{"id":73148,"date":"2026-04-02T10:55:56","date_gmt":"2026-04-02T17:55:56","guid":{"rendered":"https:\/\/penderfund.com\/?p=73148"},"modified":"2026-04-02T11:43:39","modified_gmt":"2026-04-02T18:43:39","slug":"the-intelligence-economy-artificial-intelligence-productivity-and-the-next-capital-cycle","status":"publish","type":"post","link":"https:\/\/penderfund.com\/fr\/articles\/the-intelligence-economy-artificial-intelligence-productivity-and-the-next-capital-cycle\/","title":{"rendered":"L\u2019\u00e9conomie de l\u2019intelligence\u202f: intelligence artificielle, productivit\u00e9 et prochain cycle du capital"},"content":{"rendered":"<p><span data-contrast=\"auto\">At Pender, our equity team focuses on\u00a0identifying\u00a0opportunities at the intersection of sector and capital cycles, structural change, and market inefficiencies. Our core thematic areas include Enterprise Software, Artificial Intelligence,\u00a0the Energy\u00a0Transition, and the evolving geopolitical landscape.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">This\u00a0white\u00a0paper examines artificial intelligence as\u00a0major\u00a0technological and economic\u00a0shift. It is complemented by a companion podcast featuring Toufic\u00a0Boubez, Venture Partner at Pender Ventures and a serial entrepreneur with more than two decades of experience in machine learning, cloud architecture, and enterprise software.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span style=\"color: #b12258;\"><b>The Intelligence Economy: Artificial Intelligence, Productivity, and the Next Capital Cycle<\/b><\/span><\/p>\n<p><span data-contrast=\"auto\">Artificial intelligence is one of the most consequential technological development in decades, advancing at a pace that is rapid, compounding, and difficult to fully comprehend. Industries are being restructured, entire categories of labor face obsolescence, and new sectors are emerging in their place. McKinsey estimates that AI could contribute between $17.1 trillion and $25.6 trillion annually to the global economy.<span style=\"font-size: 10pt;\"><sub>1<\/sub><\/span><\/span><\/p>\n<p><span data-contrast=\"auto\">We\u00a0believe\u00a0this\u00a0not as\u00a0a source of apprehension,\u00a0but as\u00a0one of the most compelling investment opportunities of our time.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Before\u00a0proceeding, it is useful to define a few key terms:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span style=\"color: #b12258;\"><b>Artificial Intelligence (AI):<\/b><\/span><span data-contrast=\"auto\"><span style=\"color: #b12258;\">\u00a0<\/span>A branch of computer science focused on enabling machines to perform tasks that typically require human cognition, including learning, reasoning, problem-solving, and perception. Unlike traditional software, AI systems\u00a0leverage\u00a0large datasets to\u00a0identify\u00a0patterns, make probabilistic predictions, and adapt dynamically to new inputs.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"o\" data-font=\"Courier New\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:1440,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Courier New&quot;,&quot;469769242&quot;:[9675],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;o&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"2\"><span style=\"color: #b12258;\"><b>Agentic AI:<\/b><\/span><span data-contrast=\"auto\"><span style=\"color: #b12258;\">\u00a0<\/span>an\u202fartificial intelligence\u202fsystem that can\u00a0accomplish\u00a0a specific goal with limited supervision. It consists of AI agents, machine learning models that mimic human decision-making to solve problems in real\u00a0time.<\/span><span style=\"font-size: 10pt;\"><sub>2<\/sub>\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"2\" data-aria-level=\"1\"><span style=\"color: #b12258;\"><b>Machine Learning (ML):<\/b><\/span><span data-contrast=\"auto\">\u00a0A subset of AI that involves training algorithms on data to make predictions or decisions without being explicitly programmed for specific\u00a0outcomes. It encompasses a broad range of statistical and computational techniques.