Surging memory chip prices dim outlook for consumer electronics

The artificial intelligence boom has triggered a crisis that extends far beyond the server farms powering the latest language models and image generators. As tech giants funnel unprecedented resources into AI infrastructure, the global memory chip supply has become the unlikely bottleneck constraining not just cutting-edge technology, but everyday consumer electronics. The situation has reached a critical inflection point. Data centers are now expected to consume 70 percent of all high-end DRAM production in 2026, leaving consumer device manufacturers scrambling for scraps of memory components they once took for granted. Memory prices have already surged approximately 50 percent in the final quarter of 2025, with further escalation projected. Industry analysts anticipate another 70 percent price increase throughout 2026 as the supply squeeze persists, fundamentally reshaping the economics of consumer electronics. The magnitude of this reallocation cannot be overstated. Major memory manufacturers have made a strategic calculation: abandoning or drastically reducing production of legacy memory chips used in smartphones, automobiles, televisions, and consumer devices to prioritize the higher-margin memory solutions demanded by AI data centers. This represents what industry observers are calling a permanent reallocation of global manufacturing capacity, a once-in-a-generation shift in how semiconductor production is allocated. For smartphone manufacturers, the implications are immediate and severe. Global smartphone sales are expected to decline at least 2 percent in 2026, marking the first annual shipment decline since 2023. This represents a dramatic reversal from growth forecasts issued just months earlier. Research firms IDC and Counterpoint both revised their projections downward after reassessing the severity of the shortage and its impact on device pricing. The personal computer market faces even steeper headwinds. After delivering 8.1 percent growth in 2025, the PC market is projected to contract at least 4.9 percent in 2026. The timing creates a particularly painful convergence of pressures: the Windows 10 end-of-life refresh cycle that would normally drive PC upgrades is colliding head-on with memory costs that make such upgrades financially prohibitive for both manufacturers and consumers. Major vendors including Lenovo, Dell, HP, Acer, and ASUS have already signaled price increases ranging from 15 to 20 percent as cost pressures intensify. Gaming consoles are experiencing comparable strain. Console sales are projected to fall 4.4 percent in 2026 after estimated growth of 5.8 percent in 2025. The memory constraints are forcing manufacturers to reconsider the specifications they can include in devices, potentially downmixing RAM capacity at the exact moment when content and applications demand increasing memory resources. The pricing mechanics of this crisis reveal a troubling reality for manufacturers. Some have already absorbed cost increases themselves, sacrificing margins to maintain competitive pricing. However, the scale of the shortage makes widespread absorption impossible. Industry executives face an unpalatable choice: pass price increases to consumers and risk demand destruction, or absorb costs and accept margin compression that threatens profitability. Semiconductor distributor Fusion Worldwide reported 1,000 percent price inflation in some memory products over recent quarters, with pricing continuing to escalate. Counterpoint Research estimates memory prices will jump between 40 and 50 percent in the first quarter alone, compounding the pressures from the previous quarter's 50 percent surge. Throughout 2025, DRAM prices rose 172 percent, prompting Samsung to halt new orders for DDR5 modules to reassess pricing structures and Micron to exit its consumer-focused Crucial brand entirely. The ripple effects extend across industries that most consumers rarely associate with memory chip constraints. Automotive manufacturers face particular vulnerability, with the shortage creating production delays reminiscent of COVID-era supply chain disruptions. Electronics retailers like Best Buy, already dealing with tariff-driven price pressures, now confront a second wave of cost increases that could further discourage consumer purchases. Even mundane household products face disruption. Televisions, Bluetooth speakers, set-top boxes, and smart appliances all rely on memory chips. With manufacturing capacity diverted toward AI infrastructure, these products face either severe shortages or dramatically higher prices. The economics become particularly challenging for lower-margin consumer goods where memory represented a modest percentage of production costs but now threatens to consume an outsized portion of retail pricing. Analysts project that memory could constitute as much as 10 percent of the price of most electronics and 30 percent of smartphone manufacturing costs. For low- and mid-range devices, the impact is most pronounced, as thin margins leave manufacturers unable to absorb large component cost increases without rendering products uncompetitive or unprofitable. The technical implications of this shortage extend beyond simple price escalation. Device manufacturers are reconsidering product roadmaps and specification strategies. Rather than increasing memory capacity to match software demands, manufacturers may actually downmix RAM in new systems. This occurs at precisely the wrong moment, as applications and operating systems increasingly require more memory resources. Windows 11 adoption and modern mobile applications both demand substantially more memory than their predecessors. The shortage also disrupts the natural upgrade cycle that typically drives consumer electronics replacement. Supply growth for DRAM and NAND is expected to remain below historical norms at 16 and 17 percent year-on-year respectively, insufficient to meet demand or provide relief from pricing pressures. Industry observers describe the current situation as representing fundamental supply-demand imbalance rather than temporary market volatility. This crisis originated in a structural shift: the rapid buildout of AI infrastructure by U.S. technology companies including OpenAI, Google, and Microsoft has absorbed the majority of global memory chip supply. Manufacturers have prioritized components for higher-margin data center applications over consumer devices, a rational economic decision that carries profound consequences for the broader technology industry. The situation reveals a critical vulnerability in global semiconductor manufacturing. Capacity expansion takes years to materialize, and manufacturing contracts for 2028 are already being sold out. This means relief from current shortages remains years away, and any expansion in consumer-focused memory production depends on whether manufacturers believe demand will justify the capital investment. Whether this represents an unfortunate growing pain of the AI revolution or a systemic failure of capacity planning remains contested. What is certain is that the semiconductor industry faces a choice: develop smarter capacity allocation strategies that prevent future bottlenecks, or accept a world where the infrastructure supporting artificial intelligence advancement comes at the cost of declining innovation and functionality in consumer technology.

