2026-01-27 - Final Edit
Context
Day of the year is 27.
Mega Category for today is Tech Analysis. Definition: Information regarding frontier technologies (Generative AI, Quantum Computing), space exploration, and consumer tech infrastructure (datacenters, chips). Distinguishes itself from general news by its forward-looking nature and technical depth. Consumed via specialized newsletters, tech blogs, video essays, and whitepapers. Do all you can to avoid these sorts of complaints: Readers complain about breathless hype cycles that promise revolutionary change while ignoring practical limitations and ethical concerns. There’s frustration with tech journalism that functions as PR for companies rather than critical analysis. Many criticize the lack of diversity in perspectives, with coverage dominated by Silicon Valley insiders and venture capital viewpoints. The field is faulted for ignoring labor implications, environmental costs, and social harms in favor of celebrating innovation. Users also express fatigue with constant ‘disruption’ narratives and FOMO-driven coverage. Note:
The Story Angle for today is Speculation Description: Projects the current trajectory of the category into the near future, using data and expert consensus to build a grounded scenario of what comes next. Unlike standard ‘future’ hype, this focuses on the inevitable collision of current trends and the ethical or practical dilemmas that will arise. Do all you can to avoid these sorts of complaints: Science fiction fantasy or uncritical PR hype for new technology. Avoids doom-scrolling or utopianism that ignores the friction of real-world implementation. Note:
The topic for today’s work is: Speculation in the field of Tech Analysis
Primary: Item One
Description for item one. Edit this in the-review-data.js
Secondary: Approach B
Second approach or angle
Goal
I like the version of the work I’ve included in the input section the best, it looks like it might actually work for creating a newspaper, but it needs to both be tightened up a bit and made more engaging for a layman audience. Make no mistake, this is a newspaper for highly educated people and don’t go screwing around with the details or article length. My goal is to make the look of the paper better, not the content. So make sure the headlines hook people, the first paragraph gives an overview so that they can determine to continue reading or not. I believe this is the usual editorial work done in newspapers. Also, since the drop quotes and the infographics are also scanned first, we’re going to need something good. If there are any included, re-do those as needed to make them more appealing to a layreader that’d be fine. If there are not any there, make some. We’re going to want to be able to embed, pack up, or otherwise distribute as a stand-alone static web page, pdf, and markdown for obsidian. Don’t link graphics out to other places. For Obsidian, I’m happy with svgs that I can drop in my attachment folder
We’ve got the content. We need to pay attention to the look and feel of the paper. Review the style guide and do your best to follow it.
Finally, I have an idea about one of those pencil sketches like the NYT used to run, only as a thumbnail beside each of the pro and cons for the section. Show us who wrote this. Makes the paper look more personal and official. You might need to dig around to figure out who that is. Also, this may be a bad idea. I don’t know.
We’ll probably do one more look at the overall feel of the newspaper before we’re officially done with this.
When we do content like this, many LLMs just can’t make it through without hanging and/or losing context. Take some time to orchestrate what you’re doing and use temp workspaces in order to be able to easily pick this back up where you left off.
Background
Relevant context, prior work, and constraints
Success Criteria
How will you know when this is done well?
Daily Newspaper Style Guide
This style guide ensures consistency across all editions of the daily newspaper. It applies to both human editors and large language models (LLMs) during the final polishing stage, after core content (articles, headlines, images, etc.) has been drafted. The goal is to maintain a professional, readable, and uniform appearance, fostering reader trust and brand recognition. Adhere strictly to these rules unless overridden by specific editorial decisions.
1. Overall Structure and Layout
- Edition Header (Masthead): Every edition must start with a centered masthead block including:
- Volume and issue details, day, date, and price in uppercase, small caps or equivalent, on one line (e.g., “VOL. I, NO. 47 • SUNDAY, JANUARY 11, 2026 • PRICE: ONE MOMENT OF ATTENTION”), centered, in 10-12pt font.
- Newspaper name in bold, uppercase, large font (e.g., 48pt), split across two lines if needed (e.g., “THE GLOBAL” on first line, “CONNECTOR” on second), centered.
- Tagline in quotes, italic, below the name (e.g., “Tracing the threads that hold the world together—before they snap”), centered, in 14pt font.
- A horizontal rule (---) below the masthead for separation.
