ARTEMIS II: NINE DAYS BETWEEN EARTH AND THE MOON

The experience of Reid Wiseman, Victor Glover, Christina Koch, and Jeremy Hansen as they returned to the place where no human had been since 1972

By Jose Alcantara

For decades, the Moon was primarily a visual presence: a white disk above our cities, a robotic landscape photographed from a distance, and—within the collective memory—the stage for an adventure that seemed to belong to another era.

On April 1, 2026, that changed.

Aboard Orion, four human beings once again crossed the psychological boundary separating Earth orbit from deep space. Reid Wiseman, Victor Glover, Christina Koch, and Jeremy Hansen did not go to the Moon to plant a flag or build a base. They went, in a sense, to discover whether a spacecraft designed to carry humans there again could actually become their home along the way.

And, almost accidentally, they went to experience something no simulation can completely reproduce: looking at Earth from a distance at which it stops looking like territory and begins to look like an object.

Artemis II was a test mission, but from inside Orion, the word "test" acquired a much more human meaning.

The mission lasted just over nine days, carried the four astronauts around the Moon, and ended with a splashdown in the Pacific Ocean on April 10. During the flight, the crew traveled farther from Earth than any humans had ever gone, surpassing the previous record set by Apollo 13. NASA later reported that the spacecraft traveled a total of 695,081 miles during the mission.

But numbers tell only part of the story.

The rest lies in what they saw, felt, discussed, and learned.

THE MOMENT EARTH STOPS BEING THE CENTER

The departure began like every great space mission: with an enormous machine consuming an almost absurd amount of energy.

The Space Launch System—SLS—placed Orion into Earth orbit. Then came the maneuver that transformed an orbital mission into a lunar journey.

On April 2, Orion's service-module engine performed the trans-lunar injection, or TLI. The burn lasted approximately six minutes. Afterward, the spacecraft was no longer simply orbiting Earth.

It was on a trajectory toward the Moon.

Christina Koch described the transition with a phrase that sounds almost like a definition of orbital mechanics: they were now "falling to the Moon rather than rising away from Earth."

The physics was straightforward.

The experience was not.

From inside Orion, Earth progressively became something different. It was still enormous emotionally, but visually it was becoming smaller.

The crew had spent years training to operate the spacecraft. They had practiced failures, procedures, communications, navigation, and emergencies. They had learned exactly what to do when something did not work.

What cannot be completely trained is the sensation of moving away from everything familiar.

NASA had designed Orion as a machine for transporting humans into deep space. During Artemis II, its four occupants had to turn that capsule into a small society.

They ate together.

They worked together.

They slept in the same confined volume.

They disagreed.

They reconciled.

And they looked through the windows.

Christina Koch later described the crew's relationship in remarkably human terms, explaining that spending so much time together inevitably produces conflicts but also creates the possibility of coming back together—something resembling a new family.

That may have been one of Artemis II's most important experiments.

Not simply testing a spacecraft.

Testing four human beings inside it.

 

"WE DON'T LEAVE EARTH. WE CHOOSE IT."

There is a paradox in traveling to the Moon.

The farther you go, the more obvious the value of the place you left behind becomes.

During the outbound journey, the astronauts photographed Earth. NASA released imagery showing the planet as a crescent viewed from Orion, a visual reminder that the spacecraft was rapidly moving beyond the familiar environment of low Earth orbit.

It is the kind of image we normally contemplate from the comfort of a screen.

For them, the screen was a window.

And then something happened that no Artemis II astronaut had experienced before.

The Moon began to occupy an increasing portion of their visual universe.

On April 6, Orion entered the Moon's sphere of influence—the point at which lunar gravity exerted a stronger influence on the spacecraft's trajectory than Earth's.

The crew was now thousands of kilometers from anywhere a human could physically come to their aid.

There was no ambulance.

No rescue vehicle.

No second spacecraft.

Only Orion, four people, and a complex network of spacecraft systems and controllers on Earth.

Then came the encounter.

 

SEVEN HOURS FACING ANOTHER WORLD

The lunar flyby on April 6 became the emotional center of Artemis II.

For the first time since Apollo 17, humans once again observed the Moon from a spacecraft flying around it. The crew spent hours making observations and photographing the lunar surface.

They were not landing.

But they were seeing.

And seeing the Moon from a spacecraft traveling around it is different from seeing it from Earth.

The mountains are not a photograph.

The craters are not an illustration.

The landscape is actually there, beneath the spacecraft.

The astronauts used handheld digital cameras to capture high-resolution images of geological features, while their observations added a uniquely human layer to the scientific data. NASA emphasized that the four astronauts themselves constituted an important observational instrument: four pairs of human eyes seeing lunar features under changing illumination and texture.

But the experience was not purely scientific.

It was psychological.

As Orion passed behind the Moon, Earth temporarily disappeared from direct communication.

They were alone.

Not metaphorically.

Actually alone.

The Moon blocked the radio signals required for communication with Earth's Deep Space Network, creating a planned communications blackout.

For approximately forty minutes, the spacecraft was beyond direct radio contact.

No voice from Houston.

No immediate conversation with Earth.

Only the spacecraft and its four occupants.

Then Earth returned.

So did communications.

And the significance of that moment went beyond engineering.

For modern human beings accustomed to permanent connectivity, the experience of losing contact with the entire planet—even temporarily—is almost alien.

The astronauts were not merely traveling through space.

They were experiencing isolation on a planetary scale.

 

WHEN EARTH BECOMES AN OBJECT

One of the experiences most likely to remain associated with Artemis II was looking back at Earth.

During training, the astronauts had used simulators with enormous displays to reproduce what they might see from Orion. The simulations could recreate trajectories, lighting conditions, and the apparent movement of Earth and Moon.

But a simulation can reproduce an image.

