{"id":3640,"date":"2025-10-10T09:48:22","date_gmt":"2025-10-10T08:48:22","guid":{"rendered":"https:\/\/blog.iese.edu\/expatriatus\/?p=3640"},"modified":"2025-10-10T17:51:27","modified_gmt":"2025-10-10T16:51:27","slug":"the-thinking-machine-global-work-and-the-new-burden-of-tech-leadership","status":"publish","type":"post","link":"https:\/\/blog.iese.edu\/expatriatus\/2025\/10\/10\/the-thinking-machine-global-work-and-the-new-burden-of-tech-leadership\/","title":{"rendered":"The Thinking Machine, Global Work, and the New Burden of Tech Leadership"},"content":{"rendered":"

\"\"<\/a>If you want to understand global work in 2025, start where very few people actually look: the chips. Stephen Witt\u2019s book, The Thinking Machine: Jensen Huang, Nvidia, and the World\u2019s Most Coveted Microchip<\/em>, which I just finished reading, is part biography, part business history, part supply-chain thriller. It traces how Nvidia\u2014founded in 1993, reportedly brainstormed at a Denny\u2019s\u2014went from a gaming-chip upstart to the hardware backbone of AI, and how Jensen Huang\u2019s strategic bets (notably CUDA and parallel processing) turned GPUs into essential infrastructure. The result is a vivid portrait of how a single firm\u2019s choices can reshape global industries, labor markets, and even geopolitics.<\/p>\n

Witt\u2019s story lands in a moment when Nvidia has, at times, eclipsed Microsoft and Apple to become the world\u2019s most valuable public company\u2014a symbol of how central AI hardware has become to the modern economy. That ascent, turbocharged in June 2024, gives the book a live-wire relevance: this isn\u2019t a post-mortem; it\u2019s a field report from inside an ongoing transformation.<\/p>\n

What the book does well<\/h2>\n
    \n
  1. \n

    It demystifies the hardware behind the magic.<\/strong>\u00a0Witt shows how a choice most non-engineers never heard of\u2014Nvidia\u2019s CUDA software stack\u2014made GPUs programmable for everything from physics simulations to transformer models, and in doing so, locked in a developer ecosystem that compounds with every model trained. In other words, the moat isn\u2019t just silicon\u2014it\u2019s the tools, talent, and code built atop it.<\/p>\n<\/li>\n

  2. \n

    It puts a face (and temperament) on strategy.<\/strong> The book\u2019s portrait of Huang\u2014brilliant, demanding, sometimes volcanic\u2014anchors an argument about leadership in frontier markets: conviction plus timing beats incrementalism. But it also hints at the costs and responsibilities of charismatic leadership when your product becomes essential infrastructure.<\/p>\n<\/li>\n

  3. \n

    It makes the invisible visible.<\/strong> Chips are everywhere and noticed nowhere. Witt\u2019s narrative surfaces the quiet dependencies\u2014foundry capacity, lithography bottlenecks, export controls\u2014that determine which countries and companies can build \u201cthinking machines\u201d at scale.<\/p>\n<\/li>\n<\/ol>\n

    The public\u2019s fascination\u2014without deep awareness<\/h2>\n

    I was struck that we are often enthralled by AI demos while remaining strikingly hazy on the plumbing that makes them possible. Evidence<\/a> suggests broad support for science and technology paired with shallow mental models of how complex systems work. That gap is especially wide with chips and data centers: most people encounter the spectacle of generative AI but not the racks, cooling loops, grid connections, and specialized software that convert electricity into inference. Witt\u2019s behind-the-scenes account helps close that gap.<\/p>\n

    And the stakes of that awareness gap are growing. Energy agencies<\/a> now estimate data centers consumed about 1.5% of global electricity in 2024<\/strong>, with demand rising quickly as AI workloads scale. Nvidia itself frames the future not as \u201cdata centers\u201d but \u201cAI factories,\u201d explicitly describing facilities that manufacture intelligence by turning power into models and decisions. Leaders who sell the sizzle of AI must also own the infrastructure story\u2014costs, constraints, and trade-offs included.<\/p>\n

    Lessons for global work\u2014and a new burden on leaders like Jensen Huang<\/h2>\n

    1) Platform bets reshape global labor markets.<\/strong> CUDA wasn\u2019t only a technical choice; it was a labor-market policy. By creating a programming model and toolchain, Nvidia effectively \u201cstandardized\u201d a global pool of skills\u2014from Barcelona to Bangalore\u2014around its hardware. Companies that ride these platforms gain access to talent and libraries; those that don\u2019t face recruitment, retraining, and ecosystem penalties. For HR and mobility leaders, that means skills strategies must track platform gravity<\/em>, not just generic AI skills.<\/p>\n

    2) Geopolitics is now a first-order business variable.<\/strong> Export controls, market access, and national security are no longer background noise. Nvidia\u2019s China exposure and the evolving U.S. rules on advanced chips show how quickly revenues, partnerships, and supply plans can be re-written by policy. Global leaders need real scenario planning (and country-level talent hedging) baked into product and go-to-market, not just legal compliance after the fact.<\/p>\n

