{"id":23554,"date":"2025-10-03T13:31:44","date_gmt":"2025-10-03T17:31:44","guid":{"rendered":"https:\/\/www.rbcwealthmanagement.com\/en-us\/?p=23554"},"modified":"2025-10-03T13:31:46","modified_gmt":"2025-10-03T17:31:46","slug":"ais-big-leaps-in-2025","status":"publish","type":"post","link":"https:\/\/www.rbcwealthmanagement.com\/en-us\/insights\/ais-big-leaps-in-2025","title":{"rendered":"AI\u2019s big leaps in 2025"},"content":{"rendered":"\n <div class=\"well b-blue-tint-4 mb-4\">\n      <h2 class=\"h5\">Key points<\/h2>\n      <ul class=\"list-spaced\">\n        <li>\n          The release of DeepSeek\u2019s model, with its cutting-edge performance,\n          shoestring development cost, and open-source availability, challenged\n          assumptions about which country is the AI leader.\n        <\/li>\n        <li>\n          The White House unveiled an ambitious plan and adjusted its chip\n          export policy in an attempt to secure an edge in the AI race.\n        <\/li>\n        <li>\n          The growth of alternative financing for data centers reveals the scale\n          of capital now required to fuel the AI boom\u2014and the risks associated\n          with it.\n        <\/li>\n        <li>\n          AI\u2019s potential is immense, but too much may be expected of it in the\n          short term.\n        <\/li>\n      <\/ul>\n    <\/div>\n    <!-- SECTION -->\n    <h2>Chinese AI enters the stage \u2026 with a bang<\/h2>\n    <p>\n      On Jan. 29, 2025, a little-known Chinese tech company, DeepSeek, released\n      an AI model, R1, that shook the industry. R1\u2019s cutting-edge capabilities\n      make it seemingly as good a model as those created by U.S. leader OpenAI,\n      the maker of ChatGPT. But what unsettled the industry most was its\n      shoestring development cost\u2014just $6 million, a fraction of what comparable\n      U.S. models required.\n    <\/p>\n    <p>\n      Barred from importing state-of-the-art chips from the U.S. due to the\n      export restrictions imposed by the former Biden administration in October\n      2022, DeepSeek had to rely on older hardware. To compensate, the company\n      pushed efficiency to the limit, making careful tradeoffs between accuracy\n      and computing power, replicating this method at scale, and fine-tuning\n      every other aspect of performance. This approach kept development costs\n      remarkably low while still producing a highly capable model.\n    <\/p>\n    <p>\n      Adding to the excitement, DeepSeek-R1 was released as an open-source\n      model\u2014i.e., publicly available at no cost. Users can download and run it\n      on their own computers or servers, keeping data private, and they can\n      retain, modify, or adapt it for their own needs. This means anyone, from\n      individuals to large companies, can build tools or applications without\n      seeking permission or paying for expensive access.\n    <\/p>\n    <!-- TEXT BOX -->\n    <div class=\"well mt-3\">\n      <h3 class=\"medium\">Open- vs. closed-source<\/h3>\n      <p>\n        Open-source models stand in contrast to most of today\u2019s leading AI\n        models, such as OpenAI\u2019s ChatGPT, Anthropic\u2019s Claude, or Google\u2019s\n        Gemini, which are closed-source. It wasn\u2019t always so. OpenAI initially\n        produced open-source models, but the San Francisco-based lab shifted to\n        a closed approach in 2018.\n      <\/p>\n      <p>\n        Closed-source model creators share only parts of the code, or describe\n        some of the training process but keep the full details private, much\n        like a secret recipe. Users must rely on those companies to maintain the\n        entire AI system, and are thus vulnerable to changes in model creators\u2019\n        prices or access rules.\n      <\/p>\n      <p>\n        For instance, in the case of ChatGPT, OpenAI is responsible for training\n        the model, maintaining the code that runs the model, running the model\n        on powerful servers to enable users to access ChatGPT instantly, and\n        adding features. If OpenAI were to stop doing all that, users could not\n        run it themselves.