The rapid rise of artificial intelligence (AI) is reshaping the workplace faster than most people realize. What started with automating back-office tasks and customer service roles has now expanded into programming, legal research, financial analysis, and even creative fields such as writing and design. Experts predict that by 2030, up to 30% of U.S. jobs could be automated, with as many as 300 million jobs globally at risk because of AI and related technologies.

As AI tools become smarter and more accessible, the line between human and machine work is blurring—and the pressure to adapt is mounting. If you’ve noticed your workflow getting “smarter” or your company talking more about efficiency than expertise, you’re not imagining things. The age of AI-driven disruption has arrived, and it’s rewriting the rules of the workplace worldwide.

Which Jobs Are Most At Risk from AI?

The first wave of AI automation swept through customer service, data entry, and routine administrative work, said Dima Gutzeit, CEO of LeapXpert, a New York-based tech vendor that provides modern business communication tools with AI capabilities.

Now, he said, even roles in software development, content creation, finance, law, and medicine are being reshaped by code-writing engines, AI copywriters, and data-crunching models. Entry-level and repetitive positions are especially vulnerable, as AI excels at handling foundational tasks that once helped early-career professionals gain a foothold.

A June 2025 study by the Federal Reserve Bank of Dallas argued that most claims for what AI will do are “speculative” at this point. Indeed, many—including the World Economic Forum—have argued that the jobs AI produces will far outnumber those it renders redundant—170 million versus 90 million, respectively.

Nevertheless, the jobs most at risk from language-modeling AI include clerks, administrative assistants, and certain teaching positions. The telltale signs your job could be next? Your daily workflow starts to feel more software-driven, tools gain “AI-powered” features, and management talks about “co-pilots” and “automated insights.” If your responsibilities are becoming more about overseeing software than applying your unique skills, it’s time to take action.

While AI is rapidly transforming the workplace, experts agree that the best way to stay relevant is to focus on the qualities that make us uniquely human.

Here are some strategies to avoid being replaced by AI:

1. Demonstrate Your Humanity

AI can process data, but it can’t replicate judgment, empathy, or ethical decision-making. “What sets you apart isn’t your ability to process data—it’s your ability to interpret it, communicate it, and act on it,” Gutzeit told Investopedia. Employers are increasingly valuing creativity and abilities that remain stubbornly human, like relationship-building and nuanced communication.

2. Become an AI Power User

Don’t just fear the new tools, master them. Learn how to use AI platforms relevant to your field, from prompt engineering in content creation to AI-driven analytics in finance. The fastest learners today will be tomorrow’s leaders. Experiment with AI, critique its output, and figure out how to make it work for you.

3. Automate the Repetitive, Focus on the Unique

Identify the mechanical parts of your job and automate them, freeing up time for higher-value work.

“Strip the mechanical from your day so you can invest in the interpersonal-relationships, storytelling, negotiation,” Gutzeit said. The more you focus on tasks AI can’t do, the more secure your position becomes.

4. Upskill Continuously

Stay ahead by regularly updating your technical and soft skills. Pair AI literacy with human-centric strengths: Combine analytics with storytelling, or prompt engineering with leadership. The best opportunities will go to those who can bridge the gap between algorithmic speed and human nuance.

5. Watch Industry Trends and Pivot Early

Monitor which roles and industries are being automated, and be proactive about moving into areas where human expertise is still essential. Look for companies that use AI to amplify, and not replace, human value.

“Professionals who understand that partnership create more value than either humans or machines can deliver alone,” Gutzeit said.

The Bottom Line

AI isn’t just coming for your job; it’s already transforming the workforce. But the future belongs to those who adapt early, master new tools, and double down on the skills that make us human. It’s important to stay curious, proactive, and relentlessly focused on value. You can turn the AI revolution into an opportunity instead of a threat.

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Qwen3-Next-80B-A3B addresses this challenge by incorporating a technique known as “Gated DeltaNet”, first proposed by researchers at the Massachusetts Institute of Technology and Nvidia in March.

Gated DeltaNet enhanced the model’s attention by making targeted adjustments to the input data and determining what information to retain and what to discard, said Zhou Peilin, an AI researcher at the Hong Kong University of Science and Technology.

This results in an accurate yet cost-effective attention mechanism. Citing scores from the Ruler benchmark, which evaluates AI models based on their ability to manage varying input lengths, Alibaba said Qwen3-Next-80B-A3B was comparable to its most powerful model, the Qwen3-235B-A22B-Thinking-2507, despite being smaller and more affordable.

Alibaba uses a technique known as “Gated DeltaNet” to develop its latest AI models. Photo: Handout alt=Alibaba uses a technique known as “Gated DeltaNet” to develop its latest AI models. Photo: Handout>

“It’s great to see that our DeltaNets … have been greatly scaled up by Alibaba to build excellent AI models,” said Juergen Schmidhuber, computer science professor at the King Abdullah University of Science and Technology, who contributed to the development of DeltaNets in the 1990s.

Qwen3-Next-80B-A3B also uses the “mixture-of-experts” (MoE) architecture, which has driven many efficiency gains in Chinese AI models over the past year, including DeepSeek-V3 and Moonshot’s Kimi-K2.

The MoE architecture divides a model into separate sub-networks or “experts” that specialise in subsets of input data to collaboratively perform tasks.

Alibaba enhanced the “sparsity” of its latest MoE architecture to improve efficiency. While DeepSeek-V3 and Kimi-K2 employ 256 and 384 experts, respectively, Qwen3-Next-80B-A3B features 512 experts but activates only 10 at a time.

According to Artificial Analysis, those innovations helped the model achieve parity with DeepSeek-V3.1, despite having just 3 billion active parameters compared with the latter’s 37 billion. Generally, a higher number of parameters indicates a more powerful model, but it also increases training and operational costs.

The efficiency gains are evident on Alibaba’s cloud platform, where the new model costs less to run than the Qwen3-235B-2507, which contains 235 billion parameters.

The new model architecture reflects a growing interest in smaller but more efficient AI models, amid rising concerns about the costs associated with scaling up the industry’s largest models.

According to AI research firm Epoch AI, the most expensive training run to date was xAI’s Grok 4, which cost US$490 million, with future training runs expected to exceed US$1 billion by 2027.

In August, researchers at Nvidia published a paper advocating for small language models as the future of agentic AI because of their flexibility and efficiency. The company is also experimenting with the Gated DeltaNet technique on its Nemotron models.

Meanwhile, Chinese AI giants are pushing for broader adoption of their models by ensuring they are small enough to run on laptops and smartphones.

Last month, Tencent Holdings launched four open-source AI models, each under 7 billion parameters, while Beijing-based start-up Z.ai released the GLM 4.5 Air model with just 12 billion active parameters.

Alibaba’s Qwen3-Next-80B-A3B is now compact enough to operate on a single Nvidia H200 graphics processing unit, according to Artificial Analysis. On the open-source developer platform Hugging Face, the model quickly broke into the trending leaderboard, amassing almost 20,000 downloads within 24 hours after launch.

Alibaba said its new architecture served as a preview of its next generation of AI models. The future of large language models would likely revolve around refining Alibaba’s approach to address training costs and efficiency, even if entirely different architectures emerge, said Tobias Schroder, an AI researcher at Imperial College London.

This article originally appeared in the South China Morning Post (SCMP), the most authoritative voice reporting on China and Asia for more than a century. For more SCMP stories, please explore the SCMP app or visit the SCMP’s Facebook and Twitter pages. Copyright © 2025 South China Morning Post Publishers Ltd. All rights reserved.

Copyright (c) 2025. South China Morning Post Publishers Ltd. All rights reserved.