Produced by I Xiahai fallsea, Written by I Hu Buzhi

Over the past two decades, the narrative of the global tech industry has revolved around a single dominant theme: the United States holds sway over cutting-edge technologies, erecting technological barriers through export controls, while Chinese companies break through and catch up amid the "chokehold" of technological restrictions.

From semiconductors and industrial software to advanced manufacturing, nearly every hardcore tech sector has followed this "blockade-pursuit" script. But a letter in the summer of 2026 completely flipped the story.

At 5:21 PM Eastern Time on June 12, a notice from the U.S. Department of Commerce's Bureau of Industry and Security (BIS) landed on the desk of Anthropic CEO Dario Amodei. Citing the Export Control Reform Act (ECRA), the formal letter demanded that Anthropic immediately suspend global access to its two cutting-edge large models, Fable 5 and Mythos 5—extending the restrictions not just to overseas users but also to foreign workers within the United States, including non-U.S. engineers on Anthropic's own R&D team. Violators faced up to 20 years in prison and civil penalties in the tens of millions of dollars.

This came just 72 hours after the models' official release. There was no transition period, no compliance guidance, not even a window for technical adjustments. To avoid regulatory violations, Anthropic opted for the most extreme solution: globally delisting both flagship models, instantly revoking access for all users, regardless of nationality or payment status. Overnight, tens of thousands of businesses worldwide relying on Claude-series models, along with millions of developers, were forced to confront the reality of a "supply cutoff."

On the other side of the Atlantic, Zhipu AI promptly opened full access to its new flagship model, GLM-5.2, on June 13 and, four days later, open-sourced the complete model weights under the MIT license. While top-tier models were being forcibly sealed by administrative power, an open-source model with near-cutting-edge performance was freely available. This shift rewrote the fundamental logic of the global AI industry.

This was not a technical overtaking on a curve—Chinese models had not yet surpassed U.S. closed-source labs in absolute performance. Instead, it was a textbook example of "institutional arbitrage": U.S. regulations shattered the three-decade-old business consensus of "U.S. technology for global use," turning the certainty of frontier AI supply into a geopolitical bargaining chip. Meanwhile, Chinese open-source models, unconstrained by single-jurisdiction controls, stood on the side of supply stability for the first time.

We've long talked about "chokeholds." This time, the U.S. handed its own technological moat to its competitors. How Administrative Orders Shattered Business Contracts To grasp the lethality of this letter, one must first understand its legal logic. This was not a routine product safety update or targeted controls against a specific country but the first time the U.S. had directed its export control fist at cloud-based commercial SaaS services. It broke not just Anthropic's commercial ambitions but the very foundation of global tech firms' trust in U.S. technological supply. The U.S. Department of Commerce deployed a patchwork of "control combinations."

The core legal basis came from three provisions:

First, Section 4817(b)(1) of the Export Control Reform Act (ECRA) grants the Department of Commerce authority to impose temporary controls on "emerging and foundational technologies related to national security" without lengthy public notice or legislative processes.

Second, Section 4813(a)(15) of the ECRA allows the Department of Commerce to issue control requirements directly to companies via "special notices," bypassing conventional rulemaking procedures.

Third, the "deemed export" rule in the Export Administration Regulations (EAR)—releasing controlled technology access to foreign nationals within the United States is equivalent to a cross-border export and requires a license. The ruthlessness of these rules lies in their complete blurring of "domestic" and "foreign" boundaries.

Traditional export controls target the cross-border flow of physical goods, allowing companies to mitigate risks by segmenting markets. However, the "deemed export" rule extends controls into U.S. offices. Silicon Valley tech firms have long relied on global top talent, with non-U.S. engineers accounting for over 40% of headcount in leading AI labs. Researchers from India, China, and Europe are core drivers of technological iteration. Under these controls, these foreign employees could not even access their own company's R&D models without "violating export rules."

This directly severed Anthropic's R&D iteration chain—nearly half of its core R&D team could not touch the flagship models, making subsequent optimizations, iterations, and bug fixes impossible. More critically, precisely verifying every user's nationality in real-time API services is technically nearly impossible. Enterprise account users may be distributed globally, browser-based access cannot 100% verify identities, and shared accounts or proxy access scenarios are unavoidable. For compliance-focused tech firms, rather than risk gray-zone operations, a full global delisting was the safest choice—extreme self-censorship to avoid regulatory risks. The official pretext for the controls was a "jailbreak vulnerability" reported by Amazon's security team: researchers bypassed safety safeguards using specific prompts, enabling Fable 5 to generate attack code for exploiting software vulnerabilities, posing national security risks. But this rationale was immediately rebutted by the global cybersecurity industry.

