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Vlad Saigau on X: "What will a 100 kW/ton orbital compute satellite look like? We’ve been building a satellite mass-budget model to show how compute satellites will differ architecturally from Starlink. Satcom/Starlink baselines allocate ~35 % of dry mass to phased-array antennas, gimbals, and https://t.co/47dlHmOmxN" / X

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  • Mass Reallocation: Elimination Of Phased Array Antennas And Earth Pointing Mechanisms Allows Dry Mass To Be Redirected Into Solar Generation And Thermal Management
  • Data Transit: Satellites Utilize Laser Links For Data Movement Via Existing Constellations Instead Of Traditional Radio Frequency Downlinks
  • Power Density: System Power Density Reaches Approximately One Hundred Watts Per Kilogram Meeting Industry Benchmarks For High Performance Orbital Computing
  • Thermodynamic Optimization: Efficiency Depends On The Interdependence Of Solar Generation Thermal Rejection And Compute Capability Creating A Triangular Scaling Bottleneck
  • Blackbody Radiation: Operating Chips At High Temperatures Exploits Scaled Heat Rejection To Reduce Necessary Radiator Mass And Surface Area
  • Radiator Configuration: Deployable Two Sided Fins Positioned In The Shade Of Solar Arrays Maximize Heat Dissipation To Cold Space
  • Structural Design: Compact Central Buses House Payloads While Radial Thin Film Solar Arrays Utilize Passive Gravity Gradient And Centrifugal Tensioning
  • Orbit Strategy: Deployment Into Dawn Dusk Sun Synchronous Orbits Enables Constant Solar Access And Simplified Thermal Management Profiles

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Vlad Saigau

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@VladSaigau

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What will a 100 kW/ton orbital compute satellite look like? We’ve been building a satellite mass-budget model to show how compute satellites will differ architecturally from Starlink. Satcom/Starlink baselines allocate 35 % of dry mass to phased-array antennas, gimbals, and other continuous Earth-pointing mechanisms required for RF routing. Compute satellites will have no downlink; data moves via laser links to the Starlink constellation. The freed mass is completely reallocated to solar arrays and radiators, shifting us from an RF-routing architecture to a thermodynamic-optimised one. The triangular bottleneck in orbital compute satellites is the three-way interdependence of solar generation, thermal rejection, and compute capability: any single subsystem can only scale as far as the other two allow, so true power-density gains require all three to advance in lockstep, otherwise one becomes the binding constraint. Compute sats have no need for ultra-precise Earth-pointing, so solar arrays can be far lighter and flexible, using passive gravity-gradient and centrifugal tensioning in dawn-dusk SSO. Operating the compute ASICs at ~370 K, exactly as Elon highlighted, exploits the T⁴ scaling of blackbody radiation, boosting net heat rejection significantly, and thereby slashing the required radiator area (and therefore mass) dramatically. We believe radiators must deploy two-sided and sit in the shade of the solar arrays for optimal cold-space view factors, in order to hit 100kW/ton. At first glance, thermal rejection becomes one of the largest subsystems (34 % of dry mass). Yet this dominance only appears because projected PV efficiency gains and higher chip temperature enable 7× higher power throughput, greater than the expected headroom in radiator efficiency gains. The triangular bottleneck closes cleanly at 100.2 W/kg system power density, almost exactly the line in the sand Elon and SpaceX have drawn. As finance folks who've had the privilege of learning from leading space-industry engineers, we offer this conceptual take with a grain of salt: these 100 kW/ton satellites would likely look quite different from Starlink satellites... We imagine compute satellites will have a compact central bus housing only the dense compute payload and laser links, surrounded by large, light, thin-film solar arrays deployed radially like wings or sails, kept taut by passive gravity-gradient and centrifugal tensioning in dawn-dusk SSO. Paired with them will be two-sided deployable radiator fins, deliberately positioned in the permanent shade of the arrays for optimal cold-space view factors. We are keen to learn more though so please share any suggestions. Read the full analysis here for all our modelling and charts 🧐 https://research.33fg.com/analysis/the-space-data-center-mass-budget-behind-10x-power-density…

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8:43 PM · Mar 5, 2026

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bogorad
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The Convergence: Why US-China War Is Quite Possible

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  • Strategic Energy Isolation: The United States eliminated China's primary independent energy sources in Venezuela and Iran, forcing reliance on vulnerable maritime routes and insufficient pipeline infrastructure.
  • Arctic Resource Encirclement: NATO’s consolidation of Arctic control through Arctic Sentry and reinforced military deployments in Norway and Greenland restricts China's alternative northern supply corridors.
  • Military Readiness Deadline: Xi Jinping’s mandated 2027 goal for operational readiness in Taiwan is underscored by a recent 7% increase in defense spending and massive fleet expansion.
  • Consolidation Of Power: The Central Military Commission has been reduced to two members, signaling a systematic purge of leadership to ensure absolute obedience during anticipated wartime escalations.
  • US Strategic Overextension: Significant American military assets are currently diverted to the Middle East, creating a temporary window of vulnerability for potential kinetic or blockade actions in the Indo-Pacific.
  • Autonomous Warfare Dominance: The next conflict will be decided by the industrial capacity to mass-produce autonomous systems, where China currently leads in humanoid robotics and drone manufacturing.
  • Critical Chokepoints: Semiconductor production remains the ultimate strategic prize, with 90% of advanced global output concentrated in Taiwan, alongside Chinese control over the rare earth elements essential for modern weaponry.
  • Global Logistics Infrastructure: The Belt and Road Initiative provides China with a dual-use global backbone of ports and infrastructure that can be legally commandeered for military purposes during a prolonged conflict.

By Alexander Temerev

China’s military has eighteen months left on the readiness deadline Xi Jinping set for a Taiwan operation. The United States has two carrier strike groups, 50,000 troops, and a dwindling munitions stockpile pinned down in Iran. And between January and March 2026, three seemingly unrelated American actions cut China off from every major energy supplier outside Western control.

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Each of these has its own official rationale. Together, they trace the outline of something larger: a convergence of structural conditions making a US-China military confrontation harder to avoid with each passing month.