\n      <\/p>\n    <\/div>\n \n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n <p>\n      DeepSeek\u2019s decision to make its cutting-edge AI model both cheap and\n      widely available gave China an unexpected competitive edge, as its model\n      could spread and be adapted far more widely and cheaply than those of its\n      Western rivals. This shook the U.S. tech ecosystem to its core. Until\n      then, the U.S. had been confident in its position as the world\u2019s leading\n      AI force.\n    <\/p>\n    <!-- TEXT BOX -->\n    <div class=\"well mt-3\">\n      <h3 class=\"medium\">Chinese AI: More to it than DeepSeek<\/h3>\n      <p>\n        DeepSeek may have captured the attention of the West, but China\u2019s AI\n        achievements go far beyond a single company. That should not be a\n        surprise as millions of engineers and scientists graduate from Chinese\n        universities every year, the country has the spare grid capacity\n        necessary to run the power-hungry AI models, and its permissive planning\n        laws allow data centers to be built swiftly.\n      <\/p>\n      <p>\n        Still, China faces a critical constraint: it lacks the sufficient\n        domestic supply of cutting-edge chips. Huawei, the country\u2019s hardware\n        champion, cannot yet produce top-end chips in sufficient quantities.\n        Nevertheless, the combination of these favorable conditions, remarkable\n        ingenuity, and a relentless effort at squeezing as much efficiency as\n        possible from older-generation chips has enabled Chinese firms to\n        release frontier AI models (i.e., highly capable general-purpose AI\n        models that can perform many tasks matching or exceeding the\n        capabilities of today\u2019s most advanced models).\n      <\/p>\n      <p>\n        In July 2025 alone, Alibaba, one of China\u2019s largest tech and e-commerce\n        companies, released Qwen3, a model approximately one-quarter the size of\n        the most prevalent AI models, making it significantly more\n        energy-efficient while maintaining comparable performance. Meanwhile,\n        Moonshot AI, a lesser-known AI company, unveiled Kimi K2 in July, one of\n        the largest open-source models ever released at that time. Kimi K2\n        excelled in benchmarks like MATH-500, which tests mathematical\n        reasoning, outperforming frontier class U.S. models from OpenAI (GPT-4)\n        and Anthropic, according to Venturebeat, a publication that focuses on\n        technology news.\n      <\/p>\n      <p>\n        Despite this progress, China still lags the U.S. in\n        productization\u2014turning AI models into agentic tools, or fully integrated\n        tools that can autonomously assist users in complex workflows. Agentic\n        tools take initiative, make decisions, and complete multistep tasks for\n        the user, such as reading incoming customer queries, selecting the\n        urgent ones, drafting replies, and escalating complex issues to a human.\n      <\/p>\n    <\/div>\n    <!-- SECTION -->\n    \n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2>America\u2019s response<\/h2>\n    <p>\n      The rivalry in technology between China and the U.S. has been ongoing for\n      years. Both countries see winning the AI race as a strategic advantage\u2014not\n      only will the winner be able to extend their geopolitical influence\n      through the supply of AI systems to other countries, but there are also\n      important implications for military applications.\n    <\/p>\n    <p>\n      In 2025, the White House initiatives to secure an edge in the AI race\n      included releasing its AI Action Plan in July and changing chip export\n      restrictions to China.\n    <\/p>\n    <!-- SUBSEC -->\n    <h3>Winning the race: America\u2019s AI Action Plan<\/h3>\n    <p>\n      Early in his second term, U.S. President Donald Trump directed his\n      administration to develop an AI strategy for the country. The AI Action\n      Plan is built on three core pillars: accelerating innovation, expanding\n      data center infrastructure, and promoting American technology abroad.