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        <title>INSEAD Knowledge</title>
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        <description>The business school for the world</description>
        <lastBuildDate>Wed, 13 May 2026 11:31:54 +0800</lastBuildDate>
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        <item><title>What High Oil Prices Mean for the Energy Transition</title>
                  <link>https://knowledge.insead.edu/economics-finance/what-high-oil-prices-mean-energy-transition</link>
                  <description> <![CDATA[The Iran war has sent oil prices and inflation soaring. It has also produced a less visible but no less damaging consequence: The energy transition is sliding further down the agenda of oil and gas companies.My research, published in Strategic Management Journal, offers hints as to how this could play out. I studied how oil and gas firms behaved during a previous major disruption to their industry – the 2014 oil price crash, which forced them to reckon with excess capacity. Extrapolating from those findings, today’s environment makes the transition to renewables increasingly unappealing for oil and gas firms. Even when firms have strong incentives to reposition, market forces alone are not enough, let alone when companies can instead capture war-generated windfall profits.What happened a decade agoAfter the 2014 price collapse, a small number of oil and gas companies that had already diversified into wind power cut offshore oil and gas spending and redirected it towards wind projects. These firms represented only about 2.5% of the oil and gas companies I studied. But when this minority chose to invest, the impact on offshore wind technology was significant.Where a wind farm sat within close proximity of established oil and gas assets, post-shock investment rose by as much as US$23 million per project, compared with a pre-shock average of US$6 million. These firms also deployed larger, more powerful and more technologically advanced turbines than their pure-play wind rivals. Several projects demonstrated that oil and gas companies possess transversal capabilities that can push the technological frontier in offshore wind.Equinor’s Hywind project, the world’s first commercial floating offshore wind farm, illustrates the point. Developed by a firm rooted in Norwegian oil and gas and commissioned near existing North Sea fields, it drew on engineering competencies that a pure-play renewables developer would have struggled to assemble from scratch.Policy... must create conditions in which the energy transition becomes a strategically attractive use of existing industrial capabilities – not merely a box-ticking obligation or a reputational hedge.  Post-crash conditions converged to pull energy firms towards wind in that period. The industry had idle capacity, lower returns on incremental oil and gas projects, and a pressing need to find productive uses for expensive resources – vessels, engineering and project management expertise, and an entire supply chain that would otherwise have sat idle. Combined with clear government policy favouring renewables, wind power began to look attractive by comparison – at least to some companies.What’s happening nowToday’s conditions are the opposite. In the wake of the Iran war, the incentive to redeploy resources towards renewables has collapsed. BP has announced a reorganisation of its business units that clearly reverses its push into renewable energy. TotalEnergies is swapping offshore wind projects on the United States East Coast for oil and gas projects in Texas. Left to itself, the market is plainly insufficient to drive the scale of transition required by climate targets, particularly as policy and decision-makers put climate policies themselves on the backburner.What the industry can do that others cannotThis matters because the capabilities oil and gas firms possess are not easily replicated elsewhere. The next generation of offshore wind projects – floating foundations, production sites far from shore, cables running across seabeds – would benefit enormously from the industrial project management expertise that sits inside the major oil companies and their supply chains.My research makes one conclusion difficult to avoid: The firms best equipped to accelerate the energy transition are largely not doing so, and today’s price environment gives them little reason to. Generic calls to abandon oil and gas are not only impractical but wasteful, because this industry possesses many of the elements needed to technologically advance and commercially realise renewable projects at scale.What policymakers need to doPolicy needs to do more than price carbon. It must create conditions in which the energy transition becomes a strategically attractive use of existing industrial capabilities – not merely a box-ticking obligation or a reputational hedge. That could mean targeted incentives for wind and other renewables, such as geothermal, that leverage existing oil and gas infrastructure, or long-term contracts that de-risk the investment case for firms considering redeployment. Recent moves by the European Union to shift individual behaviour are commendable, but policy needs to be far more targeted towards industrial players if it is to drive change at scale.The oil and gas industry doesn’t need to remain the villain of the energy transition story. In many respects, it is best placed to accelerate the next chapter. At current oil prices, however, this is unlikely. The gap between potential and action is what policy must target.]]></description>
                  <pubDate>Thu, 07 May 2026 01:00:48 +0000</pubDate>
                  <guid isPermaLink="false"> 48591 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/economics-finance/what-high-oil-prices-mean-energy-transition#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-05/shutterstock_1149456404_1.jpg?itok=M2VBbbcb" type="image/jpeg" length="148271" /><dc:creator>Aldona Kapačinskaitė</dc:creator></item><item><title>AI &amp; Jobs: What Workers Can Do to Protect Themselves</title>
                  <link>https://knowledge.insead.edu/career/ai-jobs-what-workers-can-do-protect-themselves</link>
                  <description> <![CDATA[The disruption unfolding across today's labour market is unlike anything that came before. Where past waves of automation swept through factory floors and manual work, AI is hitting white-collar jobs, especially entry-level ones. In the first quarter of 2026, tech companies laid off more than 78,000 workers, with 48% attributed to AI automation. Even Jerome Powell, the United States Federal Reserve chair, has warned that AI could “absolutely have implications for job creation”.How can we gird ourselves for AI’s impact? In our “AI & Jobs” series, we ask INSEAD professors to analyse the situation from the perspectives of individuals – the focus of this article – as well as organisations and policymakers. The consensus: AI is redesigning and restructuring jobs far more than it’s making them obsolete. Whether and how individuals (especially entry-level and junior workers) exploit AI while honing their own skills will decide the security of their future. Meta-skills will be a game changer Phanish Puranam, The Roland Berger Chaired Professor of Strategy and Organisation Design The jobs most vulnerable to AI displacements in the next few years will be those that are low-level, don’t require collaboration (e.g. modular work) or in-person presence (e.g. purely knowledge work), and are done the same way across companies. This isn’t necessarily because algorithms will become effective substitutes for all tasks in all such roles – the evidence suggests they aren’t (yet) – but because organisations are no longer hiring as they expect AI to eventually catch up. That said, completely new tasks and roles have been created since the advent of generative AI. I categorise them into four types: AI operations, AI compliance, jobs related to human-AI interaction (e.g. prompt librarian, AI personality coaches), and perhaps the biggest group is simply AI-augmented versions of old roles in software, medical services and other sectors where demand is elastic.Although we can’t forecast what skills will be in demand in the future, I think “meta-skills”, which allow humans to acquire new skills quickly, will