{"id":26616,"date":"2026-05-15T09:24:22","date_gmt":"2026-05-15T13:24:22","guid":{"rendered":"https:\/\/www.rbcwealthmanagement.com\/en-us\/?p=26616"},"modified":"2026-05-15T14:17:20","modified_gmt":"2026-05-15T18:17:20","slug":"the-promise-and-peril-of-ai","status":"publish","type":"post","link":"https:\/\/www.rbcwealthmanagement.com\/en-us\/insights\/the-promise-and-peril-of-ai","title":{"rendered":"The promise and peril of AI"},"content":{"rendered":"\n

\n Scenario analysis provides a useful framework for assessing the economic\n and societal impact of the AI revolution.\n <\/p>\n \n

Minor general-purpose technology: 25 percent probability<\/h2>\n

\n At the cautious end of the spectrum, perhaps large language models prove\n \u201cmerely\u201d to be a minor general-purpose technology\u2014useful, but not\n revolutionary.\n <\/p>\n

\n In this scenario, the rate of AI improvement slows significantly, or even\n stops. Hallucinations prove difficult to overcome, surmising the next\n appropriate word using a probabilistic framework falls somewhat shy of\n general intelligence, and\/or the quality of information on the internet\n used to train the models is sufficiently low to hinder output quality.\n <\/p>\n

\n Productivity growth picks up even in this conservative scenario, but\n perhaps by only 0.25 percentage points per year. The tech giants that\n spent massive sums end up with a poor return on their investment, AI\n adopters fare somewhat better and the world is not wildly different from\n today.\n <\/p>\n \n

Major general-purpose technology: 45 percent probability<\/h2>\n

\n Alternatively, AI could become a major general-purpose technology, on par\n with the invention of electricity, the internal combustion engine or the\n computer.\n <\/p>\n

\n This leads to notable job displacement and some sectors suffer, but the\n potential prosperity resulting from AI also creates new sectors and job\n opportunities.\n <\/p>\n

\n There is ample historical precedent for this. Improvements in farm\n efficiency caused the U.S. agricultural share of employment to collapse\n from 41 percent in 1900 to 21.5 percent in 1930 and then to just 12\n percent in 1950, all while food production rose and unemployment remained\n stable. Those farmhands moved to cities and secured better jobs.\n <\/p>\n

\n Importantly, demand would rise as products became less expensive. Some\n sectors, including management consulting, architecture, investment\n banking and marketing, have such elastic demand\u2014meaning demand increases\n as prices fall\u2014that it is theoretically possible AI could even increase\n the need for humans rather than reduce it.\n <\/p>\n

\n Past predictions of technology-driven doom have reliably been exaggerated.\n Brick-and-mortar stores still account for 84 percent of retail spending.\n Airplanes did not completely replace the train.\n <\/p>\n

\n It may, therefore, be the case that, while AI appears theoretically\n capable of disrupting a large share of employment, it ultimately falls\n short of doing so.\n <\/p>\n

\n AI accelerates productivity growth by a substantial 0.5\u20132.5 percentage\n points per year, unleashing what we view as a golden age of growth like\n the 1990s\u20132000s computer boom. The tech giants earn a solid return on\n their investment, AI adopters benefit substantially, and as with prior\n technological leaps, unemployment does not permanently increase.\n <\/p>\n \n

Unprecedented disruptor: 30 percent probability<\/h2>\n

\n Alternatively, maybe AI is completely different. In this scenario, AI\n proves to be an unprecedented disruptor due to its capacity for\n self-improvement, the speed of its adoption, the scale of its impact and\n its destruction of high-skill jobs. This could lead to a range of quite\n different outcomes.\n <\/p>\n

Dystopian scenario: Three percent probability<\/h3>\n

\n The dystopian AI scenario envisages companies replacing a significant\n share of their workforce with AI, with displaced workers cutting spending.\n The resulting decline in demand could be sufficiently severe to overwhelm\n the productivity gains from AI, rendering workers and businesses worse\n off.\n <\/p>\n

\n A deep recession ensues, triggering further layoffs and creating a vicious\n cycle that ends with the economy collapsing and even AI developers\n impoverished.\n <\/p>\n

AI displaced workers would face a variety of unique challenges.<\/p>\n

\n First, past technological advances reduced the number of difficult,\n dangerous, tedious and low-paying jobs. By contrast, AI is more likely to\n replace the interesting, engaging, high-paying jobs, disrupting workers\n into positions down the income hierarchy, rather than up, as has usually\n been the case in the past. This is problematic, as the top 10 percent of\n U.S. households by income\u2014disproportionately white-collar workers\u2014generate\n nearly half of consumer spending.\n <\/p>\n

\n Second, due to its flexibility and scalability, AI is seemingly capable of\n disrupting many different sectors all at once. Even if other jobs do\n eventually prove available to displaced workers over the long run, it is\n unlikely the rest of the economy can absorb them quickly enough to avoid\n significant pain.\n <\/p>\n

\n In this scenario, humans are essentially the horses that never found\n another purpose after the automobile was popularized.\n <\/p>\n

Utopian scenario: Three percent probability<\/h3>\n

\n But there are other ways the \u201cunprecedented disruptor\u201d scenario could\n unfold.\n <\/p>\n