- Example in markdown approximation:
VOL. I, NO. 47 • SUNDAY, JANUARY 11, 2026 • PRICE: ONE MOMENT OF ATTENTION THE GLOBAL CONNECTOR *"Tracing the threads that hold the world together—before they snap"* ---
- Background and Visual Style: Aim for a newspaper-like background in digital formats (e.g., light beige or subtle paper texture via CSS if possible; in plain markdown, note as a design instruction for rendering).
- Sections: Organize content into a themed newsletter format rather than rigid categories. Start with an introductory article, followed by 4-6 main stories, and end with an editorial. Each story should stand alone but tie into the edition’s theme.
- Introductory article: Begins immediately after masthead, with a main headline in bold, title case.
- Main stories: Each starts with a bold headline, followed by a subheadline in italic.
- Editorial: Labeled as “EDITORIAL” in uppercase, bold, with its own headline.
- Separate sections with ❧ ❧ ❧ or similar decorative dividers.
- Limit total content to 2000-3000 words for a daily edition.
- Page Breaks/Flow: In digital formats, use markdown or HTML breaks for readability. Aim for a “print-like” flow: no more than 800-1000 words per “page” equivalent. Use drop caps for the first letter of major articles.
- Footer: End every edition with:
- A horizontal rule.
- Production Note: A paragraph explaining the collaboration between human and AI, verification process, and encouragement of skepticism (e.g., “Production Note: This edition… Your skepticism remains appropriate and encouraged.”).
- Coming Next: A teaser for the next edition (e.g., “Coming Next Week: [Theme]—examining [details]. Also: [additional hook].”).
- Copyright notice: ”© 2026 [Newspaper Name]. All rights reserved.”
- Contact info: “Editor: [Name/Email] | Submissions: [Email]“.
- No page count; end with a clean close.
2. Typography and Formatting
- Fonts (for digital/print equivalents):
- Headlines: Serif font (e.g., Times New Roman or Georgia), bold, 18-24pt.
- Subheadlines: Serif, italic, 14-16pt.
- Body Text: Serif, regular, 12pt.
- Captions/Quotes: Sans-serif (e.g., Arial or Helvetica), 10pt, italic.
- Use markdown equivalents: # for main headlines, for sections, bold for emphasis, italic for quotes/subtle emphasis.
- Drop Caps: Introduce new articles or major sections with a drop cap for the first letter (e.g., large, bold initial like Welcome). In markdown, approximate with W and continue the paragraph; in rendered formats, use CSS for 3-4 line height drop.
- Headlines:
- Main article headlines: Capitalize major words (title case), no period at end.
- Keep to 1-2 lines (under 70 characters).
- Example: “Everything Is Connected (By Very Fragile Stuff)”
- Body Text:
- Paragraphs: 3-5 sentences each, separated by a blank line.
- Line length: 60-80 characters for readability.
- Bullet points for lists (e.g., key facts): Use - or * with consistent indentation.
- Tables: Use markdown tables for data. Align columns left for text, right for numbers.
- Pull Quotes (Drop Quotes): Insert 1-2 per story, centered, in a boxed or indented block, larger font (14pt), italic, with quotation marks. Place mid-article for emphasis. Example in markdown:
> "The tech giants in California scream about latency and 'packet loss,' viewing the outage as a software bug. The ship captain knows the truth: the internet is just a wire in the ocean." - Emphasis:
- Bold (text) for key terms or names on first mention.
- Italics (text) for book titles, foreign words, or emphasis.
- Avoid ALL CAPS except in headers.
- No underlining except for hyperlinks.
- Punctuation and Spacing:
- Use Oxford comma in lists (e.g., “apples, oranges, and bananas”).
- Single space after periods.
- Em-dashes (—) for interruptions, en-dashes (–) for ranges (e.g., 2025–2026).
- Block quotes: Indent with > or use italics in a separate paragraph for quotes longer than 2 lines.
3. Language and Tone
- Style Standard: Follow Associated Press (AP) style for grammar, spelling, and abbreviations.
- Numbers: Spell out 1-9, use numerals for 10+ (except at sentence start).
- Dates: “Jan. 12, 2026” (abbreviate months when with day).
- Titles: “President Joe Biden” on first reference, “Biden” thereafter.
- Avoid jargon; explain acronyms on first use (e.g., “Artificial Intelligence (AI)”).
- Tone: Neutral, factual, and objective for news stories, with a witty, reflective edge. Editorial may be more opinionated but balanced. Overall voice: Professional, concise, engaging—aim for a reading level of 8th-10th grade. Use direct address like “dear reader” in intros.
- Length Guidelines:
- Introductory article: 200-400 words.