matter more than specific skills. Meta-skills are, as I explain in a separate article, unlike domain knowledge or technical expertise. Meta-skills such as analogical reasoning, metacognitive regulation, higher-order thinking and social coordination don't directly produce output. Instead, they accelerate learning, enable knowledge transfer across contexts and help people adapt when tasks evolve.In new work with Alessandro Sforza and Matteo Devigili, I’m trying to pin down the signature of meta-skills by studying “super-jumpers” – individuals who make big leaps in the skills they seem to acquire when transitioning to new jobs. The danger of relying heavily on AI tools is that our meta-skills could atrophy. This means we might be more efficient in the short term but increasingly fragile and commoditised over time.AI is rewriting the rules — output is abundant, judgment and credibility are scarceSo Yeon Chun, Associate Professor of Technology and Operations ManagementAI is often discussed in terms of jobs lost or created, but this framing misses a more fundamental shift. AI is altering how work is structured, how value is defined and how opportunity is distributed. In short, AI is rewriting the rules of work.Instead of replacing jobs in their entirety, AI is increasingly transforming tasks within jobs. Rather than focusing on jobs gained or lost, it is more useful to look at how tasks are redistributed between humans and machines. Even without large-scale unemployment, AI may lead to a more invisible form of disruption in terms of responsibilities, scope and career progression.This shift changes the nature of value. When high-quality output becomes easy to generate, value moves away from production and towards judgment — the ability to interpret, evaluate and make decisions. To stay relevant, humans must become skilled at guiding AI systems, assessing their outputs, and applying context and causal reasoning. Judgment becomes critical not only for improving one’s own work, but for evaluating the contributions of others. As AI makes it easier to produce polished output at scale, distinguishing truly valuable work becomes more difficult. Thus, those who are better at making their work – valuable or not – visible may be better positioned to capture opportunity.  To stay relevant, humans must become skilled at guiding AI systems, assessing their outputs, and applying context and causal reasoning. This reflects a broader dynamic I highlight in my research: When people have less time and trust to judge an ever-increasing amount of output, visibility increasingly decides whose work is recognised. When judgment is lacking, appearing busy or visible can replace actually doing valuable work, and credibility becomes rare.In a world overflowing with output, the real advantage lies in exercising judgment and building credibility, both in the work we produce and in how it is evaluated. If you can’t beat them, use themWinnie Jiang, Assistant Professor of Organisational BehaviourIn an ongoing study of professional workers on the Upwork platform, I’ve observed that those who invest time in learning which tools are best suited for particular tasks, how to combine tools effectively, and how to use them skilfully succeed in turning AI from a threat into a resource.AI tools also free up time and cognitive capacity, enabling workers to try out new tasks, create side projects and think of new ways to create value. For example, market researchers who use AI for data collection and initial analysis can devote more effort to interpretation and application. In this way, individuals become more career-resilient, while organisations also benefit. There’s a caveat: Early-career employees should prioritise hands-on learning, which can mean avoiding AI use when it’s readily available. Our study shows that the professionals who benefit most from AI are those who already know what “good” actually looks like in a given context. In contrast, when individuals rely on AI without first developing this foundational understanding, they often struggle to detect errors or meaningfully improve AI outputs. For individuals, the prospect of being displaced by AI breeds uncertainty, anxiety and a sense of diminished status and agency. Socially, widespread job insecurity can deepen the divide between those who benefit from AI and those who are displaced. In “mass unemployment” scenarios, the legitimacy of the political and economic status quo could be destabilised. To mitigate or avoid these outcomes, workers need to be provided with not only support that helps them reskill but also support to help them reinterpret AI disruption as a temporary transition and an opportunity to find more meaningful work. Leaders, on their part, should treat workers as capable contributors who can identify and create new value, rather than as surplus labour. Cultivate deep domain knowledge and “taste”Victoria Sevcenko, Assistant Professor of StrategyMost of the labour market change happening now is the restructuring of existing roles to incorporate AI, not the appearance of new categories. The floor on acceptable output seems to have risen: People are expected to come in able to do things they might have been given more time to learn, and the average entry-level job might start to look more like a mid-level job.There will likely be more demand for deep domain knowledge and “taste” – that intuitive sense of what good work looks like, what counts as a contribution, and what is novel or sloppy. Within those redesigned roles, there will likely be more demand for deep domain knowledge and what I will call “taste”, which I shall explain below. Here are some specific actions individuals can take to stay competitive:Use AI extensively, preferably the best available models. This will teach you about what AI can and cannot do, and what you, as a human, are uniquely better at. As the models evolve, you can also spot the direction of improvement faster and more accurately.Build “taste” in your specific field, which is that intuitive sense of what good work looks like, what counts as a contribution, and what is novel or sloppy. Taste is typically socially constructed: it’s co-created by a community of practitioners and shaped by what they think matters, and much of it isn’t documented well enough for AI to pick up on. Build taste by interacting directly with people in your field and getting regular feedback from peers. You can’t rely on AI alone for this, and it’s also harder to be original in your thinking if you do.Build critical thinking. By this I mean the ability to assess what you know, what you don’t and what you are uncertain of, and to step back, reflect and adjust. It’s debatable how “trainable” people are in these skills, but some people are clearly stronger, and this gives them a big advantage. Getting feedback on your work and learning to test your own knowledge are all part of the training.Learn how AI works. You don’t need to build the models, but you need enough knowledge to anticipate what AI will be good or bad at, and to make sense of improvements as they emerge.Next week, we’ll look at what companies and organisations can do to mitigate AI’s impact on jobs and the talent pipeline.]]></description>
                  <pubDate>Mon, 04 May 2026 02:05:12 +0000</pubDate>
                  <guid isPermaLink="false"> 48571 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/career/ai-jobs-what-workers-can-do-protect-themselves#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-05/shutterstock_2452515473_1.jpg?itok=2JMUGZnY" type="image/jpeg" length="302566" /><dc:creator>Phanish Puranam</dc:creator><dc:creator>So Yeon Chun</dc:creator><dc:creator>Winnie Jiang</dc:creator><dc:creator>Victoria Sevcenko</dc:creator></item><item><title>Europe&#039;s Historic Second Chance: Leading AI’s Next Wave</title>
                  <link>https://knowledge.insead.edu/economics-finance/europes-historic-second-chance-leading-ais-next-wave</link>
