China’s AI Ambition: Huawei Hardware Powers New Model, But What’s the Real Cost?

The late afternoon sun used to stream through my office window, catching the dust motes dancing in the air above my old Pentium.

I remember the buzz, back in the 90s, when everyone swore the personal computer was going to transform everything overnight.

We’d be paperless, effortless, infinitely productive.

My colleague, a wizened old hand, would just smile and say, Patience, beta.

The seeds are sown, but the harvest takes time.

He was right, of course.

We saw a revolution, yes, but not in the straight line many imagined, nor in the immediate productivity statistics we all hoped for.

Today, as headlines trumpet breakthroughs in artificial intelligence, I feel that familiar echo.

We are told of seismic shifts, of job markets collapsing, of a new era of boundless efficiency.

Yet, beneath the surface, the story is far more intricate—a blend of geopolitical ambition, technological daring, and a healthy dose of human skepticism.

The current narrative around China’s Zhipu AI, claiming to train a model solely on Huawei hardware, perfectly encapsulates this duality.

It is a bold statement in a world increasingly focused on technological self-reliance, yet it invites critical questions about the true pace of innovation and its measurable impact.

In short: Zhipu AI claims a major leap for Chinese tech independence with its Huawei-trained model, GLM-Image.

While a symbolic victory, questions linger about performance, cost, and AI’s actual impact on productivity and job markets, reminding us to distinguish hype from verifiable progress.

This push for technological independence is particularly critical now, as global tensions and US export controls tighten, compelling nations to develop domestic alternatives to Western chip technology.

Huawei’s efforts to build a full-stack domestic computing platform—from its Kunpeng processors to its Ascend AI chips—are central to China’s self-sufficiency ambitions in AI training, as reported by The Register in 2024.

Yet, how these claims translate into real-world competitive advantage and economic benefit remains the focal point of much debate.

Zhipu AI’s Breakthrough: A Made in China Milestone?

When Zhipu AI, styling itself Z.ai, announced its GLM-Image model, claiming it was trained entirely on Huawei hardware, the tech world took notice.

This was not just another model; it was a powerful statement of domestic capability, particularly for China, a nation under immense pressure to develop its own advanced technological infrastructure.

The GLM-Image, with its autoregressive + diffusion decoder architecture, represents a significant advance in image generation, moving beyond earlier models, according to The Register in 2024.

The backbone of this achievement, Zhipu AI stated, is Huawei’s Ascend Atlas 800T A2 server, equipped with Kunpeng 920 processors and Ascend 910 AI processors.

Huawei itself claims its upcoming 2025 Ascend 910C model will achieve around 800 TFLOPS of computing power at FP16 precision, aiming for approximately 80 percent of Nvidia’s H100 chip, as noted by Huawei in 2025 via The Register.

This move demonstrates a clear intent to challenge the established giants in the semiconductor industry.

However, the critical piece of the puzzle—how many servers Zhipu used, how quickly the training was completed, and at what cost—remains undisclosed.

This lack of transparency leads to an important counterintuitive insight: a technological breakthrough is not just about what can be built, but how efficiently and competitively it can be produced.

Without these metrics, the true impact on global competition, especially against dominant players like Nvidia and AMD, is yet to be fully understood.

The Unseen Costs of Self-Reliance

Imagine a local bakery that proudly announces it is now using only domestically sourced ingredients.

A wonderful achievement for national pride, yes.

But if their bread now costs twice as much and takes three times as long to bake compared to their competitors who import some flour, the market will speak volumes.

Similarly, while Zhipu’s claim of an all-Chinese model is a symbolic victory, the global market demands performance and price.

Until Zhipu reveals the economic viability of their Huawei-powered training, the broader implications for Chinese tech independence remain nuanced.

The Solow Paradox Revisited: Is AI Really Boosting Productivity?

Beyond the China AI hardware race, there is a deeper, more fundamental question: is AI actually delivering on its promise of revolutionizing productivity?

J. P. Gownder, Forrester’s vice president and principal analyst, offers a sobering perspective.

He told The Register in 2024 that where we are today, we are not seeing it, referring to the widespread impact of AI on productivity.