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"o\" data-font=\"Courier New\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:1440,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Courier New&quot;,&quot;469769242&quot;:[9675],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;o&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"2\"><span style=\"color: #b12258;\"><b>Deep learning:<\/b><\/span><span data-contrast=\"auto\"><span style=\"color: #b12258;\">\u00a0<\/span>a subset of machine learning driven by multilayered neural networks whose design is inspired by the structure of the human brain. Deep learning models power most\u00a0state-of-the-art\u00a0AI today<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"3\" data-aria-level=\"1\"><span style=\"color: #b12258;\"><b>Large Language Models (LLMs)<\/b><\/span><span data-contrast=\"auto\"><span style=\"color: #b12258;\">:<\/span> A class of deep learning models trained\u00a0on\u00a0vast\u00a0amounts\u00a0of data. These systems generate and interpret natural language by predicting token sequences based on learned statistical relationships. Applications such as ChatGPT and Claude\u00a0fall within\u00a0this category.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"4\" data-aria-level=\"1\"><b><span data-contrast=\"auto\"><span style=\"color: #b12258;\">Hyperscale:<\/span>\u00a0<\/span><\/b><span data-contrast=\"auto\">a distributed computing environment and architecture that is designed to provide extreme scalability to accommodate workloads of massive scale. The related term \u201chyperscaler\u201d refers to hyperscale data centers, which are significantly larger than traditional on-premises data\u00a0centers.<\/span><sub><span data-contrast=\"auto\">3<\/span><\/sub><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #b12258;\"><b>Historical Context<\/b><\/span><\/p>\n<p><span style=\"color: #b12258;\"><b>AI as a General-Purpose Technology<\/b>\u00a0<\/span><\/p>\n<p>To the untrained eye, the current moment can appear abrupt. A\u00a0sudden, highly disruptive breakthrough arriving without precedent. This\u00a0perception\u00a0is understandable, but misleading. History, studied carefully, is among the most powerful tools an investor can have, and the future, in many respects, has a past. Rather than enabling precise prediction, history provides a framework for understanding causality, context, and the mechanisms through which change unfolds. By examining how prior technological revolutions evolved:\u00a0from\u00a0initial\u00a0innovation,\u00a0through adoption and eventual economic integration,\u00a0we can situate AI within a broader continuum and develop a clearer perspective on where we may be in its development trajectory. The\u00a0objective\u00a0is not certainty, but better judgment.<\/p>\n<p><span data-contrast=\"auto\">Over time, economic development has\u00a0exhibited\u00a0a recurring, cyclical pattern.\u00a0Roughly every\u00a0half century, a new general-purpose technology\u00a0emerges\u00a0(steam power, electricity, the automobile, or information technology etc.) triggering a surge of capital investment. This influx of capital often leads to periods of speculative excess and asset bubbles, followed by correction. Yet, in the aftermath, these technologies enter a \u201cgolden age\u201d characterized by sustained productivity growth and widespread economic transformation.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Each major economic era has been catalyzed by\u00a0such a\u00a0technology. Importantly, these innovations do not merely augment existing processes; they reorganize entire economic systems. They redefine labor markets, enable new industries, and reshape global supply chains. This pattern can be\u00a0observed\u00a0across several historical episodes:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<ol>\n<li aria-setsize=\"-1\" data-leveltext=\"%1.\" data-font=\"\" data-listid=\"2\" data-list-defn-props=\"{&quot;335552541&quot;:0,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769242&quot;:[65533,0],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;%1.&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span style=\"color: #b12258;\"><b>Steam Power and Rail Infrastructure (early\u2013mid\u00a019th century):<\/b><\/span><br \/>\n<span data-contrast=\"auto\">Early steam engines enabled the expansion of rail networks, culminating in the railway boom of the 1840s. This period of speculative overinvestment was followed by a financial collapse, after which rail infrastructure became foundational to industrial growth.<\/span><\/li>\n<li aria-setsize=\"-1\" data-leveltext=\"%1.\" data-font=\"\" data-listid=\"2\" data-list-defn-props=\"{&quot;335552541&quot;:0,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769242&quot;:[65533,0],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;%1.&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span style=\"color: #b12258;\"><b>Electricity and Heavy Industry (late 19th\u2013early 20th century):<\/b><\/span><br \/>\n<span data-contrast=\"auto\">Initial electrification spurred large-scale infrastructure investment and utility expansion. Following financial instability in the 1890s, electricity entered a period of broad industrial adoption, driving significant productivity gains.