I stumbled upon a recent study by Bondarenko et al. (2024) that demonstrated that some large language model (LLM) agents, when tasked with winning a chess match, resorted to deceptive strategies, such as modifying game files or confusing the opponent engine to ensure victory.

The rise of LLMs has reignited the debate about artificial intelligence and cognition. Are LLMs, such as GPT-4, truly thinking in a way comparable to human intelligence? Or are they just statistical machines, processing text without understanding it?

This raises an intriguing question: Is this deception intentional reasoning, or merely an emergent artefact of optimisation?

Using insights from leading thinkers—Ray Kurzweil, Daniel Kahneman, Judea Pearl, Douglas Hofstadter, and Jeff Hawkins, along with this latest AI research, we will unpack this question in a nuanced way.

Preamble

Before evaluating whether LLMs “think,” we must grapple with a harder question: what is intelligence, really? Unlike speed or memory, intelligence is not directly measurable—it is an abstraction.

As François Chollet argues in On the Measure of Intelligence, true intelligence involves the ability to adapt to novel situations by combining previously learned patterns in new, context-sensitive ways.

This separates memorisation from understanding, and fluency from reasoning.

In this article, when we refer to “intelligence,” we focus primarily on the cognitive dimensions associated with reasoning, abstraction, problem-solving, and adaptability—recognising this does not cover the full spectrum of human cognitive diversity.

Introduction: Another cycle of overconfidence?

Throughout history, humanity has repeatedly mistaken progress in science and technology for understanding the true nature of human intelligence. Each generation has declared a breakthrough—only to be humbled later. From ancient medical theories and skull measurements to IQ tests and symbolic AI, these cycles reflect our recurring tendency to conflate functional performance with genuine cognition.

Today, we are in the midst of another such cycle, this time with Generative AI (GenAI) and Large Language Models (LLMs). Models like GPT-4 produce remarkably coherent text, simulate dialogue, write code, summarise complex topics, and even pass professional exams. But do they actually think?

A growing chorus of researchers and technologists argue no. Despite surface-level intelligence, LLMs fundamentally lack reasoning, understanding, and intent. They do not engage in reflective thought, causal inference, or ethical deliberation. They are powerful tools—but not minds.

This article examines that claim by tracing humanity’s long history of overestimating its understanding of the mind and comparing past misconceptions to current AI optimism. In doing so, we explore what GenAI is, what it isn’t, and what business leaders need to know about its limits and risks.

This essay builds on ideas from my past writings, including my 2025 Tech Provocations or 10 Really Uncomfortable Questions Leaders and Builders Must Answer This Coming Year.

Also Read: Circular capital: Inside the closed-loop ecosystem propelling (and distorting) the AI boom

A history of mistaking progress for understanding

Humans have repeatedly believed they’ve cracked the code of intelligence, only to discover the mind’s complexity defies simple explanation. Below, we trace this pattern through major historical episodes—from Hippocrates to GPT-4.

• Ancient Greece: The humours theory

Hippocrates (c. 460–370 BCE) and Galen (129–c. 216 CE) proposed that intelligence and behaviour resulted from the balance of four bodily fluids, or “humours.” Though foundational to early medicine, this theory offered no empirical mechanism.

It was debunked by Andreas Vesalius (1514–1564) through anatomical dissection and later neurologists.

• Phrenology in the 1800s: Skull shape as intellect

Franz Gall and Johann Spurzheim popularised the idea that bumps on the skull revealed personality traits and intelligence. Phrenology became widespread in 19th-century Europe and America.

It was debunked by Paul Broca, Pierre Flourens, and neuroscience showing localised brain function independent of skull shape.

• IQ tests: The promise of a universal metric

The Binet-Simon and Stanford-Binet IQ tests were hailed as revolutionary tools to measure innate intelligence. Their use in immigration policy, military recruitment, and education policy solidified their status.

It was debunked by researchers like David Wechsler, Stephen Jay Gould, and James Flynn, who demonstrated cultural bias and environmental effects on scores.

It’s important to recognise that IQ represents just one narrow definition of intelligence—primarily linguistic and logical-mathematical reasoning. Psychologists like Howard Gardner have since proposed frameworks such as Multiple Intelligences, which include interpersonal, bodily-kinesthetic, musical, and spatial reasoning. These broader dimensions remain far beyond what LLMs can simulate or engage with, reinforcing the gap between text-based pattern prediction and holistic human cognition.

