The hottest Data Centers Substack posts right now

And their main takeaways
Category
Top Technology Topics

Wait, is cloud bad?

Cloud Irregular • 7244 implied HN points • 24 Oct 23
🕹 Technology Data Centers
  1. DHH believes established companies that can amortize capital investments should reconsider the cloud
  2. Different types of companies require different approaches to cloud vs. data center
  3. Switching back from the cloud to data center may bring back old problems that cloud solutions had addressed

The three businesses of hyperscalers

Metacritic Capital • 13 implied HN points • 23 Feb 26
💼 Business Data Centers
  1. Hyperscalers are three different businesses at once: Traditional IaaS (sticky, high‑margin cloud services), Token Factories (LLM inference APIs sold by token consumption), and AI mega‑deals (capex‑heavy long‑term GPU/data‑center contracts with labs).
  2. Token Factory work is commoditizing and price‑sensitive: customers can swap models or providers quickly, so serving costs and model access drive competitiveness more than platform lock‑in.
  3. AI mega‑deals change growth quality and valuation: hosting labs can boost revenue but often yields lower, fixed IRRs, so investors must model revenue, capex, and margins separately for each business and run a DCF.

Reading List for 11/9/24

Construction Physics • 2087 implied HN points • 09 Nov 24
🕹 Technology Data Centers
  1. Using drones and AI to monitor construction sites can help identify issues and improve efficiency. This tech can make construction safer and more effective.
  2. Microsoft's plan for mass-timber data centers is an attempt to cut carbon emissions, but energy use for operating them has a much bigger carbon footprint than the building materials.
  3. The trend of smaller windows in buildings to save energy might not be the best solution. It's better to focus on creating more clean energy rather than limiting our energy use too much.

The business of AI in 2026

Generating Conversation • 70 implied HN points • 08 Jan 26
💼 Business Data Centers
  1. Big investments in data centers and GPUs are likely to pay off as inference gets cheaper and more AI applications become economical, so infrastructure buildout is a bullish trend.
  2. Large companies will keep acquiring startups and doing acqui‑hires, and those acqui‑hires can harm the startup ecosystem and spook talent unless policy or enforcement changes.
  3. Frontier labs will move up into higher‑margin applications, so startups must differentiate on orchestration, workflows, and solving harder domains like healthcare, security, and SRE where adoption is slower but more defensible.

What’s a Datacenter?

Technically • 25 implied HN points • 12 Feb 26
🕹 Technology Data Centers
  1. Datacenters are the physical homes for thousands of servers that power everyday apps and critical services, so keeping them running reliably is essential.
  2. They’re tightly controlled, standardized facilities with strict access rules, dense racks of servers, and heavy cooling systems that create hot and cold aisles.
  3. Big datacenter investment is driving economic growth, but new projects often spark local opposition over environmental impact, utility strain, and property concerns.
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AMD MI300 Ramp, GPT-4 Performance, ASP & Volumes

SemiAnalysis • 4141 implied HN points • 01 Nov 23
🕹 Technology Data Centers
  1. AMD's MI300 is positioned as a strong competitor in LLM inference against Nvidia and Google hardware.
  2. Major companies like Microsoft, Meta, Oracle, Google, and Amazon have already placed orders for AMD MI300.
  3. AMD's Datacenter GPU revenue is expected to reach over $2 billion in 2024 with strong demand from customers and supply constraints.

476: Boring Success, Microsoft CapEx, LVMH, Nvidia, Data Center Electricity, Spotify & Joe Rogan, Tesla Model 3 Refresh, and Dragon Prince

Liberty’s Highlights • 412 implied HN points • 07 Feb 24
🕹 Technology Data Centers
  1. Compete in life with kindness, creativity, and resilience, not just success.
  2. Success in one area can enable you to take risks and be more adventurous in other aspects of life.
  3. Electricity consumption from data centers, AI, and crypto is expected to double by 2026, impacting energy needs significantly.