Act One: The Sequential Isolation of China


On January 3, 2026, the United States seized Venezuelan President Nicolás Maduro, disrupting one of China’s key sources of discounted oil that bypassed US-monitored financial channels.

On February 28, 2026, the United States and Israel launched Operation Epic Fury, a massive joint military assault on Iran. Within five days, over 1,000 Iranians were killed, Iran’s Supreme Leader was assassinated, and the Strait of Hormuz was effectively shut down. Iran was China’s other major source of sanctioned crude oil, supplying roughly 13% of China’s seaborne intake.

These events are typically analyzed separately. Venezuela is framed as a Monroe Doctrine enforcement action. Iran is framed as a nuclear nonproliferation campaign. But look at what happened in aggregate: in less than two months, the United States eliminated China’s two primary alternative energy suppliers, the ones that operated outside Western financial monitoring systems.

Coincidence? Perhaps. But consider the third move.

China does have overland energy connections that no navy can interdict. The Power of Siberia 1 pipeline delivered 38.8 bcm of Russian gas in 2025. The ESPO pipeline pumps roughly 600,000 barrels of Russian crude per day into Heilongjiang. The Central Asia-China pipeline carries 35-43 bcm annually from Turkmenistan, Uzbekistan, and Kazakhstan. All of these operate outside Western financial systems, settled in yuan and rubles through CIPS rather than SWIFT.

But these pipelines cover a fraction of China’s total energy consumption. China imported over 560 million tonnes of crude oil in 2024, of which the ESPO pipeline carries about 30 million. The rest arrives by sea, through chokepoints that the US Navy can monitor or close. And for commodities that don’t flow through pipes — LNG, iron ore, coal — China depends on maritime routes, above all the Arctic Northern Sea Route from Russia.

That route is now under pressure. NATO launched “Arctic Sentry” in February 2026, bringing all allied activities in the High North under a single operational command for the first time. Thousands of US Marines deployed to Norway for Cold Response 26. Denmark established Operation Arctic Endurance in Greenland, with a planned one-to-two-year military presence. The UK doubled its troop deployment to Norway. Trump’s push for Greenland, seemingly absurd on the surface, resulted in NATO gaining consolidated control over the Arctic’s chokepoints, including rights over Greenland’s critical minerals.

The route is already faltering on its own. Cargo volumes fell for the second straight year, dropping to 37 million tons against a target of 80 million. Western sanctions have cut Russia off from shipbuilding technology. The next-generation icebreaker meant to enable year-round navigation is only 30% complete. And the Power of Siberia 2 pipeline, which would double Russia’s overland gas capacity to China, won’t deliver gas before 2030 at the earliest.

Map this out: Venezuela — cut. Iran — cut. Strait of Hormuz — closed. Arctic route — encircled and underperforming. Overland pipelines — operational but insufficient. The net effect is a China that can survive on its existing pipelines, but cannot fuel a wartime economy through them. Whether by grand strategy or by the cumulative logic of separate decisions, China’s energy margin is shrinking.

Beijing sees this. And Beijing is drawing its own conclusions.

Act Two: The Military Clock


China’s People’s Liberation Army has a 2027 deadline, set by Xi Jinping himself, to achieve the capability for a decisive military operation against Taiwan. The Pentagon’s own assessments confirm this as stated Chinese policy.

In late 2025, China’s “Justice Mission” exercises rehearsed a full maritime blockade of Taiwan. For the first time, PLA vessels entered Taiwan’s contiguous zone in significant numbers. A Chinese destroyer withdrew only after a Taiwanese frigate locked its fire-control radar on it, signaling readiness to fire. Twenty-seven missiles were fired in or around the contiguous zone. The exercises covered a larger area than any since 2022.

China has commissioned over 18 new warships in 2025 alone, including its third aircraft carrier (the 80,000-ton Fujian, equipped with electromagnetic catapults matching US Ford-class technology) and the world’s largest amphibious assault ship (the Type 076 Sichuan). It now has over 370 ships and submarines, the world’s largest navy by hull count. Four Type 075 amphibious assault ships are in service, with more under construction.

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Jiangnan in September this year. Two 052D destroyers visible, one Type 055 fitting out bottom centre. A second Type 055 top right before launch. Image via Sinodefenceforum.

On March 5, 2026, as I write this, China announced a 7% increase in its defense budget, bringing planned military spending to $276.8 billion. This funds not just hardware, but military salary increases, training, maneuvers around Taiwan, cyberwarfare capabilities, and advanced equipment procurement.

And then there are the purges.

Xi Jinping has reduced the Central Military Commission from seven members to two: himself and one other. Over half of PLA leadership has been affected. Of 47 people who have held three-star general or above positions since 2022, 41 have been removed. Only 11 of 52 key military positions are currently occupied.

Some analysts interpret this as chaos that makes military action less likely. I read it differently. History shows that leaders purge their command structures before major military action, not to improve competence, but to ensure obedience. Stalin purged the Red Army before 1941. Hitler reshuffled his generals before every major escalation. You need commanders who will execute orders without hesitation when the stakes are existential. The purges look less like anti-corruption reform and more like consolidation of wartime command authority.

Act Three: The Window


Right now, the United States has two carrier strike groups, over 50,000 troops, and 200+ aircraft committed to the Iran theater, running operations around the clock. The US defense industrial base, already strained, is being drawn down in real time.

US military assets are being physically diverted away from the Asia-Pacific. Ships and aircraft can only be in one place at a time.

From Beijing’s perspective, this creates a window. Not necessarily for an immediate invasion of Taiwan, which would require months of visible preparation, but for actions short of invasion that are harder to respond to: a “customs quarantine” of Taiwan’s outlying islands, an escalated blockade framed as law enforcement, or a provocation-escalation spiral triggered by increasingly aggressive PLA drills that goes wrong.

Taiwan’s vulnerability is acute. It has roughly eleven days of natural gas reserves, six weeks of coal, and five months of crude oil. Food stockpiles would last seven to twelve months depending on the category. China doesn’t need to invade. Curtailing trade by even 50% would be devastating. And China has been rehearsing exactly this, with fishing militia formations of 1,400 to 2,000 boats, coast guard patrols pushing into Taiwanese waters, and AIS spoofing that makes tracking PRC vessels nearly impossible.

