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des Voeux Insights | AI Monetization Accelerates as Hardware Emerges as the Ballast of the Compute Era
dev Voeux Partners  /  News  /  Investment Views     Last Updated:2026-10-06

Over the past week, the worst phase of the macroeconomic environment appears to have passed. China has introduced supportive policies for the real estate sector, the Chinese and U.S. heads of state are expected to continue mutual visits, signaling continued communication, and falling oil prices have eased inflationary pressures, providing breathing room for interest rate hikes and U.S. Treasuries. The market is refocusing on AI fundamentals, and this time, what sustains market sentiment is no longer conceptual narrative, but tangible revenue data.


I. Model Revenue Acceleration Materializes, Demand Resilience Persists


The most direct signal comes from OpenAI. Its September annualized revenue (ARR) approached US$70 billion, a substantial increase from US$40 billion in July. In just two months, revenue nearly doubled. This not only reflects the sustainability of AI demand but also reinforces, to a certain extent, the certainty of AI monetization. Capital markets responded swiftly, with the Nasdaq and other indices reaching historic highs. From the data curves, revenue growth at OpenAI and Anthropic has shown an accelerating trajectory in 2026, while xAI and Mistral AI remain in catch-up mode, with revenue divergence among leading model companies widening.



From a medium-term perspective, demand remains resilient. The core logic lies in the fact that "intelligence" has always been a scarce commodity in any era, particularly applications capable of achieving zero-to-one breakthroughs. Several directions warrant attention: AI for coding, which directly enhances software development efficiency with the most intuitive productivity gains; AI for science, which recently achieved a breakthrough on a millennium mathematics problem, demonstrating AI's potential in fundamental research; AI for pharmaceutical, which accelerates drug discovery and shortens R&D cycles; and AI for physical world, where AMD's acquisition of WorldLab, founded by Fei-Fei Li, to develop world models signifies AI's extension from the digital realm into the physical world. The commercialization of these application scenarios constitutes the fundamental support for sustained AI revenue growth.



Of course, the market's core focus remains on model revenue growth curves, where expectation gaps may introduce volatility. For instance, Anthropic projected ARR of US$70 billion at the end of June, but the final figure came in at US$65 billion, approximately 7% below expectations. Such single-quarter deviations are not uncommon in high-growth industries, but they serve as a reminder that the pace of AI monetization is not linear, and market sentiment may fluctuate sharply due to short-term data. Nevertheless, this does not alter the broader direction—the trend of AI transitioning from technological breakthrough to commercial monetization has been established.


II. Model Landscape Remains Unsettled, Hardware Segment Offers Deeper Moats


What warrants caution is the intensifying competitive landscape in the AI model layer. Over the past year, leadership has changed frequently: from OpenAI to Google Gemini 3, then to Anthropic, and now Chinese players are rapidly catching up.



Kimi and DeepSeek are reportedly nearing completion of US$5 billion and US$12 billion financings, respectively, with financial strength not to be underestimated. Meanwhile, technologies developed by companies such as RadixARK are lowering the barriers to new model development, meaning more participants will enter the fray, and competition at the model layer will intensify. The long-term endgame remains uncertain, and the industry is likely to see divergence, with the risk of betting on a single model company rising.


However, regardless of who ultimately prevails, AI hardware will remain the underlying support. Models can be replaced, algorithms can iterate, but compute demand remains relatively certain in the medium term. Moreover, the hardware segment possesses deeper moats—energy, chip manufacturing and equipment, CPU, interconnect, and storage—each characterized by high technical barriers and capital thresholds that new entrants cannot easily overcome in the short term. Segments attracting relatively high market attention broadly include: energy, chip manufacturing and equipment, CPU, interconnect, and storage. Energy ranks first because the electricity demand of AI data centers is becoming a key bottleneck constraining compute expansion; chip manufacturing and equipment follow closely, as the scarcity of advanced process capacity persists.


For investors, the certainty of AI monetization is increasing, but the competitive landscape at the model layer remains far from settled. The moat characteristics of the hardware segment warrant continued observation, and this logic may prove more robust than chasing short-term revenue data from model companies.


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