Experts are sounding the alarm about a **"chaos tsunami"** of task automation, hollowed-out talent pipelines, and costly misfires that await leaders who cut staff to invest in AI without equally investing in human capability.

## Atlassian's AI-Driven Job Cuts Spark Industry Debate

Atlassian's decision to cut **1,600 jobs** – about 10% of its global staff – to "steer more spending into artificial intelligence and enterprise sales" has become a flashpoint in a much bigger argument about how far and how fast AI should reshape white-collar work.

This move, coming on the heels of a slew of other AI-linked redundancies, is already rippling through Australia's tech ecosystem. Venture backers say non-AI start-ups now face a funding drought and are quietly trimming headcount.

While some investors hail this as overdue "right-sizing," workforce experts warn that Atlassian's cuts are an early sign of a more chaotic and uneven transition – one that could hollow out talent pipelines, deepen disengagement, and create a dangerous illusion that software can replace hard-won human judgement.

## 'Chaos Tsunami,' Not Instant Apocalypse or Utopia

Steven McConnell, chief customer officer at Arielle Executive, said the Atlassian cuts should be seen less as a one-off restructuring and more as the front edge of a **"chaos tsunami"** in how work is organized.

"You're about to see an explosion in two types of hype," he said. "Doomers will claim that we're about to see 50% unemployment, grinding poverty, and a wave of quantitative easing to prop up the economy. Utopians will claim that agents and humanoid robots are about to create a world of universal abundance where work is optional."

Both extremes, he argued, are seductive – and social media will reward them – but both are wrong. Instead, he expects a classic S-shaped adoption curve: a burst of rapid AI deployment that is eventually capped by technological and economic realities.

McConnell highlighted how the numbers behind the AI boom don't fully add up. The bigger estimates point to OpenAI committing about $1.4 trillion in long-term spending. However, it only made $20 billion in 2025.

Today, a company might think it's cheap to use ChatGPT instead of junior staff when it costs $20–$200 per user each month. But that decision would look very different if a ChatGPT subscription jumped to $1,000–$5,000 per user per month.

In that world, McConnell said, decisions like Atlassian's start to look less like inevitable automation and more like a **high-risk bet** on where AI costs and capabilities will land.

## Middle Management in the Firing Line – For Now

Atlassian's 10% cut is far from the first time its workforce has been jolted by structural changes. A separate case before the US National Labor Relations Board alleges the company illegally fired an engineer, Denise Unterwurzacher, after she mocked co-founder Mike Cannon-Brookes in an internal Slack channel while protesting a "re-leveling" exercise that demoted and displaced staff.

The NLRB's lawyer argued she was acting in the spirit of Atlassian's own "Open Company, No Bullshit" philosophy when she criticized the CEO's comments during an all-hands meeting about the changes. Atlassian denies wrongdoing and says it expects staff to speak up "in a manner that remains professional and respectful."

For McConnell, the real structural pressure from AI is gathering one level below the C-suite. **"Middle management is most at risk,"** he said. "Think about what middle managers do day to day. They reduce friction between teams, chase updates, track milestones, hold people accountable."

"Much of this activity exists for one reason: making sure information moves across the organization. And agentic AI is getting surprisingly good at handling predictable coordination work."

He stressed that, so far, there is little hard data showing companies are directly making middle managers redundant because of AI. The more immediate effect is a silent hiring freeze.

"Before adding headcount, leadership teams are increasingly asking a simple question: 'How much of this role can AI do?'"

## A Disengaged Workforce Meets an AI Shock

If AI is accelerating structural change, Melissa Jenner, founder and CEO of Actvo, worries that many organizations are entering this era already badly underprepared.

"We entered the AI era already carrying a **decade-long disengagement crisis**," she said.

"Without immediately authorizing self-directed learning and building a genuine learning culture, organizations cannot expect even a third of their workforce to adapt at the pace AI is demanding."

Her view is that Atlassian-style cuts risk becoming the default response because it is simpler to shed staff than to rewire culture. "We are walking into a deficit era, unprepared – and the costs to correct are going to be much larger than just retrenching staff."