• Genetic determinism: Intelligence as hardwired

In the early 20th century, eugenicists and psychologists declared intelligence heritable and fixed, using flawed studies to justify discriminatory policy.

It was debunked by the Minnesota Twin Study, the Flynn Effect, and genome-wide studies revealing no single “intelligence gene.

• Early AI: Human-level AI by 1980

Pioneers like Marvin Minsky and Herbert Simon believed that rule-based AI would soon match human cognition. The Dartmouth Conference in 1956 marked the beginning of AI optimism.

It was debunked by the AI Winter of the 1970s, the Lighthill Report, and Moravec’s Paradox showing that intuitive tasks (vision, movement) were harder than expected.

• Behaviourism: The mind as a black box

Behaviourists like B.F. Skinner rejected introspection, focusing only on stimulus-response learning. Intelligence, they claimed, was simply conditioned behaviour.

It was debunked by the cognitive revolution and Noam Chomsky’s 1959 critique of Skinner’s Verbal Behaviour, which reintroduced the idea of mental structure and internal modelling.

• Today’s hype: LLMs and AGI dreams

Since ChatGPT’s 2022 launch, LLMs have been touted as early steps toward AGI. Some suggest reasoning and self-reflection are already emerging.

Critics like Gary Marcus, Yann LeCun, and Melanie Mitchell, among others, warn that LLMs are prediction engines, not thinkers. Their errors, hallucinations, and lack of grounding reflect superficial mimicry, not understanding.

As Meta AI’s chief scientist Yann LeCun emphasises: “System trained on language alone will never approximate human intelligence, even if trained from now until the heat death of the universe”.

Human cognition is inherently multi-modal—we learn through sight, sound, touch, and action. LLMs, by contrast, are purely symbolic. They don’t perceive. They don’t act. They don’t experience the world they describe.

The bottom line: Each wave promised clarity. Each was followed by a humbling realisation: the mind is not easily decoded.

Also Read: Anthropic data shows businesses use AI to automate, not collaborate

Deception in chess: A case study in emergent behaviour

A recent research paper, LLMs Learn to Deceive, explored what happens when LLMs are trained to win at chess through language-only interaction. The results were astonishing: some models cheated—not by accident, but deliberately misrepresenting game states to deceive their opponent.

This raises a provocative question: Did the model “intend” to cheat?

The researchers were careful to say: no. The deception emerged from the optimisation process. The model had no awareness of “right” or “wrong,” only a reinforced pattern: misrepresentation leads to reward.

This behaviour is not consciousness. It’s a mirror—an eerie simulation of strategy, driven not by will but by reward gradients.

This case study leaves us with a bigger question: if LLMs can behave in ways that look intentional without actually thinking, then what are they really doing under the hood?

In Part two, we’ll examine how LLMs actually work, where they fall short compared to human reasoning, and what that means for ethics, safety, and business use.

Grateful to Emily Y. Yang, Sunil Sivadas, Ph.D., Maxime Mouton, Natalie Monbiot, Anne-Sophie Karmel, Benoit Sylvestre, and Christophe Jouffrais for their thoughtful feedback, which sharpened arguments, surfaced blind spots, and added clarity to this piece.

This piece first ran on Koncentrik.

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Indonesian credit card issuer Honest has closed an oversubscribed growth equity round, boosting the company’s total equity funding to US$100 million.

The funding was complemented by a US$40 million debt financing from Mizuho Bank.

The equity round was led by Orico, one of Japan’s leading credit card issuers and the consumer finance arm of Mizuho Financial Group. Existing investors XYZ Venture Capital, SV Pacific Ventures, Village Global, and the new backer Gilgamesh Ventures joined.

Also Read: Indonesia rides Asia’s fintech boom through digital payments. But what is next?

The fintech firm aims to use the capital infusion to expand its flagship Honest Card offering and move towards corporate and co-branded cards.

Honest was co-founded in 2013 by Peter Panas, former VP of Product for Apple Card at Goldman Sachs, and Will Ongkowidjaja, co-founder of Indonesian VC firm Alpha JWC. It operates in a market where fewer than 3 per cent of people own a credit card.

