Meta will deploy Nvidia GPUs, CPUs, and networking equipment at scale, embedding Nvidia more deeply across its AI stack even as Meta develops in-house chips.

Meta Platforms is significantly expanding its reliance on Nvidia through what Nvidia described as a “multigenerational” agreement to power Meta’s next wave of artificial intelligence infrastructure.

The deal will see Meta construct data centers running on millions of Nvidia’s current and next-generation chips for both AI training and inference. The scope goes beyond graphics processing units (GPUs) to include central processing units (CPUs), networking hardware, and confidential computing technologies, signaling a deeper integration of Nvidia across Meta’s AI architecture.

From GPUs to Full-Stack AI Infrastructure

Nvidia’s dominance in AI has largely been built on its GPUs, which are optimized for the parallel computing demands of training large language models and generative AI systems. The new agreement reinforces that position, but its significance lies in how it broadens Nvidia’s role.

Meta will also deploy Nvidia CPUs, including its forthcoming Vera architecture, beyond the current Grace model. CPUs, traditionally dominated by Intel and Advanced Micro Devices, handle general-purpose computing tasks and coordinate workloads alongside GPUs inside data centers.

As AI workloads evolve from training toward inference — where models respond to user queries at scale — CPUs become increasingly important. Inference often demands lower latency and improved energy efficiency, areas where CPUs can complement GPUs effectively.

Rob Enderle of Enderle Group noted that CPUs “tend to be cheaper and a bit more power-efficient for inference,” reflecting this shift in workload balance.

By supplying GPUs, CPUs, and networking components, Nvidia is positioning itself not merely as an accelerator vendor but as a vertically integrated AI infrastructure provider. This approach increases switching costs and deepens vendor lock-in, particularly as Meta scales its data center footprint globally.

Meta’s expanded commitment to Nvidia comes even as the company pursues multiple supply strategies. The social networking giant has been developing in-house AI accelerators and has collaborated with AMD. Reports have also indicated that Meta explored the possibility of using Tensor Processing Units (TPUs) developed by Google.

Patrick Moorhead of Moor Insights & Strategy said the Nvidia deal could cool speculation around TPU adoption, though he noted that large technology firms frequently evaluate several vendors simultaneously to maintain pricing leverage and supply resilience.

The broader AI chip industry is becoming increasingly contested. While Nvidia leads in high-performance AI chips, competitors including AMD and Broadcom are investing heavily to capture market share. Google continues to expand internal TPU deployment within its own cloud ecosystem.

Yet demand for AI infrastructure remains so elevated that analysts do not expect immediate revenue contraction for Nvidia’s rivals. Hyperscale companies are collectively investing hundreds of billions of dollars in AI-related capital expenditures, creating sufficient demand to sustain multiple suppliers in the near term.

Meta’s decision to source both GPUs and CPUs from Nvidia may also mark operational pragmatism. Analysts describe a “one-throat-to-choke” procurement model in which consolidating suppliers can simplify integration, reduce interoperability risk, and streamline accountability in the event of system failures.

The agreement underscores the intensifying race among hyperscalers to secure long-term access to advanced AI silicon. Chip supply constraints have been a recurring concern as generative AI adoption accelerates, and securing multigenerational commitments provides Meta with greater visibility into capacity planning.

Beyond hardware, Meta will integrate Nvidia’s networking equipment and confidential computing technology into its data centers, including support for AI features within WhatsApp. Confidential computing enhances data security by protecting sensitive information during processing — a growing priority as AI features expand into messaging platforms and enterprise applications.

The deal reinforces Nvidia’s position as the foundational layer of AI infrastructure. The company strengthens its ecosystem moat and extends its relevance beyond pure training workloads by embedding its processors and networking systems across Meta’s stack.

The move is believed to represent a balancing act for Meta: deepen ties with the market leader to ensure performance and supply continuity, while continuing to invest in proprietary silicon and alternative partnerships to preserve strategic flexibility.

Overall, infrastructure decisions are becoming long-duration bets as AI shifts from experimentation to scaled deployment. This multigenerational alignment signals that both companies view the AI cycle not as a short-term surge, but as a structural transformation of computing that will require sustained capital investment, architectural integration, and supplier alignment over many years.

While much of the technology sector has faced sharp selloffs in early 2026 over fears that generative AI tools could disrupt traditional software, data services, and knowledge-based industries, cybersecurity stands out as one corner of the market where investors can potentially find shelter from AI disruption while still benefiting from the technology’s widespread adoption, according to a Wednesday research note from Wedbush Securities.

Led by veteran tech analyst Dan Ives—a longstanding and vocal AI bull—Wedbush argues that AI will act as a powerful tailwind for the cybersecurity industry over the coming years. The rapid proliferation of AI deployments is expected to dramatically increase attack frequency, success rates, and blast radius, making protection of data, endpoints, and use cases mission-critical.

“AI will be a major tailwind to the cyber security sector over the coming years as protection of use cases, data, and end points expand markedly,” the analysts wrote. “As attack frequency, success rates, and blast radius rise with AI deployments, cybersecurity becomes even more mission-critical risk management, reinforcing budget resilience in this new AI driven IT budget world.”