                  <description> <![CDATA[Three decades of the digital economy have been an era of missed opportunity for Europe. While it can boast of research excellence, it can hardly claim to have exported much world-conquering technology – unlike the Big Tech giants of Silicon Valley. The current AI wave looks like more of the same: dominated by the data-saturated Magnificent Seven that were forged in the internet era and have never loosened their grip since.The fact is that Europe, for all its engineering talent and capital depth, is the least competitive of the major digital economies. This has opened up a value creation gap that could run into the trillions. What’s new? Actually, a lot, if Europe plays its card right. A second wave of AI-era innovation might hand the Old Continent something the technology industry rarely offers: the chance to run the race again.Europe’s strengthsAI development today is largely about bigger foundation models mainly trained on internet data, larger data centres and ever more capital-intensive computational scale. This is where the vast majority of investment currently goes. Nobody can know today if these huge bets, often framed as a race to agentic AI or artificial general intelligence (AGI), will pay off for the latecomers to the Nvidia, OpenAI and Anthropic gamble. But one thing is now becoming clear: the next phase of AI innovation will be won where intelligence meets matter – robotics and manufacturing, chemistry and materials, bio-pharma and healthcare, energy systems, logistics networks, and industrial operations. In other words, in the physical and scientific domains. Three key factors may be even more critical than they were in the internet-dominated AI era: scientific talent, industrial strength and production know-how, and ecosystems across multiple sectors. This is precisely where Europe’s underlying advantages are hiding in plain sight. Companies like Siemens and Bosch, Airbus and Dassault Systèmes, Stellantis and Scania, BASF and Bayer, ASML and SAP, and Roche and Novo Nordisk operate some of the world’s most advanced industrial systems. Europe’s factories, supply chains, energy grids, laboratories and engineering workflows generate vast streams of high-quality real-world data. Yet Europe has barely begun to turn this resource into AI-native industrial platforms and new global champions.The following numbers may surprise European as well as American investors. The European Union produces 22% of global AI research journal articles, compared to 17% by researchers based in the United States. Some 2.2 million people graduate with STEM diplomas from European universities each year, compared to 1.4 million from American ones. Europe employs 2.15 million researchers and spent €403 billion on R&D in 2024. And unlike the US, which is strong mainly in software, Europe’s industrial base is enormous and automation-ready: EU manufacturing generates €2.5 trillion in value-add and operates 219 industrial robots per 10,000 employees – exactly the substrate where AI’s next productivity wave will land.  Scientific talent, industrial strength and production know-how, and ecosystems across multiple sectors... This is precisely where Europe’s underlying advantages are hiding in plain sight.  Finally, Europe’s underestimated advantage is that it already runs EU-funded, cross-border ecosystems that stitch together universities, industry, startups and the public sector. Horizon Europe, the EU’s key funding programme for research and innovation, pours €93.5–€95.5 billion into collaborative R&I across fields from health and energy to mobility and manufacturing between 2021 and 2027.The US has clearly taken note. It is no coincidence that Washington recently launched the Genesis Project aimed at strengthening American industrial, manufacturing and scientific domains.It’s about scientific talent, industrial strength and sectorial breadthThe depth as well as breadth of Europe’s industrial sectors provide not only data and know-how, but also the necessary market conditions for innovation. Every startup needs customers above all. Investors’ money is good, but customers’ is better. And every investor, as well as every founder, needs exit paths. And here lies another often overlooked key European advantage. For years, success in tech entrepreneurship and investment has been defined either as an IPO or an acquisition by one of the Big Tech companies. In the era of physical AI, this is about to change fundamentally, to the benefit of Europe’s tech founders and their investors. AI entrepreneurs will not have to hope for a lucky punch with the deep-pocketed Magnificent Seven Big Tech in the US but charter new exit paths with hundreds of industrial players on home soil. Europe can create a win-win environment at scale: Entrepreneurs and investors have plenty more reasons to start and fund a company, while current industrial players have access to the latest innovations from the labs. Some of today’s industrial Goliaths may be disrupted by the AI newcomers, others will only be strengthened through innovation. In both cases, value will be created and captured either by a cohort of new players: AI-native global industry leaders or incumbents infused with startup AI. It’s time to finance the commercialisation of innovationsEurope’s central challenge is not invention but innovation in the Schumpeterian sense of the term: building companies at scale to conquer markets. In 2024, US startups captured roughly 74% of global AI venture funding while Europe attracted about 12%. The US spawns about four times as many AI unicorns than Europe. Analysts estimate the broader EU-US investment gap in information and communications technology and cloud computing at US$1.36 trillion, underscoring how much industrial digital infrastructure Europe still needs to build.The new physical AI-driven market presents a huge opportunity for a comeback. The next wave of European AI companies could capture over 25% of the global next-generation AI market, in line with its research and industrial contributions – if the region manages a step change in turning breakthroughs into venture-scale businesses.The bottleneck is neither talent nor ecosystems. It is the commercialisation capacity of new ideas at speed and scale: connecting labs across borders, building stronger pathways from discovery to company formation, and linking European deep-tech founders to global capital, customers, talent and distribution networks.Encouragingly, policymakers are beginning to respond with historic ambition. Earlier this year, the European Commission launched its €200 billion InvestAI initiative, including €20 billion earmarked for AI gigafactories. For the first time, Europe is signalling its intention to match scientific excellence with industrial-scale capital.The timing matters. AI adoption is accelerating rapidly across the economy. OECD data show that the share of firms using AI has risen sharply – from 8.7% in 2023 to over 20% in 2025. AI transformation is no longer confined to Silicon Valley labs. It is spreading across factories, hospitals, laboratories, logistics networks and energy systems – precisely where Europe retains deep structural strengths.For venture capital, private equity and institutional investors alike, this second chance at AI is also one of the most compelling investment opportunities of the coming decade. Backing the next generation of research-driven AI companies can generate outsized returns as Europe converts its talent and industrial advantages into global market leadership.Importantly, Europe’s opportunity is not to emulate Silicon Valley model for model. It is to innovate differently: to build AI-native companies rooted in scientific depth, industrial integration and responsible governance. Moreover, Europe’s diversity, strong institutions and commitment to rule of law can become competitive advantages in a world increasingly shaped by trust, security and social complexity.From laggard to leaderAs the new AI wave unfolds, Europe may transform from laggard to leader. Unlike incumbent ecosystems, which have already invested, or perhaps sunk, hundreds of billions into today’s foundation-model architecture, Europe’s current generation of AI researchers and entrepreneurs can start fresh. Entire nations can be disrupted as well. Political scientist Jeffrey Ding recently argued that major technological transitions have repeatedly reshaped global power – from Britain in the first Industrial Revolution, to Germany’s rise in the age of chemicals and engineering, and American dominance in the era of mass production, computing and the internet. History rarely offers second chances. The coming AI wave might be one for Europe. The Old Continent should seize it. A version of this article was published in Fortune.]]></description>