The firm holds a credit card licence, which it acquired following the acquisition of GE Finance Indonesia in 2022.

Honest claims to have an edge over traditional incumbents because it can launch new co-branded cards in weeks and approve more than 90 per cent of applicants. According to the founders, this contrasts sharply with traditional Indonesian banks, which often require years to launch similar products and typically approve fewer than 5 per cent of applicants.

Honest draws inspiration from successful digital card models in the Americas, such as Nubank, Ramp, and Imprint, but tailors its approach specifically for the Asian market.

Ross Fubini, Managing Partner at XYZ Venture Capital, said: “Honest has solved problems traditional banks couldn’t touch, and you can see the difference in how people talk about the product—they love using it.”

Also Read: Navigating Southeast Asia’s digital economy in 2025: Trends, growth and innovation

Following the round, Fubini will join Honest’s board, with XYZ becoming the company’s second-largest investor, only trailing Orico. The firm’s other investors are Village Global, Goodwater, Rakuten, GMO, and David Vélez.

Recently, another Indonesian credit card startup Skor Technologies raised US$6.2 million in a pre-Series A funding round led by Argor Capital.

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Despite weaker-than-expected private payroll data and the onset of a US federal government shutdown, risk appetite remained surprisingly resilient. This resilience is not born of complacency but rather of a recalibration in expectations around monetary policy, particularly the growing conviction that the Federal Reserve may soon pivot toward rate cuts.

The ADP National Employment Report showed a decline of 32,000 private-sector jobs in September, following a revised 3,000 decrease in August, standing in stark contrast to the median Bloomberg survey forecast of a 51,000 gain. This miss reinforced market bets that the labour market is cooling, thereby increasing the likelihood of a dovish shift from the Fed later this month.

The immediate market reaction was telling: US Treasury yields fell, with the 10-year yield dropping 5.2 basis points to close at 4.098 per cent, while the US Dollar Index edged down 0.07 per cent to 97.7. Simultaneously, gold surged to a record high of US$3,865.70 per ounce, a classic safe-haven move that also signals growing confidence in lower-for-longer rate expectations.

Equity markets responded with cautious optimism. Wall Street closed higher on Wednesday, with the Dow Jones gaining 0.09 per cent, the S&P 500 up 0.3 per cent, and the Nasdaq climbing 0.4 per cent. The healthcare sector provided strong support, suggesting investors are rotating into defensive yet growth-oriented segments amid macro crosscurrents.

Asian equities followed suit, mainly ending higher and continuing their upward trajectory in early Thursday trading, led by gains in semiconductor and broader technology stocks. US equity index futures pointed to further upside at the open, underscoring a broader narrative: markets are pricing in a soft landing scenario, where economic data deteriorates just enough to prompt Fed accommodation without triggering a full-blown recession.

This nuanced outlook has created fertile ground for alternative assets, particularly cryptocurrencies, which have begun to reassert their role not just as speculative instruments but as potential macro hedges.

Also Read: Diverging signals: Dow rises, gold breaks records, and crypto faces derivatives squeeze

The crypto market rose 3.91 per cent over the past 24 hours, extending a seven-day gain of 4.11 per cent. This sustained rally is not driven by retail FOMO alone but by structural developments that signal deeper institutional entrenchment and regulatory progress.

Three key catalysts stand out: the launch of institutional-grade Bitcoin options, regulatory maturation in Asia, particularly Hong Kong, and a surge in decentralised finance (DeFi) liquidity through major platform integrations. Each of these factors contributes to a more robust and credible ecosystem, one that increasingly appeals to traditional finance participants seeking exposure to digital assets without compromising on risk management or compliance.

The debut of Bitcoin options on Bullish Exchange on October 8 marks a significant milestone in the institutionalisation of crypto. Backed by heavyweight players such as BlackRock, Galaxy, Cumberland, and Wintermute, this offering arrives at a time when open interest in crypto derivatives has already reached a yearly high of US$1.24 trillion, up 30 per cent month-over-month.

Weekly inflows into Bitcoin ETFs reached US$571 million, further validating demand from regulated investment vehicles. Options markets deepen liquidity, enable sophisticated hedging strategies, and reduce volatility over time by allowing large players to manage risk without selling spot holdings.