Gartner projects global spending on AI cybersecurity to reach $51 billion in 2026, roughly doubling from the prior year, underscoring the sector’s rapid growth trajectory. Cybersecurity remains one of the fastest-growing categories of enterprise IT spending, with budgets proving resilient even in periods of broader tech spending caution.

Why Cybersecurity Is Seen as AI-Resilient

Wedbush and other analysts view cybersecurity as a “double-edged sword” beneficiary of AI:

• Defensive upside — AI enables faster threat detection, automated response, behavioral analytics, and predictive intelligence, strengthening defenders’ capabilities.
• Offensive threat amplification — AI empowers attackers with more sophisticated phishing, automated vulnerability scanning, polymorphic malware, deepfakes for social engineering, and faster exploit development—driving demand for advanced protection.

Unlike many software categories facing potential disintermediation or pricing pressure from AI agents and automation, cybersecurity demand is expected to rise as organizations seek to secure AI systems themselves, protect expanding attack surfaces (cloud, edge, IoT, data pipelines), and comply with stricter regulations around AI governance and data privacy.

Wedbush identified three stocks as best-positioned to capitalize on this dynamic:

1. CrowdStrike — “We believe that CRWD’s position as the gold standard of cybersecurity remains firmly unchanged in the face of this software sell-off with the company’s innovative, best-in-class Falcon platform becoming increasingly effective in the modern threat landscape as AI adversaries become an incrementally larger threat for enterprises heading down the AI path.”
2. Palo Alto Networks — “AI is not displacing PANW’s value proposition, but has actually made it more relevant, not less, as it is forcing customers to consolidate vendors, improve visibility, and automate response as threats become more adaptive and effective.”
3. Zscaler — Highlighted for its cloud-native zero-trust architecture, which aligns well with securing distributed AI workloads, remote users, and cloud-based AI infrastructure.

The cybersecurity sector has shown relative resilience amid the broader 2026 tech selloff. While software stocks broadly declined on fears of AI-driven disruption, cybersecurity names have held up better, supported by sustained enterprise budget priority and visible secular growth drivers. CrowdStrike, Palo Alto Networks, and Zscaler have each outperformed the broader Nasdaq and software indices year-to-date, with CrowdStrike particularly benefiting from its Falcon platform’s reputation as a leading endpoint detection and response (EDR) solution in an era of increasingly sophisticated threats.

The sector’s resilience is further bolstered by regulatory tailwinds—stricter data privacy laws (GDPR, CCPA expansions), SEC cybersecurity disclosure rules, and emerging AI-specific governance frameworks—that force companies to increase security spending regardless of broader IT budget cycles.

However, there are still risks while Wedbush is bullish. A prolonged economic slowdown could pressure IT budgets, even in security. Competitive intensity is high, with legacy players (Fortinet, Check Point), cloud-native vendors (Cloudflare, Okta), and emerging startups all vying for share. Rapid AI advancement could also empower attackers faster than defenders in some scenarios, requiring continuous platform evolution.

However, the consensus view among analysts is that cybersecurity remains one of the most structurally advantaged sectors in the AI era. Demand is driven not by discretionary IT projects but by necessity—protecting assets, complying with regulations, and mitigating existential risks from AI-augmented threats.

As AI adoption accelerates across enterprises, cybersecurity spending is widely expected to remain resilient and likely accelerate. Wedbush’s note reinforces the emerging narrative: while AI may disrupt certain software categories, it is simultaneously creating a massive, secular growth driver for the companies tasked with securing the AI-powered world.

In the orchestra of modern commerce, the supply chain serves as the conductor’s baton, setting the rhythm, directing the flow, and ensuring harmony across the enterprise. Yet in many organizations, that baton has become weighed down by inefficiencies: warehouses filled with slow-moving stock, lead times stretched endlessly, and coordination across partners that struggles to keep pace with demand.

A lean supply chain represents a deliberate redesign. It is the discipline of eliminating waste, synchronizing processes, and delivering value with precision. Drawing inspiration from Toyota’s philosophy of lean production, it is about achieving more with less (less time, less inventory, and lower cost) while ensuring that the customer consistently receives greater value.

But lean is not simply about trimming excess; it is about building operational strength. Organizations that embrace it optimize sourcing, digitize logistics, and use data visibility to anticipate demand. They move from reactive replenishment to proactive planning, aligning resources closely with actual consumption patterns.

In Africa, where logistics constraints remain a major barrier to competitiveness, lean supply chain models offer a pathway to reduce the inefficiencies that inflate prices and erode margins. Properly implemented, they can transform fragmented ecosystems into coordinated networks where goods move predictably, efficiently, and at scale.

Ultimately, lean supply chain is both a strategy and a mindset. It calls on firms to see partners not as entities to be pressured, but as collaborators within a shared value system. When businesses commit to lean principles, they unlock capital, respond faster to markets, and build resilience against shocks. The reward is speed, adaptability, and enduring competitiveness.

From NATO today, we will learn from a Zen-master of lean at Tekedia Mini-MBA.

Thur, Feb 19 | 7pm-8pm WAT | Lean Supply Chain Applications in Business – Chibueze Noshiri, NATO Luxembourg  | Zoom link