    3) The energy constraint is a management constraint.<\/strong> If ever more companies become AI factories, then energy strategy becomes core strategy\u2014site selection, power purchase agreements, grid interconnects, thermal management, and sustainability claims all move from facilities to the C-suite. The IEA and others project<\/a> sharp growth in data-center load tied to AI; boards should be asking for energy budgets alongside model roadmaps.<\/p>\n

    4) Communicate the plumbing.<\/strong> Public legitimacy for AI will rest on leaders\u2019 willingness to explain the less glamorous parts: why chips matter, why supply chains are fragile, why energy use is rising, and what concrete steps are being taken (efficiency, siting near low-carbon generation, model optimization).<\/p>\n

    5) Build cosmopolitan teams\u2014and governance that travels.<\/strong> Nvidia\u2019s story is global: U.S. design culture, Asian manufacturing, European tooling, worldwide customers. The work<\/em> of making frontier tech safe and useful is likewise global\u2014standards, export compliance, privacy regimes, and labor norms vary by jurisdiction. Leaders should invest in portable governance frameworks that localize responsibly without fracturing execution.<\/p>\n

    Practical takeaways for executives and global mobility leaders<\/h2>\n
      \n
    • \n

      Map your dependency stack.<\/strong> Inventory which parts of your AI strategy rely on Nvidia\u2019s ecosystem (or others) across hardware, software, and talent. Where are your single points of failure\u2014in suppliers, skills, or sites?<\/p>\n<\/li>\n

    • \n

      Tie AI roadmaps to energy roadmaps.<\/strong> Treat power as a first-class input: model demand, secure long-term contracts (ideally low-carbon), and budget for efficiency work (quantization, sparsity, scheduling). Report progress publicly.<\/p>\n<\/li>\n

    • \n

      Run geopolitics like a product risk.<\/strong> Assign ownership, build red-team scenarios for export rules or market access changes, and prepare \u201cplan B\u201d configurations that keep your teams shippable across jurisdictions.<\/p>\n<\/li>\n

    • \n

      Upskill for the platform you bet on.<\/strong> If CUDA is central, make it explicit in hiring, L&D, and partner selection. If you\u2019re betting on alternatives, invest enough to avoid being stranded in a CUDA-centric supply of tools and talent.<\/p>\n<\/li>\n

    • \n

      Explain, don\u2019t hype.<\/strong> Borrow a page from Witt\u2019s narrative clarity: tell your workforce, customers, and the public what it really<\/em> takes to build and run AI systems\u2014chips, code, cooling, and choices\u2014and where your responsibilities begin and end.<\/p>\n<\/li>\n<\/ul>\n

      Verdict<\/h2>\n

      The Thinking Machine<\/em> is a brisk, well-reported introduction to the hardware realities behind AI\u2019s soft-focus hype. It gives us a protagonist in Jensen Huang, but it also gives us the less telegenic truths of platform moats, energy budgets, export rules, and globalized work. Read it as a leadership case: how a long-odds bet on parallel computing, married to an ecosystem play, created enormous value\u2014and how that value now carries societal obligations that can\u2019t be delegated to comms teams or regulators.<\/p>\n","protected":false},"excerpt":{"rendered":"

      If you want to understand global work in 2025, start where very few people actually look: the chips. Stephen Witt\u2019s book, The Thinking Machine: Jensen Huang, Nvidia, and the World\u2019s Most Coveted Microchip, which I just finished reading, is part biography, part business history, part supply-chain thriller. It traces how Nvidia\u2014founded in 1993, reportedly brainstormed […]<\/p>\n","protected":false},"author":345,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[18330],"tags":[102155,44833],"class_list":["post-3640","post","type-post","status-publish","format-standard","hentry","category-views-and-news-about-expatriates","tag-artificial-intelligence","tag-global-leadership","megacategoria-mc-leadership-and-people-management"],"acf":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/blog.iese.edu\/expatriatus\/wp-json\/wp\/v2\/posts\/3640","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.iese.edu\/expatriatus\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.iese.edu\/expatriatus\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.iese.edu\/expatriatus\/wp-json\/wp\/v2\/users\/345"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.iese.edu\/expatriatus\/wp-json\/wp\/v2\/comments?post=3640"}],"version-history":[{"count":3,"href":"https:\/\/blog.iese.edu\/expatriatus\/wp-json\/wp\/v2\/posts\/3640\/revisions"}],"predecessor-version":[{"id":3644,"href":"https:\/\/blog.iese.edu\/expatriatus\/wp-json\/wp\/v2\/posts\/3640\/revisions\/3644"}],"wp:attachment":[{"href":"https:\/\/blog.iese.edu\/expatriatus\/wp-json\/wp\/v2\/media?parent=3640"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.iese.edu\/expatriatus\/wp-json\/wp\/v2\/categories?post=3640"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.iese.edu\/expatriatus\/wp-json\/wp\/v2\/tags?post=3640"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}