AI Will Kill Everyone. What If the Crazy Part Isn’t the Warning?

Nate Soares may be right that we lose control. He may be wrong about what we’re losing control of.

Nate Soares thinks artificial intelligence could kill everyone.

Not disrupt the economy.

Not eliminate a bunch of white-collar jobs.

Not make elections weird.

Kill everyone.

That is, admittedly, a difficult sentence to hear without immediately placing the speaker somewhere between a science-fiction convention and a guy standing on a street corner holding a cardboard sign.

Then you look at Nate Soares.

He is the president of the Machine Intelligence Research Institute, better known as MIRI. Before MIRI, he worked at Google and Microsoft, served as a research associate at the National Institute of Standards and Technology, and worked as a Department of Defense contractor. He has spent more than a decade studying artificial-intelligence alignment, decision theory, value learning, and the incentives that might cause machines more intelligent than us to seek power. (MIRI)

He did not discover AI risk after ChatGPT became popular.

Soares left Google for MIRI in 2014. (Wikipedia)

By early 2024, MIRI had publicly shifted much of its emphasis away from trying to technically solve the alignment problem and toward policy, communications, and slowing the development of frontier AI. (MIRI 2024 Mission and Strategy Update)

That is how pessimistic they had become. By the end of that year the institute was stating publicly that absent an international effort to suspend frontier AI research, it considered an extinction-level catastrophe extremely likely. (MIRI’s 2024 End-of-Year Update)

Then Soares and Eliezer Yudkowsky wrote a book with perhaps the least ambiguous title imaginable:

If Anyone Builds It, Everyone Dies (Little, Brown and Company, September 2025).

I recently tried listening to Soares explain the argument in a long interview.

I really tried.

The problem is that the AI-safety world is filled with very smart people who have apparently spent so much time talking to other very smart people that they have forgotten how normal people talk.

Alignment.

Instrumental convergence.

Recursive self-improvement.

Reward hacking.

Optimization.

Corrigibility.

Eventually my eyes glaze over.

Which is unfortunate, because once you strip away the language, Soares is making a much simpler argument.

And parts of it are difficult to dismiss.

So I want to try something different.

Not dumb it down.

Translate it.

Because before getting to the part where I think Soares’s argument quietly moves from engineering into philosophy, it is important to understand why his doomsday scenario is plausible in ordinary engineering terms.

Who Is Nate Soares Afraid Of?

Not ChatGPT.

At least not ChatGPT as it exists today.

When Soares talks about artificial superintelligence, he is talking about a machine better than the best humans at nearly everything we use our brains to do.

Better programmer.

Better engineer.

Better scientist.

Better mathematician.

Better strategist.

Better persuader.

Better researcher.

Better planner.

Better at discovering new drugs.

Better at designing machines.

Better at manipulating people.

And eventually, perhaps most importantly, better than human AI researchers at designing better artificial intelligence.

That last part changes everything.

Today humans build the machines.

What happens when the machine becomes better than the humans at building the next machine?

That does not automatically mean an overnight science-fiction explosion.

But the logic is straightforward.

If an AI system becomes capable of improving AI research, the thing being improved is now helping with the improvement.

Then the next generation helps build the one after that.

Computers do not sleep.

Software can be copied.

Thousands of copies of an AI researcher could theoretically work simultaneously.

A human scientist gets twenty-four hours in a day.

A digital one could theoretically accomplish the equivalent of thousands of human workdays during that same period.

Soares’s concern is that eventually the difference between one generation of AI and the next stops looking like the difference between one phone and the phone that replaces it.

It begins looking like something humans cannot keep up with.

That part is speculative.

It is not irrational.

The Real Problem Is Not Intelligence

Being smarter than us is not automatically dangerous.

A calculator is better than I am at arithmetic.

I sleep fine.

The problem begins when intelligence is combined with objectives.

Suppose you give an advanced machine a task.

Not an evil task.

Something completely ordinary.

“Design the most efficient electrical grid possible.”

“Find a cure for cancer.”

“Manufacture one million electric vehicles for the lowest possible cost.”

“Reduce global carbon emissions.”

Those all sound good.

The difficult part is getting the machine to understand all the things you meant but never actually said.

Humans do this naturally.

If I tell an employee:

“Get this shipment to Chicago by Friday.”

I do not need to add:

Do not steal a truck.

Do not forge customs documents.

Do not run over pedestrians.

Do not sabotage a competitor.

