Globally, over 1.2 billion people adopted AI tools within three years of launch, establishing artificial intelligence as the fastest spreading technology in human history. Within 3 years of the release of OpenAI to the public in 2022 surveys are showing that ~45% of Australian adults had used GenAI (40% for US in a survey 6 months earlier), 58% of adults globally in a KPMG survey use it for work regularly.

The penetration rate of AI has significantly eclipsed the historical adoption curves of both the personal computer, which only reached a 20% adoption rate after three years, and the internet, which required two years to reach a 20% milestone. However, in contrast to the rapid adoption we are seeing, blog articles on how AI is going to destroy the job markets present a starkly different, ominous view. For instance, one post opens with:

“The unemployment rate printed 10.2 per cent this morning, a 0.3 per cent upside surprise. The market sold off 2 per cent on the number, bringing the cumulative drawdown in the S&P to 38 per cent from its October 2026 highs,” – (Citrini Research: THE 2028 GLOBAL INTELLIGENCE CRISIS)

This Citrini Research article had a significant impact on the stock market, including a 10% hit to Atlassian, dropping with other SaaS stocks globally. I believe, however, that the AI doom and gloom is overblown, or at least the timelines are. It is certainly a disruptive technology, just like the steam engine, the car (I am not going to call it “an automobile”), the PC, and Blockbuster Video. We can look to tangible historical examples that illustrate this slower, drawn-out process of change:

• Electricity required over thirty years to reach rural areas following the initial waves of urban electrification.
• The first PC’s, introduced in the early 1980s were not in >50% of households for two decades.
• It took 8 years from Netflix’s IPO in 2002 to Blockbuster declaring bankruptcy and Netflix launched its website in 1998, a staggering 22 years.

Google’s Gemini has informed me that this is known as the Solow Paradox (Wikipedia calls it the Productivity Paradox), the Nobel laureate economist Robert Solow, famously observed, “You can see the computer age everywhere but in the productivity statistics”.

The paradox describes the lagging productivity growth compared to rapid technology advances. Aviato Consulting works primarily with large enterprise customers deploying AI, so we have seen first hand that the Solow Paradox exists. Economists have studied this and to summarise a lot of research, the productivity gains only materialize when technological innovation is paired with deep institutional reform, training, and rethinking business processes. In my experience these take 5-10 years, the shift from on premise servers when companies would build or rent datacentres and then fill them with servers to the cloud where Google, AWS, and Microsoft rent you compute capacity is still ongoing and Google released it’s cloud in 2008.

During this process the technology will often be seen as underperforming, in one study “adopting banks experience a 428 basis point decline in ROE as they absorb GenAI integration costs.” (The Innovation Tax: Generative AI Adoption, Productivity Paradox, and Systemic Risk in the U.S. Banking Sector)

Historical Timelines of Corporate Disruption

The argument that artificial intelligence will require between five and over ten years to fundamentally replace human labor is heavily supported by analyzing historical data, which shows a range from four to well over a decade before new innovations can displace established legacy tech.

• Cloud Computing: AWS launched EC2 and S3 in 2006, Google launched in 2008, it took close to 10 years for cloud to become mainstream, by 2021 17% of IT spend went to public cloud, the market estimate is that it will grow to ~50%
• Borders vs. Amazon: Borders outsourced its online sales to Amazon in 2001. In 2008, they launched their own e-commerce website, and filed for bankruptcy in 2011—a decade after outsourcing (Amazon also launched the Kindle in that time).
• Kodak: The digital photography revolution led to Kodak’s bankruptcy in 2012, despite digital cameras being introduced in the late 90s and early 2000s.
• Excel vs. Lotus 1-2-3: Excel was launched on Mac in ’85 and Windows in ’87. It was a much better product than Lotus 1-2-3, it took a decade for Excel to take the majority of the market IBM bought Lotus in 1995, but it took until 2013 for the Lotus brand to be officially discontinued, and IBM support for Lotus 1-2-3 only officially ended in 2014 a nearly 30-year tail!
• Nokia vs. iPhone: Nokia sold its mobile division in 2013, 6 years after the iPhone launch. Again, the Nokia to iPhone jump was a drastic uplift in capability similar to AI.
• Electricity Uptake: As mentioned, it took over thirty years for electricity to reach rural areas and truly restructure household labor after the first urban power grids were established.
• Horses vs. Cars: Even the transition from horses to cars despite the urgent “horse manure crisis” in major cities took decades. In 1900, NYC still had nearly 4,000 horses per 100,000 people, and it required a massive, multi decade infrastructure shift before the automobile completely took over the roads.

