We Automated the Journey. Did We Remember the Human?

Somewhere between faster technology and smarter machines, we must never forget the ordinary people who trust them.

A Ticket, a Seat, and a Little Trust

A passenger buys a train ticket.

Not because they understand railway signalling.

Not because they have inspected the software, tested the sensors, or checked the emergency braking system.

They buy a ticket because they trust that someone else has done those things.

They find their seat.

Perhaps they look out of the window. Perhaps they send a message home. Perhaps they are thinking about work tomorrow, a family gathering, or simply getting back to someone they love.

They are not thinking about technology.

They are thinking about life.

I have travelled by high-speed train between Barcelona and Madrid many times. I remember how naturally I trusted the journey. The speed, the comfort, the feeling that everything was working as it should.

Like millions of passengers, I expected to arrive.

That expectation is ordinary.

And that is exactly why it matters.

The Journey That Made Me Think Differently

On 28 October 2026, a devastating high-speed train collision occurred near Urda, in the province of Toledo, Spain.

A Renfe train struck an Iryo train that had stopped on the tracks. Initial reports described many lives lost and many others injured.

As the news developed, reports raised serious questions about a computer-related failure in the train protection system and the decisions made afterward. The full causes and responsibilities must be established through a proper investigation.

But even before every technical question has been answered, one human truth is already clear.

The people on those trains were not travelling as statistics.

They were travelling with plans.

Someone may have been looking forward to reaching home.

Someone may have been thinking about tomorrow.

Someone may have had an ordinary day ahead that they never imagined could change so suddenly.

I do not know their individual stories, and I would not pretend to speak for them.

Yet I found myself thinking about them as fellow travellers.

People I had never met somehow felt close.

We did not know them. But their absence still hurts.

When a Green Light Becomes Too Comfortable

Technology has made our lives easier in extraordinary ways.

We automate payments, bookings, business operations, software deployments, customer enquiries, and infrastructure.

When a system works a thousand times, we begin to expect it to work the thousand-and-first time.

That confidence is understandable.

But there is a danger when confidence quietly becomes unquestioned trust.

An automated system says everything is fine.

A dashboard shows green.

The process continues.

And sometimes, the human being responsible for checking the result becomes less attentive because the system has been reliable for so long.

This is one of the challenges known as automation complacency.

The problem is not that automation exists.

The problem begins when we stop asking whether it can fail.

I Asked AI a Difficult Question

As I thought about the tragedy, I found myself asking an AI assistant something that felt almost unfair:

“You answer questions from people all over the world. Why couldn't you warn someone that a train was in danger?”

Of course, a conversational AI does not automatically have access to railway signals, live train locations, or emergency control systems.

It cannot simply see the tracks ahead.

It responds to the information and tools available to it.

And that led me to a more important question:

Why should a safety system wait for someone to ask the right question?

In an ordinary conversation, waiting for a question is natural.

In a safety-critical environment, it can be dangerous.

A system designed to protect human life must actively detect relevant hazards, respond within its defined safety limits, and move toward a safe condition when it cannot reliably confirm that an operation is safe.

It must not depend on a passenger noticing the danger.

It must not rely on someone asking an AI assistant what to do.

And it must not assume that yesterday's successful operation guarantees today's safety.

Automation Needs Independent Verification

I believe automation should make work easier without making accountability disappear.

Consider a software deployment.

A pipeline builds the application.

Automated tests pass.

A dashboard reports success.

But does that mean the application is safe to release?

Not necessarily.

The tests may have missed something. The configuration may be wrong. The deployment may behave differently in production.

That is why responsible engineering uses monitoring, validation, access controls, recovery procedures, and carefully designed approval gates.

The same principle becomes even more important when a system can affect human life.

One failure should not be allowed to turn directly into a disaster.

Safety-critical systems need multiple layers of protection.

Those layers must be designed to detect different kinds of failure, including failures in the protection system itself.

They should not all depend on the same information source or the same component, because one shared fault could defeat them together.

Human oversight also matters.

But a person watching a screen is not automatically an independent safety system. Humans can become tired, misunderstand information, or make mistakes under pressure.

The goal is not to replace automation with manual work.

