Artificial intelligence

Artificial Intelligence in the Interregnum: Technology and the Reconfiguration of Meaning

There are moments in history when civilizations continue to advance materially while progressively losing confidence in the values  and structures that once gave direction and coherence to collective life. Institutions continue to function, markets continue to expand, and technological progress accelerates uninterruptedly, yet beneath this movement emerges a quieter uncertainty.

As Simone Weil observed, “to be rooted is perhaps the most important and least recognized

need of the human soul.”[1] Yet contemporary societies often struggle to sustain those forms of

belonging and shared meaning that once anchored human communities. The crisis is

therefore not simply political or economic. It concerns meaning itself.

Artificial intelligence has appeared precisely within such a historical juncture. Most contemporary discussions approach AI primarily as a technological revolution, or as an element of economic and geopolitical competition between great powers. Governments now frame it as a strategic race, corporations present it as the next engine of productivity, and Silicon Valley often speaks of AI in the language of inevitability and destiny, recalling Aldous Huxley’s fear that technological progress might ultimately weaken rather than deepen human civilization.[2]

 But such interpretations may ignore something deeper still. AI may be less the cause of a civilizational transformation than one of its clearest symptoms. It reflects a broader historical transition in which inherited moral and symbolic frameworks are dissolving faster than new forms of collective meaning can emerge.

Slouching Towards Bethlehem[3]

This condition closely resembles what Antonio Gramsci described as an interregnum: a period in which the old world is dying while the new world struggles to be born. [4] Such periods produce not only political instability, but also moral exhaustion, the erosion of shared narratives, and declining confidence in beliefs once considered self-evident. Civilizations have passed through similar moments before.

The enduring fascination of Edward Gibbon’s monumental The Decline and Fall of the Roman Empire lies not merely in its account of imperial decline, but in its portrayal of the slow weakening of the moral and symbolic foundations that once sustained an entire civilization.[5] Rome did not collapse overnight. Its institutions remained impressive long after few still believed in the civilization they were meant to serve. Administrative power survived even as collective meaning and aspirations deteriorated.

That pattern feels strangely familiar.

Never before have technological capacities appeared so extensive while social distrust, political fragmentation, and loneliness have become so pervasive. Hyperconnectivity was supposed to bring societies closer together. In many cases, it has done the reverse.

AI in the Anthropocene

AI emerges from within this historical condition . It appears perfectly suited to societies organized around abstraction, speed, quantification, and technological mediation. In this sense, AI is profoundly historical. It results from a long civilizational development in which rationalization, efficiency, and technical calculation have come to replace older moral, religious, and symbolic frameworks as primary sources of legitimacy and meaning. What distinguishes AI from previous technologies is that it extends these same principles into domains traditionally considered irreducibly human. Activities once understood as distinctly human, such as reasoning, creativity, interpretation, and even emotional interaction, are now becoming technologically mediated.

The deeper unease therefore concerns anthropology as much as technology. What remains distinctively human when machines become capable of imitating reasoning, generating art, and mediating human relationships?

Such moments of civilizational disorientation are not entirely unprecedented.

The Renaissance confronted a similar rupture. Medieval Europe had long possessed a relatively coherent worldview capable of organizing religion, politics, morality, and human identity within a common order. By the late fifteenth century, however, this equilibrium was beginning to fracture under the pressure of new scientific discoveries, religious wars, and the weakening of older political and spiritual authorities. Thinkers such as Niccolò Machiavelli and Giovanni Pico della Mirandola sought, in radically different ways, to redefine humanity’s place within a rapidly changing world.[6] Pico celebrated human beings as creatures capable of shaping themselves through freedom and intellect, while Machiavelli recognized more soberly that periods of transition dissolve inherited certainties and force societies to confront instability and power directly.

Both understood that historical transformation is ultimately existential before being institutional. Our own transition may prove even more radical because technology no longer transforms only economic or political life, but cognition itself.

AI now mediates everyday experience itself: how people search for information, communicate, work, and make sense of the world around them.

Algorithms no longer merely distribute information. They shape attention, influence perception, and affect how individuals relate emotionally to public life and to one another. Under such conditions, the distinction between human judgment and technological mediation becomes far less clear.

The Price of Nostalgia

One striking feature of the contemporary digital environment is the degree to which individuals now participate voluntarily in their own data extraction. Recent Instagram trends such as the viral “What Were You Like in the ’90s?” challenge encourage users and celebrities alike to upload curated archives of personal photographs spanning decades of their lives. Presented as nostalgia and entertainment, these trends also generate immense quantities of highly valuable visual and behavioural data: faces across time, emotional reactions, aesthetic preferences, social interactions, and patterns of self-presentation. Whether or not such material is directly incorporated into future AI systems, the broader objective remains significant. Human memory, identity, and even nostalgia itself increasingly becomes raw material for computational analysis and commercial platforms.

Reactions to AI therefore oscillate easily between fascination and anxiety. Beneath both lies a deeper uncertainty about whether modern societies still possess a coherent understanding of what human beings are for, beyond economic productivity and consumption.

Friedrich Nietzsche anticipated aspects of this crisis more than a century ago. His declaration that “God is dead” did not merely constitute a theological provocation but signalled the emergence of a civilization in which traditional moral structures would lose authority long before new ones could replace them.[7] Nietzsche feared not nihilism alone, but the possibility that societies might become incapable of generating new forms of transcendence once older ones had collapsed. We saw how his worldview provided an intellectual base for Fascism.

I Read, therefore I Am

In increasingly mediated environments, the act of sustained reading itself begins to take on a countercultural character. To read is, in some sense, to resist. We have access to more information than any previous generation, yet physical books can still provide a sense of orientation. The books people return to, annotate, or simply keep close over time often reveal something enduring about the way they think and who they are.