On June 14, 76 global tech security leaders, including former Facebook Chief Security Officer Alex Stamos and top cryptographer Bruce Schneier, co-signed an open letter arguing that the control logic was untenable: similar jailbreak vulnerabilities are common across all frontier large models, with GPT-5.5 and Gemini Advanced sharing the same flaws. Singling out Anthropic did not reduce overall security risks. More critically, frontier large models' core security value lies in aiding code audits, vulnerability repairs, and attack simulations—they are essential tools for defenders. Removing the best defensive tools weakens U.S. cyberdefense capabilities. Behind the technical pretext lay clear political coercion. Silicon Valley knew full well why Anthropic was the first target: its ongoing break with the Pentagon.

Since 2024, Anthropic had been the most widely used AI model in U.S. military classified systems, securing up to $200 million in Pentagon contracts due to its "Constitutional AI" safety boundaries. But after Trump's second term, Defense Department demands for AI shifted toward controversial areas: requiring Anthropic to open model access for fully autonomous lethal weapon systems and large-scale domestic surveillance of U.S. citizens. These were precisely the red lines in Anthropic's Acceptable Use Policy. In February 2026, Dario Amodei publicly refused the Defense Department's demands, stating he could not ethically consent to AI for lethal weapons without human intervention or mass surveillance of citizens.

The Pentagon swiftly retaliated, placing the U.S.-based tech firm on a "supply chain risk list"—a designation previously reserved for sanctioning Chinese companies—requiring all federal agencies to find alternatives within six months.

Anthropic sued the Defense Department, accusing it of abusing administrative power and violating the First Amendment, fully exposing their conflict. Three months later, the Commerce Department's export control letter arrived. Under the guise of security vulnerabilities, it was political retaliation—a new rule from Washington: commercialization permissions for frontier AI technologies never rested with companies but with geopolitics and national will, always prioritized over business contracts. The cost of this rule was the collapse of global trust in U.S. technological supply. For decades, global firms paid a premium for U.S. tech products primarily because of "stability"—as long as they complied and paid, services would not be arbitrarily disrupted, and business contracts had clear expectations.

But after the Anthropic incident, all firms faced a harsh reality: any U.S.-jurisdiction cloud service could halt overnight due to an administrative order, even without violations or unpaid fees. When technologists supply switches are in politicians' hands, not companies', "U.S. technology" now carries a "supply cutoff risk" discount. The Aggressive Raid on Legal Gray Zones To understand why this control shocked the industry, the key lies in its redefinition of "export" boundaries—including cloud SaaS service access in export controls for the first time. Previously, the industry had debated whether AI inference services provided via the cloud to overseas users constituted "exports."

Early U.S. BIS advisory opinions leaned toward excluding pure cloud-based SaaS services from traditional "item exports," as no physical technology codes or hardware products crossed borders—users merely remotely accessed computing power deployed in the U.S. This letter shattered that consensus: as long as the accessor was a non-U.S. citizen, regardless of physical location or whether accessing cloud interfaces or locally deployed versions, granting access to frontier models was deemed an "export" requiring prior licensing. The aggressiveness of this interpretation lies in its near-total blockade of compliant global service provision.

For Anthropic, theoretically, two paths existed to comply: either precisely verify user nationality and restrict access to U.S. citizens only or halt all non-U.S. user services. The former was technically unfeasible: enterprise account users may be globally distributed, SSO login systems cannot verify each user's citizenship, and browser-based access cannot fully eliminate proxies or shared accounts. Even one Missed ones non-U.S. citizen access could trigger criminal penalties for the company and its executives. Under this "better safe than sorry" regulatory pressure, Anthropic chose the most conservative option: globally delisting both new models, even for U.S. domestic users—because the platform could not precisely distinguish each account's user nationality, forcing a blanket suspension while awaiting compliance solutions.

This regulatory logic's chilling effect rapidly spread across Silicon Valley. OpenAI immediately postponed its GPT-5.5 Enterprise Edition, originally scheduled for June 20, launching an urgent internal compliance review to assess whether existing products might trigger similar controls. Google DeepMind swiftly downgraded advanced feature access for non-U.S. IP users. Even Europe's Mistral AI, with training and inference computing deployed in the U.S., began evaluating potential EAR control impacts.