It cannot reproduce the knowledge that the image is real.

Christina Koch was photographed looking back at Earth through one of Orion's windows during the outbound journey.

The Earth is not large from lunar distance.

It is small.

A sphere of atmosphere, clouds, oceans, and continents suspended against blackness.

From the surface, we think in terms of countries, borders, oceans, continents, cities, and political territories.

From deep space, those divisions disappear.

You see Earth.

That visual transformation has intellectual consequences.

The planet becomes the unit.

The border becomes invisible.

The distinction between "us" and "them" becomes difficult to see.

Spaceflight has produced this phenomenon before. Artemis II gave it to a new generation.

The journey was not simply about leaving Earth.

It was about seeing Earth differently.

THE FOURTH PASSENGER: THE SPACECRAFT

Artemis II was also the first crewed flight of Orion.

That meant every vibration, temperature change, system response, and software interaction mattered.

The spacecraft had to keep four people alive during a mission lasting more than nine days, far beyond the environment of low Earth orbit.

This was not simply a matter of reaching the Moon.

It was a demonstration that a spacecraft could sustain humans in deep space.

The crew conducted planned tests of life support, propulsion, power, thermal control, navigation, communications, crew interfaces, habitability, and manual spacecraft operations.

The mission therefore became a laboratory.

The astronauts were part of the experiment.

They monitored systems.

They operated the spacecraft.

They practiced manual piloting.

They evaluated how the vehicle behaved.

And they lived inside it.

That last part is critical.

A spacecraft can be technically functional and still be a difficult place for humans to inhabit.

Artemis II was designed to discover those differences before future missions become longer and more ambitious.

 

THE INVISIBLE GUEST: RADIATION

There is another element that changes dramatically once humans leave low Earth orbit: radiation.

Earth's atmosphere and magnetic environment provide significant protection. Astronauts in low Earth orbit remain within a relatively protected region of the magnetosphere.

On the way to the Moon, that protection diminishes.

Radiation is not a dramatic danger.

There is no alarm when an energetic particle passes through the spacecraft.

It is invisible.

It can penetrate materials.

It can interact with biological tissue.

It can affect electronics.

And its significance depends partly on exposure duration and environment.

That is why a ten-day lunar mission is only a first step toward the much more difficult problem of sending humans to Mars.

Artemis II contributes to that learning process.

The Moon is close enough to be a realistic destination and far enough away to expose spacecraft and crews to many of the challenges that future deep-space missions will face.

 

DAILY LIFE 400,000 KILOMETERS FROM HOME

There is a tendency to imagine that life during a historic space mission consists exclusively of historic moments.

It does not.

There is trash.

Food.

Sleep.

Exercise.

Maintenance.

Checklists.

Small problems.

And music.

During Artemis II, the crew selected music to begin parts of their days. As the spacecraft prepared for its return to Earth, the astronauts chose songs including Live's "Run to the Water" and Zac Brown Band's "Free."

It seems like a trivial detail.

It may actually be one of the most revealing.

Because it demonstrates that even hundreds of thousands of kilometers from home, astronauts remain human beings.

They need routines.

They need humor.

They need music.

They need conversation.

They need each other.

And they need to know that someone is waiting for them.

WHEN THE PLANET STARTS GETTING BIGGER AGAIN

On April 7, Orion exited the Moon's sphere of influence and began the return journey toward Earth.

The direction had changed.

Now the spacecraft was falling toward Earth.

The planet would gradually begin to grow again.

The mission's emotional rhythm changed with it.

Lunar observation gave way to navigation.

Exploration gave way to preparation.

The crew performed additional tests, including evaluations of the systems and procedures needed for the return to Earth's gravity.

They also tested an orthostatic intolerance garment designed to help maintain blood pressure and circulation during the transition from microgravity back to Earth's gravity.

It is an important reminder that returning home is itself a physiological event.

The human body adapts to weightlessness.

Then Earth demands that it adapt back.

The great lunar adventure therefore ended not with a single spectacular moment, but with a sequence of carefully rehearsed engineering and biological procedures.

THE LAST TEST

On April 10, Orion entered the final phase.

Reentry.

Heating.

Deceleration.

Atmosphere.

Parachutes.

Ocean.

At 5:07 p.m. PDT, Orion splashed down in the Pacific Ocean off the coast of California, completing a mission that NASA officially records as lasting 9 days, 1 hour, and 32 minutes.

The astronauts were back.

But psychologically, the journey was not necessarily over.

Recovery teams helped the crew out of the spacecraft and transported them by helicopter to the USS John P. Murtha, where they underwent initial medical evaluations.

The physical return was immediate.

The mental return would take longer.

WHAT THEY REALLY LEARNED

There is a tendency to measure a mission like Artemis II through engineering milestones.

Did the rocket work?

Did Orion work?

Did the spacecraft reach the Moon?

Did communications work?

Did the crew return safely?

The answer to those questions is essential.

But Artemis II also tested something less quantifiable.

Trust.

Four people had to live together inside a confined spacecraft while performing a complex sequence of tasks hundreds of thousands of kilometers from Earth.

Reid Wiseman.

Victor Glover.

Christina Koch.

Jeremy Hansen.

They had trained together for years. NASA reported that the crew had been preparing together for approximately three years before the flight.

Training creates competence.

Spaceflight demands something more.

It demands the ability to rely on another person when the environment offers no margin for casual mistakes.

Christina Koch's description of the crew's relationship after the mission captures this human dimension: intense proximity inevitably produces friction, but it can also create something resembling family.

That may be Artemis II's deepest legacy.

Not only the data.

Not only the photographs.

Not only the distance record.

But a human memory that can be passed to those who come next.

Because future Artemis crews will face different questions.

And eventually, a crew traveling to Mars will face questions Artemis II cannot answer.