                  <pubDate>Thu, 23 Apr 2026 01:34:29 +0000</pubDate>
                  <guid isPermaLink="false"> 48546 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/economics-finance/europes-historic-second-chance-leading-ais-next-wave#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-04/shutterstock_2459225475_1.jpg?itok=2CltVLUS" type="image/jpeg" length="453295" /><dc:creator>François Candelon</dc:creator><dc:creator>Theodoros Evgeniou</dc:creator><dc:creator>Thomas Ramge</dc:creator></item><item><title>Tariffs and Turmoil: Negotiating the New World Order</title>
                  <link>https://knowledge.insead.edu/economics-finance/tariffs-and-turmoil-negotiating-new-world-order</link>
                  <description> <![CDATA[In the latest episode of “The INSEAD Perspective: Spotlight on Asia” podcast series, Sameer Hasija, Dean of Asia at INSEAD, sat down with Pushan Dutt, Professor of Economics and Political Science, to discuss how the new world order is creating a complex economic environment, where traditional business strategies are being upended by unpredictable political and technological shocks.As an expert in international trade, Dutt offers his insights on the impact of the fast-changing United States tariff policies for the Asian region. Ultimately, he advises firms to adopt a "wait and see" approach, suggesting that rash operational moves to counter temporary political swings could end up being a costly, and ultimately unnecessary, mistake.For him, a bigger concern is the massive investment by American and Chinese firms into AI, which could create a significant technological gap between those leaders and other countries. Organisations are historically slow to adapt, but firms in Asia need to fully understand the speed of exponential technological growth and the urgency of being prepared for the "gale of creative destruction" it will bring. In the same vein, countries like India and Indonesia need to overcome the slow pace of their bureaucratic democracies to become more agile and responsive. Whether it’s pivoting India’s IT sector to adapt to rapidly changing needs, or Indonesia’s efforts to move upstream in the nickel supply chain, speed is going to be key. The risk-taking appetite has to go up as well. Business as usual is not going to cut it. – Pushan Dutt With the prospect of an incoherent and uncertain future, at least for the short term, business leaders cannot afford to be delusional about "crises being opportunities". Instead, they need to make sure they have the slack, both in terms of finances and time, to make quick decisions in response to unexpected or unknown crises as and when they arrive.Note: This conversation was recorded before the start of the ongoing Middle East conflict.]]></description>
                  <pubDate>Tue, 31 Mar 2026 01:36:51 +0000</pubDate>
                  <guid isPermaLink="false"> 48506 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/economics-finance/tariffs-and-turmoil-negotiating-new-world-order#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/tariffs_turmoil.jpeg?itok=5y7GLXov" type="image/jpeg" length="966875" /><dc:creator>Sameer Hasija</dc:creator><dc:creator>Pushan Dutt</dc:creator></item><item><title>Will AI Eat SaaS for Lunch?</title>
                  <link>https://knowledge.insead.edu/economics-finance/will-ai-eat-saas-lunch</link>
                  <description> <![CDATA["Software is eating the world", venture capitalist and entrepreneur Marc Andreessen famously declared in 2011. The ensuing 15 years proved him prescient. In February 2026, a Substack article by Citrini Research grabbed headlines and triggered a market sell-off of SaaS (software-as-a-service) firms, wiping out nearly US$1 trillion in market value in a matter of days. Citrini’s central thesis? A cannibalistic last feast where AI eats the very software industry that’s been eating the world. The argument is simple: If anyone can prompt an LLM (large language model) and vibe code a custom enterprise resource planning or customer relationship management system in an afternoon, the multi-billion-dollar SaaS industry becomes a dinosaur overnight. It’s a frighteningly plausible thought that puts the spotlight on Citrini and the article’s author, James van Geelen, but it is fundamentally naive because it assumes that we live in a world without friction.Why firms won’t build all systems in-houseAI tools like Anthropic’s Claude are incredible at instantaneous prototyping. But as any software engineer knows, writing code is 10% of the job; maintaining, scaling and debugging it for edge cases is the other 90%. Software isn't just a static pile of logic; it’s a living organism. Production-strength software requires auditability, 24/7 reliability, API (application programming interface) stability and security compliance, all at once and all at scale. These aren’t things that LLMs, which are random systems by nature, can replicate. The “AI eats SaaS for lunch” logic naturally leads to the conclusion that all firms will build all their software in-house – because now, with the help of AI, they can. But can they really? Will they really? I bet they won’t for two reasons. For starters, a primary reason why companies buy enterprise software is to transfer risk. When a Fortune 500 company uses specialised software by a SaaS provider for cybersecurity or HR, they aren't just buying code; they're buying compliance with security frameworks, GDPR (General Data Protection Regulation) indemnity and ISO (International Organization for Standardization) certifications. If firms build systems in-house using general-purpose tools such as Claude or Google’s Gemini, what happens if (or when) things go wrong, such as data leaks? There will be no vendor to sue, no platform to blame and no security patch to purchase.Another reason relates to scale and interoperability. A third-party provider spreads the cost of high-level security and compliance across thousands of customers. An individual firm trying to replicate that in-house would find the "efficiency" of AI quickly eaten up by the massive overheads of self-certification and liability insurance.If all firms build all their systems in-house, we'll be back in the world of fragmented software with limited interoperability. Remember legacy systems? Firms will end up with isolated legacy piles of AI-written code that no one understands. Opportunities and challenges If the trend of building software in-house actually takes off and every company starts creating their own bespoke AI systems, the complexity of auditing those systems becomes exponential. Auditors would then become the most needed and sought-after profession on earth. If van Geelen really believes what he says, then he might consider auditing as a new profession to hedge against the apocalypse he hypothesised. Who knows, the auditing profession might be the answer to the problem of AI displacing jobs.Everyone can buy a shovel. Not everyone shovels their own snow. Summarising all the above, let’s not forget that AI is a general-purpose technology and software companies are specialists. It’s hard to argue that generalists will replace all specialists in the modern world, which is essentially what Citrini’s scenario argues. Everyone can buy a shovel. Not everyone shovels their own snow. What’s more, the "AI eats everything" narrative assumes that LLMs have access to all the world's intelligence. They don't. Besides every company’s proprietary data, there’s the paywall barrier, which keeps the most valuable data – highly structured, organised and predictable information required for professional-grade decisions – behind the moats of companies like LexisNexis, Thomson Reuters, Nielsen and the like. Without access to this information, generic models can’t generate deep insights; they risk simply recycling data that’s available in the public domain. Indeed, the owners of the data, not the owners of the models, hold the ultimate leverage.What will AI do to SaaS?SaaS won’t be eaten by AI, but it will be shaken and stirred by it. For instance, Block reduced its headcount by nearly half in February, culling over 4,000 positions. And in March, Atlassian, one of the SaaS companies hit hardest by the market sell-off, retrenched 10% of its staff.Gone is the cosy convention of per-user pricing and 5% annual price increases justified with the release of new features nobody asked for. Companies will need to be more discerning and can wield a credible threat to take business away from a SaaS provider (whether or not they follow through on it is another matter). This threat will inevitably shake up the SaaS industry. Those who can deliver measurable value will survive and thrive. Those who cannot will perish, and the industry will emerge leaner and stronger.A version of this article was published in The Business Times.]]></description>