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Quadric rides the shift from cloud AI to on device inference — and it's paying off

Quadric Rides the Shift From Cloud AI to On-Device Inference—And It's Paying Off The artificial intelligence industry is experiencing a fundamental realignment. After years of betting heavily on centralized cloud infrastructure and massive data centers, companies and governments are increasingly recognizing the limitations of that approach. High latency, bandwidth constraints, privacy concerns, and mounting infrastructure costs are pushing a critical inflection point: moving AI inference away from the cloud and onto devices themselves. Quadric, a San Francisco-based chip-IP startup founded by veterans of early bitcoin mining firm 21E6, is positioned squarely at the center of this shift. The company doesn't manufacture chips itself. Instead, it licenses programmable AI processor intellectual property—essentially blueprints that customers can embed into their own silicon—along with a complete software stack and toolchain to run AI models locally on devices ranging from automotive systems to laptops to industrial equipment. The market's response has been striking. Quadric posted between 15 million and 20 million dollars in licensing revenue in 2025, more than tripling from approximately 4 million dollars in 2024. The company is now targeting up to 35 million dollars in revenue this year, with CEO Veerbhan Kheterpal characterizing the expansion as driven by a "sharp business inflection" over the past eighteen months as enterprises increasingly seek to run AI locally rather than depend on cloud services. The timing matters. The proliferation of transformer-based models in 2023 fundamentally changed the economics of AI deployment. Unlike earlier vision-focused models that worked reasonably well in cloud environments, today's large language models and advanced vision systems create new imperatives: latency-sensitive applications, privacy-conscious organizations, and countries seeking to build sovereign AI capabilities independent of U.S.-based infrastructure. Quadric's core innovation addresses a persistent challenge in semiconductor design: the speed gap between hardware and software. AI models evolve rapidly, with architectures shifting from month to month. But chip design cycles stretch years. This mismatch creates a fundamental problem for traditional chip vendors. Qualcomm, for instance, typically integrates AI technology inside its own processors, which can lock customers into proprietary silicon. Other IP suppliers like Synopsys and Cadence sell neural processing engine blocks that many customers find difficult to program and update. Quadric's solution centers on programmability. The company's Chimera general-purpose NPU architecture provides what the company describes as "fully programmable" on-device AI processing. Rather than relying on fixed-function hardware that becomes obsolete when model architectures shift, customers can update their AI capabilities through software changes. This approach allows customers to support new AI models without redesigning hardware—a critical advantage in an industry where model architectures change faster than silicon can be manufactured. The technical specifications underscore the company's performance ambitions. The Chimera processor delivers up to 840 TOPS (tera operations per second) in automotive-grade safety-enhanced versions, optimized for running both vision and language model inference locally. This is not theoretical capability. Quadric demonstrated edge LLM functionality at the 2025 Embedded Vision Summit, showcasing real-time inference with minimal power consumption—a crucial metric in markets where performance-per-watt often determines adoption. Perhaps more telling is what customers can achieve practically. According to Quadric's technical specifications, chip