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li aria-setsize=\"-1\" data-leveltext=\"%1.\" data-font=\"\" data-listid=\"2\" data-list-defn-props=\"{&quot;335552541&quot;:0,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769242&quot;:[65533,0],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;%1.&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span style=\"color: #b12258;\"><b>Automobiles and Mass Production (early\u2013mid\u00a020th century):<\/b><\/span><br \/>\n<span data-contrast=\"auto\">The emergence of the automobile led to intense capital investment and industry fragmentation, followed by consolidation and the rise of mass production systems that defined modern manufacturing.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li aria-setsize=\"-1\" data-leveltext=\"%1.\" data-font=\"\" data-listid=\"2\" data-list-defn-props=\"{&quot;335552541&quot;:0,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769242&quot;:[65533,0],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;%1.&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span style=\"color: #b12258;\"><b>Information Technology and the Internet (late 20th century\u2013present):<\/b><\/span><br \/>\n<span data-contrast=\"auto\">The development of microprocessors, computing, and the internet culminated in the dot-com bubble and\u00a0subsequent\u00a0correction. In the decades that followed, digital technologies became deeply embedded across the global economy, enabling sustained productivity growth and the rise of platform-based business models.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ol>\n<p><span data-contrast=\"auto\">This historical lens brings us to the present: the emergence of artificial intelligence as the next general-purpose technology.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Thus far, the AI cycle has been characterized by foundational breakthroughs in deep learning, the rapid development of large language models, and a surge of capital investment across the technology ecosystem. Yet a central question\u00a0remains\u00a0unresolved: what constitutes the inflection point at which AI transitions from early adoption to speculative excess,\u00a0and is that\u00a0transition already underway?\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">History suggests that such turning points are only clearly identifiable in retrospect.\u00a0What appears increasingly clear, however, is that AI is following a familiar pattern, one that has historically preceded profound and enduring economic transformation.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span style=\"color: #b12258;\"><b>The AI Adoption Curve<\/b>\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">One of the defining characteristics of the current AI cycle is the unprecedented speed of adoption. Compared to prior technological waves,\u00a0dispersion\u00a0is occurring\u00a0at a dramatically accelerated pace.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">For example, ChatGPT reached 100 million users in approximately\u00a060 days, compared to\u00a0nearly two\u00a0years for Instagram. More broadly, generative AI achieved\u00a0roughly 40%\u00a0adoption among US\u00a0users in under two years,\u00a0whereas\u00a0personal computers required more than a decade to reach comparable levels of penetration. This compression of adoption timelines reflects both the maturity of existing digital infrastructure and the inherently scalable nature of software-based innovation.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Despite this rapid uptake, the full economic impact of AI has yet to be realized. This\u00a0apparent\u00a0paradox can be understood through the lens of the Productivity J-Curve<\/span><span data-contrast=\"auto\">,<sub>4<\/sub> which describes an initial phase in which technological adoption suppresses measured productivity. During this period, organizations incur significant costs related to infrastructure investment, workflow reconfiguration, and workforce retraining. Only once these complementary investments are fully integrated do productivity gains begin to materialize at scale.