It is to combine reliable automatic protection, meaningful human oversight, independent verification, and clear procedures for abnormal situations.

Automate the operation. Never automate away accountability.

What If the System Is Wrong?

This question should be part of every serious engineering discussion.

Not only:

Can we automate this?

But also:

What happens when this automation is wrong?

Can the failure be detected?

Can the operation be stopped safely?

Can another independent mechanism verify what is happening?

Can a human understand the warning and respond correctly?

Can investigators reconstruct the decisions afterward?

Can the organization learn from a near miss before it becomes a tragedy?

These are not questions that slow down innovation.

They are questions that make innovation worthy of trust.

A Lesson for AI Developers and Automation Builders

We often celebrate what AI can do.

It can generate code, analyze documents, build workflows, answer questions, and help people solve problems.

Those are useful abilities.

But intelligence should never be confused with infallibility.

An AI answer can sound confident and still be wrong.

An automated workflow can complete successfully while producing a harmful result.

A dashboard can look healthy while an important risk remains undetected.

Even the most impressive system has limitations.

That is why I believe every developer should carry a simple habit into their work:

Investigate before you modify. Verify before you conclude.

In everyday applications, this may prevent a failed deployment, an incorrect financial report, or a customer receiving the wrong information.

In safety-critical systems, carefully engineered safeguards can help prevent consequences that cannot be reversed.

Not every problem has the same level of risk.

Our verification methods should reflect what is at stake.

The Part Technology Cannot Restore

In software, we have a comforting word:

Rollback.

If a deployment fails, we may be able to restore an earlier version.

If a configuration breaks, we may be able to correct it.

If data is lost, a properly maintained backup may help us recover it.

But human life does not have a rollback button.

A person is not a failed transaction.

A family is not a database record.

A journey interrupted by tragedy cannot simply be restarted.

This is the part of engineering that should remain in our minds even when we are discussing technical diagrams, performance metrics, and system architecture.

Behind the technology are people.

People who trust us without knowing our names.

People who never see our code.

People who simply expect the system to do what it was designed to do.

To the Fellow Travellers We Never Met

I keep returning to one thought.

Where have you gone, my fellow travellers?

You were sitting on a train, heading toward your destinations.

Why has the journey become such a long wait?

We did not know you.

We had never spoken.

Yet somehow, your absence has become part of our thoughts.

We, too, are only travellers through this world.

But you left so suddenly.

And those who love you must now carry an absence that words cannot repair.

To the families, the injured, the survivors, and everyone affected: my deepest sympathy and respect.

No article can remove their pain.

But perhaps we can honour those affected by refusing to treat preventable risks as ordinary inconveniences.

Progress Should Bring Us Home

I still believe in technology.

I believe in AI, cloud engineering, automation, and the extraordinary things people can build.

But I believe even more strongly that technology must serve human life.

A faster train is an achievement.

A smarter system is an achievement.

An automated process that saves time is an achievement.

Yet none of these achievements should make us forget why we built them.

We build technology to help people live, work, connect, and reach their destinations.

The passenger trusted the journey. The engineer's responsibility was to make that trust worthy.

Perhaps the deepest measure of progress is not how quickly we can move people from one place to another.

It is how carefully we protect them along the way.

Because at the end of every journey, someone may be waiting.

And every person deserves the chance to arrive.

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Context and Sources

This reflection was prompted by the high-speed train collision near Urda, Toledo, Spain, on 28 October 2026.

Contemporary reports described a collision involving Renfe and Iryo trains and raised questions about the failure of an automatic train protection system and subsequent operational decisions. The complete causes and responsibilities remain matters for formal investigation.

This article is a personal reflection on automation, public trust, independent safety verification, and human responsibility. It is not a technical accident investigation, an eyewitness account, or a claim that AI caused the collision.

Further reporting:

• BBC News — High-speed train crash kills at least 68 people in Spain
• Reuters — At least 68 people dead in Spain's train crash
• RTVE — Timeline of the railway accident in Urda, Toledo

Reporting and investigation findings may change as more evidence becomes available.

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Written by Muhammad Naveed Ishaque

A personal reflection on technology, life, and responsible engineering.

DeTLeng — Think clearly. Build responsibly. Improve through evidence.