designers can move from initial engagement to production-ready LLM-capable silicon in under six months. The company's toolchain was built from the ground up specifically for this purpose, not retrofitted or bolted onto existing infrastructure. This allows customers to deploy computer vision and on-device LLM applications, including models up to 30 billion parameters, with industry-leading efficiency metrics. The customer roster demonstrates traction across multiple sectors. Kyocera has adopted the Chimera NPU for next-generation office automation. Denso, Japan's major auto supplier that builds chips for Toyota vehicles, is among Quadric's automotive customers. Tier IV licensed Quadric's IP for its autonomous systems, with the company's SDK now selected for AI processing evaluation and optimization supporting Autoware deployment in next-generation autonomous vehicles. These represent not merely licensing agreements but active design-wins with tier-one suppliers. The broader market context explains the urgency behind this shift. The World Economic Forum has highlighted this transition, noting how AI inference is moving closer to users and away from purely centralized architectures. EY reported in November that sovereign AI strategies have gained traction as policymakers and industry groups push for domestic AI capabilities spanning compute, models, and data infrastructure, rather than relying entirely on foreign systems. This geopolitical dimension opens new markets for Quadric. The company is exploring customers in India and Malaysia, with Moglix CEO Rahul Garg serving as a strategic investor helping shape its India-focused sovereign AI approach. Kheterpal explicitly acknowledged that the push toward on-device inference is being driven partly by rising costs of centralized infrastructure and the difficulty many countries face in building hyperscale data centers. Distributed AI setups—where inference runs on laptops or small on-premise servers inside offices rather than relying on cloud-based services for every query—represent a more realistic path for many organizations. The market opportunity aligns with these trends. According to industry reports, the on-device AI market is expected to surpass 50 billion dollars by 2030, driven by LLMs in edge servers and AIoT devices. Quadric's focus on these high-growth verticals positions it to capture significant share if its technology delivers on its promises. The company's capital efficiency also warrants attention. The recent 30 million dollar Series C funding round, while significant, remains a fraction of what chip manufacturing competitors spend on research and development. By focusing on IP licensing rather than chip manufacturing, Quadric avoids the capital intensity of foundry partnerships, allowing faster scaling and better margins. The company now employs nearly 70 people worldwide, including about 40 in the United States and around 10 in India. Yet Quadric remains relatively early in its buildout. The company has signed a handful of customers so far, and much of its longer-term upside depends on converting today's licensing deals into high-volume shipments and recurring royalties. The first products based on Quadric's technology are expected to ship this year, beginning with laptops. Success will ultimately be measured not by licensed IP but by actual silicon hitting markets and proving the efficiency and performance claims in production environments. The broader implication is clear: the era of cloud-centric AI infrastructure is giving way to a more distributed architecture. For companies like Quadric positioned at this inflection point with the right technical approach and market timing, the opportunity is substantial. The question now is execution—whether Quadric can maintain its momentum and translate design wins into the volume shipments that define success in semiconductor markets.