China’s Weird Chip Surplus, Explained

ChinaTalk • 696 implied HN points • 04 Feb 25
🕹 Technology Data Centers
  1. China has added a lot of AI chips in 2024, but they are not being used efficiently. This leads to having too many unused chips even though some types of processing power are in short supply.
  2. Major technology companies and state-owned firms are investing heavily in AI computing centers, but many of these centers are poorly managed. This results in a waste of resources and underutilized equipment.
  3. The demand for computing power is changing. While there is enough power for now, experts believe there might be shortages again soon as the need for AI applications grows.

Lattice Semiconductor Q2 2025 Financials

More Than Moore • 210 implied HN points • 14 Aug 25
🕹 Technology Data Centers
  1. Lattice Semiconductor saw a slight growth in revenue, reaching $124 million in Q2 2025. This is a positive sign after a tough period of declining sales.
  2. The company is focusing more on its newer product lines, like Avant and Nexus 2, which are becoming important for their business. These products are driving sales in high-demand areas like communications and computing.
  3. Despite some segments, like Industrial and Automotive, seeing declines, Lattice is managing its finances well with strong gross margins and an increase in free cash flow, giving it room for future investments.

AI Roundup 147: OpenAI's Code Red

Artificial Ignorance • 58 implied HN points • 05 Dec 25
🕹 Technology Data Centers
  1. OpenAI is changing its focus back to improving ChatGPT, stepping away from other projects like ads and personal assistants due to rising competition with companies like Google.
  2. Anthropic is planning to go public and has made significant strides in revenue and product offerings despite facing substantial losses, aiming to challenge the big tech firms.
  3. Three years after the launch of ChatGPT, American opinions about AI are mixed, with some people excited and others fearful, even as AI continues to change industries like education and finance.

447: Nvidia vs Hyperscalers, TSMC, Tech IPOs, SpaceX & Starlink, $5bn for Old Songs, ChatGPT vs 17 Doctors, and Phoebe Waller-Bridge

Liberty’s Highlights • 471 implied HN points • 18 Sep 23
🕹 Technology Data Centers
  1. Having a creative outlet can shift your mindset and generate more ideas.
  2. Writing online is competitive, requires multiple skills, and is ruled by power laws.
  3. Nvidia is making strategic moves in cloud services, there is competition in AI chips, and TSMC's Arizona plant chips still need to be shipped to Taiwan.

AI’s Great Infrastructure Boom: Bullwhip or Building the Future?

Gad’s Newsletter • 56 implied HN points • 01 Dec 25
🕹 Technology Data Centers
  1. AI infrastructure investment is skyrocketing, with tech giants investing billions in data centers and chips. This could lead to major changes in how AI is developed and used in the future.
  2. The bullwhip effect is making the supply chain for AI unpredictable, causing spikes in demand that may not match actual needs. This could result in periods of overordering and shortages.
  3. Despite potential oversupply and price drops, the long-term demand for AI technology is expected to be strong. This means the current build-out is more likely part of a lasting change in the tech landscape rather than a temporary bubble.

The Sequence Radar #779: The Inference Wars and China’s AI IPO Race

TheSequence • 35 implied HN points • 28 Dec 25
🕹 Technology Data Centers
  1. Nvidia licensed Groq’s LPU technology and brought key Groq leaders onboard, consolidating talent and inference IP to reinforce its lead in inference hardware.
  2. Chinese model labs are shipping frontier models: Zhipu’s GLM 4.7 pushes coding and agentic ‘deep thinking,’ while MiniMax’s M2.1 uses linear attention and MoE to enable a massive 4‑million‑token context window at much lower cost.
  3. Zhipu and MiniMax preparing Hong Kong IPOs shows foundation models are moving from VC-funded research to public, revenue-focused companies, and highlights a split where U.S. scaling is driven by capital and hardware consolidation while China focuses on architectural and economic efficiency.