\n    <\/p>\n    <p>\n      According to the Brookings Institution, an American think tank, the plan\n      can be lauded for focusing on advancing and democratizing basic and\n      applied AI research, and addressing the need to develop an AI-ready\n      workforce. However, it has a few concerns, including that adequate safety\n      measures may be compromised by the strong emphasis on accelerating U.S. AI\n      innovation and global competitiveness. Insufficient oversight\u2014particularly\n      in financial AI applications\u2014could pose significant systemic risk.\n    <\/p>\n    <p>\n      Despite these misgivings, Brookings scholars think that overall these\n      factors could strengthen the regional innovation ecosystems in the U.S. so\n      long as the federal government provides adequate support and funding.\n    <\/p>\n    <!-- EXHIBIT 1 TABLE -->\n    <h3>\n      President Trump\u2019s plan for the U.S. to achieve \u201cunquestioned global\n      technological dominance\u201d\n    <\/h3>\n    <h4>Key initiatives of the U.S. AI Action Plan<\/h4>\n    <div class=\"table-responsive mb-2\">\n    <table\n        class=\"table table-compact table-border-horizontal table-border-header\">\n        <thead>\n          <tr>\n            <th scope=\"col\" width=\"30%\" class=\"b-dark-blue-tint-1 c-white\">Initiatives<\/th>\n            <th scope=\"col\" class=\"b-dark-blue-tint-1 c-white\">Goals<\/th>\n          <\/tr>\n        <\/thead>\n        <tbody>\n          <tr>\n            <td colspan=\"2\" class=\"b-blue-tint-4 c-dark-blue\">\n              <strong>1. Accelerate AI innovation<\/strong>\n            <\/td>\n          <\/tr>\n          <tr>\n            <td>Deregulate<\/td>\n            <td>Roll back regulations seen as obstructive to AI innovation<\/td>\n          <\/tr>\n          <tr>\n            <td>Support open-source AI<\/td>\n            <td>\n              Improve access to AI compute and datasets through initiatives such\n              as the National AI Research Resource\n            <\/td>\n          <\/tr>\n          <tr>\n            <td>Facilitate adoption<\/td>\n            <td>Accelerate AI use in government and defense<\/td>\n          <\/tr>\n          <tr>\n            <td>Upgrade workforce and manufacturing<\/td>\n            <td>\n              Expand AI literacy and retraining programs, invest in robotics and\n              next-generation manufacturing\n            <\/td>\n          <\/tr>\n          <tr>\n            <td>Foster scientific advancement<\/td>\n            <td>Invest in AI-enabled laboratories and innovation test beds<\/td>\n          <\/tr>\n          <tr>\n            <td colspan=\"2\" class=\"b-blue-tint-4 c-dark-blue\">\n              <strong>2. Build American AI infrastructure<\/strong>\n            <\/td>\n          <\/tr>\n          <tr>\n            <td>Streamline permitting<\/td>\n            <td>\n              Accelerate approvals for data centers, energy projects, and\n              semiconductor facilities while safeguarding national security\n            <\/td>\n          <\/tr>\n          <tr>\n            <td>Strengthen the power grid<\/td>\n            <td>\n              Modernize infrastructure for generating and distributing\n              electricity\n            <\/td>\n          <\/tr>\n          <tr>\n            <td>Reshore semiconductor manufacturing<\/td>\n            <td>Expand domestic chip production<\/td>\n          <\/tr>\n          <tr>\n            <td>Set up secure data centers<\/td>\n            <td>\n              Build high-security data centers for military and intelligence\n              uses\n            <\/td>\n          <\/tr>\n          <tr>\n            <td>Develop the workforce<\/td>\n            <td>Train a skilled labor force to maintain AI infrastructure<\/td>\n          <\/tr>\n          <tr>\n            <td>Establish cybersecurity resilience<\/td>\n            <td>Ensure \u201csecure by design\u201d AI systems are widely used<\/td>\n          <\/tr>\n          <tr>\n            <td colspan=\"2\" class=\"b-blue-tint-4 c-dark-blue\">\n              <strong>3. Lead in international diplomacy and security<\/strong>\n            <\/td>\n          <\/tr>\n          <tr>\n            <td>Focus export strategy<\/td>\n            <td>\n              Promote U.S. AI hardware, software, and standards abroad while\n              tightening enforcement