- Main stories: 300-500 words each.
- Editorial: 400-600 words.
- Avoid fluff; prioritize who, what, when, where, why, how, with thematic connections.
- Inclusivity: Use gender-neutral language (e.g., “they” instead of “he/she”). Avoid biased terms; represent diverse perspectives fairly.
- For Further Reading: Perspectives: At the end of each story and editorial, include a “FOR FURTHER READING: PERSPECTIVES” section. Use PRO (green box) and CON (red box) for balanced views. Each entry: Bold label (PRO or CON), title in quotes, source with hyperlink. Approximate boxes in markdown with code blocks or tables; in rendered formats, use colored backgrounds (e.g., light green for PRO, light red for CON). Example:
FOR FURTHER READING: PERSPECTIVES **PRO** "Why Governments Must Control Cable Repair" — Parliament UK Joint Committee Report Source: [publications.parliament.uk](https://publications.parliament.uk) (September 2025) **CON** "Sabotage Fears Outpace Evidence" — TeleGeography Analysis Source: [blog.telegeography.com](https://blog.telegeography.com) (2025)
4. Images and Media
- Placement: Insert images after the first or second paragraph of relevant articles. Use 1-2 per article max. No images in this example, but if used, tie to stories (e.g., maps for cables, illustrations for AI).
- Formatting:
- Size: Medium (e.g., 400-600px wide) for main images; thumbnails for galleries.
- Alignment: Center with wrapping text if possible.
- In text-based formats, describe images in brackets: [Image: Description of scene, credit: Source].
- Captions: Below images, in italics, 1-2 sentences. Include credit (e.g., “Photo by Jane Doe / Reuters”).
- Alt Text (for digital): Provide descriptive alt text for accessibility (e.g., “A bustling city street during rush hour”).
- Usage Rules: Only relevant, high-quality images. No stock photos unless necessary; prefer originals or credited sources.
5. Editing and Proofing Checklist
Before finalizing:
- Consistency Check: Ensure all sections follow the structure. Cross-reference dates, names, facts, and thematic ties.
- Grammar/Spelling: Run through a tool like Grammarly or manual review. Use American English (e.g., “color” not “colour”).
- Fact-Checking: Verify claims with sources; add inline citations if needed (e.g., [Source: Reuters]).
- Readability: Read aloud for flow. Break up dense text with subheads, pull quotes, or bullets.
- LLM-Specific Notes: If using an LLM for polishing, prompt with: “Apply the style guide to this draft: [insert content]. Ensure consistency in structure, tone, formatting, including drop caps, pull quotes, and perspectives sections.”
- Variations: Minor deviations allowed for special editions (e.g., holidays), but document changes.
This guide should be reviewed annually or as needed. For questions, contact the editor-in-chief. By following these rules, each edition will maintain a polished, predictable look that readers can rely on.
Failure Indicators
Input
THE REVIEW
VOL. I, NO. 1 • TUESDAY, JANUARY 27, 2026 • PRICE: ONE MOMENT OF ATTENTION
“Where software meets atoms—and atoms push back”
When the Machines Got Thirsty
A special edition examining the collision between artificial intelligence and physical reality
Dear reader, we present to you an unusual newspaper.
For decades, the technology industry has operated on a simple faith: that computing power would continue to grow exponentially, unbound by earthly concerns. The silicon would shrink, the models would expand, and the future would arrive on schedule. Then came 2025—the year the world’s most valuable technology companies discovered they had a physics problem.
This special edition of The Review examines what might be called the Great Reckoning: the moment when artificial intelligence, that most ethereal of technologies, collided with the stubbornly material world. The stories that follow trace this collision across six continents, from Japanese glass looms to orbital satellites, from nuclear reactor control rooms to data center cooling towers. They reveal an industry scrambling to reconcile exponential ambition with finite resources—and finding, in the process, that the path forward may lie in constraint rather than abundance.
You will read about a 589 billion from a single company’s value. About nuclear reactors being resurrected from the dead to power chatbots. About the Japanese cloth that has become more strategically important than any semiconductor. About computers that must now be cooled with liquid because air is no longer cold enough. About open-source chip designs that are quietly reshaping the global balance of power. And about the ultimate escape hatch: putting data centers in space, where the sun never sets and the cooling is free.
These stories are connected by a single thread: the realization that software, for all its promise of infinite scalability, must ultimately run on hardware—and hardware is made of atoms, consumes energy, and generates heat. The companies building the future of artificial intelligence are learning, sometimes painfully, that the future must be built from physical things. Welcome to the age of material computing.
