The economic world is currently intoxicated by the promise of Artificial Intelligence. Analysts from Goldman Sachs to McKinsey are forecasting staggering jumps in global GDP—some as high as 7%—within the next decade. They look at the speed of LLMs, the automation of coding, and the streamlining of reporting, and they see a straight line to prosperity.
The Forecasts Driving the Hype
These projections stem from detailed models estimating AI’s potential to automate tasks and enhance productivity across sectors. For instance, Goldman Sachs Research has projected that generative AI could ultimately automate around 25% of labor tasks in advanced economies, leading to a 7% boost in global GDP equivalent to roughly $7 trillion over a 10-year horizon.
Similarly, McKinsey’s analysis suggests generative AI alone could add $2.6 trillion to $4.4 trillion annually across 63 use cases, increasing the overall impact of AI by 15 40% and potentially doubling if embedded into existing software.
Yet, as of early 2026, these gains remain largely theoretical. Massive AI investments contributed “basically zero” to U.S. economic growth in 2025, according to Goldman Sachs calculations, highlighting a gap between hype and reality. – washingtonpost.com
A Lesson from Robert Schad
I don’t see it. And the reason comes down to a lesson I learned years ago from a man who knew a thing or two about efficiency: Robert Schad. Schad, the founder of Husky Injection Molding Systems, was a man of rare intellect legend has it his university recommendation was penned by Albert Einstein himself. moldmakingtechnology.com
Emigrating from Germany to Canada in 1951 with just $25 in his pocket, Schad built Husky from a small Toronto garage operation into a global powerhouse, generating over $774 million in revenue by the early 2000s. plasticstoday.com
In the plastics industry, Robert lived by a singular mantra when evaluating any process: “Pellet to Pallet.”
The Chain is Only as Fast as Its Slowest Gate
“Pellet to Pallet” means you cannot claim an efficiency gain by looking at a single machine in isolation. You have to look at the entire lifecycle from the raw plastic pellet entering the hopper to the finished pallet of goods leaving the shipping dock.
AI is, without question, an incredible “pellet” processor. It can synthesize data, write reports, and analyze contracts at a speed that makes human effort look like a crawl. But a business or a country—is not a single machine. It is a long, interconnected chain of “gates.”
If AI increases the throughput of the first three links in that chain by 500%, but the fourth link is a human bureaucrat, a legacy legal requirement, or a multi layered committee approval process, you haven’t actually increased the speed of the “pallet” hitting the dock. You’ve simply created a massive, pressurized bottleneck. This mirrors historical technology diffusion lags, where innovations like electricity or computers took decades to fully impact productivity due to complementary changes needed in organizational structures and workflows. – ey.com
For AI, economists like Daron Acemoglu estimate that only about 5% of tasks will be profitably automated in the next decade, yielding a modest 1% GDP boost—far below the 7% forecasts—precisely because of these systemic frictions. – mitsloan.mit.edu
The Bureaucracy Gap
We see this everywhere in the developed world. Whether it’s getting a construction permit in Ontario or navigating federal regulations in the U.S., the “gates” are often decades old processes designed for a pre digital age.
Imagine an AI that can design a bridge and file a permit application in four seconds. That is an incredible feat of efficiency. However, if that application then sits on the desk of a zoning officer for six months, or gets mired in a three-year environmental “consultation” phase, the AI’s speed is functionally zero. In Ontario, for example, building permit approvals can stretch from months to years, with municipal reviews alone averaging a full year for multi-unit projects and significantly longer in urban centers like Toronto due to staffing shortages, outdated processes, and fragmented data systems. – altusgroup.com
Developers report rezoning taking one to two years, development permits another year, and building permits six months, with worst case scenarios hitting five years amid bureaucratic impasses. – digitalsupercluster.ca
Even as cities like Hamilton and Toronto experiment with AI tools to halve prescreening times, human oversight and legacy regulations ensure delays persist, turning AI’s potential into idle wait times. – theglobeandmail.com
The 7% GDP growth predictions assume that the “gates” will adapt to the “flow.” History suggests the opposite. Human systems are resistant to change. People who have spent thirty years managing a specific gate often view the increased throughput of AI not as a tool, but as a threat or a nuisance. They will not, and often cannot speed up their review process just because the AI delivered a thousand more reports this week.
This “productivity paradox” is well documented: AI adoption in manufacturing firms often leads to initial productivity losses before long term gains, as organizations grapple with integration, data quality, and cultural resistance.- weforum.org
Surveys show that compliance concerns and bureaucratic hurdles are cited by nearly half of executives as primary barriers to AI adoption, stalling widespread diffusion.
aei.org
The “Inertia Lag”
To realize the lofty gains being promised, we don’t just need better AI; we need Total Process Re-engineering.
Corporate Culture
• Corporate Culture: We have managers who still require three signatures for a $500 expense. In enterprises, AI experiments often fail to reach production—only 25% succeed—because agents are designed to mimic existing silos rather than dismantle them, perpetuating organizational bottlenecks. – @JimCRowan
Government Infrastructure
Government Infrastructure: We have digital systems built on top of paper-based logic. In Canada, permitting bureaucracy ranks among the slowest in OECD countries, with averages of 249 days for construction permits, driven by fragmented approvals and outdated IT. linkedin.com
Human Adaptation
Human Adaptation: We have decision makers who lack the “Pellet to Pallet” vision to see where the new bottlenecks are forming. As one expert notes, the real choke points are now cognitive—problem-solving stamina and idea generation—exacerbated by poor data infrastructure that agents must navigate before delivering value.
Adding to this, global AI adoption remains uneven, with diffusion lagging in the Global South and even in the U.S., where usage rates trail smaller digitized economies due to readiness gaps in governance and integration. – blogs.microsoft.com
Conclusion: A 15 Year Horizon, Not Five
The “Pellet to Pallet” philosophy dictates that the system’s output is limited by its slowest component. Until we’re engineer the human and bureaucratic gates that sit between the AI and the final result, the “efficiency” of AI will simply be lost in the friction of the machine.
The gains will come, but they won’t manifest in three to five years. We are looking at a 10 to 15-year horizon. It will take that long for the old guard to retire, for the “gates” to be digitized, and for the bottlenecks to be cleared. Goldman Sachs now anticipates measurable U.S. GDP impacts from AI starting in 2027, with broader acceleration in the early 2030s, aligning with this timeline. – goldmansachs.com
Historical precedents, like the 10-year lag for computers to boost productivity, reinforce that AI’s full effects will require systemic overhaul. – ey.com
AI is the most powerful engine we’ve ever built. But if the brakes are still made of 20th century bureaucracy, we aren’t going anywhere fast.
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