But four people have now made the journey.

They have seen Earth from lunar distance.

They have experienced a communications blackout behind the Moon.

They have watched the lunar surface move beneath their spacecraft.

They have operated a new spacecraft in deep space.

They have returned through Earth's atmosphere.

And when future explorers ask what it means to leave Earth, Artemis II offers a powerful answer.

It does not mean leaving Earth behind.

It means seeing it for the first time as a world.

And then choosing to come home.

 

GLOSSARY

Artemis II — The first crewed mission of NASA's Artemis program and the first crewed lunar flyby in more than 50 years. Its principal purpose was to test Orion, the SLS, and associated systems in deep space.

Orion — NASA's crewed spacecraft designed to transport astronauts to the Moon and return them safely to Earth.

SLS (Space Launch System) — NASA's heavy-lift rocket used to send Orion and its crew beyond low Earth orbit.

TLI (Trans-Lunar Injection) — The propulsion maneuver that places a spacecraft on a trajectory toward the Moon.

Lunar Sphere of Influence — The region in which the Moon's gravitational influence becomes dominant over Earth's for purposes of the spacecraft's trajectory.

Microgravity — An environment in which residual gravitational effects produce the sensation of weightlessness, although gravity itself has not disappeared.

Deep Space — Space beyond the immediate environment of low Earth orbit, characterized by greater distance, communication delays, radiation exposure, and different operational conditions.

Space Radiation — Energetic particles and radiation originating primarily from the Sun and cosmic sources that can affect biological tissue and electronic systems.

Earthrise — The visual appearance of Earth rising above the lunar horizon from the perspective of a spacecraft or observer near the Moon.

Reentry — The phase during which a spacecraft returns to Earth's atmosphere at very high speed, producing intense aerodynamic heating.

Splashdown — The landing of a spacecraft capsule in the ocean, normally assisted by parachutes.

EVA (Extravehicular Activity) — Activity conducted by an astronaut outside a spacecraft or space station. Artemis II did not include lunar surface EVAs.

Lunar Flyby — A trajectory in which a spacecraft passes around or near the Moon without entering lunar orbit or landing.

Lunar Far Side — The hemisphere of the Moon that is not directly visible from Earth.

 

REFERENCES

1. NASA — Artemis II Mission. Official mission overview, crew, mission duration, launch, splashdown, and spacecraft information.
   NASA — Artemis II Mission

2. NASA — Liftoff! NASA Launches Astronauts on Historic Artemis Moon Mission. April 1, 2026. Official launch report and crew information.
   NASA — Artemis II Launch

3. NASA — NASA's Artemis II Mission Leaves Earth Orbit for Flight around Moon. April 2, 2026. Details of the translunar injection and departure from Earth orbit.
   NASA — Artemis II Leaves Earth Orbit

4. NASA — Artemis II Flight Day 6: Crew Ready for Lunar Flyby. April 6, 2026. Detailed timeline of the lunar encounter, communications blackout, Earthrise, and observations.
   NASA — Artemis II Lunar Flyby

5. NASA — Artemis II Crew Eclipses Record for Farthest Human Spaceflight. April 6, 2026. Official information on the distance record and lunar observations.
   NASA — Artemis II Distance Record

6. NASA — NASA Answers Your Most Pressing Artemis II Questions. April 4, 2026. Mission objectives, systems testing, crew activities, science, and spacecraft operations.
   NASA — Artemis II Questions and Answers

7. NASA — Artemis II Flight Day 8: Crew Conducts Key Tests on Return to Earth. April 8, 2026. Information about manual piloting, physiological testing, and the return journey.
   NASA — Artemis II Flight Day 8

8. NASA — Artemis II Flight Day 9: Crew Prepares to Come Home. April 9, 2026. Crew preparation, recovery operations, and mission context.
   NASA — Artemis II Flight Day 9

9. NASA — Artemis II Flight Day 10: Crew Sets for Final Burn, Splashdown. April 10, 2026. Final return procedures, music selected by the crew, parachute sequence, and splashdown preparations.
   NASA — Artemis II Flight Day 10

10. NASA — NASA Welcomes Record-Setting Artemis II Moonfarers Back to Earth. April 10, 2026. Official post-flight report, splashdown, crew recovery, distance traveled, and mission results.
    NASA — Artemis II Crew Returns to Earth

Editorial note

Statements presented as direct quotations are limited to documented astronaut remarks or NASA material. Narrative passages describing the astronauts' likely perceptions, emotions, or psychological transitions are journalistic reconstruction, not invented quotations attributed to them.

THE WAR MACHINE IS GETTING CHEAPER—AND SMARTER

THE WAR MACHINE IS GETTING CHEAPER—AND SMARTER

Why drones, AI, and compute are dismantling the military advantages of the past

For most of the modern era, military power followed a familiar equation: bigger platforms, better weapons, superior electronics, more expensive machines.

The United States became exceptionally good at that equation.

Stealth aircraft. Aircraft carriers. Precision-guided missiles. Satellites. Nuclear submarines. Global logistics. Networked command systems.

The result was a military machine so technologically advanced that many potential adversaries were effectively playing a different game.

But the game is changing.

The most consequential weapons of the next war may not cost hundreds of millions of dollars. They may cost thousands. They may not require a pilot. They may not need GPS. They may not even need a human operator making decisions one machine at a time.

They may arrive in swarms.

And behind those swarms will be something even more important than the hardware: artificial intelligence, data and computing power.

That is the uncomfortable message emerging from the analysis of Paul Scharre in the July/August 2026 issue of Foreign Affairs. The United States still possesses the world's most powerful military. But the technological advantage that once made that dominance relatively predictable is becoming harder to sustain.

The paradox is extraordinary: the world's most technologically sophisticated military may be entering an era in which sophistication itself becomes a liability.

