The hottest Semiconductors Substack posts right now

And their main takeaways
Category
Top Technology Topics

The Great AI Silicon Shortage

SemiAnalysis • 11314 implied HN points • 12 Mar 26
🕹 Technology Semiconductors
  1. Advanced 3nm (TSMC N3) wafer capacity is deeply constrained because most leading AI accelerators are moving to N3, so compute deployments are bottlenecked and TSMC is prioritizing AI customers which may push others to diversify to Samsung or Intel.
  2. Memory is the next big bottleneck: HBM demand is surging, it consumes far more wafer capacity per bit than commodity DRAM, and higher HBM pin-speed requirements plus rising DRAM prices mean suppliers will struggle to meet accelerator needs without charging premiums.
  3. A small release valve exists if smartphone demand falls (freeing some N3 wafers) and CoWoS packaging constraints are easing, but memory, datacenter power, and packaging limits mean hyperscalers’ higher capex won’t immediately solve the compute shortage.

Vera Rubin – Extreme Co-Design: An Evolution from Grace Blackwell Oberon

SemiAnalysis • 15961 implied HN points • 25 Feb 26
🕹 Technology Semiconductors
  1. NVIDIA built Rubin as an "extreme co-design" where the rack is treated as one integrated compute unit, combining Rubin GPUs, Vera CPUs, NVLink‑6 switches, ConnectX‑9 NICs, BlueField‑4 DPUs and Spectrum switches to push performance and tight system control.
  2. Rubin GPUs prioritize low‑precision scaling (big FP4/FP8 gains), much higher HBM bandwidth and an adaptive compression engine for sparsity, but they also bring very large power envelopes (up to 2300W), driving big thermal and cost impacts.
  3. The NVL72 rack is redesigned for manufacturing and reliability: cableless modular trays with board‑to‑board connectors, upgraded high‑end PCBs, 100% liquid cooling and 50V power delivery, which shifts component, cooling and assembly supply chains and raises TCO considerations.

Reading List 02/21/26

Construction Physics • 24636 implied HN points • 21 Feb 26
💼 Business Semiconductors
  1. Home prices rose in parts of the Midwest and Northeast while falling in much of the South, and this pattern lines up with areas that have older housing stock versus new post-2000 construction. Places that saw the biggest COVID-era price booms are now often the hardest markets to sell in.
  2. Chinese EV makers have a major cost edge mainly because they vertically integrate much of production, cutting supplier markups. Meanwhile, global supply chains are shifting — big chip and memory fabs are being built in the U.S. even as many U.S. automakers write down or scale back costly EV investments.
  3. Political and policy changes are reshaping incentives: renewed pushes to cut property taxes and long-standing anti-growth legacies affect development and housing, while anti-vaccine political pressure and potential legal changes are squeezing vaccine makers and reducing investment and jobs.

Compatibility and Fragmentation in the AI Era

The Chip Letter • 5241 implied HN points • 11 Mar 26
🕹 Technology Semiconductors
  1. New hardware architectures keep creating compatibility headaches because different instruction sets and designs make it hard to run the same software across machines.
  2. High-level languages, intermediate representations, and architecture strategies that enforce compatibility (like IBM’s System/360) have historically reduced that burden by making software more portable and lowering support costs.
  3. A new wave of novel architectures plus AI promises more fragmentation but also new AI-driven ways to bridge differences, and how the industry manages that will shape who wins and loses.

CPUs are Back: The Datacenter CPU Landscape in 2026

SemiAnalysis • 22426 implied HN points • 09 Feb 26
🕹 Technology Semiconductors
  1. Datacenter CPUs are back in demand because reinforcement learning, agentic models, and RAG-style inference need lots of general-purpose compute for environments, tool use, data sharding and media decode, which is driving hyperscalers and AI labs to build large CPU clusters and straining inventories.
  2. CPU architecture is rapidly shifting to chiplet/disaggregated designs, higher core counts and mesh interconnects with advanced packaging, and vendors are diverging — AMD and hyperscale ARM designs are outperforming while Intel faces delays and questionable design choices that hurt competitiveness.
  3. The broader system ecosystem now matters as much as raw CPU cores: GPUs and specialized CPUs act as head nodes with shared memory, DPUs and context-memory platforms change how memory is used, and DRAM shortages plus packaging yields are shaping performance, supply and pricing.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Nvidia's Groq Plot Thickens