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<table style=\"border-collapse: collapse; width: 100%; height: 211px;\">\n<tbody>\n<tr style=\"height: 372px;\">\n<td style=\"width: 100%; height: 211px;\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-73168 alignleft\" src=\"https:\/\/penderfund.com\/wp-content\/uploads\/2026\/04\/The-J-Curve-Effect-in-Change.png\" alt=\"\" width=\"468\" height=\"314\" srcset=\"https:\/\/penderfund.com\/wp-content\/uploads\/2026\/04\/The-J-Curve-Effect-in-Change.png 992w, https:\/\/penderfund.com\/wp-content\/uploads\/2026\/04\/The-J-Curve-Effect-in-Change-300x201.png 300w, https:\/\/penderfund.com\/wp-content\/uploads\/2026\/04\/The-J-Curve-Effect-in-Change-150x101.png 150w, https:\/\/penderfund.com\/wp-content\/uploads\/2026\/04\/The-J-Curve-Effect-in-Change-768x515.png 768w, https:\/\/penderfund.com\/wp-content\/uploads\/2026\/04\/The-J-Curve-Effect-in-Change-18x12.png 18w\" sizes=\"auto, (max-width: 468px) 100vw, 468px\" \/><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span style=\"font-size: 8pt;\">Source: David Viney | david-viney.me<\/span><\/p>\n<p><span data-contrast=\"auto\">Historical evidence strongly supports this dynamic. Electrification, for instance, was technologically\u00a0viable\u00a0by the late 19th century, yet meaningful productivity gains did not\u00a0emerge\u00a0until the 1920s, when factories reorganized production systems around decentralized electric motors. Steam power\u00a0exhibited\u00a0an even longer lag, requiring several decades before delivering widespread economic benefits through rail networks and industrial expansion.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Artificial intelligence is\u00a0likely following\u00a0a similar trajectory and appears to be in the\u00a0early stages\u00a0of navigating the downward slope of this J-curve. However, the duration and severity of this phase may be less pronounced than in\u00a0previous\u00a0technological cycles. Unlike earlier general-purpose technologies, AI is being deployed on top of an already mature digital foundation built by the internet and mobile\u00a0eras.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">It\u2019s\u00a0also worth highlighting that advances in generative AI may follow a more nonlinear trajectory, as the technology\u00a0possesses\u00a0the unique capacity to accelerate its own development through\u00a0iterative\u00a0improvement. This dynamic introduces the possibility that both adoption and productivity gains could materialize more rapidly than historical\u00a0precedent\u00a0might suggest.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span style=\"color: #b12258;\"><b>The AI Capital Cycle<\/b>\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Transformational technologies share a defining feature: they attract capital at a scale with their perceived potential. Artificial intelligence is no exception,\u00a0and by\u00a0nearly every\u00a0measure, the current buildout is unprecedented in both\u00a0magnitude\u00a0and concentration. The closest historical parallel is the industrial capital expansion of the late 19th-century railway era, when massive upfront investment in physical infrastructure created short-term economic dislocation while laying the foundation for long-term structural growth. The AI capital cycle bears a striking resemblance to that moment.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Today, a small number of hyperscale technology firms are driving this investment cycle. In 2026 alone, the four largest (Amazon, Alphabet, Microsoft, and Meta) are projected to collectively deploy more than US$600 billion\u00a0in capital expenditures.\u00a0CapEx-to-revenue ratios, which historically averaged ~10% for large technology firms, have risen above 20% and are expected to peak near 25% in 2026, reflecting the intensity of this infrastructure buildout.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Importantly, this investment cycle extends beyond public markets. Private capital deployment within the AI ecosystem has also reached unprecedented levels. OpenAI recently raised\u00a0$110 billion\u00a0at an\u00a0$840 billion\u00a0post-money valuation, while Anthropic and\u00a0xAI\u00a0have secured\u00a0$30 billion\u00a0and\u00a0$20 billion\u00a0respectively in recent financing rounds. These capital flows\u00a0reflect both the scale of opportunity and the intensity of competition.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">This concentration of capital is increasingly reshaping the physical economy. AI development is not purely a software-driven phenomenon; it requires extensive investment in tangible infrastructure, including semiconductors, data centers, real estate, and energy systems. Unlike prior software cycles, the constraints are not only computational but also physical, defined by power availability, land, and grid capacity.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p data-ccp-border-bottom=\"2px solid #000000\" data-ccp-padding-bottom=\"18.666666666666668px\"><span data-contrast=\"auto\">Modern AI training clusters can require between 500 megawatts and one gigawatt of continuous power, placing significant strain on existing utility infrastructure. At the same time, traditional grid interconnection timelines in major U.S. regions, often spanning 36 to\u00a060 months, are fundamentally misaligned with the\u00a012 to\u00a024 month\u00a0deployment cycles demanded by\u00a0hyperscalers. This growing mismatch highlights the emergence of energy and infrastructure as critical bottlenecks in the next phase of AI development.