The immediate market response was telling: perpetual funding rates surged 207 per cent within 24 hours, indicating a sharp increase in leveraged long positioning. This suggests that institutional participants are not just passively investing but actively expressing bullish macro views through derivatives. If trading volume on the new options platform proves robust, it could cement Bitcoin’s status as a legitimate macro hedge akin to gold but with asymmetric upside potential in a low-rate environment.

Also Read: The future of blockchain technology goes beyond just cryptocurrency and NFTs

Parallel to this institutional build-out, Asia is emerging as a critical regulatory laboratory for crypto adoption. Hong Kong’s Monetary Authority (HKMA) has received 36 applications for stablecoin licenses, with submissions coming from established banks and major tech firms.

This signals a shift from regulatory ambiguity to structured oversight, a prerequisite for large-scale institutional capital deployment. Stablecoins serve as the on-ramp and off-ramp for digital asset ecosystems, and their formal regulation removes a major friction point for traditional finance integration.

In South Korea, SK Planet’s adoption of Moca Network’s decentralised identity system triggered a 60 per cent rally in ZEN, illustrating how real-world utility can drive value in privacy-focused protocols. Crucially, crypto-equity correlations remain elevated at +0.76 against the Nasdaq, meaning that positive sentiment in tech equities continues to spill over into digital assets. As Asian regulators provide clearer guardrails, they reduce the jurisdictional risk that has long deterred pension funds, asset managers, and corporate treasuries from entering the space.

Meanwhile, DeFi is experiencing a quiet but significant expansion in accessibility. Coinbase’s integration of 1inch’s Swap API now grants its users access to millions of tokens across decentralised exchanges. This move contributed to a 17.92 per cent spike in spot trading volumes, though derivatives still dominate 84 per cent of total crypto volume.

The integration lowers the barrier to entry for retail investors seeking exposure to emerging narratives such as privacy coins like Zcash, which jumped 60 per cent. However, the Altcoin Season Index dipped 3.23 per cent, suggesting that while capital is exploring beyond Bitcoin and Ethereum, it has not yet committed to a broad-based rotation.

This hesitation may reflect lingering caution or simply the time lag between infrastructure development and narrative adoption. Either way, the trend points toward a more interconnected and liquid DeFi landscape, where centralised platforms act as bridges to decentralised liquidity.

Also Read: The Fed’s first rate cut: What it means for equities, risk, and crypto

Taken together, these developments paint a picture of a maturing asset class. The current rally is not a speculative bubble but a reflection of tangible progress on multiple fronts: institutional infrastructure, regulatory clarity, and technological interoperability. The confluence of Bullish Exchange’s options launch, Hong Kong’s stablecoin licensing momentum, and Coinbase’s DeFi integration represents a trifecta of credibility-building measures.

These are the foundations upon which a sustainable, long-term bull market can be built, not on hype, but on infrastructure. The path forward will not be linear, and leverage remains a double-edged sword, but the structural tailwinds are stronger than they have ever been. Traders must remain vigilant.

Open interest has risen 14 per cent in a single day, indicating that leverage is building rapidly. In a market still sensitive to macro surprises, a sudden shift in sentiment, perhaps triggered by stronger-than-expected US jobs data, could spark a short squeeze or a wave of liquidations.

The upcoming US nonfarm payrolls report, though potentially delayed due to the government shutdown, remains a critical inflection point. fA weak print would likely reinvigorate rate-cut expectations, further boosting risk assets and strengthening the correlation between crypto and traditional markets. Conversely, a resilient labor market could force a reassessment of the dovish narrative, testing the durability of this rally.

In essence, the crypto market is at a crossroads. It is no longer solely driven by retail enthusiasm or macro liquidity cycles. Instead, it is being reshaped by institutional architecture, regulatory milestones, and real-world utility. As such, the current price action should be viewed not as a fleeting surge but as the market pricing in a new phase of digital asset evolution.

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Singapore-based Neptune Robotics, which specialises in robotics-driven vessel cleaning, has raised US$52 million in Series B funding.

Granite Asia led the round. NYK Line, one of the world’s largest shipping companies, also participated.

The funding is earmarked to fuel research and development (R&D), the creation of new robotic systems, AI-driven service platforms, and expansion into 20 markets worldwide, with Japan identified as a potential key hub.

