On July 28, 2026, as the first half earnings season draws to a close, the investment landscape in artificial intelligence is undergoing profound changes. Yueya Finance has compiled multi-source data revealing that global AI financing reached $38.7 billion in Q2 2026, up 42% year-on-year. However, capital flows show significant divergence—investment in foundational large models has slowed, while AI infrastructure, vertical industry applications, and AI security have become the three new focal points. This article combines the latest public information to analyze the logic behind this shift.

1. AI Infrastructure: From Building Models to Building Foundations

In H1 2026, the top ten global cloud service providers had combined capital expenditures exceeding $120 billion, of which the portion allocated to AI infrastructure surpassed 45% for the first time. Based on order data from chipmakers such as NVIDIA and AMD, quarterly shipments of AI chips for data centers grew 65% year-on-year. Notably, the adoption rate of customized AI chips (e.g., Google TPU, Amazon Trainium) surged from 12% two years ago to 34%.

Why Is Capital Shifting to Infrastructure?

• Cost Pressure Drives Efficiency Revolution: As large model parameters exceed the trillion level, training and inference costs have grown exponentially. Companies are no longer blindly pursuing the largest models and are instead optimizing compute utilization. For example, Microsoft disclosed in its Q2 2026 earnings that its self-developed AI accelerator Maia 2 reduced internal inference costs by 40%.
• Edge Computing Demand Explodes: Real-time scenarios such as autonomous driving and industrial IoT require AI inference to be completed at the edge, fueling investment in lightweight AI chips and edge servers. In Q2 2026, edge AI chip startup StarEngine Tech completed an $800 million Series C round, with a valuation exceeding $6 billion.
• Energy Constraints Become a New Focus: AI data center power consumption is expected to account for 3.5% of global electricity usage by 2027. Infrastructure solutions such as liquid cooling and efficient power supply are attracting capital. On July 25, cooling technology company ColdCore Tech announced a $200 million investment from SoftBank Vision Fund.

Analysts point out that AI infrastructure has shifted from optional to essential, and its investment certainty is higher than that of model-layer startups. This trend is similar to the internet infrastructure investment boom in 2025, but the scale is expected to be more than three times larger.

2. Vertical Industry Applications: AI from General-Purpose to Specialized

If 2024-2025 was the arms race period for general large models, then 2026 is the explosion period for AI industry applications. CB Insights data shows that in Q2 2026, the global AI application layer financing accounted for 51%, exceeding the technology layer (model layer + infrastructure layer) for the first time. Among them, healthcare, finance, and manufacturing accounted for 65% of application investments.

Latest Cases: AI Manufacturing Investment Accelerates

• Smart manufacturing platform SmartWorks AI completed a $1 billion Series D round on July 20: Its AI system optimizes factory production scheduling in real time, reducing equipment downtime by 30%. It already serves leading manufacturers such as CATL and Foxconn. This round was led by Sequoia Capital China, with a valuation exceeding $8 billion.
• Financial risk control AI company SafeGuard AI secured $500 million in funding: Using graph neural networks to detect credit fraud, it achieved 99.7% accuracy and has been integrated into the risk control systems of China's four largest state-owned banks. Investors include Hillhouse Capital and JPMorgan Chase.
• AI-assisted drug screening company PharmaSmart listed on Nasdaq: Its stock surged 58% on the first day, with a market cap exceeding $12 billion. Its AI platform shortens the candidate drug screening cycle from four years to 1.5 years, and it has partnered with Pfizer and Roche.

Why Are Vertical Applications Becoming Investment Hotspots?

“The business loop for general large models has not yet been fully closed, but vertical industry applications have already demonstrated clear ROI,” said Li Wei, Chief Analyst at Yueya Finance. “Banks are willing to pay over $50 million in software fees for every 1% reduction in bad debt. Factories are willing to invest tens of millions of yuan for a 5% improvement in yield. In these scenarios, AI pricing power is stronger and customer stickiness is extremely high.”

3. AI Security: From After-Sales Support to Core Necessity

With large-scale deployment of AI systems in critical industries, AI security and governance have become a track that capital cannot ignore. In Q2 2026, global AI security financing reached $4.5 billion, up 210% year-on-year and 178% quarter-on-quarter. This track was relatively niche in 2025, but has now become the fastest-growing segment in the AI investment landscape.

Latest Developments: Triple Drivers Fuel Demand

• Regulatory Tightening: The EU AI Act took full effect in June 2026, requiring high-risk AI systems to pass compliance certification. The U.S. Congress is also accelerating the AI Liability Act, sharply increasing corporate compliance costs.
• Frequent Security Incidents: In H1 2026, at least 87 incidents of AI system attacks or misjudgments were reported globally, involving financial transactions, autonomous driving, and medical diagnostics. For example, in May, a bank's AI risk control system was hit by an adversarial attack, resulting in tens of millions of dollars in losses.
• Technological Breakthroughs: A new generation of AI security technologies, such as explainable AI, adversarial machine learning defenses, and model watermarking, have begun commercial deployment. On July 26, AI security startup ShieldCore Tech announced a $350 million Series B round. Its products detect large model hallucinations and block harmful outputs, and it has secured government orders.

“AI security is no longer a cost item; it is a revenue item,” noted a partner at Sequoia Capital in a recent interview. “Every enterprise needs an AI auditor and an AI firewall. This market is expected to reach the $100 billion scale within three years.”

4. Investment Strategy Outlook: How to Position in H2 2026?

In summary, the key words for AI investment in H2 2026 are “deployment” and “security.” For investors, the following directions are worth attention:

• Infrastructure level: Focus on niche technologies such as liquid cooling, high-efficiency power supplies, and optical interconnects, especially companies with visible order pipelines.
• Application level: Target AI transformation needs in healthcare, finance, and manufacturing, looking for platform companies that have signed long-term contracts with leading clients.
• Security level: Invest in companies with core capabilities in compliance testing, model defense, and data privacy protection. Regulatory dividends are expected to last at least 2-3 years.

Yueya Finance reminds investors: AI investment has entered a professional phase, and the era of simply buying into concepts is ending. Only by going deep into industries and understanding scenarios can one capture true alpha in the AI wave.

Conclusion

Artificial intelligence remains the most certain growth engine of the next decade, but the investment logic is shifting from focusing on technology to focusing on applications. The window in H2 2026 belongs to companies that can deeply integrate AI technology with industry pain points. Follow Yueya Finance for more cutting-edge insights and practical strategies on the AI industry.