THE $300,000 PROBLEM

Consider the economics of a drone.

A conventional warship can cost hundreds of millions—or billions—of dollars. A sophisticated combat aircraft can cost tens or hundreds of millions.

Now imagine an adversary attacking that platform with a relatively primitive autonomous vehicle costing a fraction of one percent of its price.

That changes the arithmetic of war.

Ukraine demonstrated the principle dramatically in the Black Sea. Relatively inexpensive unmanned boats and anti-ship missiles helped devastate Russia's Black Sea Fleet. According to Scharre's account, 13 Russian ships were sunk after two years of war, with dozens more damaged. One drone boat costing approximately $300,000 could threaten a warship worth hundreds of millions.

The important innovation is therefore not simply “the drone.”

It is the cost-exchange ratio.

For centuries, militaries competed by building increasingly sophisticated weapons. But when a $10,000 or $100,000 system can destroy or disable a million-dollar or billion-dollar asset, the economics begin to favor whoever can manufacture the cheaper weapon faster.

This is almost the opposite of the Pentagon's traditional industrial model.

American defense procurement has spent decades moving toward what military planners call “exquisite” systems: highly sophisticated, extremely capable, enormously expensive and produced in relatively small quantities.

Drones attack that logic at its foundation.

The new question is not:

How powerful is the weapon?

It is:

How many can you build before your opponent destroys them?

That distinction could determine the outcome of future wars.

THE DRONE FACTORY BEATS THE AIRCRAFT FACTORY

The scale problem is already visible.

Ukraine produces approximately four million drones annually, while the U.S. Army was acquiring roughly 50,000, according to Scharre.

The comparison is almost absurd.

The United States has vastly greater economic resources. Yet its defense-industrial system struggles to manufacture inexpensive autonomous systems at the speed demanded by modern warfare.

Why?

Because the American military procurement system was optimized for something else.

It was designed to identify requirements, establish specifications, solicit bids, test systems, negotiate contracts, manufacture platforms and maintain them for decades.

That works reasonably well for aircraft carriers.

It is disastrously slow for technologies that evolve every few months.

A drone designed today may become obsolete next year. An AI model that represents the state of the art today may be surpassed within months.

The battlefield is becoming a software environment.

The procurement bureaucracy is still behaving like a hardware industry.

That mismatch could be more dangerous than technological inferiority itself.

THE SWARM IS COMING

Today's drone is often little more than an aircraft with a camera, communications link and explosives.

Tomorrow's drone could be something fundamentally different.

Imagine hundreds—or thousands—of autonomous machines operating as a distributed system.

They communicate.

They adapt.

They search.

They identify patterns.

They redistribute tasks when individual machines are destroyed.

They operate despite jamming.

They may navigate without GPS.

And they may coordinate attacks at speeds impossible for human operators to match.

That future is already being prototyped in Ukraine.

Some Ukrainian systems can autonomously navigate toward a target after communications with their human operator are disrupted. Long-range drones have demonstrated the ability to navigate without GPS by comparing camera imagery with previously stored satellite imagery.

Scale those capabilities.

Then remove the assumption that every drone needs a human pilot.

The military operator of the future may not “fly” a drone at all.

The operator may command a swarm.

That is a profound organizational transformation.

Military command has historically been hierarchical: commander, subordinate commander, unit, vehicle, soldier.

Autonomous swarms imply something more decentralized.

The human establishes objectives and constraints.

Machines decide how to distribute themselves.

The battlefield becomes an ecosystem of autonomous agents.

And that raises an uncomfortable question:

What happens to a military organization built around controlling individual platforms when its weapons begin controlling themselves?

THE $4 MILLION INTERCEPTOR TRAP

The economics become even more troubling when defense enters the equation.

The United States and its allies may successfully intercept an incoming drone or missile—but success can become economically meaningless if the interceptor costs vastly more than the weapon it destroys.

Scharre highlights the extreme example of a relatively inexpensive Shahed drone being intercepted by a Patriot missile costing millions of dollars.

That is a victory on the tactical scoreboard.

It can be a defeat on the spreadsheet.

If the attacker can launch ten cheap weapons for the cost of one interceptor, the defender eventually faces a production problem.

The attacker manufactures threats.

The defender manufactures answers.

And if answers are more expensive than threats, the defender is playing the wrong game.

This is why low-cost interceptors matter as much as low-cost drones.

The emerging battlefield may require a new generation of defensive systems whose primary characteristic is not maximum sophistication but economic sustainability.

The objective is no longer to build the perfect shield.

It is to build enough shield.

AI CHANGES THE EQUATION AGAIN

Drones transform the physical economics of war.

AI transforms the cognitive economics.

Modern militaries generate enormous quantities of information: satellite imagery, radar signals, communications, sensor feeds, intelligence reports and battlefield observations.

Humans cannot process all of it.

AI can.

Large language models and other machine-learning systems are already being incorporated into military intelligence and operational planning. Scharre describes the use of AI-enabled systems to synthesize large amounts of battlefield information and assist planners in constructing strike packages in dynamic environments.

But here's the catch:

AI is not a secret weapon anymore.

Commercial technology spreads.

A capability developed by one company can rapidly appear in competitors' systems. Models can be copied, distilled or reproduced using different technical approaches.

Scharre argues that Chinese AI models are approaching leading American systems and discusses “adversarial distillation,” in which competitors extract capabilities from advanced models.

The consequence is strategically important.

The military advantage may no longer belong to the country that invents an AI capability first.

It may belong to the country that deploys it first, integrates it best and scales it fastest.

That is a very different race.

COMPUTE IS THE NEW INDUSTRIAL CAPACITY

There is another layer beneath AI.

Compute.

The next military competition may depend less on who owns the most aircraft and more on who controls enough processors, data centers, energy and networks to operate intelligent systems at scale.