The Chip Letter • 6334 implied HN points • 04 Mar 26
🕹 Technology Semiconductors
  1. Nvidia is quickly integrating Groq’s low-latency processor technology and team and is expected to unveil a Groq-derived inference chip at GTC.
  2. Groq’s dataflow architecture plus years of compiler work could deliver extremely fast, low-latency inference if Nvidia combines it with its wider IP and engineering.
  3. If Nvidia pulls this off it could narrow the field of inference accelerators and become a major, potentially game-changing shift in computer architecture for AI.

NVIDIA Introduces Groq LP30 and LPX Nodes

More Than Moore • 957 implied HN points • 16 Mar 26
🕹 Technology Semiconductors
  1. NVIDIA has folded Groq’s engineering and chip technology into its product line and is shipping the Groq LP30 inside LPX nodes to accelerate inference decode workloads.
  2. The LP30 offers about 1.2 PFLOP FP8 performance and ~500 MB of SRAM per chip, with 8-chip LPX units giving 4 GB and full systems scaling to 256 chips / 128 GB, prioritizing huge SRAM bandwidth for high-throughput decoding.
  3. NVIDIA will use its Dynamo orchestration to split work across Rubin, Rubin CPX and Groq LPX hardware (customers can mix up to ~25% Groq) so prefill and decode are handled by the best-suited chips to boost tokens-per-second for premium use cases.

NVIDIA’s Big Week, New AI Models, Social Media On Trial

Big Technology • 6755 implied HN points • 23 Feb 26
🕹 Technology Semiconductors
  1. Nvidia has a high-stakes week: its earnings, talk of supply versus demand, and a possible $30 billion investment in OpenAI — plus hints about a new chip — could move the AI hardware market.
  2. Major AI model updates from Google, Anthropic, and Chinese firms are improving long-context reasoning, agentic tools, and multimodal generation, speeding up enterprise and creative use cases.
  3. A high-profile trial with Mark Zuckerberg could reshape whether social platforms are liable for engagement-driven, potentially 'addictive' design choices, and it underscores growing worries about mental-health harms from AI features.

Memory Mania: How a Once-in-Four-Decades Shortage Is Fueling a Memory Boom

SemiAnalysis • 15153 implied HN points • 06 Feb 26
🕹 Technology Semiconductors
  1. Memory prices are skyrocketing in a big, AI-driven supercycle and the shortage looks like it hasn’t peaked yet.
  2. DRAM scaling has slowed because of physical and process limits, so cost-per-bit improvements are much smaller and technology no longer reliably drives deflation.
  3. Memory supply is slow to change and very capex-intensive, and with fewer suppliers plus disciplined capex and massive AI demand, the shortage is harder to fix and could last longer.

How Did TVs Get So Cheap?

Construction Physics • 37998 implied HN points • 08 Jan 26
🕹 Technology Semiconductors
  1. TVs got much cheaper because LCD technology moved from niche to mass production, letting bigger, higher-resolution screens be made at much lower cost.
  2. Using ever-larger mother glass sheets and semiconductor-style fabs created big economies of scale and higher yields, which cut the price per area and pixel dramatically.
  3. A steady stream of process improvements (fewer steps, faster fills, automation) plus fierce competition and huge factory investments kept pushing costs down over decades.

Is the Great AI meltdown imminent? [NSFW]

Marcus on AI • 12370 implied HN points • 05 Feb 26
🕹 Technology Semiconductors
  1. Nvidia appears to have cut back a promised $100 billion investment in OpenAI to roughly $20 billion. That reduction could leave OpenAI exposed because it burns many billions of dollars each year.
  2. The AI industry was propped up by circular financing—chipmakers funding AI firms that then buy their chips—and those arrangements are now unraveling. If those deals fall apart, the market faces bubble-like risks similar to past tech booms.
  3. If marquee deals collapse and leading AI firms falter, the multitrillion-dollar expansion many expected might never materialize. Instead of accelerating, the industry’s growth could stall or shrink.