This sentiment echoes the famous Solow Paradox, coined by Nobel Prize-winning economist Robert Solow, who observed in 1987 that the effects of the PC revolution can be seen everywhere, except in the productivity statistics.

Gownder suggests this holds true for AI today, stating that productivity just has not soared.

Consider the historical data: US productivity grew by 2.7 percent annually from 1947 to 1973.

Yet, despite the explosion of personal computers, this dropped to 2.1 percent between 1990 and 2001, and further to 1.5 percent from 2007 to 2019, according to the US Bureau of Labour Statistics, cited by Forrester in 2024.

The notable exception was a 2.8 percent growth spurt from 2001 to 2007.

This suggests that while information technology profoundly changes how we work, its impact on macro-level productivity is not always linear or immediately evident.

The research provides key data insights.

AI’s impact on overall productivity growth is currently negligible, echoing the historical Solow Paradox with PCs.

This implies businesses should temper expectations for immediate, significant productivity boosts from current AI implementations, focusing instead on targeted applications with clear return on investment rather than a blanket AI first strategy.

Another key insight is that AI could uproot 6 percent of jobs, approximately 10.4 million, by 2030 through various automation forms.

However, many claimed AI-driven job losses are actually cost-cutting measures or outsourcing.

While structural job displacement by AI is real, the narrative is often conflated with other economic factors.

Therefore, companies and policymakers need to accurately identify the drivers of job market changes, distinguishing genuine AI-driven shifts from financial restructuring, to implement effective workforce development and support programs.

AI and Jobs: Separating Automation from Outsourcing and Budget Cuts

The fear of AI-driven job displacement looms large, and Forrester’s research does project that AI could uproot 6 percent of jobs—roughly 10.4 million—by 2030, through automation in various forms like robotic process automation and generative AI, according to Forrester in 2024.

These, Gownder notes, are structural losses, jobs gone for good, because they have been replaced.

This is a significant hit to the economy and requires serious consideration.

However, Gownder also provides a crucial distinction.

In his conversations with over 200 organizations, he found that many large-scale job cuts attributed to AI were, in fact, financial decisions masquerading as an AI job loss.

Companies would say they are hoping they will fill it with AI at some point, rather than AI actively displacing those roles, The Register reported in 2024.

This highlights a nuanced reality where a frozen white-collar job market exists—corporations hold off on hiring for open roles, hedging their bets on future AI capabilities.

Yet, as Gownder pragmatically points out, when you have work to do, it has got to get done at some point.

If AI does not work out, they will either hire or find another solution, as he shared with The Register in 2024.

A Playbook for Navigating the AI Frontier

The dual realities of ambitious AI development and uncertain productivity gains demand a thoughtful approach.

Here is a playbook for leaders:

• Demand Data, Not Just Demos: When evaluating AI solutions, particularly in critical infrastructure like China AI hardware or generative AI tools, push for transparent performance metrics—speed, cost, efficiency, and proven return on investment, as advised by The Register in 2024.
• Focus on Incremental Productivity, Not Overnight Revolution: Do not chase the Solow Paradox.

  Identify specific, measurable tasks where AI can yield immediate, albeit small, productivity gains, as suggested by the US Bureau of Labour Statistics data cited by Forrester in 2024.

• Audit AI-Driven Job Changes: Critically assess whether job reductions are genuinely due to AI replacement or if they are financial decisions, belt-tightening, or outsourcing, a distinction emphasized by J. P. Gownder of Forrester in 2024.
• Invest in Workforce Reskilling: With 6 percent of jobs potentially uprooted by 2030, proactively invest in training programs that equip your workforce with new, AI-complementary skills, based on Forrester’s 2024 research.
• Pilot with Purpose: Before large-scale deployments, run targeted pilot programs for AI tools, defining clear success metrics that extend beyond just cool factor to tangible business benefits.
• Diversify Your Tech Stack (Where Possible): For businesses reliant on AI infrastructure, closely watch developments in areas like Huawei Ascend chips.

  While a full-stack domestic computing platform may not yet rival global leaders, diversifying options can mitigate supply chain risks.

Risks, Trade-offs, and Ethics

The rapid advance of AI, coupled with geopolitical ambitions, presents inherent risks.

For China, the drive for technological self-reliance, while understandable, carries the trade-off of potentially slower development or higher costs compared to leveraging global best-in-class components.