All Silicon Valley AI firms were forced to incorporate a new risk variable into their technical roadmaps: Washington's administrative will. A firm investing billions in training a frontier model could lose its global market overnight due to a single letter, rendering ROI zero. This uncertainty fundamentally undermined industry willingness to invest in frontier large model R&D. More profoundly, it shattered global trust in the "U.S. technology foundation."

Previously, multinationals could confidently build core business systems on AWS cloud services or OpenAI model interfaces, assuming business contracts were secure. Now, all realized: as long as services fell under U.S. jurisdiction, supply cutoff risks always existed—and these risks were unrelated to a firm's own compliance, determined solely by geopolitics. Once trust collapses, it never fully recovers.

China's Institutional Arbitrage In the same week Anthropic faced service suspensions, Zhipu's GLM-5.2 release precisely targeted global firms' anxiety. This timing overlap was less deliberate marketing than an inevitable industry tipping point—Chinese open-source models' performance just reached enterprise-grade replacement thresholds, while U.S. regulatory iron fists shattered trust in closed-source models. Simultaneous supply-demand shifts fueled this "institutional arbitrage" wave. To grasp this substitution's core logic, one must look beyond "technical superiority" as the sole criterion.

For enterprise clients, a model's absolute performance is never the only—or even primary—selection criterion. A 95-point model that could vanish anytime is far less valuable than a 90-point model that is 100% controllable and always available. When closed-source models' supply certainty collapses, open-source models' "accessibility premium" rapidly erases minor performance gaps. GLM-5.2 hit precisely this critical performance threshold.

According to third-party benchmarks and Zhipu's official data, the model comprehensively approached global top-tier closed-source models in core enterprise scenarios like code, long text, and complex tasks: in the FrontierSWE benchmark measuring engineering-grade code capabilities, GLM-5.2 scored 74.4%, just 0.7 percentage points below Claude Opus 4.8's 75.1% and exceeding GPT-5.5's 72.6%; in the Terminal-Bench 2.1 terminal command task test, GLM-5.2 reached 81.0%, on par with the global first tier; more critically, GLM-5.2 expanded its stable context window to 1M tokens, capable of processing entire code repositories, business contracts, or user data sets at once—a core enterprise requirement and previously Anthropic's key competitive edge.

Of course, gaps remain. On the hardest long-cycle software engineering tasks (SWE-Marathon benchmark), GLM-5.2 scored just 13.0%, while Claude Opus 4.8 reached 26.0%—a full doubling. In multimodal deep fusion, complex logical chaining, and frontier scientific computing, domestic models still lag. But for over 80% of daily enterprise scenarios, these top-tier capability gaps are imperceptible to users. Customer service, copywriting, code assistance, knowledge base Q&A, data analysis—these highest-frequency AI functions are fully within GLM-5.2's capabilities. Once performance crosses the "good enough" threshold, supply stability, data security, and cost controllability become core decision factors. Zhipu's solution precisely addressed all enterprise anxieties: MIT-licensed open-source, supporting full private deployment.

This represents a supply model fundamentally different from that of American closed-source models. The logic of closed-source models is 'leasing': businesses pay to use APIs, but the model weights remain permanently in the hands of vendors. Services can be interrupted at any time, and data faces compliance risks related to cross-border flows. In contrast, the logic of open-source models is 'ownership': businesses can download complete model weights onto their private servers, keeping data entirely within their domain. All API calls, iterations, and optimizations are completed internally, without reliance on any third-party cloud services or susceptibility to administrative controls by any country. In other words, once privatized deployment is complete, this AI capability belongs entirely to the enterprise itself.

There are no risks of supply disruption, price hikes, or compliance violations—control rests entirely with the user. At a time of escalating geopolitical conflicts and soaring regulatory uncertainty, this 'sense of controllability' represents the rarest commercial value. According to AI industry research data from Orient Securities in the second quarter of 2026, among Chinese companies operating overseas, the proportion listing 'supply chain security and privatized deployment capability' as their top criterion for large model selection surged from 21% at the end of 2025 to 72%. Nearly 60% of companies plan to migrate core business systems from American closed-source models to domestic open-source foundations within the next 12 months.

