Has the U.S. Semiconductors Industry valuation changed over the past few years?

Investors are pessimistic on the American Semiconductors industry, indicating that they anticipate long term growth rates will be lower than they have historically. The industry is trading at a PE ratio of 28.8x which is lower than its 3-year average PE of 59.3x. The 3-year average PS ratio of 12.1x is higher than the industry's current PS ratio of 9.5x.

How does U.S. Semiconductors compare with similar industries?

There are no additional sub-industries under this industry.

U.S. Semiconductors Industry Analysis

Date	Market Cap	Revenue	Earnings	PE	Absolute PE	PS
Sat, 09 May 2026	US$5.7t	US$606.0b	US$198.8b	59.4x	28.8x	9.5x
Mon, 06 Apr 2026	US$7.9t	US$599.7b	US$194.7b	35.4x	40.8x	13.3x
Wed, 04 Mar 2026	US$8.1t	US$579.4b	US$180.5b	39x	44.9x	14x
Fri, 30 Jan 2026	US$8.5t	US$544.9b	US$157.0b	41.2x	54.3x	15.7x
Sun, 28 Dec 2025	US$8.2t	US$544.9b	US$157.4b	36.4x	52.3x	15.1x
Tue, 25 Nov 2025	US$7.7t	US$534.4b	US$146.7b	34.1x	52.4x	14.4x
Thu, 23 Oct 2025	US$7.9t	US$508.6b	US$121.0b	41.2x	65.6x	15.6x
Sat, 20 Sep 2025	US$7.6t	US$504.4b	US$118.3b	39.9x	64.3x	15.1x
Mon, 18 Aug 2025	US$7.4t	US$483.1b	US$102.2b	33.4x	72.5x	15.3x
Wed, 16 Jul 2025	US$6.8t	US$479.4b	US$102.8b	31.4x	66.3x	14.2x
Fri, 13 Jun 2025	US$6.1t	US$476.9b	US$101.3b	29.6x	60.2x	12.8x
Sun, 11 May 2025	US$5.1t	US$454.5b	US$93.9b	21.7x	53.8x	11.1x
Tue, 08 Apr 2025	US$4.1t	US$452.5b	US$94.7b	17.1x	43.1x	9x
Thu, 06 Mar 2025	US$5.0t	US$448.1b	US$89.4b	27.1x	56.4x	11.2x
Sat, 01 Feb 2025	US$5.5t	US$428.1b	US$80.0b	30.4x	68.5x	12.8x
Mon, 30 Dec 2024	US$5.9t	US$430.3b	US$82.1b	31.4x	71.9x	13.7x
Wed, 27 Nov 2024	US$5.6t	US$421.8b	US$78.8b	32.6x	70.9x	13.2x
Fri, 25 Oct 2024	US$5.7t	US$406.3b	US$85.7b	30.4x	67x	14.1x
Sun, 22 Sep 2024	US$5.1t	US$403.0b	US$83.9b	27.9x	61.2x	12.7x
Tue, 20 Aug 2024	US$5.3t	US$384.7b	US$79.0b	31.8x	67.3x	13.8x
Thu, 18 Jul 2024	US$5.5t	US$385.2b	US$82.8b	28.8x	65.9x	14.2x
Sat, 15 Jun 2024	US$5.7t	US$382.2b	US$80.6b	30x	70.3x	14.8x
Mon, 13 May 2024	US$4.3t	US$362.1b	US$69.6b	28.9x	62.3x	12x
Wed, 10 Apr 2024	US$4.3t	US$363.1b	US$67.4b	26.5x	64.2x	11.9x
Fri, 08 Mar 2024	US$4.6t	US$358.7b	US$66.9b	29x	69x	12.9x
Sun, 04 Feb 2024	US$3.6t	US$344.6b	US$57.0b	30.7x	63.6x	10.5x
Tue, 02 Jan 2024	US$3.2t	US$343.3b	US$53.6b	30.8x	59.5x	9.3x
Thu, 30 Nov 2023	US$2.8t	US$342.4b	US$54.7b	27x	51.8x	8.3x
Sat, 28 Oct 2023	US$2.4t	US$331.9b	US$47.5b	23.1x	51.2x	7.3x
Mon, 25 Sep 2023	US$2.5t	US$336.9b	US$51.7b	25.2x	48.6x	7.5x
Wed, 23 Aug 2023	US$2.6t	US$327.2b	US$46.0b	26x	56.6x	8x
Fri, 21 Jul 2023	US$2.7t	US$333.1b	US$47.2b	25.1x	57x	8.1x
Sun, 18 Jun 2023	US$2.6t	US$338.2b	US$51.8b	26.2x	50.7x	7.8x
Tue, 16 May 2023	US$2.0t	US$338.3b	US$50.4b	21.2x	40.6x	6x


US Market	2.14%
Tech	6.51%
Semiconductors	10.49%
Semiconductors	10.46%
Semiconductor Equipment	10.79%

Company	Last Price	7D	1Y	Valuation
NVDA NVIDIA	US$211.50	6.0% +US$289.8b	80.2%	PE42.8x
MU Micron Technology	US$646.47	25.0% +US$145.8b	659.2%	PE30.2x
AMD Advanced Micro Devices	US$408.46	15.2% +US$88.1b	301.6%	PE135x
INTC Intel	US$109.62	16.0% +US$76.1b	422.0%	PS10.2x
QCOM QUALCOMM	US$202.57	12.8% +US$24.2b	39.6%	PE21.5x