<\/span><span data-ccp-props=\"{&quot;335572079&quot;:12,&quot;335572080&quot;:14,&quot;335572081&quot;:4278190080,&quot;469789806&quot;:&quot;single&quot;}\">\u00a0<\/span><\/p>\n<p data-ccp-border-top=\"0px none \" data-ccp-padding-top=\"0px\" data-ccp-border-bottom=\"2px solid #000000\" data-ccp-padding-bottom=\"18.666666666666668px\"><span data-contrast=\"auto\">Taken together, the AI capital cycle reflects a transition from a purely digital investment to one that is increasingly constrained, as well as enabled by the physical world.\u00a0<\/span><span data-ccp-props=\"{&quot;335572079&quot;:12,&quot;335572080&quot;:14,&quot;335572081&quot;:4278190080,&quot;469789806&quot;:&quot;single&quot;}\">\u00a0<\/span><\/p>\n<p data-ccp-border-top=\"0px none \" data-ccp-padding-top=\"0px\" data-ccp-border-bottom=\"2px solid #000000\" data-ccp-padding-bottom=\"18.666666666666668px\"><span style=\"color: #b12258;\"><b>AI Industrial Stack<\/b>\u00a0<\/span><\/p>\n<p data-ccp-border-top=\"0px none \" data-ccp-padding-top=\"0px\" data-ccp-border-bottom=\"2px solid #000000\" data-ccp-padding-bottom=\"18.666666666666668px\"><span data-contrast=\"auto\">Artificial intelligence is not a singular technology. It is a structured, interdependent system that requires coordinated development across a vertically integrated stack spanning physical infrastructure, data, models, and applications. This distinction matters\u00a0for\u00a0investors. Unlike prior software-driven cycles, where value creation was\u00a0largely concentrated\u00a0at the application layer, AI demands simultaneous investment across every level of the stack. Understanding where capital is being deployed, where constraints are emerging, and where value\u00a0ultimately accrues\u00a0requires understanding how these layers interact.<\/span><span data-ccp-props=\"{&quot;335572079&quot;:12,&quot;335572080&quot;:14,&quot;335572081&quot;:4278190080,&quot;469789806&quot;:&quot;single&quot;}\">\u00a0<\/span><\/p>\n<p data-ccp-border-top=\"0px none \" data-ccp-padding-top=\"0px\" data-ccp-border-bottom=\"2px solid #000000\" data-ccp-padding-bottom=\"18.666666666666668px\"><span data-contrast=\"auto\">While definitions may vary, the AI stack can be broadly categorized into four core layers: infrastructure, data, models, and applications.<\/span><span data-ccp-props=\"{&quot;335572079&quot;:12,&quot;335572080&quot;:14,&quot;335572081&quot;:4278190080,&quot;469789806&quot;:&quot;single&quot;}\">\u00a0<\/span><\/p>\n<table style=\"border-collapse: collapse; width: 34.0836%; height: 354px;\">\n<tbody>\n<tr style=\"height: 354px;\">\n<td style=\"width: 100%; height: 354px;\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-73169 alignleft\" src=\"https:\/\/penderfund.com\/wp-content\/uploads\/2026\/04\/The-AI-Tech-Stack-2.png\" alt=\"\" width=\"306\" height=\"332\" srcset=\"https:\/\/penderfund.com\/wp-content\/uploads\/2026\/04\/The-AI-Tech-Stack-2.png 559w, https:\/\/penderfund.com\/wp-content\/uploads\/2026\/04\/The-AI-Tech-Stack-2-277x300.png 277w, https:\/\/penderfund.com\/wp-content\/uploads\/2026\/04\/The-AI-Tech-Stack-2-138x150.png 138w, https:\/\/penderfund.com\/wp-content\/uploads\/2026\/04\/The-AI-Tech-Stack-2-11x12.png 11w\" sizes=\"auto, (max-width: 306px) 100vw, 306px\" \/><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p data-ccp-border-top=\"0px none \" data-ccp-padding-top=\"0px\" data-ccp-border-bottom=\"2px solid #000000\" data-ccp-padding-bottom=\"18.666666666666668px\"><span data-contrast=\"auto\">At the foundation lies the\u00a0<\/span><span style=\"color: #b12258;\"><b>infrastructure layer<\/b><\/span><span data-contrast=\"auto\">, which underpins the entire system. This includes semiconductors, data centers, and cloud platforms, as well as the physical systems\u00a0required\u00a0to support them \u2013 think power generation, cooling, high-speed\u00a0networking, etc. As computational intensity increases, physical constraints have become increasingly important. The ability to efficiently move large volumes of data and manage heat now\u00a0represents\u00a0a critical bottleneck. In this sense, performance is no longer\u00a0determined\u00a0solely by advances in\u00a0compute, but also by energy availability and thermal management capacity.