This shift isn't limited to domestic companies. Enterprises in emerging markets like Southeast Asia, the Middle East, and Latin America are also accelerating their transition to Chinese open-source models. These regions' companies lack both the capability to develop large models independently and the capacity to bear the risks of sudden supply disruptions from American models. An open-source, controllable AI solution without geopolitical strings perfectly fills this market gap. Public data from Hugging Face shows that within a week of GLM-5.2's open-source release, overseas downloads accounted for over 60%, with Southeast Asia and the Middle East leading growth. Capital markets have priced in this 'substitution narrative' with real money. Following the incident, Zhipu's Hong Kong stock price surged, peaking at over 40% intra-day gains and closing up 15.09%. By late June 2026, Zhipu's cumulative gains from its IPO price exceeded 1,900%, with a total market cap surpassing HK$1 trillion, making it China's first independent AI company to break the trillion-dollar valuation barrier.

The market isn't valuing 'global technical supremacy' but 'global substitution preference.' Investors aren't betting that Zhipu will technically surpass Anthropic outright, but that U.S. regulations will continuously raise the risk premium of closed-source models, driving more companies toward open-source solutions. As one of the highest-performing open-source models currently available, Zhipu stands to be the primary beneficiary of this migration wave. This is the essence of institutional arbitrage: it's not that our technology suddenly became stronger, but that our competitors' regulatory environment actively undermines their products' commercial value.

When the U.S. weaponizes technological supply as a geopolitical tool, China's unregulated open-source models naturally acquire scarce 'certainty premiums.' Restructuring of Power Dynamics The Anthropic incident is no isolated commercial event—it marks a paradigm shift in global AI competition. Previously, AI competition resembled a 100-meter sprint, focusing on parameter scale, benchmark scores, and technical iteration speed. Closed-source labs sat atop the pyramid, defining industry technical standards. Now, AI competition has transformed into a cross-country rally, emphasizing stable, sustained, and uninterrupted delivery of capabilities to clients. Supply stability now rivals technical performance in importance. This power restructuring is propagating upward and downward from the model layer, reshaping value allocation across the entire AI supply chain. The most visible change occurs at the model layer: open-source models are evolving from 'niche alternatives' to 'mainstream options,' tilting the power balance toward open-source vendors. Historically, open-source models served as 'budget substitutes' for SMEs and individual developers, with cost as their core selling point. Now, their core value proposition has shifted to 'security and controllability,' making them the default choice for finance, government, and high-end manufacturing sectors—even becoming core business infrastructures for multinationals.

Accompanying this is business model restructuring: closed-source models charge per token, essentially operating as traffic-based businesses where higher consumption drives revenue, but clients may churn at any time. Open-source models adopt a 'base licensing + custom development + technical services' model, yielding higher average contract values, stronger client retention, and more stable revenue structures. Long-term, the global large model market will inevitably form a clear 'dual-track' landscape: cutting-edge exploratory scenarios and extreme performance demands will remain dominated by U.S. closed-source models, but market size will be confined to the U.S. and its core allies' specific domains. Meanwhile, the vast majority of enterprise production scenarios requiring supply chain security will gradually migrate to Chinese open-source models, creating a larger market covering more industries.

The second layer of restructuring occurs at the compute layer, defying intuitive logic: model open-sourcing doesn't reduce compute demand but drives exponential growth in total compute requirements. Many assume that enterprises shifting from cloud API calls to local deployment will reduce cloud compute purchases, but the opposite is true: when AI model costs decrease and controllability improves, companies will AI-enable many scenarios previously deemed uneconomical or risky. Previously, high API costs limited AI adoption to core business functions. Now, with privatized deployment's near-zero marginal costs, AI permeates every business detail—from full-process intelligent assistance and massive data batch processing to edge device localization. The token consumption from these new scenarios far exceeds previous API call volumes. More critically, open-source model proliferation opens crucial market space for domestic AI chips.

Historically, domestic AI chips faced the chicken-and-egg problem: lacking upper-layer model ecosystems, clients saw no incentive to purchase hardware without usable models. Now, leading open-source vendors like Zhipu have completed deep optimizations with full-stack domestic compute platforms including Huawei Ascend, Cambricon, Hygon, and Biren, establishing a complete domestic 'chip-framework-model' stack. Enterprises seeking to eliminate supply chain risks will choose full-stack domestic solutions, creating a virtuous cycle: wider model adoption drives greater domestic compute demand; a more mature compute ecosystem lowers model deployment costs, accelerating adoption.