and closing loopholes in regulations for AI\n              compute and semiconductor export\n            <\/td>\n          <\/tr>\n          <tr>\n            <td>Counter authoritarian influence<\/td>\n            <td>\n              Push back against Chinese influence in international AI governance\n              forums such as the United Nations\n            <\/td>\n          <\/tr>\n          <tr>\n            <td>Assess security risks<\/td>\n            <td>\n              Proactively evaluate cutting-edge AI systems for national security\n              vulnerabilities including chemical, biological, and nuclear\n              threats\n            <\/td>\n          <\/tr>\n        <\/tbody>\n      <\/table>\n    <\/div>\n    <p class=\"disclaimer mb-4\">\n      Source &#8211; Executive Office of the President, July 2025\n    <\/p>\n    <!-- SUBSEC -->\n    <h3>Export controls: Chips as a geopolitical weapon?<\/h3>\n    <p>\n      The Trump administration is also using export controls, a strategy used in\n      Trump\u2019s first term and followed up by former President Joe Biden to\n      respond to the Chinese threat. This time, however, the controls have sent\n      confusing signals.\n    <\/p>\n\n    <p>\n      In April 2025, the Trump administration banned exports of NVIDIA\u2019s H20\n      chips to China over concerns the technology could strengthen Beijing\u2019s\n      defense industry. After strong lobbying from the semiconductor industry,\n      the ban was lifted in July. A few weeks later, Washington announced a new\n      framework: NVIDIA and Advanced Micro Devices would be granted export\n      licenses to sell specific chips to China so long as they shared 15 percent\n      of their revenue from these chip sales with the U.S. government.\n    <\/p>\n\n    <!-- EXHIBIT 2 -->\n    <div class=\"well mt-3\">\n      <h3 class=\"medium\">U.S. chip export controls span three administrations<\/h3>\n      <h4 class=\"mb-1-half\">\n        Timeline of U.S. strategy for semiconductor exports to China since 2018\n      <\/h4>\n\n      <p class=\"subheader mb-quarter\">\n        April 2018: Initial restrictions\n      <\/p>\n      <p>\n        Trump administration blocks sales of advanced chips to Chinese telecom\n        firm ZTE. (R)\n      <\/p>\n      <p class=\"mb-quarter subheader\">\n        June 2020: Expanded restrictions\n      <\/p>\n      <p>\n        Trump administration extends prohibition of advanced chips sales to\n        Chinese conglomerate Huawei. (R)\n      <\/p>\n      <p class=\"mb-quarter subheader\">\n        2020\u20132021: Pressure on allies\n      <\/p>\n      <p>\n        Trump administration pressures allies to impose similar restrictions\n        (e.g., the Dutch government restricts ASML from selling its most\n        advanced semiconductor equipment to China). (R)\n      <\/p>\n      <p class=\"mb-quarter subheader\">\n        October 2022: Continued export ban\n      <\/p>\n      <p>\n        Biden administration imposes broad restrictions on export of high-end\n        chips (e.g., H100) to all Chinese entities based on national security\n        concerns. (D)\n      <\/p>\n      <p class=\"mb-quarter subheader\">November 2023<\/p>\n      <p>\n        NVIDIA unveils the H20 chip, which complies with U.S. export\n        restrictions.\n      <\/p>\n\n      <p class=\"mb-quarter subheader\">\n        April 2025: Further expansion of restrictions\n      <\/p>\n      <p>\n        Trump administration bans export of H20 chips, believed to be powerful\n        enough to support Chinese defense industry. (R)\n      <\/p>\n      <p class=\"mb-quarter subheader\">\n        May 2025: Further expansion of restrictions\n      <\/p>\n      <p>\n        Export ban expanded to include newer models (e.g., cutting-edge H200\n        chip) to curb China\u2019s technological advancement. (R)\n      <\/p>\n\n      <p class=\"mb-quarter subheader\">July 2025<\/p>\n      <p>\n        U.S. government reverses ban on H20 chips; H100 and H200 export bans\n        remain in effect. (R)\n      <\/p>\n      <p class=\"mb-quarter subheader\">\n        August 2025: Revenue sharing agreement\n      <\/p>\n      <p>\n        NVIDIA and AMD agree to pay 15% of their China chip revenue to the U.S.