❧ ❧ ❧
The $6 Million Earthquake
How a Chinese startup trained a world-class AI model for the price of a modest Manhattan apartment—and terrified Wall Street
A PDF posted to the internet cost Nvidia $589 billion in a single day.
On Jan. 27, 2025—exactly one year ago today—the Chinese artificial intelligence company DeepSeek released a technical report alongside its R1 reasoning model. The document claimed the final training run had cost approximately $5.6 million. American laboratories had spent hundreds of millions, sometimes billions, reaching similar capabilities. Nvidia’s stock suffered the largest single-day value destruction in market history. Investors suddenly wondered if they had been funding an arms race with a water pistol.
The number was simultaneously misleading and profound. Critics correctly noted that DeepSeek’s 1.6 billion.
Yet to dismiss the achievement as accounting trickery misses the strategic earthquake beneath the spreadsheets. DeepSeek’s engineers, denied access to Nvidia’s fastest chips by U.S. export controls, had been forced to innovate around their hardware limitations. The H800 processors available to Chinese buyers featured deliberately crippled communication speeds—they could think fast but struggled to talk to their neighbors. American engineers, blessed with unlimited budgets and top-tier equipment, had no such constraints and therefore no such incentives.
Constraint became catalyst. DeepSeek developed techniques—multi-head latent attention, auxiliary-loss-free load balancing, aggressive quantization—that allowed their 671 billion parameter model to train with the compute budget typically reserved for models one-tenth its size. They eliminated the expensive “Critic” model from their reinforcement learning pipeline, halving alignment costs. They open-sourced everything.
“We had no incentive to find the efficiency frontier when investors were providing billions to find the capability frontier instead,” a senior researcher at a major American AI lab admitted anonymously. “DeepSeek, backed into a corner by geopolitics, discovered that the path we weren’t taking actually led somewhere.”
The irony was not lost on policy analysts: U.S. export controls designed to hobble Chinese AI had instead produced the AI equivalent of high-altitude training. One year later, Chinese labs are leaner, more rigorous, and arguably better prepared for the thermodynamic constraints now facing the entire industry. American labs have adopted many of DeepSeek’s techniques—a tacit admission that efficiency matters after all.
DeepSeek’s R2 model is expected within weeks.
[Image placeholder: Infographic comparing Western “brute force” vs. DeepSeek “efficiency” approaches across key technical dimensions]
For Further Reading: Perspectives
PRO “DeepSeek’s Latest Breakthrough Is Redefining the AI Race” — CSIS Analysis
Argues the efficiency gains signal the end of the “winner-takes-all” assumption in AI and open new paths for smaller nations.
Source: csis.org (December 2025)
CON “Challenging US Dominance: China’s DeepSeek Model and the Pluralisation of AI Development” — EU Institute for Security Studies
Notes DeepSeek still relied on foundational U.S. research and Nvidia hardware, limiting claims of true independence.
Source: iss.europa.eu (July 2025)
❧ ❧ ❧
The Nuclear Renaissance Nobody Expected
Big Tech discovers that chatbots need power plants—and the only ones available are the kind that split atoms
The machines that will think for us must first be fed, and what they eat is power.
American data centers consumed 183 terawatt-hours of electricity in 2024—roughly equivalent to the entire nation of Pakistan. By 2030, the International Energy Agency projects this will grow to 426 terawatt-hours. In Virginia’s Loudoun County, already nicknamed “Data Center Alley,” server farms consume more electricity than all the homes combined.
When a minor disturbance in Fairfax County last year caused 60 data centers to switch simultaneously to backup generators, the sudden disappearance of 1,500 megawatts—Boston’s entire demand—nearly triggered cascading grid failures. Residential electricity bills in western Maryland are expected to rise $18 per month just to accommodate the neighbors.
The technology industry’s response has been, in retrospect, inevitable: nuclear power. In late 2025, Microsoft announced a 20-year agreement to restart Three Mile Island Unit 1—not the infamous reactor, but its undamaged twin, mothballed in 2019 for economic reasons. A tech company was bringing a nuclear power plant back from the dead to serve as its captive power source.
Within months, everyone followed. Google signed agreements for up to 500 megawatts of small modular reactors from Kairos Power. Amazon invested over $20 billion in nuclear-powered data center projects and backed 5 gigawatts of X-energy reactors. Meta, not to be outdone, announced last week it had signed deals totaling over 4 gigawatts of nuclear capacity—enough to power a medium-sized European country—with partners including Vistra, Oklo, and TerraPower.