The third restructuring layer occurs at the application layer, where migration dividends are fostering new industry barriers. For downstream SaaS vendors and application developers, base model switching requires painful reconstruction but also offers reshuffling opportunities. Vendors that adapt earliest to open-source bases and support multi-base flexible scheduling can better meet enterprise clients' security needs, rapidly capture market share, and enjoy first-mover advantages. More profoundly, when foundational models converge in capabilities, application vendors' core competitiveness shifts from 'access to stronger models' to 'deep customization based on open-source models combined with industry data and know-how.' Previously, many AI applications were mere 'API assemblers' without proprietary barriers, vulnerable to model vendor price hikes. In the open-source era, vendors with true industry expertise, data assets, and deep customization capabilities will build genuine competitive moats.

Industrial value will gradually shift from upstream model layers to downstream scenario application layers. This entire supply chain restructuring essentially revolves around 'certainty': model layers compete on supply certainty, compute layers on supply chain certainty, and application layers on business implementation certainty. When U.S. regulations shatter the certainty they once dominated, global industrial gravity naturally shifts toward more stable supply ends. Restructuring of Pricing Logic Alongside supply chain logic changes, AI model valuation frameworks are undergoing a silent revolution.

J.P. Morgan's June 2026 research report explicitly states that global large model valuation logic has shifted from 'parameter scale narratives' to 'task completion capability and pricing power,' with enterprise payment logic moving from per-token charging to task-outcome-based pricing. Zhipu's pricing strategy for GLM-5.2 validates this trend, adopting a peak-valley quota system: peak period calls consume 3x quotas, off-peak 2x, with promotional periods as low as 1x. Pricing now depends on 'task completion timeliness and compute costs' rather than 'character generation volume.' Data shows Zhipu's API prices have surged over 80% year-to-date, yet call volumes grew 400%—indicating enterprise clients' willingness to pay premiums for stable, implementable task capabilities, with far lower price sensitivity than supply stability sensitivity. This pricing logic shift reflects AI's transition from 'technical novelty' to 'production tool.'

In early industry stages, companies paid for 'possibility': larger parameters and novel capabilities justified trial purchases, making per-token charging logical—after all, call volumes were small and costs controllable. But as AI enters production workflows, enterprises care less about 'how many characters were generated' than 'whether tasks can be completed properly.' A Typical Case (typical example) in code development illustrates this: enterprises need models that can independently develop complete functional modules, pass tests, and fix production bugs—not just generate code snippets requiring heavy manual revisions. If a model can deliver end-to-end task completion, enterprises will pay higher per-token prices; if it only generates fragments needing extensive modification, even cheap pricing offers limited value.

This explains why closed-source models' performance advantages are diluting: for most production scenarios, the experience gap between top-tier and first-tier models pales compared to the reliability gap between 'always available' and 'potentially disrupted.' When open-source models handle 80% of production tasks with 100% supply controllability, enterprises see no need to assume disruption risks for the remaining 20% of extreme performance. For model vendors, this implies new valuation anchors: mere parameter stacking and benchmark chasing no longer suffice. The ability to penetrate industry scenarios, solve real production problems, and build stable supply capabilities now defines long-term value. Don't Mistake Narratives for Inevitability Amid this institutional arbitrage wave, we must avoid overconfidence in 'China's complete AI rise.' This represents merely a policy window-driven phase of opportunity, not comprehensive technological superiority or the final outcome of industrial competition. Whether redistributions can be sustained and advantages converted into long-term barriers remains uncertain, with multiple risks and boundaries.

GLM-5.2 has reached cutting-edge thresholds in mainstream scenarios but still shows clear performance gaps with U.S. closed-source models in top-tier capabilities. Beyond the previously mentioned 1x gap in SWE-Marathon benchmarks, shortcomings persist in multimodal deep fusion, complex logical chain reasoning, and frontier scientific computing. At the foundational research level, the U.S. maintains leads in model alignment techniques, efficient training frameworks, new model architectures, and underlying mechanism exploration. Domestic models primarily catch up in engineering and application implementation, with insufficient bottom-layer original breakthroughs. Technological catch-up is a marathon—a policy window can accelerate market penetration but cannot directly erase performance gaps. If we reduce R&D investment amid temporary market enthusiasm, gaps will quickly reopen. Institutional dividends are gifts from others; only technological strength is truly our own. Current advantages rest on a premise: regulations target only Anthropic, leaving other U.S. models usable. But if Washington extends this logic industry-wide, requiring strict nationality screening for all frontier closed-source models and even regulating open-source ecosystems, the global AI industry would face fragmentation shocks. Already, U.S. congressional members have proposed bills to include all large models exceeding certain parameter thresholds in export control regimes, restricting U.S. entities from using Chinese-backed open-source large models. If controls extend to open-source ecosystems, today's 'openness advantages' could become new compliance risks.