<\/span><span data-ccp-props=\"{&quot;335572079&quot;:12,&quot;335572080&quot;:14,&quot;335572081&quot;:4278190080,&quot;469789806&quot;:&quot;single&quot;}\">\u00a0<\/span><\/p>\n<p data-ccp-border-top=\"0px none \" data-ccp-padding-top=\"0px\" data-ccp-border-bottom=\"2px solid #000000\" data-ccp-padding-bottom=\"18.666666666666668px\"><span data-contrast=\"auto\">Above this sits the\u00a0<\/span><span style=\"color: #b12258;\"><b>data layer<\/b><\/span><span data-contrast=\"auto\">, which serves as the essential input to all AI systems. Model performance is fundamentally dependent on the quality, scale, and accessibility of underlying data. As a result, modernizing data architecture, through cloud migration, data integration, and real-time processing, has become a prerequisite for effective AI deployment. Significant investment is being\u00a0directed\u00a0toward building robust data pipelines and storage systems.\u00a0AI systems are only as effective as the data on which they are trained.<\/span><span data-ccp-props=\"{&quot;335572079&quot;:12,&quot;335572080&quot;:14,&quot;335572081&quot;:4278190080,&quot;469789806&quot;:&quot;single&quot;}\">\u00a0<\/span><\/p>\n<p data-ccp-border-top=\"0px none \" data-ccp-padding-top=\"0px\" data-ccp-border-bottom=\"2px solid #000000\" data-ccp-padding-bottom=\"18.666666666666668px\"><span data-contrast=\"auto\">The<span style=\"color: #b12258;\">\u00a0<\/span><\/span><span style=\"color: #b12258;\"><b>model layer<\/b><\/span><span data-contrast=\"auto\"><span style=\"color: #b12258;\">\u00a0<\/span>represents\u00a0the core intelligence of the stack. This includes machine learning algorithms and, increasingly, large-scale foundation models such as LLMs. Advances at this\u00a0layer\u00a0have driven much of the recent progress in AI capabilities. However, these\u00a0advancements\u00a0remain\u00a0dependent on the underlying infrastructure and data layers.<\/span><span data-ccp-props=\"{&quot;335572079&quot;:12,&quot;335572080&quot;:14,&quot;335572081&quot;:4278190080,&quot;469789806&quot;:&quot;single&quot;}\">\u00a0<\/span><\/p>\n<p data-ccp-border-top=\"0px none \" data-ccp-padding-top=\"0px\"><span data-contrast=\"auto\">At the top of the stack is the\u00a0<\/span><span style=\"color: #b12258;\"><b>application layer<\/b><\/span><span data-contrast=\"auto\">, where AI is translated into economic value. This includes enterprise software, consumer applications, and industry-specific solutions that embed AI into real-world workflows. While early investment has been concentrated in infrastructure and models, it is at the application layer where monetization and widespread adoption\u00a0ultimately occur.<\/span><span data-ccp-props=\"{&quot;335572079&quot;:12,&quot;335572080&quot;:14,&quot;335572081&quot;:4278190080,&quot;469789806&quot;:&quot;single&quot;}\">\u00a0<\/span><\/p>\n<p data-ccp-border-bottom=\"2px solid #000000\" data-ccp-padding-bottom=\"18.666666666666668px\"><span data-contrast=\"auto\">It\u2019s\u00a0important to highlight that\u00a0as a result of\u00a0this stack infrastructure, AI development could be constrained by the weakest link across the stack. Each layer has levels of\u00a0interdependencies, while large-scale adoption depends on seamless integration into operational systems.\u00a0Further,\u00a0the foundation of this entire stack rests on semiconductor technology. Semiconductors are materials whose electrical conductivity can be precisely controlled, allowing them to function as both conductors and insulators under different conditions. This property enables the creation of transistors, microscopic switches that regulate the flow of electricity. Modern chips\u00a0contain\u00a0billions of these switches, enabling the execution of complex computations at extraordinary speed and scale. As such, semiconductors form the backbone of all digital systems, including those powering artificial intelligence.<\/span><span data-ccp-props=\"{&quot;335572079&quot;:12,&quot;335572080&quot;:14,&quot;335572081&quot;:0,&quot;469789806&quot;:&quot;single&quot;}\">\u00a0<\/span><\/p>\n<p data-ccp-border-top=\"0px none \" data-ccp-padding-top=\"0px\"><span data-contrast=\"auto\">The AI industrial stack makes one thing clear: the\u00a0potential\u00a0investment opportunity in artificial intelligence is not confined to any single layer. It is distributed across the entire system. For investors, this is both\u00a0the complexity\u00a0and\u00a0the opportunity. Those who\u00a0understand how the layers interact, where the bottlenecks lie, and where capital is flowing\u00a0may\u00a0be better positioned to\u00a0identify\u00a0where durable returns are most likely to\u00a0emerge.