\n        government in exchange for export licenses to resume sales to China. (R)\n      <\/p>\n      <p class=\"footnote\">\n        R = Republican government initiative; D = Democratic government\n        initiative\n      <\/p>\n      <p class=\"disclaimer\">Source &#8211; RBC Wealth Management<\/p>\n    <\/div>\n\n   \n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n <p>\n      This policy U-turn points to the difficulty of calibrating the security\n      and economic interests of the U.S., and exposes tensions between China\n      hawks in the U.S. government pushing for tighter export controls and\n      businesses eager to access the world\u2019s second-largest economy.\n    <\/p>\n    <p>\n      The Trump administration seems to have adopted NVIDIA CEO Jensen Huang\u2019s\n      view that providing China access to NVIDIA\u2019s AI chips could serve both the\n      company\u2019s interests and U.S. strategic goals by creating Chinese\n      dependence on American technology. NVIDIA supplies not just the chips, but\n      also the hardware and infrastructure that support entire data centers. If\n      major Chinese AI firms such as Alibaba, ByteDance, and Tencent build their\n      data centers around U.S. hardware, it could give Washington a geopolitical\n      advantage and greater leverage in any future negotiations with China. The\n      logic is that if U.S. technology were completely banned from China,\n      Beijing would likely accelerate efforts to develop its own AI\n      infrastructure.\n    <\/p>\n    <!-- TEXT BOX -->\n    <div class=\"well mt-3\">\n      <h3 class=\"medium\">H20: NVIDIA\u2019s export-compliant chip for China<\/h3>\n      <p>\n        The H20 chip had been developed two years earlier by NVIDIA specifically\n        for the Chinese market and to comply with the Biden administration\u2019s\n        2022 export controls. It was designed mainly for AI inference\u2014the\n        process by which trained models generate insights and suggest\n        decisions\u2014but lacks the power needed to train new models. By offering a\n        chip that China could use but one that is markedly weaker than the\n        next-generation H100 and H200, Washington sought to maintain China\u2019s\n        dependency on American hardware, while at the same time limiting its\n        ability to advance in frontier AI.\n      <\/p>\n    <\/div>\n   \n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n <p>\n      Many observers worry that engineering a financial payout for the U.S.\n      government has now taken precedence over national security. Reversing the\n      H20 export ban, they argue, may be a strategic mistake, effectively\n      providing China with the hardware it needs to surge ahead in AI.\n    <\/p>\n    <p>\n      But Chris Miller, acclaimed author of\n      <span class=\"italic\">Chip War<\/span> and professor of international\n      history at Tufts University, offers a more nuanced perspective, drawing on\n      his expertise in the global semiconductor industry and geopolitics. He\n      believes that despite the 15 percent financial arrangement, national\n      security is still at the heart of Trump\u2019s policies. He argues that despite\n      criticizing Biden\u2019s CHIPS Act, the Trump administration continues to\n      disburse grants promised under the 2022 law to semiconductor companies and\n      research institutions. Moreover, the U.S. government has taken a 9.9\n      percent equity stake in Intel, the only U.S. semiconductor firm that both\n      designs and manufactures leading-edge logic chips. Miller believes the\n      U.S. administration sees Intel as relevant to the broad future of U.S.\n      technological leadership.\n    <\/p>\n    <p>\n      Whether the strategy of making China \u201caddicted\u201d to U.S. tech, in Commerce\n      Secretary Howard Lutnick\u2019s terms, will prove successful remains to be\n      seen. In August, China urged local companies to avoid using NVIDIA\u2019s H20\n      processors particularly for government-related purposes, according to\n      Bloomberg. Then in September, it banned its largest tech firms from buying\n      NVIDIA\u2019s AI chips in an effort to foster domestic production.