Sam Altman and the day Nvidia’s meteoric rise came to an end

Marcus on AI • 12173 implied HN points • 04 Feb 26
🕹 Technology Semiconductors
  1. OpenAI presented GPT-5 as AGI-capable, but the release showed it wasn’t and that claim undermined confidence in promises of imminent AGI.
  2. Belief that scaling alone would create AGI helped drive Nvidia and GPU stocks skyward, but after the GPT-5 disappointment those stocks have stalled, showing the ascent has lost steam.
  3. Investors are rotating out of hyped LLM plays as models prove expensive, unreliable, and commoditized, which means smaller profits and price wars but also creates space for newcomers and new AI approaches.

Co Packaged Optics (CPO) – Scaling with Light for the Next Wave of Interconnect

SemiAnalysis • 21820 implied HN points • 01 Jan 26
🕹 Technology Semiconductors
  1. Co-packaged optics (CPO) is moving from labs to shipping products and will be the key way to scale high-bandwidth, low-latency AI scale-up networks because it offers much higher bandwidth density and longer reach than copper.
  2. CPO cuts or removes power-hungry DSPs and long-reach SerDes, unlocking big energy and density gains by integrating optical engines near the chip and using enablers like TSMC COUPE, modulators (MRM/MZM/EAM), WDM, and FAUs.
  3. Wide adoption still faces real hurdles — supply chain, manufacturability, reliability, serviceability and standards — so early wins will be limited, but hyperscaler commitments and compelling scale-up economics should drive a larger ramp later this decade.

Apple-TSMC: The Partnership That Built Modern Semiconductors

SemiAnalysis • 14850 implied HN points • 08 Jan 26
🕹 Technology Semiconductors
  1. Apple’s huge, predictable orders and upfront funding were the anchor that let TSMC build and scale bleeding‑edge fabs, turning TSMC into the dominant foundry.
  2. The rise of AI/HPC demand (led by Nvidia and hyperscalers) has shifted the industry to a two‑anchor model, splitting wafer and packaging demand and reducing Apple’s relative share on some nodes while creating fierce competition for advanced packaging capacity.
  3. Apple vertically integrated chip design through acquisitions and internal teams to boost margins and product differentiation, while quietly diversifying non‑core production (and managing Taiwan concentration risk) with alternatives like Intel, Samsung, and Arizona fabs.

The Origins and Limitations of AMD's Revival

The Chip Letter • 5023 implied HN points • 12 Feb 26
🕹 Technology Semiconductors
  1. In the 2000s AMD reshaped itself by selling its flash-memory unit, buying ATI for graphics, and spinning off its chip factories, which changed the company’s business model.
  2. The company mounted a major legal and strategic challenge to Intel that was a high-risk move, producing intense conflict and short-term financial pain that led to leadership change.
  3. AMD’s fortunes later recovered under new leadership, so today’s success is the result of both those risky early moves and subsequent execution rather than any single decision.

1,000 CFETs, SK Hynix Next-Gen NAND, Interconnects Beyond Copper, 2D Materials, and More

SemiAnalysis • 9799 implied HN points • 13 Jan 26
🕹 Technology Semiconductors
  1. 3D NAND makers are still squeezing more bits by adding layers and decks; SK Hynix’s 321-layer V9 boosts capacity a lot and its multi-site 5-bits-per-cell idea shows big logical-density potential, but these tricks add serious process complexity and cost.
  2. Metals are changing to beat copper limits: Samsung is using molybdenum to cut wordline resistance in NAND, and ruthenium is emerging for ultra-fine interconnects with textured ALD that can greatly lower line resistance at tiny pitches.
  3. Two-dimensional materials keep promise for sub‑10 nm logic because they reduce source‑to‑drain tunneling, but real-world barriers—wafer‑scale integration, low‑bias contacts (especially p‑type), variability, doping methods, and modeling—still need to be solved before they become manufacturable.