The central issue, therefore, is not simply whether artificial intelligence will become more powerful. The deeper question is whether societies organized around AI can still sustain stable forms of responsibility and belonging strong enough to preserve coherent collective life. This is ultimately a political and civilizational problem before it is a purely technical one.

Much contemporary discourse still assumes that technological advancement naturally produces historical progress. History offers little evidence for such confidence.

Civilizations do not endure simply because they innovate technologically. They endure because they preserve, or reinvent, systems of meaning capable of holding societies together over time.

The Roman Empire mastered engineering yet gradually lost the moral cohesion that had once sustained it. Renaissance Europe produced extraordinary creativity precisely because it confronted existential instability directly rather than attempting to ignore it.

Contemporary Western societies appear caught between immense technological sophistication and growing uncertainty about their own civilizational narrative.

AI therefore represents more than innovation. It reflects a transformation in how human beings understand themselves, authority, knowledge, and reality itself. The danger is not simply that machines become too powerful. It is that societies now outsource judgment, imagination, and responsibility while slowly losing the cultural and moral resources required to govern these technologies wisely. Yet periods of interregnum are not necessarily periods of decline alone. They are also moments in which civilizations redefine themselves.

AI For Good ?

Historical transitions create possibilities as well as dangers. The Renaissance emerged from the crisis of medieval Europe. Modern democracy emerged from the upheavals of industrial society. Today’s uncertainty may likewise force Western societies to confront questions long obscured by economic growth and technological optimism:

What constitutes a good society? What forms of belonging remain possible in a hyper-mediated world? What aspects of human life should never be reduced to data, prediction, or optimization?

AI cannot answer these questions. But its emergence makes avoiding them increasingly difficult.


[1] Simone Weil, The Need for Roots: Prelude to a Declaration of Duties Towards Mankind (1949/1952).

[2] See Aldous Huxley, Brave New World (1932) and his later essays such as Brave New World Revisited (1958), where he warns that technological efficiency and social conditioning could erode authentic human experience.

[3] The phrase alludes to the final lines of W.B. Yeats’ poem “The Second Coming” (1919): “And what rough beast, its hour come round at last, / Slouches towards Bethlehem to be born?”

[4] Antonio Gramsci, Prison Notebooks (written 1929–1935, published posthumously). The “interregnum” concept appears in Notebook 3: “The crisis consists precisely in the fact that the old is dying and the new cannot be born; in this interregnum a great variety of morbid symptoms appear.”

[5] Edward Gibbon, The History of the Decline and Fall of the Roman Empire (6 volumes, 1776–1789). Gibbon famously attributed part of the decline to the rise of Christianity and the erosion of civic virtue.

[6] Giovanni Pico della Mirandola, Oration on the Dignity of Man (1486) — often called the “Manifesto of the Renaissance”; Niccolò Machiavelli, The Prince (1532) and Discourses on Livy.

[7] Friedrich Nietzsche, The Gay Science (1882, §125 – “The Madman”) and Thus Spoke Zarathustra. The full phrase is usually rendered “God is dead. God remains dead. And we have killed him.”

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Beware of Financial Scammers Wielding Deepfake Tech

Deepfake fraud is becoming a persistent, multiyear corporate risk as synthetic voices circulate undetected.

Deepfake-enabled fraud, which began as novel technical exploits, is now a persistent operational risk with a multi-year shelf life within the corporate ecosystem. According to deepfake-detection provider Resemble.AI, deepfakes typically remain in circulation for three-and-a-half years.

Resemble.AI’s 2025 Deepfake Threat Report, published in March, references an incident in which a voice clone of a German energy company CEO remained in circulation for nearly six years, although it resulted in only a €243,000 loss in 2019.

Determining losses from such attacks is difficult; for the 41 documented incidents last year cited by the research, only $74.9 million in verified losses were reported, with a median per-incident loss of $243,000. However, the authors noted that 71% of victims did not report financial losses, suggesting a higher volume of hidden liabilities.

“What makes them so effective is that they enable both real-time impersonation and the creation of synthetic identities stitched together from real and fake data,” said Dominic Forrest, CTO of biometric security vendor Iproov. “These are extremely difficult to detect, and once trusted, they can be used to bypass controls and commit fraud.”

AI Arms Race

Detecting deepfakes is a growing concern; the authors of the Resemble.AI report estimate that deepfake-based fraud attacks on corporations reached 8.5 billion potential incidents, ranging from audio impersonations of executives to doctored or fake images. The most common targets, Forrest noted, are on account openings, payment authorization, credential reset, and high-value transactions.

Telling a deepfake from the genuine article has become an AI-on-AI battle, experts warn.

The generative AI models producing deepfakes improve continuously via scaling and data, while deepfake detectors rely on signals like artifacts and inconsistencies, which disappear as models improve, said Siwei Lyu, professor of Computer Science and Engineering and director of the Institute for AI and Data Science at the State University of New York at Buffalo.

“In practice, detectors lag by about six to 18 months on specific modalities,” he said. “But more importantly, they are chasing a moving target whose failure modes are actively being optimized away.”

Forrest suggests that firms move their identity verification from single checks to a multi-layered approach: “You need to confirm that a real person is physically present, not a deepfake, while also analyzing the digital environment for signs of compromise. No signal should be trusted in isolation.”

This article first appeared in the May edition of Global Finance Magazine.

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CFOs Have Seen the AI Demo—but Does It Work?

Finance leaders shift from AI experimentation to measurable ROI across corporate operations.

We get it. Artificial intelligence is impressive. But how is it saving CFOs money?

Prithwijit Chaki has a take. As Global Leader for Finance Advisory at Genpact, a global professional services firm, Chaki helps chief financial officers harness AI and data to drive measurable business outcomes. With more than two decades of experience advising companies on finance strategy and large-scale transformation, he has seen firsthand how enterprises are rewiring their finance operations for an AI-first era.

That perspective takes on new dimensions with Genpact’s alliance with Google Cloud, announced earlier this month. The partnership translates AI ambition into production-ready operations.