Announcement • May 07

Broadcom Inc Announces Vmware Cloud Foundation 9.1

Broadcom Inc. had announced VMware Cloud Foundation (VCF) 9.1, a secure and cost-effective infrastructure platform for production AI workloads. VCF 9.1 delivers an AI and Kubernetes native private cloud platform with integrated security and mixed compute infrastructure support across AMD, Intel, and NVIDIA. This enables enterprises to deploy inference and agentic AI applications with significantly lower costs, enhanced security, and freedom to choose best-of-breed GPU and CPU hardware. A preview of Broadcom’s Private Cloud Outlook 2026 report reveals private cloud continues to be the preferred platform for production AI. More than half of organizations surveyed (56%) are running or planning to run production inferencing in a private cloud. More importantly, public cloud use for production inference was 41%, down 15% year over year. Additionally, 62% of IT leaders reported being very or extremely concerned about generative AI infrastructure costs while 36% report AI is driving new requirements for data protection, privacy, security controls and risk management. VMware Cloud Foundation provides a better alternative to public cloud for production workloads through intelligent software that maximizes infrastructure efficiency on existing servers while providing architectural control and regulatory compliance capabilities essential for production AI deployments. VMware Cloud Foundation 9.1 will enable enterprises to deploy production workloads including inference and agentic AI with: Up to 40% reduction in server costs through intelligent memory tiering for clusters running a mix of AI and non-AI workloads; Up to 39% lower storage TCO through enhanced compression and deduplication for AI data pipelines; Up to 46% reduction in Kubernetes operational costs for running AI workloads at scale; 4x faster cluster upgrades and 2x increased fleet capacity to rapidly scale AI infrastructure. VCF 9.1 maximizes density for both VM and containerized AI workloads on existing infrastructure while dramatically reducing operational complexity. Through intelligent resource management and automated operations, enterprises can deploy more production workloads on current servers, scale efficiently across distributed environments, and eliminate the need for costly infrastructure expansion during a period of hardware shortage and rising costs. Key capabilities include: Intelligent resource optimization that maximizes infrastructure utilization through advanced memory tiering and next-generation storage compression for AI data pipelines, enabling higher AI workload density without performance compromises or expensive hardware refresh. Automated fleet operations at scale that deliver doubled management capacity to 5,000 hosts and 4x faster cluster upgrades across distributed and air-gapped environments, eliminating manual patching overhead while supporting rapid AI infrastructure expansion. Multi-tenant infrastructure for AI isolation that enables enterprises and service providers to run multiple AI projects and customers on shared infrastructure with strict security boundaries, maximizing utilization of expensive GPU and CPU resources while supporting data sovereignty for sensitive models. Open ecosystem integration that delivers multi-accelerator GPU choice across AMD and NVIDIA, support for leading AMD and Intel CPU platforms, and standards-based EVPN and VXLAN interoperability with Arista Universal Cloud Network, demonstrating VCF's commitment to providing the high-performance connectivity and compute flexibility production AI demands. High speed networking for AI workloads through VCF support for NVIDIA ConnectX-7 NICs and NVIDIA BlueField-3 with Enhanced DirectPath I/O. With this enhancement high-speed, multi-host AI model training and data transfer, crucial for demanding Gen AI workloads is enabled. Virtualized load balancing and security with VMware Avi Load Balancer and VMware vDefend eliminate hardware appliance requirements for AI inference endpoints and agentic applications, reducing capital expense while providing enterprise-grade resilience and automated lifecycle management. VCF 9.1 delivers a unified platform that accelerates AI application deployment by running inference workloads, agentic applications, containerized services, and traditional VMs on a single infrastructure layer. This eliminates operational fragmentation and the cost of managing separate stacks while providing the developer velocity and platform governance that production AI requires. Key capabilities include: Kubernetes scale and performance for AI that delivers 2.6x increased cluster scale, 70% faster deployments, 75% shorter upgrade windows compared to preview versions, and seamless scaling that enables zero downtime for production AI services. Mixed compute management that efficiently handles both CPU-intensive agentic AI workflows and GPU-accelerated inference on a unified platform, addressing the reality that agentic workloads demand significantly more CPU than GPU capacity for workflow execution and decision orchestration. AI observability and governance that provides detailed metrics for time to first token, token throughput, and GPU utilization across multiple accelerator types, enabling enterprises to maximize infrastructure ROI through precise hardware utilization monitoring while centralized policy injection and data sovereignty controls enable AI compliance enforcement and secure model access. Live application stack blueprints that capture multi-VM applications as reusable templates for rapid environment deployment, eliminating manual configuration errors and preventing configuration drift across development, test, and production environments while accelerating infrastructure delivery velocity. VCF 9.1 integrates security at the infrastructure layer to protect AI workloads, proprietary models, and training data from hypervisor to application. By delivering zero-trust segmentation, sovereign recovery, and continuous patching without bolt-on tools, VCF strengthens the security posture essential for production AI deployments that public cloud environments cannot match. Key capabilities include: On-premises ransomware recovery that provides isolated recovery environments and integrated validation tools including new CrowdStrike Falcon Endpoint Security support protect AI models and training data – significant intellectual property – from cross-border movement while avoiding massive bandwidth fees during crisis restoration. Continuous compliance enforcement that maintains regulatory adherence through centralized monitoring and automated desired state remediation for workloads and VCF stack components, enabling enterprises to demonstrate audit readiness for production AI deployments without manual overhead or separate compliance tools.

U.S. Semiconductors Industry Analysis

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