\n    <\/p>\n    <!-- TEXT BOX -->\n    <div class=\"well mt-3\">\n      <h3 class=\"medium\">China\u2019s technological rise: A longstanding U.S. concern<\/h3>\n      <p>\n        Slowing China\u2019s technological progress has long been a preoccupation of\n        U.S. administrations, and it is instructive to look at how U.S. export\n        control policy evolved over time.\n      <\/p>\n      <p class=\"mb-half\">The first Trump administration realized that:<\/p>\n      <ul class=\"list-spaced\">\n        <li>\n          Semiconductors were key to not only a wide range of day-to-day\n          technologies, such as smartphones and computers, but also to military\n          applications and winning the AI race.\n        <\/li>\n        <li>\n          The U.S. had a real competitive advantage in designing cutting-edge\n          semiconductors.\n        <\/li>\n        <li>\n          By blocking semiconductor exports, the U.S. could slow China\u2019s\n          technological advancement.\n        <\/li>\n      <\/ul>\n      <p class=\"mb-half\">As a result, its strategy was two-pronged:<\/p>\n      <ul class=\"list-spaced\">\n        <li>\n          The administration tried to attract some of the largest foreign\n          semiconductor manufacturers to the U.S. (hence TSMC, the Taiwanese\n          behemoth that produces most of the world\u2019s cutting-edge\n          semiconductors, started its multibillion dollar investment in advanced\n          semiconductor manufacturing operations in Arizona).\n        <\/li>\n        <li>\n          The White House also imposed a ban on chip exports to Huawei, the\n          Chinese technology giant, and pressured U.S. allies to enforce similar\n          restrictions.\n        <\/li>\n      <\/ul>\n      <p class=\"mb-half\">\n        Under the Biden administration the strategy evolved as follows:\n      <\/p>\n      <ul class=\"list-spaced\">\n        <li>\n          The CHIPS Act funded and strengthened the domestic semiconductor\n          industry (the administration recognized that while designed in the\n          U.S., most cutting-edge semiconductors were manufactured abroad,\n          leaving the U.S. vulnerable to supply chain disruptions).\n        <\/li>\n        <li>\n          Biden also tightened export controls on semiconductor equipment and\n          imposed broad restrictions on the export of AI chips to China one\n          month before the release of ChatGPT.\n        <\/li>\n      <\/ul>\n    <\/div>\n    \n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2>New sources of financing<\/h2>\n    <p>\n      Another key AI development in 2025 has been the shift in funding sources\n      for the substantial investment needed to build the infrastructure to\n      support AI models.\n    <\/p>\n    <p>\n      According to McKinsey, a consulting company, global data centers will need\n      between $3.7 trillion and $5.2 trillion by 2030 to meet demand for AI\n      computing power, including hardware, processors, memory, storage, and\n      energy.\n    <\/p>\n    <p>\n      Much of this will be shouldered by Big Tech. Companies like Alphabet\n      (Google), Amazon, Microsoft, and Meta Platforms (Facebook)\u2014also referred\n      to as \u201chyperscalers\u201d\u2014are building large data centers to support their\n      cloud services and AI initiatives. Traditionally, they often preferred to\n      self-fund these investments, and were able to while maintaining robust\n      balance sheets with minimal debt. But this is changing due to the scale of\n      financing needs.\n    <\/p>\n    <p>\n      Other tech companies are boosting the demand for data center financing as\n      well. OpenAI has formed a joint venture with Oracle and SoftBank to invest\n      up to $500 billion in AI infrastructure across the U.S. in the next four\n      years. Property developers are also increasingly building data centers,\n      further fueling the financing demand.