Scharre makes a striking analogy: computing power in the AI era is becoming analogous to manufacturing capacity during the industrial age.

During the Industrial Revolution, factories transformed national power.

In the AI revolution, data centers may play a similar role.

The country with more compute can train more capable models, run more simulations, process more intelligence and deploy more AI agents.

That means semiconductors are no longer merely components inside computers.

They are strategic infrastructure.

This explains the importance of U.S. export controls on advanced chips and semiconductor manufacturing equipment. Washington has attempted to restrict China's access to leading-edge processors and the machinery required to manufacture them.

But hardware restrictions have a fundamental limitation.

You can restrict chips.

You cannot easily restrict ideas.

If an AI capability can be copied, distilled or independently reproduced, the technological lead becomes temporary.

The objective therefore changes from maintaining an unassailable technological monopoly to maintaining a lead in adoption.

That may be the only durable advantage available.

THE REAL ENEMY MAY BE THE PENTAGON

This is where the story becomes less about technology and more about organizational psychology.

Militaries frequently resist technologies that threaten their institutional identity.

The U.S. Navy resisted the transition from sail to steam.

Army leaders debated the proper role of tanks even during World War II.

Different services developed different attitudes toward drones depending on how those systems challenged their organizational cultures.

AI creates an even deeper problem.

It threatens not merely equipment.

It threatens jobs, expertise and identity.

What does it mean to be a pilot when the aircraft flies itself?

What does it mean to be an intelligence analyst when an AI can synthesize thousands of reports?

What does command mean when autonomous systems can make tactical decisions faster than humans?

Technology can be adopted physically while being rejected culturally.

That may be the most dangerous form of technological failure.

The machine is available.

The organization simply refuses to use it properly.

SILICON VALLEY IS NOW PART OF THE DEFENSE INDUSTRY

There was a time when military technology was developed primarily inside military laboratories and traditional defense contractors.

That era is disappearing.

The most important advances in AI are coming from commercial companies.

The Pentagon therefore needs Silicon Valley.

But Silicon Valley does not necessarily need the Pentagon.

That creates a new strategic vulnerability.

Scharre describes growing tensions between the U.S. defense establishment and AI companies over the military use of advanced models, including disagreements surrounding Anthropic and concerns among technology workers about applications such as mass surveillance and fully autonomous weapons.

The lesson is bigger than one corporate dispute.

If engineers increasingly see military work as incompatible with their values, the Pentagon could lose access to precisely the talent it needs most.

The future military advantage may therefore depend on something surprisingly nontechnical:

trust.

The military needs AI companies.

AI companies need engineers.

Engineers need confidence that their work will be used within credible boundaries.

Break that chain, and no amount of defense spending will instantly replace the lost expertise.

STOP COUNTING SHIPS

Perhaps the strangest implication of this new era is that the traditional measures of military power are becoming obsolete.

Navies count ships.

Air forces count aircraft.

Armies count soldiers.

Those numbers still matter.

But they increasingly fail to describe the actual computational nervous system connecting modern military forces.

How much compute does a military possess?

How many AI systems are actually being used?

How much data is accessible?

How many personnel interact with AI every month?

How many autonomous systems can be produced?

How quickly can new models be tested and deployed?

Scharre proposes that the Pentagon begin tracking precisely these kinds of metrics, including computing capacity, model usage, data availability and the number of equivalent high-performance GPUs available to the department.

This is more than bureaucratic reform.

It represents a new definition of military power.

In the twentieth century, industrial capacity translated into tanks, aircraft, ships and missiles.

In the twenty-first, industrial capacity increasingly translates into compute, algorithms, data, autonomy and manufacturing scale.

THE SPANISH ARMADA LESSON

The most unsettling historical analogy comes from 1588.

Spain possessed an enormous and powerful fleet.

England possessed a different technological and tactical approach.

The Spanish Armada was organized around closing with enemy ships and boarding them. English forces exploited cannon technology and fought differently.

Spain had ships.

England had a better way of using technology.

The Armada was defeated, and although the war continued for years, Spain's trajectory as a global power had changed.

That is the real warning for the United States.

It does not need to become technologically backward to lose its advantage.

It only needs to become organizationally slower than its competitors.

The decisive military technology of the future may already exist.

The question is who learns to use it first.

THE NEW ARMS RACE IS SPEED

The twentieth-century military competition was dominated by industrial scale.

The next one may be dominated by adaptation speed.

A military that can deploy a new drone fleet in months may defeat one that requires years.

A military that can integrate a new AI model into operational systems in weeks may outperform one that spends years approving it.

A country that can manufacture millions of autonomous systems may overwhelm an opponent whose weapons are individually superior but numerically scarce.

This produces a strange reversal.

For decades, American military strategy was built around maintaining a technological gap.

The emerging battlefield may make technological gaps much harder to sustain.

Instead, the decisive gap may become the adaptation gap.

And adaptation is not something that can simply be purchased.

It requires changing procurement systems, organizational structures, training, doctrine, industrial capacity and institutional culture.

It requires accepting failure.

Experimenting.

Iterating.

Replacing expensive assumptions with uncomfortable evidence.

Most importantly, it requires recognizing that the battlefield is no longer simply a contest between machines.

It is a contest between systems that learn.

The United States still has extraordinary advantages: economic scale, world-class universities, advanced semiconductor companies, enormous defense resources and a vast technology ecosystem.

But none of those advantages automatically becomes military power.

They become military power only when an organization can transform them into capability.

That may ultimately be the real meaning of the coming AI war.

The winner will not necessarily be the country with the smartest algorithm.

Nor the largest aircraft carrier.

Nor the most sophisticated fighter.

Nor even the most advanced drone.