The diplomatic “thaw” between the US and China, including a planned Trump-Xi summit, means nothing if you’re watching ship movements instead of handshakes. Germany and the Soviet Union were trading partners right up until Barbarossa. Japan’s diplomats were literally in Washington when Pearl Harbor was launched.

Act Four: The Autonomous Revolution


Most analysis of a Taiwan conflict focuses on ship counts and troop numbers, and misses the part that will actually decide it.

The next major war will be fought with autonomous systems at scale — drones, ground robots, naval drones, and increasingly, the factories that produce them.

Autonomous systems also lower the threshold for starting a war. Unmanned operations mean fewer body bags in the first weeks, making escalation easier to absorb domestically. Drone blockades can be framed as law enforcement. AI-driven kill chains operate faster than diplomatic channels can respond, so a limited action can escalate beyond recall before anyone picks up a phone.

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Jiu Tian: Super-high altitude UAV with 7,000km range and ability to release up to 100 units of small drones. Source: South China Morning Post

Ukraine has already demonstrated the mechanics. Ukrainian drone production scaled from 2.2 million units in 2024 to 4.5 million in 2025, with some estimates suggesting capacity for 10 million per year with adequate funding. FPV drones now account for 60-70% of all destroyed Russian equipment. Ukraine conducted the world’s first fully unmanned assault on Russian positions in December 2024. Drone swarms of up to 25 units coordinate strikes autonomously. AI-enabled terminal guidance allows drones to operate independently of radio links, making them unjammable.

Both sides now organize strike campaigns around factory production rhythms rather than traditional military planning. The war is being won or lost in manufacturing output, not on the battlefield alone.

Scale this logic to a US-China conflict over Taiwan. The decisive factor won’t be which side has more ships in the strait on day one. It will be which side can manufacture and replace autonomous systems fastest, who can sustain industrial-scale production of drones, unmanned ground vehicles, autonomous naval systems, and the AI systems that coordinate them.

This is where self-replicating manufacturing enters the picture. We are approaching the point where autonomous systems can build the factories that build more autonomous systems. Apptronik’s Apollo humanoid robots are being tested at Jabil to build more of themselves, with commercial production targeted for 2026. Tesla’s Optimus program envisions autonomous robots constructing their own successors. China controls approximately 85-90% of the humanoid robotics market by shipment, with Unitree alone targeting 10,000-20,000 units in 2026.

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Chekhov said that if a gun appears on stage in the first act, it must be fired by the third. You don’t build 200 military-convertible RO-RO ships, rehearse beach landings with civilian ferries, train fishing militia in blockade formations, construct the world’s largest amphibious assault ship, and set a 2027 readiness deadline just to let it all sit on the shelf.

And you don’t build self-replicating autonomous manufacturing systems and not use them in the domain where they matter most.

The Chokepoints of the Autonomous War


If the next war is won by whoever can produce autonomous systems at the greatest scale and speed, the critical resources shift from oil wells to mines, fabrication plants, and AI chips. The picture is troubling for both sides, but in different ways.

China’s advantages:

China controls 90% of global rare earth processing: the magnets in every drone motor, every robot actuator, every missile guidance system. It dominates gallium and germanium production (essential for semiconductors). It has banned exports of these materials for military use. It controls the integrated supply chain from mine to finished product. And it has the manufacturing workforce and infrastructure to scale production faster than any other country.

The US vulnerability:

Over 40% of semiconductor components in US weapons systems are sourced from China. The US has no large-scale domestic production of ultra-high-density printed circuit boards used in defense applications. A US defense assessment concluded bluntly: the US “currently cannot surge or scale the electronics needed for sustained conflict unless it relies on adversarial sources.” It would take 18-24 months to bring even limited domestic production of critical electronics online.

And then there’s the ultimate chokepoint: Taiwan itself. TSMC produces roughly 90% of the world’s most advanced semiconductors. If China takes or blockades Taiwan, it simultaneously gains control of advanced chip production and denies it to the United States. The AI chips that power autonomous weapons systems, on both sides, flow through this single island.

The rare earth export bans are already having effect. A US drone manufacturer supplying the military has delayed orders by two months searching for non-Chinese magnet sources. Leonardo DRS is down to “safety stock” of germanium. Factory shutdowns have occurred in the US, Europe, and Japan.

The race for self-sufficient production:

Both sides understand this. The US launched a Critical Minerals Ministerial, committed $30 billion in supply chain investments, and is pursuing deep-sea mining to reduce dependence. China is accelerating domestic chip manufacturing and consolidating control over its processing monopolies. Europe’s E5 defense ministers launched the LEAP program to produce autonomous drones at Ukrainian-inspired scale.

But reshoring takes years. And the convergence of timelines (China’s 2027 military readiness target, the 18-24 month US electronics reshoring estimate, the current US military overextension in Iran) creates a window where the balance of industrial capacity favors China more than it may at any point in the future.

The Global Board


China’s preparations extend well beyond the Taiwan Strait. Over the past decade, Beijing has built a global infrastructure network that would sustain it through a prolonged conflict.

In Africa, Chinese state-owned enterprises control 15 of the DRC’s 19 cobalt mining operations, giving China effective control over 80% of the world’s cobalt production — essential for batteries in everything from drones to submarines. Chinese firms have poured $4.5 billion into lithium projects across Zimbabwe, the DRC, Mali, and Namibia. Chinese companies are involved in 78 African ports across 32 countries, a third of the continent’s port infrastructure. China’s military base in Djibouti, its first overseas, overlooks the Bab el-Mandeb strait through which 12-20% of global trade passes. A second base opened in Cambodia in 2025, and the Pentagon has identified over 20 countries where Beijing is actively considering more.

In Europe, China has built a logistics corridor from the Indian Ocean to Central Europe. COSCO controls 67% of Greece’s Piraeus port, now the eastern Mediterranean’s largest. The $2.89 billion Belgrade-Budapest high-speed railway, the first Chinese-built rail link in Europe, opened its Serbian section in October 2025 and completes through Hungary in March 2026. Together, these create a Chinese-controlled supply chain from the Suez Canal into the heart of Europe. Serbia itself has become China’s primary military partner on the continent: Chinese arms accounted for 57% of Serbia’s weapons imports from 2020-2024, including advanced air defense systems and combat drones. Joint military exercises followed in 2025.