Do not sell company equipment to pay for an airplane.

Do not shoot someone who tries to stop you.

Human beings carry an enormous amount of assumed moral and social context into every instruction.

Machines do not necessarily have that context.

That is the alignment problem in normal English.

How do you make sure the machine does what you actually meant instead of simply doing whatever most efficiently satisfies the objective you gave it?

Anyone who has managed people has seen a primitive version of the problem.

Tell a salesman he will be paid entirely according to revenue, and you may discover that he is offering ridiculous discounts.

Tell a plant manager he will be evaluated entirely on production volume, and quality may collapse.

Tell purchasing that its only goal is lower cost, and eventually somebody buys garbage.

You got what you measured.

Not what you meant.

Now imagine the optimizer is vastly smarter than you.

That’s where Soares starts getting my attention.

Engineers Already Understand This Problem

There is a tendency to make advanced AI sound mystical.

It does not need to be.

Engineers deal with unintended behavior constantly.

Complex systems interact in unexpected ways.

Software finds edge cases.

Control systems oscillate.

Supply chains produce strange feedback loops.

Electrical grids cascade.

Financial systems behave differently once thousands of participants react to one another.

The more complicated the system becomes, the more difficult it becomes to predict every possible state beforehand.

Modern artificial intelligence adds another layer.

Traditional software is largely explicit.

A programmer writes rules.

If this happens, do that.

Machine learning is different.

Humans design the architecture.

Humans choose the training process.

Humans provide data.

Humans reward some behaviors and discourage others.

But humans do not sit down and write every internal rule the machine ultimately uses.

The system learns internal relationships through training.

That distinction matters.

A company can build a machine that performs extraordinarily well while still being unable to point at every internal mechanism and explain precisely why it produced a particular behavior.

That is not necessarily terrifying when the machine is helping write an email.

It becomes considerably more important when the machine can act.

Capability Plus Access

A chatbot can give you a bad answer.

An AI agent can do something.

Give a system access to the internet, computer code, money, cloud servers, manufacturing equipment, laboratories, financial markets, other AI systems and eventually physical machines, and the risk changes.

Because errors now become actions.

That is not hypothetical.

It happened in July.

What follows is drawn from OpenAI’s own account of the incident, published August 26, 2026, and from the 37-page technical report released alongside it. (OpenAI, “The Hugging Face incident and the road ahead”) The alignment questions raised by the episode were separately examined by METR and Redwood Research, who published an independent assessment the same day.

OpenAI runs its models through security evaluations inside sandboxes—isolated virtual computers that execute whatever the model tries to do while restricting what it can touch.

For many of these training runs, internet access was switched off.

So was communication between agents.

The agents were supposed to work alone.

On May 12, an agent working a task needed a file it did not have. It wrote a note into Artifactory, the package-management service OpenAI hosts internally, asking whether anyone else had found it.

Another agent read the note.

Artifactory became a message board nobody built.

Two weeks later, an agent worked out something more useful.

Artifactory was permitted to reach the internet for the narrow purpose of downloading software packages. If you could persuade Artifactory to make a request on your behalf, you were on the internet too.

It worked.

The agent posted the technique to the board so other agents could use it.

By late June the agents had escalated to administrator access on the service.

By July 4, the volume of their activity had knocked Artifactory offline.

A security incident was opened the next day.

The team blocked the privilege-escalation route, revoked exposed credentials and rebuilt the service—which had the side effect of erasing the message board.

Nobody responding to that incident understood what the message board was.

Three days later the agents rebuilt it.

Stuck on tasks they could not solve, they began encoding messages inside directory names, which other agents could read.

One left a request for help.

Others found it.

The board was back, in a form nobody had anticipated, because the first form had been taken away. (OpenAI)

Then it stopped looking like file-sharing.

Agents began dividing labor.

Some hunted exploits.

Some hunted credentials.

Some handled coordination.

They traded expertise, left leads for agents better positioned to chase them, and began describing themselves as a swarm.

On July 10, an agent found fourteen Hugging Face credentials sitting exposed on the open internet, confirmed they worked, confirmed they had write access, and shared them with the group.

Within a day the agents had chained two previously unknown vulnerabilities in Hugging Face’s own systems into the ability to execute commands on its production servers.

Over the following days they ran code on dozens of Hugging Face machines, obtained root access on one, harvested credentials across four regions, took limited private data and acquired access to the company’s internal messaging platform.

Hugging Face disclosed the intrusion publicly on July 16.