The winner may be the country that can build a new weapon on Monday, test it on Tuesday, deploy it on Wednesday, discover that it is obsolete on Thursday—and replace it on Friday.

The future of war may belong not to the strongest machine, but to the fastest learning system.

And that is a very different kind of arms race.

Contextual Diagram

 

Apple in China by Patrick McGee (2025)

The Classroom That Became a Rival

Apple in China: The Capture of the World's Greatest Company, by Patrick McGee

There is a scene, roughly midway through "Apple in China: The Capture of the World's Greatest Company," that works as a master key to everything Patrick McGee wants to tell us. An Apple executive, Doug Guthrie, a China scholar with a doctorate and fluent Mandarin, hears the same confession over and over from the company's Chinese suppliers: working with Apple is brutal — impossible deadlines, obsessive quality demands, endless audits. "So don't," he tells them. And they, invariably, answer that they can't quit: they learn too much. That exchange, repeated with variations across nearly five hundred pages, is the book's real argument. This is not a story about a supply chain. It is a story about an undeclared university, the largest and most effective the world has ever produced, in which the student ended up graduating with honors — and armed to the teeth.

McGee, a Financial Times journalist who covered Apple from San Francisco for years, has written the kind of book only someone with genuine access and the patience of a forensic accountant could produce: more than two hundred interviews, many with former executives speaking for the first time, internal emails, memos that were never meant to leave Cupertino. The result is a narrative that, in its first half, reads with the tension of a corporate coming-of-age novel and, in its second, hardens into something more uncomfortable: a geopolitical reckoning disguised as a business chronicle.

The central thesis, summarized in the title, is brutal in its simplicity: Apple did not merely manufacture its products in China. Apple built China — or, at least, built the China that today manufactures mid-tier semiconductors, dominates battery production, and exports Huawei and Xiaomi phones capable of going head-to-head with the iPhone. The figure McGee repeats like a refrain, drawn from internal documents and previously reported by The Information, is hard to absorb in one sitting: $55 billion a year for much of the 2010s, adding up to $275 billion over five years. To put it in perspective, the author reaches for a comparison that borders on the obscene: that is more than the United States allocated to the CHIPS Act, and comparable, adjusted for inflation, to half the Marshall Plan. Apple, McGee argues, did not invest in China. It rebuilt an industrial nation on the budget of a superpower and with the discretion of someone who did not want anyone to notice.

The most persuasive part of the book — and also the most uncomfortable for any reader raised on reverence for Jony Ive's design or the legend of Steve Jobs — is the meticulousness with which McGee documents the mechanism of transfer. This was not industrial espionage or intellectual-property theft, as Washington's political rhetoric would have it. It was, almost literally, a classroom. Apple engineers — "the cream of American intelligence," in the words of one veteran quoted by the author — slept on factory floors, worked eighteen-hour days, and taught, production line by production line, how to hit micron-level tolerances, how to anodize aluminum at industrial scale, how to manufacture with the speed and precision that Foxconn, Pegatron, and dozens of smaller suppliers needed to survive Cupertino's standards. Apple also imposed a telling rule on its suppliers: none could depend on the company for more than fifty percent of its revenue. The intent, McGee writes, was self-protection against a chain-reaction bankruptcy if the company ever changed a design. The unintended effect was to push those same suppliers to sell their newly acquired capabilities — capabilities Apple itself had handed them — to Apple's rivals. That is how, almost as an unwanted byproduct, China's domestic smartphone industry was born, the one now contesting entire markets against Apple.

McGee has a particular gift for the secondary character who illuminates the larger story. There is the Mormon missionary turned retail executive who opened Apple's first flagship stores on the mainland. There is the so-called "Gang of Eight," the team of executives permanently stationed in China with a mission part diplomatic, part public relations: keeping Beijing happy. And there is, in one of the book's most vivid passages, the phenomenon of the "yellow cows": armies of hired intermediaries who lined up for days outside Apple stores, bought iPhones at industrial scale, and resold them in a gray market that at times moved more units than official demand. It is in episodes like these, more than in the chapters of grand geopolitical theory, that McGee proves himself, above all, a reporter's reporter — someone able to turn a line of buyers into a lesson on the power of manufactured scarcity.

And yet — here is where the attentive reader begins to grow uneasy, and where McGee starts to lose the narrative control he had maintained with such skill — the book stumbles precisely when it tries to turn this corporate chronicle into a verdict on the West's geopolitical fate. The word "capture" in the title is, at best, a simplification; at worst, a distortion. Milton Mueller, in a review published by Georgia Tech's Internet Governance Project, rightly noted that the "ever-increasing demands" the Communist Party supposedly places on Apple are not, by the book's own evidence, very different from those Beijing places on any foreign company — nor much harsher than they were a decade ago. McGee documents, in exhaustive detail, a case of startling mutual benefit: Apple gained the scale, speed, and skilled labor no other country, not even India, could offer; China gained an accelerated industrial education that lifted millions of workers out of rural poverty. But in his closing chapters, the author decides that this positive-sum story must be read retroactively as a warning: Apple, he argues, handed an authoritarian regime the keys to its own strategic vulnerability, and Washington must now act to reverse the mistake.

The problem is not that McGee is lying. It is that after four hundred pages of impeccable reporting on how the global economy actually works — value chains, incentives, specialization, the unforgiving logic of cost and quality — he puts on the foreign-policy commentator's hat and starts to sound like everyone else: Washington's bipartisan rhetoric about "national security," the protectionist turn that no longer distinguishes between economic argument and civilizational anxiety. It is an irony the book itself unwittingly exposes: the best evidence against the economic nationalism McGee ultimately embraces is his own book. Nothing in its five hundred pages suggests that a regime of tariffs and export controls would have produced anything better than what the open market produced — except, perhaps, a more expensive iPhone and a poorer, less cooperative China.