Across 150 countries and $1.4 trillion in cumulative investment, the Belt and Road Initiative has given Beijing something no other country possesses: a global logistics backbone built for dual use. Chinese law mandates that overseas infrastructure meet military standards and authorizes the PLA to commandeer Chinese company assets in wartime. Every BRI port, railway, and airport is a potential military asset by statute.

Why the Brakes Are Failing


The standard objections to a US-China war — nuclear deterrence, economic interdependence, rational self-interest — each look weaker under scrutiny than they do on instinct.

Nuclear weapons have prevented direct great-power conflict for eighty years. But a Taiwan scenario is precisely the case where both sides might convince themselves the weapons stay holstered. The fight would be naval and aerial, concentrated around a single island. Using nuclear weapons over Taiwan would irradiate the prize. Both Beijing and Washington have reason to believe the other would keep it conventional — and that mutual belief is itself what makes the conventional fight thinkable.

The economic brake is even weaker. After Russia’s invasion of Ukraine, the West imposed the heaviest sanctions package in history. But sanctioning China is a different proposition entirely. China processes 90% of the world’s rare earths, manufactures the majority of global electronics, and supplies over 40% of the semiconductor components in US weapons systems. You cannot sanction your own supply chain. Beijing knows this. The economic weapon that was supposed to make great-power war obsolete may simply not function against the one country most likely to test it.

The regional picture adds tripwires rather than stability. Japan passed its largest-ever defense budget. AUKUS is delivering nuclear-powered submarines to Australia. The Philippines has opened new bases to US forces. Each move is individually rational, but collectively they multiply the actors and incidents that could trigger a crisis. More tripwires means more ways for a confrontation to start and fewer ways to contain it once it does.

Taiwan itself is not passive — its “silicon shield” strategy, the implicit argument that destroying TSMC would hurt China as much as anyone, provides some deterrent logic. But it also makes Taiwan the ultimate prize: whoever controls the island controls the world’s most advanced chip production. The deterrent and the incentive pull in opposite directions.

What Comes Next


None of this means war is certain. Complex systems with many actors retain the capacity to surprise. The Cuban Missile Crisis came closer than any event in modern history, and was still averted — though that resolution depended on Penkovsky’s intelligence and a power asymmetry that left the Soviets no viable option. Today the intelligence picture is murkier, the off-ramps are narrower, and the local power asymmetry favors China.

But the structural conditions are converging faster than public discourse acknowledges. The sequential isolation of China’s resource networks, the military buildup around Taiwan, the window while the US is committed in the Middle East, the approaching 2027 readiness deadline, the autonomous weapons production race, and the failure of every traditional brake on great-power conflict: these all wind together into a single trajectory.

The people who saw Ukraine coming were reading the same kind of indicators: buildup patterns, diplomatic dead ends, a leader who had consolidated power and narrowed his circle. Those patterns are present now, at a larger scale, with higher stakes.

What to watch for: large-scale Chinese military exercises that don’t stand down on schedule. Coast guard or fishing militia operations that establish a persistent presence around Taiwan’s outlying islands. Acceleration of PLA logistics posture — fuel depots, field hospitals, blood supply stockpiling — beyond what exercises require. Evacuation of Chinese nationals from Taiwan. Any of these would suggest the timeline has shortened.

The most dangerous trigger remains a miscalculation: a blockade that escalates, a drill that goes wrong, an incident in the Taiwan Strait while the US is stretched thin. Once autonomous systems are deployed at scale, the speed of conflict accelerates beyond human decision-making capacity. AI-powered kill chains measured in seconds. Swarms that coordinate without human oversight. Factory output measured in millions of units per year.

We are entering an era where the machines that fight wars also build the machines that fight wars. The chokepoints have shifted from straits and canals to chip fabs, rare earth mines, and drone factories. And the nations that control those chokepoints will determine who wins the next war, and whether humanity retains meaningful control over how it is fought.

The gun is on stage.


Alexander Temerev builds autonomous agent systems. He simulated neural circuits at EPFL Blue Brain, studied emergent behavior in multi-agent systems at CERN, and designed epidemic models adopted by Swiss and Italian policymakers during COVID-19. He is currently completing a PhD at the University of Geneva and building Lethe — an autonomous agent with brain-inspired memory and structural oversight. He lives in Geneva.

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A new chapter for the Nix language, courtesy of WebAssembly

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  • Language Constraints: The Nix language is domain-specific, Turing complete, and interpreted, lacking the efficiency required for complex or computationally intensive programming tasks.
  • Integration Challenges: Current methods for extending functionality include implementing builtins directly in C++, creating Nix plugins, or using import-from-derivation, each presenting significant maintenance, compatibility, or performance trade-offs.
  • Wasm Capability: Determinate Nix incorporates WebAssembly to enable the execution of performant, portable, and secure code modules directly within the evaluation context.
  • Functional Purity: The implementation maintains reproducibility by ensuring that WebAssembly modules operate in isolation without access to impure host functions, preserving the language's integrity.
  • Execution Mechanism: The builtins.wasm function facilitates the execution of compiled modules, allowing Nix to pass and receive complex data structures handled by foreign compiled code.
  • Performance Gains: Benchmarking reveals that migrating computationally expensive operations, such as Fibonacci calculations, to WebAssembly can achieve significant improvements in execution speed and memory consumption.
  • Deployment Options: WebAssembly modules can be integrated by committing binary files to source repositories, fetching them via URL, or managing them through flake inputs to ensure reproducibility.
  • Current Status: The feature is currently experimental, with ongoing efforts to upstream the functionality and refine the Wasm host interface for broader language support.

The Nix language is a domain-specific language for describing software configurations. Although it’s Turing complete, it was never really intended as a general-purpose language. For complex programming tasks, it lacks the conveniences of modern languages like Rust.

The Nix language is also a fully interpreted language without any kind of just-in-time compilation, so it’s not all that well suited for computationally intensive tasks. In most cases this isn’t much of a blocker for Nix users, but it does become a problem when you need to do something in Nix that isn’t provided as a builtin function in the language.