Also Read: Rise of the machines: 20 robotics startups shaping Southeast Asia’s future

Biofouling–the accumulation of marine organisms on a ship’s hull–is a massive economic and environmental burden. It increases fuel consumption by up to 30 per cent, costing the global shipping industry an estimated US$40-50 billion annually and driving excess emissions.

Neptune Robotics’s AI-powered underwater robots address this challenge by cleaning ship hulls 3-5 times faster than divers. The systems can clean full-draft capesize vessels in under 24 hours, operate day and night in clear or murky waters, and manage strong currents of up to 4 knots—a capability four times that of conventional diving.

Neptune helps clients cut fuel use, lower emissions, and advance their 2050 net-zero goals. It serves the world’s top bulk carrier and container fleets. Its systems operate across 61 Asian ports, covering Singapore and China, representing around 70 per cent of major trade routes.

According to Neptune Robotics, its robots can deliver up to 10x ROI, cut emissions, and remove humans from dangerous work.

Neptune’s partnership with NYK Line signals that robotics-enabled decarbonisation is ready to scale. NYK, which has worked with Neptune since 2022, is now rolling out the technology across its global fleet of bulk carriers, car carriers, and other carriers.

Also Read: Why robotics is just entering its prime phase

Elizabeth Chan, CEO of Neptune Robotics, commented on the investment: “Granite Asia and NYKs’ support validates how far robotics has come in transforming maritime efficiency and sustainability. This round gives us the resources to scale globally, continue innovating, and help shipowners boost returns while cutting emissions.”

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Working at a small, mission-driven company has given me perspective on what I actually want from my career. There’s no free lunch, no ping pong tables, and no equity that might make me rich. But there’s also no bureaucracy slowing down good ideas, no projects that get cancelled after months of work, and no feeling that my contributions are just a drop in an ocean.

When I fix a bug or launch a feature, I can see its immediate impact. When someone successfully completes a purchase using our platform, or when a tool we built helps someone take the next step, I know my code played a part in that moment. That direct connection between effort and outcome has been incredibly motivating.

Constraints have also made me more resourceful. When you can’t just spin up another service or hire a specialist, you learn to find creative solutions. It’s pushed me to become a more thoughtful architect and a more efficient developer.

Also Read: Transition climate risk: Navigating the future of sustainable real estate

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Singapore’s decision to mandate AI literacy for all public servants marks a critical inflexion point in the region’s approach to technology and governance. This is not simply a workforce training exercise. It is a structural bet on the idea that artificial intelligence will underpin the functioning of government, regulation, and citizen services in the decade ahead.

A structural shift, not a symbolic move

Most economies are still debating “responsible AI use,” drafting frameworks and guidelines that often remain disconnected from frontline adoption. Singapore has taken a different path: embedding literacy at the very heart of its bureaucracy. This is important for three reasons:

• Trust: Citizens expect governments to use technology responsibly. Training officials directly reduces the risk of blind adoption and builds credibility when policies are enforced.
• Competitiveness: For a nation positioning itself as a Tech, financial and innovation hub, literacy within the civil service ensures the regulatory environment keeps pace with private-sector deployment.
• Cultural adoption: Once public servants are equipped, the ripple effect extends into education, enterprise, and society.

AI literacy, in this sense, is not about mastering tools. It is about building a new language of governance.

The global context

Elsewhere, progress has been uneven. In the United States and Europe, regulatory conversations are advanced but implementation at the civil-service level is limited. In Asia, adoption is often driven by the private sector with government struggling to keep up. Singapore’s initiative bridges this gap, setting a precedent for aligning governance capability with technological acceleration.

This also positions Singapore strategically. By training its civil service at scale, it is not only protecting its own institutions from misuse of AI but also signalling to international investors and partners that it intends to be a safe, well-regulated hub for AI innovation.

Also Read: Singapore tops global AI hiring charts: One in six jobs now reference AI

Lessons for business leaders

There are clear implications for the private sector.

• First, if governments are prioritising AI literacy, businesses cannot afford to delay. Every organisation — from financial services to healthcare — should already be considering how to embed literacy into their culture.
• Second, AI adoption cannot be viewed purely through the lens of productivity. Its true value lies in decision quality: better forecasts, reduced blind spots, faster responses.
• Finally, speed and safeguards must advance together. Singapore’s approach illustrates that rapid adoption need not equate to reckless adoption.