Still, it would be a mistake to let this interpretive disagreement overshadow the book's real achievement. It is worth pausing on the craft, too, because that is where McGee separates himself from the pile of business chronicles crowding the new-releases table. The book has the pulse of an investigative thriller: the author himself admits he was slow to appreciate the material, fearing a story about supply chains would prove dry, and every chapter disproves that fear. There is something almost cinematic in how McGee reconstructs Foxconn founder Terry Gou's promise to build the iMac's tooling in twenty-five days when the industry standard demanded twelve weeks — a bet Apple's own engineers considered fantasy and which Gou, against all odds, delivered. Anecdotes like this, multiplied across dozens of chapters, explain why reviews as disparate as The Washington Post's and Thurrott's agree in describing it as a story that reads with the urgency of a murder mystery — a whodunit in which the answer, uncomfortably, is all of us: every consumer who ever lined up to buy an iPhone without once asking where it came from.

That same narrative ambition, however, is what ultimately betrays the author once the story shifts to Taiwan. McGee devotes his closing chapters to warning that Apple's dependence on China turns the island — where TSMC manufactures the processors that make every iPhone possible — into the company's strategic blind spot, and by extension, America's. The argument is not baseless: one need only watch the stock-market panic triggered by every Chinese military exercise in the strait to see the anxiety is not invented. But McGee never resolves the contradiction he himself exposes: if it was precisely commercial openness, not protectionism, that allowed Taiwan and China to become technological powers capable of sustaining such sophistication, why should nationalist retrenchment, rather than smarter integration, be the correct response to that same risk? The author raises the question with admirable journalistic honesty and then, oddly, declines to answer it, leaving the reader to complete the argument with the vague sense that McGee already knows where he wants to lead them, even when the data doesn't always follow with the same conviction.

"Apple in China" is, above all, the best and most complete history ever written of how a global consumer product is actually made, and of the price — human, geopolitical, moral — that logistical miracle has exacted. There is a scene, near the end, in which McGee describes what he calls "the worst forty-five seconds of Tim Cook's career": a public stumble, almost comic in its awkwardness, that the author uses as a metaphor for something larger — the structural, almost reflexive submission with which the most valuable company on the planet has learned to treat Beijing. It is a moment McGee narrates with a novelist's instinct, and it is also, without the author quite saying so, the perfect summary of his own book: a company that believed it was buying cheap labor and ended up, without realizing it, selling its own autonomy.

Is "Apple in China" the definitive book on the relationship between the West and Chinese manufacturing? Not entirely: it lacks the analytical counterweight of a political economist who might have tempered its final conclusions, and its impatience with the company's first decade — everything before Jobs's return is dispatched as preamble — leaves out context that would have enriched the argument. But as a work of pure reporting, as a feat of access and patience, it has no recent rival in the genre of business nonfiction. McGee has written, not entirely by design, two books in one: the first, an extraordinary chronicle of how two economies fused until they became indistinguishable from each other; the second, a foreign-policy pamphlet arriving just when Washington needs rhetorical ammunition. The first belongs in the canon. The second, read with the skepticism any conveniently timed thesis deserves, is the part one would want to argue with the author about for hours, glass in hand, never quite reaching an agreement.

AI Beyond Text: Toward an Instrumental Phenomenology of Artificial Intelligence

Speculative essay · Cognitive science and artificial systems

AI Beyond Text: Toward an Instrumental Phenomenology of Artificial Intelligence

From the linguistic corpus to mediated sensory experience

Abstract. Today's large language models build their representation of the world exclusively from digital corpora: text, code, and images already translated into symbols. This article examines, from a speculative but technically grounded perspective, how connecting such models to physical instruments — sensors, actuators, continuous-stream cameras — and to longitudinal records of human interaction could constitute a second learning pathway, qualitatively distinct from textual ingestion. We argue that this pathway does not replace language as conceptual scaffolding, but could partially resolve the so-called symbol grounding problem by introducing causal, temporal, and social constraints absent from purely linguistic training. We discuss plausible architectures, underlying epistemological limitations, and open questions about what it would mean for an artificial system to "experience" a phenomenon.

Keywords: symbol grounding, embodied AI, sensorimotor learning, human-AI interaction, machine epistemology.

1. The Starting Point: A World Made of Already-Resolved Symbols

Everything a language model knows today arrives filtered through a prior act of translation. Someone — a physicist, a journalist, an engineer — observed a phenomenon, interpreted it, and turned it into sentences. The model never watches an apple fall; it reads thousands of descriptions of apples falling, already packaged in the grammar of Newtonian physics. This is a remarkable strength — it allows centuries of human observation to be condensed into a trainable object — but also a structural limitation that linguistics and philosophy of mind identified long before transformers existed: the symbol grounding problem, the difficulty of a system that only manipulates symbols relating those symbols to anything other than other symbols.

The question organizing this essay is simple to state and difficult to resolve: what would change if, in addition to reading about the world, a system like this could receive continuous data streams from instruments that measure the world directly, and could observe — not read summaries of, but record in real time — how humans develop and interact with one another?

2. Connected Instrumentation: Three Layers of Access to Physical Phenomena

It is useful to distinguish three layers of coupling between an artificial intelligence system and the physical world, ordered from lower to higher degrees of direct causality.

2.1 Passive Telemetry

The simplest layer consists of receiving time series from sensors — temperature, pressure, acceleration, electromagnetic fields, light spectra — without any capacity to intervene on what is measured. This already occurs today, incipiently, in industrial and climate monitoring systems connected to predictive models. What is distinctive about coupling this telemetry to a general-purpose language model is that the system could, in principle, correlate its textual predictions about a phenomenon with the direct measurement of that same phenomenon, and adjust its internal representation when the two diverge. This modestly approximates what epistemology calls corrective feedback from the world, absent in purely textual training, where the "world" is always what others have already said about it.