The quality and scalability of domestic AI solutions, like GLM-Image, need to be rigorously proven.

Ethically, the conversation around AI and jobs requires sensitivity.

The potential for 10.4 million jobs to be uprooted by 2030 is significant, according to Forrester in 2024.

Leaders have a moral responsibility to manage this transition with dignity, ensuring transparent communication and robust support systems for affected employees.

Misattributing job losses to AI when they are financial decisions only erodes trust and hinders effective planning.

We must uphold a grounded empathy in our approach.

Tools, Metrics, and Cadence

For organizations looking to implement AI effectively, a structured approach is key.

Recommended Tool Stacks (Conceptual):

• Data Prep & Management: Leverage cloud-based data lakes/warehouses with integrated data governance.
• AI Development & Deployment: Utilize platforms with MLOps capabilities for model training, deployment, and monitoring.
• Performance Monitoring: Implement dashboards that track real-time operational metrics and business impact.

Key Performance Indicators (KPIs) to Track:

• Productivity Lift: Percent increase in output per employee for AI-assisted tasks, targeting 5-10 percent per specific process.
• Cost Reduction: Percent decrease in operational cost due to AI automation, targeting 10-15 percent on automated tasks.
• Error Rate Reduction: Percent decrease in human errors in AI-supported processes, aiming for 20 percent improvement.
• Time to Market (AI): Cycle time from AI project inception to deployment, targeting a reduction of 15-20 percent.
• Job Transition Rate: Percent of displaced employees successfully retrained/reassigned, targeting over 70 percent.

Review Cadence:

Establish a monthly review of AI project performance against KPIs, a quarterly strategic review of the AI roadmap, and an annual assessment of AI’s overall impact on business strategy, workforce, and market competitiveness.

This continuous feedback loop is crucial for adapting to the evolving AI landscape.

FAQ

Q: Has China truly achieved self-sufficiency in AI hardware?

A: Zhipu AI claims to have trained its GLM-Image model entirely on Huawei hardware, showcasing domestic capability.

However, the true level of self-sufficiency and competitiveness with global rivals like Nvidia remains unproven as Zhipu has not released performance benchmarks or costs, as reported by The Register in 2024.

Q: Is AI currently improving productivity in businesses?

A: According to Forrester analyst J.P. Gownder, current evidence does not show AI significantly boosting productivity, drawing parallels to the Solow Paradox from the PC revolution where IT investment did not immediately translate to productivity growth, as noted by The Register in 2024 and the US Bureau of Labour Statistics, cited by Forrester in 2024.

Q: Are job losses widely attributed to AI actually caused by AI?

A: While AI is expected to displace some jobs, J.P. Gownder suggests many reported AI-driven job losses are often financial decisions, belt-tightening, or even outsourcing masquerading as AI replacement.

A frozen white-collar job market exists where companies hold off hiring to see if AI can fill roles, but if AI does not perform, they will eventually have to hire, as explained by The Register in 2024.

Conclusion

Just as the sun eventually set on my Pentium-powered office, ushering in new eras of computing, so too will AI redefine our world.

Zhipu AI’s bold claim, powered by Huawei’s ambition, is a testament to the relentless drive for innovation and technological independence.

It is a significant marker on China’s journey to build a robust domestic computing platform.

Yet, the real victory, like the true promise of the PC, will not be measured by the claims alone, but by the tangible, measurable impact on productivity, the thoughtful management of workforce transitions, and the ethical grounding of its development.

We are indeed sowing the seeds of a new technological era.

But as my old colleague knew, we must approach this harvest with clear eyes, discerning hype from reality, and ensuring that our technological advancements ultimately serve human progress.

The future of AI is not just about what can be built, but what should be built, and for whom.

Let us build wisely, with dignity and purpose.

References

• Forrester. AI job replacement research. The Register, 2024.
• Huawei. Huawei claims. The Register, 2025.
• The Register. China’s Z.ai claims it trained a model using only Huawei hardware. 2024.
• University of Oxford. The Future of Employment: How Susceptible Are Jobs to Computerisation? 2013.
• US Bureau of Labour Statistics. US Bureau of Labour Statistics cited by Forrester. The Register, 2024.