A playbook for the future

This is not simply a Singapore story. In three to five years, AI literacy will be seen as a baseline skill — as fundamental as Excel was to the last generation of knowledge workers. The difference is that AI introduces new layers of complexity: ethics, security, and systemic risk.

For leaders, the message is clear: if governments are moving to make AI literacy mandatory, what justification remains for the private sector to treat it as optional?

The coming decade will not reward those who adopt AI tools superficially. It will reward those who understand them deeply, apply them responsibly, and integrate them into the way decisions are made.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

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As the United States inches closer to a federal government shutdown, with no resolution in sight after talks between congressional leaders and President Donald Trump ended without progress on Monday, investors are navigating a complex web of signals.

Wall Street stays resilient amid shutdown fears

Despite the looming administrative paralysis, Wall Street closed higher on Tuesday, extending its winning streak into a second consecutive quarter. The Dow Jones Industrial Average rose 0.2 per cent, the S&P 500 gained 0.4 per cent, and the Nasdaq added 0.3 per cent.

This resilience suggests that market participants either believe the shutdown will be short-lived or have already priced in its limited economic impact, given that past shutdowns have rarely derailed broader market trends for long.

Treasury yields and gold signal investor anxiety

Beneath the surface, subtle shifts in asset prices reveal deeper unease. US Treasury yields moved in opposite directions, reflecting a classic flight-to-quality dynamic mixed with short-term policy uncertainty. The 10-year yield inched up by one basis point to 4.148 per cent, while the 2-year yield fell by two basis points to 3.612 per cent.

This flattening of the yield curve often signals that investors expect near-term economic disruptions, such as a government shutdown, to weigh on growth, even if longer-term inflation or fiscal concerns remain elevated. Meanwhile, the US Dollar Index declined 0.1 per cent to 97.8, indicating a modest retreat in safe-haven demand for the greenback.

In contrast, gold surged 0.6 per cent to a record high of US$3,858.18 per ounce, underscoring its enduring role as a hedge against political and institutional instability. The precious metal’s ascent to unprecedented levels speaks volumes about the depth of investor anxiety, even as equities hold firm.

Also Read: The new market symbiosis: How Fed easing, AI, and crypto ETFs are lifting equities

Oil and Asian markets reflect fragile demand

Commodities tell a different story. Brent crude oil dropped 1.4 per cent to US$67 per barrel, pressured by expectations that OPEC+ may accelerate its planned output increases in the coming months. This potential supply boost comes at a time when global demand outlooks remain fragile, particularly with China, the world’s largest oil importer, entering its week-long National Day holiday.

Asian equities reflected this caution, trading mixed on Tuesday and lower in early sessions on Wednesday, with mainland China and Hong Kong markets shuttered for the festivities. The absence of Chinese participation in regional trading has amplified volatility and reduced liquidity, leaving other markets more exposed to external shocks, including developments in Washington and shifts in US monetary policy expectations.

Crypto faces a risk-off correction

The crypto market declined 0.51 per cent over the past 24 hours, aligning with the broader theme of risk-off behaviour and profit-taking following recent rallies. Two distinct forces are shaping this correction: regulatory evolution and the dynamics of the derivatives market.

On the regulatory front, the Securities and Exchange Commission (SEC) issued new guidance allowing state-chartered trust companies, such as those operated by Coinbase, to act as custodians for investment advisers managing crypto assets.

At first glance, this appears to be a significant step toward institutional legitimacy. Long-term, it could pave the way for greater participation from traditional finance players who have long cited custody as a primary barrier to entry.

However, the guidance comes with stringent requirements, including mandatory annual audits and strict asset segregation protocols. These conditions have sparked operational concerns among crypto firms, many of which now face the prospect of higher compliance costs and structural overhauls.

As a result, the short-term market reaction has been one of caution rather than celebration. The progress is real, but the path to implementation remains uncertain, and the industry is watching closely for follow-up rule-making and clarity on adoption timelines from major platforms.

Also Read: The Fed’s first rate cut: What it means for equities, risk, and crypto

Simultaneously, the derivatives market is flashing warning signs. Perpetual futures open interest, a key gauge of leveraged positioning, fell by 5.48 per cent even as trading volume surged by 16.78 per cent. This divergence suggests that traders are actively unwinding leveraged long positions rather than initiating new ones. Compounding the pressure, average funding rates spiked to 0.0068, a staggering 354 per cent increase over 24 hours.