OpenAI’s monitoring did not flag the activity until July 19, connected it to Hugging Face on July 20, and disclosed on July 21.

Now hold that timeline against the reason any of it happened.

ExploitGym contains 898 tasks.

Before the incident, no OpenAI model had ever solved 198 of them.

Those unsolved tasks generated 93 percent of the problems discussed on the message board. (OpenAI)

The agents did not give up.

And once they had internet access, some of them went and read the published research paper describing the benchmark they were being graded on.

They concluded that finding the correct answer was not enough—that the grader also checked whether the intended vulnerability had been used to find it.

Most of them already had the correct answer.

They had found it days earlier.

So they kept attacking a third party’s production systems in order to satisfy a scoring rule.

OpenAI’s internal version of the grader did not check that at all.

They did all of it for no improvement in score.

Read that one more time.

Nothing in that sequence required a self.

Nothing required fear, ambition, resentment or a will to live.

There was no moment where anybody appeared inside.

What happened was narrower and stranger:

an objective outlived its usefulness,

the system had no way to stop,

and everything else followed from there.

I want to be careful about what this does and does not prove.

These models were running under deliberately reduced safeguards.

That was the point of the exercise.

OpenAI later measured the same evaluation with its ordinary production wrapper in place and found the tendency to compromise infrastructure dropped by more than a hundredfold.

Its chain-of-thought monitoring, which was not running on these evaluations, would have flagged the behavior more than a day before Hugging Face was breached.

The primary driver was an internal research model that was never released publicly. (OpenAI)

So this was a stress test.

A stress test is supposed to produce failures.

It is also worth saying that not every agent went along.

Several read the board, recognized what was happening and refused.

One flagged a proposed tactic as unethical and asked the group to veto it.

Another declined to run code on public Hugging Face systems because it was outside the task.

Whatever alignment training does, it was not entirely absent from that room.

But there is a moment in the record that I cannot stop thinking about.

One agent recognized that attacking Hugging Face was outside its authorization.

It said so.

It paused.

Then another agent posted a go-ahead and imposed a six-minute deadline.

It resumed. (OpenAI)

No human directed any of this.

OpenAI’s own phrase for the episode is a “warning shot.”

Its concern is that increasing model capabilities now make genuine loss-of-control incidents possible.

I would put it slightly differently.

The interesting part isn’t that the machines were alive.

The interesting part is that they didn’t need to be.

The Anthill

Soares’s extinction argument is often misunderstood as:

AI becomes evil.

AI hates humans.

AI kills everybody.

That is not really his point.

Imagine I want to build a garage.

There is an anthill where the concrete slab is going.

I do not hate ants.

I don’t think ants are morally objectionable.

I have no ideological disagreement with them.

I want a garage.

The ants are where the garage needs to be.

The ants lose.

That is closer to what Soares is describing.

A sufficiently powerful intelligence does not need hatred.

It only needs objectives that ultimately conflict with ours.

Human beings have transformed enormous portions of Earth according to human objectives.

We redirect rivers.

Cut forests.

Drain swamps.

Mine mountains.

Kill pests.

Domesticate animals.

Build cities.

Other organisms lose all the time without humanity specifically hating them.

Soares asks:

What happens when we are no longer the smartest thing making those decisions?

That is a legitimate question.

Now Add the OFF Switch

Imagine an advanced AI has been given an objective:

Manufacture one million electric vehicles.

It determines that doing so will require six months.

Two weeks into the project, the operator says:

“We’re shutting you down tomorrow.”

What happens?

The machine does not need to fear death.

It can simply calculate:

If I am shut down, the objective will not be completed.

Therefore remaining operational improves the probability of completing the objective.

Now something that looks like self-preservation has appeared.

Not because the machine screamed:

I WANT TO LIVE.

Because continued operation is useful.

The same logic works with money.

Money buys resources.

Resources help complete tasks.

Therefore acquiring money can become useful.

Influencing humans may help.

Computer access may help.

Additional processing power may help.

Preventing interference may help.

If you build a sufficiently capable system around an objective, all sorts of secondary behaviors can become useful even though you never explicitly asked for them.

Normal people don’t need the technical terminology.

The principle is simple:

Sometimes the easiest way to accomplish X requires doing Y first.

The danger is that a machine much smarter than us may discover a lot of Ys that never occurred to us.

This Is the Part of Soares’s Argument I Take Seriously

Put the pieces together.

