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Forbes
2 days ago
- Business
- Forbes
20 Tech Experts On Emerging Hardware Trends Businesses Must Watch
While emerging technologies like artificial intelligence and blockchain have been capturing headlines, businesses must also keep an eye on evolving hardware trends. As companies pursue faster, smarter and more secure operations, hardware configuration is becoming a critical competitive differentiator. In an era shaped by AI adoption, rising end-user expectations and tightening privacy regulations, IT leaders are reevaluating not only what hardware their organizations need, but also where it should reside and how it should be deployed. Below, members of Forbes Technology Council share key hardware strategies designed to deliver the flexibility, security and cost efficiency modern enterprises require. 1. AI-Embedded Hardware Security At The Edge AI-embedded hardware security at the edge is becoming essential. By integrating intelligent processing directly into devices—servers, endpoints and storage—companies can achieve real-time, autonomous security; reduce latency; and protect privacy without cloud dependence. This hardware-native AI trend will be critical for secure, scalable operations in the near term. - Camellia Chan, Flexxon 2. Inference-Optimized Hardware We're seeing a shift toward inference-optimized hardware—systems designed specifically for running, not training, AI models. As model deployment scales, general-purpose GPUs waste energy and rack space. Purpose-built accelerators with high memory bandwidth utilization will be essential for cost-effective, real-time AI. - Thomas Sohmers, Positron AI Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify? 3. Disaggregated Infrastructure Disaggregated infrastructure is rising fast—separating compute, storage and memory lets companies scale AI workloads efficiently. Paired with smart NICs and GPUs, it's the backbone for low-latency, high-throughput architectures in tomorrow's data centers. - Sai Krishna Manohar Cheemakurthi, U.S. Bank 4. Field-Deployed Edge AI Accelerators Edge AI accelerators are gaining traction—particularly in the insurance industry, where devices that can analyze claims locally are deployed in field adjusters' kits. These devices slash cloud costs while preserving privacy. Key benefits include TOPS/watt efficiency, hardware-encrypted data pipelines, and precertification for IEC 62304 medical-grade reliability. The future is distributed intelligence. - Srinath Chandramohan, EY 5. Hybrid Cloud Infrastructure Hybrid cloud—combining locally hosted servers and public cloud providers—is a trending configuration. This strategy can help businesses reduce costs while maintaining the flexibility to scale when needed. - Anto Joseph, Eigen Labs 6. Edge-Enabled Safety Infrastructure As a public safety company, we're seeing increasing demand for edge-enabled safety infrastructure—devices like smart panic buttons, mobile gateways and compact edge processors that can locally process video, audio or wellness data before syncing with cloud-based platforms. This reduces latency, enables real-time decision-making in emergencies, and enhances privacy by limiting data exposure. - Kevin Mullins, SaferMobility 7. AI-Enabled Edge Computing A key trend is integrating edge computing with AI, enabling real-time data processing near the source. This reduces latency and bandwidth, which is crucial for IoT and smart systems. Advances in processors and AI chips facilitate local data analysis, enhancing decision-making and efficiency. Adopting this trend can help companies in data-driven industries cut costs, improve performance and stay competitive. - Gautam Nadkarni, Wipro 8. Localized Foundation Model Deployment Enabling hardware to run localized foundational models is key. Current foundation models and large language models require significant infrastructure and pose security risks due to generalization and centralized data processing. New approaches deploy personalized models on small devices like watches or phones, enabling secure local processing and paving the way for personalized AI assistants. - Abhijeet Mukkawar, Siemens Digital Industries Software 9. On-Site Edge Computing In Factories And Telecom Sites A growing trend is building edge computing setups equipped with GPUs or AI chips close to where data is generated. The advantages include reduced delays, because information is processed locally; bandwidth savings; and scalability. These