“While other sectors of the economy are backing away from this, large tech companies are still talking about it,” observed Bloomberg Intelligence analyst Rob Barnett, referring to clean energy commitments. “It’s clear that nuclear energy has to be a big part of meeting the demand for power from AI.”
The fundamental appeal is physical: nuclear plants provide “baseload” power—constant, weather-independent output that precisely matches AI training’s requirements. There is no Dunkelflaute in fission, no cloudy days or calm winds. And the technology industry is willing to pay premium prices for that reliability.
But a fundamental timing problem looms. AI compute demand doubles every six to eighteen months. Nuclear reactors take years to license and build. The Three Mile Island restart is targeted for 2028 at the earliest. Kairos’s first SMRs are expected around 2030. Until then, an awkward truth: the industry will likely bridge the gap by extending the life of fossil fuel plants.
A perverse near-term result: the greenest technology companies in history are indirectly prolonging the age of coal.
[Image placeholder: Timeline showing committed nuclear deals from Microsoft, Google, Amazon, and Meta, totaling over 10 GW]
For Further Reading: Perspectives
PRO “2026: The Year Nuclear Power Reclaims Relevance With 15 Reactors, AI Demand, and China’s Expansion” — Carbon Credits
Analyzes how grid constraints and AI demand are reshaping nuclear’s role as essential low-carbon baseload.
Source: carboncredits.com (December 2025)
CON “Microsoft Wants to Resurrect Three Mile Island. It Will Never Happen.” — The Hill (Opinion by Chatterjee)
Former FERC chair argues regulatory, material, and logistical hurdles make nuclear restarts unrealistic.
Source: thehill.com (January 2026)
❧ ❧ ❧
When Fans Aren’t Enough
The AI industry’s cooling systems are failing—and there aren’t enough plumbers to fix them
Air can no longer keep computers cool enough. This simple physical fact has upended decades of data center engineering.
A modern Nvidia Blackwell rack consumes 120 kilowatts—six times what air cooling can handle. The heat generated by next-generation GPUs cannot be removed by fans alone; it requires liquid flowing directly over the processors. The semiconductor industry’s roadmap calls for 4,400-watt chips by 2028. Heat dissipation per square centimeter now approaches 50 watts—comparable to a nuclear reactor core.
The industry has responded with a rushed transition to direct-to-chip liquid cooling. Cold plates channel coolant within millimeters of the silicon. The physics are sound. The implementation has been, at times, catastrophic.
When Microsoft’s Quincy data center experienced a cooling failure lasting 37 minutes, GPU temperatures spiked to 94 degrees Celsius. The result: $3.2 million in hardware damage and 72 hours of downtime. Post-incident analysis revealed the failure mode—chemical incompatibility between coolant and gasket materials—would have been obvious to anyone with process engineering training. The staff on-site were IT professionals, not chemical engineers.
This is the labor crisis beneath the cooling crisis. Managing a liquid-cooled data center requires the skills of a chemical engineer and a master plumber. The current workforce is trained in swapping hard drives and managing airflow. The gap cannot be closed through short-term hiring; the pipeline of qualified professionals simply does not exist at scale.
Galvanic corrosion has emerged as the silent destroyer. In the rush to deploy liquid cooling, many facilities mixed incompatible metals—copper cold plates connected to aluminum radiators. When coolant flows between them, the system becomes a battery. The aluminum sacrifices itself, dissolving into the fluid and precipitating as sludge that clogs microscopic fins. Eventually, the aluminum wall thins until it bursts, spraying glycol-water mixture onto racks worth millions.
A November 2025 incident at a CME Group data center in Aurora, Ill., illustrated the stakes. A chiller malfunction caused cooling to fail across multiple units, halting trading operations. H100 downtime now costs between 40,000 per GPU per day.
The industry’s response has been to engineer humans out of the loop. Vendors are racing to build “idiot-proof” systems—hermetically sealed, modular cooling cartridges that generalist technicians can swap without understanding fluid chemistry. Industry insiders call this “cartridge-ification”: the deliberate trade-off of peak efficiency for absolute reliability.
At sufficient scale, complexity becomes the enemy of survival.
[Image placeholder: Thermal density progression chart showing GPU power evolution from A100 (400W) to projected “Feynman” (4,400W)]
For Further Reading: Perspectives
PRO “The Data Center Cooling State of Play (2025)” — Tom’s Hardware
Comprehensive analysis of liquid cooling technologies rising to meet AI thermal demands.