                  <pubDate>Mon, 27 Apr 2026 01:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48491 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/economics-finance/will-ai-eat-saas-lunch#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/shutterstock_2210055765.jpg?itok=edLWRtz_" type="image/jpeg" length="879547" /><dc:creator>Lily Fang</dc:creator></item><item><title>INSEAD Insights: Secrets, Innovations and Alcohol</title>
                  <link>https://knowledge.insead.edu/economics-finance/insead-insights-secrets-innovations-and-alcohol</link>
                  <description> <![CDATA[The outsized influence of alcohol on financial markets in China and how legal protection for trade secrets spurs innovation are among our featured research this month. Other notable papers include an examination of the factors that influence an airline’s adoption of new innovations, and how a form of dementia impacts people’s impatience for rewards. How drinking “clubs” impact China’s financial marketsBusiness-related drinking culture is contributing to companies in China distorting or faking public financial information. That’s the surprising finding of a new study Massimo Massa and his co-authors published in the Journal of Financial and Quantitative Analysis. The researchers found that business leaders, auditors and even regulators who drink together create informal networks where everyone in the "club" protects one another, even if it hurts the public market. The evidence? Toxic alcohol scandals that shook up local drinking habits also significantly reduced firms’ distortion of their financial information in that location.Read the full paperHow radical innovations impact adoption Henrich Greve and his co-author looked at the speed of adoption of new technologies or products within the aviation sector. They discovered that the nature of the innovation was crucial, and that companies relied on different factors to help make those decisions. Specifically, adoption of simple upgrades, such as the Airbus A320neo (new engine/same plane), were based on cost advantages. For more radical technological or organisational innovations, such as investing in the new Boeing 787 or point-to-point flying, the experiences of early adopters and extensive trials were crucial before firms felt comfortable in adopting the innovation.Read the paperCan dementia make people more impatient?Individuals with a particular form of dementia are much more impatient than healthy adults when it comes to food or financial rewards, Hilke Plassmann and her co-authors found in a study published in Communications Biology. Using MRI scans, the team identified that the impatience was the result of atrophy in specific regions of the brain that typically help process emotional value and the ability to imagine future consequences. The findings show that reward impatience is a core feature of the disease and such behaviour could eventually serve as a marker for very early neurodegenerative risk. Read the paperThe benefits of keeping trade secretsWhile patents often dominate the conversation around intellectual property, trade secrets – which account for some US$5 trillion in value among US firms – are the unsung hero. A study published in Management Science by Aldona Kapacinskaite and her co-author provides rare, granular evidence on how firms manage these secrets in high-stakes environments. Analysing the US hydraulic fracturing industry before and after the 2016 Defend Trade Secrets Act (DTSA), the authors found that stronger legal protection did more than help firms hide information – they actively encouraged firms to deploy more novel, productive technologies that were previously deemed too "risky" to use in the field.Read the paper]]></description>
                  <pubDate>Tue, 24 Mar 2026 01:04:18 +0000</pubDate>
                  <guid isPermaLink="false"> 48486 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/economics-finance/insead-insights-secrets-innovations-and-alcohol#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/insightsmar_2026.jpg?itok=OvXz6AnQ" type="image/jpeg" length="989218" /><dc:creator>Lily Fang</dc:creator></item><item><title>Is the Asian Economic Model Breaking?</title>
                  <link>https://knowledge.insead.edu/economics-finance/asian-economic-model-breaking</link>
                  <description> <![CDATA[Here’s a question for you: In which economies do exports account for more than half of GDP? If you guessed Germany (41 percent) or China (20 percent), you’d be wrong. The answers are Vietnam (90 percent), Cambodia and Malaysia (70 percent), Thailand (60 percent) and Singapore – which, at 175 percent, exports more than it produces, the economic equivalent of a magic trick. These aren’t merely export-oriented economies. They are the beating heart of globalisation. Strip away the flows of goods, capital and technology of the last five decades, and you strip away the Asian model itself. For more than 50 years, that model delivered the most compressed period of mass prosperity in human history. It rested on three pillars: open trade, a stable geopolitical order anchored by American power, and the assumption that technology would continue to flow from rich to poor countries, allowing catch-up growth.Unfortunately, all three pillars are currently under strain as the world undergoes a structural break. The break isn’t yet visible in headline growth numbers, which is precisely what makes it dangerous. The IMF projects that most of Asia will grow respectably through 2026. However, many factors that sustained GDP growth last year – tariff exemptions, TACO (Trump Always Chickens Out), stockpiling and the AI boom – may all wane in the years ahead, curtailing growth.The trade pillarDonald Trump’s first stint as President of the United States was annoying but manageable. Tariffs were narrow, targeted, telegraphed in advance and phased in slowly. Crucially, firms could plan for them, with many rerouting supply chains through Vietnam and Malaysia. Trump 2.0 is different. The tariffs are sudden, volatile, whimsical and coercive. Unpredictability is a feature, not a bug. The current conflict in the Middle East has only deepened the sense of chaos and uncertainty.After the tariffs were announced last year, countries raced to strike deals: Vietnam accepted 20-percent tariffs in exchange for zero tariffs on its goods and commitments to buy American liquefied natural gas and aircraft. Malaysia agreed to 19-percent tariffs, giving Washington a say over its export controls. Japan and Korea got 15 percent in exchange for investment pledges, while Singapore, despite its free trade pact with the US, quietly accepted a 10-percent baseline tariff. Last month, the Supreme Court stepped in and deemed these tariffs unconstitutional. Almost immediately, Trump invoked presidential powers under a 1974 trade law to impose blanket 15-percent global tariffs. Everyone now gets 15 percent, and the trade deals and bilateral concessions, extracted under duress, are in limbo. Some will be slow-walked, some renegotiated, others reneged on. However, even these tariffs are likely illegal, as they are used to address balance-of-payments issues that the US simply doesn’t face. They expire in 150 days, and what happens after that is anyone’s guess.Beyond tariffs, the more immediate threat is transhipment. Goods in Asian supply chains cross borders an average of six times before becoming a final product. Transhipment tariffs of 40 percent, designed to prevent rerouting and reclassification, expose Singapore, Vietnam and Thailand to a compliance nightmare they are only beginning to map. The advice for companies is to invest heavily in data, trace what fraction of your inputs originate from each source, and prepare for rules that will