Open-Weight LLMs Are Reshaping the AI Value Chain: What the Sector Pullback Means for Investors

From API Calls to Open Weights: Restructuring Value in the AI Industry Chain

A landmark event recently unfolded in the large model space: a leading AI startup officially released the complete weights of its flagship large language model. The move sparked wide discussion across global developer communities and even drew official congratulations from major international compute vendors. Open weights mean developers and enterprises are no longer confined to a single API channel—they can download the model parameters directly and deploy, fine-tune, or build on them within their own or third-party compute environments. For technical teams, this is more than a change in how models are consumed; it fundamentally strengthens data privacy control, creates room to optimize inference costs, and unlocks deeper model customization.

From an industry chain perspective, this trend is reshaping how value is distributed across the AI stack. In the past, large model capability was concentrated in the API endpoints of a handful of cloud providers, creating a de facto bundled sale of “compute + model.” With weights opened up, the technological premium of the model itself will gradually migrate toward compute infrastructure, inference optimization toolchains, and industry-specific solutions. In other words, whoever holds efficient compute orchestration, mature deployment tooling, and deep domain know-how will take the initiative in the next phase of competition. This explains why in China’s A-share market, compute chips, optical modules, and servers have traded in close correlation with model-opening events.

Layered Evolution of Compute Demand: Training, Inference, and Edge Deployment

One direct effect of open weights is exponential growth in inference demand. Once models can be freely deployed, enterprise users tend to migrate high-frequency, sensitive workloads into private environments, directly boosting demand for inference servers, accelerator cards, and supporting networking equipment. Meanwhile, model fine-tuning and domain-specific training are creating new compute procurement cycles.

Notably, compute demand is evolving in clearly defined layers. On the cloud training side, large clusters, high bandwidth, and low-latency interconnects remain the competitive focus. On the inference side, cost sensitivity and latency requirements are driving a partial migration from general-purpose GPUs to specialized ASICs. On the edge, as storage cost pressures ease at the margin, demand for AI-capable SoC chips is expected to recover. Industry research firms also point out that the spillover effect of AI demand will improve conditions in mature-node segments such as analog chips and power devices—this is not baseless optimism, but is driven jointly by recovering downstream device shipments and rising silicon content per unit.

For enterprise technology decision-makers, this means compute planning can no longer be a crude “pile up more GPUs” exercise. It requires fine-grained cost-performance modeling across training, inference, and edge workloads, along with hybrid architectures spanning multi-cloud, hybrid cloud, and on-premises deployment. Platforms that offer unified scheduling, elastic scaling, and heterogeneous compute management will win favor with more enterprise customers.

Semiconductor Supply Chain Localization: Deepening from Equipment to Materials

Beyond the open-weights milestone, the push for supply chain autonomy—particularly visible in China’s semiconductor sector—is accelerating. Expansion of domestic memory and advanced logic fab capacity continues to advance, directly lifting the localization rate of semiconductor equipment and components. Demand for materials—from photoresists and electronic specialty gases to CMP polishing pads—is growing steadily as new production lines ramp up and process categories expand. This process is not driven by policy alone; it has clear commercial logic behind it. Geopolitical uncertainty has made supply chain security a hard requirement for enterprises, while domestic equipment and materials have already achieved cost-performance advantages in certain segments.

The deeper shift is that the ecosystem for domestic AI chips is crossing over from “usable” to “good to use.” Early domestic accelerators suffered from immature software stacks and missing operator libraries, so real-world performance fell well short of theoretical peaks. But as leading vendors continue investing in compiler optimization and framework adaptation, that gap is narrowing. For developers, the learning cost of switching to domestic compute platforms still exists, but has dropped to an acceptable range. Some early adopters have even formed internal “porting task forces” dedicated to migrating CUDA code to domestic platforms, and have distilled a workable methodology—one that offers a replicable path for those who follow.

Reading the Index Pullback: Expectation Gaps and Structural Divergence

Turning to capital markets: as of recent trading days, China’s CSI Artificial Intelligence Theme Index has pulled back to a certain degree. On the surface, this looks like a technical correction after a strong run-up, but at a deeper level it reflects a tug-of-war between market expectations and actual earnings. Among index constituents, optical module leaders have fallen the most, while certain consumer electronics chip stocks have risen against the trend—a divergence that reveals capital rebalancing across sub-sectors.

Specifically, the optical module pullback is tied to expectations of a temporary slowdown in overseas cloud providers’ capital spending. Although training demand from large models remains robust, the market worries that the procurement peak for 800G optical modules may have already passed within the year, with subsequent growth depending on the ramp of 1.6T products. By contrast, companies with edge AI chip design capabilities have attracted capital thanks to steadily rising AI feature penetration in smartphones and IoT devices. This “barbell” allocation strategy—one end anchored in compute infrastructure, the other in terminal application chips—mirrors the medium-term direction of the industry’s evolution.

From a valuation standpoint, after this round of adjustment, the P/E percentile of the CSI Artificial Intelligence Theme Index has fallen from historical highs to slightly above its central range. Given that the top ten constituents account for more than 54% of the index, index performance is highly correlated with the earnings of leading compute chip and optical module companies. If the next earnings season validates order visibility, the current position may offer a certain margin of safety. That said, investors should also watch for valuation compression risk stemming from marginal shifts in Federal Reserve monetary policy, as well as potential supply chain disruptions from geopolitics.

Positioning Strategy: Matching Growth Momentum with Valuation

For investors focused on the AI theme, the core tension at this stage is balancing high growth momentum against high volatility. Looking back at the past two years of the AI rally, every major pullback was followed by substantive fundamental validation—whether it was the debut of ChatGPT or the leap forward in multimodal capabilities, both were later confirmed in earnings reports. Whether the current moment represents a good entry point therefore depends on tracking several key indicators:

First, order visibility. Pay close attention to inventory turnover and contract liabilities at leading compute chip makers. If production schedules extend into the next fiscal year, downstream demand is real and sustained. Second, marginal changes in gross margin. During periods of capacity expansion, stable or rising gross margins are an important signal that pricing power has not been eroded. Third, R&D conversion efficiency. In an industry where technology iterates extremely fast, the match between R&D spending and patent output often determines whether a company can maintain a competitive edge through the next technology cycle.

On strategy, a “barbell” approach makes sense: build a core position around compute infrastructure leaders with stronger earnings certainty, and allocate moderately to edge AI application chip companies with breakout potential to capture upside elasticity. For investors with lower risk tolerance, gaining diversified exposure through index-tracking funds at a low entry threshold is also a prudent option.

Conclusion: An Investment Methodology for a Time of Technological Change

The opening of large model weights marks AI’s formal transition from the “model arms race” to the era of “engineering deployment.” For developers, it represents an unprecedented opportunity: freed from API vendor lock-in, they can build intelligent applications that truly belong to them on top of open weights. For investors, the challenge is to identify core assets capable of crossing market cycles amid the noise of relentless technological iteration. A pullback is not a trend reversal—it is the market repricing technological maturity. At moments like this, rational analysis matters far more than chasing rallies or panic selling, and a solid base of information combined with a clear logical framework is the prerequisite for sound decisions.