\n At the opposite extreme, there is a utopian scenario rooted in the idea\n that AI-driven productivity gains will be so large that they create a\n world of abundance. If the annual productivity growth rate accelerates by\n five percent or even 10 percent, the global economy is doubling or better\n in size every decade.\n <\/p>\n

\n The resulting positive supply shock lowers business costs, inducing\n falling prices and surging corporate profits. Real wages rise while\n government coffers swell, allowing policymakers to amply compensate\n displaced workers via basic income or unemployment transfers.\n <\/p>\n

\n Displaced workers effectively retire early or work part-time. People also\n enjoy access to an unprecedented amount of high-quality knowledge and\n advice, improving their quality of life.\n <\/p>\n

\n At the societal level, AI makes breakthroughs in fields such as drug\n development and medical treatments, materials science and nuclear fusion.\n Human life expectancy soars and climate change slows.\n <\/p>\n

Mixed AI outcome: 24 percent probability<\/h3>\n

\n It has to be conceded that the aforementioned dystopian scenario feels\n more plausible than the utopian one. So why have we assigned it such a low\n likelihood?\n <\/p>\n

\n This is because if the world were to find itself steering toward the\n dystopian sub-scenario, governments would likely intervene. A tax on AI\u2019s\n \u201ccompute\u201d would make using AI relatively more expensive for businesses,\n rendering human labor comparatively more competitive, and generating tax\n revenue to retrain and at least partially compensate structurally\n displaced workers.\n <\/p>\n

\n Productivity growth remains rapid in this mixed income scenario despite\n being somewhat dimmed by the AI tax. Businesses are the disproportionate\n beneficiaries, while displaced workers are somewhat worse off even after\n the government remedy.\n <\/p>\n \n

A major general-purpose technology<\/h2>\n

\n In conclusion, there are a number of ways this AI revolution could unfold.\n The table below provides a preliminary attempt at quantifying the impact\n of the scenarios on key variables.\n <\/p>\n

\n In our view, the most likely outcome, albeit with only a 45 percent\n probability, is that AI technology proves to be a \u201cmajor general-purpose\n technology,\u201d accelerating the rate of productivity growth, increasing\n corporate profits and displacing some workers\u2014but without permanently\n increasing the unemployment rate.\n <\/p>\n \n

\n Of all scenarios, AI is most likely to emerge as a major general-purpose\n technology\n <\/h3>\n
\n
\n \"The\n \n

\n As of 3\/9\/26. Productivity shock is initial increase in annual\n productivity growth. Symbols indicate the expected direction and\n magnitude of impact: (+) to (+ + + + +) denotes an increasingly positive\n effect, (~) a neutral effect, and (\u2013) to (\u2013 \u2013 \u2013 \u2013 \u2013) an increasingly\n negative effect.\n <\/p>\n

Source – RBC Global Asset Management<\/p>\n

\n The graphic presents the projected impacts of AI technology and the\n probability of various outcomes. Two main scenarios are presented. The\n first scenario is that AI becomes a general-purpose technology; this\n is broken down into two variants, \u201cMinor\u201d (25% probability, 0.25%\n productivity increase) and \u201cMajor\u201d (45% probability, 0.5% to 2.5%\n productivity increase). The second scenario is that AI proves to be an\n unprecedented disruptor; this is broken down into three variants, all\n of which entail a 5% to 10% productivity increase: \u201cDystopian\u201d (3%\n probability), \u201cUtopian\u201d (3% probability), and \u201cMixed\u201d (24%\n probability). Impacts are shown across eight areas in addition to\n productivity: growth, AI makers, AI adopters, AI disrupted firms,\n workers, inflation, interest rates, and stock markets. The\n \u201cUnprecedented disruptor\u201d scenario would have greater impacts in all\n areas than the \u201cGeneral-purpose technology\u201d scenario. The \u201cDystopian\u201d\n variant has more negative impacts, particularly in the areas of\n growth, AI disrupted firms, workers, inflation, interest rates, and\n stock markets. The \u201cUtopian\u201d variant has more positive impacts,\n particularly in the areas of growth, AI makers, AI adopters, and stock\n markets.\n <\/p>\n","protected":false},"excerpt":{"rendered":"

Until quite recently, the AI boom had been viewed in an almost entirely positive light. But for all of the promise, fears are now growing that AI could also disrupt sectors, hurt workers and pose a risk to the economy.<\/p>\n","protected":false},"author":22,"featured_media":26608,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"rbcwm_post_date":"2026-05-14T15:36:05","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":[448,945,946,944,947],"rbcwm_content_owner":[609],"rbcwm_need":[],"rbcwm_segment":[],"rbcwm_solution":[],"rbcwm_topic":[468],"rbcwm_channel":[],"rbcwm_format":[],"class_list":["post-26616","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analysis","tag-ai","tag-ai-disrupting-jobs","tag-ai-disruption","tag-u-s-employment","tag-u-s-unemployment","rbcwm_content_owner-pag","rbcwm_topic-global-insights"],"acf":{"rbcwm_subtitle":"Until quite recently, the AI boom had been viewed in an almost entirely positive light. 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