reliable, flexible and modular hardware configurations allow factories and telecom sites to run AI-powered tasks on site, enabling faster responses, stronger data protection and more efficient workload management. - Maman Ibrahim, EugeneZonda Cyber Consulting Services 10. Data Center Layouts Built For AI Local compute is making a comeback. Everyone chased the cloud—until inference costs punched them in the face. We used to fight over RAM; now it's NVMe lanes, PCIe bandwidth and power delivery. Welcome to the AI hardware wars! But AI-native workloads demand rack design, not just chip choice. If your data center layout hasn't changed since 2015, you're not ready. - Mirror Tang, ZEROBASE 11. Energy-Optimized Hardware We're in the early stages of a shift toward energy-optimized hardware. Organizations are investing in renewable-powered data centers to meet ESG goals and reduce their carbon footprints. - Ohm Kundurthy, Santander Bank 12. Accelerated Compute AI Clusters I'm seeing accelerated compute AI clusters doing double duty for both training and inference. The push toward agentic and multimodal AI requires significant processing power to solve complex problems and advance AI autonomy. - Steven Carlini, Schneider Electric 13. Modular Hardware Setups One significant trend is the shift to modular hardware setups, such as servers and storage, which allow for scaling up or down as needed. This lets companies add power or space without a full rebuild, making it easier to keep up with changing needs and control costs. It's a flexible approach that's quickly becoming standard. - Ganesh Ariyur, Gainwell Technologies 14. Edge Computing With NPUs And Specialized Chips One essential hardware trend is AI-accelerated edge computing. By processing data closer to its source with neural processing units and specialized chips, companies reduce latency, improve privacy and enable real-time decision-making. As AI becomes core to operations, edge intelligence will be critical for speed, scalability and resilience. - Rishit Lakhani, Nile 15. Heterogeneous Hardware Compatibility Adopting heterogeneous hardware compatibility and mixed-hardware serving is essential. This enables the flexible use of diverse hardware types—GPUs, CPUs and ASICs—across generations and vendors, boosting capacity utilization and cutting costs. It supports scalable AI workloads by running models efficiently on mixed hardware fleets, increasing agility and sustainability. - Pooja Jain, Meta (Facebook) 16. Privacy-Driven Edge Computing A growing hardware trend is edge computing—processing data closer to the user instead of relying entirely on the cloud. It's becoming essential for real-time decision-making in privacy-sensitive environments. For example, in AdTech, edge setups enable brands to deliver faster, more compliant, personalized ads without sacrificing speed or data security. - Ivan Guzenko, SmartyAds Inc. 17. Hybrid CPU-GPU Architectures A clear short-term trend is the adoption of hybrid CPU-GPU architectures optimized for AI and data analytics workloads. It's important to understand that AI is no longer optional—it must be integrated into workflows. These architectures improve performance without requiring full infrastructure replacement, helping companies balance cost and efficiency. - David Barberá Costarrosa, Beeping Fulfilment 18. Chip-Level Security Integration A key hardware trend is the integration of security at the silicon level—such as trusted platform modules, secure enclaves and hardware-based authentication. With rising cyberthreats and remote workforces, companies must adopt hardware that enforces zero-trust principles from the chip up to protect sensitive data and systems. - Raj Jhaveri, Greenlane™ Infrastructure 19. On-Device NPUs One essential hardware trend is the adoption of neural processing units on personal devices. Newer PCs and devices come equipped with NPUs to handle AI workloads efficiently—on the device. As AI becomes integral to everyday workflows, devices without these chips risk falling behind in performance and capability. - Tarun Eldho Alias, Neem Inc. 20. Heterogeneous Compute One key trend is the move to heterogeneous compute—combining CPUs, GPUs and AI accelerators—to handle growing machine learning workloads. Traditional CPUs can't keep up with large models. Adopting specialized hardware like H100s, faster interconnects and memory-rich nodes is essential for faster training, cost efficiency and staying competitive in the AI era. - Karan Alang, Versa Networks Inc.