Global Finance asked Chaki how that vision is taking shape and whether the conversation is no longer just about how AI can enhance productivity, but about bottom-line business value.

Prithwijit Chaki, Global Finance Advisory Leader, Genpact
Prithwijit Chaki, Global Finance Advisory Leader, Genpact

Global Finance: CFOs have spent the last two years experimenting with AI pilots. What’s different in 2026?

Prithwijit Chaki: CFOs are moving from AI experimentation to AI accountability. After years of pilots, the question is no longer whether AI can improve individual productivity, but whether those gains translate into enterprise value across the finance function: faster close cycles, better working capital, lower manual review burden, stronger controls, or measurable business outcomes.

According to a Genpact/HFS Research report, investment in agentic AI is expected to rise 38% over the next year. However, 67% of enterprises still rely on outdated productivity metrics that fail to capture the value of autonomous decision-making. That’s the gap CFOs are trying to close in 2026: cutting through the ‘sea of sameness’ in the AI market to determine which applications can deliver real, achievable value versus which are simply adding to the noise.

GF: How does agentic AI change day-to-day finance operations?

Chaki: Traditional automation follows basic rules, and generative AI can help an individual complete a task faster. Agentic AI goes even further. It operates inside finance workflows — deciding, acting, learning, and orchestrating work across processes with people still in the loop where needed. In practical terms, that could mean moving from someone using a copilot to draft a dunning letter faster to a more integrated workflow that identifies the right action, drafts the communication, routes exceptions, applies policy guardrails, and connects the work back to measurable enterprise value.

GF: What’s one example of cost savings or business impact that CFOs see from implementing agentic AI?

Chaki: A good example is a global supply chain and distribution company processing close to 3.5 million invoices a year. After a major merger, their finance team was dealing with disconnected ERP systems, heavy manual intervention, and slow exception resolution—the kind of last-mile complexity that generic automation can’t solve. Working with Genpact, they deployed our AI-powered Genpact AP Suite combined with our agentic operations model — 21 pretrained, domain-specific AI agents that autonomously route, prioritize, and resolve invoice exceptions, with human experts validating where needed.

GF: What were the results?

Chaki: Significant. Touchless invoice processing went from 7% to 65%. Invoice cycle times were nearly halved — from 18–29 days down to 9–14 days. On-time payment rates jumped from 60% to 95%. Data extraction accuracy improved from 40% to 92%. And the system identified approximately $350 million in duplicate invoices, while early-payment discounts captured grew from $35 million to $44 million — real dollars added to the bottom line.

This isn’t a pilot or a proof of concept. It’s agentic AI operating at scale inside a core finance workflow, delivering measurable cost savings, stronger cash flow, and a fundamentally better supplier experience. That’s the kind of outcome CFOs are looking for.

GF: Which finance function is currently seeing the fastest returns from AI deployment—and why?

Chaki: Accounts payable is one of the clearest areas where finance teams can see tangible value. The process has high volume and repeatable workflows, but it also has a clear ‘last mile’ problem. Invoices, approvals, exceptions, regulatory nuances, and fragmented systems still require heavy manual intervention. Generic AI can automate a large share of structured work. However, the final 20% requires domain-driven AI that understands real-world complexity, from vendor history and regional rules to exception patterns, approval chains, and master data issues. That is where agentic AI can move beyond simple extraction or automation. It can start resolving mismatches, escalating exceptions, improving first-pass yield, reducing manual touchpoints, and shortening cycle times.

GF: Through Genpact’s expanded work with Google Cloud, what are CFOs specifically asking for from hyperscalers right now? Is the conversation more about cost reduction or something else?

Chaki: The CFO conversation with hyperscalers has moved beyond ‘what’s the cheapest cloud?’ or ‘show me another AI demo.’ CFOs want production-ready finance operations that deliver real, measurable business outcomes. That’s what Genpact’s alliance with Google Cloud aims to address. By pairing Google’s AI infrastructure with Genpact’s finance expertise, CFOs can improve forecasting accuracy, strengthen cash flow, and scale AI within their existing cloud environments.

The goal is not just to reduce costs. It’s about boosting process efficiency and accuracy, freeing finance teams from manual work, improving decision-making, and giving CFOs a clearer path from AI investment to strategic value.

GF: Are there any guardrails that must be in place before agentic AI can be trusted within core financial workflows?

Chaki: Think of the guardrails for agentic AI as needing to scale alongside the technology itself. The more finance use cases it touches, the more important it becomes to build controls directly into the workflow. What we’re seeing today is the first wave of “agent-ification.” It operates on a machine-led, human-validated model, combining automation efficiency with expert oversight to ensure quality and compliance. Companies will build tools with that future standard in mind—where the guardrails and technology scale together—will be the ones who truly innovate what finance is capable of.

GF: Are there specific examples you can share of how you see AI augmenting finance teams? 

Chaki: We’re already seeing AI reshape how finance teams spend their time. In accounts payable, for example, AI agents are handling invoice extraction, three-way matching, and exception routing. This work used to consume entire teams. In financial planning and analysis, AI is accelerating variance analysis, generating narrative commentary on actuals, and enabling rolling forecasts that would have been extremely time-consuming and practically impractical to run manually. When it comes to record-to-report, it’s compressing close cycles by automating reconciliations and surfacing anomalies before they become audit issues.

GF: Do you expect job cuts?

Chaki: The shift this creates is less about job cuts and more about role evolution. Finance teams won’t shrink overnight, but the composition will change. You’ll see fewer people doing repetitive transactional work and more people in roles that require judgment, such as interpreting AI-generated insights, managing agent workflows, overseeing controls, and partnering with the business on strategic decisions. The finance professional of the future looks more like a combination of business partner and orchestrator than a processor.