Source: tomshardware.com (December 2025)
CON “Why Liquid Cooling for AI Data Centers Is Harder Than It Looks” — Schneider Electric
Industry vendor warns that trial-and-error approaches at extreme heat densities will fail catastrophically.
Source: blog.se.com (August 2025)
❧ ❧ ❧
The Trillion-Dollar Loom
The entire AI industry is waiting on output from a few specialized looms in Japan—and the glass that might replace them
The most important material in artificial intelligence is not silicon. It is a specialized fiberglass cloth called T-Glass, and a single Japanese company controls nearly all of it.
To understand why, one must first understand the “reticle limit.” The lithography machines that print chips have a maximum exposure area of roughly 858 square millimeters. No single silicon die can exceed this size. Yet frontier AI chips require trillions of transistors—far more than can fit on a single chip.
The industry’s solution has been “chiplet” architectures: stitching together multiple smaller dies onto a base layer called a substrate. This package acts as a motherboard for the silicon, providing the electrical connections between components. For decades, these substrates were made from organic resin reinforced with fiberglass cloth. The approach worked adequately for traditional chips.
It is failing for AI.
When a massive AI package—100mm by 100mm—is heated during manufacturing, the organic substrate expands at a different rate than the silicon sitting on it. This mismatch causes warping. For chips with thousands of microscopic connections, warping severs circuits and destroys yields.
T-Glass, or low-CTE glass cloth, is the current solution. Unlike standard fiberglass, T-Glass is chemically formulated to expand at nearly the same rate as silicon. But manufacturing it requires spinning molten glass into yarn finer than a human hair and weaving it into defect-free cloth. The process demands specialized furnaces that take years to build.
Global T-Glass production is dominated by Nitto Boseki of Japan. When Nvidia and AMD ramped production of chiplet-based AI accelerators, demand exploded beyond anything Nittobo anticipated. The company is reportedly sold out through 2027. The entire trillion-dollar AI hardware market is throttled by the output of a few glass looms.
The long-term solution—and the subject of an intense international competition—is abandoning organic cloth entirely in favor of solid glass substrates. A glass core uses a sheet of borosilicate or quartz glass as the package’s structural foundation. The advantages are profound: atomic-level flatness, tunable thermal expansion, 10 times the interconnect density.
Intel, having invested over a decade and $1 billion in its Arizona facility, displayed its first thick-core glass substrate with embedded EMIB bridges just yesterday at NEPCON Japan. SK’s Absolics facility in Georgia began commercial shipments late last year. Samsung plans glass interposers by 2028. Rapidus of Japan has demonstrated the world’s first 600mm × 600mm glass interposer.
The race is not merely commercial but strategic. Glass substrate capability is becoming a prerequisite for manufacturing AI chips at frontier scale. Nations without domestic glass substrate production will be dependent on those who have it.
The future of artificial intelligence may depend on mastering one of humanity’s oldest materials.
[Image placeholder: Substrate evolution comparison table—organic, T-Glass, glass core—with feature sizes and status]
For Further Reading: Perspectives
PRO “Glass Substrates: The Breakthrough Material for Next-Generation AI Chip Packaging” — Financial Content
Analysis of glass as a critical enabler for the next decade of AI computing.
Source: markets.financialcontent.com (January 2026)
CON “The Race To Glass Substrates” — SemiEngineering
Notes brittleness challenges, handling issues, and uncertainty about Intel’s internal commitment.
Source: semiengineering.com (August 2025)
❧ ❧ ❧
The Chip Design Nobody Owns
China has embraced an open-source processor architecture governed from Switzerland—and America is worried
For decades, the computing world has been a duopoly. Intel and AMD controlled the x86 architecture that runs servers and PCs. ARM Holdings, a British company, dominated mobile devices. Both require licensing and royalties.
RISC-V is different. It is an open-standard instruction set architecture—the fundamental language that software uses to command hardware. Anyone can implement RISC-V without paying royalties or seeking permission. The specification is maintained by a non-profit based in Switzerland.
Until recently, RISC-V was dismissed as a “toy” architecture—adequate for microcontrollers but lacking the software ecosystem for serious computing. That assessment is now obsolete.