To give an example, suppose that you need to parse a YAML file in Nix to extract some configuration data. Unfortunately, Nix has no builtin YAML parser. So you have a few possibilities:

  • Write a YAML parser in Nix. This is a pretty daunting, not-so-fun task because Nix is not a great language for this kind of string processing. The resulting parser will also be rather slow and memory hungry.

  • Add a YAML parser to Nix as a builtin function. This has to be written in C++, but it does allow you to reuse any existing YAML parser library for C++. But you’re going to have a hard time getting this accepted upstream. The main reason is that YAML is complex, while the Nix language is intended to be reproducible across releases. So updating the YAML parser dependency could cause differences in evaluation results across Nix versions, which has been a real problem with builtins.fromTOML. And even if you do get your new builtin function accepted, it’s going to be a while before it makes it into a release and everybody can use it.

  • Write a Nix plugin. This is similar to the previous approach—in that the plugin would need to be written in C++—except that you don’t need to get it accepted upstream. But now you do need to ensure that everybody who uses a Nix expression that calls your YAML parser has the plugin installed. This means that Nix flakes using it are no longer self-contained, and there is no convenient mechanism to declare that a flake requires a specific plugin.

  • Use “import-from-derivation” (IFD), that is, do the YAML parsing using any language or tool of your choice and run it inside a derivation, and then import the result. Here’s an example:

    builtins.fromJSON (  runCommand    "parse-yaml"    { src = ./input.yaml; }    ``...run some command that converts $src from YAML into JSON...``)
    

    But IFD is an expensive mechanism, as realising the derivation may require downloading and building a lot of dependencies. It also breaks the separation between evaluating and building configurations, so an operation like nix flake show may unexpectedly start downloading and building lots of stuff.

    IFD is particularly unsuited when you want to do a traversal over a large source tree (for example to discover dependencies of source files), since it requires the entire source tree to be copied to the Nix store—even with lazy trees.

Determinate Nix now has a better way to extend the Nix language: through the power of WebAssembly.

WebAssembly

WebAssembly (Wasm) was created for pretty much the same reason it’s attractive for Nix: to allow JavaScript programs in web browsers to offload computationally expensive tasks to a more performant language. Wasm is a low-level binary instruction format that can be compiled from many high-level languages, including Rust, C++, and Zig. It is designed to be fast, portable, and secure. It has many implementations, including several that can be embedded in C++, such as Wasmtime and WasmEdge.

WebAssembly has a precisely defined semantics: a call to a WebAssembly function will always produce the same result when executed, as long as it has no access to impure external functions (“host functions” in Wasm parlance). For instance, WebAssembly by default has no access to a source of random numbers. This is critically important to Nix, as it is intended to be reproducible. Without it, Wasm functions could break the purity of the language.

builtins.wasm

The builtins.wasm function allows you to call a WebAssembly function from Nix. Here is an example of calling a Wasm function that computes the nth Fibonacci number:

nix-repl> builtins.wasm { path = ./nix_wasm_plugin_fib.wasm; function = "fib"; } 33warning: 'nix_wasm_plugin_fib.wasm' function 'fib': greetings from Wasm!5702887

So to call a Wasm function, you need to provide the path to the Wasm module and the name of the function you want to call. The Wasm function takes a single Nix value as input (in this case 33), and returns a single Nix value as output. These values, however, can be arbitrarily complex Nix values, such as attribute sets.

Wasm modules can be written in any language for which there is a compiler that targets Wasm. nix_wasm_plugin_fib.wasm was written in Rust. Here is its source code:

use nix_wasm_rust::{warn, Value};
#[no_mangle]pub extern "C" fn fib(arg: Value) -> Value {    warn!("greetings from Wasm!");
    fn fib2(n: i64) -> i64 {        if n < 2 {            1        } else {            fib2(n - 1) + fib2(n - 2)        }    }
    Value::make_int(fib2(arg.get_int()))}

nix_wasm_rust is a support crate that provides Rust wrappers around the Wasm host functions that Nix makes available to Wasm modules. The type Value represents a (possibly not yet evaluated) Nix value. The call arg.get_int() makes a host function call to Nix to check that the value arg evaluates to an integer and return its value. Conversely, Value::make_int() creates a new Nix integer value. There are similar functions to access or construct other Nix data types, including attribute sets and lists. The macro warn!() calls a host function that prints out a message to stderr.

YAML in Nix

Using builtins.wasm, adding support for YAML is pretty trivial, since Rust already has a crate for parsing and generating YAML. Here is fromYAML implemented in Rust:

use nix_wasm_rust::{Type, Value};use yaml_rust2::{Yaml, YamlLoader};
#[no_mangle]pub extern "C" fn fromYAML(arg: Value) -> Value {    Value::make_list(        &YamlLoader::load_from_str(&arg.get_string())            .unwrap()            .iter()            .map(yaml_to_value)            .collect::<Vec<_>>(),    )}
fn yaml_to_value(yaml: &Yaml) -> Value {    match yaml {        Yaml::Integer(n) => Value::make_int(*n),        Yaml::String(s) => Value::make_string(s),        Yaml::Array(array) => {            Value::make_list(&array.iter().map(yaml_to_value).collect::<Vec<_>>())        }        Yaml::Hash(hash) => Value::make_attrset(...),        ...    }}

Performance

Nix uses Wasmtime, a Wasm runtime written in Rust that features a just-in-time code generator named Cranelift. The resulting code is much faster than equivalent Nix code. For example, here is Fibonacci in Nix:

Terminal window

# command time nix eval --expr 'let fib = n: if n < 2 then 1 else fib (n - 1) + fib (n - 2); in fib 40'16558014177.52user 1.66system 1:19.33elapsed 99%CPU (0avgtext+0avgdata 4570812maxresident)k

And here we are using the Rust Wasm version shown above:

Terminal window

# command time nix eval --impure --expr 'builtins.wasm { path = ./nix_wasm_plugin_fib.wasm; function = "fib"; } 40'warning: 'nix_wasm_plugin_fib.wasm' function 'fib': greetings from Wasm!1655801410.31user 0.02system 0:00.33elapsed 100%CPU (0avgtext+0avgdata 30076maxresident)k

79.33 seconds to 0.33 seconds, a 240x speedup! It’s worth noting that the 0.33 seconds includes the code generation overhead, which Nix could cache on disk across invocations but currently doesn’t. Not only that, but Nix uses much less memory using the Wasm version: 30 MB instead of 4.5 GB, a 151x reduction.