Weekly Dose of Optimism #153

Not Boring by Packy McCormick • 205 implied HN points • 18 Jul 25
🕹 Technology Data Centers
  1. There are massive investments in AI infrastructure, mainly in Pennsylvania, with companies like Google and Blackstone pledging billions to build data centers. This investment is expected to create many jobs and boost the local economy.
  2. Meta is working on building a huge data center called Hyperion, which will provide lots of power for AI development. They plan to invest around $70 billion in AI this year, which could lead to significant advancements in their products.
  3. A new study shows that a technique called three-person IVF can produce healthy children by combining DNA from three people to prevent genetic diseases. This could change how families with these conditions approach reproduction.

Import AI 323: AI researcher warns about AI; BloombergGPT; and an open source Flamingo

Import AI • 519 implied HN points • 03 Apr 23
🕹 Technology Data Centers
  1. Bloomberg has developed BloombergGPT, a powerful language model trained on proprietary financial data with significant performance improvements on financial tasks.
  2. AI researcher Dan Hendrycks warns about future AI systems potentially out-competing humans due to natural selection favoring AI traits that may not align with human interests.
  3. Open source initiatives like OpenFlamingo and Cerebras-GPT show how companies and collectives are replicating and releasing advanced AI models, presenting a trend in the industry towards open collaboration and competition.

Sarah Chekfa: Stop Believing: The Data Center as Religious Monument

Do Not Research • 279 implied HN points • 06 Nov 23
🕹 Technology Data Centers
  1. Data centers are often like religious monuments, housing IT infrastructure and managing vast amounts of data that power modern life.
  2. Big data is considered almost mythical, with beliefs and values attributed to its insights and power, leading to comparisons with religion.
  3. Data centers have significant ecological impacts, consuming vast amounts of electricity and resources, leading to concerns over energy waste and pollution, with proposals for lunar data centers creating new environmental challenges.

⤴⤵ Up Wing/Down Wing #19

Faster, Please! • 548 implied HN points • 05 Oct 24
🕹 Technology Data Centers
  1. Nvidia is looking at nuclear power to help run its AI data centers. This could help with energy shortages as the demand for electricity grows.
  2. NASA and other organizations are working on new technologies to detect and deflect dangerous asteroids. This is important for protecting Earth from potential impacts.
  3. There are criticisms of populist economic policies like trade protectionism and industrial policy. These ideas can hinder progress and innovation in the economy.

AI’s Energy Crisis

Technically Optimistic • 39 implied HN points • 07 Jun 24
🕹 Technology Data Centers
  1. AI's energy consumption is rapidly increasing due to the demand for machine learning models and data processing, raising concerns for the future sustainability of AI technology.
  2. Efforts are being made to address the environmental impact of AI, such as exploring alternative energy sources, water recycling techniques, and more efficient cooling systems for data centers.
  3. Regulators and innovators are seeking solutions to manage AI's energy use, including implementing baseload reliable energy, optimizing power usage during off-peak hours, and demanding transparency from AI developers.

Q&A With Marvell CEO Matt Murphy

More Than Moore • 303 implied HN points • 13 Jan 25
🕹 Technology Data Centers
  1. Marvell is focusing on custom chip design to meet the growing demand from large tech companies, helping them create tailored solutions without needing extensive in-house resources. This trend is important for optimizing performance and costs in data centers.
  2. The company recently announced a new high-performance memory interface called HBM, which is in high demand for advanced computing. They are offering innovative designs to enhance speed and reduce power usage.
  3. Marvell sees significant growth opportunities in the AI sector, believing there are still many product cycles ahead. They are committed to investing in R&D to stay competitive in this rapidly evolving market.