China’s government announced plans earlier this month to promote nationwide adoption of RISC-V across eight government agencies. The guidelines, if enacted, would mark the first time a major world power has officially designated an open-source hardware standard as a national security priority. Beijing has concluded that reliance on x86 or ARM—both entangled with American or British intellectual property law—represents an existential risk to Chinese technological sovereignty.
RISC-V’s Swiss governance makes it legally difficult for any single nation to restrict access to the standard itself. For China, it is a strategic loophole.
The investment has been substantial. Alibaba’s XuanTie C930, unveiled in early 2025, targets server-grade performance previously thought impossible for an open-source design. The Chinese Academy of Sciences’ XiangShan project has produced the Kunminghu architecture, aiming for 3 GHz clock speeds that match ARM’s Neoverse N2. Chinese entities have filed more than 2,500 RISC-V patents, far surpassing American filings.
India has emerged as a second front. Under its Digital India RISC-V program, India launched DHRUV64 in December—its first homegrown dual-core processor. The nation is building a parallel ecosystem that mirrors China’s pursuit of sovereignty.
In a move that stunned analysts, Nvidia announced last year that it would port CUDA—its proprietary software platform that locks the AI industry to its GPUs—to RISC-V processors. The company would not invest engineering resources in a platform it expected to fail.
“If a server vendor chooses RISC-V, we want to support that too,” explained Nvidia’s Frans Sijstermans.
American policymakers have expressed alarm. Some members of Congress have called for export controls on contributions to the RISC-V standard—a proposal that critics warn could accelerate rather than slow the architecture’s development outside U.S. control.
The era of the proprietary monopoly on instruction sets is ending. The question is what replaces it.
[Image placeholder: Global compute stack diagram showing Western proprietary stack vs. sovereign open-source alternative]
For Further Reading: Perspectives
PRO “What RISC-V Means for the Future of Chip Development” — CSIS Analysis
Argues RISC-V offers firms control over their technology without expensive licensing fees.
Source: csis.org (July 2025)
CON “RISC-V Deserves the Same Scrutiny China Gives Nvidia” — Washington Times (Opinion by Whitley)
Warns the open architecture enables Chinese access and calls for expanded export controls.
Source: washingtontimes.com (October 2025)
❧ ❧ ❧
The Final Frontier Has Wi-Fi
Some companies think the solution to AI’s earthly constraints is obvious: leave Earth
In November, Nvidia-backed startup Starcloud successfully trained an AI model in orbit. The model learned from Shakespeare’s complete works, began speaking in Elizabethan English, and made history. Google’s Gemma ran inference 400 kilometers above the Earth’s surface.
“Greetings, Earthlings!” wrote the orbital AI. “Let’s see what wonders this view of your world holds.”
The achievement demonstrated that space-based data centers are no longer science fiction. They are a startup category.
The appeal is fundamentally physical. In a sun-synchronous orbit, a satellite receives continuous solar energy—no night, no clouds, no weather. Irradiance is 40 percent higher than Earth’s surface. And deep space is an effectively infinite heat sink at approximately 2.7 Kelvin. A properly designed radiator can reject hundreds of watts per square meter to the cosmic background—three times the electricity generated by equivalent solar panels.
There are no grid interconnection queues in orbit. No neighbors to complain about noise. No aquifers to drain. No permit hearings.
Starcloud’s Philip Johnston projects 10 times lower energy costs than terrestrial data centers, even including launch expenses. “In 10 years, nearly all new data centers will be built in outer space,” he predicts. Google’s “Project Suncatcher” aims to launch demonstration missions by 2027. Elon Musk has indicated SpaceX is exploring orbital data centers for his xAI company. Jeff Bezos has said Blue Origin expects gigawatt data centers in space within a decade.
Yet the skeptics enumerate substantial obstacles. Launch costs, even with reusable rockets, run thousands of dollars per kilogram. A gigawatt-scale data center would require mass measured in thousands of tons. There are no technicians in orbit; a failed GPU cannot be swapped. Cosmic rays cause bit-flips in electronics. And a Saarland University study calculated that accounting for rocket launch and atmospheric reentry emissions, orbital data centers might create an order of magnitude more carbon than Earth-based alternatives.
Perhaps most profound is the jurisdictional question. An AI model trained on a private station in international orbit is technically outside the reach of the EU AI Act, American executive orders, or Chinese regulations. The prospect of “orbital data havens” has sparked discussion of an international treaty for space-based computing—though negotiating such agreements while orbital AI remains experimental faces the eternal challenge of anticipatory governance.