The global market would then split into two ecosystems: the U.S. and its allies using closed-source models, China and emerging markets using open-source models, with no interconnection—a lose-lose scenario for global AI development. Geopolitical games are always two-way streets; no unilateral benefits exist indefinitely. While we enjoy institutional advantage dividends, we must prepare for escalated controls and ecosystem fragmentation. Capital market exuberance often outpaces industrial fundamentals. Behind the trillion-dollar valuation lies a grand narrative of global substitution, but real commercialization proves far more complex.

Currently, Zhipu's core revenue still comes from privatized deployment projects—highly customized, long-delivery-cycle, and labor-intensive initiatives difficult to scale rapidly. The global open-source model monetization path remains experimental: offering free base weights while charging for services, solutions, and value-added features. Whether this model can support a trillion-dollar valuation awaits time-based validation. Overseas market expansion also proves tougher than imagined. European and U.S. markets enforce strict data compliance reviews and deep-seated geopolitical trust barriers, making core enterprise clients reluctant to adopt Chinese-backed large models at scale. While emerging markets grow rapidly, their weak payment capacity and low average contract values struggle to generate sufficient revenue. Ecosystem gaps further complicate matters. U.S. closed-source models have built mature developer toolchains, plugin ecosystems, third-party app markets, and industry solutions over years, enabling low-barrier application development. Domestic open-source ecosystems remain nascent, with toolchains, communities, and developer networks still under construction—requiring time to handle large-scale enterprise migration demands. Ultimately, this represents a window of opportunity granted by competitors' mistakes, not a victory in technological Decisive battle! (decisive battle). Window periods don't last forever; seizing opportunities to address shortcomings, build ecosystems, and strengthen internal capabilities will determine long-term outcomes.

Epilogue:

Looking back at the past 40 years of global tech industry history, the U.S. has consistently occupied the pinnacle of technological hegemony, constructing a 'U.S. R&D, global payment' industrial paradigm through open markets, mature commercial rules, and global talent aggregation. Global enterprises long accepted this framework: paying premiums for U.S. technology in exchange for stable service supply and commercial guarantees. The Anthropic regulatory incident shattered this decades-old paradigm. When administrative orders can arbitrarily disrupt commercial services, when geopolitical games override contractual spirit, and when technological supply becomes a revocable bargaining chip,

global enterprises must reassess: How much controllability remains when building core businesses on U.S. technological foundations? The answer's scales are tilting. Chinese open-source models stand on the side of supply certainty—they don't restrict markets through regulations but release technological capabilities through open-source collaboration. They don't attach geopolitical conditions to commercial cooperation but offer global enterprises autonomous and controllable alternatives. Amid escalating geopolitical conflicts and rising uncertainty, 'stability and controllability' itself constitutes core commercial competitiveness. This marks the first time in global tech industry history that China's frontier technology sector has formed comparative advantages through institutional environment certainty. This advantage doesn't stem from comprehensive technological reversal but from U.S. regulatory self-sabotage that voluntarily ceded global market share. Its emergence contains contingent factors, but the industrial opportunities it releases are real and profound.

However, it must be made clear that institutional dividends are phased (phased, here referring to temporary advantages gained from institutional factors, keep the original word as it's a specialized term contextually understood) in nature, while technological hard power is the foundation for long-term industrial competitiveness. The window of opportunity will not remain open indefinitely. Whether one can seize this wave of market demand and transform temporary supply advantages into long-term technological and ecological barriers depends on the long-term deep cultivation of domestic model manufacturers in R&D investment, ecological construction, and industrial implementation. If one indulges in the hype of short-term narratives and misses the window to address shortcomings, once U.S. regulations adjust and the ecosystem recovers, the industry landscape may still revert to its original trajectory. The global industrial table in the AI era is undergoing a new round of reshuffling.

By having U.S. regulators voluntarily remove their own cutting-edge models from the global market, China's open-source models have gained new leverage to enter the global market. However, the game is far from over. The ultimate outcome of competition is never determined by an opponent's mistakes but by one's own strength. In the next decade, whoever can continuously and stably output inclusive AI capabilities globally and build a more open and dynamic industrial ecosystem will truly stand at the core of the global AI industry. All the current changes are merely the opening act of this prolonged competition.