It’s not all great, however. Wasm calls have a non-trivial overhead due to the need to create a new Wasm instance for every call. We can’t reuse instances between calls to the same function, because then the function could do impure things like maintain a global counter. We do use Wasmtime’s pre-instantiation feature to parse and compile Wasm modules only once per Nix process. On an Intel i7-1260P, Nix can do around 123,000 Wasm calls per second. By contrast, it can do around 2.8 million “native” function calls per second. Thus, Wasm is best used for larger tasks.

Managing Wasm functions

Wasm modules are often small enough that you can commit them into your Git repositories directly. For example, the compiled Wasm module for parsing and generating YAML is 180 KiB—probably still an acceptable size for adding to a repository like Nixpkgs.

Alternatively, you can fetch the Wasm module at evaluation time like this:

builtins.wasm {  path = builtins.fetchurl <a href="https://.../nix_wasm_plugin_fib.wasm;" rel="nofollow">https://.../nix_wasm_plugin_fib.wasm;</a>  function = "fib";} 33

If you’re using flakes, you can use the file flake input type to fetch a single Wasm module via HTTP. This allows you to update the Wasm dependency automatically using nix flake update.

Finally, you could use import-from-derivation to declaratively build the Wasm module from source. But then you’re back to using import-from-derivation, which somewhat defeats the purpose!

Status

builtins.wasm is currently an experimental feature in Determinate Nix. There is also a PR to add it to upstream Nix.

If you want to give builtins.wasm a try, either install Determinate Nix or add the Determinate Nix CLI to your shell session:

Terminal window

nix shell github:DeterminateSystems/nix-src

To get a set of example Wasm functions from the nix-wasm-rust repo, run:

Terminal window

nix build github:DeterminateSystems/nix-wasm-rust

Then test whether it works:

Terminal window

nix eval --extra-experimental-features wasm-builtin \  --impure --raw --expr \  'builtins.wasm { path = ./result/nix_wasm_plugin_mandelbrot.wasm; function = "mandelbrot"; } { width = 60; }'

If you want to write Wasm functions in Rust, the nix-wasm-rust crate provides you with everything you need to interface with Nix. We have a blog post on compiling Rust to Wasm using Nix that you may find useful. For other languages, please consult the Wasm Host Interface documentation in the Determinate Nix manual. This interface is subject to change, which is the main reason builtins.wasm is still experimental. We welcome your feedback on writing Nix Wasm functions—in particular, please let us know if you run into limitations with the host interface.

Extending the Nix language isn’t the only application of Wasm in Nix. Wasm also enables platform-independent derivation builders, which also opens up many compelling possibilities. But that’s a topic for another blog post.

The Nix language has its detractors but it’s nonetheless provided a stable foundation for Nix for many years. With Nix usage pushing ever upward, now feels like an opportune—and exciting—time to push beyond some of the language’s historical limitations and see what the Nix ecosystem does with it.

Acknowledgments

Wasm support is the result of a collaboration between Determinate Systems and Shopify. Surma at Shopify developed the first prototype and wrote a function for running JavaScript in Nix via Wasm.


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Written by Eelco Dolstra

Eelco started the Nix project as a PhD student at Utrecht University. He is a co-founder at Determinate Systems and a member of the Nix team.

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Sam Altman Wants Elected Officials, Not OpenAI, to Decide How Military Uses AI - WSJ

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  • Democratic Oversight: Elected officials possess the final authority to dictate the boundaries of artificial intelligence deployment within national defense sectors.
  • Institutional Trust: Preference remains for the established democratic process over corporate influence when addressing complex systemic issues and governance.
  • Contractual Security: OpenAI successfully finalized an agreement with the Pentagon to implement AI models within classified environments.
  • Strategic Implementation: The corporate decision to pursue this military partnership followed a failed negotiation between the Department of Defense and Anthropic.
  • Internal Accountability: Management expressed regret regarding the rapid execution and appearance of the securing of government contracts.
  • Public Perception: Acknowledgement of negative reputational consequences regarding concerns over potential data collection involving United States citizens.
  • Legislative Necessity: Identification of a requirement for updated legal frameworks to effectively govern modern technological capabilities and prevent harms.
  • Civil Liberties: Emphasis on the preservation of established privacy protections for citizens against unauthorized government surveillance or overreach.

By

Berber Jin

March 5, 2026 1:47 pm ET

41


Sam Altman speaking at the AI Summit in New Delhi.

OpenAI’s CEO Sam Altman at an event last month. Associated Press

OpenAI believes elected officials, not technology company executives, should ultimately determine the limits of how artificial intelligence can be used in national defense, Chief Executive Sam Altman said at an investor conference Thursday. 

Speaking at the Morgan Stanley Tech, Media and Telecom Conference in San Francisco, Altman said “we have to trust in the democratic process” to supply some of the answers to the questions OpenAI and rival Anthropic are wrangling over in their separate negotiations with the Department of Defense. 

“This process is messy. This process has some deep flaws, but it is better than all other systems,” he said. “If we start abandoning that process and our commitment to it because, you know, some people don’t like the person or people currently in charge, that is challenged no matter what. I think it’s bad for society no matter what.”

Altman’s remarks came after OpenAI reached a deal with the Pentagon to use its models in classified settings, capitalizing on an impasse between the agency and Anthropic over red lines the company refused to relinquish over its control of the technology for certain military applications. 

In a memo to employees earlier this week, Altman said he regretted moving so quickly to secure the deal, saying it looked “opportunistic and sloppy.”

In a Tuesday all-hands meeting, he acknowledged the move generated “very negative PR” over the appearance that OpenAI was willing to allow its technology to be used for purposes that include collecting data on Americans. 

“I feel terrible for subjecting you all to this,” he added.

