to strengthen distinctly human skills. AI-powered pitch training platforms now leverage machine learning and natural language processing to analyze tone, clarity, body language, and verbal patterns, delivering insights not readily apparent to human observers. These algorithms, trained on massive datasets of pitching examples, generate insights across multiple dimensions including clarity, emotion, and momentum.
New AI tools guide students through entrepreneurship principles while keeping human judgment front and center, allowing them to pressure-test go-to-market strategies and product ideas. Business schools are converging around four key competencies for the AI era: critical thinking and judgment, emotional intelligence and empathy, creative problem-solving, and ethical reasoning. Yet critics worry this approach trivializes profound human qualities. Some educators argue that technology may render specific skills obsolete, and that learning content represents a commodity accessible to anyone. The concern is whether creativity and empathy can truly be taught through structured pedagogy, or if some qualities remain best “caught, not taught.”
The shift reflects a broader recognition that as AI becomes ubiquitous, technology alone won’t provide differentiation. Research has identified that work dependent on human characteristics such as empathy, judgment, and hope is less likely to be replaced by machines. As one student reflected: “I came to this module expecting to learn how to use AI tools. I left understanding how to think with them”.
The Central Question: Grading the Soul?
This debate cuts to the heart of business education’s identity. Advocates argue that while empathy itself may be intangible, organizations can assess whether candidates identify emotional needs of colleagues, take meaningful action, and achieve positive outcomes in workplace interactions. Assessment rubrics interpret expectations so students can focus on work instead of guessing what instructors want.
The business case is captivating. According to GMAC’s 2023 Corporate Recruiters Survey, 81% of recruiters identify interpersonal skills as important, more than any other kind of skills. As one recruiter noted, “Everyone coming out of business school seems to have the technological familiarity we require. Soft skills, like communications and people skills, tend to make the difference”. Some now ask candidates to describe times they “failed with empathy,” seeking graduates who can articulate and apply these qualities in corporate contexts.
Yet critics fear that quantifying the human diminishes it. Applying abstract descriptions of “poor” work to individual students can have an objectifying effect, especially if rubric descriptions lack emotional sensitivity. Can creativity truly be captured in learning outcomes, or are we cultivating “creativity technicians” rather than visionaries?
According to McKinsey, 87% of organizations already face skill gaps or expect to within a few years, particularly in communication, empathy, and problem-solving. AI will handle repetitive, data-heavy tasks, but human creativity, perception, and emotional intelligence will continue to drive innovation.
The era of STEM supremacy may be ending, but the dawn of the “Creativity Quotient” raises a profound question: In trying to future-proof leaders against the rise of machines, are business schools programming the humanity out of them, or democratizing soft skills once reserved for those lucky enough to find the right mentor? The answer will define both business education and the nature of leadership for decades to come.
Dr. George Sammouris Associate Professor at Princess Sumaya University for Technology, Jordan. His expertise includes data analytics, business intelligence, and e-learning. He serves on editorial boards and accreditation committees, mentors universities in AACSB accreditation, and has published widely while leading quality assurance and academic development initiatives in higher education.
By Terence Tse
CFOs are evolving into AI-driven transformation orchestrators, balancing finance, technology, and strategy while upskilling teams, managing risks, and driving measurable business value.
A key insight from this year’s AI for CFOs event, organized...
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Dr Dan Steinbock
Dr. Gleb Tsipursky PhD
Dr. George Sammour
