China can narrow its technological gap with the United States by leaning into greater risk-taking and homegrown innovation, according to leading artificial intelligence researchers, though restrictions on access to advanced semiconductor manufacturing tools continue to weigh heavily on the sector.

Speaking at an AI conference in Beijing on Saturday, senior figures from China’s fast-rising AI ecosystem said recent momentum in capital markets and research breakthroughs point to growing confidence in the country’s ability to challenge U.S. dominance in artificial intelligence, even as structural bottlenecks persist.

The comments come after China’s so-called “AI tiger” startups MiniMax and Zhipu AI posted strong debuts on the Hong Kong Stock Exchange this week, a milestone for Beijing’s push to accelerate AI and chip-related listings as it seeks domestic alternatives to advanced U.S. technology amid intensifying geopolitical rivalry.

Yao Shunyu, a former senior researcher at ChatGPT maker OpenAI who was appointed Tencent’s chief AI scientist in December, said there was a high likelihood that a Chinese company could emerge as the world’s leading AI firm within the next three to five years. However, he cautioned that the lack of advanced chipmaking equipment remains the sector’s biggest technical hurdle.

“Currently, we have a significant advantage in electricity and infrastructure,” Yao said at the conference. “The main bottlenecks are production capacity, including lithography machines, and the software ecosystem.”

U.S. export controls have severely limited China’s access to cutting-edge semiconductor manufacturing tools, particularly extreme-ultraviolet lithography machines, which are essential for producing the most advanced chips used to train and deploy large AI models. While China has made progress toward developing its own alternatives, those efforts remain years away from commercial maturity.

Reuters reported last month that China has completed a working prototype of an extreme-ultraviolet lithography machine that could, in theory, produce chips rivaling Western technology. However, the machine has yet to manufacture functional chips. It may not do so until around 2030, according to people familiar with the matter, underscoring the long runway China still faces in closing the hardware gap.

Investment and Infrastructure Divide

Conference participants also acknowledged that the U.S. retains a significant lead in computing power, driven by massive capital spending by American technology giants and deep pools of private investment.

“The U.S. computer infrastructure is likely one to two orders of magnitude larger than ours,” said Lin Junyang, technical lead for Alibaba’s flagship Qwen large language model. “But I see that whether it’s OpenAI or other platforms, they’re investing heavily in next-generation research.”

By contrast, Lin said Chinese AI developers face tighter financial constraints, which shape how resources are allocated.

“We, on the other hand, are relatively strapped for cash; delivery alone likely consumes the majority of our computer infrastructure,” he said during a panel discussion at the AGI-Next Frontier Summit, hosted by the Beijing Key Laboratory of Foundational Models at Tsinghua University.

That funding gap has forced Chinese firms to prioritize efficiency over brute-force scaling, a dynamic that some researchers see as a competitive advantage rather than a weakness. Lin said limited resources have pushed Chinese engineers to pursue algorithm-hardware co-design, an approach that optimizes software to run large AI models on smaller, less expensive hardware.

Such techniques have helped Chinese companies deploy competitive models despite restrictions on access to the most advanced chips from U.S. suppliers like Nvidia, whose top products are subject to export bans.

A Shift in Risk Culture

Beyond technology and capital, industry leaders pointed to a cultural shift within China’s AI sector, particularly among younger entrepreneurs, as a critical factor in narrowing the gap with Silicon Valley.

Tang Jie, founder of Zhipu AI, which raised HK$4.35 billion in its Hong Kong initial public offering, said a growing willingness to embrace high-risk ventures is changing the innovation landscape.

“I think if we can improve this environment, allowing more time for these risk-taking, intelligent individuals to engage in innovative endeavors,” Tang said, “this is something our government and the country can help improve.”

That mindset represents a notable evolution in China’s technology sector, which has traditionally favored incremental improvement and commercial certainty over high-risk experimentation. Beijing’s recent moves to fast-track AI listings, support domestic chipmakers, and shield strategic industries from external shocks suggest policymakers are increasingly aligned with that shift.

Still, analysts say China’s path to global AI leadership will depend on whether it can translate innovation and efficiency into sustained breakthroughs while overcoming hardware constraints that remain largely outside its control.

In sum, Beijing’s AI elite is currently saying that the gap with the U.S. is real, but it is not insurmountable—and the next phase of the competition may be shaped as much by ingenuity and risk appetite as by raw computing power.

Elon Musk has announced plans to make X’s recommendation algorithm fully open-source, a move he says is intended to bring unprecedented transparency to how content and advertising are ranked on the platform.

The decision, unveiled on Saturday, lands at a moment when X is under sustained regulatory scrutiny in Europe, facing investigations, fines, and ongoing demands from authorities to explain how its algorithms shape the spread of content online.

In a post on X, Musk said the company would release its new algorithm within seven days, including “all code for organic and advertising post recommendations.” He added that the disclosure would not be a one-time exercise.

According to Musk, X plans to repeat the process every four weeks, publishing updated versions of the code alongside detailed developer notes explaining what has changed and why.

The announcement positions X as an outlier among major social media platforms, which typically guard recommendation systems as closely held intellectual property. Algorithms sit at the heart of how platforms drive engagement and monetize attention, influencing what users see, what goes viral, and how advertising is targeted. By pledging to open-source this technology, Musk is framing transparency as both a principle and a differentiator, consistent with his repeated claims that X should function as a digital public square.