Forbes
09-04-2025
- Forbes
The Cybersecurity Wake-Up Call: Hardware And Trust Are Our Future
Camellia Chan is the CEO and Cofounder of Flexxon, a next-generation hardware cybersecurity solutions provider with a global presence. getty News broke in February 2025 that hackers chained three vulnerabilities in Palo Alto Networks' PAN-OS firewalls, turning a trusted security gatekeeper into an open door. Thousands of unpatched systems fell, exposing sensitive networks to root-level compromise. But before we go placing the blame on the company, it's important to recognize that this was not just a vendor slip-up. It was a flare illuminating a deeper truth: Software-centric security is crumbling under modern threats. To survive, we need to evolve and expand our approach. Strengthening community-rooted trust anchored in hardware—through innovations at the physical layer—must be our next step. The nightmare unfolded with CVE-2025-0108, CVE-2025-0111 and CVE-2024-9474—flaws in PAN-OS that let attackers bypass authentication, read files and escalate to root access via exposed management interfaces. Proof-of-concept code spread quickly, and exploits followed even faster, outpacing organizations' ability to patch. The impact? Firewalls meant to protect became backdoors. This highlights a harsh truth: Relying on software updates alone leaves us forever reacting—often too late. This recent attack isn't an outlier; it's a symptom. In 2020, the attack involving SolarWinds saw hackers lace a software update with malware, hitting 18,000 organizations. This major incident captured headlines and ignited massive downstream repercussions, reinforcing that vendor trust can be a single point of failure. Yet almost five years down the road, here we still are. The 2023 breach involving Barracuda went further. A zero-day attack forced hardware replacements, not just quick software patches, showing that when software fails you can't always patch your way out of the problem. In a 2021 incident, a critical vulnerability named Log4Shell was discovered in a Log4j library. Hackers could remotely run malicious code, and the damage spread like wildfire. These aren't isolated incidents—they reveal a pattern. Patching after the fact isn't enough. We need a foundation that stops breaches before they spread. A community root of trust flips the script. Trust isn't any single vendor's burden. It is a shared fortress built by hardware makers, software developers, researchers and users. Think collective threat intelligence spotting exploits early, or ecosystem-wide standards ensuring devices aren't weak links. Palo Alto's race to patch couldn't match hackers' speed, but a community model could have shrunk that window because shared accountability outpaces solo fixes. Contrast this with today's reality: isolated vendors, running alone, while attackers feast on the gaps. It's time to stop seeing cyber threats as somebody else's problem, it is ours. If software is the lock, hardware is the door. If the door's flimsy, no lock will hold back invaders. The most recent case proves it: Software fell like dominoes because the hardware beneath lacked intrinsic defenses. Hardware-based security is harder to crack remotely, and offering bedrock software cannot match. Imagine if those firewalls had integrity checks built into their silicon. Exploits might have hit a wall before root access was theirs. A more resilient approach must combine hardware-rooted protections that prevent software failures from escalating. Cryptographic roots of trust, embedded in hardware, verify system integrity from the moment a device boots up. Firmware-level security prevents unauthorized modifications, ensuring attackers can't manipulate the system undetected. Beyond these, AI-driven security at the memory level adds another layer of defense, autonomously detecting ransomware and unauthorized access in real time. Unlike software-based monitoring, these AI-enhanced solutions operate within the hardware itself—responding instantly without relying on external updates or human intervention. Establishing trust at the system level begins with ensuring that the foundation—both the hardware and firmware—remains uncompromised. The Trusted Platform Module (TPM) plays a key role here. As a security chip or firmware solution, TPM provides cryptographic verification, ensuring a device's boot process, encryption keys and firmware remain untampered. It acts as a safeguard, preventing compromised software from executing unchecked. However, TPM alone is not a silver bullet. It is part of a broader movement toward hardware-integrated security, working alongside newer advancements such as AI-powered storage security, secure enclaves and tamper-resistant firmware. The goal is to create multilayered trust mechanisms that harden security. This layered approach significantly reduces the attack surface and ensures systems are resilient against both remote and physical exploits. The Palo Alto breach demonstrates why this shift is necessary—software defenses alone are not enough. A strategic mix of TPM, AI-driven security and other hardware protections represents the path forward, ensuring security isn't an afterthought but a built-in standard. So how do we begin fixing this? Community-Driven Trust: Vendors, researchers and users must collaborate. Think of shared threat databases or a 'trust certification' for devices meeting hardware/software benchmarks. Hardware Mandate: Critical devices such as firewalls, routers and servers need security baked in. TPM should be standard, verifying integrity from boot to runtime. AI Hardware Integration: Push for smart AI-embedded security technology at the storage level, paired with TPM for systemwide resilience. A community could standardize this combo, ensuring no layer is left exposed. User Action: Enterprises should restrict management interfaces (as Palo Alto urged), but also demand TPM-enabled devices and verify it pre-deployment. Policy Push: Governments could incentivize hardware security through tax breaks for TPM adoption and penalties for repeat breaches to make resilience a mandate not a cost comparison. Today, we stand on a mountain of major cyber incidents. The recent cybersecurity breaches—they are not just warnings; they're a blueprint for failure or success. Software's fragility demands a community-rooted trust model, reinforced by hardware security. This isn't a solo sprint by any one vendor—it's a collective stand to ensure trust spans the entire ecosystem, not just a single patch. IT pros, policymakers and vendors must act now and build the fortress, making it silicon-strong and community-wide, before the next exploit chain strikes. The future's not secure until we make it so. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