Quadric rides the shift from cloud AI to on device inference and its paying off

Quadric Rides the Shift From Cloud AI to On-Device Inference and Its Paying Off The artificial intelligence industry is experiencing a fundamental realignment. For years, cloud computing dominated AI deployment, with massive data centers processing queries from millions of users. But that model is beginning to crack under its own weight. Rising infrastructure costs, latency concerns, data privacy demands, and geopolitical pressures are pushing companies and governments to move AI workloads from centralized clouds onto devices themselves. Quadric, a relatively under-the-radar chip-IP startup, is positioning itself as a crucial infrastructure player in this shift, and the early financial results suggest the bet is paying off. The San Francisco-based company just announced a $30 million Series C funding round led by ACCELERATE Fund, managed by BEENEXT Capital Management, bringing its total capital raised to $72 million. But the more telling number is the revenue trajectory. Quadric posted between $15 million and $20 million in licensing revenue in 2025, up from around $4 million in 2024, according to CEO Veerbhan Kheterpal. That represents a tripling of business in a single year. The company is now targeting up to $35 million in 2026 as it builds what it describes as a royalty-driven on-device AI business. These aren't vanity metrics. They reflect genuine market momentum in one of the most important infrastructure transitions happening in tech right now. Understanding Quadric's opportunity requires understanding what the company actually does. Quadric doesn't manufacture chips. Instead, it licenses what Kheterpal calls a "blueprint"—programmable AI processor IP that customers can embed into their own silicon. The company provides the architectural design, software stack, and toolchain necessary to run AI models, including vision and voice applications, directly on devices rather than routing them through the cloud. This approach addresses a critical problem facing the semiconductor industry. AI models are evolving at a pace that outstrips traditional chip design cycles. A new chip architecture might take years to move from conception to production. Meanwhile, AI models shift from vision-focused systems to transformer-based language models to whatever comes next in a matter of months. This creates a fundamental mismatch: by the time a fixed-function chip ships, the models it was optimized for may already be obsolete. Quadric's Chimera processor IP solves this through programmability. Unlike traditional neural processing units that lock customers into specific model architectures, Chimera runs any AI model—current or future—on a single unified architecture. Customers can update the software stack when models change without requiring costly hardware redesigns. According to the company, customers can move from initial engagement to production-ready silicon capable of running language models up to 30 billion parameters in under six months. The timing of this technology is crucial. Cloud AI infrastructure has hit scalability and cost walls. Data center power consumption is becoming prohibitive. Bandwidth requirements continue climbing. Privacy regulations increasingly restrict sending sensitive data to external servers. And an expanding list of countries want to reduce reliance on U.S.-based AI infrastructure, pushing what they call "sovereign AI" strategies emphasizing domestic compute capability. Companies and governments are responding by prioritizing on-device inference. Rather than sending every query to the cloud, they're pushing as much computation as possible to laptops, edge servers, and embedded devices. This reduces costs, improves latency, strengthens data governance, and builds local capability. The World Economic Forum has documented this shift. EY reported in November that the sovereign AI approach gained significant traction as policymakers push for domestic AI capabilities spanning compute, models, and data. Quadric's customer base reflects this emerging market. The company has design wins spanning printers, automobiles, and AI laptops. Customers include Kyocera, Japan's auto supplier Denso (which builds chips for Toyota vehicles), and Tier IV, which licensed Quadric's IP for autonomous systems. The first products based on Quadric's technology are expected to begin shipping this year, starting with laptops. The company is also explicitly targeting sovereign AI markets. Quadric is exploring customers in India and Malaysia, with Moglix CEO Rahul Garg as a strategic investor helping shape its India approach. This geographic diversification is significant. It signals that the on-device AI shift isn't merely a technological transition but a geopolitical one, with profound implications for how AI infrastructure gets built and distributed globally. From an architectural perspective, Quadric positions itself as an alternative to established players in different ways. Qualcomm typically integrates AI technology inside its own processors, which can lock customers into Qualcomm silicon. Traditional IP suppliers like Synopsys and Cadence sell neural processing engine blocks that many customers find difficult to program. Quadric offers a middle path: programmable processor IP licensed to customers who integrate it into their own chips, avoiding lock-in while maintaining accessibility through comprehensive developer tools. The market opportunity is substantial. The on-device AI market is expected to surpass $50 billion by 2030, driven by language models in edge servers and AI-enabled IoT devices. Quadric currently has a post-money valuation of between $270 million and $300 million, up from around $100 million in its 2022 Series B. With only around 70 employees worldwide, the company is capital-efficient compared to chip manufacturers that require billions in fabrication investments. That said, Quadric remains early. The company has a handful of signed customers, and much of its longer-term upside depends on converting today's licensing deals into high-volume shipments and recurring royalties. Transforming design wins into actual production silicon takes time. And Quadric faces competition from multiple directions—established chip vendors investing in edge AI, new startups pursuing similar opportunities, and the possibility that cloud AI infrastructure improvements could shift the economics back toward centralization. But the fundamental forces driving the shift toward on-device inference appear durable. Energy costs will continue rising. Data privacy regulations will keep tightening. Geopolitical tensions around U.S.-based AI infrastructure will likely intensify. Model architectures will keep changing faster than chip design cycles. These aren't temporary phenomena. They're structural features of the AI landscape reshaping how infrastructure gets built. Quadric's timing in providing programmable processor IP built for this reality could position it as a crucial enabler of the next phase of AI infrastructure development. The revenue growth and market traction suggest the market agrees.

Apple needs a hit. Is a wearable AI 'pin' the answer?