At the conference Thursday, Altman said the Defense Department had been “extremely understanding” about the need to “clarify” some details of the contract, but that new law was needed to reflect the state of the technology and prevent harms.

“I think one of the civil liberties of this country that’s most important is the government does not spy without, you know, warrants and good legal process on its own citizens,” he said. “The definition of what that is going to mean needs to change with technology.”

News Corp, owner of The Wall Street Journal, has a content-licensing partnership with OpenAI.

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AI-Generated Writing is Everywhere, and It’s Still Easy to Spot—For Now AI-Generated Writing is Everywhere, and It’s Still Easy to Spot—For Now

OpenAI CEO Sam Altman Defends Pentagon Work to Staff, Calls Backlash ‘Really Painful’ OpenAI CEO Sam Altman Defends Pentagon Work to Staff, Calls Backlash ‘Really Painful’

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Amazon Tries Its Low-Cost Approach to Winning the AI Race Amazon Tries Its Low-Cost Approach to Winning the AI Race

Copyright ©2026 Dow Jones & Company, Inc. All Rights Reserved. 87990cbe856818d5eddac44c7b1cdeb8

Berber Jin covers startups and venture capital out of the Wall Street Journal's San Francisco office. His articles focus on the money and people powering Silicon Valley, with a recent focus on artificial intelligence. He previously covered the same topic for the Information, where he won a Best in Business award from the Society for Advancing Business Editing and Writing.

Berber is originally from Scarsdale, N.Y., and graduated from Stanford University.


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Altman Says OpenAI Is Working on Pentagon Deal Amid Anthropic Standoff

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Altman waded into the standoff between Anthropic and the Pentagon over the use of AI on the battlefield, telling his staff that the company was working on a deal that might help solve the impasse.

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Pentagon Formally Labels Anthropic Supply-Chain Risk, Escalating Conflict - WSJ

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  • Official Designation: The U.S. Department of Defense has formally classified Anthropic as a supply-chain risk.
  • Operational Consequences: The designation prohibits Pentagon partners and contractors from conducting business with Anthropic.
  • Security Justification: Military officials state the company restricts the lawful use of capabilities and poses a threat to the chain of command.
  • Disputed Terms: Anthropic seeks explicit prohibitions against the use of its technology in autonomous weapons and domestic surveillance.
  • Legal Challenge: Anthropic has announced plans to contest the Pentagon’s designation in court.
  • Executive Allegations: CEO Dario Amodei asserts the action stems from the company's refusal to align with the current administration's AI agenda.
  • Competitive Landscape: Rival AI firms, including OpenAI and xAI, have secured approvals to operate within classified Defense Department systems.
  • Industry Impact: Analysts suggest the broad application of this supply-chain designation may deter private companies from engaging in government contracts.

By

Amrith Ramkumar

18


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Dario Amodei on Safety, Job Displacement and Anthropic's $350B Valuation

Dario Amodei on Safety, Job Displacement and Anthropic's $350B ValuationPlay video: Dario Amodei on Safety, Job Displacement and Anthropic's $350B Valuation

Keep hovering to play

In January, Anthropic CEO Dario Amodei discussed his company’s $10 billion raise, the fields that are most at risk of AI-driven employment disruption and the state of safety in AI development. Photo: Maurizio Martorana for WSJ

The Defense Department has officially told Anthropic it is a supply-chain risk and will be cut off from partners who work with the Pentagon, following through on a threat to escalate its battle with the artificial-intelligence company. 

The Defense Department on Thursday formally notified Anthropic’s leadership that the company and its AI tools present security threats that require an action normally reserved for businesses from foreign adversaries, a senior Pentagon official said. 

The official said the military wouldn’t allow a vendor to insert itself into the chain of command and put members of the armed services at risk by restricting the lawful use of a critical capability.

Defense Secretary Pete Hegseth said last week he would designate Anthropic a supply-chain risk, but both sides had continued talking in hopes of finding a deal, people familiar with the matter said.

The move could have far-reaching consequences for Anthropic partners and investors including Lockheed Martin, Amazon.com and Google. 

The Pentagon wanted Anthropic to let it use AI in all lawful use cases, while the company is fighting for explicit guarantees that its technology won’t be used in autonomous weapons and for mass domestic surveillance. 

Anthropic said last week it would challenge the designation in court. 

It is one of the first times the supply-chain risk designation has been applied to a U.S. company, a move that tech analysts warn could have a chilling effect on other businesses wanting to do business with the government.

The supply-chain risk designation can be used very broadly or in a narrowly tailored way. Hegseth’s post last week said any company working with the military can’t do business with Anthropic, a directive that some analysts said goes beyond the Pentagon’s legal authority. Those analysts said the Defense Department could only tell companies not to use Claude specifically in their work with the Pentagon.

Thursday’s move came a day after The Information reported that Dario Amodei, Anthropic’s chief executive and co-founder, sent Anthropic staff a memo last week stating that the administration is targeting the company because it hasn’t “given dictator-style praise to Trump” and has fought against the administration’s AI agenda.

The memo also criticized rival OpenAI, which accepted a deal with the Defense Department to have its systems used in classified systems. The company added explicit bans on surveillance to its deal earlier this week following backlash from employees.

Founded by former OpenAI executives, Anthropic previously was the only company approved for use in classified settings. Elon Musk’s xAI also recently won approval.

Anthropic’s Claude is being used by the military in operation in Iran and was used in Venezuela in the operation to capture former Venezuelan leader Nicolás Maduro. The company has a partnership with data-mining firm Palantir, which supplies software to the Defense Department.


Watch: Anthropic CEO Discusses Safety, Jobs and Valuation

Dario Amodei, CEO and Co-Founder of Anthropic, looking on at the World Economic Forum meeting in Davos.

Anthropic CEO and co-founder Dario Amodei Denis Balibouse/Reuters

Copyright ©2026 Dow Jones & Company, Inc. All Rights Reserved. 87990cbe856818d5eddac44c7b1cdeb8

Amrith Ramkumar is a reporter for The Wall Street Journal in Washington covering tech and crypto policy. He previously covered clean energy and was a Journal markets reporter in New York who wrote about special-purpose acquisition companies, or SPACs, when SPAC mergers were a popular alternative to traditional initial public offerings. He also previously wrote about stocks and commodities, including battery metals such as lithium and cobalt.