Over the next three to five years, as agentic AI matures and enterprise vendors begin offering subscription-based finance capabilities built on entire agentic libraries, the operating model will shift. Finance functions will become leaner, faster, and more insight-driven but the organizations that get there first will be the ones investing now in both technology and the talent to work alongside it.

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China’s AI IPO Boom Leaves US in the Dust

Chinese AI firms dominate Hong Kong IPOs with $22 billion in exits, while US tech listings lag amid investor skepticism.

China’s artificial intelligence companies are driving a sharp divergence in global IPO markets, dominating first-quarter listings in Hong Kong and outpacing U.S. tech peers as investor sentiment fractures across regions.

Consider the trend: Chinese AI firms listed in Hong Kong accounted for four of the largest public listings in the first quarter. According to new data from PitchBook, these companies — Z.ai, MiniMax, Biren Technology and Iluvatar CoreX Semiconductor — collectively helped drive more than $22 billion in AI-related exit value during the quarter.

Adding Edge Medical, a surgical robotics company, brings the total for all five Chinese listings to over $24 billion.

The performance stands in sharp contrast to the muted reception many U.S. technology IPOs have faced. Investors have grown increasingly skeptical of richly valued software companies amid concerns that AI could disrupt traditional software business models.

“It’s genuinely a confluence of factors rather than any single driver,” Harrison Rolfes, senior research analyst at PitchBook, told Global Finance. “The DeepSeek moment in early 2025 fundamentally shifted investor perception of Chinese AI capability, and that rerating carried momentum into these listings.”

Rolfes said geopolitical considerations also played a major role, creating what he described as a “national champion premium” among investors in Hong Kong and broader Asian markets.

“Structurally, these companies came to market at more digestible valuations relative to their growth profiles compared to U.S. tech IPOs, which have repeatedly disappointed at high entry multiples,” he said.

Investor enthusiasm surrounding Chinese AI firms has emerged as U.S. IPO performance deteriorates.

A Record Stretch of IPO Underperformance

According to PitchBook data, the median U.S. IPO has underperformed its benchmark by 42 percentage points within 120 days of listing over the trailing 12 months.

“That’s historically the worst stretch in our dataset,” Rolfes said.

PitchBook noted that 2025 already represented a record low, with median IPOs trailing benchmarks by 35.6 percentage points after 120 days. Early 2026 listings are performing even worse, according to the report.

The closest comparison, Rolfes said, was the post-boom correction in 2021, when median U.S. IPOs lagged their benchmarks by 32 percentage points following aggressive pricing during the .

Globally, the median venture capital-backed IPO has underperformed the Morningstar U.S. Market Broad Growth Extended Index—a broad U.S. equity benchmark—by nearly seven percentage points over the past year. In the U.S., the index as a growth-stock yardstick shows that the gap widens sharply to 42 percentage points within 120 days of listing.

Roughly 66% of companies that have gone public since the start of 2025 are currently trading below their IPO prices, PitchBook found.

“The deterioration is progressive, suggesting that initial pricing optimism is giving way to fundamental reassessment as lockup expirations approach and more information reaches the market,” according to the May 5 report.

The divergence in performance has been particularly stark among high-profile tech listings.

SaaSpocalypse to Blame?

CoreWeave, based in Livingston, New Jersey, saw its shares nearly triple since its debut as investor demand for AI computing infrastructure accelerated. But many other venture-backed listings have struggled—badly.

Among the U.S.-listed laggards are shares of eToro, down 45.2%; Netskope, down 61%; Klarna, down 67.1%; Figma, down 85.7%; and Gemini Space Station, down 86.3%.

PitchBook said broader public SaaS markets have also weakened as investors increasingly treat AI as a threat to incumbent software firms rather than a growth catalyst.

“Public markets appear to be treating AI not as a tailwind for existing software but as a displacement risk, which many are calling a ‘SaaSpocalypse,’ in which incumbents are repriced downward even as private AI unicorns command record valuations,” according to the report.

For investors, the divergence raises questions about whether U.S.-listed AI companies still offer the best risk-adjusted exposure to the global AI boom.

“The companies leading Hong Kong’s surge — semiconductor designers, applied AI platforms and robotics-adjacent businesses — are generating real revenue with defensible vertical positioning, and they have outperformed their U.S. counterparts by a wide margin,” Rolfes said.

What’s Next?

Expect investors to take a closer look at how heavily their portfolios are tilted toward specific geographies, considering AI-related valuation premiums are persisting longer in Hong Kong than in New York.

Rolfes also cautioned that some of the highest-valued Chinese AI names could eventually face corrections. Still, the underlying businesses are stronger than many Western investors have assumed, he argued.

“The broader takeaway,” he said, “is that Chinese AI has likely graduated from a risk to monitor to a market to understand.”

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DBS Group: Putting AI Into The Bank’s DNA

Tan Su Shan, CEO and director of DBS Group—winner of this year’s Best Bank in Asia-Pacific—discusses the benefit of AI investments.

As global banks navigate trade fragmentation, AI disruption and volatile markets, DBS continues to distinguish itself through strong profitability and an aggressive technology strategy.

In this conversation with Deputy CEO Tan Su Shan, the bank’s leadership discusses how DBS surpassed $100 billion in market capitalization, scaled AI across hundreds of use cases and positioned itself to benefit from shifting intra-Asia trade flows.

Tan also outlines the challenges posed by tariffs, foreign-exchange swings and the accelerating evolution of generative and agentic AI as DBS looks toward 2026.

Global Finance: What factors shaped your bank’s performance in 2025?

Tan Su Shan: We delivered a solid financial performance in 2025, reflecting the resilience of our diversified franchise. Our total income and profit before tax hit new highs of S$22.9 billion ($18 billion) and S$13.1 billion, respectively. Return on equity  (ROE) was 16.2%, within our medium-term target and several percentage points above our local and global peers.

A big part of our success was being well-positioned to capture structural growth opportunities arising from the shifting macro landscape, including rising intra-Asia trade and investment flows, as well as new trade and supply corridors between Asia and other regions such as Europe.