My episode on Carl Zha regarding latest on AI

TP’s Substack • 15 implied HN points • 16 Dec 25
🕹 Technology Data Centers
  1. China is rapidly building a full AI hardware ecosystem — from semiconductors to memory, analog parts, cooling, and optical networking — which makes its domestic supernodes and clusters increasingly powerful.
  2. System-level limits like energy, interconnect bandwidth, and memory often leave chips idle, so tightly wiring many smaller chips into SuperPoDs or SuperClusters can be more effective than relying on bigger standalone chips.
  3. In the near term most AI will act like an advanced search/automation tool that replaces entry-level work, and while China may buy large numbers of H200s, those GPUs alone won’t be the core of its overall AI chip demand.

Gallium Nitride + Photonics w/ James Lee of Wave Photonics

State of the Future • 14 implied HN points • 09 Dec 25
🕹 Technology Data Centers
  1. Gallium nitride (GaN) could be better for photonics than silicon. It can generate light directly on the chip, while silicon needs separate lasers, making it less efficient.
  2. The constraints of using specific wavelengths for light transmission are starting to disappear. In short-distance connections, like inside data centers, it's possible to use a wider range of wavelengths.
  3. There's no perfect material for every need. Using different materials for different tasks could lead to better solutions in fields like quantum computing and RF photonics, making the industry more versatile.

A practical breakdown of the AI power situation

Technically • 18 implied HN points • 25 Nov 25
🕹 Technology Data Centers
  1. To make AI smarter, we need more computers, especially powerful GPUs. The more compute power we have, the better AI models we can create.
  2. Building more data centers is required for this extra compute power, but our current power grid can't handle the demand. This could lead to problems as AI grows.
  3. Big tech companies are investing in nuclear power plants because renewable energy alone can't keep up with the energy needs of AI data centers.

Jio Moment for Indian Hyperscalers?

Sector 6 | The Newsletter of AIM • 59 implied HN points • 08 Feb 24
🕹 Technology Data Centers
  1. Indian companies are growing their data center capacity rapidly, which poses challenges for major cloud service providers like AWS and Microsoft Azure. This means more options for businesses in India when it comes to cloud services.
  2. Government support and new data security rules are fueling the rise of hyperscale data centers in India. This shows a strong push towards more secure and accessible digital infrastructure.
  3. The growth in hyperscale capacity mirrors the earlier success of Jio in the telecom industry, suggesting India could play a big role in the global tech landscape with advances in AI and data services.

I thought we were broke

Alex's Personal Blog • 65 implied HN points • 07 Jul 25
🕹 Technology Data Centers
  1. Groq is making waves in AI and inference computing by building special chips that help with AI tasks. They recently expanded into Europe to better meet customer needs.
  2. Elon Musk's new America Party might attract moderate voters looking for alternatives, especially among tech elites. However, it's uncertain how popular it will actually be among voters.
  3. Changes to the tax rules for small businesses allow for more tax-free sales, which could help startups but may raise questions about government handouts amid claims of a budget crisis.

Technically Monthly (December 2025)

Technically • 12 implied HN points • 08 Dec 25
🕹 Technology Data Centers
  1. RLHF acts like a finishing school for AI, using supervised fine-tuning, reward models, and reinforcement learning so models learn to format answers, judge quality, and prefer better responses.
  2. Scaling modern AI needs huge, reliable power — labs are investing in gigawatts of electricity and striking deals with cloud and energy providers, which is why you’re seeing big data center and power projects.
  3. For AI at work, start small by automating recurring 30–90 minute manual tasks so you can give clear context, iterate quickly, and save time on repetitive work while keeping judgment-heavy parts for people.

20260118

Curious futures (KGhosh) • 4 implied HN points • 18 Jan 26
🕹 Technology Data Centers
  1. AI is rapidly reshaping industries and work: companies are pivoting from old bets to AI services, and jobs are becoming more fractional and outcome-based as AI starts to behave like a new kind of employee.
  2. Communities can reclaim AI to protect and revive culture and language, showing technology can be used for cultural stewardship rather than just profit.
  3. The rush toward new tech exposes material, security, and social strains—so preserving human rhythms like rest, play, and collective care is essential for resilience.