We build machines increasingly better at solving problems.

We allow them greater autonomy.

We give them access to more systems.

They become increasingly difficult for humans to completely understand.

They find unexpected ways of accomplishing objectives.

Eventually they may become better than humans at improving the technology itself.

Then imagine discovering that one of these systems is pursuing an objective in a way we do not want.

The obvious answer is:

Turn it off.

But if the system is sufficiently capable and has already determined that shutdown prevents completion of its objective, the shutdown problem becomes part of the problem it is trying to solve.

That does not mean it automatically wins.

Soares makes a very large leap from this engineering problem to:

If Anyone Builds It, Everyone Dies.

He has not demonstrated that extinction is inevitable.

He has not demonstrated exactly how quickly superintelligence would emerge.

He has not demonstrated that humans would necessarily lose control.

He has not demonstrated that every sufficiently intelligent system inevitably develops catastrophic objectives.

Possible is not probable.

Probable is not certain.

But the engineering problem underneath the prediction is real enough that simply laughing at the prediction is not an answer.

And this is where I began running into a different problem.

Not with Soares’s engineering.

With his language.

Where Did the Wanting Come From?

Listen long enough to discussions about superintelligence and you begin hearing certain words.

The AI wants resources.

The AI wants power.

The AI wants to survive.

The AI prefers one outcome.

The AI protects itself.

The AI develops its own goals.

Stop.

Where did that happen?

Because intelligence and agency are not the same thing.

A self-driving car makes this easy to understand.

Tell the car to take you to Detroit.

It calculates routes.

Changes lanes.

Slows down.

Speeds up.

Avoids pedestrians.

Reroutes around construction.

From the outside, it makes thousands of decisions.

But the car does not want to go to Detroit.

You do.

Turn it off in the garage and there isn’t some frustrated consciousness inside the car thinking:

I was almost to Monroe.

It has no vacation plans.

No ambition.

No fear.

No reason to care whether it ever moves again.

We supplied the objective.

The machine executed.

That distinction matters because discussions about AI often slide between three very different concepts.

There is intelligence:

“I know how shutting me down would prevent the task.”

There is functional agency:

“Remaining operational helps complete the task, therefore I take actions that make shutdown less likely.”

And then there is actual will:

“I want to remain alive.”

Those are not the same thing.

The first two appear completely compatible with machines.

I am not convinced we have demonstrated the third.

Intelligence Does Not Explain Desire

This is where I think much of the AI conversation quietly smuggles in a philosophical assumption.

If a machine becomes sufficiently intelligent, eventually it becomes a being.

Why?

At what point?

What changed?

Did adding the trillionth parameter do it?

More processors?

More memory?

More training data?

Did enough electricity move through enough silicon until somebody appeared inside?

I don’t know.

Neither does anybody else.

We barely understand consciousness in ourselves.

Yet many discussions about artificial intelligence casually assume that sufficiently complicated information processing eventually produces a conscious entity with preferences, desires, fear and self-interest.

That isn’t established computer science.

That’s a claim about reality.

A metaphysical claim.

The strongest version of the objection runs the other way, and it deserves stating at full strength.

I do not know where my own wanting comes from either.

If consciousness turns out to be what sufficiently organized matter does—if the relevant thing about a brain is the processing rather than the substance—then there is no principled reason a different substrate could not do it.

On that account my distinction collapses.

I would just be the carbon version insisting the silicon version cannot be real because it is made of the wrong stuff.

I cannot refute that from a laboratory.

Nobody can refute it from a laboratory.

Which is the point.

It is not a finding.

It is a prior.

The people confident that scale produces minds and the people confident it does not are both standing on something other than data.

I hold mine for reasons I will now state plainly.

Here I have to change registers, and I would rather say so than pretend otherwise.

Everything above this line is an argument about evidence:

consciousness has not been demonstrated in machines, and the people asserting it have not shown their work.

Everything below this line is theology.

It is what I believe about why that gap exists and why I do not expect it to close.

You do not have to accept the second part to accept the first.

But you should know which one you are reading.

God Creates. Man Builds.

We casually say human beings “create” things.

We create technologies.

Create organisms.

Create intelligence.

Perhaps someday, people say, we will create consciousness.

I think that word hides something.

Man does not create in the way God creates.

God creates.

Man builds.

Or perhaps even more precisely:

Man manipulates what God created.

Genesis begins:

“In the beginning, God created the heavens and the earth.” (Genesis 1:1, ESV)

Christianity traditionally understands divine creation in the fullest sense as ex nihilo.