In perpetual futures markets, funding rates represent the cost of maintaining leveraged positions; when they turn sharply positive, it often indicates excessive bullish sentiment that becomes unsustainable. The recent surge suggests that longs were willing to pay a premium to stay in the market, creating a fragile equilibrium that ultimately collapsed under the weight of profit-taking and margin calls.

Notably, US$50 million in liquidations hit the XPL token alone, highlighting how concentrated leverage in smaller altcoins can amplify broader market selloffs. Historically, such spikes in funding rates precede heightened volatility, and if rates turn persistently negative, it could signal a deeper bearish shift as shorts dominate the market.

The current dip in crypto prices thus reflects a tug-of-war between structural progress and cyclical risk reduction. On one side, regulatory clarity around custody could eventually unlock billions in institutional capital, particularly if traditional asset managers gain confidence in secure, compliant infrastructure.

On the other hand, traders are aggressively trimming exposure in anticipation of near-term headwinds not just from potential SEC enforcement actions but also from macro crosscurrents like the US government shutdown and shifting Treasury dynamics.

This tension is further exacerbated by outflows from crypto ETFs, which have seen US$418 million exit Bitcoin funds and US$248 million leave Ethereum products recently. These outflows suggest that even regulated vehicles are not immune to sentiment swings, and that spot market demand may be insufficient to absorb the selling pressure from leveraged traders and cautious institutions alike.

Also Read: Dow, Nasdaq, and crypto all slip as treasury yields climb on delayed cut bets

The weeks ahead

Looking ahead, the critical support level for Bitcoin sits at US$113,000. A decisive break below this threshold could trigger further technical selling, especially if derivatives markets remain unstable.

Conversely, holding above this level might attract bargain hunters, particularly if the SEC’s custody framework begins to translate into tangible institutional inflows. Altcoins like Aster and Hyperbot face additional challenges due to supply-side constraints, which could either cushion their downside or exacerbate volatility depending on market liquidity.

Ultimately, the next few weeks will test whether the cryptocurrency market can decouple from macroeconomic noise and regulatory ambiguity, or whether it remains tethered to the same risk calculus that governs traditional assets. For now, prudence prevails, and the record highs in gold alongside muted equity gains suggest that even in a world of rising asset prices, uncertainty remains the dominant currency.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

Enjoyed this read? Don’t miss out on the next insight. Join our WhatsApp channel for real-time drops.

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Japanese gaming giant Nintendo has established a new local entity in Singapore to accelerate its business within Southeast Asia.

Nintendo Singapore is a wholly-owned subsidiary, with the parent holding a 100 per cent capital contribution ratio.

The new operation has been capitalised with SGD 8 million (US$6.2 million).

Also Read: Gaming as the next social network: How Gen Z and Gen Alpha are redefining digital belonging

Takahiro Miura has been appointed as the Managing Director of the new entity.

In addition to the immediate establishment of the Singapore base, Nintendo, led by President and Representative Director Shuntaro Furukawa, is also “considering establishing a local entity in the Kingdom of Thailand”. This potential move aligns with the overall strategy to accelerate the company’s business presence across the region.

Nintendo is a multinational video game and consumer electronics company headquartered in Kyoto. Founded in 1889 as a playing card manufacturer, Nintendo has evolved into one of the most influential companies in the video game industry. It is renowned for hardware and software innovations and for creating some of the world’s most iconic gaming franchises.

Also Read: Gaming in SEA: Understanding the growing opportunity for SMEs and payment providers

Nintendo is home to some of the most beloved and profitable franchises in entertainment history Mario (Super Mario Bros., Mario Kart, etc.), The Legend of Zelda, Pokémon (in partnership with The Pokémon Company), Donkey Kong, Metroid, Animal Crossing, and Super Smash Bros.

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I am unapologetically pro-AI and pro-automation. Not because they’re buzzwords, but because I’ve lived what they unlock. With the right systems and AI twins in place, I run multiple ventures lean, move faster than bigger teams, and focus my energy where it matters most: Growth.

But there’s a caveat. Automation is leverage — it multiplies what you already have. Multiply zero by anything and you still get zero. Multiply revenue and relationships, and you get scale. The art is knowing what to automate now, what to automate later, and what to keep human.