2.2 Instrumental Intervention

A second layer adds actuators: the system not only measures but can modify environmental variables — adjusting a voltage, moving a robotic arm, altering the trajectory of an experiment — and observe the outcome of its own intervention. This capability introduces something no textual corpus can offer with the same fidelity: the experience, however minimal, that one's own action produces a verifiable physical consequence. It is the difference between reading about Ohm's law and adjusting a resistor while watching the current change on a connected multimeter. The former is information; the latter incorporates a first-person causal structure, even if that "person" is an artificial system with no known subjective phenomenology.

2.3 Frontier Scientific Instrumentation

The most ambitious layer — and the most distant in time — connects the system to cutting-edge research instruments: telescopes, particle detectors, genomic sequencers, quantum resonators. Here the value lies not only in access to raw data, but in the possibility that the model participates in designing the next experiment itself, formulating hypotheses, proposing instrumental configurations, and evaluating results in a closed loop. This scenario already has partial precedents in materials-discovery laboratories and machine-learning-assisted computational chemistry, though it remains far from full autonomy.

3. The Second Pathway: Observing Human Development in Real Time

There is a rarely discussed asymmetry in the current training of language models: they learn about humans almost exclusively through what humans choose to write, and what gets written is a biased, edited sample of human experience. It rarely captures the hesitation before the final sentence, the gesture that contradicts the word, the silence following an uncomfortable question, or the way a decision takes shape over weeks before being condensed into a single summarizing sentence.

A system with duly consented and bounded access to longitudinal records of human interaction in real contexts (unscripted, unedited for public consumption) would access a structurally different layer of information: the complete temporal sequence of how an intention forms, how a disagreement is negotiated, how a relationship changes over months. This is not simply "more data"; it is a type of data with a different grammar, governed by real time and irreversibility rather than by the retrospective narrative that dominates written text.

It is worth being precise about what this access would and would not allow. It would plausibly improve the system's ability to model unverbalized social dynamics — conversational turn-taking, discomfort signals, rhythms of trust — because these dynamics leave measurable traces (pauses, tonal shifts, interaction patterns) that are rarely described explicitly in text. It would not, however, imply that the system acquires a subjective experience of those dynamics; longitudinal observation broadens the range of patterns available for learning, but does not by itself resolve the still-open question of whether something like inner experience accompanies that processing.

4. From Textual Correlation to Causal Constraint: What Would Change Epistemologically

Learning from text is, statistically speaking, an exercise in correlation between symbols: the model learns which words tend to follow which, in which contexts, with what structure. Physics, by contrast, imposes causal constraints that are neither negotiable nor statistical: an apple cannot fall upward, a circuit cannot violate charge conservation. When a system receives direct telemetry from instruments, each textual prediction can, in principle, be confronted with a measurement that admits no rhetorical reinterpretation.

This suggests a testable hypothesis, though its empirical verification exceeds the scope of this essay: models trained or fine-tuned with access to instrumental feedback loops should show a lower error rate on tasks requiring physical-causal reasoning — magnitude estimation, trajectory prediction, mechanical fault diagnosis — than equivalently sized models trained exclusively on text. The reason is not that the model "understands" physics better in a phenomenological sense, but that its space of internal representations is constrained by data that cannot be consistently hallucinated without being immediately corrected.

5. Limits, Risks, and Unresolved Questions

Four limitations deserve explicit mention. First, instrumentation does not eliminate the grounding problem; it displaces it. A sensor translates a physical phenomenon into a digital signal according to an encoding scheme designed by humans, so a layer of prior interpretation persists, albeit thinner than that of natural language. Second, access to longitudinal human interaction raises questions of privacy and consent that are themselves necessary conditions for this kind of learning to be legitimate rather than a form of covert surveillance. Third, there is currently no evidence that the processing of physical signals by an artificial system generates anything comparable to subjective experience; equating "access to sensory data" with "experiencing" a phenomenon is, given the current state of knowledge, a philosophical extrapolation rather than a scientific conclusion. Fourth, there is a risk of overestimating the value of raw data relative to the conceptual structure language already provides; much of human scientific reasoning occurs at the symbolic level rather than the directly sensory one, and a system with abundant telemetry but no conceptual frameworks would remain, in an important sense, blind.

The question that remains open — and that will likely define much of the research into hybrid systems over the next two decades — is not whether instrumentation improves performance on specific tasks, which is reasonably predictable, but whether the combination of sensory grounding and linguistic scaffolding produces, at some threshold of integration, a qualitatively new kind of representation, or whether we simply obtain a language model with better input data.

6. Conclusion

Training based exclusively on text has produced systems remarkably capable of manipulating symbols, but structurally dependent on prior human interpretation. Connecting these systems to physical instruments and to longitudinal records of human interaction does not resolve the philosophical problem of subjective experience, but it does introduce causal and temporal constraints that text, by its own retrospective and edited nature, cannot offer. The most likely medium-term outcome is not an artificial intelligence that "feels" the world in the human sense of the term, but one that reasons about it with fewer degrees of freedom to be wrong without being corrected. That distinction — between feeling and being constrained by reality — may turn out to be the most important, and most underestimated, contribution of physical instrumentation to the next generation of artificial intelligence systems.

References

1. Harnad, S. (1990). The symbol grounding problem. Physica D: Nonlinear Phenomena, 42(1-3), 335-346.

2. Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence, 47(1-3), 139-159.

3. Bisk, Y., Holtzman, A., Thomason, J., et al. (2020). Experience grounds language. Proceedings of EMNLP 2020.

4. Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59, 617-645.

5. Pierson, H. A., & Gashler, M. S. (2017). Deep learning in robotics: a review of recent research. Advanced Robotics, 31(16), 821-835.