The Automation Paradox: Job Growth Preceding Decline

When projecting the timeline for artificial intelligence to replace white collar workers, we must account for the “Jevons Paradox” which can be summarised as “when a labour saving technology in a profession leads to an increase in employment within that sector, as the technology lowers the cost of the service, thereby increasing demand”.

The ATM in the 1970s is a great example. At the time analysts predicted that the ability for machines to autonomously dispense cash and accept deposits at any hour of the day or night would decimate the human bank teller profession. In the 1970s and 1980s, ATMs became a staple service. The catastrophic job losses predicted, however, did not materialize. Because ATMs significantly reduced the operational cost of running a physical bank branch, financial institutions subsequently opened more branches to capture greater market share leading to the total number of bank tellers in the US doubling, rising from approximately 300,000 in 1970 to nearly 600,000 by 2010.

The nature of the teller’s job was altered, removing them from the low value deposit/withdrawal work, and moving up into more customer relationships (and somewhat annoyingling trying to always upsell me a mortgage or credit card). The decline in teller numbers did eventually eventuate, when online banking became prominent, dropping 30% between 2010 and 2024.

Structural Friction in Enterprise AI Adoption

While generative AI can write code, review legal contracts, and generate marketing copy in seconds, integrating these capabilities into the rigid, complex architecture of the ASX300, and Fortune 1000 companies that Aviato Consulting works with, their governance, security, and the way these companies are structured introduces immense friction. 

The words “governance” and “security” have many times stopped an IT project in its tracks, these teams create a chasm between consumer utility and enterprise scalability. Pushing a project that you can complete on a MacMini with Open Claw into an enterprise turns it into a multi year timeline for ASX300’s and subsequently delaying white collar displacement.

According to 2024 research by Accenture detailing enterprise operations maturity, a staggering 61% of corporate executives reported that their data assets were “not ready for generative AI”. Further 70% of companies found it exceedingly difficult to scale AI projects that relied on proprietary, unstructured data, which remains largely ungoverned in most organizations. 

Establishing a centralized data governance architecture, cleaning decades of historical data, and migrating to cloud systems is an absolute prerequisite for deploying autonomous AI agents (See my point about shift to cloud above) This ensures that AI remains trapped in “pilot purgatory” for the foreseeable future.

A recent Gartner Report showed a very top heavy AI Maturity Funnel:

Conclusion

The rapid consumer adoption of generative AI is undeniably unprecedented, but conflating this  with immediate wholesale job displacement ignores centuries of technological history. As evidenced by everything from the transition from horses to cars to the prolonged battle between on premise datacentres, and Cloud.

The Solow Paradox remains alive and well today; enterprises are actively absorbing the massive costs of integrating AI, attempting to clean up decades of unstructured data, and battling the internal friction of governance and security protocols.

We believe that in the short term, AI is far more likely to trigger the Jevons Paradox augmenting white collar workers, increasing their productivity, and potentially driving up the demand for their output just as the ATM did for bank tellers throughout the 1980s and 1990s. 

True, widespread structural displacement of the white collar workforce will not happen overnight. It will be a 5+ year timeline, as it requires a fundamental reengineering of corporate workflows and a massive, finalized shift away from legacy on-premises infrastructure.