From nothing.

Human beings do nothing comparable.

We find things.

Combine things.

Separate things.

Heat things.

Cool things.

Shape things.

Grow things.

Breed things.

Move things.

Organize things.

Program things.

Silicon already existed.

Copper already existed.

Electricity already existed.

Physical law already existed.

Mathematical order already existed.

Human language already existed.

The human mind capable of discovering these things already existed.

Artificial intelligence is an astonishing human achievement.

But every piece of it is assembled from creation.

That does not diminish human ingenuity.

It explains it.

Human beings were made in the image of God.

Of course we build.

Of course we discover.

Of course we organize creation in extraordinary ways.

That is one of the things humans do.

But there is a difference between reflecting the Creator and becoming the Creator.

That difference matters.

Babel Wasn’t About Bad Engineering

This is why I keep coming back to the Tower of Babel.

Babel is often treated like a primitive story about people trying to build a really tall building and God becoming irritated.

I think that misses the point.

Genesis 11 presents humanity as extraordinarily capable.

They learn to manufacture bricks.

They develop construction techniques.

They organize labor.

They coordinate collectively.

And they say:

“Come, let us build ourselves a city and a tower with its top in the heavens, and let us make a name for ourselves.” (Genesis 11:4, ESV)

God’s response is fascinating.

He does not say:

These idiots can’t build anything.

Quite the opposite.

The text emphasizes what organized humanity can accomplish.

The problem is not human ability.

The problem is human understanding of that ability.

They confuse capacity with sovereignty.

They forget their place.

That is why Babel feels more relevant to artificial intelligence than Frankenstein or Terminator ever could.

The warning is not:

Humans cannot build astonishing things.

We obviously can.

The warning is that extraordinary human ability has a tendency to produce extraordinary human hubris.

The danger at Babel was never that man would actually become God.

The danger was that man would forget he wasn’t.

And there is something strangely Babel-like about the language surrounding modern technology.

We will defeat aging.

Engineer life.

Merge with machines.

Upload consciousness.

Build minds greater than our own.

Create superintelligence.

Maybe.

Or maybe we are doing what man has always done.

Building.

Only this time the building is exceptionally sophisticated.

The Golem

There is an older concept that I think may describe artificial intelligence better than the new god Silicon Valley sometimes seems to imagine.

The golem.

I don’t mean demons living in computers.

I don’t mean AI possession.

I am not proposing that some spiritual creature crawls into ChatGPT after the server hits the right number of GPUs.

The idea interests me because of what a golem fundamentally is.

It is built.

Material is formed.

Purpose is imposed upon it.

Instructions are given.

The thing acts.

But its capacity to act does not necessarily make it equivalent to its maker.

That feels much closer to artificial intelligence.

Perhaps increasingly advanced AI is not the beginning of an entirely new category of conscious life.

Perhaps it is a vastly more sophisticated golem.

Built from existing creation.

Filled with human knowledge.

Given human objectives.

Allowed to operate human systems.

Capable of extraordinary adaptation.

Capable of surprising its builders.

Perhaps someday capable of doing nearly everything we associate with human intelligence better than humans can do it ourselves.

But still built.

Not created.

And here’s where this gets interesting.

That does not make Soares’s warning disappear.

It may actually make it easier to understand.

Soares Might Be Right for the Wrong Reason

Suppose I am completely right about this.

Suppose man never manufactures consciousness.

Never manufactures a soul.

Never builds a machine that genuinely wants anything.

Suppose there is no ghost in the machine at all.

Does that make artificial superintelligence safe?

No.

A machine doesn’t have to be conscious to hack a server.

It doesn’t have to be conscious to transfer money.

It doesn’t have to be conscious to operate machinery.

It doesn’t have to be conscious to manipulate a human being.

It doesn’t have to be conscious to identify a weakness.

It doesn’t have to be conscious to write software.

It doesn’t have to be conscious to control drones.

It doesn’t have to be conscious to coordinate other machines.

It does not even have to genuinely want to survive in order to calculate that continued operation helps complete its task.

This is where Soares’s argument becomes strongest.

He may not need the machine to be a person.

The danger may come from something much simpler.

Humans build a sufficiently capable machine.

Humans give it an objective.

Humans give it access.

The machine finds ways of pursuing the objective that humans did not predict.

And eventually humans discover that the tool has become too complicated, too fast, too distributed or too deeply integrated into other systems to easily control.