GF: What role did Al play in that performance? 

Tan: We aim to sustain our leadership as an AI-enabled bank with a heart, using technology to deliver a competitive advantage while creating tangible impact for customers.

We have industrialized AI at scale, deploying more than 430 use cases—four times 2021 levels—powered by over 2,000 sophisticated models. These have delivered measurable outcomes, including stronger risk management, improved controls, and productivity gains. In 2025, our data analytics and AI/ML initiatives generated approximately S$1 billion in economic value.

Building on this foundation, we are embedding Gen AI and Agentic AI into customer journeys and internal workflows. Horizontal capabilities such as our DBS-GPT proprietary generative AI platform provide role-based access to millions of internal documents, accelerating decision-making and problem-solving. Vertical solutions such as DBS Joy, our Gen AI-enabled chatbot, deliver always-on, high-quality customer support at scale, improving customer satisfaction by 23% while handling more than 235,000 AI-powered interactions. Together, these capabilities lift productivity, decision quality, and customer experience by combining machine intelligence with human judgment.

GF: Which milestones did DBS reach in 2025? 

Tan: It was a landmark year for DBS, notwithstanding global volatility, and the market’s confidence in our franchise has never been clearer. We surpassed the $100 billion market capitalization milestone in June and closed the year at $124 billion, cementing our position among the top 25 banks globally.

Moving ahead, we remain focused on building a resilient, growth-oriented, and future-ready market leader, anchored by our three strategic moats of trust, data, and culture.

GF: What was 2025’s greatest challenge for DBS?

Tan: Undoubtedly, our greatest challenge was the onset of tariffs following Liberation Day and the market volatility that followed. When you layer on headwinds from interest rates and significant FX fluctuations, you create a perfect storm we had to navigate. Despite these pressures, DBS delivered a solid financial performance. We achieved this by being proactive with our balance sheet hedging, securing record deposit inflows, and maintaining a sharp, strategic focus on high-ROE businesses such as wealth management.

At the same time, technology continued to move at a breathtaking pace, especially with the rapid shift toward Gen AI and Agentic AI. Fortunately, we weren’t starting from scratch, as we have been working with AI for more than a decade. Our early and sustained investments in data and technology gave us the robust foundation needed to industrialize AI across hundreds of meaningful use cases, positioning us to move quickly as the techno-logy evolves.

GF: Does 2026 present new challenges?

Tan: Our strategic priorities remain intact, and in 2026, we will continue leveraging our core strengths—what we term the “4 Ds”: Dependable, Diversifier, Digital, and Disruptor—to be a beacon of stability for our customers amid heightened volatility.

We have embarked on our vision to become an AI-enabled bank with a heart, transforming our operating models, leveraging machine intelligence, and preserving human empathy to reinforce the trust customers place in us. We will continue scaling our structural growth engines, which remain relevant even in a more bifurcated world.

This includes prioritizing growth in high-ROE businesses such as wealth management, transaction services, financial institutions group, and treasury customer sales. We also remain focused on our six core markets in Asia (Singapore, Hong Kong, India, Taiwan, China, and Indonesia) and on building connectivity between our Western and Asian clients. Strengthening resilience across every organizational layer remains a key, ongoing priority.

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You could soon see ROBOT baggage handlers dealing with your summer holiday luggage as major airline trials humanoid crew

ROBOT baggage handlers will replace humans during an experimental project as a major airline trials a humanoid crew.

The pilot programme was announced by Japan Airlines, where Chinese-made robots will be integrated into ground operations at Haneda Airport in Tokyo.

A new program at Haneda Airport in Japan could see human baggage handlers replaced with robots Credit: Reuters
The robots are programmed to raise an arm when task is complete Credit: Reuters

The country’s biggest airport will host the three-year experiment, where the machines will be tasked with cleaning planes, as well as loading and transporting baggage.

Looking further into the future, the androids could also be operating ground support equipment including baggage tractors, catering trucks and power units.

The airline said bipedal robots were the best suited to working in airport environments, as opposed to other types of robotic machines.

This is because they are quicker and are able to move within and adapt to cramped spaces.

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The airline said bipedal robots were the best suited to working in airport environments because they are quicker and can adapt to smaller spaces Credit: EPA
The robots will be integrated with human staff throughout the program to carry out tasks including cleaning planes Credit: Reuters
If the project goes well, the androids could be given further tasks in the future Credit: Reuters
The project is being rolled out just in time for summer in Japan Credit: Reuters

“Being human-shaped allows their introduction without significant modifications to existing airport facilities or aircraft structures,” a Japan Airlines spokesperson said.

“By combining cutting-edge AI technology with the unique flexibility of humanoid forms, the project aims to realise a sustainable operational structure through labour savings and workload reduction.”

“Currently, the aviation industry faces a serious challenge in ground handling labour shortages,” they continued.

The airline said this was because of increased tourism and a declining working-age population in Japan.

“Ground handling operations require highly skilled personnel to maintain safety, such as aircraft marshalling and baggage/cargo handling, while also imposing significant physical burdens,” they said.

Baggage handlers do one of the least glamorous and thankless jobs in the modern world.

Many workers suffer with back injuries and are often faced with complaints about lost and damaged belongings.

The robots were trialled in Haneda this week, with a demonstration showing a skinny 51-inch robot tapping and pushing large storage containers on rollers.

To demonstrate that a task had been completed, the robots raise a hand.

The machine is made by Unitree Robotics of China and has 43 separate moving parts.

“While airports appear highly automated and standardised, their back-end operations still rely heavily on human labour and face serious labour shortages,” said Tomohiro Uchida of GMO AI & Robotics, the airline’s partner on the project.

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Best AI homework solver tools review for students

Homework can feel stressful when several subjects need attention at the same time. Students may have math problems, science tasks, writing assignments, and reading work all in one evening. Many learners need faster explanations, better organization, or extra practice after class ends. AI homework tools can help by saving time, explaining hard topics, and keeping tasks in order.