\n    <\/p>\n    <p>\n      Financing requirements are so large that companies are turning to\n      different sources of funding. Debt is gaining in popularity:\n      investment-grade borrowing by U.S. tech firms was up 70 percent year over\n      year in the first half of 2025, according to Bloomberg, with Alphabet\n      issuing bonds for the first time in five years in April 2025. Smaller or\n      fast-growing firms, such as CoreWeave, are even turning to borrowing\n      arrangements that use graphics processing units (GPUs)\u2014specialized chips\n      that accelerate AI computations\u2014as collateral.\n    <\/p>\n    <p>\n      Debt securitization is growing, whereby data center-related borrowing is\n      pooled and sold to investors in tranches, much like mortgages are. The\n      data center-related debt securitization market, virtually negligible five\n      years ago, has grown rapidly and is now valued at around $30 billion,\n      according to AInvest.\n    <\/p>\n    <p>\n      Private capital is playing an important role too, with large private\n      equity firms increasingly acting as direct lenders to businesses and\n      infrastructure projects, alongside their traditional equity investments.\n      In August 2025, Meta finalized a $29 billion deal for its Hyperion data\n      center project including a debt portion of $26 billion led by PIMCO, the\n      investment management firm.\n    <\/p>\n    <p>\n      Data center lending and investing carry additional risks beyond cost\n      overruns. Overcapacity from rapid capital investment can leave assets\n      underutilized. For example, in the late 1990s, U.S. telecom companies laid\n      more than 80 million miles of fiber optic cables across the country after\n      overestimating future demand. Prices plummeted and many companies entered\n      bankruptcy proceedings.\n    <\/p>\n    <p>\n      Technology risk is also substantial. Much of the current spending goes to\n      data centers built to train powerful AI models, but as demand shifts\n      toward running those models, the need for computing power could drop,\n      lowering the value of these assets. Newer, higher-performance chips could\n      make older facilities less useful, and some may even require innovative\n      cooling systems, leaving existing data centers obsolete.\n    <\/p>\n    <p>\n      Hyperscalers are diversified enough to weather these challenges, in our\n      view, though they now carry substantial infrastructure and capital\n      commitments\u2014they are no longer asset light. We believe smaller investors\n      and lenders will need to be particularly vigilant.\n    <\/p>\n    <!-- SECTION -->\n    <h2>Superintelligence around the corner?<\/h2>\n    <p>\n      Some observers are optimistic that progress in AI will be swift, raising\n      hopes that Artificial General Intelligence (an AI model with human-like\n      cognitive abilities) and even Artificial Superintellingence (an AI model\n      with an intellectual scope beyond human intelligence) could be achieved\n      within their lifetimes. On July 30, 2025, Mark Zuckerberg, CEO of Meta\n      Platforms, perhaps driven by ambition and competitive positioning, stated\n      that \u201cdeveloping superintelligence is now in sight.\u201d\n    <\/p>\n    <p>\n      Such enthusiasm is understandable. The pace of AI progress has been\n      remarkable. OpenAI\u2019s GPT-2, released in 2019, could write coherent\n      paragraphs but often lapsed into meaningless output, while by early 2023,\n      the company\u2019s GPT-4 model had advanced enough to pass the U.S. bar exam,\n      scoring in the top 10 percent of test takers, as reported by Reuters.\n      Remarkably, this leap in performance was achieved without changing the\n      science behind AI, but rather just by feeding the model more data and\n      using more powerful GPUs.\n    <\/p>\n    <p>\n      Yet others guard against too much enthusiasm. Rodney Brooks, robotics\n      pioneer and former director of the MIT Computer Science and Artificial\n      Intelligence Laboratory, and best known as the founder of the company that\n      developed and supplied the search-and-rescue robots used at Ground Zero\n      after the 9\/11 attacks, offers a clear-eyed take on AI\u2019s current hype.\n    <\/p>\n    <p>\n      In a February 2025 interview with <span class=\"italic\">Newsweek<\/span>, he\n      emphasized that while AI models can use language fluidly, they are\n      essentially pattern recognizers, really good at spotting and repeating\n      patterns in data. That, in his view, is not the same as truly\n      understanding or thinking for themselves.