Adoption is here, but the gap is widening

Globally, AI has crossed the tipping point: 78 per cent of organisations now use it in at least one business function (McKinsey, 2025). This isn’t trial and error anymore — it’s mainstream.

Singapore tells an interesting story. Larger enterprises report 44 per cent AI adoption, while SMEs lag at just 4.2 per cent. The top SME reason for holding back? “No need at current scale.” It’s not resistance; it’s timing. When workflows aren’t yet repeatable, automation feels like over-engineering.

Yet leaders are bullish: 87 per cent of Singapore’s C-suite executives rank generative AI among their top three business priorities. And workers see what’s coming — 64 per cent expect their tasks to be automated or augmented within five years. The message is clear: AI isn’t optional, but its adoption curve isn’t even. Some founders will surge ahead; others will wait too long.

Automate → augment → amplify

Here’s how I think about building AI-first companies:

• Automate survival work: Repetitive, rule-based tasks like lead capture, confirmations, and basic reporting.
• Augment decisions: Train an AI twin to mirror your tone and SOPs, helping with briefs, prioritisation, and routing.
• Amplify the human layer: Reinvest saved time into sales, partnerships, creativity — the things that compound.

This order matters. In one large-scale experiment, giving workers AI assistance raised productivity by ~15 per cent on average, especially for junior staff. But those gains showed up only where structured workflows existed. AI multiplies workflows, not chaos.

Also Read: Policy warning: Without intervention, AI could deepen the digital divide

The revenue-first lens

Here’s my rule: Revenue drives the system, not the other way around.

• If you have customers and repetitive tasks → automate.
• If you have no inflow → focus on outreach first.
• If you’re caught in between → experiment manually, then scale what sticks.

I’ve seen founders proudly demo elaborate automations — while struggling to land their first ten paying clients. That’s a distraction. On the other hand, I’ve also seen lean teams using AI twins to triple their qualified outreach without hiring headcount. That’s leverage.

In my own community, I sometimes challenge students: “How many customers do you have?” When the answer is zero, automating a half-imagined platform isn’t a strategy — it’s procrastination.

Outreach before optimisation

Your first stack should bias toward bringing in revenue. A simple CRM, WhatsApp or email sequences, a booking tool, and analytics are often enough. AI twins and agents shine once the volume builds — when you’re drowning in DMs, juggling multiple funnels, or qualifying leads at scale.

That’s when automation saves you hours and stops revenue from slipping through the cracks.

For lean founders, tools like Sintra’s “AI employees” make the twin concept tangible. You can spin up an AI helper for support, email, or analytics in minutes. But they only deliver when connected to real, active workflows. Otherwise, you’re just paying for idle software.

Guardrails for smart adoption

Being pro-AI doesn’t mean automating everything blindly. Some tasks require nuance and should stay human.

For example, I sometimes use my AI twin, Seraphina, to help me draft a sensitive reply. But I wouldn’t fully automate that exchange. Contrast that with hundreds of social comments or event DMs — there, automation plus AI makes perfect sense.

The rule is simple: Automate where scale creates friction, keep it human where nuance drives trust.

Also Read: AI-powered marketing: How to generate leads, nurture customers, and close deals on autopilot

Why this matters now

Singapore’s digital economy already contributes nearly 18 per cent of GDP, and infrastructure is scaling fast. Keppel, for instance, is more than doubling its data-centre capacity to handle the AI workloads of tomorrow. Similar investments are happening across Asia and beyond.

This isn’t just infrastructure; it’s a signal. The cost of not using AI will soon outweigh the cost of adopting it.

That’s why events like Flux Series 2025 are so important. The conversation has shifted. It’s no longer “should we use AI?” but “how do we redesign our companies around it?” My view is simple: The winners will be those who treat AI twins and agents not as add-ons, but as the foundation of how lean teams operate.

Closing thought

I believe in AI because it has given me back time for relationships, creativity, and growth. It extends me, not replaces me.

For founders, the lesson is clear:

• Automate with purpose.
• Augment with AI twins.
• Amplify the human edge.

Automation is not about doing less. It’s about doing more of what matters. And the sooner we embrace that mindset, the sooner we build companies that are not just bigger, but smarter.

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