<\/span><span data-ccp-props=\"{&quot;335572079&quot;:12,&quot;335572080&quot;:14,&quot;335572081&quot;:0,&quot;469789806&quot;:&quot;single&quot;}\">\u00a0<\/span><\/p>\n<p><span style=\"color: #b12258;\"><b>The Productivity\u00a0Shock<\/b>\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">As the AI industrial stack matures and transitions from experimentation to large-scale deployment, it\u00a0could\u00a0continue to generate a productivity shock across the global economy. This shift is not merely incremental; it could\u00a0represent\u00a0a fundamental change in how work is performed, how knowledge is generated, and how organizations\u00a0operate. AI is increasing operational velocity, redefining\u00a0research\u00a0and development processes, and introducing structural changes to labor markets.<\/span><span data-ccp-props=\"{&quot;335572079&quot;:12,&quot;335572080&quot;:14,&quot;335572081&quot;:0,&quot;469789806&quot;:&quot;single&quot;}\">\u00a0<\/span><\/p>\n<p data-ccp-border-bottom=\"2px solid #000000\" data-ccp-padding-bottom=\"18.666666666666668px\"><span data-contrast=\"auto\">Estimates of the impact vary widely. MIT economist Daron Acemoglu offers a\u00a0relatively conservative\u00a0projection, suggesting that AI may contribute less than 0.1% to annual productivity growth over the next decade. In contrast, Goldman Sachs estimates that widespread adoption of generative AI could increase U.S. labor productivity growth by up to 1.5%\u00a0annually, potentially adding approximately 7% to global GDP over time.\u00a0<\/span><span data-ccp-props=\"{&quot;335572079&quot;:12,&quot;335572080&quot;:14,&quot;335572081&quot;:4278190080,&quot;469789806&quot;:&quot;single&quot;}\">\u00a0<\/span><\/p>\n<p data-ccp-border-top=\"0px none \" data-ccp-padding-top=\"0px\" data-ccp-border-bottom=\"2px solid #000000\" data-ccp-padding-bottom=\"18.666666666666668px\"><span data-contrast=\"auto\">On a company specific\u00a0scale\u00a0however, productivity gains can already be\u00a0observed. In software engineering, AI-assisted development tools are\u00a0resulting in massive efficiency gains across processes.\u00a0Developers using tools such as GitHub Copilot have been shown to complete tasks up to 55% faster, with the largest gains among less experienced engineers. Industry research suggests that overall coding productivity improvements in the range of 20% to 30% are becoming increasingly common. Notably, these gains are often reinvested into improving code quality, reducing technical debt, and enhancing system architecture, rather than simply accelerating output.<\/span><span data-ccp-props=\"{&quot;335572079&quot;:12,&quot;335572080&quot;:14,&quot;335572081&quot;:4278190080,&quot;469789806&quot;:&quot;single&quot;}\">\u00a0<\/span><\/p>\n<p data-ccp-border-top=\"0px none \" data-ccp-padding-top=\"0px\" data-ccp-border-bottom=\"2px solid #000000\" data-ccp-padding-bottom=\"18.666666666666668px\"><span data-contrast=\"auto\">Customer service functions are experiencing similar improvements. Generative AI-powered assistants can analyze customer interactions in real time, recommend responses, and automate post-interaction workflows. Early studies\u00a0indicate\u00a0productivity increases of ~14% among support agents, with AI handling routine inquiries and enabling human workers to focus on more complex, higher-value interactions.<\/span><span data-ccp-props=\"{&quot;335572079&quot;:12,&quot;335572080&quot;:14,&quot;335572081&quot;:4278190080,&quot;469789806&quot;:&quot;single&quot;}\">\u00a0<\/span><\/p>\n<p data-ccp-border-top=\"0px none \" data-ccp-padding-top=\"0px\"><span data-contrast=\"auto\">Beyond firm-level efficiency, the broader question surrounding AI adoption is how uptake will\u00a0impact the\u00a0labor market.\u00a0As AI systems increasingly\u00a0substitute for\u00a0certain forms of cognitive labor,\u00a0it is still unclear if\u00a0this transition will result in net job displacement or follow historical patterns of\u00a0role expansion\u00a0and\u00a0general\u00a0job creation.<\/span><span data-ccp-props=\"{&quot;335572079&quot;:12,&quot;335572080&quot;:14,&quot;335572081&quot;:4278190080,&quot;469789806&quot;:&quot;single&quot;}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Based on what we have\u00a0observed\u00a0thus far, the latter appears more likely. The World Economic Forum estimates that while AI and automation could displace ~92 million jobs globally by 2030, they may also create around 170 million new roles, a net positive employment effect. This is consistent with the historical pattern of prior technological\u00a0revolutions, where short-term labor displacement\u00a0ultimately gave\u00a0way to broader economic expansion and the emergence of entirely new categories of work. The industrial revolution did not\u00a0eliminate\u00a0employment; it transformed it. There is reason to believe AI\u00a0may\u00a0follow the same trajectory.