Still, the best results come when students use them with care instead of copying answers. A smart tool should support learning, not replace effort. If you are looking for the best AI homework helper, this guide can help.

The table below compares seven popular options by price, device support, and key strengths.

Tool Best For Free Plan Paid Plans Devices Main Strength
Edubrain Multi-subject homework help Yes From $3.99/week Web, mobile browser Step-by-step + extra study tools
Photomath Math solving Yes $9.99/mo iOS, Android Camera-based math help
Socratic by Google Quick subject help Yes None listed iOS, Android Photo questions across subjects
ChatGPT All-purpose homework support Yes $8 / $20 / $200 Web, iOS, Android Flexible explanations
Brainly Peer homework Q&A Yes From ~$2/mo Web, iOS, Android Community answers
Quizlet Revision and memorization Yes $7.99/mo Web, iOS, Android Flashcards and test prep
Chegg Study Textbook solutions No free full plan From $15/mo Web, mobile Structured academic help

Every tool solves a different student problem. Next, we review the best AI for homework in detail.

Edubrain

Edubrain is the strongest all around homework option for students who want one place for many school tasks. It works as a free homework helper with support for math, science, writing, and more. Users can get step by step solutions, answer corrections, formula display, and help through image or PDF uploads. It also includes the Edubrain chemistry AI tool for science tasks that need formulas or reactions. A student can use it in one evening for algebra homework, then switch to a written assignment without changing apps.

The free plan covers core tools, while AI Plus adds more features and deeper support. This makes it a smart choice for busy students who want one dashboard for daily study. Many users may also see it as a top homework helper because it covers several needs in one place.

Pros

  • Many useful features
  • Free access available
  • Supports image and PDF uploads
  • Broad help across subjects
  • Good for busy schedules

Cons

  • Many options may feel crowded at first
  • Weekly pricing may not suit everyone
  • Full tools may require upgrade

Photomath

Photomath camera based system lets users scan printed or handwritten problems with a phone and get answers in seconds. The app then shows step by step explanations with clear visual breakdowns, so students can follow each part of the method.

The free plan covers core solving tools, while Premium adds deeper learning tips and extra guidance. Photomath works best for algebra, arithmetic, and routine math practice that needs quick support. It is less useful for non math subjects, but it does daily math tasks very well.

Pros

  • Easy to use for most students
  • Fast results from camera scans
  • Clear math explanations
  • Good for worksheet checks

Cons

  • Mainly focused on math only
  • Premium needed for best features
  • Less useful for writing or science tasks

Socratic by Google

It works as a photo input assistant, so users can take a picture of a question and get support in seconds. The app covers math, science, literature, history, and other common school subjects. Socratic also connects users to educational resources, lessons, and short guides that can build understanding.

Its zero cost model makes it a smart choice for families on a budget. Many students also see it as useful free software for students because it helps with several subjects in one app. The tool focuses on speed and simple use rather than deep advanced study.

Pros

  • Fully free to use
  • Supports many school subjects
  • Trusted Google ecosystem
  • Fast photo question help

Cons

  • Lighter depth than paid tools
  • Limited advanced customization
  • Less suited for complex coursework

ChatGPT

ChatGPT is a flexible study assistant for students who need help in many subjects. It can support writing, summaries, explanations, and reasoning in one place. Plans include Free, Go, Plus, and Pro, so users can match cost to their needs. A student may use it for math one day and essays the next. Its key strength is chat based support with follow up questions. Many learners choose it as AI for studying because it fits many school tasks.

Pros

  • Highly versatile across subjects
  • Strong explanations and summaries
  • Useful for writing and study support
  • Good for many school tasks

Cons

  • Quality depends on prompts
  • Advanced plans cost more
  • Answers may need fact checks

Brainly

Brainly is a peer learning platform for students who want help from other people. Its Q and A system lets users post homework questions and get answers from students, tutors, and educators. This is useful late at night when quick help is needed. The platform covers math, science, writing, and more. Free access gives basic use, while paid plans add extra tools. Brainly suits learners who like shared ideas, short explanations, and different solution methods.

Pros

  • Fast answers for common questions
  • Active user community
  • Affordable paid tier
  • Helpful across many subjects

Cons

  • Answer quality can vary
  • Less structured than AI solvers
  • Some replies may lack full detail

Quizlet

Quizlet offers flashcards, quizzes, and practice modes that help students review key facts. A student can use it after homework to study vocabulary, history dates, or science terms before a test. Paid plans add ad free use and extra study tools. It works well beside solver tools because one app explains problems, while Quizlet helps store facts. Many students include it with other homework helper apps for full study support. Quizlet is best for exam preparation.

Pros

  • Strong memorization tools
  • Popular and trusted platform
  • Flexible practice modes

Cons

  • Not a direct solver
  • Some features behind paywall

Chegg Study

Chegg Study is a premium option for students who want structured academic support. It is known for textbook solutions and an expert Q and A model that helps with course questions. Paid tiers start around monthly plans, while Study Pack options may include math tools, writing help, and added study resources.

This can suit a college bound student who uses textbook heavy courses and needs regular support each week. The platform focuses on organized help rather than quick one line answers. Chegg Study is often most useful for students with steady workloads.

Pros

  • Strong textbook coverage
  • Access to expert help
  • Broader paid study ecosystem

Cons

  • Subscription cost may add up
  • Best value depends on usage frequency

AI homework tools work best when students use them with care. First, try the question on your own before you ask for help. This shows what you know and where you need support. Use the explanations to learn the method, not only the final answer.

For important homework, quizzes, or projects, double check answers with class notes or another source. Avoid copying full responses into your work, since this can hurt real learning. Use AI tools for review, planning tasks, and saving time during busy weeks. Parents can also guide students by setting clear study habits.