\n    <\/p>\n    <p>\n      He believes change will come more slowly than is generally expected\n      because rolling out new technology almost always runs into practical\n      hurdles like cost, integration with other systems, and regulation. And\n      because today\u2019s AI models are pattern-matchers, they still need a great\n      deal of careful oversight\u2014they are far from being plug-and-play as the\n      hype often suggests. Finally, he emphasized that corporate adoption of the\n      new technology will be based on return on investment, not \u201cglitziness.\u201d\n    <\/p>\n    <p>\n      On that front, a July 2025 report from the Massachusetts Institute of\n      Technology revealed that 95 percent of generative AI (GenAI) pilot\n      programs in enterprises yielded no measurable return on investment,\n      despite the $30 billion\u2013$40 billion enterprise investment in GenAI so far.\n      The authors conceded that over 80 percent of organizations surveyed have\n      explored or piloted tools such as ChatGPT and Copilot, with 40 percent\n      reporting to have purchased an official large language model subscription.\n      Interestingly, they found that workers at more than 90 percent of\n      companies surveyed say they use personal AI tools like chatbots for work,\n      but the report highlighted that these tools seem to primarily enhance\n      individual productivity, not profitability.\n    <\/p>\n    <!-- SECTION -->\n    <h2>Promise \u2026 and challenges?<\/h2>\n    <p>\n      2025 has been a pivotal year for the AI industry, and 2026 will likely be\n      as eventful. Some of the significant developments to watch in the next\n      year include:\n    <\/p>\n    <ul class=\"list-spaced\">\n      <li>\n        OpenAI rolling out its first in-house AI chip, potentially reducing\n        third-party hardware dependence;\n      <\/li>\n      <li>\n        The evolution of the OpenAI-NVIDIA relationship now that the latter has\n        taken a $100 billion stake in the former\u2014a move that could reshape the\n        balance of power in AI hardware and model development;\n      <\/li>\n      <li>\n        NVIDIA\u2019s new Rubin AI chip, which holds promise to be even more\n        efficient;\n      <\/li>\n      <li>\n        Meta\u2019s massive multi-gigawatt AI data center, called Prometheus, being\n        built in Ohio, which illustrates the escalating scale of investment\n        needed to support cutting-edge AI.\n      <\/li>\n    <\/ul>\n    <p>\n      Beyond these developments, which will likely keep enthusiasm high, we\n      believe investors should also keep on eye on whether such investments and\n      the application of AI in business are generating adequate returns. As has\n      almost always been the case in the past, the risk remains that investors\n      may overestimate what the new technology can deliver in the short to\n      medium term.\n    <\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI is seldom out of the headlines in 2025, with defining developments coming one after another. We look at where AI is today and how its promise is matched against technological, economic, and geopolitical challenges.<\/p>\n","protected":false},"author":15,"featured_media":23552,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"rbcwm_post_date":"2025-10-03T09:28:53","editor_notices":[],"rbc_url_alias":"","rbcwm_featured_desktop_image_position":"","rbcwm_featured_mobile_image_position":"","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[71],"tags":[448,822,824,823],"rbcwm_content_owner":[609],"rbcwm_need":[],"rbcwm_segment":[],"rbcwm_solution":[],"rbcwm_topic":[468],"rbcwm_channel":[],"rbcwm_format":[],"class_list":["post-23554","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analysis","tag-ai","tag-ai-models","tag-closed-source-ai-model","tag-open-source-ai-model","rbcwm_content_owner-pag","rbcwm_topic-global-insights"],"acf":{"rbcwm_subtitle":"AI is seldom out of the headlines in 2025, with defining developments coming one after another. 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