On Dwarkesh Patel's 2026 Podcast With Elon Musk and Other Recent Elon Musk Things

Don't Worry About the Vase • 3404 implied HN points • 17 Feb 26
🕹 Technology Semiconductors
  1. Elon appears confused about alignment and is willing to build AI that could far exceed human intelligence. He frames expanding intelligence as acceptable or even desirable even if humans become a tiny fraction of total intelligence.
  2. He’s betting big on engineering fixes: data centers and chip fabs in space, mass-produced robots, and digital humans as the path to massive compute and revenue. Those plans depend on huge energy, new chip capacity, and rapid scaling via rockets.
  3. xAI’s safety stance looks weak, with high safety-team turnover and leadership downplaying dedicated safety roles while encouraging fast pushes to production. That combination raises real concerns about inadequate oversight and testing.

Intel's Larabee Legacy

The Chip Letter • 7426 implied HN points • 24 Jan 26
🕹 Technology Semiconductors
  1. Larrabee was Intel's attempt to build a GPU by extending x86, but the design proved uncompetitive and the project was cancelled.
  2. The project added large new vector instructions (LRBni / 512-bit vectors) and architectural baggage that increased complexity without producing a viable graphics product.
  3. Larrabee's failure left Intel without a competitive discrete GPU, costing time and money and contributing to long-term cultural and strategic problems that weakened its position in AI and graphics markets.

TPU Mania

The Chip Letter • 18128 implied HN points • 13 Dec 25
🕹 Technology Semiconductors
  1. Google’s TPU program is the result of a long, steady effort dating back to 2013, evolving from a simple TPU v1 co‑processor into massive cloud AI supercomputers using systolic-array ideas and iterative hardware improvements up to TPU v7.
  2. Google’s control of the full stack, huge resources, and datacenter expertise give TPUs a strong practical advantage, but selling TPUs externally creates strategic trade‑offs and means customers should avoid becoming fully dependent on a single vendor.
  3. The TPU vs GPU contest is still open: architectural strengths matter, but ecosystem, software, and execution will likely decide market share, and we should expect convergence rather than one clear winner.

How ASML Got EUV

Construction Physics • 23801 implied HN points • 20 Nov 25
🕹 Technology Semiconductors
  1. EUV lithography is an advanced technology that uses extremely short wavelengths of light to make tiny patterns on computer chips. This allows for the production of smaller and more powerful transistors.
  2. Despite early advancements and significant US research, a Dutch company called ASML became the sole producer of EUV machines. This highlights how developing technology and successfully marketing it can be very different.
  3. The journey of EUV technology took several decades and required massive investments from major companies. This shows that bringing a complex technology to production is often a challenging and lengthy process.

Links For December 2025

Astral Codex Ten • 18651 implied HN points • 10 Dec 25
🕹 Technology Semiconductors
  1. AI is now the dominant political and technological battleground, driving fights over regulation, funding, and geopolitics like chip exports and PAC spending.
  2. Many hyped tech and biotech ventures make grand claims and show warning signs of fraud or shaky science, so investors and users should be skeptical and favor proven alternatives.
  3. AI’s spread will upend jobs and even the role of wealthy capitalists, creating pressure for redistribution or new power dynamics, so governments need better transparency, auditing, and realistic regulation.

Two takes on AI and the future of America

Marcus on AI • 11461 implied HN points • 23 Dec 25
🕹 Technology Semiconductors
  1. Huge bets on large language models have driven a boom in chips and data center construction, but real-world performance and trust are lagging, so those assets could become overvalued and risky.
  2. Multiple studies and company experiences show generative AI often fails to deliver the promised productivity gains and can sometimes harm outcomes, so it’s premature to treat it as a guaranteed productivity revolution.
  3. Putting an entire economy or national strategy all-in on generative AI is dangerous; diversification and cautious risk management are needed to avoid big losses or calls for bailouts.

Qualcomm's RISC-Ventana Fusion

The Chip Letter • 6770 implied HN points • 13 Jan 26
🕹 Technology Semiconductors
  1. Qualcomm bought Ventana mainly to add experienced RISC-V CPU engineers and IP so it can accelerate its own CPU plans and reduce reliance on Arm.
  2. Ventana produced promising Veyron CPU designs (V1 then V2 with vector support) but appears to have struggled to convert that tech into clear customer wins.
  3. A bigger Qualcomm push into RISC-V could hurt Arm’s revenues given Qualcomm’s size, but RISC-V still faces major software and ecosystem hurdles before it can fully replace Arm for high-performance workloads.