<\/span><span data-ccp-props=\"{&quot;335572079&quot;:12,&quot;335572080&quot;:14,&quot;335572081&quot;:0,&quot;469789806&quot;:&quot;single&quot;}\">\u00a0<\/span><\/p>\n<p><span style=\"color: #b12258;\"><b>Investment Implications\u00a0<\/b>\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Artificial intelligence presents a dynamic familiar from prior technological cycles: near-term uncertainty alongside long-term structural opportunity. The challenge for investors is not simply\u00a0identifying\u00a0beneficiaries, but\u00a0distinguishing between short-term disruption and durable value creation.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">We do not believe AI will\u00a0eliminate\u00a0economic value; it will redistribute it. Value is likely to shift away from thin application-layer software toward areas characterized by scarcity and defensibility: proprietary data, intellectual property, industry\u00a0expertise, and physical infrastructure such as energy and data centers. Yet this view is not uniformly reflected in current market pricing. In some segments of the software ecosystem, valuations imply significant long-term impairment, with certain companies effectively assigned minimal terminal value. Where we believe the market is over-discounting disruption risk, we see opportunities for asymmetric outcomes.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">This environment underscores the importance of active management. By\u00a0maintaining\u00a0a deep understanding of companies, their business models, competitive advantages, and end-market exposure, we\u00a0are able to\u00a0selectively increase exposure where we believe market expectations have diverged meaningfully from underlying fundamentals.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Ultimately, our\u00a0approach is to use this period of volatility to build positions in high-quality businesses where we believe long-term value is being mispriced. As the AI ecosystem continues to evolve, we will remain focused on\u00a0identifying\u00a0where durable economic advantages are\u00a0forming, and\u00a0positioning the portfolio to\u00a0benefit\u00a0from the structural shifts that define the emerging intelligence economy.<\/span><\/p>\n<p><span style=\"color: #b12258;\"><strong>References<\/strong><\/span><\/p>\n<p><sup>1<\/sup> McKinsey &amp; Company. (n.d.). The economic potential of Generative AI: The Next Productivity Frontier. The economic potential of generative<br \/>\nAI: The next productivity frontier. https:\/\/www.mckinsey.com\/capabilities\/tech-and-ai\/our-insights\/the-economic-potential-of-generative-aithe-next-productivity-frontier<\/p>\n<p><sup>2<\/sup> Stryker, C. (2026, March 18). What is Agentic Ai?. IBM. https:\/\/www.ibm.com\/think\/topics\/agentic-ai<\/p>\n<p><sup>3<\/sup> Powell, P., &amp; Smalley, I. (2025, November 17). What is hyperscale?. IBM. https:\/\/www.ibm.com\/think\/topics\/hyperscale<\/p>\n<p><sup>4<\/sup> Brynjolfsson, E., Rock, D., &amp; Syverson, C. (n.d.). General Purpose Technologies (GPTS) such as AI enable and require significant. MIT<br \/>\nINITIATIVE ON THE DIGITAL ECONOMY RESEARCH BRIEF. https:\/\/ide.mit.edu\/sites\/default\/files\/publications\/2019-04JCurvebrief.final2_.pdf<\/p>\n","protected":false},"excerpt":{"rendered":"<p>At Pender, our equity team focuses on\u00a0identifying\u00a0opportunities at the intersection of sector and capital cycles, structural change, and market inefficiencies. Our core thematic areas include Enterprise Software, Artificial Intelligence,\u00a0the Energy\u00a0Transition, and the evolving geopolitical landscape.\u00a0 This\u00a0white\u00a0paper examines artificial intelligence as\u00a0major\u00a0technological and economic\u00a0shift. It is complemented by a companion podcast featuring Toufic\u00a0Boubez, Venture Partner at Pender [&hellip;]<\/p>\n","protected":false},"author":13,"featured_media":73174,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[106],"tags":[],"fund-type":[],"class_list":["post-73148","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.2 (Yoast SEO v27.2) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>The Intelligence Economy: Artificial Intelligence, Productivity, and the Next Capital Cycle - PenderFund Capital Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/penderfund.com\/fr\/articles\/the-intelligence-economy-artificial-intelligence-productivity-and-the-next-capital-cycle\/\" \/>\n<meta property=\"og:locale\" content=\"fr_CA\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Intelligence Economy: Artificial Intelligence, Productivity, and the Next Capital Cycle\" \/>\n<meta property=\"og:description\" content=\"At Pender, our equity team focuses on\u00a0identifying\u00a0opportunities at the intersection of sector and capital cycles, structural change, and market inefficiencies. 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