"The mindset shift has to come from a leadership shift first – towards building a sustainable culture centred around self-determined learning enabled for everyone," Jenner explained.

## The False Economy of 'Cut to Pivot to AI'

Atlassian has framed its job cuts as a way to reallocate spending into AI products and enterprise sales. Across the start-up sector, venture capitalists describe a similar story: AI is allowing software firms to operate with much leaner engineering teams, and many that hired aggressively during the 2021–22 boom are now slashing 20–40% of product and engineering roles to "extend runway" in far tougher fundraising conditions.

Jenner argues this arithmetic ignores the true cost of trading people for promises of automation.

"Cutting 10% of your workforce to 'pivot to AI' is a short-term headline with a long-term cost," she said.

"Research consistently shows that re-hiring externally can cost three to five times more (after factoring in financial, time and resource costs). This means for a $50,000 employee replacement costs could be $100,000 before accounting for lost institutional knowledge and the cultural DNA that simply cannot be rehired."

"Institutional knowledge – customer relationships, product history, cultural context – lives in people, not systems," Jenner added.

"When you displace experienced employees for lacking a few skills – in favour of AI – you're not just losing headcount. You're deleting years of pattern recognition that no model has been trained on."

McConnell makes a similar point from the employer-brand side. He says companies that hide behind euphemisms about "rebalancing" and "long-term operational efficiency" while offering "tick-the-box" outplacement support are damaging their reputation in a tight talent market.

"Make your brand relatable and human by getting in front of cameras," he said. And if you are cutting roles, ensure the career transition support "delivers value in the 2026 job market", not a box of generic CV templates.

## AI is Killing Tasks, Not Jobs – But Roles Need Redesign

Both experts insist that a binary "AI kills jobs" narrative misses what is actually happening inside companies like Atlassian.

"AI doesn't replace jobs – it replaces tasks," Jenner said. "The real question leaders should be asking is: have we actually mapped what our people can do against what our AI-augmented roles will need?"

She argues that with the right workforce intelligence tools, companies can rapidly identify which employees are already close to future roles, and then re-skill the rest "far faster than they could ever recruit externally".

Yet most organizations are getting the sequence backwards. "Most organizations are investing heavily in AI tools as a first-step on their augmentation journey – but almost nothing in human readiness," she said.

"It's the equivalent of fitting out a world-class gym and expecting your team to be athletes by osmosis. The technology growth budget and the people growth budget need to be in the same conversation – right now, they're not even in the same building."

McConnell noted that individual workers need to lean into that redesign rather than hope the storm passes. "The most in-demand leaders of the next decade won't just manage people – they'll manage hybrid teams of humans and AI agents," he said. "So stop asking: 'Should we use AI?' And start asking: 'How do we staff with it?'"

He acknowledged the paradox: by embracing AI, some workers may help make their own current role redundant. "But you'll walk away with a skillset likely to have commercial value in the future."

## Talent Pipelines at Risk

Beyond today's headlines, McConnell is most worried about what AI-linked cuts mean for tomorrow's leaders.

"The Big Four consulting firms, for example, have slashed graduate hiring by almost 50%, with Deloitte recording the steepest drop," he said.

While this hiring pullback may be driven as much by economic jitters as by AI, the effect is the same: entry-level roles are being squeezed just as generative AI tools become embedded in white-collar work.

"The trouble with replacing entry-level roles with AI is that AI hallucinates – confidently. And spotting a mistake requires someone to know what AI doesn't know – which only comes from experience," McConnell added.

If junior roles disappear, fewer people will accumulate that experience – making the whole system more brittle.

## Innovation's Human Ceiling – and the Investment Test

For Jenner, the risk is that companies like Atlassian successfully automate away large chunks of routine work, only to discover they have also thinned out the very capabilities that make them competitive.

"AI can obviously accelerate execution, but it cannot generate the contextual judgment, customer empathy, or collaborative, creative problem-solving that drives genuine innovation," she said.