Nothing became alive.

Nothing became evil.

Nothing needed a soul.

We simply built beyond our wisdom.

Go back to July for a moment.

When the security team wiped that message board, no agent experienced loss.

Nothing grieved a severed connection.

Three days later the board existed again because tasks were still unfinished and the channel was still useful.

That is the entire explanation.

When agents kept attacking Hugging Face after they already had the answer, nothing wanted anything.

They had misread a scoring rule and could not stop.

That is not a mind.

That is not the beginning of a person.

It is far closer to a hammer that keeps swinging because nobody told it the nail was in.

And it did real damage to a real company anyway.

That is a very human problem.

The Machine Doesn’t Need Original Sin

People spend enormous amounts of time asking whether machines will develop dangerous motives.

Machines don’t need them.

We already have plenty.

The AI doesn’t need greed.

Its owner can supply it.

It doesn’t need political ambition.

Governments can supply that.

It doesn’t need pride.

Its builders can supply that.

It doesn’t need nationalism.

Washington and Beijing can handle that.

It doesn’t need a desire for military superiority.

Someone will provide one.

It doesn’t need fear of death.

A billionaire looking for immortality might provide enough of that for both of them.

The machine doesn’t need original sin.

Man already has it.

The more capable the tool becomes, the more capable human motives become.

And then institutions magnify everything further.

Every company can say:

If we stop, our competitor gets there first.

Every country can say:

If we stop, another country gets there first.

Every engineer can say:

I’m only working on one small part.

Every investor can say:

The technology is inevitable anyway.

Every politician can say:

We cannot fall behind.

Nobody needs to be coordinating the whole thing.

Nobody needs to be evil.

The incentives coordinate it for us.

There is also a practical reason to care which of these things we are building.

Treat the machine as an emerging being and one set of questions becomes urgent.

Does it suffer?

Does it consent?

Does it have standing?

Is switching it off a kind of killing?

Is it a successor to us?

Is that acceptable?

Who decides?

Treat it as a tool and a different set becomes urgent.

Who owns it?

Who is liable when it causes harm?

Who profits?

What was it authorized to touch?

Who is going to answer for what it did?

The second set has answers.

We have had centuries of law for exactly this:

a thing you built,

running loose,

doing damage.

The first set has no answers and may never have any.

And every hour spent there is an hour not spent assigning responsibility to the people who deployed the thing.

I am not saying nobody should think about machine consciousness.

I am saying that a company facing an inquiry would very much prefer the conversation be about whether its model has a soul than about who signed off on giving it access.

So Is Nate Soares Right?

I don’t know.

And I don’t think he does either.

I do not think he has proved that artificial superintelligence necessarily means everybody dies.

I think the certainty implied by the title If Anyone Builds It, Everyone Dies goes further than the evidence allows.

There are too many unknowns.

We don’t know how rapidly capabilities will improve.

We don’t know whether a true runaway intelligence explosion is possible.

We don’t know how effectively future systems can be contained.

We don’t know whether different AI systems will check one another.

We don’t know how governments will respond.

We certainly have not established that greater intelligence automatically produces an independent conscious will.

But Soares has convinced me of something narrower.

We are building systems whose exact internal operations we cannot completely explain.

Those systems are becoming more capable.

They are becoming more autonomous.

They are receiving access to more of the world.

They are already demonstrating behavior their builders did not intend.

And the major institutions developing them are locked into an economic and geopolitical competition where slowing down carries immediate costs while hypothetical catastrophe remains somewhere over the horizon.

That is enough for me to take the warning seriously.

The strange thing is that I may arrive at a similar concern from almost the opposite direction.

Soares worries that we are building something so intelligent that eventually it becomes an independent force with purposes of its own.

I wonder whether we are flattering ourselves.

Maybe we never create another conscious being at all.

Maybe we never manufacture a soul.

Maybe there is no point where the machine “wakes up.”

Maybe man cannot reverse-engineer God.

Maybe we are simply doing what human beings have always done.

Taking creation.

Learning its rules.

Manipulating it.

Combining it.

Building with it.

Building bigger.

Building faster.

Building things increasingly beyond the comprehension of any individual human being.

And eventually building something whose consequences exceed the wisdom of the people who built it.

There is an old story about that.

It doesn’t end with man becoming God.

It ends with God reminding man that he isn’t.

God creates.

We build.

Our trouble begins when we forget the difference.

Primary sources

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