Apple Needs a Hit. Is a Wearable AI 'Pin' the Answer? The smartwatch market has been kind to Apple. The Apple Watch generates billions in revenue annually and has become the company's most successful wearable. Yet in the broader universe of AI-powered devices, the tech giant has found itself playing catch-up. Other companies have attempted to crack the wearable AI problem with limited success, but that hasn't deterred Apple from entering the race. According to reports from The Information, Apple is actively developing an AI-powered wearable pin roughly the size of an AirTag, a project that could represent the company's most ambitious wearable venture since the Apple Watch itself. The timing is significant. As artificial intelligence reshapes the tech landscape, Apple has positioned itself as a cautious but committed player through its "Apple Intelligence" initiative. This AI pin would mark a fundamental shift in how the company envisions human interaction with AI, moving beyond smartphones and watches to a truly standalone wearable that operates independently. If successful, it could establish Apple as the company that finally cracked the wearable AI problem—a challenge that has humbled competitors for years. The Device Itself The AI pin project remains in early development stages, which means specifics could change dramatically before any potential launch. What we know comes from sources familiar with the initiative who spoke to The Information. The device is designed as a thin, circular disk made of aluminum and glass, resembling an AirTag in overall dimensions but slightly thicker. This form factor represents a deliberate design choice—something compact enough to attach to clothing or accessories without creating bulk, yet substantial enough to house meaningful processing power. The hardware specifications reveal Apple's ambitions for the device. Two front-facing cameras—one with a standard lens and another with a wide-angle lens—will capture images and video in close proximity to the wearer. Three microphones are built into the device, ostensibly designed to pick up the user's voice for commands while also capturing ambient audio from the environment. A speaker allows the pin to communicate back to users through audio feedback. Physical interaction comes through a single button mounted on the edge of the device, providing tactile control without complicating the minimalist design. Power management represents another crucial technical consideration. The pin will reportedly use magnetic inductive charging similar to the Apple Watch, eliminating the need for proprietary cables or complex docking mechanisms. This charging approach aligns with Apple's ecosystem and reduces friction for users already invested in the company's products. The software story remains incomplete. Reports suggest the pin will run on Apple Intelligence and be powered by the company's revamped Siri chatbot. However, critics and analysts have repeatedly noted that Siri's AI capabilities have lagged behind competitors like Google Assistant and OpenAI's ChatGPT. For an AI-focused wearable to succeed, Siri would need to demonstrate genuine intelligence and responsiveness. Whether Apple's forthcoming upgrades will prove sufficient remains an open question. Why This Matters The wearable AI space has become increasingly crowded, yet remarkably unsuccessful. Humane's AI Pin launched with tremendous hype and a premium $699 price tag, only to face widespread criticism for limited functionality, poor battery life, and unclear use cases. The device failed to gain traction and became a cautionary tale about the risks of pursuing hardware innovation before the software and user experience are truly ready. Yet the industry hasn't abandoned the concept. OpenAI is reportedly developing its own AI-powered device in collaboration with Jony Ive, Apple's former design chief. Meta continues investing in AI-powered glasses. Amazon, Motorola, and numerous startups are all exploring their own takes on AI wearables. This suggests the market genuinely believes in the potential even as current implementations struggle. For Apple specifically, a successful AI pin would address a strategic vulnerability. While the company dominates in premium smartphones and watches, it has failed to establish itself as an AI-forward company in the way Google, OpenAI, and Microsoft have done. An Apple Intelligence-powered wearable that actually works—that provides genuine utility and intelligence—could reshape perceptions about the company's AI capabilities and commitment to innovation. The device's specification suggests it's designed for passive observation and active interaction. The dual cameras and microphones imply a system designed to understand context, capture relevant information from the user's environment, and respond intelligently. Potential use cases might include real-time translation, ambient object recognition, voice-based information retrieval, or health monitoring. However, none of these are confirmed, and speculation ahead of actual product details can mislead. The Timeline and Uncertainty Apple is reportedly targeting a 2027 launch, with plans to manufacture approximately 20 million units. This timeline seems aggressive given the project's current early-stage status, but Apple has demonstrated the ability to accelerate hardware development when committed. The 20 million unit projection also signals serious commercial intent rather than a limited experimental release. However, history suggests caution is warranted. The Information explicitly notes that the project could face delays or be cancelled entirely. Apple has worked on numerous wearable concepts that never reached consumers. An AI Pin prototype that functions well in controlled environments might prove impractical for real-world use. Battery concerns, processing limitations, privacy objections, or regulatory hurdles could all derail the project. There's also the question of positioning. Will Apple sell the pin as a standalone accessory, bundle it with future smart glasses or other devices, or integrate it into a broader ecosystem? These decisions will fundamentally affect its market potential and pricing strategy. The Bigger Picture What an Apple AI pin ultimately represents is a bet on future computing. The company believes wearable AI devices will eventually become as essential as smartphones, and it wants to be the company that defines that category. Whether the 2027 timeline is realistic, whether 20 million units will actually be produced, and whether consumers will embrace the device remain open questions. But the mere fact that Apple is investing seriously in wearable AI suggests the company sees it as strategically essential rather than experimental. For now, the tech industry watches. A successful Apple AI pin would validate years of investment in wearable technology and prove that the category can achieve mainstream adoption. A failure would further confirm the skepticism that has greeted every AI wearable attempted so far.

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Surging memory chip prices dim outlook for consumer electronics

More Hardware News

Quadric rides the shift from cloud AI to on device inference — and it's paying off

Quadric rides the shift from cloud AI to on device inference and its paying off

Apple needs a hit. Is a wearable AI 'pin' the answer?