Amrith joined the Journal as a markets intern after graduating from Duke in 2017.


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How Long Can Anthropic Play Defense? | WSJ AI & Business for March 3 - WSJ

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  • Governmental Conflict: Anthropic faces a ban on federal business following a dispute between CEO Dario Amodei and Defense Secretary Pete Hegseth regarding supply-chain security.
  • Commercial Presence: The company maintains widespread adoption of its Claude AI tools among private sector organizations for automation and coding tasks.
  • Market Valuation: Anthropic intends to pursue an initial public offering later this year with an anticipated valuation exceeding $380 billion.
  • Financial Status: Current business operations involve a reported annual cash burn of approximately $3 billion, complicating the path to profitability without federal contracts.
  • Consumer Reception: The Claude mobile application reached the top position in the Apple App Store, surpassing OpenAI’s ChatGPT following the public dispute.
  • Industry Impact: Technological advancements by Anthropic are identified as a primary catalyst for the recent selloff in broader software sector valuations.
  • Hardware Developments: Nvidia is planning to launch a specialized AI chip designed for inferencing, aiming to address a specific niche in their compute-heavy product portfolio.
  • Market Trends: OpenAI recently secured $110 billion in new funding commitments, establishing a valuation of $730 billion ahead of its planned public listing.

Dan Gallagher

By

Dan Gallagher

March 3, 2026 1:14 pm ET

7


BPC > Only use to renew if text is incomplete or updated: | archive.li

BPC > Full article text fetched from (no need to report issue for external site): | archive.today | archive.md

Anthropic Co-founder and CEO Dario Amodei

Anthropic Co-founder and CEO Dario Amodei Chance Yeh/HubSpot/Getty Images

Many tech companies survive without contracts with the federal government. None are planning to go public later this year at a valuation hopefully well north of $380 billion—while still burning loads of cash.

Anthropic’s standoff with the Pentagon has snowballed into the AI company being effectively banned from doing business with any federal agency, at least according to a missive fired off by President Trump on Friday. That followed an apparently tense face-to-face meeting between Anthropic CEO Dario Amodei and Defense Secretary Pete Hegseth, who later designated Anthropic as a supply-chain risk. 

That designation could impair Anthropic’s ability to work with any company that does business with the federal government. Anthropic says it will challenge that designation in court.

But even if successful on that legal front, Anthropic will likely have to go forward without the U.S. government as a customer. Guggenheim analyst John DiFucci estimates the U.S. federal government accounts for about 8%-9% of worldwide spending on software, which makes it difficult for any software vendor to ignore it as a customer.

But Anthropic isn’t your typical software vendor. The company’s Claude AI tools are widely used across large and small businesses for tasks like coding automation. That success has made it into one of the most important names in AI—Anthropic’s software was being used by the U.S. government as part of the Iran attacks just hours after the ban was announced, the WSJ reported.

The company’s announcements of new services and capabilities have been the major spark behind the “SaaSpocalypse.” That has been a brutal selloff of software stocks that has cost companies like Salesforce, Workday and ServiceNow more than one-quarter of their market capitalizations so far this year. 

Anthropic, in other words, already has its hooks deep into corporate America. And the company’s resistance to the Pentagon’s demands also seems to have rallied strong support among consumers, with the Claude app hitting number one in Apple’s App Store. It also surpassed OpenAI’s ChatGPT in that ranking for the first time. 

Tech companies have taken seemingly costly stands before and stuck by them. Google famously exited the Chinese internet search market in 2010 after deciding it wasn’t comfortable censoring results to abide by the government’s censorship rules. But Google was already public at that point, very profitable and generated about $8.5 billion in annual free cash flow. 

Anthropic, by contrast, is reportedly burning through about $3 billion a year in cash. Losing federal-government contract dollars, and possibly more from government contractors, could dent the company’s efforts to cast itself as a stronger business than arch-rival OpenAI. The Wall Street Journal reported late last year that the company was aiming to turn a profit ahead of the ChatGPT maker, which doesn’t expect to get to that level until 2030.

Pulling that off without Uncle Sam’s support will be much tougher. It’s IPO might be as well. 

This is an edition of the WSJ AI & Business newsletter, a weekly digest to help you make sense of AI’s impact on business with news, insights and data from our global team of technology journalists. If you’re not subscribed, sign up here.


Nvidia Goes After New AI Chip Market

Jensen Huang, CEO of Nvidia

Jensen Huang, CEO of Nvidia REUTERS/ANN WANG

Nvidia will use its widely followed GTC conference later this month to unveil a new type of AI chip, one that may fill what is seen as a notable gap in the company’s portfolio. Nvidia, by far, rules the market for compute-heavy systems that handle the training of AI models. Now it is designing a new system for AI “inferencing,” which refers to AI models generating output, according to The Wall Street Journal.


The Number

$110 Billion

The amount of secured commitments for new funding that OpenAI has received in a deal valuing the company at $730 billion ahead of an expected initial public offering later this year.


What the Humans Are Saying

“Frontier AI systems are simply not reliable enough to power fully autonomous weapons.”

— Anthropic chief Dario Amodei speaking last week before the Trump administration banned the government from working with the company.


AI in Charts

Nvidia now makes more revenue in a single quarter than most other chip companies generate in an entire year. In a turbulent market awash in a new class of AI fears, that’s no longer enough. In fact, the company’s runaway success could be seen as a sign of the industrywide destabilization to come, given the massive amounts of capital spending that are filling its coffers while financially weakening the world’s largest companies—and employers.


AI in the Wild

PHD Student Kayley Waltz reflected in a wafer. Johns Hopkins Whiting School of Engineering, Baltimore, Maryland.

PhD Student Kayley Waltz reflected in a wafer. Justin T. Gellerson for WSJ

Sometime in the next 15 years, the same technology that produced the world’s first photograph will allow us to make the most powerful, densely packed silicon microchips allowed by physics. It will be a triumph of engineering—and the final step in the march of chip-industry progress known as Moore’s Law. Getting there is the multitrillion-dollar challenge the entire semiconductor industry now faces.


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