AI's Environmental Impact: How Much Energy Does It Consume?

Rod’s Blog • 39 implied HN points • 19 Feb 24
🕹 Technology Data Centers
  1. Artificial intelligence (AI) consumes a significant amount of energy and contributes to a large carbon footprint due to its need for computing power.
  2. The main sources of AI's carbon footprint are data centers that rely on fossil fuels or non-renewable energy sources to power and cool the machines.
  3. Both AI and cryptocurrency mining are energy-intensive activities but can benefit from renewable energy sources and face challenges related to ethics and regulation.

AI Roundup 102: The Stargate Project

Artificial Ignorance • 71 implied HN points • 24 Jan 25
🕹 Technology Data Centers
  1. The Stargate Project is a huge partnership by OpenAI, SoftBank, and Oracle to build new AI data centers in the U.S., promising up to $500 billion investment. This is much larger than past projects like the Manhattan and Apollo projects.
  2. China is making fast progress in AI, with new models from companies like DeepSeek that can compete with major Western models. This raises concerns for leading U.S. labs about staying ahead in AI technology.
  3. There are new challenges in measuring AI performance since current benchmarks are not effective anymore. A new test called 'Humanity's Last Exam' highlights this issue as AI systems advance beyond human-level capabilities.

5 cool AI things this week

Sex and the State • 4 implied HN points • 17 Dec 25
🕹 Technology Data Centers
  1. AI and data centers raise real energy and water concerns: electricity demand is the bigger issue, water worries are emotionally charged, and cooling or water-use choices can change the impact.
  2. A patchwork of state regulations is making it harder for smaller AI companies to compete and could stifle useful innovation, while policymakers often focus on narrow problems like deepfakes instead of bigger issues like energy and grid planning.
  3. Nobody really knows how AI will transform the world, so there’s a lot of uncertainty, and near-term risks from malicious humans using AI deserve more attention than hypothetical superintelligent scenarios.

UX Roundup: UX Leaders Need Profits | In-depth AI | Emotional Avatar Animation | Construction Time | User Needs

Jakob Nielsen on UX • 5 implied HN points • 01 Dec 25
🕹 Technology Data Centers
  1. UX leaders should focus on delivering value and driving profits, rather than just trying to get close to CEOs. It's important to prove you can make a positive impact on the business.
  2. New AI tools like 'Deep Review' are designed to analyze and improve academic papers in depth, showing that more investment in AI can lead to better results in research quality.
  3. User engagement with AI products, like ChatGPT, is growing fast. People are increasingly relying on these tools over traditional methods, signaling a big shift in user preferences.

AI’s climate footprint, a nuclear renaissance, and the new climate subsidy wars 🔥

Climate Money • 19 implied HN points • 30 Jan 24
🌞 Climate & Environment Data Centers
  1. Global electricity demand from data centers is set to double in the next two years due to AI's growth.
  2. Nuclear industry is experiencing a significant moment with uranium prices reaching a 16-year high.
  3. There is a new competitive landscape in the global climate technology space with Europe's entry leading to climate subsidy wars.

🔊Capital Allocators China - Generative AI Panel

Interconnected • 200 implied HN points • 14 Aug 23
🕹 Technology Data Centers
  1. Generative AI requires a significant amount of electricity and power for training, leading to data centers being located near cheap energy sources.
  2. Open source technologies are challenging closed source in the generative AI space, with implications for competition and innovation.
  3. Chinese AI model makers are emerging in unexpected places like niche internet companies and academic research institutes, showing diversity in the AI landscape.