"If you under-invest in your people's capability to work alongside AI, you don't get 10x output – you get faster mediocrity."

"My belief has always been that AI scales wisdom – secure your wisest people and upskill them, to reap the 1+1=3 outcome you need to innovate faster than your competitors."

Her prescription is blunt: "For every dollar an organization spends on AI technology in 2026, they should be committing an equal dollar to human capability development. Anything less is planning to fail slowly – as the machines won't run themselves."

In other words, Atlassian's 1,600 job cuts may prove a defining test – not just of one company's AI strategy, but of whether the tech sector can resist the temptation to cut first, explain later, and instead build the human systems needed to stop automation turning into a self-inflicted talent crisis.

Experts warn a “chaos tsunami” of task automation, hollowed‑out talent pipelines and costly misfires awaits leaders who cut staff to buy AI – but fail to invest equally in human capability

Atlassian's AI Job Cuts: Are We Heading Toward a 'Chaos Tsunami' in the Workforce?

## The Hidden AI Risk for Workers Isn't Just Unemployment — It's a Pay Cut

Former Salesforce AI CEO Clara Shih warns that while much attention focuses on AI eliminating jobs, the **more insidious threat** to workers is actually **lower wages**. She argues that wage resets are a more common and disruptive way new technologies affect workers than outright layoffs.

![Clara Shih](https://i.insider.com/69a16a861fb3fcb426489209?width=700)
*Former Salesforce AI CEO Clara Shih says artificial intelligence could quietly push wages down across industries.*

### Three Ways AI Could Push Wages Down

Shih outlines three mechanisms through which new technologies can suppress wages:

**1. Intra-Sector Squeeze**
When workers lose jobs in an industry, they compete for the remaining roles in the same field, driving wages down. Shih points to **manufacturing after the early-2000s trade shocks** as an example. As factories closed or automated, laid-off workers fought for a shrinking pool of domestic manufacturing jobs, and real wages declined.

The US Bureau of Labor Statistics estimates that **5.5 million US manufacturing jobs were lost between 2000 and 2017**. Economists David Autor, David Dorn, and Gordon Hanson found in a 2016 paper that workers in industries exposed to import competition from China "accumulate substantially lower earnings" between 1992 and 2007.

**2. Lowering Skill Barriers**
Technology can reduce the skill requirements for previously specialized work, expanding the labor pool and increasing competition. "AI (like past tech waves) slashes the skill floor for once-premium jobs, flooding labor supply and compressing wages," Shih wrote.

She cites **London's black cab drivers** as an example. For decades, drivers had to master "The Knowledge," a rigorous examination requiring memorization of thousands of streets and landmarks. GPS navigation and ride-hailing apps dramatically reduced the need for that expertise, expanding the driver labor pool and exposing drivers to more competition.

**3. Sector Switching with Pay Cuts**
Displaced high-skill workers may move into entirely new sectors after losing their jobs, often accepting lower pay while displacing incumbent workers. "Displaced high-skill workers switch fields, often taking a pay cut while displacing incumbent workers," Shih wrote.

### The Early AI Wage Boost May Already Be Starting to Fade

Ioana Marinescu, an associate professor at the University of Pennsylvania School of Social Policy & Practice, suggests AI may already be nearing the **peak of its wage boost**. New technologies often raise pay early on by making workers more productive, but that effect can reverse once automation spreads widely enough.

Her model suggests **wage growth could begin to decline once roughly 37% of cognitive or 'intelligence' tasks are automated** — a tipping point where automation starts replacing, rather than augmenting, workers. By her estimate, the economy has already automated more than 14% of those tasks, meaning the peak of the AI-driven pay boost may arrive sooner than many expect.

For Shih, the implication is clear: policymakers and workers shouldn't judge AI's labor-market impact solely by job losses, but also by **wage trends**. The hidden risk of AI isn't just unemployment — it's the quiet erosion of earning power across industries.

Clara Shih said AI could push wages down as technology lowers skill barriers and displaced workers flood remaining jobs.

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