For now, Starcloud-2 is scheduled for launch this October. It will carry multiple H100 chips and Nvidia’s new Blackwell B200. The next generation of AI may quite literally be looking down on us.
[Image placeholder: Rendering of Starcloud’s proposed 5-gigawatt orbital data center with 4km solar array]
For Further Reading: Perspectives
PRO “How Starcloud Is Bringing Data Centers to Outer Space” — NVIDIA Blog
Details the technical and environmental case for orbital computing.
Source: blogs.nvidia.com (October 2025)
CON “Space-Based Data Centers Could Power AI with Solar Energy—At a Cost” — Scientific American
Independent researchers warn orbital facilities may create greater emissions than terrestrial alternatives.
Source: scientificamerican.com (December 2025)
❧ ❧ ❧
EDITORIAL
When Software Met Atoms
The age of unlimited digital growth is over. What comes next?
There is a famous line in technology punditry: “Software is eating the world.” The phrase captured the early 21st century’s dominant dynamic—digital systems absorbing and transforming industry after industry.
The artificial intelligence era represents something different. Software has eaten enough of the world that it is now bumping into the world’s physical constraints. The data centers that house AI models require concrete, steel, water, and electricity in quantities that register on national resource statistics. The chips that run AI workloads require materials sourced from specific mines, processed in specific factories, and packaged using techniques that cannot be trivially replicated.
The stories in this special edition trace a single theme: the collision between exponential digital ambition and finite material reality. DeepSeek proved that efficiency is not optional; it is mandatory when hardware constraints bite. The nuclear rush proves that clean energy commitments matter less than reliable baseload when models need constant power. The cooling crisis proves that thermodynamics does not care about investor timelines. The glass shortage proves that supply chains have chokepoints nobody predicted. RISC-V proves that architectural openness may matter more than raw performance when sovereignty is at stake. And orbital computing proves that when you run out of room on Earth, some will simply leave it.
What unites these stories is not pessimism but pragmatism. The AI industry is learning—sometimes at the cost of hundreds of billions of dollars in market value—that there is no software solution to a hardware problem. You cannot optimize your way around a grid that does not have enough power. You cannot abstract away the need for specialized glass weavers in Japan. You cannot virtualize the laws of thermodynamics.
This is, in its way, good news. The recognition of material limits opens the door to material solutions. Efficiency innovations forced by export controls have improved the entire field. Nuclear investments forced by grid constraints may finally break the decades-long stalemate in clean energy deployment. Cooling standardization forced by complexity will improve reliability across the industry.
The next decade of AI will not be determined solely by who writes the cleverest algorithms. It will be determined by who can source T-Glass, restart nuclear reactors, train cooling technicians, manufacture glass substrates, and perhaps—eventually—escape the gravity well entirely.
The era of software eating the world is giving way to something more complex: a dialectic between digital ambition and material constraint. The world is eating back. And for those who understand this, the collision may prove more opportunity than obstacle.
“By the end of this decade, the semiconductor industry will likely reach its limits on being able to scale transistors on a silicon package using organic materials.”
— Intel Corporation, Glass Substrate Announcement
For Further Reading: Perspectives
PRO “The Great Decoupling: How RISC-V Became China’s Ultimate Weapon for Semiconductor Sovereignty” — Financial Content
Argues open-source standards create a more diverse, accessible technology future.
Source: markets.financialcontent.com (December 2025)
CON “The Public Pays the Price for Big Tech’s Data Centers” — The Invading Sea (Opinion by Trota)
Argues tech giants should be held accountable for resource consumption and climate impact.
Source: theinvadingsea.com (December 2025)
Production Note
This edition of The Review was produced on Monday, January 26, 2026, in collaboration between human editorial direction and artificial intelligence drafting and research capabilities. The research underpinning these articles draws on a comprehensive investigation titled “The Weight of Intelligence: How the Physical World Is Reshaping the Future of AI,” with additional current events and opinion pieces verified through web search.
All factual claims have been sourced from reputable publications including Bloomberg, CNBC, Tom’s Hardware, Scientific American, the International Energy Agency, and official corporate announcements. Opinion pieces presented in “For Further Reading” sections represent genuine published perspectives and are linked to their original sources.
Your skepticism remains appropriate and encouraged.
Coming Next Week: The Talent War—examining how the shortage of cooling engineers, nuclear physicists, and glass substrate specialists is reshaping technology hiring. Also: Updates on DeepSeek R2 and Meta’s nuclear deals.
© 2026 The Review. All rights reserved.
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