change before the compliance systems are built.Indonesia's position deserves a separate note. Its 2020 nickel ore export ban was a device to force Chinese and Western battery manufacturers to invest in downstream processing within Indonesia. It didn’t want to be an upstream commodity exporter, susceptible to the resource curse and Dutch disease of deindustrialisation. In fact, it wanted the entire value chain. The complication is that Chinese firms currently control an estimated 70 to 80 percent of Indonesia’s nickel processing capacity. The mineral is Indonesian; the value chain is largely not. Battery facilities have been slow to ramp up, while Chinese electric vehicle manufacturers are shifting to battery chemistries that use far less nickel.The geopolitical pillarJapan and South Korea built their entire defence posture around one assumption: that the American security umbrella would be deployed in times of need. That assumption is now a question mark. The recent behaviour of the Trump administration – the Venezuela operation, the Greenland threats, the treatment of NATO allies as supplicants rather than partners – has communicated something specific to every Asian capital: Sovereignty has a price, and the US no longer considers itself unconditionally bound by the architecture it built.The Taiwan issue concentrates this anxiety. A recent article in The New York Times put a number on what a Taiwan crisis would cost: an 11-percent decline in US GDP, while China’s economy would contract by 16 percent. Taiwan produces roughly 90 percent of the world’s high-end chips and underpins an estimated US$10 trillion of global GDP. Such a scenario would not merely threaten its flagship chipmaker TSMC; it would detonate the entire regional supply chain, including Singapore, Malaysia, Vietnam, Thailand and the Philippines.ASEAN countries don’t primarily buy from TSMC to consume chips. Rather, they sit downstream of it, specialising in the assembly, testing and packaging (ATP) of chips before re-export to end markets globally. In 2024, Taiwan exported close to US$40 billion in semiconductors and components to ASEAN. Roughly 80 percent of these exports flowed to Singapore and Malaysia alone. The two countries differ in an important way, however. Malaysia remains almost entirely a back-end operation: It handles global chip ATP but produces almost no wafers itself. Singapore is further up the value chain and hosts multiple active wafer fabrication plants, or fabs, with more under construction. But even in Singapore, the fabs are foreign-owned and dependent on an unbroken supply of wafers and equipment from Taiwan, the Netherlands and the US.What makes this especially uncomfortable is that ASEAN is exposed to the downside of both the current system and the transition away from it. A Taiwan crisis would cut off inputs. But a successful American reshoring effort (the Trump administration is pushing for 50 percent of chips to be made on US soil) would also shrink the flow of Taiwanese inputs through Asian ATP hubs.The question ASEAN planners aren’t yet asking loudly enough is: What is our exposure not to a Taiwan invasion, but to a Taiwan disruption – elevated tensions before any shots are fired, when shipping insurance spikes, investment freezes, flows of chips are disrupted, and the great powers start asking smaller states whose side they are on?The AI technology pillarAn AI Impact Summit was organised in India just last month. The summit dismissed the idea of “superintelligence soon” as an American imperialist narrative and instead bet on the diffusion of small models, open-source models and the need for some edge compute. This may be right. But what if it happens to be wrong?American hyperscalers including Microsoft, Google, Meta and Amazon collectively committed over US$300 billion to AI capital expenditure in 2025 and US$690 billion in 2026. The Stargate Project alone commands US$500 billion over four years. These are investments in recursively self-improving systems and building “god in a box”. Again, it’s right to question the fragility and sustainability of this investment and lament the relentless AI hype. Then again, Claude Cowork and Claude Code wiped out US$300 billion in software company valuations in a week!Against this, Singapore’s recent commitment of S$1 billion over five years to its national AI strategy is a rounding error. This isn’t a criticism of the city-state; it simply can’t match American numbers. The question is whether "good enough" local models will be sufficient as the frontier accelerates, or whether economies that make this bet find themselves locked into technological dependence on whoever controls frontier systems – almost certainly either the US or China – at precisely the moment when AI-driven productivity gains are widening the gap between frontier and non-frontier economies faster than any previous technology.China is exerting a different kind of pressure. Its relentless application of AI-enabled automation in manufacturing has compressed the low-cost labour advantage that Vietnam, Thailand, Indonesia, the Philippines and India are counting on. Robot density in Chinese manufacturing rose from 25 per 10,000 workers in 2015 to roughly 392 in 2023 – nearly matching Germany, the world’s most automated large manufacturing economy. The window for labour-cost-based manufacturing competition may be closing faster than anyone in Jakarta or New Delhi is prepared to acknowledge.India’s position is the most paradoxical. Its US$250 billion IT services sector, employing five million people, was built on one comparative advantage: large numbers of English-speaking engineers who could do, at lower cost, what Western firms needed. That advantage is being structurally eroded by the very AI systems India now hopes to deploy for growth. And it’s being built by the major AI companies, staffed and often led by Indians. The middle rung of the ladder is being pulled up while the climbers are still on it.My fear is not that AI disrupts. Every technology disrupts. My fear is that the disruption arrives before the adaptation does, and that governments currently building five-year AI roadmaps premised on a plateau that may not materialise will look up in 2028 and find that the world they planned for no longer exists.The Asian trilemmaEvery piece about Asia in crisis is supposed to end on a positive note, with every challenge reframed as an opportunity. But sometimes, a crisis is just a crisis. And compulsory or compulsive optimism is a form of denial.The threats to Malaysia and Singapore in semiconductors and to Japan and South Korea on security are real. So are the opportunities for India and Indonesia. The fracturing of the China-centric supply chain creates space. The demand for alternative manufacturing bases, mineral suppliers and technology partners is genuine. But the opportunity is narrow, time-bound and conditional on decisions that no government has fully committed to: continued investment in logistics, reforms and state capacity in India’s case, diversification away from Chinese processing dominance in Indonesia’s.The Asian miracle was built on the assumption that the system would hold. The trilemma is the likelihood that it won’t. What is new for firms and governments in this region is that the pillars are cracking, and the pivot has to happen faster than the displacement.Read a longer version of this article.]]></description>
                  <pubDate>Mon, 09 Mar 2026 01:30:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48396 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/economics-finance/asian-economic-model-breaking#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/shutterstock_2624487499.jpg?itok=LW8e_rH7" type="image/jpeg" length="1005483" /><dc:creator>Pushan Dutt</dc:creator></item><item><title>Are We in an AI Bubble?</title>
                  <link>https://knowledge.insead.edu/economics-finance/are-we-ai-bubble</link>