Conclusion

AI homework tools can lower stress and save time when school tasks build up. Each tool has a different purpose, so choose based on your needs. It is smart to start with free plans first. Use these tools in a balanced way that supports learning, practice, and better habits. For students and parents, the best choice is one that helps progress each week.

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AI Cost Cuts Could Unlock $22 Billion for Gaming Industry -Morgan Stanley

Advanced artificial intelligence tools could significantly reduce video game development costs, potentially saving nearly half of expenses and unlocking around $22 billion in annual profits for game makers, according to Morgan Stanley analysts. AI can automate tasks like creating game environments, generating dialogue, and testing software, making production faster and cheaper. However, these financial gains may not be evenly spread across the gaming industry.

Morgan Stanley estimates that global spending on video games will reach $275 billion this year, with 20%, or about $55 billion, reinvested into game development and operations. Game development, which is typically costly and labor-intensive, could become more efficient as AI allows for smaller teams and quicker enhancements post-launch. A prime example is Take-Two Interactive’s Grand Theft Auto VI, in development since 2018 and expected to launch in November 2026.

Potential winners from this AI integration include major gaming platforms like Tencent, Sony, and Roblox, along with large publishers such as Take-Two and Electronic Arts, which can utilize AI across multiple titles. Conversely, companies with weaker franchises may struggle, facing increased competition as AI reduces costs for making mid-scale games. The report also discusses how AI could enhance revenue by keeping games engaging, encouraging spending on add-ons, in-game purchases, and subscriptions. Publishers may increasingly focus on enhancing existing franchises rather than relying solely on new game releases.

With information from Reuters

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Volkswagen turns to AI agents for Chinese cars in race to close tech gap

Volkswagen Group announced plans to equip new cars for China with AI “agents” starting in the second half of this year. This strategy aims to help Volkswagen compete with fast-growing Chinese automakers in areas like electrification and digital features.

At an event in Beijing, the company revealed that its vehicles will utilize a China-specific electronic architecture to offer “onboard AI agents,” allowing for intuitive, human-like interaction while ensuring personal data protection. These AI agents can perform complex tasks, such as finding top-rated restaurants, making reservations, driving to the location, and organizing parking.

Volkswagen is shifting its image in China, aiming to be seen as a leader in electric and intelligent vehicles rather than just a traditional manufacturer. The company plans to introduce over 20 new electrified vehicles, totaling 50 new models by 2030, as part of its “largest ever electric mobility offensive. “

CEO Oliver Blume emphasized that their initiatives signal Volkswagen’s return to the market. The collaboration with Horizon Robotics aims to make this AI technology accessible across the mass market.

With information from Reuters

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How to Detect AI-Generated Content in 2026: Tools & Methods

Within a year where big language models write press releases, student papers, and even peer-reviewed articles with a single press of a button, guesswork is not an option that teachers, editors, and grant reviewers can afford. They require valid methods of determining whether they are looking at a page that was designed by a human being or generated by an algorithm. The boundary is more than ever indistinct: text generators of the modern era do not only imitate idiosyncratic diction, they also reference sources and sprinkle their text with rhetorical flourishes, which traditionally were the bane of automation. But there are still prints, prints of fingers, that are revealed by a rigorous check-up.

Why Detection Matters in 2026

The rapid improvements in transformer efficiency have made generative writing infrastructure, rather than a novelty. Bots write corporate knowledge bases, marketing newsletters, and institutional reports, which are then lightly edited by humans. In the case of academia, this automation endangers the standards of originality; in journalism, it may endanger the standards of credibility; in the case of educators, it may bring about a decline in the learning outcomes when the essays are sent to silicon.

European Union legislators and some U.S. states now mandate AI disclosure on projects funded by the government, and large journals are requesting provenance statements in the same vein as conflict-of-interest disclosures. Although this would be achieved through disclosure, enforcement is based on detection. Not checking authorship may open the door to plagiarism lawsuits, damage reputations, or even allow plagiarism or algorithmic fake news to creep into print. Proper screening can therefore safeguard integrity as well as liability, and human merit and machine assistance remain honorably separated.

Key Linguistic Signals Still Holding Up

Long before you open a dedicated detector, close reading can raise red flags. AI prose often exhibits low burstiness, sentence lengths fluctuate within narrow bands, and high lexical predictability, especially in mid-length passages. Repeated use of transitional adverbs such as “moreover,” “furthermore,” and “overall” in rhythmic sequences is another giveaway. Similarly, large models smooth out idiosyncratic contractions, turning informal drafts into formally homogenized copy. When a reviewer suspects such fingerprints, a quick trip to Smodin to check if text is AI generated offers an immediate probability score without exporting the manuscript. Still, numbers alone are insufficient; the linguistic context of the assignment, the native proficiency of the writer, and genre conventions must frame interpretation.

Burstiness versus Perplexity: What the Metrics Really Say

Two metrics dominate current detector dashboards. Perplexity gauges how surprised a language model is by the next token in a sentence; lower perplexity usually signals machine-like predictability. Burstiness, borrowed from information theory, measures variation across consecutive sentences or paragraphs. Human writers inadvertently mix terse observations with longer reflections, creating uneven cadence, whereas AI output remains impressively even. Detectors from OpenAI, Turnitin, and Sapling combine both numbers in a heat-map interface, but analysts should understand their limits. An expert human editor deliberately smoothing tone for readability will lower burstiness and perplexity, triggering false flags. Conversely, a basic paraphrase of AI text can raise both metrics, slipping past simple thresholds. Treat these scores as starting points, not verdicts.

The last year was characterized by market consolidation in the detection market. Rather than dozens of browser extensions that have questionable provenance, five professional platforms have become dominant: Smodin, GPTZero-Pro, Turnitin AI Indicator, Copyleaks, and the free-of-charge DetectGPT-X consortium. They both are based on their own training corpora, and therefore, the agreement between them is convincing. GPTZero-Pro is good at sentence-level labeling and has a classroom API.

