Billy Joel wrote We Didn’t Start the Fire after a younger musician accused the boomer generation of never facing the kind of challenges modern society must endure. Joel responded with a rapid-fire list of wars, assassinations, recessions, and cultural upheavals to prove the point that every generation feels like its struggles are unprecedented. In reality, the fire has always been burning.
The same is true of today’s layoffs.
There are plenty of headlines decrying the storyline that AI is taking jobs. Thousands of people are losing work at Amazon, Microsoft, Google, Meta, Dell, Salesforce, Spotify, Oracle. Lots of commentary around artificial intelligence as the inevitable cause. The framing is clean, modern, and irresistible. But it’s also misleading.
Layoffs are not new. Not in any industry but certainly not in tech. They are woven into the fabric of our industry, and indeed into corporate life more broadly. Consider a few significant milestones like when IBM cut 60,000 jobs in 1993 in what remains one of the largest corporate layoffs in history. I remember that time well because I was entering the workforce for the first time in Australia and there were no jobs. We were recovering from the 80’s hangover and “the recession we had to have”.
In 1996, AT&T eliminated 40,000 roles as part of a sweeping reorganisation. During the dot-com crash of 2000–2001, companies like Cisco slashed tens of thousands. And following the financial crisis of 2009 GM cut over 45,000 jobs in its bankruptcy restructuring.
It has been a decade since Microsoft let go 18,000 employees, largely tied to the failed Nokia acquisition. Even Deutsche Bank (18,000) and HSBC (10,000) around 2019 cut hard, as did Boeing (16,000) and other airlines in 2020 amid the pandemic.
Cut to today, and the scale of the current wave is significant. Amazon (27,000), Alphabet (12,000), Meta (21,000), Salesforce (7,000), Dell (25,000), Spotify (1,500). Significant yes. And counting. But the precedent is undeniable.
Each time, with each cycle, the language used to justify the cuts has reflected the zeitgeist. In the 1990s it was global competition. In the 2000s it was dot-com rationalisation. In the 2010s it was financial crisis and cloud transformation. Today, the buzz is AI.
The claim that AI is directly responsible for the current wave of job losses doesn’t hold up when set against the numbers. Adoption is certainly growing, but it is nowhere near the scale required to explain tens of thousands of redundancies in one stroke.
A 2024 McKinsey survey found around sixty-five percent of firms were experimenting with generative AI, yet most of that activity was confined to narrow, highly specific use cases.
A Gartner poll in 2024 reached a similar conclusion. Two-thirds of enterprises reported using AI across multiple business units, but in practice this often meant pilots, proofs of concept, or limited automation rather than wholesale transformation.
Even the U.S. Census Bureau’s own data (2025) reinforces the point, showing that adoption among large firms actually slipped from roughly fourteen percent to twelve percent over the course of mid-year surveys. Servicenow study data reflected the same.
Meanwhile, OECD studies suggest AI use is clustered in knowledge-intensive services, where scaling remains uneven and governance challenges continue to slow meaningful deployment. Put together, these findings show enthusiasm and experimentation, not mass substitution.
That is why this is not the footprint of a technology eliminating entire workforces at industrial scale. If anything, it reveals a hype-lag where the rhetoric of AI is running ahead of its operational impact.
The real drivers are the same ones we’ve seen before. Over-hiring during booms, rising interest rates, investor pressure to control costs, global instability, and shifting product portfolios. These are financial and structural decisions first, technological ones second.
So why do firms lean so heavily on AI in their explanations? Because it’s rhetorically powerful.
In the 2010s, saying “we’re restructuring to focus on the cloud” conveyed progress, even if it meant job cuts. Today, saying “we’re restructuring to focus on AI” sends the same signal. It ties a company’s strategy to the most hyped technology of the moment, reassuring investors that leadership has a future-focused plan.
It also conveniently shifts attention away from the uncomfortable truth that layoffs are part of our industry’s DNA. By framing them as the inevitable consequence of AI, executives are positioning themselves as visionaries riding the wave of change, rather than managers cutting costs, or worse, gamblers taking massive risks.
This is where the narrative needs to shift. Layoffs are not extraordinary events triggered by some unprecedented force. They are part of the ongoing cycle of disruption that defines modern business. So we should stop treating disruption as an anomaly and treating it more like business-as-usual.
For decades, companies have expanded during periods of optimism and cut back when the cycle turns. They have pivoted technologies, reorganised business units, and exited markets. Each time, the language has changed but the structural pattern is the same.
Which leads to the more important question. If disruption is constant, why do we still build technology and organisations as though stability is the norm? This is where the AI conversation actually matters. Not as a scapegoat for layoffs, but as a catalyst for rethinking how we design systems.
Organisations that assume disruption is normal will design differently. They will invest in platforms that are flexible, composable, and able to integrate new capabilities quickly. They will emphasise data governance and interoperability so that when the next pivot comes, they don’t collapse under the weight of technical debt. They will use AI not as a reason to cut, but as a way to augment, orchestrate, and absorb change.
This is the same argument I’ve made in other contexts. HYPA as the new architecture model because disruption is not an event, it is actually the operating environment. If we accept that, then the question can shift from “what caused this layoff wave?” to “how do we build our business with technologies that thrive in a world where waves never stop?”
Billy Joel’s refrain holds true in every respect: we didn’t start the fire, it’s always been burning. So let’s get real. AI did not suddenly ignite a wave of layoffs. It has simply become, at this point at least, the soundtrack for this cycle.
And the real challenge is not to find the villain of the moment, but to recognise the pattern that disruption is the new normal. The winners will be the organisations that stop pretending otherwise and build technology, governance, and culture for a world where the fire will never go out.