                  <description> <![CDATA[Fears of an AI bubble have picked up steam in recent months. Some point to surging AI capital expenditure that lacks corresponding immediate returns. Others have raised red flags about the influx of circular deals between OpenAI and the likes of Nvidia, Microsoft and Oracle, which could be artificially inflating demand. Still others question the sky-high valuations of AI-linked stocks, despite the recent market pullback.Are we in an AI bubble reminiscent of the dotcom era? If so, what are some signs that it may be about to pop? INSEAD faculty analyse how we got here, whether investors have cause for concern and what to pay attention to in the months ahead.The market is pricing in continued exceptional growthBen Charoenwong, Associate Professor of FinanceThe question of whether we are in an AI bubble requires some definition. A bubble exists not only when prices exceed current fundamentals, but when prices exceed what future fundamentals can realistically deliver. It implies that the market is behaving like expected future returns are negative, which can happen when the market is “wrong” or when risk-taking capacity turns from risk-aversion to risk-lovingness – distinguishing one from the other is notoriously difficult.So, bubbles can form when market participants unrealistically extrapolate recent growth rates to the indefinite future, either by ignoring competitive dynamics or when circular validation (rising prices confirming the narrative that justified the price increase) crowds out rigorous analyses of sustainable cash flows.By this framework, the current AI market is not obviously a bubble – yet. Earnings growth has largely matched price increases for AI infrastructure leaders, but the market is pricing in continued exceptional growth for years to come. Whether those expectations prove realistic or represent overextrapolation will determine if current valuations are justified or become the foundation of a correction.There are warning signs. The web of circular financing deals in AI has reached a scale and complexity that warrants serious scrutiny. These arrangements bear uncomfortable similarities to the vendor-financing structures that characterised the late-stage dotcom bubble. Consider the arrangement between Nvidia and OpenAI. Nvidia invests up to US$100 billion in OpenAI. OpenAI uses that capital to build data centres, which are filled with Nvidia chips. OpenAI receives cash to expand, and Nvidia is guaranteed to be the supplier for that expansion. The arrangement between Oracle and OpenAI follows the same pattern.The distinction between "flywheel" and "house of cards" comes down to whether real end-user demand is being generated, or whether money is simply moving in circles. Bull markets and elevated sentiment are forgiving of circular financing in hopes of future growth. But if demand fails to materialise in earnings, these hopes can be dashed. What appears as a virtuous cycle today becomes the mechanism of collapse tomorrow.While markets have focused on generative AI and cloud infrastructure, the next phase of the AI cycle may be physical, comprising humanoid robotics and embodied intelligence. Goldman Sachs projects that the global market for humanoid robots could reach US$38 billion by 2035. This wave is beginning to materialise commercially but seems less of a factor in current valuations compared to the AI darlings – and could be a significant upcoming catalyst for capital investment beyond AI data centres. Investors focused solely on generative AI may be underweighting the longer-term transformation that physical AI represents.Investors are already asking firms to “show me”Lily Fang, Dean of Research and Innovation, Professor of Finance and the UBS Chair in Investment BankingIt’s always dangerous to say, “this time is different”. From a pure valuation perspective, there are some parallels between current market conditions and the dotcom bubble. For example, if you look at the Shiller P/E ratio, which is the ratio of the current price over the trailing 10-year inflation-adjusted earnings of the S&P 500, we are at a level (40) that’s very close to the peak in 1999 (45).But there are some fundamental differences. In the dotcom era, the most expensive stocks were loss-making, newly listed tech stocks that drove up overall market valuation. Today, valuation is propelled by massive – and massively profitable – firms such as Nvidia, Google and their Big Tech peers. These firms’ stock prices have risen a lot, but so have their earnings. Based on the Shiller P/E ratio, they look expensive. However, if you look at the regular P/E ratio of the S&P 500, which is calculated as the current price divided by only the trailing 12-month earnings, then we are at 29 – quite a bit lower than the peak of 45. Though 29 is hardly cheap, it indicates that current earnings are significantly higher than historical earnings, so valuations regarding current and future earnings are more reasonable compared to the dotcom era.That being said, the market wants assurances that its investments are yielding the desired ROI. Investors are already asking firms to “show me”. Recently, both Meta and Microsoft reported top- and bottom-line earnings that beat expectations. But Microsoft’s stock price dropped by 10 percent, while Meta’s rose by 10 percent the next day. Why? Meta was able to convince investors that AI was improving its ad targeting and profitability, whereas Microsoft couldn’t show a clear ROI. Let’s not forget that not too long ago, the market was unforgiving to Meta. In the previous quarter, when it couldn’t show such data, its stock price was duly punished.I think the bubble is less in the listed market and more in pre-IPO AI start-ups. Many early-stage investors, including venture capitalists, will lose money if they continue pouring capital into loss-making, cash-burning AI start-ups with no clear path to profitability. In this sense, there’s a parallel to the dotcom bubble: countless VC firms disappeared after it burst. The valuations of AI firms are eye-wateringBoris Vallée, Associate Professor of FinanceI see many similarities between the current AI boom and the dotcom era. First, there’s no question that we are facing an innovation that will have a profound impact on our economies and societies, much like the internet. Contrast this with the crypto ecosystem, where compelling use cases have been challenging to find. Second, the valuations of AI companies are eye-watering and have risen very quickly, despite the absence of profit for most of these firms. This is reminiscent of the dotcom era. On the other hand, Nvidia is a highly profitable company, which reminds me of the gold rush in some ways – shovel-sellers were the ones with the best business case, given the uncertainty of gold prospection.There’s a key difference between the hundreds of billions that companies are pouring into AI-related capex and the spending that occurred during the dotcom era. Large and extraordinarily deep-pocketed incumbents are participating in and financing the development of AI technologies and capacity, either on their own or by partnering with the most innovative firms. This capex is mostly being shelled out on infrastructure, which should be largely redeployable. This is very different from the dotcom bubble, during which marketing spend, for instance, was particularly large.General academic research has shown that complex financing structures often serve the purpose of hiding risk. My understanding is that the circular transactions we’re seeing aren’t simply providing financing across the supply chain (e.g. a client financing a supplier) but are structured in opaque ways to limit recourse over the general assets of the large tech firms. There are some concerns here that deep-pocketed incumbents are trying to design options for themselves, thereby fuelling the financing of investments while limiting their own downside exposure. So, if things go awry, the losses would be transferred to the financial system.Looking ahead, I’d personally keep an eye on adoption trends by both corporate clients and individuals and the associated revenues, to monitor their willingness to pay. We’ll need to be able to start rationalising both valuations and capex with more precisely estimated cash flows. An interesting parallel is that Nvidia’s market cap, at roughly US$4 trillion, is as large as the entire amount of PE assets under management in the United States. When we think about the footprint of firms under PE ownership, we have to start seeing AI building a similar one.