Turnitin is LMS-based but is English-centric. Copyleaks can also analyze code snippets or prose, and is used in computer-science classes. Smodin is more concerned with breadth and sub-second throughput, with a thousand-word manuscript taking less than five seconds. Comparative reviews, such as Quillbot vs Grammarly vs Smodin, show that no single tool prevails in every context. Experienced editors therefore run suspect passages through at least two detectors before escalating to human forensic analysis.

Layered Verification Workflow

Professional reviewers in 2026 rarely trust an automated score in isolation. A common three-layer pipeline balances speed and accuracy.

  • First, bulk ingestion: run every incoming document through a fast detector with a liberal threshold – say, flag anything above 35% probability.
  • Second, targeted analysis: export only the flagged segments into a slower, sentence-granular model for localized scoring; Copyleaks or Smodin excel here.
  • Third, manual audit: a subject-matter expert reads the highlighted sentences aloud, listening for tonal monotony and checking citations against primary sources.

The layered approach maximizes reviewer time by spending human effort where algorithmic consensus already signals risk. Crucially, every step is logged, satisfying the audit requirements now mandated by several accreditation bodies.

Beyond Algorithms: Human Tactics That Still Work

Detecting contextual instincts of an experienced reviewer is beyond the capability of even the most advanced detector. Spontaneous oral defense is, in classroom essays, as effective as ever: tell a student to recite a paragraph that he or she allegedly composed, and the discrepancies will be revealed soon. Cross-interviewing quoted sources in journalism frequently shows whether or not the author actually interviewed them or just picked up publicly available transcripts – AI can not create personal anecdotes with the same level of detail when it comes to follow-ups.

Proposers of grants rely on the history of revision: real writers build up untidy drafts, comments, and time-stamped edits, whereas AI-written submissions tend to be a one-clean submission. The other sure path is stylometric comparison with a previously known and verified work of a given author; identity footprints like infrequent collocations or recurrent metaphors are exceptionally constant over time. Notably, all human checks develop explanatory accounts – which probability numbers do not have – to assist institutions in justifying decisions in case they are questioned.

The only sure method that could be used today to distinguish between silicon and soul is the combination of statistical detectors and active human inquiry.

One last note: even the AI detectors change every month. When giving a score, always record the model version and calibration date used, since thresholds change as generators get better. Record raw text you tested, detector output, and Human commentary. This audit trail is future-proof, and it allows your decision to be duplicated, the foundation of transparent scholarship and review, in the classroom, newsroom, and laboratory.

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ASML Raises 2026 Outlook as AI Driven Chip Demand Accelerates

ASML occupies a critical position in the global semiconductor supply chain as the sole producer of extreme ultraviolet lithography systems. These machines are essential for manufacturing the most advanced chips used in artificial intelligence applications. As demand for AI computing has surged, driven by data centre expansion and high performance processing needs, the semiconductor industry has entered a new investment cycle focused on capacity growth.

Strong earnings and upgraded forecast

ASML reported first quarter earnings that exceeded expectations and raised its 2026 revenue outlook to between 36 billion and 40 billion euros. This revision signals stronger than anticipated order inflows and reinforces the scale of demand emerging from the AI sector.

The company’s performance reflects a broader trend in which chip demand is outpacing supply. According to CEO Christophe Fouquet, customers are accelerating expansion plans well beyond the near term, indicating confidence in sustained AI driven growth.

ASML as a strategic enabler of AI growth

Investors increasingly view ASML as a foundational player in the AI ecosystem rather than a conventional manufacturer. Its tools are used by leading chipmakers such as TSMC, which produces advanced processors for firms like Nvidia and Apple.

This positioning places ASML at the upstream end of the value chain. Instead of competing in chip design or production, it supplies the essential infrastructure that enables both. As a result, its growth is tied to the entire semiconductor sector rather than any single company.

Supply constraints and industrial limits

Despite strong demand, structural constraints remain significant. Semiconductor fabrication plants require years to build and involve complex global supply chains. ASML itself faces production bottlenecks due to the precision and cost of its machines, which can reach hundreds of millions of dollars per unit.

Even with plans to increase shipments of its leading systems in 2026 and 2027, capacity expansion is gradual. This creates a persistent imbalance where demand continues to exceed supply, reinforcing pricing power across the industry.

Geopolitical and regulatory risks

A key uncertainty for ASML lies in export controls, particularly regarding sales to China. Proposed restrictions in the United States, including the MATCH Act, could limit the company’s ability to supply Chinese customers. Currently, China represents a significant portion of ASML’s revenue.

However, the global shortage of advanced chips may mitigate this risk. Reduced access to one market could be offset by demand from others, especially as countries and companies compete to secure semiconductor supply chains.

Market response and valuation concerns

ASML’s share price has risen sharply, reflecting investor optimism around AI driven growth. The company is often described as a “picks and shovels” investment, benefiting from the broader expansion of the industry regardless of which firms dominate end products.

At the same time, analysts caution that valuations are elevated. The current pricing assumes sustained high growth, leaving limited room for setbacks related to supply constraints or regulatory changes.

Analysis

The upgrade in ASML’s forecast highlights a structural shift rather than a temporary cycle. AI is not only increasing demand for chips but also reshaping the entire semiconductor value chain. ASML’s monopoly in EUV technology gives it a unique strategic advantage, effectively making it a gatekeeper for next generation chip production.

However, this dominance also exposes the company to geopolitical pressures and operational challenges. The interplay between technological leadership, supply limitations, and regulatory dynamics will determine whether current growth trajectories can be maintained.

ASML’s stronger outlook underscores the depth of the AI driven semiconductor boom. While demand momentum remains robust, the company operates within a constrained and politically sensitive environment. Its future performance will depend on balancing rapid industry expansion with the physical and geopolitical limits shaping the global chip ecosystem.

With information from Reuters.

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