TSMC Overseas Fabs – A Success?

SemiAnalysis • 13334 implied HN points • 01 Dec 25
🕹 Technology Semiconductors
  1. TSMC is a key player in semiconductor manufacturing, but most of its production happens in Taiwan. Their overseas expansions to the U.S., Japan, and Germany face challenges in replicating the efficiency and ecosystem found in Taiwan.
  2. The founder, Morris Chang, is skeptical about the success of U.S. fabs, suggesting that high costs and a lack of local supply chains could make them less competitive compared to TSMC's operations in Taiwan.
  3. The U.S. government is pushing for onshore semiconductor production for national security reasons, but building and operating fabs in places like Arizona is complicated and significantly more expensive than in Taiwan.

Apple Mobile Processors Are Now Made in the USA. By TSMC

Tim Culpan’s Position • 880 HN points • 17 Sep 24
🕹 Technology Semiconductors
  1. TSMC is now making Apple's mobile processors in Arizona, marking a big shift for tech manufacturing in the U.S.
  2. The A16 chip, which was first used in the iPhone 14 Pro, will be the first product produced at this new facility.
  3. This move shows Apple and TSMC's commitment to making advanced chips domestically, which is a key part of the U.S. government's efforts to boost local tech production.

How Much AI Does $1 Get You in China vs America?

ChinaTalk • 1096 implied HN points • 19 Feb 26
🕹 Technology Semiconductors
  1. The U.S. gets more usable AI compute per dollar because its data centers use higher‑efficiency, higher‑performance hardware, even though building and labor costs are higher.
  2. If China gets broad access to Nvidia H200s, its data centers could close the raw performance gap a lot, but limited H200 supply and export rules mean the boost won’t be complete or immediate.
  3. Most cost differences come from construction and hardware while electricity, water, and staff are relatively small; the decisive constraints are chip supply for China and power capacity for the U.S., so solving those bottlenecks will determine the outcome.

AI Davos: Four Reflections From The World’s Most Exclusive Tech Conference

Big Technology • 3502 implied HN points • 23 Jan 26
🕹 Technology Semiconductors
  1. People are debating whether the AI surge is a bubble or just a strong tech investment cycle. Some parts of the industry look frothy and a correction and consolidation are likely, which will make the next few years volatile.
  2. The market for AI devices could be enormous — forecasts talk about billions of always‑with‑you agents in the form of glasses, rings, watches, or desk devices. These products will only take off if they prove more useful than an app on your phone.
  3. Big tech is racing to ship wearable AI products: Google is gearing up for a major push in AI glasses soon, and other firms, including OpenAI, are moving on device plans while pursuing large funding and scaling revenue.

Intel Board Chair Frank Yeary Retires

More Than Moore • 280 implied HN points • 09 Mar 26
🕹 Technology Semiconductors
  1. Frank Yeary is retiring as Intel’s board chair effective May 13, and Dr. Craig H. Barratt will become the new chair, with the board shrinking by one member.
  2. Barratt’s rapid promotion underscores Intel’s move to prioritize technical and operational experience on its board given his background at Atheros, Google Fiber, and Barefoot Networks.
  3. The chair change is primarily a signal to engineers, customers, and investors about Intel’s focus on proving its 18A nodes and foundry strategy, but it won’t solve manufacturing or yield issues—public 18A yield data and customer commitments will be the real test.

Davos Dispatch: Is AI The New Altruism?

Big Technology • 3252 implied HN points • 19 Jan 26
🕹 Technology Semiconductors
  1. Davos has shifted into an AI-heavy event where companies are framing artificial intelligence as the new face of corporate social good. Hundreds of AI sessions and branded “AI houses” show tech is using the meeting to sell altruism alongside products.
  2. Top tech CEOs, political leaders, and nation-states are converging to shape AI policy and business, turning Davos into a hub for dealmaking and national AI ambitions like sovereign models and new pavilions. The event blends publicity, partnerships, and product pitches in equal measure.
  3. Big tensions remain unresolved: AI’s rising energy use vs. sustainability, who will govern powerful systems, and whether all the benevolent rhetoric will translate into real action. Companies have announced worker-training and access commitments, but follow-through is the real test.

