‘Grim news’: How Atlassian’s move highlights the rise of AI in recruiting and workplace change

This article expands on a Sky News Australia segment and commentary by Chris Kenny about the recent announcement from Atlassian. In a prerecorded message, cofounder and billionaire CEO Mike Cannon-Brookes told staff that about 150 roles would be cut and "replaced in large measure with artificial intelligence." That decision — and the blunt way it was delivered — raises urgent questions about technology, jobs, and how companies will increasingly use AI in recruiting, staffing and customer-facing work.

What happened at Atlassian — the short version

Atlassian, the Sydney-founded software giant, has announced it will cut roughly 150 positions. The affected roles sit largely in customer service, with company leadership saying that many customer complaints and support queries will be handled through AI systems going forward. The news was delivered to staff in a prerecorded video from CEO Mike Cannon-Brookes, who appeared in a hoodie and explained the change.

The move comes at a fraught time for the company: market capitalization has dropped significantly, cofounders have taken different paths, and the firm has dealt with a number of controversies recently. One cofounder, speaking positively about the broader AI opportunity, suggested Australia could become a regional hub for data centers — provided the country secures the reliable power needed to run them.

Why customer service is often the first casualty

Customer service is among the most automatable areas of a business. Many support tasks are repetitive: answering common queries, providing status updates, and triaging tickets. Modern AI systems — from chatbots to automated ticket routing and knowledge-base retrieval — can reduce response times and operate 24/7.

That efficiency is attractive to companies, especially when they are under financial pressure or facing investor scrutiny. But replacing people with AI is not just a financial calculation. It is a human one: those roles are livelihoods. Executing such changes poorly — with little notice or empathy — compounds the harm and damages employer reputation.

Atlassian’s internal culture and a CEO’s candid admission

Part of the story here is also the tone set by leadership. Cannon-Brookes has previously reflected candidly on his mistakes as a CEO. One admission stands out:

"It's probably, learning to fire people quickly enough. Actually, if I had to have one big mistake, the only times we've made huge egregious errors is where we've let someone, who was a good person in the wrong job, stay around for too long just because we're gutless."

That frankness can be read a number of ways. On one hand, it signals a ruthless focus on performance and rapid decision-making. On the other, it invites scrutiny about how sundering changes are communicated and managed. Delivering layoffs in a prerecorded video further underscores the tension between a leader’s strategic clarity and the need for humane execution.

AI in recruiting: where this trend intersects with hiring and HR

The Atlassian case speaks directly to a broader trend: companies will increasingly rely on AI in recruiting. When a firm automates customer service, it often simultaneously rethinks headcount, HR workflows, and how it identifies future talent needs. AI in recruiting will touch every phase of hiring: sourcing, screening, interviewing, onboarding and even workforce planning.

Here are key ways AI in recruiting is already reshaping hiring practice:

• Sourcing and screening: Algorithms can scan résumés, match skills to roles, and prioritize candidates based on patterns derived from historical hires.
• Candidate engagement: Chatbots can answer applicant questions, schedule interviews, and deliver timely updates — substituting for initial recruiter interactions.
• Assessment and interviewing: AI-driven assessments can evaluate technical skills, while automated interview platforms can analyze verbal and non-verbal cues.
• Onboarding and training: Personalized learning paths, driven by AI, can shorten ramp-up times and reduce the need for large training teams.
• Workforce planning: Predictive analytics can forecast skill gaps, enabling companies to hire more strategically or reskill existing employees.

Each of these applications brings efficiency and scale, but also raises risks: bias encoded in models, a dehumanized candidate experience, privacy concerns, and the potential for automated decisions to entrench existing inequities. The way companies deploy AI in recruiting will determine whether the technology elevates hiring or simply makes outdated processes faster and more opaque.

Practical consequences for recruiters and applicants

For recruiters, AI in recruiting is a double-edged sword. On one hand, automation frees up time for high-value work: relationship-building, interviewing nuanced candidates, and strategic workforce planning. On the other hand, it can reduce the number of hiring roles if companies decide to centralize or automate the recruiter’s traditional tasks.

For applicants, the experience changes too. Quick responses and clear timelines are positives. But applicants may also find there is no human touch early in the process, and appealing an automated rejection can be difficult or impossible. Transparency about how AI decisions are made — and the ability to request human review — will be critical to maintain trust.

Jobs lost versus jobs created: the big unknown

Chris Kenny’s closing note in the Sky News segment was blunt: "The AI revolution is bound to cost many jobs in coming years. The big question is how many others it might create." That’s the central debate. Historical waves of automation have both destroyed certain roles and spawned new ones. But the pace and scale of AI changes could make this transition more abrupt.

Potential new roles include AI trainers, model auditors, prompt engineers, data centre technicians, and specialists in AI ethics and compliance. Governments and private sector leaders can help by investing in retraining programs, facilitating transitions, and encouraging companies to invest a portion of productivity gains in workforce development.

The infrastructure paradox: data centers and power needs

Another side of the AI economy is physical: data centers. One of Atlassian’s cofounders highlighted the opportunity for Australia to attract investment to build data centers, becoming a regional hub. That is possible, but it requires reliable and abundant power — a practical constraint that will shape the geography of AI growth.

Data centers are capital-intensive and energy-hungry. If governments want to host the next generation of AI infrastructure, they will need to plan for power, cooling, connectivity, and environmental impacts. These are policy choices that affect where jobs tied to AI (both tech and construction, operations and maintenance) end up being located.

Ethics, reputation, and the human cost

Decisions about automation aren’t only economic. They are reputational and ethical. Companies that replace people with AI will be judged not only on shareholder returns but also on how they treat workers. Sudden layoffs delivered impersonally create negative cascades: decreased morale among remaining staff, brand damage with customers, and political backlash.

Policy makers will face pressure to respond — through social safety nets, reskilling programs, and potentially regulations governing how AI is used in employment decisions. Meanwhile, boards and CEOs must weigh short-term gains against long-term talent and brand risks.

Practical advice for employees, recruiters and leaders

What should individuals and organizations do now? Here are practical steps:

1. For employees: Upskill in areas where humans have comparative advantage — complex problem-solving, creativity, emotional intelligence, and roles that require multidisciplinary judgment.
2. For recruiters: Learn to work with AI in recruiting tools, but push for transparency and candidate advocacy. Advocate that automation augment rather than replace human judgment where fairness and nuance matter.
3. For leaders: Communicate change clearly and compassionately. Invest some gains from automation into reskilling and transition support. Consider phased implementations that preserve institutional knowledge during change.
4. For policy makers: Prepare workforce programs, incentivize responsible AI deployment, and ensure energy and infrastructure plans align with digital ambitions.

Conclusion — a turning point, not an inevitability

Atlassian’s decision to cut 150 jobs in favor of AI-driven customer service is striking because it encapsulates so many trends: the speed of technological adoption, the pressure on public companies to cut costs, the promise of new infrastructure investment, and the human consequences of automation. It also puts a spotlight on how the same AI systems used in customer service can be applied to hiring — which is why the topic of AI in recruiting matters so much.

AI in recruiting will not be a neutral tool. It will shape career paths, hiring outcomes, and corporate cultures. The big question remains: will organizations use AI to make work more humane and productive, or will they use it primarily to reduce headcount? The answer will depend on leaders' choices, regulatory frameworks, and how society decides to share the benefits and burdens of technological progress.

The AI revolution is here. How we manage the human side of it — in recruiting, in customer service, and across the economy — will determine whether this moment becomes a net gain for workers and communities, or a source of prolonged disruption.