“Strategic necessity” isn’t a blank cheque Lin Shen, Assistant Professor of FinanceOn valuation alone, today’s AI-led market looks hot, but it still doesn’t match dotcom extremes. The current rally is more tightly anchored to profits and cash flow. The Nasdaq-100 is currently trading at a trailing 12-month price-to-earnings multiple of just over 33 vs. around 60 in March 2000.The dotcom peak was more extreme because a large share of market value was tied to companies that hadn’t yet built real earnings or cash flow, so prices were driven more by future stories than by current results. In the current AI cycle, many of the key beneficiaries are cash-flow machines. Nvidia – arguably the emblematic AI winner – reported a record US$57 billion in revenue for its fiscal Q3 2025, up 62 percent year-over-year, and US$23.8 billion in cash flow from operating activities for that same quarter. These numbers make it easier to justify paying up via discounted cash-flow logic, not just “eyeballs”.That same focus on fundamentals emerges in how quickly the market pushes back when the cash-flow story gets stretched by spending. For instance, when Alphabet declared it would target US$175 billion to US$185 billion of capex in 2026 (roughly double 2025’s level), it triggered an immediate sell-off as investors questioned whether AI spend would convert into earnings.Alphabet isn’t alone. The scale of the arms race is now explicit: Big Tech players are estimated to shell out US$660 billion on AI this year, a number that crystallises both the ambition and the risk. The market’s reaction to these capex disclosures – sharp repricing rather than applause – underscores how different today’s mindset is from the late-1990s: Investors aren’t blindly buying a vision; they are policing the cash conversion path.So, why are firms spending so aggressively anyway? Because AI is a “winner-takes-most” game: distribution, proprietary data, model quality and developer ecosystems can compound, and the prize for becoming the default platform is enormous. That belief turns AI into an arms race, with each firm fearing that under-investing today risks irrelevance tomorrow.But the capex-driven sell-offs are a reminder that “strategic necessity” isn’t a blank cheque. The past couple of years were largely about building capacity. Going forward, investors will demand proof of utilisation, pricing power and profits.]]></description>
                  <pubDate>Mon, 23 Feb 2026 02:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48341 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/economics-finance/are-we-ai-bubble#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-02/shutterstock_2465304407.jpg?itok=x08Z_HYq" type="image/jpeg" length="934813" /><dc:creator>Ben Charoenwong</dc:creator><dc:creator>Lily Fang</dc:creator><dc:creator>Boris Vallée</dc:creator><dc:creator>Lin Shen</dc:creator></item><item><title>INSEAD Insights: Strong Cultures, Supply Chains and Surprise</title>
                  <link>https://knowledge.insead.edu/leadership-organisations/insead-insights-strong-cultures-supply-chains-and-surprise</link>
                  <description> <![CDATA[From understanding the role of surprise in organisations to finding ways to improve disease tracking and analysis, this month’s selection of recently published research by INSEAD faculty spans a diverse array of topics. Other papers explore how context shapes the effectiveness of fiscal rules; how market-based incentives can drive the energy transition; and how firms with less hierarchical structures manage to get their employees pulling in the same direction.The role of surprise in organisationsFrom Aristotle to Charles Darwin, surprise has been a topic of fascination and the source of many questions in philosophy, the social sciences and beyond. However, a history of inconsistent definitions and fragmented perspectives has led to conceptual confusion, limiting our understanding of how surprise affects individuals in organisations.In a paper published in the Journal of Applied Psychology, Spencer Harrison and his co-authors blend insights from psychology, management and other related fields to provide a comprehensive understanding of surprise in organisational contexts. They explore the cognitive and emotional mechanisms that underlie surprise and identify how key factors – such as organisational memory and emotional capabilities – shape how it is experienced and managed within organisations.Read the full paperImproving disease surveillance in Sub-Saharan AfricaPathogen genomic sequencing is central to disease surveillance, enabling laboratories to track the spread of diseases and inform public health responses. A study by Luk Van Wassenhove and his co-authors evaluates two types of donor interventions aimed at improving underdeveloped pathogen genomic sequencing supply chains in Sub-Saharan Africa: in-kind donations and supply chain management capability-building. The study, published in the International Journal of Operations & Production Management, revealed that although in-kind donations can mitigate acute shortages, frequent use risks creating dependency and suppressing learning. In contrast, supply chain management capability-building brings more sustainable improvements, particularly for laboratories that are unlikely to improve without external support.Read the full paperWell-designed fiscal rules are no silver bulletFiscal rules have been shown to improve government budget balances and restrain debt growth. But while they generally improve a country’s cyclically adjusted primary balance, their impact depends on both the time horizon and the context in which they are adopted, according to research by Antonio Fatás and his co-authors published in the Journal of International Money and Finance.In advanced economies and countries with strong political institutions, the effects strengthen over time. But in emerging markets and developing economies – especially those with weaker institutions – their impact tends to fade as time passes. This suggests that fiscal rules introduced during periods of economic hardship or under highly concentrated political power are often less effective in the medium term.Read the full paperHow firms redeploy assets in response to industry shocksHow should firms respond when a core industry experiences a downturn? Research by Aldona Kapačinskaitė, published in the Strategic Management Journal, examines how energy giants reacted to the 2014 oil price crash. Focusing on oil and gas companies that diversified into wind power, she shows that these firms reduced investment in oil and gas – especially in offshore projects – while increasing investment in wind power.Importantly, firms were more likely to invest in newer, higher-capacity wind technologies when these could be co-located with existing offshore oil and gas assets. This shows how firms facing industry shocks redeploy resources into more promising sectors. However, their willingness to do so may depend on the possibility of leveraging existing assets – meaning that market-based incentives alone may be insufficient to drive the switch to renewable energy sources.Read the full paperThe ties that bind less hierarchical firmsInstead of depending on traditional forms of managerial hierarchy to align the work of employees, can strong cultures – made up of systems of widely shared beliefs and values – do the job? To investigate this, Phanish Puranam and his co-author analysed 1.5 million Glassdoor employee reviews and 42 million professional social media profiles from 23,000 American firms.Their research, published in the Strategic Management Journal, found that organisations with stronger cultures do indeed have a lower proportion of managers to total employees. This suggests that attempts to flatten organisational hierarchies by eliminating layers of managers is more likely to succeed if accompanied by efforts to build strong cultures. This can be facilitated in various ways, including the careful selection and socialisation of employees.Read the full paper]]></description>
                  <pubDate>Mon, 16 Feb 2026 01:10:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48336 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/leadership-organisations/insead-insights-strong-cultures-supply-chains-and-surprise#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-02/shutterstock_2100721102.jpg?itok=afP9-t-x" type="image/jpeg" length="1008987" /><dc:creator>Lily Fang</dc:creator></item>
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