AI #154: Claw Your Way To The Top

Don't Worry About the Vase • 2329 implied HN points • 05 Feb 26
🕹 Technology Semiconductors
  1. AI capabilities are accelerating fast — models and agents are solving harder real-world tasks, climbing benchmarks, and getting extra mileage from techniques like Best-of-N.
  2. Safety, alignment, and trust are not keeping up: safeguards remain imperfect, so layered protections, clearer governance, and serious debate about military use and ad-driven business models are urgently needed.
  3. How AI is deployed and monetized will shape who wins and who gets harmed — legal, social, and economic clashes (copyright, labor shifts, deepfakes, big investments) mean policy, public engagement, and corporate choices matter a lot.

Is China Cooking Waymo?

ChinaTalk • 1200 implied HN points • 11 Feb 26
🕹 Technology Semiconductors
  1. Chinese AV companies have outpaced U.S. peers in real-world deployment and international deals, offering not just robotaxis but also delivery vans, trucks, and integrated vehicle-cloud-road systems.
  2. China controls much of the LiDAR and EV battery supply chain, giving its firms cost and supply advantages. The U.S. still holds leverage through automotive-grade chipmakers and advanced semiconductor manufacturing, so both sides remain interdependent.
  3. China’s centralized pilot zones, friendlier regulations, and higher public acceptance let firms scale fast and win overseas infrastructure deals. Still, rapid expansion hasn’t guaranteed profits and raises safety, regulatory, and labor tensions.

Jensen Groks Groq

The Chip Letter • 5241 implied HN points • 31 Dec 25
🕹 Technology Semiconductors
  1. Groq’s LPUs deliver much faster, low‑latency AI inference by storing model parameters in on‑chip SRAM and linking many chips together, avoiding reliance on scarce HBM.
  2. Nvidia struck a non‑exclusive licence and talent deal that moves most Groq employees to Nvidia and pays shareholders, while Groq remains operating with a new CEO and GroqCloud continuing.
  3. Bringing Groq’s processors into Nvidia’s AI platform could let real‑time, high‑speed inference scale broadly and shift the economics and architecture of AI inference.

How the US Won Back Chip Manufacturing

ChinaTalk • 504 implied HN points • 17 Feb 26
🕹 Technology Semiconductors
  1. A focused mix of big incentives (like an investment tax credit and targeted grants) plus a small, execution‑focused team is what actually accelerated a large semiconductor fab buildout in the U.S., not just market demand alone.
  2. Effective industrial policy needs the right balance of simple market tools and discretionary powers for urgent problems, and it must be governed with transparency and insulation from politics or public trust breaks down.
  3. To make this repeatable, the country needs durable state capacity that can attract talent, deploy capital, accept some failures, and differentiate between defensive fixes for chokepoints and offensive bets on future enabling R&D.

Cursed circuits #3: true mathematics

lcamtuf’s thing • 3877 implied HN points • 22 Dec 25
🕹 Technology Semiconductors
  1. An op-amp simply amplifies the voltage difference between its inputs by a huge factor, and with feedback you force its inputs to be nearly equal so passive parts (resistors, diodes, caps) can be arranged to perform math instead of just gain.
  2. Addition and subtraction are straightforward: resistor networks can average or sum signals and a non‑inverting amplifier scales them to produce a true sum, while difference amplifiers give Vout ≈ VA − VB and can be biased to work on a single supply.
  3. Harder operations are possible too: multiplication/division can be done with log/antilog converters that use the diode’s exponential V–I curve plus a summing stage, and integration is implemented by charging a capacitor with a controlled current to produce precise ramps, though these analog tricks need careful biasing and have practical limits (rails, linearity, noise).