However, the timing of the move is difficult to separate from the regulatory challenges X is facing in the European Union. Earlier this week, the European Commission said it had decided to extend a retention order sent to X last year, prolonging it until the end of 2026. According to commission spokesperson Thomas Regnier, the order relates to X’s algorithms and the dissemination of illegal content on the platform. Such retention orders require companies to preserve internal documents, data, and technical materials that could be relevant to enforcement actions under EU law.

The extension suggests that regulators remain concerned about how X’s systems operate and whether the company is meeting its obligations under the Digital Services Act (DSA). The DSA imposes strict requirements on large online platforms, including duties to assess and mitigate systemic risks, provide transparency around recommender systems, and grant vetted researchers access to platform data. Algorithms that amplify content are a central focus of the law, given fears that they can promote harmful material or unlawful content at scale.

X’s relationship with European authorities has been strained for months. In July 2025, Paris prosecutors opened an investigation into the platform over suspected algorithmic bias and fraudulent data extraction. At the time, X described the probe as a “politically-motivated criminal investigation” and warned that it threatened users’ free speech. French authorities have not publicly detailed the full scope of the case, but it added to growing pressure on the company across the bloc.

That pressure intensified last month when the EU imposed a 120 million euro ($140 million) fine on X for breaching transparency obligations under the DSA. Regulators said the violations were linked to multiple issues, including the platform’s “blue checkmark” subscription model, shortcomings in transparency around its advertising repository, and failures to provide researchers with access to public data. EU officials argued that these gaps made it harder to scrutinize how X manages risks associated with content dissemination.

Musk responded angrily to the fine, replying with an obscenity under a European Commission post announcing the penalty. The reaction underscored the increasingly confrontational tone between X’s owner and EU regulators, even as authorities insist that compliance with the DSA is non-negotiable for platforms operating in the bloc.

Against this backdrop, Musk’s plan to open-source X’s algorithm can be read in multiple ways. Supporters are likely to view it as a bold step toward accountability, giving developers, researchers, and users the ability to inspect how recommendations are generated. Musk has argued in the past that exposing algorithms to public scrutiny can build trust and counter claims of hidden manipulation or political bias.

Regulators and critics, however, may argue that publishing code alone does not resolve their core concerns. Recommendation systems are complex and constantly evolving, shaped not just by code but by data inputs, training processes, and real-time adjustments that may not be fully captured in an open-source release. There are also fears that making algorithms public could enable bad actors to game the system, amplifying spam, misinformation, or illegal content.

Still, the move raises broader questions for the industry. If X follows through on regular, detailed releases of its recommendation code, it could challenge rivals to explain why similar transparency is not possible elsewhere. It may also force regulators to clarify what meaningful algorithmic transparency should look like in practice, beyond access to source code.

They were men of immense capabilities, and seasoned professionals of their era. Masters of the waters. Experts who understood the moods of the sea, its temperaments, its rhythms and its paths. Their competence was not theoretical; it was earned through dawns and dusks spent navigating the waves as fishermen.

Yet one day, on the familiar Sea of Galilee, shallow in depth but fierce in temperament, the lowest freshwater lake on earth, fed by the River Jordan and framed by the Golan Heights, the currents gathered unusual momentum. Trouble rose from a place they thought they had mastered! Yes, a place they imagined they knew and understood.

On that fateful day, the disciples, four of whom were recruited by Christ from that very same sea, suddenly saw their core capabilities stretched beyond limits. The storm was merciless. The waves disrespected experience, mocked expertise, and humbled mastery.

The men battled with every ounce of strength. They rewired their survival instincts, rebuilt their procedures on the fly, and re-engineered new playbooks in real time to confront the tempest. But nothing worked.

Then, when their strength and skill were no longer enough, they reached upward for help. “Peace, be still,” came the command, and the storm obeyed. Yes, the storm stopped!

In our careers, we take pride in our education, knowledge, and experiences. We believe they can carry us through any turbulence in the professional seas. Yet, life will always produce moments when our competencies alone are insufficient. At those points, the storms require mentors, and higher anchors, systems greater than our personal capabilities.

Today’s labour market mirrors the Sea of Galilee, unpredictable, tech-disrupted, globalized, and unforgiving. Skills remain essential, but so do alternate pathways, new networks, and supportive structures for moments when capabilities are temporarily overwhelmed, just as the fishermen were nearly submerged in the waters they had mastered. Their saving grace was simple: they were in the right company with a Higher Mentor, to guide them to safety. You need someone to guide you to a safer professional and career journey.

Invest to be in the right company because sometimes, survival is not about the skill of the sailor, but the strength of the boat and the greatness of the One you sail with.

The next praise will be better because new songs will be discovered. Have a great Sunday and anchor to a Higher Mentor in your career and life.

– Ndubuisi Ekekwe, ex-unit cell coordinator, Scripture Union Nigeria, Secondary Technical School Ovim; Sunday School teacher, All Saints Chapel, FUTO; a Bible Teacher. Ndubuisi writes business and professional career cases from Biblical stories.