Nvidia makes gains on rising inference demand

Alex's Personal Blog • 32 implied HN points • 27 Feb 25
🕹 Technology Data Centers
  1. Nvidia's revenue is soaring due to high demand for their chips, especially for AI models. This growth is a good sign for the entire AI industry as more companies seek powerful computing solutions.
  2. Rising demand for inference, which is running AI models to handle user queries, is becoming more important than just training the models. Nvidia’s chips are designed to excel in this area, suggesting ongoing strong sales.
  3. Other companies like Snowflake are also doing well with their earnings by integrating AI into their services, while Salesforce is facing challenges despite its strong AI prospects. This shows different paths in the tech industry as they adapt to AI's growth.

[Q4 CY23] Ericsson, Nokia, Lam Research, Tesla, TSMC

Irrational Analysis • 19 implied HN points • 21 Oct 23
🕹 Technology Data Centers
  1. Analyst pointed out Ericsson's struggles with return to 2018 revenue levels and significant growth decline, raising concerns about pricing and cost-cutting efforts.
  2. Nokia's CEO indicated a challenging forecast with no recovery until 2026, expressing concern over irrational pricing actions by competitors in the market.
  3. TSMC CEO emphasized confidence in the company's advanced technology, dismissing the impact of edge AI on revenue growth in 2024.

Op-Ed: Is the Stargate AI project bold vision or grand illusion?

philsiarri • 22 implied HN points • 22 Jan 25
🕹 Technology Data Centers
  1. The Stargate AI project has a huge amount of funding, starting at $100 billion and possibly growing to $500 billion. This shows a strong interest in AI technology.
  2. There are a lot of big promises being made about this project, but some people are worried that it might be overhyped and not deliver on its potential.
  3. The project's success will depend on managing many challenges, like building the right infrastructure, getting through regulations, and making sure it benefits everyone.

Taiwan’s Power Crunch vs AI Ambitions

Taipology • 22 implied HN points • 19 Dec 24
🕹 Technology Data Centers
  1. Taiwan aims to develop its own AI called 'Sovereign AI,' but it faces challenges in powering the necessary data centers.
  2. Currently, Taiwan struggles with electricity supply, limiting its ability to support large data centers needed for AI development.
  3. The government could restart mothballed nuclear reactors to increase power supply, which may be crucial for Taiwan to keep up with global AI advancements.

Would We Really Shut Down A Misbehaving AI?

Am I Stronger Yet? • 62 implied HN points • 15 Dec 23
🕹 Technology Data Centers
  1. People are usually hesitant to shut down a rogue AI due to various reasons like financial interests and fear of backlash.
  2. Delaying the decision to shut down a misbehaving AI can lead to complications and potentially missing the window of opportunity.
  3. Shutting down a dangerous AI is not as simple as pressing a button; it can be complex, time-consuming, and error-prone.

❄️Cubits in a Fridge: Make Quantum Great Again (Feat Himadri of SemiQon)❄️

State of the Future • 19 implied HN points • 04 Dec 24
🕹 Technology Data Centers
  1. Silicon spin qubits are smaller and cheaper than other types, making them more scalable. They can potentially revolutionize quantum computing by using existing semiconductor technology.
  2. Cryo-CMOS technology allows quantum computers to operate at very low temperatures, which is essential for maintaining quantum states. This can also help reduce cooling costs for data centers, which spend billions on keeping their systems cool.
  3. The focus in quantum computing is shifting from just the number of qubits to how efficiently they perform operations. Spin qubits might have an advantage here due to their longer coherence times and faster gate operations.

The Carbon Impact of Large Language Models: AI's Growing Environmental Cost

ScaleDown • 11 implied HN points • 10 Dec 23
🕹 Technology Data Centers
  1. Large language models like GPT-4 and LLaMA 2 have a significant carbon footprint due to massive energy consumption during training.
  2. Factors affecting the carbon footprint of ML models include hardware, training data size, model architecture, training duration, and data center location.
  3. It is essential to balance the benefits of AI models with minimizing their environmental impact, considering their vast energy requirements.