Chinese AI Rings in the Year of the Horse

ChinaTalk • 415 implied HN points • 18 Feb 26
🕹 Technology Semiconductors
  1. China’s AI firms are racing to ship bigger multimodal and agentic models aimed at coding and long-horizon tasks, often boasting huge context windows and trillion-parameter systems. These pushes bring IP, copyright, and misuse worries—accusations of covert distillation, Hollywood pushback, and easy deepfake generation have all emerged.
  2. Humanoid robotics made a high-profile leap with fluid performances and a surge in consumer interest, while companies and competitions showcase more advanced motor skills; at the same time, firms like Alibaba are releasing robotics AI tools that help close the software gap. This combination suggests China is seriously pushing to win in both robot hardware and control software.
  3. A global memory shortage is creating opportunities for Chinese memory makers to expand supply to PC and phone makers, but new fabs and capacity will take years to materialize. Regulators are sending mixed signals—encouraging commercialization and subsidies while cracking down on misleading AIGC, anti-competitive promotions, and harmful content—making the policy environment uncertain for companies.

Fungible Compute, Mortal Computing and Why the IoT won't use Silicon

State of the Future • 12 implied HN points • 10 Mar 26
🕹 Technology Semiconductors
  1. Flexible thin‑film IGZO chips let you add cheap, bendable compute to everyday objects that never had it, creating a new class of semiconductor separate from cutting‑edge silicon.
  2. Process times measured in days and a tiny, modular 20×30m fab footprint make manufacturing much cheaper and faster, enabling billions of units and even the possibility of deploying fabs at customer sites.
  3. Edge intelligence can be very simple but valuable: tiny classifiers of a few hundred gates plus basic sensors can capture huge amounts of real‑world data for use in supply chains, healthcare, and agriculture, shifting value to the aggregate data layer.

The Sequence Opinion #819: How AI Chips are Made?

TheSequence • 238 implied HN points • 05 Mar 26
🕹 Technology Semiconductors
  1. Hardware drives modern deep learning: algorithms explain maybe 40% of progress and the rest comes from the compute, memory, and system-level engineering that makes training and inference practical.
  2. GPUs were a lucky fit for neural nets because their high arithmetic density matched the workload, but custom AI chips are needed to close remaining gaps by optimizing dataflow, precision, and memory access.
  3. Designing an AI chip is a layered engineering craft from architecture to physics and tape‑out, involving RTL/Verilog work, hardware–software co‑design, and careful trade‑offs across performance, power, and manufacturability.

Morris Chang and the Origins of TSMC

Construction Physics • 46767 implied HN points • 31 Dec 24
🕹 Technology Semiconductors
  1. Morris Chang founded TSMC in 1985, turning it into a key player in the semiconductor industry. He saw the need for a company that could manufacture chips for others, which allowed many new companies to emerge.
  2. Chang's journey was not smooth; he faced many challenges and failures before achieving success with TSMC. Much of his early career included tough breaks, but he persevered and created something significant.
  3. TSMC's unique business model changed how semiconductor companies operated by providing manufacturing services without competing directly with clients. This innovation helped TSMC grow quickly and become vital for tech giants like Apple and Intel.

🚀 FP! Week In Review, Briefly #19

Faster, Please! • 1005 implied HN points • 31 Jan 26
🕹 Technology Semiconductors
  1. AI is starting to improve the systems that build AI, creating a possible self-reinforcing “boom loop” that could speed up discovery and long-run economic growth beyond past trends.
  2. This week brought lots of pro-innovation signs—faster chips and chip competition, AI applied to genomics and retail, progress on self-driving and renewables—showing broad technological momentum across sectors.
  3. At the same time, social and political risks are rising, from AI-related mental-health concerns and anti-AI political strategies to financial and regulatory worries, so the gains come with important trade-offs.

AMD's Ryzen 7 9850X3D: A Review

More Than Moore • 186 implied HN points • 01 Mar 26
🕹 Technology Semiconductors
  1. The Ryzen 7 9850X3D is basically a higher‑binned 9800X3D with faster clocks, but it only delivers tiny performance gains while drawing significantly more power and costing more.
  2. AMD’s 3D V‑Cache really helps CPU‑bound, cache‑hungry games and makes memory speed matter less, but it doesn’t improve compute‑heavy workloads and offers no advantage for AI paths that need an NPU.
  3. On value, the 9800X3D or cheaper Intel options give better performance‑per‑dollar, so most buyers should pick the cheaper chip and spend any savings on other parts like memory amid volatile DRAM prices.